tcga_benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T18:34:14.131624Z",
     "start_time": "2019-07-20T18:34:13.972790Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "import meta_dataloader.TCGA\n",
    "\n",
    "#import models.mlp, models.gcn\n",
    "import numpy as np\n",
    "import data.gene_graphs\n",
    "import collections\n",
    "import sklearn.metrics\n",
    "import sklearn.model_selection\n",
    "import random\n",
    "from collections import OrderedDict\n",
    "import pandas as pd\n",
    "from torch.optim import Optimizer\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from torch import Tensor\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7.3 (default, Mar 27 2019, 22:11:17) \n",
      "[GCC 7.3.0]\n"
     ]
    }
   ],
   "source": [
    "print(sys.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T15:27:04.953227Z",
     "start_time": "2019-07-20T15:26:44.932267Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tasks = meta_dataloader.TCGA.TCGAMeta(\n",
    "                                      min_samples_per_class=10\n",
    "                                     )\n",
    "#task = tasks[113]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T19:13:10.934622Z",
     "start_time": "2019-07-20T19:13:10.840520Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174\n"
     ]
    }
   ],
   "source": [
    "print(len(tasks.task_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T19:13:54.388264Z",
     "start_time": "2019-07-20T19:13:54.315016Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Expression_Subtype', 'LUAD')\n",
      "('Expression_Subtype', 'LUNG')\n",
      "('GeneExp_Subtype', 'GBM')\n",
      "('Metastasis_nature2012', 'BRCA')\n",
      "('Node_nature2012', 'BRCA')\n",
      "('PAM50Call_RNAseq', 'BRCA')\n",
      "('_EVENT', 'ACC')\n",
      "('_EVENT', 'BLCA')\n",
      "('_EVENT', 'BRCA')\n",
      "('_EVENT', 'CESC')\n",
      "('_EVENT', 'CHOL')\n",
      "('_EVENT', 'COAD')\n",
      "('_EVENT', 'COADREAD')\n",
      "('_EVENT', 'ESCA')\n",
      "('_EVENT', 'GBM')\n",
      "('_EVENT', 'GBMLGG')\n",
      "('_EVENT', 'HNSC')\n",
      "('_EVENT', 'KICH')\n",
      "('_EVENT', 'KIRP')\n",
      "('_EVENT', 'LAML')\n",
      "('_EVENT', 'LGG')\n",
      "('_EVENT', 'LIHC')\n",
      "('_EVENT', 'LUAD')\n",
      "('_EVENT', 'LUNG')\n",
      "('_EVENT', 'LUSC')\n",
      "('_EVENT', 'MESO')\n",
      "('_EVENT', 'OV')\n",
      "('_EVENT', 'PAAD')\n",
      "('_EVENT', 'READ')\n",
      "('_EVENT', 'SARC')\n",
      "('_EVENT', 'SKCM')\n",
      "('_EVENT', 'STAD')\n",
      "('_EVENT', 'THCA')\n",
      "('_EVENT', 'UCEC')\n",
      "('_EVENT', 'UCS')\n",
      "('_EVENT', 'UVM')\n",
      "('_PANCAN_DNAMethyl_BLCA', 'BLCA')\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA')\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC')\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD')\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC')\n",
      "('_PANCAN_mirna_BLCA', 'BLCA')\n",
      "('_PANCAN_mirna_BRCA', 'BRCA')\n",
      "('_PANCAN_mirna_HNSC', 'HNSC')\n",
      "('_PANCAN_mirna_LAML', 'LAML')\n",
      "('_PANCAN_mirna_LUAD', 'LUAD')\n",
      "('_PANCAN_mirna_LUSC', 'LUSC')\n",
      "('_PANCAN_mirna_OV', 'OV')\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML')\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC')\n",
      "('alcohol_history_documented', 'ESCA')\n",
      "('alcohol_history_documented', 'HNSC')\n",
      "('alcohol_history_documented', 'PAAD')\n",
      "('anatomic_neoplasm_subdivision', 'OV')\n",
      "('anatomic_neoplasm_subdivision', 'PAAD')\n",
      "('animal_insect_allergy_history', 'GBMLGG')\n",
      "('animal_insect_allergy_history', 'LGG')\n",
      "('antireflux_treatment', 'STAD')\n",
      "('asthma_history', 'GBMLGG')\n",
      "('asthma_history', 'LGG')\n",
      "('atypical_mitotic_figures', 'ACC')\n",
      "('barretts_esophagus', 'STAD')\n",
      "('biochemical_recurrence', 'PRAD')\n",
      "('birth_control_pill_history_usage_category', 'CESC')\n",
      "('birth_control_pill_history_usage_category', 'UCEC')\n",
      "('clinical_M', 'ACC')\n",
      "('clinical_M', 'KICH')\n",
      "('clinical_M', 'KIRP')\n",
      "('clinical_stage', 'KIRP')\n",
      "('colon_polyps_present', 'COAD')\n",
      "('colon_polyps_present', 'COADREAD')\n",
      "('colon_polyps_present', 'READ')\n",
      "('diabetes', 'UCEC')\n",
      "('family_history_of_cancer', 'GBMLGG')\n",
      "('family_history_of_cancer', 'LGG')\n",
      "('family_history_of_cancer', 'PAAD')\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG')\n",
      "('family_history_of_primary_brain_tumor', 'LGG')\n",
      "('family_history_of_stomach_cancer', 'STAD')\n",
      "('family_history_other_cancer', 'TGCT')\n",
      "('gender', 'ACC')\n",
      "('gender', 'BLCA')\n",
      "('gender', 'BRCA')\n",
      "('gender', 'CHOL')\n",
      "('gender', 'COAD')\n",
      "('gender', 'COADREAD')\n",
      "('gender', 'DLBC')\n",
      "('gender', 'ESCA')\n",
      "('gender', 'GBM')\n",
      "('gender', 'GBMLGG')\n",
      "('gender', 'HNSC')\n",
      "('gender', 'KICH')\n",
      "('gender', 'KIRP')\n",
      "('gender', 'LAML')\n",
      "('gender', 'LGG')\n",
      "('gender', 'LIHC')\n",
      "('gender', 'LUAD')\n",
      "('gender', 'LUNG')\n",
      "('gender', 'LUSC')\n",
      "('gender', 'MESO')\n",
      "('gender', 'PAAD')\n",
      "('gender', 'PCPG')\n",
      "('gender', 'READ')\n",
      "('gender', 'SARC')\n",
      "('gender', 'SKCM')\n",
      "('gender', 'STAD')\n",
      "('gender', 'THCA')\n",
      "('gender', 'THYM')\n",
      "('gender', 'UVM')\n",
      "('histological_type', 'ESCA')\n",
      "('histological_type', 'LGG')\n",
      "('histological_type', 'PCPG')\n",
      "('histological_type', 'PRAD')\n",
      "('histological_type', 'UCEC')\n",
      "('histological_type', 'UCS')\n",
      "('histological_type', 'UVM')\n",
      "('hypertension', 'UCEC')\n",
      "('hypertension', 'UCS')\n",
      "('icd_10', 'TGCT')\n",
      "('ldh1_mutation_found', 'GBMLGG')\n",
      "('ldh1_mutation_found', 'LGG')\n",
      "('lymphatic_invasion', 'COAD')\n",
      "('lymphatic_invasion', 'COADREAD')\n",
      "('lymphatic_invasion', 'OV')\n",
      "('lymphatic_invasion', 'READ')\n",
      "('lymphovascular_invasion_present', 'BLCA')\n",
      "('lymphovascular_invasion_present', 'HNSC')\n",
      "('lymphovascular_invasion_present', 'TGCT')\n",
      "('menopause_status', 'BRCA')\n",
      "('mental_status_changes', 'GBMLGG')\n",
      "('mental_status_changes', 'LGG')\n",
      "('metastatic_breast_carcinoma_estrogen_receptor_status', 'BRCA')\n",
      "('metastatic_breast_carcinoma_progesterone_receptor_status', 'BRCA')\n",
      "('metastatic_diagnosis', 'SARC')\n",
      "('necrosis', 'ACC')\n",
      "('oct_embedded', 'ACC')\n",
      "('oct_embedded', 'BLCA')\n",
      "('oct_embedded', 'BRCA')\n",
      "('oct_embedded', 'CESC')\n",
      "('oct_embedded', 'ESCA')\n",
      "('oct_embedded', 'GBMLGG')\n",
      "('oct_embedded', 'HNSC')\n",
      "('oct_embedded', 'KIRP')\n",
      "('oct_embedded', 'LGG')\n",
      "('oct_embedded', 'LIHC')\n",
      "('oct_embedded', 'LUAD')\n",
      "('oct_embedded', 'LUNG')\n",
      "('oct_embedded', 'LUSC')\n",
      "('oct_embedded', 'MESO')\n",
      "('oct_embedded', 'PAAD')\n",
      "('oct_embedded', 'PCPG')\n",
      "('oct_embedded', 'PRAD')\n",
      "('oct_embedded', 'SARC')\n",
      "('oct_embedded', 'SKCM')\n",
      "('oct_embedded', 'STAD')\n",
      "('oct_embedded', 'TGCT')\n",
      "('oct_embedded', 'THCA')\n",
      "('oct_embedded', 'THYM')\n",
      "('oct_embedded', 'UCEC')\n",
      "('oct_embedded', 'UCS')\n",
      "('pregnancies', 'UCEC')\n",
      "('synchronous_colon_cancer_present', 'COAD')\n",
      "('synchronous_colon_cancer_present', 'COADREAD')\n",
      "('tumor_tissue_site', 'COADREAD')\n",
      "('tumor_tissue_site', 'GBMLGG')\n",
      "('tumor_tissue_site', 'PCPG')\n",
      "('tumor_tissue_site', 'THYM')\n",
      "('venous_invasion', 'COAD')\n",
      "('venous_invasion', 'COADREAD')\n",
      "('venous_invasion', 'OV')\n",
      "('venous_invasion', 'READ')\n",
      "('weiss_venous_invasion', 'ACC')\n",
      "('white_cell_count_result', 'KICH')\n",
      "('white_cell_count_result', 'KIRP')\n"
     ]
    }
   ],
   "source": [
    "for taskid in sorted(tasks.task_ids):\n",
    "    print(taskid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T22:36:51.228686Z",
     "start_time": "2019-07-19T22:36:33.730712Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('histological_type', 'LGG')\n",
      "(529, 20530)\n",
      "(529,)\n",
      "Counter({2: 198, 0: 197, 1: 134})\n"
     ]
    }
   ],
   "source": [
    "task = meta_dataloader.TCGA.TCGATask(('histological_type', 'LGG'))\n",
    "print(task.id)\n",
    "print(task._samples.shape)\n",
    "print(np.asarray(task._labels).shape)\n",
    "print(collections.Counter(task._labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T19:12:57.416302Z",
     "start_time": "2019-07-20T19:12:14.536672Z"
    },
    "code_folding": [
     0
    ],
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-193-29b0d2bce25c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mtask\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'gender'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'COAD'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_samples\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/PycharmProjects/gene-graph-conv/meta_dataloader/TCGA.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mAn\u001b[0m \u001b[0mexample\u001b[0m \u001b[0mof\u001b[0m \u001b[0ma\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0mvariable\u001b[0m \u001b[0;32mis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'gender-BRCA'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m \u001b[0mwe\u001b[0m \u001b[0mpredict\u001b[0m \u001b[0mgender\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbreast\u001b[0m \u001b[0mcancer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBRCA\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mpatients\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m         \"\"\"\n\u001b[0;32m---> 99\u001b[0;31m         \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTCGATask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_ids\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_transform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_transform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdownload\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloaded\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreloaded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgene_symbol_map_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgene_symbol_map_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset_transform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/PycharmProjects/gene-graph-conv/meta_dataloader/TCGA.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, task_id, data_dir, transform, target_transform, download, preloaded, gene_symbol_map_file)\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0mavailable_elements\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mattribute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sampleID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_all_sample_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0msample_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mavailable_elements\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         \u001b[0mfiltered_attribute\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mattribute\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mavailable_elements\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'category'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered_attribute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcodes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategories\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered_attribute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategories\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    909\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    910\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 911\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    912\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    944\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    945\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'boolean'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 946\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    948\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_values\u001b[0;34m(self, indexer)\u001b[0m\n\u001b[1;32m    975\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 977\u001b[0;31m             return self._constructor(self._data.get_slice(indexer),\n\u001b[0m\u001b[1;32m    978\u001b[0m                                      fastpath=True).__finalize__(self)\n\u001b[1;32m    979\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mget_slice\u001b[0;34m(self, slobj, axis)\u001b[0m\n\u001b[1;32m   1509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1510\u001b[0m         return self.__class__(self._block._slice(slobj),\n\u001b[0;32m-> 1511\u001b[0;31m                               self.index[slobj], fastpath=True)\n\u001b[0m\u001b[1;32m   1512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1513\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, block, axis, do_integrity_check, fastpath)\u001b[0m\n\u001b[1;32m   1479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1480\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBlock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1481\u001b[0;31m             \u001b[0mblock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_block\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1483\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mmake_block\u001b[0;34m(values, placement, klass, ndim, dtype, fastpath)\u001b[0m\n\u001b[1;32m   3086\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mklass\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3087\u001b[0m         \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3088\u001b[0;31m         \u001b[0mklass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_block_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3089\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3090\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mklass\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mDatetimeTZBlock\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mget_block_type\u001b[0;34m(values, dtype)\u001b[0m\n\u001b[1;32m   3060\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mis_interval_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_period_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3061\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mObjectValuesExtensionBlock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3062\u001b[0;31m     \u001b[0;32melif\u001b[0m \u001b[0mis_extension_array_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3063\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExtensionBlock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3064\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloating\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/dtypes/common.py\u001b[0m in \u001b[0;36mis_extension_array_dtype\u001b[0;34m(arr_or_dtype)\u001b[0m\n\u001b[1;32m   1747\u001b[0m     \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr_or_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'dtype'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr_or_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1748\u001b[0m     return (isinstance(dtype, ExtensionDtype) or\n\u001b[0;32m-> 1749\u001b[0;31m             registry.find(dtype) is not None)\n\u001b[0m\u001b[1;32m   1750\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1751\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for task in sorted(tasks):\n",
    "    print(task)\n",
    "    if task.id == ('gender', 'COAD'):\n",
    "        print(task.id)\n",
    "        print(task._samples.shape)\n",
    "        print(np.asarray(task._labels).shape)\n",
    "        print(len(collections.Counter(task._labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T07:03:14.440948Z",
     "start_time": "2019-07-22T07:03:14.354924Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def load_sets(task, valid=False):\n",
    "     \n",
    "    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(task._samples, \n",
    "                                                                                task._labels, \n",
    "                                                                                stratify=task._labels,\n",
    "                                                                                train_size=50,\n",
    "                                                                                test_size=100,\n",
    "                                                                                shuffle=True,\n",
    "                                                                                random_state=0\n",
    "                                                                                 )\n",
    "    \n",
    "    train_set = TensorDataset( Tensor(X_train), Tensor(y_train))\n",
    "    test_set = TensorDataset( Tensor(X_test), Tensor(y_test))\n",
    "\n",
    "    if valid:\n",
    "        X_test, X_valid, y_test, y_valid = sklearn.model_selection.train_test_split(X_test, \n",
    "                                                                                y_test, \n",
    "                                                                                stratify=y_test,\n",
    "                                                                                train_size=50,\n",
    "                                                                                test_size=50,\n",
    "                                                                                shuffle=True,\n",
    "                                                                                random_state=0\n",
    "                                                                               )\n",
    "        valid_set = TensorDataset( Tensor(X_valid), Tensor(y_valid))\n",
    "        return train_set, valid_set, test_set\n",
    "    \n",
    "    return train_set, test_set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T15:27:06.001534Z",
     "start_time": "2019-07-20T15:27:05.943840Z"
    },
    "code_folding": [
     1
    ]
   },
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "def Majority(X_train, X_test, y_train, y_test, random_state):\n",
    "    \n",
    "    classifier = DummyClassifier(strategy='most_frequent', random_state=random_state)\n",
    "    classifier.fit(X_train, y_train)\n",
    "    predicted = classifier.predict(X_test)\n",
    "    accuracy = (predicted == y_test).mean()*100.\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T18:34:18.276906Z",
     "start_time": "2019-07-20T18:34:18.211837Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "class LogisticRegression(torch.nn.Module):\n",
    "    \n",
    "    def __init__(self, seed, input_size, num_classes, learning_rate, batch_size, epochs):\n",
    "        super(LogisticRegression, self).__init__()\n",
    "        self.learning_rate = learning_rate\n",
    "        self.batch_size = batch_size\n",
    "        self.epochs = epochs\n",
    "        self.linear = torch.nn.Linear(input_size, num_classes)\n",
    "        \n",
    "        random.seed(seed)\n",
    "        torch.manual_seed(seed)\n",
    "    \n",
    "    def forward(self, x, params=None):\n",
    "        return self.linear(x)\n",
    "\n",
    "    @property\n",
    "    def criterion(self):\n",
    "        return torch.nn.CrossEntropyLoss()\n",
    "    \n",
    "    @property\n",
    "    def optimizer(self):\n",
    "        return torch.optim.LBFGS(self.parameters(), lr=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T03:32:01.550893Z",
     "start_time": "2019-07-21T03:32:01.509028Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def train(model, dataset, task_id, stop_early=False):\n",
    "    train_loss = []\n",
    "    criterion = model.criterion\n",
    "    if type(model).__name__ == \"LogisticRegression\":\n",
    "        optimizer = torch.optim.LBFGS(model.parameters(), lr=1)\n",
    "    else:\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=model.learning_rate, weight_decay= model.weight_decay)\n",
    "     \n",
    "    if stop_early:\n",
    "        print(\"\")\n",
    "    else:\n",
    "        train_set = dataset\n",
    "        valid_set = None\n",
    " \n",
    "    for i in range(model.epochs):\n",
    "        for batch, labels in torch.utils.data.DataLoader(train_set, batch_size=model.batch_size, shuffle=True):\n",
    "            labels = torch.autograd.Variable(labels.long())\n",
    "            \n",
    "            def closure():\n",
    "                # Forward + Backward + Optimize\n",
    "                optimizer.zero_grad()\n",
    "                out = model(batch)\n",
    "                loss = criterion(out, labels)\n",
    "                loss.backward()\n",
    "                return loss\n",
    "            \n",
    "            intermediate = optimizer.step(closure)\n",
    "            loss = intermediate.item()\n",
    "            train_loss.append(loss)\n",
    "\n",
    "    return model, loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T18:34:20.773602Z",
     "start_time": "2019-07-20T18:34:20.708587Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def test(model, test_set):\n",
    "    # Test the Model\n",
    "    batch, labels = next(iter(torch.utils.data.DataLoader(test_set, batch_size=len(test_set), shuffle=False)))\n",
    "    model.eval()\n",
    "    outputs = model(batch)\n",
    "    _, predicted = torch.max(outputs.data, 1)\n",
    "\n",
    "    predicted = predicted.numpy()\n",
    "    labels = labels.numpy()\n",
    "    accuracy = (predicted == labels).mean()*100.\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logisitic Regression and Majority Experiments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T00:41:53.180000Z",
     "start_time": "2019-07-20T19:17:14.403897Z"
    },
    "code_folding": [
     4
    ],
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Expression_Subtype', 'LUAD') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUAD') LR Accuracy is: 62.0\n",
      "('Expression_Subtype', 'LUAD') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUAD') LR Accuracy is: 56.99999999999999\n",
      "('Expression_Subtype', 'LUAD') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUAD') LR Accuracy is: 64.0\n",
      "('Expression_Subtype', 'LUAD') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUAD') LR Accuracy is: 57.99999999999999\n",
      "('Expression_Subtype', 'LUAD') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUAD') LR Accuracy is: 69.0\n",
      "('Expression_Subtype', 'LUAD') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUAD') LR Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUAD') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUAD') LR Accuracy is: 61.0\n",
      "('Expression_Subtype', 'LUAD') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUAD') LR Accuracy is: 67.0\n",
      "('Expression_Subtype', 'LUAD') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUAD') LR Accuracy is: 60.0\n",
      "('Expression_Subtype', 'LUAD') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUAD') LR Accuracy is: 70.0\n",
      "('Expression_Subtype', 'LUAD') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUAD') LR Accuracy is: 61.0\n",
      "('Expression_Subtype', 'LUNG') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUNG') LR Accuracy is: 62.0\n",
      "('Expression_Subtype', 'LUNG') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUNG') LR Accuracy is: 56.99999999999999\n",
      "('Expression_Subtype', 'LUNG') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUNG') LR Accuracy is: 64.0\n",
      "('Expression_Subtype', 'LUNG') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUNG') LR Accuracy is: 57.99999999999999\n",
      "('Expression_Subtype', 'LUNG') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUNG') LR Accuracy is: 69.0\n",
      "('Expression_Subtype', 'LUNG') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUNG') LR Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUNG') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUNG') LR Accuracy is: 61.0\n",
      "('Expression_Subtype', 'LUNG') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUNG') LR Accuracy is: 67.0\n",
      "('Expression_Subtype', 'LUNG') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUNG') LR Accuracy is: 60.0\n",
      "('Expression_Subtype', 'LUNG') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUNG') LR Accuracy is: 70.0\n",
      "('Expression_Subtype', 'LUNG') Majority Accuracy is: 38.0\n",
      "('Expression_Subtype', 'LUNG') LR Accuracy is: 61.0\n",
      "('GeneExp_Subtype', 'GBM') Majority Accuracy is: 33.0\n",
      "('GeneExp_Subtype', 'GBM') LR Accuracy is: 68.0\n",
      "('GeneExp_Subtype', 'GBM') Majority Accuracy is: 33.0\n",
      "('GeneExp_Subtype', 'GBM') LR Accuracy is: 25.0\n",
      "('GeneExp_Subtype', 'GBM') Majority Accuracy is: 33.0\n",
      "('GeneExp_Subtype', 'GBM') LR Accuracy is: 69.0\n",
      "('GeneExp_Subtype', 'GBM') Majority Accuracy is: 33.0\n",
      "('GeneExp_Subtype', 'GBM') LR Accuracy is: 64.0\n",
      "('GeneExp_Subtype', 'GBM') Majority Accuracy is: 33.0\n",
      "('GeneExp_Subtype', 'GBM') LR Accuracy is: 65.0\n",
      "('GeneExp_Subtype', 'GBM') Majority Accuracy is: 33.0\n",
      "('GeneExp_Subtype', 'GBM') LR Accuracy is: 70.0\n",
      "('GeneExp_Subtype', 'GBM') Majority Accuracy is: 33.0\n",
      "('GeneExp_Subtype', 'GBM') LR Accuracy is: 66.0\n",
      "('GeneExp_Subtype', 'GBM') Majority Accuracy is: 33.0\n",
      "('GeneExp_Subtype', 'GBM') LR Accuracy is: 51.0\n",
      "('GeneExp_Subtype', 'GBM') Majority Accuracy is: 33.0\n",
      "('GeneExp_Subtype', 'GBM') LR Accuracy is: 72.0\n",
      "('GeneExp_Subtype', 'GBM') Majority Accuracy is: 33.0\n",
      "('GeneExp_Subtype', 'GBM') LR Accuracy is: 68.0\n",
      "('GeneExp_Subtype', 'GBM') Majority Accuracy is: 33.0\n",
      "('GeneExp_Subtype', 'GBM') LR Accuracy is: 68.0\n",
      "('Metastasis_nature2012', 'BRCA') Majority Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') LR Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') Majority Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') LR Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') Majority Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') LR Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') Majority Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') LR Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') Majority Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') LR Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') Majority Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') LR Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') Majority Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') LR Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') Majority Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') LR Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') Majority Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') LR Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') Majority Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') LR Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') Majority Accuracy is: 98.0\n",
      "('Metastasis_nature2012', 'BRCA') LR Accuracy is: 98.0\n",
      "('Node_nature2012', 'BRCA') Majority Accuracy is: 49.0\n",
      "('Node_nature2012', 'BRCA') LR Accuracy is: 34.0\n",
      "('Node_nature2012', 'BRCA') Majority Accuracy is: 49.0\n",
      "('Node_nature2012', 'BRCA') LR Accuracy is: 40.0\n",
      "('Node_nature2012', 'BRCA') Majority Accuracy is: 49.0\n",
      "('Node_nature2012', 'BRCA') LR Accuracy is: 42.0\n",
      "('Node_nature2012', 'BRCA') Majority Accuracy is: 49.0\n",
      "('Node_nature2012', 'BRCA') LR Accuracy is: 45.0\n",
      "('Node_nature2012', 'BRCA') Majority Accuracy is: 49.0\n",
      "('Node_nature2012', 'BRCA') LR Accuracy is: 32.0\n",
      "('Node_nature2012', 'BRCA') Majority Accuracy is: 49.0\n",
      "('Node_nature2012', 'BRCA') LR Accuracy is: 42.0\n",
      "('Node_nature2012', 'BRCA') Majority Accuracy is: 49.0\n",
      "('Node_nature2012', 'BRCA') LR Accuracy is: 50.0\n",
      "('Node_nature2012', 'BRCA') Majority Accuracy is: 49.0\n",
      "('Node_nature2012', 'BRCA') LR Accuracy is: 42.0\n",
      "('Node_nature2012', 'BRCA') Majority Accuracy is: 49.0\n",
      "('Node_nature2012', 'BRCA') LR Accuracy is: 42.0\n",
      "('Node_nature2012', 'BRCA') Majority Accuracy is: 49.0\n",
      "('Node_nature2012', 'BRCA') LR Accuracy is: 42.0\n",
      "('Node_nature2012', 'BRCA') Majority Accuracy is: 49.0\n",
      "('Node_nature2012', 'BRCA') LR Accuracy is: 39.0\n",
      "('PAM50Call_RNAseq', 'BRCA') Majority Accuracy is: 45.0\n",
      "('PAM50Call_RNAseq', 'BRCA') LR Accuracy is: 79.0\n",
      "('PAM50Call_RNAseq', 'BRCA') Majority Accuracy is: 45.0\n",
      "('PAM50Call_RNAseq', 'BRCA') LR Accuracy is: 74.0\n",
      "('PAM50Call_RNAseq', 'BRCA') Majority Accuracy is: 45.0\n",
      "('PAM50Call_RNAseq', 'BRCA') LR Accuracy is: 77.0\n",
      "('PAM50Call_RNAseq', 'BRCA') Majority Accuracy is: 45.0\n",
      "('PAM50Call_RNAseq', 'BRCA') LR Accuracy is: 15.0\n",
      "('PAM50Call_RNAseq', 'BRCA') Majority Accuracy is: 45.0\n",
      "('PAM50Call_RNAseq', 'BRCA') LR Accuracy is: 77.0\n",
      "('PAM50Call_RNAseq', 'BRCA') Majority Accuracy is: 45.0\n",
      "('PAM50Call_RNAseq', 'BRCA') LR Accuracy is: 73.0\n",
      "('PAM50Call_RNAseq', 'BRCA') Majority Accuracy is: 45.0\n",
      "('PAM50Call_RNAseq', 'BRCA') LR Accuracy is: 70.0\n",
      "('PAM50Call_RNAseq', 'BRCA') Majority Accuracy is: 45.0\n",
      "('PAM50Call_RNAseq', 'BRCA') LR Accuracy is: 68.0\n",
      "('PAM50Call_RNAseq', 'BRCA') Majority Accuracy is: 45.0\n",
      "('PAM50Call_RNAseq', 'BRCA') LR Accuracy is: 69.0\n",
      "('PAM50Call_RNAseq', 'BRCA') Majority Accuracy is: 45.0\n",
      "('PAM50Call_RNAseq', 'BRCA') LR Accuracy is: 78.0\n",
      "('PAM50Call_RNAseq', 'BRCA') Majority Accuracy is: 45.0\n",
      "('PAM50Call_RNAseq', 'BRCA') LR Accuracy is: 79.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('_EVENT', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'BLCA') LR Accuracy is: 59.0\n",
      "('_EVENT', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'BLCA') LR Accuracy is: 54.0\n",
      "('_EVENT', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'BLCA') LR Accuracy is: 59.0\n",
      "('_EVENT', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'BLCA') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'BLCA') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'BLCA') LR Accuracy is: 54.0\n",
      "('_EVENT', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'BLCA') LR Accuracy is: 56.99999999999999\n",
      "('_EVENT', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'BLCA') LR Accuracy is: 51.0\n",
      "('_EVENT', 'BLCA') Majority Accuracy is: 55.00000000000001\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('_EVENT', 'BLCA') LR Accuracy is: 51.0\n",
      "('_EVENT', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'BLCA') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'BLCA') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'BRCA') Majority Accuracy is: 83.0\n",
      "('_EVENT', 'BRCA') LR Accuracy is: 77.0\n",
      "('_EVENT', 'BRCA') Majority Accuracy is: 83.0\n",
      "('_EVENT', 'BRCA') LR Accuracy is: 77.0\n",
      "('_EVENT', 'BRCA') Majority Accuracy is: 83.0\n",
      "('_EVENT', 'BRCA') LR Accuracy is: 81.0\n",
      "('_EVENT', 'BRCA') Majority Accuracy is: 83.0\n",
      "('_EVENT', 'BRCA') LR Accuracy is: 61.0\n",
      "('_EVENT', 'BRCA') Majority Accuracy is: 83.0\n",
      "('_EVENT', 'BRCA') LR Accuracy is: 77.0\n",
      "('_EVENT', 'BRCA') Majority Accuracy is: 83.0\n",
      "('_EVENT', 'BRCA') LR Accuracy is: 72.0\n",
      "('_EVENT', 'BRCA') Majority Accuracy is: 83.0\n",
      "('_EVENT', 'BRCA') LR Accuracy is: 80.0\n",
      "('_EVENT', 'BRCA') Majority Accuracy is: 83.0\n",
      "('_EVENT', 'BRCA') LR Accuracy is: 81.0\n",
      "('_EVENT', 'BRCA') Majority Accuracy is: 83.0\n",
      "('_EVENT', 'BRCA') LR Accuracy is: 81.0\n",
      "('_EVENT', 'BRCA') Majority Accuracy is: 83.0\n",
      "('_EVENT', 'BRCA') LR Accuracy is: 73.0\n",
      "('_EVENT', 'BRCA') Majority Accuracy is: 83.0\n",
      "('_EVENT', 'BRCA') LR Accuracy is: 70.0\n",
      "('_EVENT', 'CESC') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') LR Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') LR Accuracy is: 72.0\n",
      "('_EVENT', 'CESC') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') LR Accuracy is: 71.0\n",
      "('_EVENT', 'CESC') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') LR Accuracy is: 69.0\n",
      "('_EVENT', 'CESC') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') LR Accuracy is: 60.0\n",
      "('_EVENT', 'CESC') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') LR Accuracy is: 69.0\n",
      "('_EVENT', 'CESC') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') LR Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') LR Accuracy is: 67.0\n",
      "('_EVENT', 'CESC') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') LR Accuracy is: 68.0\n",
      "('_EVENT', 'CESC') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') LR Accuracy is: 68.0\n",
      "('_EVENT', 'CESC') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'CESC') LR Accuracy is: 61.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('_EVENT', 'COAD') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'COAD') LR Accuracy is: 71.0\n",
      "('_EVENT', 'COAD') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'COAD') LR Accuracy is: 73.0\n",
      "('_EVENT', 'COAD') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'COAD') LR Accuracy is: 66.0\n",
      "('_EVENT', 'COAD') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'COAD') LR Accuracy is: 72.0\n",
      "('_EVENT', 'COAD') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'COAD') LR Accuracy is: 72.0\n",
      "('_EVENT', 'COAD') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'COAD') LR Accuracy is: 73.0\n",
      "('_EVENT', 'COAD') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'COAD') LR Accuracy is: 67.0\n",
      "('_EVENT', 'COAD') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'COAD') LR Accuracy is: 73.0\n",
      "('_EVENT', 'COAD') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'COAD') LR Accuracy is: 74.0\n",
      "('_EVENT', 'COAD') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'COAD') LR Accuracy is: 74.0\n",
      "('_EVENT', 'COAD') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'COAD') LR Accuracy is: 70.0\n",
      "('_EVENT', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('_EVENT', 'COADREAD') LR Accuracy is: 64.0\n",
      "('_EVENT', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('_EVENT', 'COADREAD') LR Accuracy is: 54.0\n",
      "('_EVENT', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('_EVENT', 'COADREAD') LR Accuracy is: 63.0\n",
      "('_EVENT', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('_EVENT', 'COADREAD') LR Accuracy is: 72.0\n",
      "('_EVENT', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('_EVENT', 'COADREAD') LR Accuracy is: 62.0\n",
      "('_EVENT', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('_EVENT', 'COADREAD') LR Accuracy is: 61.0\n",
      "('_EVENT', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('_EVENT', 'COADREAD') LR Accuracy is: 71.0\n",
      "('_EVENT', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('_EVENT', 'COADREAD') LR Accuracy is: 68.0\n",
      "('_EVENT', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('_EVENT', 'COADREAD') LR Accuracy is: 68.0\n",
      "('_EVENT', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('_EVENT', 'COADREAD') LR Accuracy is: 70.0\n",
      "('_EVENT', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('_EVENT', 'COADREAD') LR Accuracy is: 66.0\n",
      "('_EVENT', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') LR Accuracy is: 60.0\n",
      "('_EVENT', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') LR Accuracy is: 56.99999999999999\n",
      "('_EVENT', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') LR Accuracy is: 53.0\n",
      "('_EVENT', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') LR Accuracy is: 46.0\n",
      "('_EVENT', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') LR Accuracy is: 56.99999999999999\n",
      "('_EVENT', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') LR Accuracy is: 48.0\n",
      "('_EVENT', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'ESCA') LR Accuracy is: 52.0\n",
      "('_EVENT', 'GBM') Majority Accuracy is: 77.0\n",
      "('_EVENT', 'GBM') LR Accuracy is: 63.0\n",
      "('_EVENT', 'GBM') Majority Accuracy is: 77.0\n",
      "('_EVENT', 'GBM') LR Accuracy is: 65.0\n",
      "('_EVENT', 'GBM') Majority Accuracy is: 77.0\n",
      "('_EVENT', 'GBM') LR Accuracy is: 68.0\n",
      "('_EVENT', 'GBM') Majority Accuracy is: 77.0\n",
      "('_EVENT', 'GBM') LR Accuracy is: 71.0\n",
      "('_EVENT', 'GBM') Majority Accuracy is: 77.0\n",
      "('_EVENT', 'GBM') LR Accuracy is: 72.0\n",
      "('_EVENT', 'GBM') Majority Accuracy is: 77.0\n",
      "('_EVENT', 'GBM') LR Accuracy is: 64.0\n",
      "('_EVENT', 'GBM') Majority Accuracy is: 77.0\n",
      "('_EVENT', 'GBM') LR Accuracy is: 59.0\n",
      "('_EVENT', 'GBM') Majority Accuracy is: 77.0\n",
      "('_EVENT', 'GBM') LR Accuracy is: 64.0\n",
      "('_EVENT', 'GBM') Majority Accuracy is: 77.0\n",
      "('_EVENT', 'GBM') LR Accuracy is: 66.0\n",
      "('_EVENT', 'GBM') Majority Accuracy is: 77.0\n",
      "('_EVENT', 'GBM') LR Accuracy is: 65.0\n",
      "('_EVENT', 'GBM') Majority Accuracy is: 77.0\n",
      "('_EVENT', 'GBM') LR Accuracy is: 71.0\n",
      "('_EVENT', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('_EVENT', 'GBMLGG') LR Accuracy is: 74.0\n",
      "('_EVENT', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('_EVENT', 'GBMLGG') LR Accuracy is: 71.0\n",
      "('_EVENT', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('_EVENT', 'GBMLGG') LR Accuracy is: 75.0\n",
      "('_EVENT', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('_EVENT', 'GBMLGG') LR Accuracy is: 71.0\n",
      "('_EVENT', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('_EVENT', 'GBMLGG') LR Accuracy is: 70.0\n",
      "('_EVENT', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('_EVENT', 'GBMLGG') LR Accuracy is: 75.0\n",
      "('_EVENT', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('_EVENT', 'GBMLGG') LR Accuracy is: 75.0\n",
      "('_EVENT', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('_EVENT', 'GBMLGG') LR Accuracy is: 73.0\n",
      "('_EVENT', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('_EVENT', 'GBMLGG') LR Accuracy is: 75.0\n",
      "('_EVENT', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('_EVENT', 'GBMLGG') LR Accuracy is: 73.0\n",
      "('_EVENT', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('_EVENT', 'GBMLGG') LR Accuracy is: 73.0\n",
      "('_EVENT', 'HNSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'HNSC') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'HNSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'HNSC') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'HNSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'HNSC') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'HNSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'HNSC') LR Accuracy is: 63.0\n",
      "('_EVENT', 'HNSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'HNSC') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'HNSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'HNSC') LR Accuracy is: 61.0\n",
      "('_EVENT', 'HNSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'HNSC') LR Accuracy is: 59.0\n",
      "('_EVENT', 'HNSC') Majority Accuracy is: 55.00000000000001\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('_EVENT', 'HNSC') LR Accuracy is: 51.0\n",
      "('_EVENT', 'HNSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'HNSC') LR Accuracy is: 51.0\n",
      "('_EVENT', 'HNSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'HNSC') LR Accuracy is: 59.0\n",
      "('_EVENT', 'HNSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'HNSC') LR Accuracy is: 60.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('_EVENT', 'KIRP') Majority Accuracy is: 84.0\n",
      "('_EVENT', 'KIRP') LR Accuracy is: 80.0\n",
      "('_EVENT', 'KIRP') Majority Accuracy is: 84.0\n",
      "('_EVENT', 'KIRP') LR Accuracy is: 83.0\n",
      "('_EVENT', 'KIRP') Majority Accuracy is: 84.0\n",
      "('_EVENT', 'KIRP') LR Accuracy is: 84.0\n",
      "('_EVENT', 'KIRP') Majority Accuracy is: 84.0\n",
      "('_EVENT', 'KIRP') LR Accuracy is: 80.0\n",
      "('_EVENT', 'KIRP') Majority Accuracy is: 84.0\n",
      "('_EVENT', 'KIRP') LR Accuracy is: 86.0\n",
      "('_EVENT', 'KIRP') Majority Accuracy is: 84.0\n",
      "('_EVENT', 'KIRP') LR Accuracy is: 86.0\n",
      "('_EVENT', 'KIRP') Majority Accuracy is: 84.0\n",
      "('_EVENT', 'KIRP') LR Accuracy is: 87.0\n",
      "('_EVENT', 'KIRP') Majority Accuracy is: 84.0\n",
      "('_EVENT', 'KIRP') LR Accuracy is: 86.0\n",
      "('_EVENT', 'KIRP') Majority Accuracy is: 84.0\n",
      "('_EVENT', 'KIRP') LR Accuracy is: 86.0\n",
      "('_EVENT', 'KIRP') Majority Accuracy is: 84.0\n",
      "('_EVENT', 'KIRP') LR Accuracy is: 88.0\n",
      "('_EVENT', 'KIRP') Majority Accuracy is: 84.0\n",
      "('_EVENT', 'KIRP') LR Accuracy is: 81.0\n",
      "('_EVENT', 'LAML') Majority Accuracy is: 65.0\n",
      "('_EVENT', 'LAML') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'LAML') Majority Accuracy is: 65.0\n",
      "('_EVENT', 'LAML') LR Accuracy is: 50.0\n",
      "('_EVENT', 'LAML') Majority Accuracy is: 65.0\n",
      "('_EVENT', 'LAML') LR Accuracy is: 53.0\n",
      "('_EVENT', 'LAML') Majority Accuracy is: 65.0\n",
      "('_EVENT', 'LAML') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LAML') Majority Accuracy is: 65.0\n",
      "('_EVENT', 'LAML') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LAML') Majority Accuracy is: 65.0\n",
      "('_EVENT', 'LAML') LR Accuracy is: 52.0\n",
      "('_EVENT', 'LAML') Majority Accuracy is: 65.0\n",
      "('_EVENT', 'LAML') LR Accuracy is: 51.0\n",
      "('_EVENT', 'LAML') Majority Accuracy is: 65.0\n",
      "('_EVENT', 'LAML') LR Accuracy is: 35.0\n",
      "('_EVENT', 'LAML') Majority Accuracy is: 65.0\n",
      "('_EVENT', 'LAML') LR Accuracy is: 56.99999999999999\n",
      "('_EVENT', 'LAML') Majority Accuracy is: 65.0\n",
      "('_EVENT', 'LAML') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LAML') Majority Accuracy is: 65.0\n",
      "('_EVENT', 'LAML') LR Accuracy is: 59.0\n",
      "('_EVENT', 'LGG') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'LGG') LR Accuracy is: 73.0\n",
      "('_EVENT', 'LGG') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'LGG') LR Accuracy is: 62.0\n",
      "('_EVENT', 'LGG') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'LGG') LR Accuracy is: 72.0\n",
      "('_EVENT', 'LGG') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'LGG') LR Accuracy is: 70.0\n",
      "('_EVENT', 'LGG') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'LGG') LR Accuracy is: 73.0\n",
      "('_EVENT', 'LGG') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'LGG') LR Accuracy is: 80.0\n",
      "('_EVENT', 'LGG') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'LGG') LR Accuracy is: 66.0\n",
      "('_EVENT', 'LGG') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'LGG') LR Accuracy is: 69.0\n",
      "('_EVENT', 'LGG') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'LGG') LR Accuracy is: 64.0\n",
      "('_EVENT', 'LGG') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'LGG') LR Accuracy is: 77.0\n",
      "('_EVENT', 'LGG') Majority Accuracy is: 75.0\n",
      "('_EVENT', 'LGG') LR Accuracy is: 73.0\n",
      "('_EVENT', 'LIHC') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'LIHC') LR Accuracy is: 64.0\n",
      "('_EVENT', 'LIHC') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'LIHC') LR Accuracy is: 60.0\n",
      "('_EVENT', 'LIHC') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'LIHC') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LIHC') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'LIHC') LR Accuracy is: 62.0\n",
      "('_EVENT', 'LIHC') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'LIHC') LR Accuracy is: 51.0\n",
      "('_EVENT', 'LIHC') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'LIHC') LR Accuracy is: 56.99999999999999\n",
      "('_EVENT', 'LIHC') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'LIHC') LR Accuracy is: 56.99999999999999\n",
      "('_EVENT', 'LIHC') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'LIHC') LR Accuracy is: 60.0\n",
      "('_EVENT', 'LIHC') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'LIHC') LR Accuracy is: 59.0\n",
      "('_EVENT', 'LIHC') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'LIHC') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LIHC') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'LIHC') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'LUAD') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'LUAD') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUAD') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'LUAD') LR Accuracy is: 53.0\n",
      "('_EVENT', 'LUAD') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'LUAD') LR Accuracy is: 53.0\n",
      "('_EVENT', 'LUAD') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'LUAD') LR Accuracy is: 50.0\n",
      "('_EVENT', 'LUAD') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'LUAD') LR Accuracy is: 59.0\n",
      "('_EVENT', 'LUAD') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'LUAD') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'LUAD') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'LUAD') LR Accuracy is: 61.0\n",
      "('_EVENT', 'LUAD') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'LUAD') LR Accuracy is: 63.0\n",
      "('_EVENT', 'LUAD') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'LUAD') LR Accuracy is: 47.0\n",
      "('_EVENT', 'LUAD') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'LUAD') LR Accuracy is: 62.0\n",
      "('_EVENT', 'LUAD') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'LUAD') LR Accuracy is: 52.0\n",
      "('_EVENT', 'LUNG') Majority Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') LR Accuracy is: 56.99999999999999\n",
      "('_EVENT', 'LUNG') Majority Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'LUNG') Majority Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') LR Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') Majority Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') LR Accuracy is: 56.99999999999999\n",
      "('_EVENT', 'LUNG') Majority Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') LR Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') Majority Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') LR Accuracy is: 61.0\n",
      "('_EVENT', 'LUNG') Majority Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') LR Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') Majority Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') LR Accuracy is: 62.0\n",
      "('_EVENT', 'LUNG') Majority Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') LR Accuracy is: 53.0\n",
      "('_EVENT', 'LUNG') Majority Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') LR Accuracy is: 56.99999999999999\n",
      "('_EVENT', 'LUNG') Majority Accuracy is: 59.0\n",
      "('_EVENT', 'LUNG') LR Accuracy is: 60.0\n",
      "('_EVENT', 'LUSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUSC') LR Accuracy is: 52.0\n",
      "('_EVENT', 'LUSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUSC') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'LUSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUSC') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'LUSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUSC') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'LUSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUSC') LR Accuracy is: 54.0\n",
      "('_EVENT', 'LUSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUSC') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'LUSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUSC') LR Accuracy is: 54.0\n",
      "('_EVENT', 'LUSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUSC') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUSC') LR Accuracy is: 59.0\n",
      "('_EVENT', 'LUSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUSC') LR Accuracy is: 56.99999999999999\n",
      "('_EVENT', 'LUSC') Majority Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'LUSC') LR Accuracy is: 56.00000000000001\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('_EVENT', 'OV') Majority Accuracy is: 60.0\n",
      "('_EVENT', 'OV') LR Accuracy is: 60.0\n",
      "('_EVENT', 'OV') Majority Accuracy is: 60.0\n",
      "('_EVENT', 'OV') LR Accuracy is: 49.0\n",
      "('_EVENT', 'OV') Majority Accuracy is: 60.0\n",
      "('_EVENT', 'OV') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'OV') Majority Accuracy is: 60.0\n",
      "('_EVENT', 'OV') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'OV') Majority Accuracy is: 60.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('_EVENT', 'OV') LR Accuracy is: 56.99999999999999\n",
      "('_EVENT', 'OV') Majority Accuracy is: 60.0\n",
      "('_EVENT', 'OV') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'OV') Majority Accuracy is: 60.0\n",
      "('_EVENT', 'OV') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'OV') Majority Accuracy is: 60.0\n",
      "('_EVENT', 'OV') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'OV') Majority Accuracy is: 60.0\n",
      "('_EVENT', 'OV') LR Accuracy is: 54.0\n",
      "('_EVENT', 'OV') Majority Accuracy is: 60.0\n",
      "('_EVENT', 'OV') LR Accuracy is: 45.0\n",
      "('_EVENT', 'OV') Majority Accuracy is: 60.0\n",
      "('_EVENT', 'OV') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'PAAD') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'PAAD') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'PAAD') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'PAAD') LR Accuracy is: 49.0\n",
      "('_EVENT', 'PAAD') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'PAAD') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'PAAD') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'PAAD') LR Accuracy is: 63.0\n",
      "('_EVENT', 'PAAD') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'PAAD') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'PAAD') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'PAAD') LR Accuracy is: 62.0\n",
      "('_EVENT', 'PAAD') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'PAAD') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'PAAD') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'PAAD') LR Accuracy is: 48.0\n",
      "('_EVENT', 'PAAD') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'PAAD') LR Accuracy is: 48.0\n",
      "('_EVENT', 'PAAD') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'PAAD') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'PAAD') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'PAAD') LR Accuracy is: 61.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('_EVENT', 'SARC') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'SARC') LR Accuracy is: 59.0\n",
      "('_EVENT', 'SARC') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'SARC') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'SARC') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'SARC') LR Accuracy is: 47.0\n",
      "('_EVENT', 'SARC') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'SARC') LR Accuracy is: 59.0\n",
      "('_EVENT', 'SARC') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'SARC') LR Accuracy is: 53.0\n",
      "('_EVENT', 'SARC') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'SARC') LR Accuracy is: 51.0\n",
      "('_EVENT', 'SARC') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'SARC') LR Accuracy is: 56.99999999999999\n",
      "('_EVENT', 'SARC') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'SARC') LR Accuracy is: 54.0\n",
      "('_EVENT', 'SARC') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'SARC') LR Accuracy is: 63.0\n",
      "('_EVENT', 'SARC') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'SARC') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'SARC') Majority Accuracy is: 63.0\n",
      "('_EVENT', 'SARC') LR Accuracy is: 50.0\n",
      "('_EVENT', 'SKCM') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'SKCM') LR Accuracy is: 63.0\n",
      "('_EVENT', 'SKCM') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'SKCM') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'SKCM') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'SKCM') LR Accuracy is: 60.0\n",
      "('_EVENT', 'SKCM') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'SKCM') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'SKCM') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'SKCM') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'SKCM') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'SKCM') LR Accuracy is: 61.0\n",
      "('_EVENT', 'SKCM') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'SKCM') LR Accuracy is: 60.0\n",
      "('_EVENT', 'SKCM') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'SKCM') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'SKCM') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'SKCM') LR Accuracy is: 54.0\n",
      "('_EVENT', 'SKCM') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'SKCM') LR Accuracy is: 64.0\n",
      "('_EVENT', 'SKCM') Majority Accuracy is: 52.0\n",
      "('_EVENT', 'SKCM') LR Accuracy is: 59.0\n",
      "('_EVENT', 'STAD') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'STAD') LR Accuracy is: 60.0\n",
      "('_EVENT', 'STAD') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'STAD') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'STAD') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'STAD') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'STAD') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'STAD') LR Accuracy is: 60.0\n",
      "('_EVENT', 'STAD') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'STAD') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'STAD') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'STAD') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'STAD') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'STAD') LR Accuracy is: 56.00000000000001\n",
      "('_EVENT', 'STAD') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'STAD') LR Accuracy is: 54.0\n",
      "('_EVENT', 'STAD') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'STAD') LR Accuracy is: 57.99999999999999\n",
      "('_EVENT', 'STAD') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'STAD') LR Accuracy is: 55.00000000000001\n",
      "('_EVENT', 'STAD') Majority Accuracy is: 61.0\n",
      "('_EVENT', 'STAD') LR Accuracy is: 59.0\n",
      "('_EVENT', 'THCA') Majority Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') LR Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') Majority Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') LR Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') Majority Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') LR Accuracy is: 85.0\n",
      "('_EVENT', 'THCA') Majority Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') LR Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') Majority Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') LR Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') Majority Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') LR Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') Majority Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') LR Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') Majority Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') LR Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') Majority Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') LR Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') Majority Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') LR Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') Majority Accuracy is: 97.0\n",
      "('_EVENT', 'THCA') LR Accuracy is: 97.0\n",
      "('_EVENT', 'UCEC') Majority Accuracy is: 81.0\n",
      "('_EVENT', 'UCEC') LR Accuracy is: 80.0\n",
      "('_EVENT', 'UCEC') Majority Accuracy is: 81.0\n",
      "('_EVENT', 'UCEC') LR Accuracy is: 79.0\n",
      "('_EVENT', 'UCEC') Majority Accuracy is: 81.0\n",
      "('_EVENT', 'UCEC') LR Accuracy is: 79.0\n",
      "('_EVENT', 'UCEC') Majority Accuracy is: 81.0\n",
      "('_EVENT', 'UCEC') LR Accuracy is: 77.0\n",
      "('_EVENT', 'UCEC') Majority Accuracy is: 81.0\n",
      "('_EVENT', 'UCEC') LR Accuracy is: 78.0\n",
      "('_EVENT', 'UCEC') Majority Accuracy is: 81.0\n",
      "('_EVENT', 'UCEC') LR Accuracy is: 75.0\n",
      "('_EVENT', 'UCEC') Majority Accuracy is: 81.0\n",
      "('_EVENT', 'UCEC') LR Accuracy is: 80.0\n",
      "('_EVENT', 'UCEC') Majority Accuracy is: 81.0\n",
      "('_EVENT', 'UCEC') LR Accuracy is: 80.0\n",
      "('_EVENT', 'UCEC') Majority Accuracy is: 81.0\n",
      "('_EVENT', 'UCEC') LR Accuracy is: 79.0\n",
      "('_EVENT', 'UCEC') Majority Accuracy is: 81.0\n",
      "('_EVENT', 'UCEC') LR Accuracy is: 76.0\n",
      "('_EVENT', 'UCEC') Majority Accuracy is: 81.0\n",
      "('_EVENT', 'UCEC') LR Accuracy is: 81.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') Majority Accuracy is: 30.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') LR Accuracy is: 55.00000000000001\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') Majority Accuracy is: 30.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') LR Accuracy is: 47.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') Majority Accuracy is: 30.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') LR Accuracy is: 49.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') Majority Accuracy is: 30.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') LR Accuracy is: 54.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') Majority Accuracy is: 30.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') LR Accuracy is: 48.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') Majority Accuracy is: 30.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') LR Accuracy is: 40.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') Majority Accuracy is: 30.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') LR Accuracy is: 53.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') Majority Accuracy is: 30.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') LR Accuracy is: 47.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') Majority Accuracy is: 30.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') LR Accuracy is: 44.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') Majority Accuracy is: 30.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') LR Accuracy is: 42.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') Majority Accuracy is: 30.0\n",
      "('_PANCAN_DNAMethyl_BRCA', 'BRCA') LR Accuracy is: 45.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') Majority Accuracy is: 41.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') LR Accuracy is: 70.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') Majority Accuracy is: 41.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') LR Accuracy is: 65.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') Majority Accuracy is: 41.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') LR Accuracy is: 66.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') Majority Accuracy is: 41.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') LR Accuracy is: 70.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') Majority Accuracy is: 41.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') LR Accuracy is: 61.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') Majority Accuracy is: 41.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') LR Accuracy is: 66.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') Majority Accuracy is: 41.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') LR Accuracy is: 64.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') Majority Accuracy is: 41.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') LR Accuracy is: 16.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') Majority Accuracy is: 41.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') LR Accuracy is: 70.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') Majority Accuracy is: 41.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') LR Accuracy is: 67.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') Majority Accuracy is: 41.0\n",
      "('_PANCAN_DNAMethyl_HNSC', 'HNSC') LR Accuracy is: 71.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') Majority Accuracy is: 43.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') LR Accuracy is: 55.00000000000001\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') Majority Accuracy is: 43.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') LR Accuracy is: 48.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') Majority Accuracy is: 43.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') LR Accuracy is: 53.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') Majority Accuracy is: 43.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') LR Accuracy is: 47.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') Majority Accuracy is: 43.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') LR Accuracy is: 49.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') Majority Accuracy is: 43.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') LR Accuracy is: 23.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') Majority Accuracy is: 43.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') LR Accuracy is: 38.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') Majority Accuracy is: 43.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') LR Accuracy is: 54.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') Majority Accuracy is: 43.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') LR Accuracy is: 44.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') Majority Accuracy is: 43.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') LR Accuracy is: 49.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') Majority Accuracy is: 43.0\n",
      "('_PANCAN_DNAMethyl_LUAD', 'LUAD') LR Accuracy is: 49.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') LR Accuracy is: 63.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') LR Accuracy is: 56.00000000000001\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') LR Accuracy is: 30.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') LR Accuracy is: 63.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') LR Accuracy is: 56.99999999999999\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') LR Accuracy is: 60.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') LR Accuracy is: 30.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') LR Accuracy is: 57.99999999999999\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') LR Accuracy is: 52.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') LR Accuracy is: 56.00000000000001\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_DNAMethyl_LUSC', 'LUSC') LR Accuracy is: 54.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') Majority Accuracy is: 32.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') LR Accuracy is: 7.000000000000001\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') Majority Accuracy is: 32.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') LR Accuracy is: 7.000000000000001\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') Majority Accuracy is: 32.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') LR Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') Majority Accuracy is: 32.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') LR Accuracy is: 33.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') Majority Accuracy is: 32.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') LR Accuracy is: 7.000000000000001\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') Majority Accuracy is: 32.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') LR Accuracy is: 36.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') Majority Accuracy is: 32.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') LR Accuracy is: 35.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') Majority Accuracy is: 32.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') LR Accuracy is: 7.000000000000001\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') Majority Accuracy is: 32.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') LR Accuracy is: 31.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') Majority Accuracy is: 32.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') LR Accuracy is: 33.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') Majority Accuracy is: 32.0\n",
      "('_PANCAN_mirna_BRCA', 'BRCA') LR Accuracy is: 37.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') LR Accuracy is: 51.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') LR Accuracy is: 49.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') LR Accuracy is: 56.00000000000001\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') LR Accuracy is: 49.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') LR Accuracy is: 55.00000000000001\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') LR Accuracy is: 49.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') LR Accuracy is: 53.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') LR Accuracy is: 53.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') LR Accuracy is: 55.00000000000001\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') LR Accuracy is: 46.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') Majority Accuracy is: 38.0\n",
      "('_PANCAN_mirna_HNSC', 'HNSC') LR Accuracy is: 55.00000000000001\n",
      "('_PANCAN_mirna_LAML', 'LAML') Majority Accuracy is: 35.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') LR Accuracy is: 7.000000000000001\n",
      "('_PANCAN_mirna_LAML', 'LAML') Majority Accuracy is: 35.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') LR Accuracy is: 57.99999999999999\n",
      "('_PANCAN_mirna_LAML', 'LAML') Majority Accuracy is: 35.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') LR Accuracy is: 67.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') Majority Accuracy is: 35.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') LR Accuracy is: 60.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') Majority Accuracy is: 35.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') LR Accuracy is: 55.00000000000001\n",
      "('_PANCAN_mirna_LAML', 'LAML') Majority Accuracy is: 35.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') LR Accuracy is: 68.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') Majority Accuracy is: 35.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') LR Accuracy is: 68.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') Majority Accuracy is: 35.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') LR Accuracy is: 67.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') Majority Accuracy is: 35.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') LR Accuracy is: 67.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') Majority Accuracy is: 35.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('_PANCAN_mirna_LAML', 'LAML') LR Accuracy is: 66.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') Majority Accuracy is: 35.0\n",
      "('_PANCAN_mirna_LAML', 'LAML') LR Accuracy is: 68.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') Majority Accuracy is: 31.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') LR Accuracy is: 33.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') Majority Accuracy is: 31.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') LR Accuracy is: 39.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') Majority Accuracy is: 31.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') LR Accuracy is: 43.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') Majority Accuracy is: 31.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') LR Accuracy is: 43.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') Majority Accuracy is: 31.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') LR Accuracy is: 12.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') Majority Accuracy is: 31.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') LR Accuracy is: 34.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') Majority Accuracy is: 31.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') LR Accuracy is: 39.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') Majority Accuracy is: 31.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') LR Accuracy is: 43.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') Majority Accuracy is: 31.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') LR Accuracy is: 35.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') Majority Accuracy is: 31.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') LR Accuracy is: 12.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') Majority Accuracy is: 31.0\n",
      "('_PANCAN_mirna_LUAD', 'LUAD') LR Accuracy is: 39.0\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') Majority Accuracy is: 56.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') LR Accuracy is: 55.00000000000001\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') Majority Accuracy is: 56.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') LR Accuracy is: 64.0\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') Majority Accuracy is: 56.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') LR Accuracy is: 57.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') Majority Accuracy is: 56.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') LR Accuracy is: 57.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') Majority Accuracy is: 56.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') LR Accuracy is: 56.00000000000001\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') Majority Accuracy is: 56.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') LR Accuracy is: 11.0\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') Majority Accuracy is: 56.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') LR Accuracy is: 50.0\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') Majority Accuracy is: 56.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') LR Accuracy is: 59.0\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') Majority Accuracy is: 56.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') LR Accuracy is: 60.0\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') Majority Accuracy is: 56.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') LR Accuracy is: 11.0\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') Majority Accuracy is: 56.99999999999999\n",
      "('_PANCAN_mirna_LUSC', 'LUSC') LR Accuracy is: 54.0\n",
      "('_PANCAN_mirna_OV', 'OV') Majority Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_OV', 'OV') LR Accuracy is: 26.0\n",
      "('_PANCAN_mirna_OV', 'OV') Majority Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_OV', 'OV') LR Accuracy is: 30.0\n",
      "('_PANCAN_mirna_OV', 'OV') Majority Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_OV', 'OV') LR Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_OV', 'OV') Majority Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_OV', 'OV') LR Accuracy is: 23.0\n",
      "('_PANCAN_mirna_OV', 'OV') Majority Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_OV', 'OV') LR Accuracy is: 26.0\n",
      "('_PANCAN_mirna_OV', 'OV') Majority Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_OV', 'OV') LR Accuracy is: 23.0\n",
      "('_PANCAN_mirna_OV', 'OV') Majority Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_OV', 'OV') LR Accuracy is: 27.0\n",
      "('_PANCAN_mirna_OV', 'OV') Majority Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_OV', 'OV') LR Accuracy is: 19.0\n",
      "('_PANCAN_mirna_OV', 'OV') Majority Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_OV', 'OV') LR Accuracy is: 39.0\n",
      "('_PANCAN_mirna_OV', 'OV') Majority Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_OV', 'OV') LR Accuracy is: 27.0\n",
      "('_PANCAN_mirna_OV', 'OV') Majority Accuracy is: 28.000000000000004\n",
      "('_PANCAN_mirna_OV', 'OV') LR Accuracy is: 27.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') Majority Accuracy is: 60.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') LR Accuracy is: 80.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') Majority Accuracy is: 60.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') LR Accuracy is: 64.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') Majority Accuracy is: 60.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') LR Accuracy is: 73.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') Majority Accuracy is: 60.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') LR Accuracy is: 70.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') Majority Accuracy is: 60.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') LR Accuracy is: 79.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') Majority Accuracy is: 60.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') LR Accuracy is: 72.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') Majority Accuracy is: 60.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') LR Accuracy is: 67.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') Majority Accuracy is: 60.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') LR Accuracy is: 73.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') Majority Accuracy is: 60.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') LR Accuracy is: 19.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') Majority Accuracy is: 60.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') LR Accuracy is: 75.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') Majority Accuracy is: 60.0\n",
      "('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') LR Accuracy is: 79.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') Majority Accuracy is: 52.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') LR Accuracy is: 51.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') Majority Accuracy is: 52.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') LR Accuracy is: 56.00000000000001\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') Majority Accuracy is: 52.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') LR Accuracy is: 55.00000000000001\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') Majority Accuracy is: 52.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') LR Accuracy is: 56.99999999999999\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') Majority Accuracy is: 52.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') LR Accuracy is: 55.00000000000001\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') Majority Accuracy is: 52.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') LR Accuracy is: 51.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') Majority Accuracy is: 52.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') LR Accuracy is: 52.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') Majority Accuracy is: 52.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') LR Accuracy is: 49.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') Majority Accuracy is: 52.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') LR Accuracy is: 53.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') Majority Accuracy is: 52.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') LR Accuracy is: 50.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') Majority Accuracy is: 52.0\n",
      "('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') LR Accuracy is: 49.0\n",
      "('alcohol_history_documented', 'ESCA') Majority Accuracy is: 73.0\n",
      "('alcohol_history_documented', 'ESCA') LR Accuracy is: 63.0\n",
      "('alcohol_history_documented', 'ESCA') Majority Accuracy is: 73.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alcohol_history_documented', 'ESCA') LR Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'ESCA') Majority Accuracy is: 73.0\n",
      "('alcohol_history_documented', 'ESCA') LR Accuracy is: 59.0\n",
      "('alcohol_history_documented', 'ESCA') Majority Accuracy is: 73.0\n",
      "('alcohol_history_documented', 'ESCA') LR Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'ESCA') Majority Accuracy is: 73.0\n",
      "('alcohol_history_documented', 'ESCA') LR Accuracy is: 65.0\n",
      "('alcohol_history_documented', 'ESCA') Majority Accuracy is: 73.0\n",
      "('alcohol_history_documented', 'ESCA') LR Accuracy is: 66.0\n",
      "('alcohol_history_documented', 'ESCA') Majority Accuracy is: 73.0\n",
      "('alcohol_history_documented', 'ESCA') LR Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'ESCA') Majority Accuracy is: 73.0\n",
      "('alcohol_history_documented', 'ESCA') LR Accuracy is: 70.0\n",
      "('alcohol_history_documented', 'ESCA') Majority Accuracy is: 73.0\n",
      "('alcohol_history_documented', 'ESCA') LR Accuracy is: 62.0\n",
      "('alcohol_history_documented', 'ESCA') Majority Accuracy is: 73.0\n",
      "('alcohol_history_documented', 'ESCA') LR Accuracy is: 52.0\n",
      "('alcohol_history_documented', 'ESCA') Majority Accuracy is: 73.0\n",
      "('alcohol_history_documented', 'ESCA') LR Accuracy is: 59.0\n",
      "('alcohol_history_documented', 'HNSC') Majority Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') LR Accuracy is: 62.0\n",
      "('alcohol_history_documented', 'HNSC') Majority Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') LR Accuracy is: 68.0\n",
      "('alcohol_history_documented', 'HNSC') Majority Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') LR Accuracy is: 66.0\n",
      "('alcohol_history_documented', 'HNSC') Majority Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') LR Accuracy is: 68.0\n",
      "('alcohol_history_documented', 'HNSC') Majority Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') LR Accuracy is: 65.0\n",
      "('alcohol_history_documented', 'HNSC') Majority Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') LR Accuracy is: 65.0\n",
      "('alcohol_history_documented', 'HNSC') Majority Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') LR Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') Majority Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') LR Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') Majority Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') LR Accuracy is: 64.0\n",
      "('alcohol_history_documented', 'HNSC') Majority Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') LR Accuracy is: 63.0\n",
      "('alcohol_history_documented', 'HNSC') Majority Accuracy is: 67.0\n",
      "('alcohol_history_documented', 'HNSC') LR Accuracy is: 64.0\n",
      "('alcohol_history_documented', 'PAAD') Majority Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'PAAD') LR Accuracy is: 52.0\n",
      "('alcohol_history_documented', 'PAAD') Majority Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'PAAD') LR Accuracy is: 43.0\n",
      "('alcohol_history_documented', 'PAAD') Majority Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'PAAD') LR Accuracy is: 55.00000000000001\n",
      "('alcohol_history_documented', 'PAAD') Majority Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'PAAD') LR Accuracy is: 39.0\n",
      "('alcohol_history_documented', 'PAAD') Majority Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'PAAD') LR Accuracy is: 52.0\n",
      "('alcohol_history_documented', 'PAAD') Majority Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'PAAD') LR Accuracy is: 54.0\n",
      "('alcohol_history_documented', 'PAAD') Majority Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'PAAD') LR Accuracy is: 54.0\n",
      "('alcohol_history_documented', 'PAAD') Majority Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'PAAD') LR Accuracy is: 53.0\n",
      "('alcohol_history_documented', 'PAAD') Majority Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'PAAD') LR Accuracy is: 55.00000000000001\n",
      "('alcohol_history_documented', 'PAAD') Majority Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'PAAD') LR Accuracy is: 55.00000000000001\n",
      "('alcohol_history_documented', 'PAAD') Majority Accuracy is: 61.0\n",
      "('alcohol_history_documented', 'PAAD') LR Accuracy is: 55.00000000000001\n",
      "('anatomic_neoplasm_subdivision', 'OV') Majority Accuracy is: 73.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') LR Accuracy is: 69.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') Majority Accuracy is: 73.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') LR Accuracy is: 71.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') Majority Accuracy is: 73.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') LR Accuracy is: 61.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') Majority Accuracy is: 73.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') LR Accuracy is: 54.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') Majority Accuracy is: 73.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') LR Accuracy is: 63.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') Majority Accuracy is: 73.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') LR Accuracy is: 71.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') Majority Accuracy is: 73.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') LR Accuracy is: 59.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') Majority Accuracy is: 73.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') LR Accuracy is: 69.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') Majority Accuracy is: 73.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') LR Accuracy is: 53.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') Majority Accuracy is: 73.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') LR Accuracy is: 69.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') Majority Accuracy is: 73.0\n",
      "('anatomic_neoplasm_subdivision', 'OV') LR Accuracy is: 67.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') Majority Accuracy is: 77.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') LR Accuracy is: 72.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') Majority Accuracy is: 77.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') LR Accuracy is: 76.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') Majority Accuracy is: 77.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') LR Accuracy is: 62.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') Majority Accuracy is: 77.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') LR Accuracy is: 72.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') Majority Accuracy is: 77.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') LR Accuracy is: 71.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') Majority Accuracy is: 77.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') LR Accuracy is: 74.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') Majority Accuracy is: 77.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') LR Accuracy is: 70.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') Majority Accuracy is: 77.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') LR Accuracy is: 77.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') Majority Accuracy is: 77.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') LR Accuracy is: 68.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') Majority Accuracy is: 77.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') LR Accuracy is: 69.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') Majority Accuracy is: 77.0\n",
      "('anatomic_neoplasm_subdivision', 'PAAD') LR Accuracy is: 75.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') LR Accuracy is: 86.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') LR Accuracy is: 85.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') LR Accuracy is: 86.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') LR Accuracy is: 94.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') LR Accuracy is: 87.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') LR Accuracy is: 93.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') LR Accuracy is: 92.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') LR Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') LR Accuracy is: 89.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') LR Accuracy is: 94.0\n",
      "('animal_insect_allergy_history', 'GBMLGG') Majority Accuracy is: 95.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('animal_insect_allergy_history', 'GBMLGG') LR Accuracy is: 88.0\n",
      "('animal_insect_allergy_history', 'LGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'LGG') LR Accuracy is: 86.0\n",
      "('animal_insect_allergy_history', 'LGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'LGG') LR Accuracy is: 85.0\n",
      "('animal_insect_allergy_history', 'LGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'LGG') LR Accuracy is: 86.0\n",
      "('animal_insect_allergy_history', 'LGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'LGG') LR Accuracy is: 94.0\n",
      "('animal_insect_allergy_history', 'LGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'LGG') LR Accuracy is: 87.0\n",
      "('animal_insect_allergy_history', 'LGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'LGG') LR Accuracy is: 93.0\n",
      "('animal_insect_allergy_history', 'LGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'LGG') LR Accuracy is: 92.0\n",
      "('animal_insect_allergy_history', 'LGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'LGG') LR Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'LGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'LGG') LR Accuracy is: 89.0\n",
      "('animal_insect_allergy_history', 'LGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'LGG') LR Accuracy is: 94.0\n",
      "('animal_insect_allergy_history', 'LGG') Majority Accuracy is: 95.0\n",
      "('animal_insect_allergy_history', 'LGG') LR Accuracy is: 88.0\n",
      "('antireflux_treatment', 'STAD') Majority Accuracy is: 79.0\n",
      "('antireflux_treatment', 'STAD') LR Accuracy is: 75.0\n",
      "('antireflux_treatment', 'STAD') Majority Accuracy is: 79.0\n",
      "('antireflux_treatment', 'STAD') LR Accuracy is: 79.0\n",
      "('antireflux_treatment', 'STAD') Majority Accuracy is: 79.0\n",
      "('antireflux_treatment', 'STAD') LR Accuracy is: 77.0\n",
      "('antireflux_treatment', 'STAD') Majority Accuracy is: 79.0\n",
      "('antireflux_treatment', 'STAD') LR Accuracy is: 75.0\n",
      "('antireflux_treatment', 'STAD') Majority Accuracy is: 79.0\n",
      "('antireflux_treatment', 'STAD') LR Accuracy is: 74.0\n",
      "('antireflux_treatment', 'STAD') Majority Accuracy is: 79.0\n",
      "('antireflux_treatment', 'STAD') LR Accuracy is: 72.0\n",
      "('antireflux_treatment', 'STAD') Majority Accuracy is: 79.0\n",
      "('antireflux_treatment', 'STAD') LR Accuracy is: 78.0\n",
      "('antireflux_treatment', 'STAD') Majority Accuracy is: 79.0\n",
      "('antireflux_treatment', 'STAD') LR Accuracy is: 77.0\n",
      "('antireflux_treatment', 'STAD') Majority Accuracy is: 79.0\n",
      "('antireflux_treatment', 'STAD') LR Accuracy is: 73.0\n",
      "('antireflux_treatment', 'STAD') Majority Accuracy is: 79.0\n",
      "('antireflux_treatment', 'STAD') LR Accuracy is: 74.0\n",
      "('antireflux_treatment', 'STAD') Majority Accuracy is: 79.0\n",
      "('antireflux_treatment', 'STAD') LR Accuracy is: 78.0\n",
      "('asthma_history', 'GBMLGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') LR Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') LR Accuracy is: 93.0\n",
      "('asthma_history', 'GBMLGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') LR Accuracy is: 92.0\n",
      "('asthma_history', 'GBMLGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') LR Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') LR Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') LR Accuracy is: 92.0\n",
      "('asthma_history', 'GBMLGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') LR Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') LR Accuracy is: 93.0\n",
      "('asthma_history', 'GBMLGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') LR Accuracy is: 92.0\n",
      "('asthma_history', 'GBMLGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') LR Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'GBMLGG') LR Accuracy is: 90.0\n",
      "('asthma_history', 'LGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') LR Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') LR Accuracy is: 93.0\n",
      "('asthma_history', 'LGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') LR Accuracy is: 92.0\n",
      "('asthma_history', 'LGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') LR Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') LR Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') LR Accuracy is: 92.0\n",
      "('asthma_history', 'LGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') LR Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') LR Accuracy is: 93.0\n",
      "('asthma_history', 'LGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') LR Accuracy is: 92.0\n",
      "('asthma_history', 'LGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') LR Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') Majority Accuracy is: 94.0\n",
      "('asthma_history', 'LGG') LR Accuracy is: 90.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('barretts_esophagus', 'STAD') Majority Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') LR Accuracy is: 82.0\n",
      "('barretts_esophagus', 'STAD') Majority Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') LR Accuracy is: 88.0\n",
      "('barretts_esophagus', 'STAD') Majority Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') LR Accuracy is: 89.0\n",
      "('barretts_esophagus', 'STAD') Majority Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') LR Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') Majority Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') LR Accuracy is: 89.0\n",
      "('barretts_esophagus', 'STAD') Majority Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') LR Accuracy is: 87.0\n",
      "('barretts_esophagus', 'STAD') Majority Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') LR Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') Majority Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') LR Accuracy is: 89.0\n",
      "('barretts_esophagus', 'STAD') Majority Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') LR Accuracy is: 85.0\n",
      "('barretts_esophagus', 'STAD') Majority Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') LR Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') Majority Accuracy is: 91.0\n",
      "('barretts_esophagus', 'STAD') LR Accuracy is: 89.0\n",
      "('biochemical_recurrence', 'PRAD') Majority Accuracy is: 87.0\n",
      "('biochemical_recurrence', 'PRAD') LR Accuracy is: 76.0\n",
      "('biochemical_recurrence', 'PRAD') Majority Accuracy is: 87.0\n",
      "('biochemical_recurrence', 'PRAD') LR Accuracy is: 86.0\n",
      "('biochemical_recurrence', 'PRAD') Majority Accuracy is: 87.0\n",
      "('biochemical_recurrence', 'PRAD') LR Accuracy is: 87.0\n",
      "('biochemical_recurrence', 'PRAD') Majority Accuracy is: 87.0\n",
      "('biochemical_recurrence', 'PRAD') LR Accuracy is: 78.0\n",
      "('biochemical_recurrence', 'PRAD') Majority Accuracy is: 87.0\n",
      "('biochemical_recurrence', 'PRAD') LR Accuracy is: 85.0\n",
      "('biochemical_recurrence', 'PRAD') Majority Accuracy is: 87.0\n",
      "('biochemical_recurrence', 'PRAD') LR Accuracy is: 81.0\n",
      "('biochemical_recurrence', 'PRAD') Majority Accuracy is: 87.0\n",
      "('biochemical_recurrence', 'PRAD') LR Accuracy is: 80.0\n",
      "('biochemical_recurrence', 'PRAD') Majority Accuracy is: 87.0\n",
      "('biochemical_recurrence', 'PRAD') LR Accuracy is: 82.0\n",
      "('biochemical_recurrence', 'PRAD') Majority Accuracy is: 87.0\n",
      "('biochemical_recurrence', 'PRAD') LR Accuracy is: 83.0\n",
      "('biochemical_recurrence', 'PRAD') Majority Accuracy is: 87.0\n",
      "('biochemical_recurrence', 'PRAD') LR Accuracy is: 80.0\n",
      "('biochemical_recurrence', 'PRAD') Majority Accuracy is: 87.0\n",
      "('biochemical_recurrence', 'PRAD') LR Accuracy is: 82.0\n",
      "('birth_control_pill_history_usage_category', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('birth_control_pill_history_usage_category', 'CESC') LR Accuracy is: 49.0\n",
      "('birth_control_pill_history_usage_category', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('birth_control_pill_history_usage_category', 'CESC') LR Accuracy is: 50.0\n",
      "('birth_control_pill_history_usage_category', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('birth_control_pill_history_usage_category', 'CESC') LR Accuracy is: 52.0\n",
      "('birth_control_pill_history_usage_category', 'CESC') Majority Accuracy is: 55.00000000000001\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('birth_control_pill_history_usage_category', 'CESC') LR Accuracy is: 47.0\n",
      "('birth_control_pill_history_usage_category', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('birth_control_pill_history_usage_category', 'CESC') LR Accuracy is: 50.0\n",
      "('birth_control_pill_history_usage_category', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('birth_control_pill_history_usage_category', 'CESC') LR Accuracy is: 48.0\n",
      "('birth_control_pill_history_usage_category', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('birth_control_pill_history_usage_category', 'CESC') LR Accuracy is: 9.0\n",
      "('birth_control_pill_history_usage_category', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('birth_control_pill_history_usage_category', 'CESC') LR Accuracy is: 51.0\n",
      "('birth_control_pill_history_usage_category', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('birth_control_pill_history_usage_category', 'CESC') LR Accuracy is: 48.0\n",
      "('birth_control_pill_history_usage_category', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('birth_control_pill_history_usage_category', 'CESC') LR Accuracy is: 50.0\n",
      "('birth_control_pill_history_usage_category', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('birth_control_pill_history_usage_category', 'CESC') LR Accuracy is: 47.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('clinical_M', 'KIRP') Majority Accuracy is: 75.0\n",
      "('clinical_M', 'KIRP') LR Accuracy is: 68.0\n",
      "('clinical_M', 'KIRP') Majority Accuracy is: 75.0\n",
      "('clinical_M', 'KIRP') LR Accuracy is: 71.0\n",
      "('clinical_M', 'KIRP') Majority Accuracy is: 75.0\n",
      "('clinical_M', 'KIRP') LR Accuracy is: 68.0\n",
      "('clinical_M', 'KIRP') Majority Accuracy is: 75.0\n",
      "('clinical_M', 'KIRP') LR Accuracy is: 73.0\n",
      "('clinical_M', 'KIRP') Majority Accuracy is: 75.0\n",
      "('clinical_M', 'KIRP') LR Accuracy is: 69.0\n",
      "('clinical_M', 'KIRP') Majority Accuracy is: 75.0\n",
      "('clinical_M', 'KIRP') LR Accuracy is: 74.0\n",
      "('clinical_M', 'KIRP') Majority Accuracy is: 75.0\n",
      "('clinical_M', 'KIRP') LR Accuracy is: 73.0\n",
      "('clinical_M', 'KIRP') Majority Accuracy is: 75.0\n",
      "('clinical_M', 'KIRP') LR Accuracy is: 67.0\n",
      "('clinical_M', 'KIRP') Majority Accuracy is: 75.0\n",
      "('clinical_M', 'KIRP') LR Accuracy is: 70.0\n",
      "('clinical_M', 'KIRP') Majority Accuracy is: 75.0\n",
      "('clinical_M', 'KIRP') LR Accuracy is: 57.99999999999999\n",
      "('clinical_M', 'KIRP') Majority Accuracy is: 75.0\n",
      "('clinical_M', 'KIRP') LR Accuracy is: 66.0\n",
      "('clinical_stage', 'KIRP') Majority Accuracy is: 66.0\n",
      "('clinical_stage', 'KIRP') LR Accuracy is: 67.0\n",
      "('clinical_stage', 'KIRP') Majority Accuracy is: 66.0\n",
      "('clinical_stage', 'KIRP') LR Accuracy is: 65.0\n",
      "('clinical_stage', 'KIRP') Majority Accuracy is: 66.0\n",
      "('clinical_stage', 'KIRP') LR Accuracy is: 65.0\n",
      "('clinical_stage', 'KIRP') Majority Accuracy is: 66.0\n",
      "('clinical_stage', 'KIRP') LR Accuracy is: 56.00000000000001\n",
      "('clinical_stage', 'KIRP') Majority Accuracy is: 66.0\n",
      "('clinical_stage', 'KIRP') LR Accuracy is: 53.0\n",
      "('clinical_stage', 'KIRP') Majority Accuracy is: 66.0\n",
      "('clinical_stage', 'KIRP') LR Accuracy is: 60.0\n",
      "('clinical_stage', 'KIRP') Majority Accuracy is: 66.0\n",
      "('clinical_stage', 'KIRP') LR Accuracy is: 68.0\n",
      "('clinical_stage', 'KIRP') Majority Accuracy is: 66.0\n",
      "('clinical_stage', 'KIRP') LR Accuracy is: 59.0\n",
      "('clinical_stage', 'KIRP') Majority Accuracy is: 66.0\n",
      "('clinical_stage', 'KIRP') LR Accuracy is: 60.0\n",
      "('clinical_stage', 'KIRP') Majority Accuracy is: 66.0\n",
      "('clinical_stage', 'KIRP') LR Accuracy is: 60.0\n",
      "('clinical_stage', 'KIRP') Majority Accuracy is: 66.0\n",
      "('clinical_stage', 'KIRP') LR Accuracy is: 60.0\n",
      "('colon_polyps_present', 'COAD') Majority Accuracy is: 66.0\n",
      "('colon_polyps_present', 'COAD') LR Accuracy is: 64.0\n",
      "('colon_polyps_present', 'COAD') Majority Accuracy is: 66.0\n",
      "('colon_polyps_present', 'COAD') LR Accuracy is: 54.0\n",
      "('colon_polyps_present', 'COAD') Majority Accuracy is: 66.0\n",
      "('colon_polyps_present', 'COAD') LR Accuracy is: 56.99999999999999\n",
      "('colon_polyps_present', 'COAD') Majority Accuracy is: 66.0\n",
      "('colon_polyps_present', 'COAD') LR Accuracy is: 48.0\n",
      "('colon_polyps_present', 'COAD') Majority Accuracy is: 66.0\n",
      "('colon_polyps_present', 'COAD') LR Accuracy is: 63.0\n",
      "('colon_polyps_present', 'COAD') Majority Accuracy is: 66.0\n",
      "('colon_polyps_present', 'COAD') LR Accuracy is: 48.0\n",
      "('colon_polyps_present', 'COAD') Majority Accuracy is: 66.0\n",
      "('colon_polyps_present', 'COAD') LR Accuracy is: 57.99999999999999\n",
      "('colon_polyps_present', 'COAD') Majority Accuracy is: 66.0\n",
      "('colon_polyps_present', 'COAD') LR Accuracy is: 52.0\n",
      "('colon_polyps_present', 'COAD') Majority Accuracy is: 66.0\n",
      "('colon_polyps_present', 'COAD') LR Accuracy is: 56.99999999999999\n",
      "('colon_polyps_present', 'COAD') Majority Accuracy is: 66.0\n",
      "('colon_polyps_present', 'COAD') LR Accuracy is: 61.0\n",
      "('colon_polyps_present', 'COAD') Majority Accuracy is: 66.0\n",
      "('colon_polyps_present', 'COAD') LR Accuracy is: 59.0\n",
      "('colon_polyps_present', 'COADREAD') Majority Accuracy is: 71.0\n",
      "('colon_polyps_present', 'COADREAD') LR Accuracy is: 62.0\n",
      "('colon_polyps_present', 'COADREAD') Majority Accuracy is: 71.0\n",
      "('colon_polyps_present', 'COADREAD') LR Accuracy is: 65.0\n",
      "('colon_polyps_present', 'COADREAD') Majority Accuracy is: 71.0\n",
      "('colon_polyps_present', 'COADREAD') LR Accuracy is: 51.0\n",
      "('colon_polyps_present', 'COADREAD') Majority Accuracy is: 71.0\n",
      "('colon_polyps_present', 'COADREAD') LR Accuracy is: 64.0\n",
      "('colon_polyps_present', 'COADREAD') Majority Accuracy is: 71.0\n",
      "('colon_polyps_present', 'COADREAD') LR Accuracy is: 60.0\n",
      "('colon_polyps_present', 'COADREAD') Majority Accuracy is: 71.0\n",
      "('colon_polyps_present', 'COADREAD') LR Accuracy is: 65.0\n",
      "('colon_polyps_present', 'COADREAD') Majority Accuracy is: 71.0\n",
      "('colon_polyps_present', 'COADREAD') LR Accuracy is: 65.0\n",
      "('colon_polyps_present', 'COADREAD') Majority Accuracy is: 71.0\n",
      "('colon_polyps_present', 'COADREAD') LR Accuracy is: 52.0\n",
      "('colon_polyps_present', 'COADREAD') Majority Accuracy is: 71.0\n",
      "('colon_polyps_present', 'COADREAD') LR Accuracy is: 60.0\n",
      "('colon_polyps_present', 'COADREAD') Majority Accuracy is: 71.0\n",
      "('colon_polyps_present', 'COADREAD') LR Accuracy is: 59.0\n",
      "('colon_polyps_present', 'COADREAD') Majority Accuracy is: 71.0\n",
      "('colon_polyps_present', 'COADREAD') LR Accuracy is: 60.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('family_history_of_cancer', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'GBMLGG') LR Accuracy is: 49.0\n",
      "('family_history_of_cancer', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'GBMLGG') LR Accuracy is: 49.0\n",
      "('family_history_of_cancer', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'GBMLGG') LR Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'GBMLGG') LR Accuracy is: 55.00000000000001\n",
      "('family_history_of_cancer', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'GBMLGG') LR Accuracy is: 50.0\n",
      "('family_history_of_cancer', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'GBMLGG') LR Accuracy is: 50.0\n",
      "('family_history_of_cancer', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'GBMLGG') LR Accuracy is: 52.0\n",
      "('family_history_of_cancer', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'GBMLGG') LR Accuracy is: 52.0\n",
      "('family_history_of_cancer', 'GBMLGG') Majority Accuracy is: 62.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('family_history_of_cancer', 'GBMLGG') LR Accuracy is: 50.0\n",
      "('family_history_of_cancer', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'GBMLGG') LR Accuracy is: 49.0\n",
      "('family_history_of_cancer', 'GBMLGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'GBMLGG') LR Accuracy is: 50.0\n",
      "('family_history_of_cancer', 'LGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'LGG') LR Accuracy is: 49.0\n",
      "('family_history_of_cancer', 'LGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'LGG') LR Accuracy is: 49.0\n",
      "('family_history_of_cancer', 'LGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'LGG') LR Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'LGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'LGG') LR Accuracy is: 55.00000000000001\n",
      "('family_history_of_cancer', 'LGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'LGG') LR Accuracy is: 50.0\n",
      "('family_history_of_cancer', 'LGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'LGG') LR Accuracy is: 50.0\n",
      "('family_history_of_cancer', 'LGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'LGG') LR Accuracy is: 52.0\n",
      "('family_history_of_cancer', 'LGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'LGG') LR Accuracy is: 52.0\n",
      "('family_history_of_cancer', 'LGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'LGG') LR Accuracy is: 50.0\n",
      "('family_history_of_cancer', 'LGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'LGG') LR Accuracy is: 49.0\n",
      "('family_history_of_cancer', 'LGG') Majority Accuracy is: 62.0\n",
      "('family_history_of_cancer', 'LGG') LR Accuracy is: 50.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') LR Accuracy is: 95.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') LR Accuracy is: 95.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') LR Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') LR Accuracy is: 93.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') LR Accuracy is: 91.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') LR Accuracy is: 95.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') LR Accuracy is: 94.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') LR Accuracy is: 95.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') LR Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') LR Accuracy is: 95.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'GBMLGG') LR Accuracy is: 92.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') LR Accuracy is: 95.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') LR Accuracy is: 95.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') LR Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') LR Accuracy is: 93.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') LR Accuracy is: 91.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') LR Accuracy is: 95.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') LR Accuracy is: 94.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') LR Accuracy is: 95.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') LR Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') LR Accuracy is: 95.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') Majority Accuracy is: 96.0\n",
      "('family_history_of_primary_brain_tumor', 'LGG') LR Accuracy is: 92.0\n",
      "('family_history_of_stomach_cancer', 'STAD') Majority Accuracy is: 95.0\n",
      "('family_history_of_stomach_cancer', 'STAD') LR Accuracy is: 85.0\n",
      "('family_history_of_stomach_cancer', 'STAD') Majority Accuracy is: 95.0\n",
      "('family_history_of_stomach_cancer', 'STAD') LR Accuracy is: 93.0\n",
      "('family_history_of_stomach_cancer', 'STAD') Majority Accuracy is: 95.0\n",
      "('family_history_of_stomach_cancer', 'STAD') LR Accuracy is: 92.0\n",
      "('family_history_of_stomach_cancer', 'STAD') Majority Accuracy is: 95.0\n",
      "('family_history_of_stomach_cancer', 'STAD') LR Accuracy is: 94.0\n",
      "('family_history_of_stomach_cancer', 'STAD') Majority Accuracy is: 95.0\n",
      "('family_history_of_stomach_cancer', 'STAD') LR Accuracy is: 87.0\n",
      "('family_history_of_stomach_cancer', 'STAD') Majority Accuracy is: 95.0\n",
      "('family_history_of_stomach_cancer', 'STAD') LR Accuracy is: 87.0\n",
      "('family_history_of_stomach_cancer', 'STAD') Majority Accuracy is: 95.0\n",
      "('family_history_of_stomach_cancer', 'STAD') LR Accuracy is: 91.0\n",
      "('family_history_of_stomach_cancer', 'STAD') Majority Accuracy is: 95.0\n",
      "('family_history_of_stomach_cancer', 'STAD') LR Accuracy is: 93.0\n",
      "('family_history_of_stomach_cancer', 'STAD') Majority Accuracy is: 95.0\n",
      "('family_history_of_stomach_cancer', 'STAD') LR Accuracy is: 84.0\n",
      "('family_history_of_stomach_cancer', 'STAD') Majority Accuracy is: 95.0\n",
      "('family_history_of_stomach_cancer', 'STAD') LR Accuracy is: 89.0\n",
      "('family_history_of_stomach_cancer', 'STAD') Majority Accuracy is: 95.0\n",
      "('family_history_of_stomach_cancer', 'STAD') LR Accuracy is: 82.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('gender', 'BLCA') Majority Accuracy is: 73.0\n",
      "('gender', 'BLCA') LR Accuracy is: 83.0\n",
      "('gender', 'BLCA') Majority Accuracy is: 73.0\n",
      "('gender', 'BLCA') LR Accuracy is: 78.0\n",
      "('gender', 'BLCA') Majority Accuracy is: 73.0\n",
      "('gender', 'BLCA') LR Accuracy is: 77.0\n",
      "('gender', 'BLCA') Majority Accuracy is: 73.0\n",
      "('gender', 'BLCA') LR Accuracy is: 80.0\n",
      "('gender', 'BLCA') Majority Accuracy is: 73.0\n",
      "('gender', 'BLCA') LR Accuracy is: 79.0\n",
      "('gender', 'BLCA') Majority Accuracy is: 73.0\n",
      "('gender', 'BLCA') LR Accuracy is: 77.0\n",
      "('gender', 'BLCA') Majority Accuracy is: 73.0\n",
      "('gender', 'BLCA') LR Accuracy is: 81.0\n",
      "('gender', 'BLCA') Majority Accuracy is: 73.0\n",
      "('gender', 'BLCA') LR Accuracy is: 80.0\n",
      "('gender', 'BLCA') Majority Accuracy is: 73.0\n",
      "('gender', 'BLCA') LR Accuracy is: 81.0\n",
      "('gender', 'BLCA') Majority Accuracy is: 73.0\n",
      "('gender', 'BLCA') LR Accuracy is: 76.0\n",
      "('gender', 'BLCA') Majority Accuracy is: 73.0\n",
      "('gender', 'BLCA') LR Accuracy is: 85.0\n",
      "('gender', 'BRCA') Majority Accuracy is: 99.0\n",
      "('gender', 'BRCA') LR Accuracy is: 99.0\n",
      "('gender', 'BRCA') Majority Accuracy is: 99.0\n",
      "('gender', 'BRCA') LR Accuracy is: 99.0\n",
      "('gender', 'BRCA') Majority Accuracy is: 99.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('gender', 'BRCA') LR Accuracy is: 99.0\n",
      "('gender', 'BRCA') Majority Accuracy is: 99.0\n",
      "('gender', 'BRCA') LR Accuracy is: 99.0\n",
      "('gender', 'BRCA') Majority Accuracy is: 99.0\n",
      "('gender', 'BRCA') LR Accuracy is: 99.0\n",
      "('gender', 'BRCA') Majority Accuracy is: 99.0\n",
      "('gender', 'BRCA') LR Accuracy is: 99.0\n",
      "('gender', 'BRCA') Majority Accuracy is: 99.0\n",
      "('gender', 'BRCA') LR Accuracy is: 99.0\n",
      "('gender', 'BRCA') Majority Accuracy is: 99.0\n",
      "('gender', 'BRCA') LR Accuracy is: 99.0\n",
      "('gender', 'BRCA') Majority Accuracy is: 99.0\n",
      "('gender', 'BRCA') LR Accuracy is: 99.0\n",
      "('gender', 'BRCA') Majority Accuracy is: 99.0\n",
      "('gender', 'BRCA') LR Accuracy is: 99.0\n",
      "('gender', 'BRCA') Majority Accuracy is: 99.0\n",
      "('gender', 'BRCA') LR Accuracy is: 99.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('gender', 'COAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COAD') LR Accuracy is: 85.0\n",
      "('gender', 'COAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COAD') LR Accuracy is: 90.0\n",
      "('gender', 'COAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COAD') LR Accuracy is: 92.0\n",
      "('gender', 'COAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COAD') LR Accuracy is: 87.0\n",
      "('gender', 'COAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COAD') LR Accuracy is: 96.0\n",
      "('gender', 'COAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COAD') LR Accuracy is: 94.0\n",
      "('gender', 'COAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COAD') LR Accuracy is: 95.0\n",
      "('gender', 'COAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COAD') LR Accuracy is: 94.0\n",
      "('gender', 'COAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COAD') LR Accuracy is: 86.0\n",
      "('gender', 'COAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COAD') LR Accuracy is: 96.0\n",
      "('gender', 'COAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COAD') LR Accuracy is: 88.0\n",
      "('gender', 'COADREAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COADREAD') LR Accuracy is: 85.0\n",
      "('gender', 'COADREAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COADREAD') LR Accuracy is: 81.0\n",
      "('gender', 'COADREAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COADREAD') LR Accuracy is: 87.0\n",
      "('gender', 'COADREAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COADREAD') LR Accuracy is: 72.0\n",
      "('gender', 'COADREAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COADREAD') LR Accuracy is: 82.0\n",
      "('gender', 'COADREAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COADREAD') LR Accuracy is: 96.0\n",
      "('gender', 'COADREAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COADREAD') LR Accuracy is: 91.0\n",
      "('gender', 'COADREAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COADREAD') LR Accuracy is: 83.0\n",
      "('gender', 'COADREAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COADREAD') LR Accuracy is: 93.0\n",
      "('gender', 'COADREAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COADREAD') LR Accuracy is: 80.0\n",
      "('gender', 'COADREAD') Majority Accuracy is: 54.0\n",
      "('gender', 'COADREAD') LR Accuracy is: 78.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('gender', 'ESCA') Majority Accuracy is: 84.0\n",
      "('gender', 'ESCA') LR Accuracy is: 82.0\n",
      "('gender', 'ESCA') Majority Accuracy is: 84.0\n",
      "('gender', 'ESCA') LR Accuracy is: 83.0\n",
      "('gender', 'ESCA') Majority Accuracy is: 84.0\n",
      "('gender', 'ESCA') LR Accuracy is: 84.0\n",
      "('gender', 'ESCA') Majority Accuracy is: 84.0\n",
      "('gender', 'ESCA') LR Accuracy is: 84.0\n",
      "('gender', 'ESCA') Majority Accuracy is: 84.0\n",
      "('gender', 'ESCA') LR Accuracy is: 82.0\n",
      "('gender', 'ESCA') Majority Accuracy is: 84.0\n",
      "('gender', 'ESCA') LR Accuracy is: 83.0\n",
      "('gender', 'ESCA') Majority Accuracy is: 84.0\n",
      "('gender', 'ESCA') LR Accuracy is: 84.0\n",
      "('gender', 'ESCA') Majority Accuracy is: 84.0\n",
      "('gender', 'ESCA') LR Accuracy is: 85.0\n",
      "('gender', 'ESCA') Majority Accuracy is: 84.0\n",
      "('gender', 'ESCA') LR Accuracy is: 86.0\n",
      "('gender', 'ESCA') Majority Accuracy is: 84.0\n",
      "('gender', 'ESCA') LR Accuracy is: 83.0\n",
      "('gender', 'ESCA') Majority Accuracy is: 84.0\n",
      "('gender', 'ESCA') LR Accuracy is: 83.0\n",
      "('gender', 'GBM') Majority Accuracy is: 65.0\n",
      "('gender', 'GBM') LR Accuracy is: 80.0\n",
      "('gender', 'GBM') Majority Accuracy is: 65.0\n",
      "('gender', 'GBM') LR Accuracy is: 66.0\n",
      "('gender', 'GBM') Majority Accuracy is: 65.0\n",
      "('gender', 'GBM') LR Accuracy is: 71.0\n",
      "('gender', 'GBM') Majority Accuracy is: 65.0\n",
      "('gender', 'GBM') LR Accuracy is: 78.0\n",
      "('gender', 'GBM') Majority Accuracy is: 65.0\n",
      "('gender', 'GBM') LR Accuracy is: 72.0\n",
      "('gender', 'GBM') Majority Accuracy is: 65.0\n",
      "('gender', 'GBM') LR Accuracy is: 76.0\n",
      "('gender', 'GBM') Majority Accuracy is: 65.0\n",
      "('gender', 'GBM') LR Accuracy is: 83.0\n",
      "('gender', 'GBM') Majority Accuracy is: 65.0\n",
      "('gender', 'GBM') LR Accuracy is: 81.0\n",
      "('gender', 'GBM') Majority Accuracy is: 65.0\n",
      "('gender', 'GBM') LR Accuracy is: 66.0\n",
      "('gender', 'GBM') Majority Accuracy is: 65.0\n",
      "('gender', 'GBM') LR Accuracy is: 77.0\n",
      "('gender', 'GBM') Majority Accuracy is: 65.0\n",
      "('gender', 'GBM') LR Accuracy is: 84.0\n",
      "('gender', 'GBMLGG') Majority Accuracy is: 56.99999999999999\n",
      "('gender', 'GBMLGG') LR Accuracy is: 80.0\n",
      "('gender', 'GBMLGG') Majority Accuracy is: 56.99999999999999\n",
      "('gender', 'GBMLGG') LR Accuracy is: 79.0\n",
      "('gender', 'GBMLGG') Majority Accuracy is: 56.99999999999999\n",
      "('gender', 'GBMLGG') LR Accuracy is: 75.0\n",
      "('gender', 'GBMLGG') Majority Accuracy is: 56.99999999999999\n",
      "('gender', 'GBMLGG') LR Accuracy is: 94.0\n",
      "('gender', 'GBMLGG') Majority Accuracy is: 56.99999999999999\n",
      "('gender', 'GBMLGG') LR Accuracy is: 82.0\n",
      "('gender', 'GBMLGG') Majority Accuracy is: 56.99999999999999\n",
      "('gender', 'GBMLGG') LR Accuracy is: 90.0\n",
      "('gender', 'GBMLGG') Majority Accuracy is: 56.99999999999999\n",
      "('gender', 'GBMLGG') LR Accuracy is: 88.0\n",
      "('gender', 'GBMLGG') Majority Accuracy is: 56.99999999999999\n",
      "('gender', 'GBMLGG') LR Accuracy is: 81.0\n",
      "('gender', 'GBMLGG') Majority Accuracy is: 56.99999999999999\n",
      "('gender', 'GBMLGG') LR Accuracy is: 83.0\n",
      "('gender', 'GBMLGG') Majority Accuracy is: 56.99999999999999\n",
      "('gender', 'GBMLGG') LR Accuracy is: 95.0\n",
      "('gender', 'GBMLGG') Majority Accuracy is: 56.99999999999999\n",
      "('gender', 'GBMLGG') LR Accuracy is: 82.0\n",
      "('gender', 'HNSC') Majority Accuracy is: 73.0\n",
      "('gender', 'HNSC') LR Accuracy is: 75.0\n",
      "('gender', 'HNSC') Majority Accuracy is: 73.0\n",
      "('gender', 'HNSC') LR Accuracy is: 74.0\n",
      "('gender', 'HNSC') Majority Accuracy is: 73.0\n",
      "('gender', 'HNSC') LR Accuracy is: 71.0\n",
      "('gender', 'HNSC') Majority Accuracy is: 73.0\n",
      "('gender', 'HNSC') LR Accuracy is: 80.0\n",
      "('gender', 'HNSC') Majority Accuracy is: 73.0\n",
      "('gender', 'HNSC') LR Accuracy is: 80.0\n",
      "('gender', 'HNSC') Majority Accuracy is: 73.0\n",
      "('gender', 'HNSC') LR Accuracy is: 75.0\n",
      "('gender', 'HNSC') Majority Accuracy is: 73.0\n",
      "('gender', 'HNSC') LR Accuracy is: 84.0\n",
      "('gender', 'HNSC') Majority Accuracy is: 73.0\n",
      "('gender', 'HNSC') LR Accuracy is: 78.0\n",
      "('gender', 'HNSC') Majority Accuracy is: 73.0\n",
      "('gender', 'HNSC') LR Accuracy is: 68.0\n",
      "('gender', 'HNSC') Majority Accuracy is: 73.0\n",
      "('gender', 'HNSC') LR Accuracy is: 77.0\n",
      "('gender', 'HNSC') Majority Accuracy is: 73.0\n",
      "('gender', 'HNSC') LR Accuracy is: 82.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('gender', 'KIRP') Majority Accuracy is: 73.0\n",
      "('gender', 'KIRP') LR Accuracy is: 83.0\n",
      "('gender', 'KIRP') Majority Accuracy is: 73.0\n",
      "('gender', 'KIRP') LR Accuracy is: 79.0\n",
      "('gender', 'KIRP') Majority Accuracy is: 73.0\n",
      "('gender', 'KIRP') LR Accuracy is: 79.0\n",
      "('gender', 'KIRP') Majority Accuracy is: 73.0\n",
      "('gender', 'KIRP') LR Accuracy is: 73.0\n",
      "('gender', 'KIRP') Majority Accuracy is: 73.0\n",
      "('gender', 'KIRP') LR Accuracy is: 83.0\n",
      "('gender', 'KIRP') Majority Accuracy is: 73.0\n",
      "('gender', 'KIRP') LR Accuracy is: 79.0\n",
      "('gender', 'KIRP') Majority Accuracy is: 73.0\n",
      "('gender', 'KIRP') LR Accuracy is: 85.0\n",
      "('gender', 'KIRP') Majority Accuracy is: 73.0\n",
      "('gender', 'KIRP') LR Accuracy is: 82.0\n",
      "('gender', 'KIRP') Majority Accuracy is: 73.0\n",
      "('gender', 'KIRP') LR Accuracy is: 77.0\n",
      "('gender', 'KIRP') Majority Accuracy is: 73.0\n",
      "('gender', 'KIRP') LR Accuracy is: 76.0\n",
      "('gender', 'KIRP') Majority Accuracy is: 73.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('gender', 'KIRP') LR Accuracy is: 87.0\n",
      "('gender', 'LAML') Majority Accuracy is: 54.0\n",
      "('gender', 'LAML') LR Accuracy is: 95.0\n",
      "('gender', 'LAML') Majority Accuracy is: 54.0\n",
      "('gender', 'LAML') LR Accuracy is: 77.0\n",
      "('gender', 'LAML') Majority Accuracy is: 54.0\n",
      "('gender', 'LAML') LR Accuracy is: 88.0\n",
      "('gender', 'LAML') Majority Accuracy is: 54.0\n",
      "('gender', 'LAML') LR Accuracy is: 78.0\n",
      "('gender', 'LAML') Majority Accuracy is: 54.0\n",
      "('gender', 'LAML') LR Accuracy is: 90.0\n",
      "('gender', 'LAML') Majority Accuracy is: 54.0\n",
      "('gender', 'LAML') LR Accuracy is: 70.0\n",
      "('gender', 'LAML') Majority Accuracy is: 54.0\n",
      "('gender', 'LAML') LR Accuracy is: 91.0\n",
      "('gender', 'LAML') Majority Accuracy is: 54.0\n",
      "('gender', 'LAML') LR Accuracy is: 82.0\n",
      "('gender', 'LAML') Majority Accuracy is: 54.0\n",
      "('gender', 'LAML') LR Accuracy is: 67.0\n",
      "('gender', 'LAML') Majority Accuracy is: 54.0\n",
      "('gender', 'LAML') LR Accuracy is: 69.0\n",
      "('gender', 'LAML') Majority Accuracy is: 54.0\n",
      "('gender', 'LAML') LR Accuracy is: 83.0\n",
      "('gender', 'LGG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'LGG') LR Accuracy is: 77.0\n",
      "('gender', 'LGG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'LGG') LR Accuracy is: 78.0\n",
      "('gender', 'LGG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'LGG') LR Accuracy is: 95.0\n",
      "('gender', 'LGG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'LGG') LR Accuracy is: 93.0\n",
      "('gender', 'LGG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'LGG') LR Accuracy is: 76.0\n",
      "('gender', 'LGG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'LGG') LR Accuracy is: 94.0\n",
      "('gender', 'LGG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'LGG') LR Accuracy is: 93.0\n",
      "('gender', 'LGG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'LGG') LR Accuracy is: 94.0\n",
      "('gender', 'LGG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'LGG') LR Accuracy is: 82.0\n",
      "('gender', 'LGG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'LGG') LR Accuracy is: 89.0\n",
      "('gender', 'LGG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'LGG') LR Accuracy is: 88.0\n",
      "('gender', 'LIHC') Majority Accuracy is: 66.0\n",
      "('gender', 'LIHC') LR Accuracy is: 89.0\n",
      "('gender', 'LIHC') Majority Accuracy is: 66.0\n",
      "('gender', 'LIHC') LR Accuracy is: 85.0\n",
      "('gender', 'LIHC') Majority Accuracy is: 66.0\n",
      "('gender', 'LIHC') LR Accuracy is: 85.0\n",
      "('gender', 'LIHC') Majority Accuracy is: 66.0\n",
      "('gender', 'LIHC') LR Accuracy is: 93.0\n",
      "('gender', 'LIHC') Majority Accuracy is: 66.0\n",
      "('gender', 'LIHC') LR Accuracy is: 77.0\n",
      "('gender', 'LIHC') Majority Accuracy is: 66.0\n",
      "('gender', 'LIHC') LR Accuracy is: 83.0\n",
      "('gender', 'LIHC') Majority Accuracy is: 66.0\n",
      "('gender', 'LIHC') LR Accuracy is: 93.0\n",
      "('gender', 'LIHC') Majority Accuracy is: 66.0\n",
      "('gender', 'LIHC') LR Accuracy is: 90.0\n",
      "('gender', 'LIHC') Majority Accuracy is: 66.0\n",
      "('gender', 'LIHC') LR Accuracy is: 92.0\n",
      "('gender', 'LIHC') Majority Accuracy is: 66.0\n",
      "('gender', 'LIHC') LR Accuracy is: 88.0\n",
      "('gender', 'LIHC') Majority Accuracy is: 66.0\n",
      "('gender', 'LIHC') LR Accuracy is: 80.0\n",
      "('gender', 'LUAD') Majority Accuracy is: 54.0\n",
      "('gender', 'LUAD') LR Accuracy is: 92.0\n",
      "('gender', 'LUAD') Majority Accuracy is: 54.0\n",
      "('gender', 'LUAD') LR Accuracy is: 81.0\n",
      "('gender', 'LUAD') Majority Accuracy is: 54.0\n",
      "('gender', 'LUAD') LR Accuracy is: 83.0\n",
      "('gender', 'LUAD') Majority Accuracy is: 54.0\n",
      "('gender', 'LUAD') LR Accuracy is: 93.0\n",
      "('gender', 'LUAD') Majority Accuracy is: 54.0\n",
      "('gender', 'LUAD') LR Accuracy is: 85.0\n",
      "('gender', 'LUAD') Majority Accuracy is: 54.0\n",
      "('gender', 'LUAD') LR Accuracy is: 91.0\n",
      "('gender', 'LUAD') Majority Accuracy is: 54.0\n",
      "('gender', 'LUAD') LR Accuracy is: 73.0\n",
      "('gender', 'LUAD') Majority Accuracy is: 54.0\n",
      "('gender', 'LUAD') LR Accuracy is: 84.0\n",
      "('gender', 'LUAD') Majority Accuracy is: 54.0\n",
      "('gender', 'LUAD') LR Accuracy is: 89.0\n",
      "('gender', 'LUAD') Majority Accuracy is: 54.0\n",
      "('gender', 'LUAD') LR Accuracy is: 90.0\n",
      "('gender', 'LUAD') Majority Accuracy is: 54.0\n",
      "('gender', 'LUAD') LR Accuracy is: 78.0\n",
      "('gender', 'LUNG') Majority Accuracy is: 60.0\n",
      "('gender', 'LUNG') LR Accuracy is: 84.0\n",
      "('gender', 'LUNG') Majority Accuracy is: 60.0\n",
      "('gender', 'LUNG') LR Accuracy is: 80.0\n",
      "('gender', 'LUNG') Majority Accuracy is: 60.0\n",
      "('gender', 'LUNG') LR Accuracy is: 88.0\n",
      "('gender', 'LUNG') Majority Accuracy is: 60.0\n",
      "('gender', 'LUNG') LR Accuracy is: 77.0\n",
      "('gender', 'LUNG') Majority Accuracy is: 60.0\n",
      "('gender', 'LUNG') LR Accuracy is: 92.0\n",
      "('gender', 'LUNG') Majority Accuracy is: 60.0\n",
      "('gender', 'LUNG') LR Accuracy is: 80.0\n",
      "('gender', 'LUNG') Majority Accuracy is: 60.0\n",
      "('gender', 'LUNG') LR Accuracy is: 91.0\n",
      "('gender', 'LUNG') Majority Accuracy is: 60.0\n",
      "('gender', 'LUNG') LR Accuracy is: 90.0\n",
      "('gender', 'LUNG') Majority Accuracy is: 60.0\n",
      "('gender', 'LUNG') LR Accuracy is: 88.0\n",
      "('gender', 'LUNG') Majority Accuracy is: 60.0\n",
      "('gender', 'LUNG') LR Accuracy is: 90.0\n",
      "('gender', 'LUNG') Majority Accuracy is: 60.0\n",
      "('gender', 'LUNG') LR Accuracy is: 88.0\n",
      "('gender', 'LUSC') Majority Accuracy is: 74.0\n",
      "('gender', 'LUSC') LR Accuracy is: 86.0\n",
      "('gender', 'LUSC') Majority Accuracy is: 74.0\n",
      "('gender', 'LUSC') LR Accuracy is: 82.0\n",
      "('gender', 'LUSC') Majority Accuracy is: 74.0\n",
      "('gender', 'LUSC') LR Accuracy is: 67.0\n",
      "('gender', 'LUSC') Majority Accuracy is: 74.0\n",
      "('gender', 'LUSC') LR Accuracy is: 76.0\n",
      "('gender', 'LUSC') Majority Accuracy is: 74.0\n",
      "('gender', 'LUSC') LR Accuracy is: 86.0\n",
      "('gender', 'LUSC') Majority Accuracy is: 74.0\n",
      "('gender', 'LUSC') LR Accuracy is: 81.0\n",
      "('gender', 'LUSC') Majority Accuracy is: 74.0\n",
      "('gender', 'LUSC') LR Accuracy is: 81.0\n",
      "('gender', 'LUSC') Majority Accuracy is: 74.0\n",
      "('gender', 'LUSC') LR Accuracy is: 79.0\n",
      "('gender', 'LUSC') Majority Accuracy is: 74.0\n",
      "('gender', 'LUSC') LR Accuracy is: 82.0\n",
      "('gender', 'LUSC') Majority Accuracy is: 74.0\n",
      "('gender', 'LUSC') LR Accuracy is: 85.0\n",
      "('gender', 'LUSC') Majority Accuracy is: 74.0\n",
      "('gender', 'LUSC') LR Accuracy is: 79.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('gender', 'PAAD') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PAAD') LR Accuracy is: 83.0\n",
      "('gender', 'PAAD') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PAAD') LR Accuracy is: 87.0\n",
      "('gender', 'PAAD') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PAAD') LR Accuracy is: 90.0\n",
      "('gender', 'PAAD') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PAAD') LR Accuracy is: 94.0\n",
      "('gender', 'PAAD') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PAAD') LR Accuracy is: 84.0\n",
      "('gender', 'PAAD') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PAAD') LR Accuracy is: 89.0\n",
      "('gender', 'PAAD') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PAAD') LR Accuracy is: 88.0\n",
      "('gender', 'PAAD') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PAAD') LR Accuracy is: 90.0\n",
      "('gender', 'PAAD') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PAAD') LR Accuracy is: 90.0\n",
      "('gender', 'PAAD') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PAAD') LR Accuracy is: 81.0\n",
      "('gender', 'PAAD') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PAAD') LR Accuracy is: 91.0\n",
      "('gender', 'PCPG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PCPG') LR Accuracy is: 79.0\n",
      "('gender', 'PCPG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PCPG') LR Accuracy is: 73.0\n",
      "('gender', 'PCPG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PCPG') LR Accuracy is: 83.0\n",
      "('gender', 'PCPG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PCPG') LR Accuracy is: 81.0\n",
      "('gender', 'PCPG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PCPG') LR Accuracy is: 82.0\n",
      "('gender', 'PCPG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PCPG') LR Accuracy is: 94.0\n",
      "('gender', 'PCPG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PCPG') LR Accuracy is: 85.0\n",
      "('gender', 'PCPG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PCPG') LR Accuracy is: 77.0\n",
      "('gender', 'PCPG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PCPG') LR Accuracy is: 82.0\n",
      "('gender', 'PCPG') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'PCPG') LR Accuracy is: 80.0\n",
      "('gender', 'PCPG') Majority Accuracy is: 55.00000000000001\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('gender', 'PCPG') LR Accuracy is: 81.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('gender', 'SARC') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'SARC') LR Accuracy is: 71.0\n",
      "('gender', 'SARC') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'SARC') LR Accuracy is: 73.0\n",
      "('gender', 'SARC') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'SARC') LR Accuracy is: 74.0\n",
      "('gender', 'SARC') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'SARC') LR Accuracy is: 55.00000000000001\n",
      "('gender', 'SARC') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'SARC') LR Accuracy is: 77.0\n",
      "('gender', 'SARC') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'SARC') LR Accuracy is: 78.0\n",
      "('gender', 'SARC') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'SARC') LR Accuracy is: 76.0\n",
      "('gender', 'SARC') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'SARC') LR Accuracy is: 69.0\n",
      "('gender', 'SARC') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'SARC') LR Accuracy is: 78.0\n",
      "('gender', 'SARC') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'SARC') LR Accuracy is: 74.0\n",
      "('gender', 'SARC') Majority Accuracy is: 55.00000000000001\n",
      "('gender', 'SARC') LR Accuracy is: 66.0\n",
      "('gender', 'SKCM') Majority Accuracy is: 62.0\n",
      "('gender', 'SKCM') LR Accuracy is: 78.0\n",
      "('gender', 'SKCM') Majority Accuracy is: 62.0\n",
      "('gender', 'SKCM') LR Accuracy is: 77.0\n",
      "('gender', 'SKCM') Majority Accuracy is: 62.0\n",
      "('gender', 'SKCM') LR Accuracy is: 75.0\n",
      "('gender', 'SKCM') Majority Accuracy is: 62.0\n",
      "('gender', 'SKCM') LR Accuracy is: 75.0\n",
      "('gender', 'SKCM') Majority Accuracy is: 62.0\n",
      "('gender', 'SKCM') LR Accuracy is: 80.0\n",
      "('gender', 'SKCM') Majority Accuracy is: 62.0\n",
      "('gender', 'SKCM') LR Accuracy is: 72.0\n",
      "('gender', 'SKCM') Majority Accuracy is: 62.0\n",
      "('gender', 'SKCM') LR Accuracy is: 74.0\n",
      "('gender', 'SKCM') Majority Accuracy is: 62.0\n",
      "('gender', 'SKCM') LR Accuracy is: 76.0\n",
      "('gender', 'SKCM') Majority Accuracy is: 62.0\n",
      "('gender', 'SKCM') LR Accuracy is: 71.0\n",
      "('gender', 'SKCM') Majority Accuracy is: 62.0\n",
      "('gender', 'SKCM') LR Accuracy is: 71.0\n",
      "('gender', 'SKCM') Majority Accuracy is: 62.0\n",
      "('gender', 'SKCM') LR Accuracy is: 74.0\n",
      "('gender', 'STAD') Majority Accuracy is: 65.0\n",
      "('gender', 'STAD') LR Accuracy is: 77.0\n",
      "('gender', 'STAD') Majority Accuracy is: 65.0\n",
      "('gender', 'STAD') LR Accuracy is: 80.0\n",
      "('gender', 'STAD') Majority Accuracy is: 65.0\n",
      "('gender', 'STAD') LR Accuracy is: 82.0\n",
      "('gender', 'STAD') Majority Accuracy is: 65.0\n",
      "('gender', 'STAD') LR Accuracy is: 73.0\n",
      "('gender', 'STAD') Majority Accuracy is: 65.0\n",
      "('gender', 'STAD') LR Accuracy is: 81.0\n",
      "('gender', 'STAD') Majority Accuracy is: 65.0\n",
      "('gender', 'STAD') LR Accuracy is: 81.0\n",
      "('gender', 'STAD') Majority Accuracy is: 65.0\n",
      "('gender', 'STAD') LR Accuracy is: 88.0\n",
      "('gender', 'STAD') Majority Accuracy is: 65.0\n",
      "('gender', 'STAD') LR Accuracy is: 83.0\n",
      "('gender', 'STAD') Majority Accuracy is: 65.0\n",
      "('gender', 'STAD') LR Accuracy is: 77.0\n",
      "('gender', 'STAD') Majority Accuracy is: 65.0\n",
      "('gender', 'STAD') LR Accuracy is: 74.0\n",
      "('gender', 'STAD') Majority Accuracy is: 65.0\n",
      "('gender', 'STAD') LR Accuracy is: 89.0\n",
      "('gender', 'THCA') Majority Accuracy is: 73.0\n",
      "('gender', 'THCA') LR Accuracy is: 93.0\n",
      "('gender', 'THCA') Majority Accuracy is: 73.0\n",
      "('gender', 'THCA') LR Accuracy is: 86.0\n",
      "('gender', 'THCA') Majority Accuracy is: 73.0\n",
      "('gender', 'THCA') LR Accuracy is: 87.0\n",
      "('gender', 'THCA') Majority Accuracy is: 73.0\n",
      "('gender', 'THCA') LR Accuracy is: 92.0\n",
      "('gender', 'THCA') Majority Accuracy is: 73.0\n",
      "('gender', 'THCA') LR Accuracy is: 93.0\n",
      "('gender', 'THCA') Majority Accuracy is: 73.0\n",
      "('gender', 'THCA') LR Accuracy is: 94.0\n",
      "('gender', 'THCA') Majority Accuracy is: 73.0\n",
      "('gender', 'THCA') LR Accuracy is: 89.0\n",
      "('gender', 'THCA') Majority Accuracy is: 73.0\n",
      "('gender', 'THCA') LR Accuracy is: 90.0\n",
      "('gender', 'THCA') Majority Accuracy is: 73.0\n",
      "('gender', 'THCA') LR Accuracy is: 92.0\n",
      "('gender', 'THCA') Majority Accuracy is: 73.0\n",
      "('gender', 'THCA') LR Accuracy is: 93.0\n",
      "('gender', 'THCA') Majority Accuracy is: 73.0\n",
      "('gender', 'THCA') LR Accuracy is: 93.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('histological_type', 'ESCA') Majority Accuracy is: 49.0\n",
      "('histological_type', 'ESCA') LR Accuracy is: 96.0\n",
      "('histological_type', 'ESCA') Majority Accuracy is: 49.0\n",
      "('histological_type', 'ESCA') LR Accuracy is: 97.0\n",
      "('histological_type', 'ESCA') Majority Accuracy is: 49.0\n",
      "('histological_type', 'ESCA') LR Accuracy is: 97.0\n",
      "('histological_type', 'ESCA') Majority Accuracy is: 49.0\n",
      "('histological_type', 'ESCA') LR Accuracy is: 98.0\n",
      "('histological_type', 'ESCA') Majority Accuracy is: 49.0\n",
      "('histological_type', 'ESCA') LR Accuracy is: 95.0\n",
      "('histological_type', 'ESCA') Majority Accuracy is: 49.0\n",
      "('histological_type', 'ESCA') LR Accuracy is: 98.0\n",
      "('histological_type', 'ESCA') Majority Accuracy is: 49.0\n",
      "('histological_type', 'ESCA') LR Accuracy is: 97.0\n",
      "('histological_type', 'ESCA') Majority Accuracy is: 49.0\n",
      "('histological_type', 'ESCA') LR Accuracy is: 98.0\n",
      "('histological_type', 'ESCA') Majority Accuracy is: 49.0\n",
      "('histological_type', 'ESCA') LR Accuracy is: 97.0\n",
      "('histological_type', 'ESCA') Majority Accuracy is: 49.0\n",
      "('histological_type', 'ESCA') LR Accuracy is: 97.0\n",
      "('histological_type', 'ESCA') Majority Accuracy is: 49.0\n",
      "('histological_type', 'ESCA') LR Accuracy is: 97.0\n",
      "('histological_type', 'LGG') Majority Accuracy is: 38.0\n",
      "('histological_type', 'LGG') LR Accuracy is: 59.0\n",
      "('histological_type', 'LGG') Majority Accuracy is: 38.0\n",
      "('histological_type', 'LGG') LR Accuracy is: 59.0\n",
      "('histological_type', 'LGG') Majority Accuracy is: 38.0\n",
      "('histological_type', 'LGG') LR Accuracy is: 49.0\n",
      "('histological_type', 'LGG') Majority Accuracy is: 38.0\n",
      "('histological_type', 'LGG') LR Accuracy is: 37.0\n",
      "('histological_type', 'LGG') Majority Accuracy is: 38.0\n",
      "('histological_type', 'LGG') LR Accuracy is: 56.00000000000001\n",
      "('histological_type', 'LGG') Majority Accuracy is: 38.0\n",
      "('histological_type', 'LGG') LR Accuracy is: 56.00000000000001\n",
      "('histological_type', 'LGG') Majority Accuracy is: 38.0\n",
      "('histological_type', 'LGG') LR Accuracy is: 54.0\n",
      "('histological_type', 'LGG') Majority Accuracy is: 38.0\n",
      "('histological_type', 'LGG') LR Accuracy is: 53.0\n",
      "('histological_type', 'LGG') Majority Accuracy is: 38.0\n",
      "('histological_type', 'LGG') LR Accuracy is: 55.00000000000001\n",
      "('histological_type', 'LGG') Majority Accuracy is: 38.0\n",
      "('histological_type', 'LGG') LR Accuracy is: 53.0\n",
      "('histological_type', 'LGG') Majority Accuracy is: 38.0\n",
      "('histological_type', 'LGG') LR Accuracy is: 50.0\n",
      "('histological_type', 'PCPG') Majority Accuracy is: 82.0\n",
      "('histological_type', 'PCPG') LR Accuracy is: 83.0\n",
      "('histological_type', 'PCPG') Majority Accuracy is: 82.0\n",
      "('histological_type', 'PCPG') LR Accuracy is: 82.0\n",
      "('histological_type', 'PCPG') Majority Accuracy is: 82.0\n",
      "('histological_type', 'PCPG') LR Accuracy is: 84.0\n",
      "('histological_type', 'PCPG') Majority Accuracy is: 82.0\n",
      "('histological_type', 'PCPG') LR Accuracy is: 78.0\n",
      "('histological_type', 'PCPG') Majority Accuracy is: 82.0\n",
      "('histological_type', 'PCPG') LR Accuracy is: 80.0\n",
      "('histological_type', 'PCPG') Majority Accuracy is: 82.0\n",
      "('histological_type', 'PCPG') LR Accuracy is: 85.0\n",
      "('histological_type', 'PCPG') Majority Accuracy is: 82.0\n",
      "('histological_type', 'PCPG') LR Accuracy is: 79.0\n",
      "('histological_type', 'PCPG') Majority Accuracy is: 82.0\n",
      "('histological_type', 'PCPG') LR Accuracy is: 77.0\n",
      "('histological_type', 'PCPG') Majority Accuracy is: 82.0\n",
      "('histological_type', 'PCPG') LR Accuracy is: 77.0\n",
      "('histological_type', 'PCPG') Majority Accuracy is: 82.0\n",
      "('histological_type', 'PCPG') LR Accuracy is: 85.0\n",
      "('histological_type', 'PCPG') Majority Accuracy is: 82.0\n",
      "('histological_type', 'PCPG') LR Accuracy is: 82.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('histological_type', 'PRAD') Majority Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') LR Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') Majority Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') LR Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') Majority Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') LR Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') Majority Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') LR Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') Majority Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') LR Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') Majority Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') LR Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') Majority Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') LR Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') Majority Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') LR Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') Majority Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') LR Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') Majority Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') LR Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') Majority Accuracy is: 97.0\n",
      "('histological_type', 'PRAD') LR Accuracy is: 97.0\n",
      "('histological_type', 'UCEC') Majority Accuracy is: 62.0\n",
      "('histological_type', 'UCEC') LR Accuracy is: 69.0\n",
      "('histological_type', 'UCEC') Majority Accuracy is: 62.0\n",
      "('histological_type', 'UCEC') LR Accuracy is: 71.0\n",
      "('histological_type', 'UCEC') Majority Accuracy is: 62.0\n",
      "('histological_type', 'UCEC') LR Accuracy is: 64.0\n",
      "('histological_type', 'UCEC') Majority Accuracy is: 62.0\n",
      "('histological_type', 'UCEC') LR Accuracy is: 71.0\n",
      "('histological_type', 'UCEC') Majority Accuracy is: 62.0\n",
      "('histological_type', 'UCEC') LR Accuracy is: 73.0\n",
      "('histological_type', 'UCEC') Majority Accuracy is: 62.0\n",
      "('histological_type', 'UCEC') LR Accuracy is: 69.0\n",
      "('histological_type', 'UCEC') Majority Accuracy is: 62.0\n",
      "('histological_type', 'UCEC') LR Accuracy is: 64.0\n",
      "('histological_type', 'UCEC') Majority Accuracy is: 62.0\n",
      "('histological_type', 'UCEC') LR Accuracy is: 71.0\n",
      "('histological_type', 'UCEC') Majority Accuracy is: 62.0\n",
      "('histological_type', 'UCEC') LR Accuracy is: 67.0\n",
      "('histological_type', 'UCEC') Majority Accuracy is: 62.0\n",
      "('histological_type', 'UCEC') LR Accuracy is: 61.0\n",
      "('histological_type', 'UCEC') Majority Accuracy is: 62.0\n",
      "('histological_type', 'UCEC') LR Accuracy is: 71.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('lymphatic_invasion', 'COAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COAD') LR Accuracy is: 56.99999999999999\n",
      "('lymphatic_invasion', 'COAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COAD') LR Accuracy is: 63.0\n",
      "('lymphatic_invasion', 'COAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COAD') LR Accuracy is: 53.0\n",
      "('lymphatic_invasion', 'COAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COAD') LR Accuracy is: 54.0\n",
      "('lymphatic_invasion', 'COAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COAD') LR Accuracy is: 57.99999999999999\n",
      "('lymphatic_invasion', 'COAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COAD') LR Accuracy is: 56.99999999999999\n",
      "('lymphatic_invasion', 'COAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COAD') LR Accuracy is: 53.0\n",
      "('lymphatic_invasion', 'COAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COAD') LR Accuracy is: 56.00000000000001\n",
      "('lymphatic_invasion', 'COAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COAD') LR Accuracy is: 56.99999999999999\n",
      "('lymphatic_invasion', 'COAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COAD') LR Accuracy is: 61.0\n",
      "('lymphatic_invasion', 'COAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COAD') LR Accuracy is: 56.99999999999999\n",
      "('lymphatic_invasion', 'COADREAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COADREAD') LR Accuracy is: 60.0\n",
      "('lymphatic_invasion', 'COADREAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COADREAD') LR Accuracy is: 61.0\n",
      "('lymphatic_invasion', 'COADREAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COADREAD') LR Accuracy is: 56.99999999999999\n",
      "('lymphatic_invasion', 'COADREAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COADREAD') LR Accuracy is: 67.0\n",
      "('lymphatic_invasion', 'COADREAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COADREAD') LR Accuracy is: 65.0\n",
      "('lymphatic_invasion', 'COADREAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COADREAD') LR Accuracy is: 60.0\n",
      "('lymphatic_invasion', 'COADREAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COADREAD') LR Accuracy is: 54.0\n",
      "('lymphatic_invasion', 'COADREAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COADREAD') LR Accuracy is: 56.00000000000001\n",
      "('lymphatic_invasion', 'COADREAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COADREAD') LR Accuracy is: 60.0\n",
      "('lymphatic_invasion', 'COADREAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COADREAD') LR Accuracy is: 63.0\n",
      "('lymphatic_invasion', 'COADREAD') Majority Accuracy is: 69.0\n",
      "('lymphatic_invasion', 'COADREAD') LR Accuracy is: 66.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('lymphovascular_invasion_present', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('lymphovascular_invasion_present', 'BLCA') LR Accuracy is: 50.0\n",
      "('lymphovascular_invasion_present', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('lymphovascular_invasion_present', 'BLCA') LR Accuracy is: 53.0\n",
      "('lymphovascular_invasion_present', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('lymphovascular_invasion_present', 'BLCA') LR Accuracy is: 49.0\n",
      "('lymphovascular_invasion_present', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('lymphovascular_invasion_present', 'BLCA') LR Accuracy is: 55.00000000000001\n",
      "('lymphovascular_invasion_present', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('lymphovascular_invasion_present', 'BLCA') LR Accuracy is: 55.00000000000001\n",
      "('lymphovascular_invasion_present', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('lymphovascular_invasion_present', 'BLCA') LR Accuracy is: 51.0\n",
      "('lymphovascular_invasion_present', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('lymphovascular_invasion_present', 'BLCA') LR Accuracy is: 53.0\n",
      "('lymphovascular_invasion_present', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('lymphovascular_invasion_present', 'BLCA') LR Accuracy is: 54.0\n",
      "('lymphovascular_invasion_present', 'BLCA') Majority Accuracy is: 55.00000000000001\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('lymphovascular_invasion_present', 'BLCA') LR Accuracy is: 60.0\n",
      "('lymphovascular_invasion_present', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('lymphovascular_invasion_present', 'BLCA') LR Accuracy is: 50.0\n",
      "('lymphovascular_invasion_present', 'BLCA') Majority Accuracy is: 55.00000000000001\n",
      "('lymphovascular_invasion_present', 'BLCA') LR Accuracy is: 52.0\n",
      "('lymphovascular_invasion_present', 'HNSC') Majority Accuracy is: 64.0\n",
      "('lymphovascular_invasion_present', 'HNSC') LR Accuracy is: 66.0\n",
      "('lymphovascular_invasion_present', 'HNSC') Majority Accuracy is: 64.0\n",
      "('lymphovascular_invasion_present', 'HNSC') LR Accuracy is: 61.0\n",
      "('lymphovascular_invasion_present', 'HNSC') Majority Accuracy is: 64.0\n",
      "('lymphovascular_invasion_present', 'HNSC') LR Accuracy is: 63.0\n",
      "('lymphovascular_invasion_present', 'HNSC') Majority Accuracy is: 64.0\n",
      "('lymphovascular_invasion_present', 'HNSC') LR Accuracy is: 70.0\n",
      "('lymphovascular_invasion_present', 'HNSC') Majority Accuracy is: 64.0\n",
      "('lymphovascular_invasion_present', 'HNSC') LR Accuracy is: 61.0\n",
      "('lymphovascular_invasion_present', 'HNSC') Majority Accuracy is: 64.0\n",
      "('lymphovascular_invasion_present', 'HNSC') LR Accuracy is: 65.0\n",
      "('lymphovascular_invasion_present', 'HNSC') Majority Accuracy is: 64.0\n",
      "('lymphovascular_invasion_present', 'HNSC') LR Accuracy is: 56.99999999999999\n",
      "('lymphovascular_invasion_present', 'HNSC') Majority Accuracy is: 64.0\n",
      "('lymphovascular_invasion_present', 'HNSC') LR Accuracy is: 54.0\n",
      "('lymphovascular_invasion_present', 'HNSC') Majority Accuracy is: 64.0\n",
      "('lymphovascular_invasion_present', 'HNSC') LR Accuracy is: 61.0\n",
      "('lymphovascular_invasion_present', 'HNSC') Majority Accuracy is: 64.0\n",
      "('lymphovascular_invasion_present', 'HNSC') LR Accuracy is: 65.0\n",
      "('lymphovascular_invasion_present', 'HNSC') Majority Accuracy is: 64.0\n",
      "('lymphovascular_invasion_present', 'HNSC') LR Accuracy is: 50.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('menopause_status', 'BRCA') Majority Accuracy is: 69.0\n",
      "('menopause_status', 'BRCA') LR Accuracy is: 64.0\n",
      "('menopause_status', 'BRCA') Majority Accuracy is: 69.0\n",
      "('menopause_status', 'BRCA') LR Accuracy is: 66.0\n",
      "('menopause_status', 'BRCA') Majority Accuracy is: 69.0\n",
      "('menopause_status', 'BRCA') LR Accuracy is: 56.99999999999999\n",
      "('menopause_status', 'BRCA') Majority Accuracy is: 69.0\n",
      "('menopause_status', 'BRCA') LR Accuracy is: 62.0\n",
      "('menopause_status', 'BRCA') Majority Accuracy is: 69.0\n",
      "('menopause_status', 'BRCA') LR Accuracy is: 72.0\n",
      "('menopause_status', 'BRCA') Majority Accuracy is: 69.0\n",
      "('menopause_status', 'BRCA') LR Accuracy is: 57.99999999999999\n",
      "('menopause_status', 'BRCA') Majority Accuracy is: 69.0\n",
      "('menopause_status', 'BRCA') LR Accuracy is: 66.0\n",
      "('menopause_status', 'BRCA') Majority Accuracy is: 69.0\n",
      "('menopause_status', 'BRCA') LR Accuracy is: 3.0\n",
      "('menopause_status', 'BRCA') Majority Accuracy is: 69.0\n",
      "('menopause_status', 'BRCA') LR Accuracy is: 64.0\n",
      "('menopause_status', 'BRCA') Majority Accuracy is: 69.0\n",
      "('menopause_status', 'BRCA') LR Accuracy is: 70.0\n",
      "('menopause_status', 'BRCA') Majority Accuracy is: 69.0\n",
      "('menopause_status', 'BRCA') LR Accuracy is: 67.0\n",
      "('mental_status_changes', 'GBMLGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'GBMLGG') LR Accuracy is: 70.0\n",
      "('mental_status_changes', 'GBMLGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'GBMLGG') LR Accuracy is: 62.0\n",
      "('mental_status_changes', 'GBMLGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'GBMLGG') LR Accuracy is: 61.0\n",
      "('mental_status_changes', 'GBMLGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'GBMLGG') LR Accuracy is: 69.0\n",
      "('mental_status_changes', 'GBMLGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'GBMLGG') LR Accuracy is: 68.0\n",
      "('mental_status_changes', 'GBMLGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'GBMLGG') LR Accuracy is: 66.0\n",
      "('mental_status_changes', 'GBMLGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'GBMLGG') LR Accuracy is: 65.0\n",
      "('mental_status_changes', 'GBMLGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'GBMLGG') LR Accuracy is: 68.0\n",
      "('mental_status_changes', 'GBMLGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'GBMLGG') LR Accuracy is: 68.0\n",
      "('mental_status_changes', 'GBMLGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'GBMLGG') LR Accuracy is: 62.0\n",
      "('mental_status_changes', 'GBMLGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'GBMLGG') LR Accuracy is: 62.0\n",
      "('mental_status_changes', 'LGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'LGG') LR Accuracy is: 70.0\n",
      "('mental_status_changes', 'LGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'LGG') LR Accuracy is: 62.0\n",
      "('mental_status_changes', 'LGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'LGG') LR Accuracy is: 61.0\n",
      "('mental_status_changes', 'LGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'LGG') LR Accuracy is: 69.0\n",
      "('mental_status_changes', 'LGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'LGG') LR Accuracy is: 68.0\n",
      "('mental_status_changes', 'LGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'LGG') LR Accuracy is: 66.0\n",
      "('mental_status_changes', 'LGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'LGG') LR Accuracy is: 65.0\n",
      "('mental_status_changes', 'LGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'LGG') LR Accuracy is: 68.0\n",
      "('mental_status_changes', 'LGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'LGG') LR Accuracy is: 68.0\n",
      "('mental_status_changes', 'LGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'LGG') LR Accuracy is: 62.0\n",
      "('mental_status_changes', 'LGG') Majority Accuracy is: 75.0\n",
      "('mental_status_changes', 'LGG') LR Accuracy is: 62.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('metastatic_diagnosis', 'SARC') Majority Accuracy is: 67.0\n",
      "('metastatic_diagnosis', 'SARC') LR Accuracy is: 60.0\n",
      "('metastatic_diagnosis', 'SARC') Majority Accuracy is: 67.0\n",
      "('metastatic_diagnosis', 'SARC') LR Accuracy is: 54.0\n",
      "('metastatic_diagnosis', 'SARC') Majority Accuracy is: 67.0\n",
      "('metastatic_diagnosis', 'SARC') LR Accuracy is: 51.0\n",
      "('metastatic_diagnosis', 'SARC') Majority Accuracy is: 67.0\n",
      "('metastatic_diagnosis', 'SARC') LR Accuracy is: 50.0\n",
      "('metastatic_diagnosis', 'SARC') Majority Accuracy is: 67.0\n",
      "('metastatic_diagnosis', 'SARC') LR Accuracy is: 59.0\n",
      "('metastatic_diagnosis', 'SARC') Majority Accuracy is: 67.0\n",
      "('metastatic_diagnosis', 'SARC') LR Accuracy is: 61.0\n",
      "('metastatic_diagnosis', 'SARC') Majority Accuracy is: 67.0\n",
      "('metastatic_diagnosis', 'SARC') LR Accuracy is: 56.99999999999999\n",
      "('metastatic_diagnosis', 'SARC') Majority Accuracy is: 67.0\n",
      "('metastatic_diagnosis', 'SARC') LR Accuracy is: 54.0\n",
      "('metastatic_diagnosis', 'SARC') Majority Accuracy is: 67.0\n",
      "('metastatic_diagnosis', 'SARC') LR Accuracy is: 56.99999999999999\n",
      "('metastatic_diagnosis', 'SARC') Majority Accuracy is: 67.0\n",
      "('metastatic_diagnosis', 'SARC') LR Accuracy is: 57.99999999999999\n",
      "('metastatic_diagnosis', 'SARC') Majority Accuracy is: 67.0\n",
      "('metastatic_diagnosis', 'SARC') LR Accuracy is: 57.99999999999999\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('oct_embedded', 'BLCA') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'BLCA') LR Accuracy is: 84.0\n",
      "('oct_embedded', 'BLCA') Majority Accuracy is: 85.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('oct_embedded', 'BLCA') LR Accuracy is: 86.0\n",
      "('oct_embedded', 'BLCA') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'BLCA') LR Accuracy is: 82.0\n",
      "('oct_embedded', 'BLCA') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'BLCA') LR Accuracy is: 79.0\n",
      "('oct_embedded', 'BLCA') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'BLCA') LR Accuracy is: 76.0\n",
      "('oct_embedded', 'BLCA') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'BLCA') LR Accuracy is: 79.0\n",
      "('oct_embedded', 'BLCA') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'BLCA') LR Accuracy is: 83.0\n",
      "('oct_embedded', 'BLCA') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'BLCA') LR Accuracy is: 81.0\n",
      "('oct_embedded', 'BLCA') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'BLCA') LR Accuracy is: 78.0\n",
      "('oct_embedded', 'BLCA') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'BLCA') LR Accuracy is: 78.0\n",
      "('oct_embedded', 'BLCA') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'BLCA') LR Accuracy is: 78.0\n",
      "('oct_embedded', 'BRCA') Majority Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') LR Accuracy is: 59.0\n",
      "('oct_embedded', 'BRCA') Majority Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') LR Accuracy is: 61.0\n",
      "('oct_embedded', 'BRCA') Majority Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') LR Accuracy is: 60.0\n",
      "('oct_embedded', 'BRCA') Majority Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') LR Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') Majority Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') LR Accuracy is: 54.0\n",
      "('oct_embedded', 'BRCA') Majority Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') LR Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'BRCA') Majority Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') LR Accuracy is: 56.00000000000001\n",
      "('oct_embedded', 'BRCA') Majority Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') LR Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') Majority Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') LR Accuracy is: 52.0\n",
      "('oct_embedded', 'BRCA') Majority Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') LR Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'BRCA') Majority Accuracy is: 62.0\n",
      "('oct_embedded', 'BRCA') LR Accuracy is: 61.0\n",
      "('oct_embedded', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'CESC') LR Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'CESC') LR Accuracy is: 54.0\n",
      "('oct_embedded', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'CESC') LR Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'CESC') LR Accuracy is: 59.0\n",
      "('oct_embedded', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'CESC') LR Accuracy is: 52.0\n",
      "('oct_embedded', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'CESC') LR Accuracy is: 54.0\n",
      "('oct_embedded', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'CESC') LR Accuracy is: 53.0\n",
      "('oct_embedded', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'CESC') LR Accuracy is: 59.0\n",
      "('oct_embedded', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'CESC') LR Accuracy is: 59.0\n",
      "('oct_embedded', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'CESC') LR Accuracy is: 56.00000000000001\n",
      "('oct_embedded', 'CESC') Majority Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'CESC') LR Accuracy is: 54.0\n",
      "('oct_embedded', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'ESCA') LR Accuracy is: 64.0\n",
      "('oct_embedded', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'ESCA') LR Accuracy is: 71.0\n",
      "('oct_embedded', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'ESCA') LR Accuracy is: 65.0\n",
      "('oct_embedded', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'ESCA') LR Accuracy is: 69.0\n",
      "('oct_embedded', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'ESCA') LR Accuracy is: 68.0\n",
      "('oct_embedded', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'ESCA') LR Accuracy is: 64.0\n",
      "('oct_embedded', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'ESCA') LR Accuracy is: 65.0\n",
      "('oct_embedded', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'ESCA') LR Accuracy is: 70.0\n",
      "('oct_embedded', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'ESCA') LR Accuracy is: 73.0\n",
      "('oct_embedded', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'ESCA') LR Accuracy is: 71.0\n",
      "('oct_embedded', 'ESCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'ESCA') LR Accuracy is: 69.0\n",
      "('oct_embedded', 'GBMLGG') Majority Accuracy is: 63.0\n",
      "('oct_embedded', 'GBMLGG') LR Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'GBMLGG') Majority Accuracy is: 63.0\n",
      "('oct_embedded', 'GBMLGG') LR Accuracy is: 56.99999999999999\n",
      "('oct_embedded', 'GBMLGG') Majority Accuracy is: 63.0\n",
      "('oct_embedded', 'GBMLGG') LR Accuracy is: 59.0\n",
      "('oct_embedded', 'GBMLGG') Majority Accuracy is: 63.0\n",
      "('oct_embedded', 'GBMLGG') LR Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'GBMLGG') Majority Accuracy is: 63.0\n",
      "('oct_embedded', 'GBMLGG') LR Accuracy is: 59.0\n",
      "('oct_embedded', 'GBMLGG') Majority Accuracy is: 63.0\n",
      "('oct_embedded', 'GBMLGG') LR Accuracy is: 60.0\n",
      "('oct_embedded', 'GBMLGG') Majority Accuracy is: 63.0\n",
      "('oct_embedded', 'GBMLGG') LR Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'GBMLGG') Majority Accuracy is: 63.0\n",
      "('oct_embedded', 'GBMLGG') LR Accuracy is: 56.00000000000001\n",
      "('oct_embedded', 'GBMLGG') Majority Accuracy is: 63.0\n",
      "('oct_embedded', 'GBMLGG') LR Accuracy is: 62.0\n",
      "('oct_embedded', 'GBMLGG') Majority Accuracy is: 63.0\n",
      "('oct_embedded', 'GBMLGG') LR Accuracy is: 65.0\n",
      "('oct_embedded', 'GBMLGG') Majority Accuracy is: 63.0\n",
      "('oct_embedded', 'GBMLGG') LR Accuracy is: 56.99999999999999\n",
      "('oct_embedded', 'HNSC') Majority Accuracy is: 51.0\n",
      "('oct_embedded', 'HNSC') LR Accuracy is: 50.0\n",
      "('oct_embedded', 'HNSC') Majority Accuracy is: 51.0\n",
      "('oct_embedded', 'HNSC') LR Accuracy is: 45.0\n",
      "('oct_embedded', 'HNSC') Majority Accuracy is: 51.0\n",
      "('oct_embedded', 'HNSC') LR Accuracy is: 48.0\n",
      "('oct_embedded', 'HNSC') Majority Accuracy is: 51.0\n",
      "('oct_embedded', 'HNSC') LR Accuracy is: 53.0\n",
      "('oct_embedded', 'HNSC') Majority Accuracy is: 51.0\n",
      "('oct_embedded', 'HNSC') LR Accuracy is: 52.0\n",
      "('oct_embedded', 'HNSC') Majority Accuracy is: 51.0\n",
      "('oct_embedded', 'HNSC') LR Accuracy is: 51.0\n",
      "('oct_embedded', 'HNSC') Majority Accuracy is: 51.0\n",
      "('oct_embedded', 'HNSC') LR Accuracy is: 48.0\n",
      "('oct_embedded', 'HNSC') Majority Accuracy is: 51.0\n",
      "('oct_embedded', 'HNSC') LR Accuracy is: 47.0\n",
      "('oct_embedded', 'HNSC') Majority Accuracy is: 51.0\n",
      "('oct_embedded', 'HNSC') LR Accuracy is: 52.0\n",
      "('oct_embedded', 'HNSC') Majority Accuracy is: 51.0\n",
      "('oct_embedded', 'HNSC') LR Accuracy is: 48.0\n",
      "('oct_embedded', 'HNSC') Majority Accuracy is: 51.0\n",
      "('oct_embedded', 'HNSC') LR Accuracy is: 50.0\n",
      "('oct_embedded', 'KIRP') Majority Accuracy is: 59.0\n",
      "('oct_embedded', 'KIRP') LR Accuracy is: 61.0\n",
      "('oct_embedded', 'KIRP') Majority Accuracy is: 59.0\n",
      "('oct_embedded', 'KIRP') LR Accuracy is: 56.99999999999999\n",
      "('oct_embedded', 'KIRP') Majority Accuracy is: 59.0\n",
      "('oct_embedded', 'KIRP') LR Accuracy is: 60.0\n",
      "('oct_embedded', 'KIRP') Majority Accuracy is: 59.0\n",
      "('oct_embedded', 'KIRP') LR Accuracy is: 63.0\n",
      "('oct_embedded', 'KIRP') Majority Accuracy is: 59.0\n",
      "('oct_embedded', 'KIRP') LR Accuracy is: 59.0\n",
      "('oct_embedded', 'KIRP') Majority Accuracy is: 59.0\n",
      "('oct_embedded', 'KIRP') LR Accuracy is: 60.0\n",
      "('oct_embedded', 'KIRP') Majority Accuracy is: 59.0\n",
      "('oct_embedded', 'KIRP') LR Accuracy is: 60.0\n",
      "('oct_embedded', 'KIRP') Majority Accuracy is: 59.0\n",
      "('oct_embedded', 'KIRP') LR Accuracy is: 53.0\n",
      "('oct_embedded', 'KIRP') Majority Accuracy is: 59.0\n",
      "('oct_embedded', 'KIRP') LR Accuracy is: 61.0\n",
      "('oct_embedded', 'KIRP') Majority Accuracy is: 59.0\n",
      "('oct_embedded', 'KIRP') LR Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'KIRP') Majority Accuracy is: 59.0\n",
      "('oct_embedded', 'KIRP') LR Accuracy is: 54.0\n",
      "('oct_embedded', 'LGG') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'LGG') LR Accuracy is: 46.0\n",
      "('oct_embedded', 'LGG') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'LGG') LR Accuracy is: 47.0\n",
      "('oct_embedded', 'LGG') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'LGG') LR Accuracy is: 46.0\n",
      "('oct_embedded', 'LGG') Majority Accuracy is: 60.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('oct_embedded', 'LGG') LR Accuracy is: 56.99999999999999\n",
      "('oct_embedded', 'LGG') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'LGG') LR Accuracy is: 44.0\n",
      "('oct_embedded', 'LGG') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'LGG') LR Accuracy is: 51.0\n",
      "('oct_embedded', 'LGG') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'LGG') LR Accuracy is: 44.0\n",
      "('oct_embedded', 'LGG') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'LGG') LR Accuracy is: 46.0\n",
      "('oct_embedded', 'LGG') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'LGG') LR Accuracy is: 51.0\n",
      "('oct_embedded', 'LGG') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'LGG') LR Accuracy is: 41.0\n",
      "('oct_embedded', 'LGG') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'LGG') LR Accuracy is: 46.0\n",
      "('oct_embedded', 'LIHC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'LIHC') LR Accuracy is: 62.0\n",
      "('oct_embedded', 'LIHC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'LIHC') LR Accuracy is: 56.00000000000001\n",
      "('oct_embedded', 'LIHC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'LIHC') LR Accuracy is: 62.0\n",
      "('oct_embedded', 'LIHC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'LIHC') LR Accuracy is: 59.0\n",
      "('oct_embedded', 'LIHC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'LIHC') LR Accuracy is: 60.0\n",
      "('oct_embedded', 'LIHC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'LIHC') LR Accuracy is: 61.0\n",
      "('oct_embedded', 'LIHC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'LIHC') LR Accuracy is: 62.0\n",
      "('oct_embedded', 'LIHC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'LIHC') LR Accuracy is: 63.0\n",
      "('oct_embedded', 'LIHC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'LIHC') LR Accuracy is: 55.00000000000001\n",
      "('oct_embedded', 'LIHC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'LIHC') LR Accuracy is: 70.0\n",
      "('oct_embedded', 'LIHC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'LIHC') LR Accuracy is: 64.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('oct_embedded', 'LUNG') Majority Accuracy is: 69.0\n",
      "('oct_embedded', 'LUNG') LR Accuracy is: 67.0\n",
      "('oct_embedded', 'LUNG') Majority Accuracy is: 69.0\n",
      "('oct_embedded', 'LUNG') LR Accuracy is: 68.0\n",
      "('oct_embedded', 'LUNG') Majority Accuracy is: 69.0\n",
      "('oct_embedded', 'LUNG') LR Accuracy is: 67.0\n",
      "('oct_embedded', 'LUNG') Majority Accuracy is: 69.0\n",
      "('oct_embedded', 'LUNG') LR Accuracy is: 62.0\n",
      "('oct_embedded', 'LUNG') Majority Accuracy is: 69.0\n",
      "('oct_embedded', 'LUNG') LR Accuracy is: 66.0\n",
      "('oct_embedded', 'LUNG') Majority Accuracy is: 69.0\n",
      "('oct_embedded', 'LUNG') LR Accuracy is: 71.0\n",
      "('oct_embedded', 'LUNG') Majority Accuracy is: 69.0\n",
      "('oct_embedded', 'LUNG') LR Accuracy is: 60.0\n",
      "('oct_embedded', 'LUNG') Majority Accuracy is: 69.0\n",
      "('oct_embedded', 'LUNG') LR Accuracy is: 69.0\n",
      "('oct_embedded', 'LUNG') Majority Accuracy is: 69.0\n",
      "('oct_embedded', 'LUNG') LR Accuracy is: 66.0\n",
      "('oct_embedded', 'LUNG') Majority Accuracy is: 69.0\n",
      "('oct_embedded', 'LUNG') LR Accuracy is: 66.0\n",
      "('oct_embedded', 'LUNG') Majority Accuracy is: 69.0\n",
      "('oct_embedded', 'LUNG') LR Accuracy is: 70.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('oct_embedded', 'PRAD') Majority Accuracy is: 89.0\n",
      "('oct_embedded', 'PRAD') LR Accuracy is: 78.0\n",
      "('oct_embedded', 'PRAD') Majority Accuracy is: 89.0\n",
      "('oct_embedded', 'PRAD') LR Accuracy is: 75.0\n",
      "('oct_embedded', 'PRAD') Majority Accuracy is: 89.0\n",
      "('oct_embedded', 'PRAD') LR Accuracy is: 69.0\n",
      "('oct_embedded', 'PRAD') Majority Accuracy is: 89.0\n",
      "('oct_embedded', 'PRAD') LR Accuracy is: 76.0\n",
      "('oct_embedded', 'PRAD') Majority Accuracy is: 89.0\n",
      "('oct_embedded', 'PRAD') LR Accuracy is: 75.0\n",
      "('oct_embedded', 'PRAD') Majority Accuracy is: 89.0\n",
      "('oct_embedded', 'PRAD') LR Accuracy is: 81.0\n",
      "('oct_embedded', 'PRAD') Majority Accuracy is: 89.0\n",
      "('oct_embedded', 'PRAD') LR Accuracy is: 78.0\n",
      "('oct_embedded', 'PRAD') Majority Accuracy is: 89.0\n",
      "('oct_embedded', 'PRAD') LR Accuracy is: 77.0\n",
      "('oct_embedded', 'PRAD') Majority Accuracy is: 89.0\n",
      "('oct_embedded', 'PRAD') LR Accuracy is: 77.0\n",
      "('oct_embedded', 'PRAD') Majority Accuracy is: 89.0\n",
      "('oct_embedded', 'PRAD') LR Accuracy is: 75.0\n",
      "('oct_embedded', 'PRAD') Majority Accuracy is: 89.0\n",
      "('oct_embedded', 'PRAD') LR Accuracy is: 83.0\n",
      "('oct_embedded', 'SARC') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'SARC') LR Accuracy is: 76.0\n",
      "('oct_embedded', 'SARC') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'SARC') LR Accuracy is: 81.0\n",
      "('oct_embedded', 'SARC') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'SARC') LR Accuracy is: 72.0\n",
      "('oct_embedded', 'SARC') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'SARC') LR Accuracy is: 60.0\n",
      "('oct_embedded', 'SARC') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'SARC') LR Accuracy is: 73.0\n",
      "('oct_embedded', 'SARC') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'SARC') LR Accuracy is: 73.0\n",
      "('oct_embedded', 'SARC') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'SARC') LR Accuracy is: 83.0\n",
      "('oct_embedded', 'SARC') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'SARC') LR Accuracy is: 78.0\n",
      "('oct_embedded', 'SARC') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'SARC') LR Accuracy is: 83.0\n",
      "('oct_embedded', 'SARC') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'SARC') LR Accuracy is: 79.0\n",
      "('oct_embedded', 'SARC') Majority Accuracy is: 85.0\n",
      "('oct_embedded', 'SARC') LR Accuracy is: 84.0\n",
      "('oct_embedded', 'SKCM') Majority Accuracy is: 80.0\n",
      "('oct_embedded', 'SKCM') LR Accuracy is: 78.0\n",
      "('oct_embedded', 'SKCM') Majority Accuracy is: 80.0\n",
      "('oct_embedded', 'SKCM') LR Accuracy is: 78.0\n",
      "('oct_embedded', 'SKCM') Majority Accuracy is: 80.0\n",
      "('oct_embedded', 'SKCM') LR Accuracy is: 75.0\n",
      "('oct_embedded', 'SKCM') Majority Accuracy is: 80.0\n",
      "('oct_embedded', 'SKCM') LR Accuracy is: 79.0\n",
      "('oct_embedded', 'SKCM') Majority Accuracy is: 80.0\n",
      "('oct_embedded', 'SKCM') LR Accuracy is: 74.0\n",
      "('oct_embedded', 'SKCM') Majority Accuracy is: 80.0\n",
      "('oct_embedded', 'SKCM') LR Accuracy is: 78.0\n",
      "('oct_embedded', 'SKCM') Majority Accuracy is: 80.0\n",
      "('oct_embedded', 'SKCM') LR Accuracy is: 72.0\n",
      "('oct_embedded', 'SKCM') Majority Accuracy is: 80.0\n",
      "('oct_embedded', 'SKCM') LR Accuracy is: 72.0\n",
      "('oct_embedded', 'SKCM') Majority Accuracy is: 80.0\n",
      "('oct_embedded', 'SKCM') LR Accuracy is: 68.0\n",
      "('oct_embedded', 'SKCM') Majority Accuracy is: 80.0\n",
      "('oct_embedded', 'SKCM') LR Accuracy is: 78.0\n",
      "('oct_embedded', 'SKCM') Majority Accuracy is: 80.0\n",
      "('oct_embedded', 'SKCM') LR Accuracy is: 80.0\n",
      "('oct_embedded', 'STAD') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'STAD') LR Accuracy is: 61.0\n",
      "('oct_embedded', 'STAD') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'STAD') LR Accuracy is: 61.0\n",
      "('oct_embedded', 'STAD') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'STAD') LR Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'STAD') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'STAD') LR Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'STAD') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'STAD') LR Accuracy is: 56.99999999999999\n",
      "('oct_embedded', 'STAD') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'STAD') LR Accuracy is: 61.0\n",
      "('oct_embedded', 'STAD') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'STAD') LR Accuracy is: 56.00000000000001\n",
      "('oct_embedded', 'STAD') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'STAD') LR Accuracy is: 63.0\n",
      "('oct_embedded', 'STAD') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'STAD') LR Accuracy is: 63.0\n",
      "('oct_embedded', 'STAD') Majority Accuracy is: 60.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('oct_embedded', 'STAD') LR Accuracy is: 61.0\n",
      "('oct_embedded', 'STAD') Majority Accuracy is: 60.0\n",
      "('oct_embedded', 'STAD') LR Accuracy is: 53.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('oct_embedded', 'THCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') LR Accuracy is: 64.0\n",
      "('oct_embedded', 'THCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') LR Accuracy is: 67.0\n",
      "('oct_embedded', 'THCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') LR Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') LR Accuracy is: 67.0\n",
      "('oct_embedded', 'THCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') LR Accuracy is: 67.0\n",
      "('oct_embedded', 'THCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') LR Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') LR Accuracy is: 56.99999999999999\n",
      "('oct_embedded', 'THCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') LR Accuracy is: 69.0\n",
      "('oct_embedded', 'THCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') LR Accuracy is: 66.0\n",
      "('oct_embedded', 'THCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') LR Accuracy is: 66.0\n",
      "('oct_embedded', 'THCA') Majority Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'THCA') LR Accuracy is: 60.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('oct_embedded', 'UCEC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'UCEC') LR Accuracy is: 61.0\n",
      "('oct_embedded', 'UCEC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'UCEC') LR Accuracy is: 60.0\n",
      "('oct_embedded', 'UCEC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'UCEC') LR Accuracy is: 57.99999999999999\n",
      "('oct_embedded', 'UCEC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'UCEC') LR Accuracy is: 54.0\n",
      "('oct_embedded', 'UCEC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'UCEC') LR Accuracy is: 61.0\n",
      "('oct_embedded', 'UCEC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'UCEC') LR Accuracy is: 60.0\n",
      "('oct_embedded', 'UCEC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'UCEC') LR Accuracy is: 66.0\n",
      "('oct_embedded', 'UCEC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'UCEC') LR Accuracy is: 54.0\n",
      "('oct_embedded', 'UCEC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'UCEC') LR Accuracy is: 53.0\n",
      "('oct_embedded', 'UCEC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'UCEC') LR Accuracy is: 54.0\n",
      "('oct_embedded', 'UCEC') Majority Accuracy is: 65.0\n",
      "('oct_embedded', 'UCEC') LR Accuracy is: 51.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('synchronous_colon_cancer_present', 'COAD') Majority Accuracy is: 95.0\n",
      "('synchronous_colon_cancer_present', 'COAD') LR Accuracy is: 91.0\n",
      "('synchronous_colon_cancer_present', 'COAD') Majority Accuracy is: 95.0\n",
      "('synchronous_colon_cancer_present', 'COAD') LR Accuracy is: 87.0\n",
      "('synchronous_colon_cancer_present', 'COAD') Majority Accuracy is: 95.0\n",
      "('synchronous_colon_cancer_present', 'COAD') LR Accuracy is: 92.0\n",
      "('synchronous_colon_cancer_present', 'COAD') Majority Accuracy is: 95.0\n",
      "('synchronous_colon_cancer_present', 'COAD') LR Accuracy is: 94.0\n",
      "('synchronous_colon_cancer_present', 'COAD') Majority Accuracy is: 95.0\n",
      "('synchronous_colon_cancer_present', 'COAD') LR Accuracy is: 93.0\n",
      "('synchronous_colon_cancer_present', 'COAD') Majority Accuracy is: 95.0\n",
      "('synchronous_colon_cancer_present', 'COAD') LR Accuracy is: 94.0\n",
      "('synchronous_colon_cancer_present', 'COAD') Majority Accuracy is: 95.0\n",
      "('synchronous_colon_cancer_present', 'COAD') LR Accuracy is: 94.0\n",
      "('synchronous_colon_cancer_present', 'COAD') Majority Accuracy is: 95.0\n",
      "('synchronous_colon_cancer_present', 'COAD') LR Accuracy is: 94.0\n",
      "('synchronous_colon_cancer_present', 'COAD') Majority Accuracy is: 95.0\n",
      "('synchronous_colon_cancer_present', 'COAD') LR Accuracy is: 93.0\n",
      "('synchronous_colon_cancer_present', 'COAD') Majority Accuracy is: 95.0\n",
      "('synchronous_colon_cancer_present', 'COAD') LR Accuracy is: 92.0\n",
      "('synchronous_colon_cancer_present', 'COAD') Majority Accuracy is: 95.0\n",
      "('synchronous_colon_cancer_present', 'COAD') LR Accuracy is: 93.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') Majority Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') LR Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') Majority Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') LR Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') Majority Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') LR Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') Majority Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') LR Accuracy is: 95.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') Majority Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') LR Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') Majority Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') LR Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') Majority Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') LR Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') Majority Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') LR Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') Majority Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') LR Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') Majority Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') LR Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') Majority Accuracy is: 96.0\n",
      "('synchronous_colon_cancer_present', 'COADREAD') LR Accuracy is: 96.0\n",
      "('tumor_tissue_site', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'COADREAD') LR Accuracy is: 73.0\n",
      "('tumor_tissue_site', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'COADREAD') LR Accuracy is: 71.0\n",
      "('tumor_tissue_site', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'COADREAD') LR Accuracy is: 72.0\n",
      "('tumor_tissue_site', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'COADREAD') LR Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'COADREAD') LR Accuracy is: 68.0\n",
      "('tumor_tissue_site', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'COADREAD') LR Accuracy is: 67.0\n",
      "('tumor_tissue_site', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'COADREAD') LR Accuracy is: 68.0\n",
      "('tumor_tissue_site', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'COADREAD') LR Accuracy is: 70.0\n",
      "('tumor_tissue_site', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'COADREAD') LR Accuracy is: 68.0\n",
      "('tumor_tissue_site', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'COADREAD') LR Accuracy is: 67.0\n",
      "('tumor_tissue_site', 'COADREAD') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'COADREAD') LR Accuracy is: 73.0\n",
      "('tumor_tissue_site', 'GBMLGG') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'GBMLGG') LR Accuracy is: 91.0\n",
      "('tumor_tissue_site', 'GBMLGG') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'GBMLGG') LR Accuracy is: 93.0\n",
      "('tumor_tissue_site', 'GBMLGG') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'GBMLGG') LR Accuracy is: 92.0\n",
      "('tumor_tissue_site', 'GBMLGG') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'GBMLGG') LR Accuracy is: 92.0\n",
      "('tumor_tissue_site', 'GBMLGG') Majority Accuracy is: 76.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('tumor_tissue_site', 'GBMLGG') LR Accuracy is: 69.0\n",
      "('tumor_tissue_site', 'GBMLGG') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'GBMLGG') LR Accuracy is: 94.0\n",
      "('tumor_tissue_site', 'GBMLGG') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'GBMLGG') LR Accuracy is: 93.0\n",
      "('tumor_tissue_site', 'GBMLGG') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'GBMLGG') LR Accuracy is: 91.0\n",
      "('tumor_tissue_site', 'GBMLGG') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'GBMLGG') LR Accuracy is: 95.0\n",
      "('tumor_tissue_site', 'GBMLGG') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'GBMLGG') LR Accuracy is: 94.0\n",
      "('tumor_tissue_site', 'GBMLGG') Majority Accuracy is: 76.0\n",
      "('tumor_tissue_site', 'GBMLGG') LR Accuracy is: 93.0\n",
      "('tumor_tissue_site', 'PCPG') Majority Accuracy is: 82.0\n",
      "('tumor_tissue_site', 'PCPG') LR Accuracy is: 87.0\n",
      "('tumor_tissue_site', 'PCPG') Majority Accuracy is: 82.0\n",
      "('tumor_tissue_site', 'PCPG') LR Accuracy is: 82.0\n",
      "('tumor_tissue_site', 'PCPG') Majority Accuracy is: 82.0\n",
      "('tumor_tissue_site', 'PCPG') LR Accuracy is: 85.0\n",
      "('tumor_tissue_site', 'PCPG') Majority Accuracy is: 82.0\n",
      "('tumor_tissue_site', 'PCPG') LR Accuracy is: 86.0\n",
      "('tumor_tissue_site', 'PCPG') Majority Accuracy is: 82.0\n",
      "('tumor_tissue_site', 'PCPG') LR Accuracy is: 85.0\n",
      "('tumor_tissue_site', 'PCPG') Majority Accuracy is: 82.0\n",
      "('tumor_tissue_site', 'PCPG') LR Accuracy is: 88.0\n",
      "('tumor_tissue_site', 'PCPG') Majority Accuracy is: 82.0\n",
      "('tumor_tissue_site', 'PCPG') LR Accuracy is: 88.0\n",
      "('tumor_tissue_site', 'PCPG') Majority Accuracy is: 82.0\n",
      "('tumor_tissue_site', 'PCPG') LR Accuracy is: 88.0\n",
      "('tumor_tissue_site', 'PCPG') Majority Accuracy is: 82.0\n",
      "('tumor_tissue_site', 'PCPG') LR Accuracy is: 86.0\n",
      "('tumor_tissue_site', 'PCPG') Majority Accuracy is: 82.0\n",
      "('tumor_tissue_site', 'PCPG') LR Accuracy is: 87.0\n",
      "('tumor_tissue_site', 'PCPG') Majority Accuracy is: 82.0\n",
      "('tumor_tissue_site', 'PCPG') LR Accuracy is: 84.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('venous_invasion', 'COAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COAD') LR Accuracy is: 73.0\n",
      "('venous_invasion', 'COAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COAD') LR Accuracy is: 63.0\n",
      "('venous_invasion', 'COAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COAD') LR Accuracy is: 72.0\n",
      "('venous_invasion', 'COAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COAD') LR Accuracy is: 70.0\n",
      "('venous_invasion', 'COAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COAD') LR Accuracy is: 70.0\n",
      "('venous_invasion', 'COAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COAD') LR Accuracy is: 68.0\n",
      "('venous_invasion', 'COAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COAD') LR Accuracy is: 67.0\n",
      "('venous_invasion', 'COAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COAD') LR Accuracy is: 76.0\n",
      "('venous_invasion', 'COAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COAD') LR Accuracy is: 71.0\n",
      "('venous_invasion', 'COAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COAD') LR Accuracy is: 73.0\n",
      "('venous_invasion', 'COAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COAD') LR Accuracy is: 72.0\n",
      "('venous_invasion', 'COADREAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COADREAD') LR Accuracy is: 67.0\n",
      "('venous_invasion', 'COADREAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COADREAD') LR Accuracy is: 79.0\n",
      "('venous_invasion', 'COADREAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COADREAD') LR Accuracy is: 70.0\n",
      "('venous_invasion', 'COADREAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COADREAD') LR Accuracy is: 73.0\n",
      "('venous_invasion', 'COADREAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COADREAD') LR Accuracy is: 76.0\n",
      "('venous_invasion', 'COADREAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COADREAD') LR Accuracy is: 78.0\n",
      "('venous_invasion', 'COADREAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COADREAD') LR Accuracy is: 76.0\n",
      "('venous_invasion', 'COADREAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COADREAD') LR Accuracy is: 76.0\n",
      "('venous_invasion', 'COADREAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COADREAD') LR Accuracy is: 78.0\n",
      "('venous_invasion', 'COADREAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COADREAD') LR Accuracy is: 75.0\n",
      "('venous_invasion', 'COADREAD') Majority Accuracy is: 77.0\n",
      "('venous_invasion', 'COADREAD') LR Accuracy is: 74.0\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "Not enough samples!\n",
      "('white_cell_count_result', 'KIRP') Majority Accuracy is: 74.0\n",
      "('white_cell_count_result', 'KIRP') LR Accuracy is: 61.0\n",
      "('white_cell_count_result', 'KIRP') Majority Accuracy is: 74.0\n",
      "('white_cell_count_result', 'KIRP') LR Accuracy is: 68.0\n",
      "('white_cell_count_result', 'KIRP') Majority Accuracy is: 74.0\n",
      "('white_cell_count_result', 'KIRP') LR Accuracy is: 67.0\n",
      "('white_cell_count_result', 'KIRP') Majority Accuracy is: 74.0\n",
      "('white_cell_count_result', 'KIRP') LR Accuracy is: 67.0\n",
      "('white_cell_count_result', 'KIRP') Majority Accuracy is: 74.0\n",
      "('white_cell_count_result', 'KIRP') LR Accuracy is: 54.0\n",
      "('white_cell_count_result', 'KIRP') Majority Accuracy is: 74.0\n",
      "('white_cell_count_result', 'KIRP') LR Accuracy is: 60.0\n",
      "('white_cell_count_result', 'KIRP') Majority Accuracy is: 74.0\n",
      "('white_cell_count_result', 'KIRP') LR Accuracy is: 62.0\n",
      "('white_cell_count_result', 'KIRP') Majority Accuracy is: 74.0\n",
      "('white_cell_count_result', 'KIRP') LR Accuracy is: 51.0\n",
      "('white_cell_count_result', 'KIRP') Majority Accuracy is: 74.0\n",
      "('white_cell_count_result', 'KIRP') LR Accuracy is: 61.0\n",
      "('white_cell_count_result', 'KIRP') Majority Accuracy is: 74.0\n",
      "('white_cell_count_result', 'KIRP') LR Accuracy is: 66.0\n",
      "('white_cell_count_result', 'KIRP') Majority Accuracy is: 74.0\n",
      "('white_cell_count_result', 'KIRP') LR Accuracy is: 63.0\n"
     ]
    }
   ],
   "source": [
    "avg_maj_acc, avg_lr_acc = {}, {}\n",
    "weight_decay = 0.05\n",
    "batch_size = 32\n",
    "epochs=100\n",
    "for taskid in sorted(tasks.task_ids):\n",
    "    task = meta_dataloader.TCGA.TCGATask(taskid)\n",
    "    input_size = task._samples.shape[1]\n",
    "    num_classes = len(collections.Counter(task._labels))\n",
    "    for seed in [0,1,2,3,4,5,6,7,8,9,10]:\n",
    "        try:\n",
    "            X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(task._samples, \n",
    "                                                                                        task._labels, \n",
    "                                                                                        stratify=task._labels,\n",
    "                                                                                        train_size=50,\n",
    "                                                                                        test_size=100,\n",
    "                                                                                        shuffle=True,\n",
    "                                                                                        random_state=0\n",
    "                                                                                         )\n",
    "            train_set = TensorDataset( Tensor(X_train), Tensor(y_train))\n",
    "            test_set = TensorDataset( Tensor(X_test), Tensor(y_test))\n",
    "\n",
    "            #-----Majority-----\n",
    "            mj_acc = Majority(X_train, X_test, y_train, y_test, seed)\n",
    "            print(\"{} Majority Accuracy is: {}\" .format(task.id, mj_acc))\n",
    "            if task.id in avg_maj_acc.keys():\n",
    "                avg_maj_acc[task.id].append(mj_acc)\n",
    "            else:\n",
    "                avg_maj_acc[task.id] = [mj_acc]\n",
    "\n",
    "            #-----Logidtic Regression-----\n",
    "            lr = 1.\n",
    "            LR_model = LogisticRegression(seed, input_size, num_classes,lr, batch_size, epochs)\n",
    "            trained_model, train_loss = train(LR_model, train_set, task.id, False)\n",
    "            lr_result = test(trained_model, test_set)\n",
    "            print(\"{} LR Accuracy is: {}\" .format(task.id, lr_result))\n",
    "            if task.id in avg_lr_acc.keys():\n",
    "                avg_lr_acc[task.id].append(lr_result)\n",
    "            else:\n",
    "                avg_lr_acc[task.id] = [lr_result]\n",
    "\n",
    "        except:\n",
    "            print(\"Not enough samples!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T00:50:20.421763Z",
     "start_time": "2019-07-21T00:50:20.333361Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('Expression_Subtype', 'LUAD'): [38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0], ('Expression_Subtype', 'LUNG'): [38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0], ('GeneExp_Subtype', 'GBM'): [33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0], ('Metastasis_nature2012', 'BRCA'): [98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0], ('Node_nature2012', 'BRCA'): [49.0, 49.0, 49.0, 49.0, 49.0, 49.0, 49.0, 49.0, 49.0, 49.0, 49.0], ('PAM50Call_RNAseq', 'BRCA'): [45.0, 45.0, 45.0, 45.0, 45.0, 45.0, 45.0, 45.0, 45.0, 45.0, 45.0], ('_EVENT', 'BLCA'): [55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001], ('_EVENT', 'BRCA'): [83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0], ('_EVENT', 'CESC'): [75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0], ('_EVENT', 'COAD'): [75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0], ('_EVENT', 'COADREAD'): [76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0], ('_EVENT', 'ESCA'): [57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999], ('_EVENT', 'GBM'): [77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0], ('_EVENT', 'GBMLGG'): [62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0], ('_EVENT', 'HNSC'): [55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001], ('_EVENT', 'KIRP'): [84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0], ('_EVENT', 'LAML'): [65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0], ('_EVENT', 'LGG'): [75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0], ('_EVENT', 'LIHC'): [61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0], ('_EVENT', 'LUAD'): [63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0], ('_EVENT', 'LUNG'): [59.0, 59.0, 59.0, 59.0, 59.0, 59.0, 59.0, 59.0, 59.0, 59.0, 59.0], ('_EVENT', 'LUSC'): [55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001], ('_EVENT', 'OV'): [60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0], ('_EVENT', 'PAAD'): [52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0], ('_EVENT', 'SARC'): [63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0], ('_EVENT', 'SKCM'): [52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0], ('_EVENT', 'STAD'): [61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0], ('_EVENT', 'THCA'): [97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0], ('_EVENT', 'UCEC'): [81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0], ('_PANCAN_DNAMethyl_BRCA', 'BRCA'): [30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0], ('_PANCAN_DNAMethyl_HNSC', 'HNSC'): [41.0, 41.0, 41.0, 41.0, 41.0, 41.0, 41.0, 41.0, 41.0, 41.0, 41.0], ('_PANCAN_DNAMethyl_LUAD', 'LUAD'): [43.0, 43.0, 43.0, 43.0, 43.0, 43.0, 43.0, 43.0, 43.0, 43.0, 43.0], ('_PANCAN_DNAMethyl_LUSC', 'LUSC'): [38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0], ('_PANCAN_mirna_BRCA', 'BRCA'): [32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0], ('_PANCAN_mirna_HNSC', 'HNSC'): [38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0], ('_PANCAN_mirna_LAML', 'LAML'): [35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0], ('_PANCAN_mirna_LUAD', 'LUAD'): [31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0], ('_PANCAN_mirna_LUSC', 'LUSC'): [56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999], ('_PANCAN_mirna_OV', 'OV'): [28.000000000000004, 28.000000000000004, 28.000000000000004, 28.000000000000004, 28.000000000000004, 28.000000000000004, 28.000000000000004, 28.000000000000004, 28.000000000000004, 28.000000000000004, 28.000000000000004], ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML'): [60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0], ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC'): [52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0, 52.0], ('alcohol_history_documented', 'ESCA'): [73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0], ('alcohol_history_documented', 'HNSC'): [67.0, 67.0, 67.0, 67.0, 67.0, 67.0, 67.0, 67.0, 67.0, 67.0, 67.0], ('alcohol_history_documented', 'PAAD'): [61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0], ('anatomic_neoplasm_subdivision', 'OV'): [73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0], ('anatomic_neoplasm_subdivision', 'PAAD'): [77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0], ('animal_insect_allergy_history', 'GBMLGG'): [95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0], ('animal_insect_allergy_history', 'LGG'): [95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0], ('antireflux_treatment', 'STAD'): [79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0], ('asthma_history', 'GBMLGG'): [94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0], ('asthma_history', 'LGG'): [94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0], ('barretts_esophagus', 'STAD'): [91.0, 91.0, 91.0, 91.0, 91.0, 91.0, 91.0, 91.0, 91.0, 91.0, 91.0], ('biochemical_recurrence', 'PRAD'): [87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0], ('birth_control_pill_history_usage_category', 'CESC'): [55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001], ('clinical_M', 'KIRP'): [75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0], ('clinical_stage', 'KIRP'): [66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0], ('colon_polyps_present', 'COAD'): [66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0], ('colon_polyps_present', 'COADREAD'): [71.0, 71.0, 71.0, 71.0, 71.0, 71.0, 71.0, 71.0, 71.0, 71.0, 71.0], ('family_history_of_cancer', 'GBMLGG'): [62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0], ('family_history_of_cancer', 'LGG'): [62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0], ('family_history_of_primary_brain_tumor', 'GBMLGG'): [96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0], ('family_history_of_primary_brain_tumor', 'LGG'): [96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0], ('family_history_of_stomach_cancer', 'STAD'): [95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0], ('gender', 'BLCA'): [73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0], ('gender', 'BRCA'): [99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0], ('gender', 'COAD'): [54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0], ('gender', 'COADREAD'): [54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0], ('gender', 'ESCA'): [84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0], ('gender', 'GBM'): [65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0], ('gender', 'GBMLGG'): [56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999, 56.99999999999999], ('gender', 'HNSC'): [73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0], ('gender', 'KIRP'): [73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0], ('gender', 'LAML'): [54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0], ('gender', 'LGG'): [55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001], ('gender', 'LIHC'): [66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0], ('gender', 'LUAD'): [54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0, 54.0], ('gender', 'LUNG'): [60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0], ('gender', 'LUSC'): [74.0, 74.0, 74.0, 74.0, 74.0, 74.0, 74.0, 74.0, 74.0, 74.0, 74.0], ('gender', 'PAAD'): [55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001], ('gender', 'PCPG'): [55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001], ('gender', 'SARC'): [55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001], ('gender', 'SKCM'): [62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0], ('gender', 'STAD'): [65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0], ('gender', 'THCA'): [73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0], ('histological_type', 'ESCA'): [49.0, 49.0, 49.0, 49.0, 49.0, 49.0, 49.0, 49.0, 49.0, 49.0, 49.0], ('histological_type', 'LGG'): [38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0, 38.0], ('histological_type', 'PCPG'): [82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0], ('histological_type', 'PRAD'): [97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0], ('histological_type', 'UCEC'): [62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0], ('lymphatic_invasion', 'COAD'): [69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0], ('lymphatic_invasion', 'COADREAD'): [69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0], ('lymphovascular_invasion_present', 'BLCA'): [55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001], ('lymphovascular_invasion_present', 'HNSC'): [64.0, 64.0, 64.0, 64.0, 64.0, 64.0, 64.0, 64.0, 64.0, 64.0, 64.0], ('menopause_status', 'BRCA'): [69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0], ('mental_status_changes', 'GBMLGG'): [75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0], ('mental_status_changes', 'LGG'): [75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0, 75.0], ('metastatic_diagnosis', 'SARC'): [67.0, 67.0, 67.0, 67.0, 67.0, 67.0, 67.0, 67.0, 67.0, 67.0, 67.0], ('oct_embedded', 'BLCA'): [85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0], ('oct_embedded', 'BRCA'): [62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0], ('oct_embedded', 'CESC'): [55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001, 55.00000000000001], ('oct_embedded', 'ESCA'): [57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999], ('oct_embedded', 'GBMLGG'): [63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0, 63.0], ('oct_embedded', 'HNSC'): [51.0, 51.0, 51.0, 51.0, 51.0, 51.0, 51.0, 51.0, 51.0, 51.0, 51.0], ('oct_embedded', 'KIRP'): [59.0, 59.0, 59.0, 59.0, 59.0, 59.0, 59.0, 59.0, 59.0, 59.0, 59.0], ('oct_embedded', 'LGG'): [60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0], ('oct_embedded', 'LIHC'): [65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0], ('oct_embedded', 'LUNG'): [69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0], ('oct_embedded', 'PRAD'): [89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0], ('oct_embedded', 'SARC'): [85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0], ('oct_embedded', 'SKCM'): [80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0], ('oct_embedded', 'STAD'): [60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0, 60.0], ('oct_embedded', 'THCA'): [57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999, 57.99999999999999], ('oct_embedded', 'UCEC'): [65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0], ('synchronous_colon_cancer_present', 'COAD'): [95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0], ('synchronous_colon_cancer_present', 'COADREAD'): [96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0], ('tumor_tissue_site', 'COADREAD'): [76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0], ('tumor_tissue_site', 'GBMLGG'): [76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0], ('tumor_tissue_site', 'PCPG'): [82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0], ('venous_invasion', 'COAD'): [77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0], ('venous_invasion', 'COADREAD'): [77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0], ('white_cell_count_result', 'KIRP'): [74.0, 74.0, 74.0, 74.0, 74.0, 74.0, 74.0, 74.0, 74.0, 74.0, 74.0]}\n"
     ]
    }
   ],
   "source": [
    "print(avg_maj_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T00:50:24.941695Z",
     "start_time": "2019-07-21T00:50:24.894859Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('Expression_Subtype', 'LUAD'): [62.0, 56.99999999999999, 64.0, 57.99999999999999, 69.0, 38.0, 61.0, 67.0, 60.0, 70.0, 61.0], ('Expression_Subtype', 'LUNG'): [62.0, 56.99999999999999, 64.0, 57.99999999999999, 69.0, 38.0, 61.0, 67.0, 60.0, 70.0, 61.0], ('GeneExp_Subtype', 'GBM'): [68.0, 25.0, 69.0, 64.0, 65.0, 70.0, 66.0, 51.0, 72.0, 68.0, 68.0], ('Metastasis_nature2012', 'BRCA'): [98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0], ('Node_nature2012', 'BRCA'): [34.0, 40.0, 42.0, 45.0, 32.0, 42.0, 50.0, 42.0, 42.0, 42.0, 39.0], ('PAM50Call_RNAseq', 'BRCA'): [79.0, 74.0, 77.0, 15.0, 77.0, 73.0, 70.0, 68.0, 69.0, 78.0, 79.0], ('_EVENT', 'BLCA'): [59.0, 54.0, 59.0, 56.00000000000001, 55.00000000000001, 54.0, 56.99999999999999, 51.0, 51.0, 56.00000000000001, 57.99999999999999], ('_EVENT', 'BRCA'): [77.0, 77.0, 81.0, 61.0, 77.0, 72.0, 80.0, 81.0, 81.0, 73.0, 70.0], ('_EVENT', 'CESC'): [75.0, 72.0, 71.0, 69.0, 60.0, 69.0, 75.0, 67.0, 68.0, 68.0, 61.0], ('_EVENT', 'COAD'): [71.0, 73.0, 66.0, 72.0, 72.0, 73.0, 67.0, 73.0, 74.0, 74.0, 70.0], ('_EVENT', 'COADREAD'): [64.0, 54.0, 63.0, 72.0, 62.0, 61.0, 71.0, 68.0, 68.0, 70.0, 66.0], ('_EVENT', 'ESCA'): [55.00000000000001, 57.99999999999999, 60.0, 57.99999999999999, 56.99999999999999, 53.0, 46.0, 56.99999999999999, 48.0, 56.00000000000001, 52.0], ('_EVENT', 'GBM'): [63.0, 65.0, 68.0, 71.0, 72.0, 64.0, 59.0, 64.0, 66.0, 65.0, 71.0], ('_EVENT', 'GBMLGG'): [74.0, 71.0, 75.0, 71.0, 70.0, 75.0, 75.0, 73.0, 75.0, 73.0, 73.0], ('_EVENT', 'HNSC'): [57.99999999999999, 55.00000000000001, 56.00000000000001, 63.0, 55.00000000000001, 61.0, 59.0, 51.0, 51.0, 59.0, 60.0], ('_EVENT', 'KIRP'): [80.0, 83.0, 84.0, 80.0, 86.0, 86.0, 87.0, 86.0, 86.0, 88.0, 81.0], ('_EVENT', 'LAML'): [57.99999999999999, 50.0, 53.0, 55.00000000000001, 55.00000000000001, 52.0, 51.0, 35.0, 56.99999999999999, 55.00000000000001, 59.0], ('_EVENT', 'LGG'): [73.0, 62.0, 72.0, 70.0, 73.0, 80.0, 66.0, 69.0, 64.0, 77.0, 73.0], ('_EVENT', 'LIHC'): [64.0, 60.0, 55.00000000000001, 62.0, 51.0, 56.99999999999999, 56.99999999999999, 60.0, 59.0, 55.00000000000001, 56.00000000000001], ('_EVENT', 'LUAD'): [55.00000000000001, 53.0, 53.0, 50.0, 59.0, 56.00000000000001, 61.0, 63.0, 47.0, 62.0, 52.0], ('_EVENT', 'LUNG'): [56.99999999999999, 56.00000000000001, 59.0, 56.99999999999999, 59.0, 61.0, 59.0, 62.0, 53.0, 56.99999999999999, 60.0], ('_EVENT', 'LUSC'): [52.0, 56.00000000000001, 56.00000000000001, 57.99999999999999, 54.0, 56.00000000000001, 54.0, 55.00000000000001, 59.0, 56.99999999999999, 56.00000000000001], ('_EVENT', 'OV'): [60.0, 49.0, 56.00000000000001, 56.00000000000001, 56.99999999999999, 55.00000000000001, 56.00000000000001, 56.00000000000001, 54.0, 45.0, 55.00000000000001], ('_EVENT', 'PAAD'): [56.00000000000001, 49.0, 57.99999999999999, 63.0, 57.99999999999999, 62.0, 55.00000000000001, 48.0, 48.0, 57.99999999999999, 61.0], ('_EVENT', 'SARC'): [59.0, 55.00000000000001, 47.0, 59.0, 53.0, 51.0, 56.99999999999999, 54.0, 63.0, 55.00000000000001, 50.0], ('_EVENT', 'SKCM'): [63.0, 56.00000000000001, 60.0, 57.99999999999999, 57.99999999999999, 61.0, 60.0, 57.99999999999999, 54.0, 64.0, 59.0], ('_EVENT', 'STAD'): [60.0, 55.00000000000001, 56.00000000000001, 60.0, 55.00000000000001, 57.99999999999999, 56.00000000000001, 54.0, 57.99999999999999, 55.00000000000001, 59.0], ('_EVENT', 'THCA'): [97.0, 97.0, 85.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0], ('_EVENT', 'UCEC'): [80.0, 79.0, 79.0, 77.0, 78.0, 75.0, 80.0, 80.0, 79.0, 76.0, 81.0], ('_PANCAN_DNAMethyl_BRCA', 'BRCA'): [55.00000000000001, 47.0, 49.0, 54.0, 48.0, 40.0, 53.0, 47.0, 44.0, 42.0, 45.0], ('_PANCAN_DNAMethyl_HNSC', 'HNSC'): [70.0, 65.0, 66.0, 70.0, 61.0, 66.0, 64.0, 16.0, 70.0, 67.0, 71.0], ('_PANCAN_DNAMethyl_LUAD', 'LUAD'): [55.00000000000001, 48.0, 53.0, 47.0, 49.0, 23.0, 38.0, 54.0, 44.0, 49.0, 49.0], ('_PANCAN_DNAMethyl_LUSC', 'LUSC'): [63.0, 56.00000000000001, 30.0, 63.0, 56.99999999999999, 60.0, 30.0, 57.99999999999999, 52.0, 56.00000000000001, 54.0], ('_PANCAN_mirna_BRCA', 'BRCA'): [7.000000000000001, 7.000000000000001, 28.000000000000004, 33.0, 7.000000000000001, 36.0, 35.0, 7.000000000000001, 31.0, 33.0, 37.0], ('_PANCAN_mirna_HNSC', 'HNSC'): [51.0, 49.0, 56.00000000000001, 49.0, 55.00000000000001, 49.0, 53.0, 53.0, 55.00000000000001, 46.0, 55.00000000000001], ('_PANCAN_mirna_LAML', 'LAML'): [7.000000000000001, 57.99999999999999, 67.0, 60.0, 55.00000000000001, 68.0, 68.0, 67.0, 67.0, 66.0, 68.0], ('_PANCAN_mirna_LUAD', 'LUAD'): [33.0, 39.0, 43.0, 43.0, 12.0, 34.0, 39.0, 43.0, 35.0, 12.0, 39.0], ('_PANCAN_mirna_LUSC', 'LUSC'): [55.00000000000001, 64.0, 57.99999999999999, 57.99999999999999, 56.00000000000001, 11.0, 50.0, 59.0, 60.0, 11.0, 54.0], ('_PANCAN_mirna_OV', 'OV'): [26.0, 30.0, 28.000000000000004, 23.0, 26.0, 23.0, 27.0, 19.0, 39.0, 27.0, 27.0], ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML'): [80.0, 64.0, 73.0, 70.0, 79.0, 72.0, 67.0, 73.0, 19.0, 75.0, 79.0], ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC'): [51.0, 56.00000000000001, 55.00000000000001, 56.99999999999999, 55.00000000000001, 51.0, 52.0, 49.0, 53.0, 50.0, 49.0], ('alcohol_history_documented', 'ESCA'): [63.0, 67.0, 59.0, 61.0, 65.0, 66.0, 61.0, 70.0, 62.0, 52.0, 59.0], ('alcohol_history_documented', 'HNSC'): [62.0, 68.0, 66.0, 68.0, 65.0, 65.0, 67.0, 67.0, 64.0, 63.0, 64.0], ('alcohol_history_documented', 'PAAD'): [52.0, 43.0, 55.00000000000001, 39.0, 52.0, 54.0, 54.0, 53.0, 55.00000000000001, 55.00000000000001, 55.00000000000001], ('anatomic_neoplasm_subdivision', 'OV'): [69.0, 71.0, 61.0, 54.0, 63.0, 71.0, 59.0, 69.0, 53.0, 69.0, 67.0], ('anatomic_neoplasm_subdivision', 'PAAD'): [72.0, 76.0, 62.0, 72.0, 71.0, 74.0, 70.0, 77.0, 68.0, 69.0, 75.0], ('animal_insect_allergy_history', 'GBMLGG'): [86.0, 85.0, 86.0, 94.0, 87.0, 93.0, 92.0, 95.0, 89.0, 94.0, 88.0], ('animal_insect_allergy_history', 'LGG'): [86.0, 85.0, 86.0, 94.0, 87.0, 93.0, 92.0, 95.0, 89.0, 94.0, 88.0], ('antireflux_treatment', 'STAD'): [75.0, 79.0, 77.0, 75.0, 74.0, 72.0, 78.0, 77.0, 73.0, 74.0, 78.0], ('asthma_history', 'GBMLGG'): [94.0, 93.0, 92.0, 94.0, 94.0, 92.0, 94.0, 93.0, 92.0, 94.0, 90.0], ('asthma_history', 'LGG'): [94.0, 93.0, 92.0, 94.0, 94.0, 92.0, 94.0, 93.0, 92.0, 94.0, 90.0], ('barretts_esophagus', 'STAD'): [82.0, 88.0, 89.0, 91.0, 89.0, 87.0, 91.0, 89.0, 85.0, 91.0, 89.0], ('biochemical_recurrence', 'PRAD'): [76.0, 86.0, 87.0, 78.0, 85.0, 81.0, 80.0, 82.0, 83.0, 80.0, 82.0], ('birth_control_pill_history_usage_category', 'CESC'): [49.0, 50.0, 52.0, 47.0, 50.0, 48.0, 9.0, 51.0, 48.0, 50.0, 47.0], ('clinical_M', 'KIRP'): [68.0, 71.0, 68.0, 73.0, 69.0, 74.0, 73.0, 67.0, 70.0, 57.99999999999999, 66.0], ('clinical_stage', 'KIRP'): [67.0, 65.0, 65.0, 56.00000000000001, 53.0, 60.0, 68.0, 59.0, 60.0, 60.0, 60.0], ('colon_polyps_present', 'COAD'): [64.0, 54.0, 56.99999999999999, 48.0, 63.0, 48.0, 57.99999999999999, 52.0, 56.99999999999999, 61.0, 59.0], ('colon_polyps_present', 'COADREAD'): [62.0, 65.0, 51.0, 64.0, 60.0, 65.0, 65.0, 52.0, 60.0, 59.0, 60.0], ('family_history_of_cancer', 'GBMLGG'): [49.0, 49.0, 62.0, 55.00000000000001, 50.0, 50.0, 52.0, 52.0, 50.0, 49.0, 50.0], ('family_history_of_cancer', 'LGG'): [49.0, 49.0, 62.0, 55.00000000000001, 50.0, 50.0, 52.0, 52.0, 50.0, 49.0, 50.0], ('family_history_of_primary_brain_tumor', 'GBMLGG'): [95.0, 95.0, 96.0, 93.0, 91.0, 95.0, 94.0, 95.0, 96.0, 95.0, 92.0], ('family_history_of_primary_brain_tumor', 'LGG'): [95.0, 95.0, 96.0, 93.0, 91.0, 95.0, 94.0, 95.0, 96.0, 95.0, 92.0], ('family_history_of_stomach_cancer', 'STAD'): [85.0, 93.0, 92.0, 94.0, 87.0, 87.0, 91.0, 93.0, 84.0, 89.0, 82.0], ('gender', 'BLCA'): [83.0, 78.0, 77.0, 80.0, 79.0, 77.0, 81.0, 80.0, 81.0, 76.0, 85.0], ('gender', 'BRCA'): [99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0], ('gender', 'COAD'): [85.0, 90.0, 92.0, 87.0, 96.0, 94.0, 95.0, 94.0, 86.0, 96.0, 88.0], ('gender', 'COADREAD'): [85.0, 81.0, 87.0, 72.0, 82.0, 96.0, 91.0, 83.0, 93.0, 80.0, 78.0], ('gender', 'ESCA'): [82.0, 83.0, 84.0, 84.0, 82.0, 83.0, 84.0, 85.0, 86.0, 83.0, 83.0], ('gender', 'GBM'): [80.0, 66.0, 71.0, 78.0, 72.0, 76.0, 83.0, 81.0, 66.0, 77.0, 84.0], ('gender', 'GBMLGG'): [80.0, 79.0, 75.0, 94.0, 82.0, 90.0, 88.0, 81.0, 83.0, 95.0, 82.0], ('gender', 'HNSC'): [75.0, 74.0, 71.0, 80.0, 80.0, 75.0, 84.0, 78.0, 68.0, 77.0, 82.0], ('gender', 'KIRP'): [83.0, 79.0, 79.0, 73.0, 83.0, 79.0, 85.0, 82.0, 77.0, 76.0, 87.0], ('gender', 'LAML'): [95.0, 77.0, 88.0, 78.0, 90.0, 70.0, 91.0, 82.0, 67.0, 69.0, 83.0], ('gender', 'LGG'): [77.0, 78.0, 95.0, 93.0, 76.0, 94.0, 93.0, 94.0, 82.0, 89.0, 88.0], ('gender', 'LIHC'): [89.0, 85.0, 85.0, 93.0, 77.0, 83.0, 93.0, 90.0, 92.0, 88.0, 80.0], ('gender', 'LUAD'): [92.0, 81.0, 83.0, 93.0, 85.0, 91.0, 73.0, 84.0, 89.0, 90.0, 78.0], ('gender', 'LUNG'): [84.0, 80.0, 88.0, 77.0, 92.0, 80.0, 91.0, 90.0, 88.0, 90.0, 88.0], ('gender', 'LUSC'): [86.0, 82.0, 67.0, 76.0, 86.0, 81.0, 81.0, 79.0, 82.0, 85.0, 79.0], ('gender', 'PAAD'): [83.0, 87.0, 90.0, 94.0, 84.0, 89.0, 88.0, 90.0, 90.0, 81.0, 91.0], ('gender', 'PCPG'): [79.0, 73.0, 83.0, 81.0, 82.0, 94.0, 85.0, 77.0, 82.0, 80.0, 81.0], ('gender', 'SARC'): [71.0, 73.0, 74.0, 55.00000000000001, 77.0, 78.0, 76.0, 69.0, 78.0, 74.0, 66.0], ('gender', 'SKCM'): [78.0, 77.0, 75.0, 75.0, 80.0, 72.0, 74.0, 76.0, 71.0, 71.0, 74.0], ('gender', 'STAD'): [77.0, 80.0, 82.0, 73.0, 81.0, 81.0, 88.0, 83.0, 77.0, 74.0, 89.0], ('gender', 'THCA'): [93.0, 86.0, 87.0, 92.0, 93.0, 94.0, 89.0, 90.0, 92.0, 93.0, 93.0], ('histological_type', 'ESCA'): [96.0, 97.0, 97.0, 98.0, 95.0, 98.0, 97.0, 98.0, 97.0, 97.0, 97.0], ('histological_type', 'LGG'): [59.0, 59.0, 49.0, 37.0, 56.00000000000001, 56.00000000000001, 54.0, 53.0, 55.00000000000001, 53.0, 50.0], ('histological_type', 'PCPG'): [83.0, 82.0, 84.0, 78.0, 80.0, 85.0, 79.0, 77.0, 77.0, 85.0, 82.0], ('histological_type', 'PRAD'): [97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0], ('histological_type', 'UCEC'): [69.0, 71.0, 64.0, 71.0, 73.0, 69.0, 64.0, 71.0, 67.0, 61.0, 71.0], ('lymphatic_invasion', 'COAD'): [56.99999999999999, 63.0, 53.0, 54.0, 57.99999999999999, 56.99999999999999, 53.0, 56.00000000000001, 56.99999999999999, 61.0, 56.99999999999999], ('lymphatic_invasion', 'COADREAD'): [60.0, 61.0, 56.99999999999999, 67.0, 65.0, 60.0, 54.0, 56.00000000000001, 60.0, 63.0, 66.0], ('lymphovascular_invasion_present', 'BLCA'): [50.0, 53.0, 49.0, 55.00000000000001, 55.00000000000001, 51.0, 53.0, 54.0, 60.0, 50.0, 52.0], ('lymphovascular_invasion_present', 'HNSC'): [66.0, 61.0, 63.0, 70.0, 61.0, 65.0, 56.99999999999999, 54.0, 61.0, 65.0, 50.0], ('menopause_status', 'BRCA'): [64.0, 66.0, 56.99999999999999, 62.0, 72.0, 57.99999999999999, 66.0, 3.0, 64.0, 70.0, 67.0], ('mental_status_changes', 'GBMLGG'): [70.0, 62.0, 61.0, 69.0, 68.0, 66.0, 65.0, 68.0, 68.0, 62.0, 62.0], ('mental_status_changes', 'LGG'): [70.0, 62.0, 61.0, 69.0, 68.0, 66.0, 65.0, 68.0, 68.0, 62.0, 62.0], ('metastatic_diagnosis', 'SARC'): [60.0, 54.0, 51.0, 50.0, 59.0, 61.0, 56.99999999999999, 54.0, 56.99999999999999, 57.99999999999999, 57.99999999999999], ('oct_embedded', 'BLCA'): [84.0, 86.0, 82.0, 79.0, 76.0, 79.0, 83.0, 81.0, 78.0, 78.0, 78.0], ('oct_embedded', 'BRCA'): [59.0, 61.0, 60.0, 62.0, 54.0, 57.99999999999999, 56.00000000000001, 62.0, 52.0, 57.99999999999999, 61.0], ('oct_embedded', 'CESC'): [57.99999999999999, 54.0, 57.99999999999999, 59.0, 52.0, 54.0, 53.0, 59.0, 59.0, 56.00000000000001, 54.0], ('oct_embedded', 'ESCA'): [64.0, 71.0, 65.0, 69.0, 68.0, 64.0, 65.0, 70.0, 73.0, 71.0, 69.0], ('oct_embedded', 'GBMLGG'): [55.00000000000001, 56.99999999999999, 59.0, 57.99999999999999, 59.0, 60.0, 57.99999999999999, 56.00000000000001, 62.0, 65.0, 56.99999999999999], ('oct_embedded', 'HNSC'): [50.0, 45.0, 48.0, 53.0, 52.0, 51.0, 48.0, 47.0, 52.0, 48.0, 50.0], ('oct_embedded', 'KIRP'): [61.0, 56.99999999999999, 60.0, 63.0, 59.0, 60.0, 60.0, 53.0, 61.0, 57.99999999999999, 54.0], ('oct_embedded', 'LGG'): [46.0, 47.0, 46.0, 56.99999999999999, 44.0, 51.0, 44.0, 46.0, 51.0, 41.0, 46.0], ('oct_embedded', 'LIHC'): [62.0, 56.00000000000001, 62.0, 59.0, 60.0, 61.0, 62.0, 63.0, 55.00000000000001, 70.0, 64.0], ('oct_embedded', 'LUNG'): [67.0, 68.0, 67.0, 62.0, 66.0, 71.0, 60.0, 69.0, 66.0, 66.0, 70.0], ('oct_embedded', 'PRAD'): [78.0, 75.0, 69.0, 76.0, 75.0, 81.0, 78.0, 77.0, 77.0, 75.0, 83.0], ('oct_embedded', 'SARC'): [76.0, 81.0, 72.0, 60.0, 73.0, 73.0, 83.0, 78.0, 83.0, 79.0, 84.0], ('oct_embedded', 'SKCM'): [78.0, 78.0, 75.0, 79.0, 74.0, 78.0, 72.0, 72.0, 68.0, 78.0, 80.0], ('oct_embedded', 'STAD'): [61.0, 61.0, 57.99999999999999, 57.99999999999999, 56.99999999999999, 61.0, 56.00000000000001, 63.0, 63.0, 61.0, 53.0], ('oct_embedded', 'THCA'): [64.0, 67.0, 57.99999999999999, 67.0, 67.0, 57.99999999999999, 56.99999999999999, 69.0, 66.0, 66.0, 60.0], ('oct_embedded', 'UCEC'): [61.0, 60.0, 57.99999999999999, 54.0, 61.0, 60.0, 66.0, 54.0, 53.0, 54.0, 51.0], ('synchronous_colon_cancer_present', 'COAD'): [91.0, 87.0, 92.0, 94.0, 93.0, 94.0, 94.0, 94.0, 93.0, 92.0, 93.0], ('synchronous_colon_cancer_present', 'COADREAD'): [96.0, 96.0, 96.0, 95.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0], ('tumor_tissue_site', 'COADREAD'): [73.0, 71.0, 72.0, 76.0, 68.0, 67.0, 68.0, 70.0, 68.0, 67.0, 73.0], ('tumor_tissue_site', 'GBMLGG'): [91.0, 93.0, 92.0, 92.0, 69.0, 94.0, 93.0, 91.0, 95.0, 94.0, 93.0], ('tumor_tissue_site', 'PCPG'): [87.0, 82.0, 85.0, 86.0, 85.0, 88.0, 88.0, 88.0, 86.0, 87.0, 84.0], ('venous_invasion', 'COAD'): [73.0, 63.0, 72.0, 70.0, 70.0, 68.0, 67.0, 76.0, 71.0, 73.0, 72.0], ('venous_invasion', 'COADREAD'): [67.0, 79.0, 70.0, 73.0, 76.0, 78.0, 76.0, 76.0, 78.0, 75.0, 74.0], ('white_cell_count_result', 'KIRP'): [61.0, 68.0, 67.0, 67.0, 54.0, 60.0, 62.0, 51.0, 61.0, 66.0, 63.0]}\n"
     ]
    }
   ],
   "source": [
    "print(avg_lr_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T01:29:07.457421Z",
     "start_time": "2019-07-21T01:29:07.397897Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.1666444503210265\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(np.std(avg_lr_acc[('white_cell_count_result', 'KIRP')]))\n",
    "print(np.std(avg_maj_acc[('white_cell_count_result', 'KIRP')]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T01:28:13.572060Z",
     "start_time": "2019-07-21T01:28:13.517823Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def measure_mean_std(model_dict):\n",
    "    mean_std_results = {}\n",
    "    for task in model_dict.keys():\n",
    "        mean = np.mean(model_dict[task])\n",
    "        std =  np.std(model_dict[task])\n",
    "        mean_std_results[task] = {'mean':mean,'std': std}\n",
    "    return mean_std_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T01:30:34.997694Z",
     "start_time": "2019-07-21T01:30:34.941200Z"
    }
   },
   "outputs": [],
   "source": [
    "Maj_dict = measure_mean_std(avg_maj_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T02:09:13.295509Z",
     "start_time": "2019-07-21T02:09:13.212257Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "path = '/Users/mandanasamiei/PycharmProjects/TCGA_Benchmark'\n",
    "result_dataframe = pd.DataFrame.from_dict(Maj_dict, orient='index')\n",
    "result_dataframe.to_csv(os.path.join(path,'Majority_result_10seeds.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T02:11:30.801883Z",
     "start_time": "2019-07-21T02:11:30.754216Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('Expression_Subtype', 'LUAD'): {'mean': 38.0, 'std': 0.0}, ('Expression_Subtype', 'LUNG'): {'mean': 38.0, 'std': 0.0}, ('GeneExp_Subtype', 'GBM'): {'mean': 33.0, 'std': 0.0}, ('Metastasis_nature2012', 'BRCA'): {'mean': 98.0, 'std': 0.0}, ('Node_nature2012', 'BRCA'): {'mean': 49.0, 'std': 0.0}, ('PAM50Call_RNAseq', 'BRCA'): {'mean': 45.0, 'std': 0.0}, ('_EVENT', 'BLCA'): {'mean': 55.00000000000001, 'std': 0.0}, ('_EVENT', 'BRCA'): {'mean': 83.0, 'std': 0.0}, ('_EVENT', 'CESC'): {'mean': 75.0, 'std': 0.0}, ('_EVENT', 'COAD'): {'mean': 75.0, 'std': 0.0}, ('_EVENT', 'COADREAD'): {'mean': 76.0, 'std': 0.0}, ('_EVENT', 'ESCA'): {'mean': 57.99999999999999, 'std': 0.0}, ('_EVENT', 'GBM'): {'mean': 77.0, 'std': 0.0}, ('_EVENT', 'GBMLGG'): {'mean': 62.0, 'std': 0.0}, ('_EVENT', 'HNSC'): {'mean': 55.00000000000001, 'std': 0.0}, ('_EVENT', 'KIRP'): {'mean': 84.0, 'std': 0.0}, ('_EVENT', 'LAML'): {'mean': 65.0, 'std': 0.0}, ('_EVENT', 'LGG'): {'mean': 75.0, 'std': 0.0}, ('_EVENT', 'LIHC'): {'mean': 61.0, 'std': 0.0}, ('_EVENT', 'LUAD'): {'mean': 63.0, 'std': 0.0}, ('_EVENT', 'LUNG'): {'mean': 59.0, 'std': 0.0}, ('_EVENT', 'LUSC'): {'mean': 55.00000000000001, 'std': 0.0}, ('_EVENT', 'OV'): {'mean': 60.0, 'std': 0.0}, ('_EVENT', 'PAAD'): {'mean': 52.0, 'std': 0.0}, ('_EVENT', 'SARC'): {'mean': 63.0, 'std': 0.0}, ('_EVENT', 'SKCM'): {'mean': 52.0, 'std': 0.0}, ('_EVENT', 'STAD'): {'mean': 61.0, 'std': 0.0}, ('_EVENT', 'THCA'): {'mean': 97.0, 'std': 0.0}, ('_EVENT', 'UCEC'): {'mean': 81.0, 'std': 0.0}, ('_PANCAN_DNAMethyl_BRCA', 'BRCA'): {'mean': 30.0, 'std': 0.0}, ('_PANCAN_DNAMethyl_HNSC', 'HNSC'): {'mean': 41.0, 'std': 0.0}, ('_PANCAN_DNAMethyl_LUAD', 'LUAD'): {'mean': 43.0, 'std': 0.0}, ('_PANCAN_DNAMethyl_LUSC', 'LUSC'): {'mean': 38.0, 'std': 0.0}, ('_PANCAN_mirna_BRCA', 'BRCA'): {'mean': 32.0, 'std': 0.0}, ('_PANCAN_mirna_HNSC', 'HNSC'): {'mean': 38.0, 'std': 0.0}, ('_PANCAN_mirna_LAML', 'LAML'): {'mean': 35.0, 'std': 0.0}, ('_PANCAN_mirna_LUAD', 'LUAD'): {'mean': 31.0, 'std': 0.0}, ('_PANCAN_mirna_LUSC', 'LUSC'): {'mean': 56.99999999999999, 'std': 0.0}, ('_PANCAN_mirna_OV', 'OV'): {'mean': 28.000000000000004, 'std': 0.0}, ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML'): {'mean': 60.0, 'std': 0.0}, ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC'): {'mean': 52.0, 'std': 0.0}, ('alcohol_history_documented', 'ESCA'): {'mean': 73.0, 'std': 0.0}, ('alcohol_history_documented', 'HNSC'): {'mean': 67.0, 'std': 0.0}, ('alcohol_history_documented', 'PAAD'): {'mean': 61.0, 'std': 0.0}, ('anatomic_neoplasm_subdivision', 'OV'): {'mean': 73.0, 'std': 0.0}, ('anatomic_neoplasm_subdivision', 'PAAD'): {'mean': 77.0, 'std': 0.0}, ('animal_insect_allergy_history', 'GBMLGG'): {'mean': 95.0, 'std': 0.0}, ('animal_insect_allergy_history', 'LGG'): {'mean': 95.0, 'std': 0.0}, ('antireflux_treatment', 'STAD'): {'mean': 79.0, 'std': 0.0}, ('asthma_history', 'GBMLGG'): {'mean': 94.0, 'std': 0.0}, ('asthma_history', 'LGG'): {'mean': 94.0, 'std': 0.0}, ('barretts_esophagus', 'STAD'): {'mean': 91.0, 'std': 0.0}, ('biochemical_recurrence', 'PRAD'): {'mean': 87.0, 'std': 0.0}, ('birth_control_pill_history_usage_category', 'CESC'): {'mean': 55.00000000000001, 'std': 0.0}, ('clinical_M', 'KIRP'): {'mean': 75.0, 'std': 0.0}, ('clinical_stage', 'KIRP'): {'mean': 66.0, 'std': 0.0}, ('colon_polyps_present', 'COAD'): {'mean': 66.0, 'std': 0.0}, ('colon_polyps_present', 'COADREAD'): {'mean': 71.0, 'std': 0.0}, ('family_history_of_cancer', 'GBMLGG'): {'mean': 62.0, 'std': 0.0}, ('family_history_of_cancer', 'LGG'): {'mean': 62.0, 'std': 0.0}, ('family_history_of_primary_brain_tumor', 'GBMLGG'): {'mean': 96.0, 'std': 0.0}, ('family_history_of_primary_brain_tumor', 'LGG'): {'mean': 96.0, 'std': 0.0}, ('family_history_of_stomach_cancer', 'STAD'): {'mean': 95.0, 'std': 0.0}, ('gender', 'BLCA'): {'mean': 73.0, 'std': 0.0}, ('gender', 'BRCA'): {'mean': 99.0, 'std': 0.0}, ('gender', 'COAD'): {'mean': 54.0, 'std': 0.0}, ('gender', 'COADREAD'): {'mean': 54.0, 'std': 0.0}, ('gender', 'ESCA'): {'mean': 84.0, 'std': 0.0}, ('gender', 'GBM'): {'mean': 65.0, 'std': 0.0}, ('gender', 'GBMLGG'): {'mean': 56.99999999999999, 'std': 0.0}, ('gender', 'HNSC'): {'mean': 73.0, 'std': 0.0}, ('gender', 'KIRP'): {'mean': 73.0, 'std': 0.0}, ('gender', 'LAML'): {'mean': 54.0, 'std': 0.0}, ('gender', 'LGG'): {'mean': 55.00000000000001, 'std': 0.0}, ('gender', 'LIHC'): {'mean': 66.0, 'std': 0.0}, ('gender', 'LUAD'): {'mean': 54.0, 'std': 0.0}, ('gender', 'LUNG'): {'mean': 60.0, 'std': 0.0}, ('gender', 'LUSC'): {'mean': 74.0, 'std': 0.0}, ('gender', 'PAAD'): {'mean': 55.00000000000001, 'std': 0.0}, ('gender', 'PCPG'): {'mean': 55.00000000000001, 'std': 0.0}, ('gender', 'SARC'): {'mean': 55.00000000000001, 'std': 0.0}, ('gender', 'SKCM'): {'mean': 62.0, 'std': 0.0}, ('gender', 'STAD'): {'mean': 65.0, 'std': 0.0}, ('gender', 'THCA'): {'mean': 73.0, 'std': 0.0}, ('histological_type', 'ESCA'): {'mean': 49.0, 'std': 0.0}, ('histological_type', 'LGG'): {'mean': 38.0, 'std': 0.0}, ('histological_type', 'PCPG'): {'mean': 82.0, 'std': 0.0}, ('histological_type', 'PRAD'): {'mean': 97.0, 'std': 0.0}, ('histological_type', 'UCEC'): {'mean': 62.0, 'std': 0.0}, ('lymphatic_invasion', 'COAD'): {'mean': 69.0, 'std': 0.0}, ('lymphatic_invasion', 'COADREAD'): {'mean': 69.0, 'std': 0.0}, ('lymphovascular_invasion_present', 'BLCA'): {'mean': 55.00000000000001, 'std': 0.0}, ('lymphovascular_invasion_present', 'HNSC'): {'mean': 64.0, 'std': 0.0}, ('menopause_status', 'BRCA'): {'mean': 69.0, 'std': 0.0}, ('mental_status_changes', 'GBMLGG'): {'mean': 75.0, 'std': 0.0}, ('mental_status_changes', 'LGG'): {'mean': 75.0, 'std': 0.0}, ('metastatic_diagnosis', 'SARC'): {'mean': 67.0, 'std': 0.0}, ('oct_embedded', 'BLCA'): {'mean': 85.0, 'std': 0.0}, ('oct_embedded', 'BRCA'): {'mean': 62.0, 'std': 0.0}, ('oct_embedded', 'CESC'): {'mean': 55.00000000000001, 'std': 0.0}, ('oct_embedded', 'ESCA'): {'mean': 57.99999999999999, 'std': 0.0}, ('oct_embedded', 'GBMLGG'): {'mean': 63.0, 'std': 0.0}, ('oct_embedded', 'HNSC'): {'mean': 51.0, 'std': 0.0}, ('oct_embedded', 'KIRP'): {'mean': 59.0, 'std': 0.0}, ('oct_embedded', 'LGG'): {'mean': 60.0, 'std': 0.0}, ('oct_embedded', 'LIHC'): {'mean': 65.0, 'std': 0.0}, ('oct_embedded', 'LUNG'): {'mean': 69.0, 'std': 0.0}, ('oct_embedded', 'PRAD'): {'mean': 89.0, 'std': 0.0}, ('oct_embedded', 'SARC'): {'mean': 85.0, 'std': 0.0}, ('oct_embedded', 'SKCM'): {'mean': 80.0, 'std': 0.0}, ('oct_embedded', 'STAD'): {'mean': 60.0, 'std': 0.0}, ('oct_embedded', 'THCA'): {'mean': 57.99999999999999, 'std': 0.0}, ('oct_embedded', 'UCEC'): {'mean': 65.0, 'std': 0.0}, ('synchronous_colon_cancer_present', 'COAD'): {'mean': 95.0, 'std': 0.0}, ('synchronous_colon_cancer_present', 'COADREAD'): {'mean': 96.0, 'std': 0.0}, ('tumor_tissue_site', 'COADREAD'): {'mean': 76.0, 'std': 0.0}, ('tumor_tissue_site', 'GBMLGG'): {'mean': 76.0, 'std': 0.0}, ('tumor_tissue_site', 'PCPG'): {'mean': 82.0, 'std': 0.0}, ('venous_invasion', 'COAD'): {'mean': 77.0, 'std': 0.0}, ('venous_invasion', 'COADREAD'): {'mean': 77.0, 'std': 0.0}, ('white_cell_count_result', 'KIRP'): {'mean': 74.0, 'std': 0.0}}\n",
      "121\n"
     ]
    }
   ],
   "source": [
    "print(Maj_dict)\n",
    "print(len(Maj_dict))#121"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.unique(Maj_dict.keys()[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T01:31:22.763573Z",
     "start_time": "2019-07-21T01:31:22.704731Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('Expression_Subtype', 'LUAD'): {'mean': 60.63636363636363, 'std': 8.227147161271832}, ('Expression_Subtype', 'LUNG'): {'mean': 60.63636363636363, 'std': 8.227147161271832}, ('GeneExp_Subtype', 'GBM'): {'mean': 62.36363636363637, 'std': 12.928282788822674}, ('Metastasis_nature2012', 'BRCA'): {'mean': 98.0, 'std': 0.0}, ('Node_nature2012', 'BRCA'): {'mean': 40.90909090909091, 'std': 4.640817824763715}, ('PAM50Call_RNAseq', 'BRCA'): {'mean': 69.0, 'std': 17.49805183961398}, ('_EVENT', 'BLCA'): {'mean': 55.45454545454545, 'std': 2.675261630829483}, ('_EVENT', 'BRCA'): {'mean': 75.45454545454545, 'std': 5.852173432520393}, ('_EVENT', 'CESC'): {'mean': 68.63636363636364, 'std': 4.617609126981925}, ('_EVENT', 'COAD'): {'mean': 71.36363636363636, 'std': 2.568081253059188}, ('_EVENT', 'COADREAD'): {'mean': 65.36363636363636, 'std': 5.032128182944745}, ('_EVENT', 'ESCA'): {'mean': 54.54545454545455, 'std': 4.185768884807758}, ('_EVENT', 'GBM'): {'mean': 66.18181818181819, 'std': 3.785575057028548}, ('_EVENT', 'GBMLGG'): {'mean': 73.18181818181819, 'std': 1.7486712783337586}, ('_EVENT', 'HNSC'): {'mean': 57.09090909090909, 'std': 3.703917961356974}, ('_EVENT', 'KIRP'): {'mean': 84.27272727272727, 'std': 2.73332661424962}, ('_EVENT', 'LAML'): {'mean': 52.72727272727273, 'std': 6.22445191586316}, ('_EVENT', 'LGG'): {'mean': 70.81818181818181, 'std': 5.131333015152759}, ('_EVENT', 'LIHC'): {'mean': 57.81818181818182, 'std': 3.4855071841237835}, ('_EVENT', 'LUAD'): {'mean': 55.54545454545455, 'std': 4.942646261479341}, ('_EVENT', 'LUNG'): {'mean': 58.18181818181818, 'std': 2.4052284646041735}, ('_EVENT', 'LUSC'): {'mean': 55.72727272727273, 'std': 1.8630819574471986}, ('_EVENT', 'OV'): {'mean': 54.45454545454545, 'std': 3.893202382793429}, ('_EVENT', 'PAAD'): {'mean': 56.0, 'std': 5.222329678670934}, ('_EVENT', 'SARC'): {'mean': 54.81818181818182, 'std': 4.3655298921974435}, ('_EVENT', 'SKCM'): {'mean': 59.18181818181818, 'std': 2.757409252382382}, ('_EVENT', 'STAD'): {'mean': 56.90909090909091, 'std': 2.0650575802910054}, ('_EVENT', 'THCA'): {'mean': 95.9090909090909, 'std': 3.4497574474564137}, ('_EVENT', 'UCEC'): {'mean': 78.54545454545455, 'std': 1.776801844153824}, ('_PANCAN_DNAMethyl_BRCA', 'BRCA'): {'mean': 47.63636363636363, 'std': 4.637254816311582}, ('_PANCAN_DNAMethyl_HNSC', 'HNSC'): {'mean': 62.36363636363637, 'std': 14.950054589579429}, ('_PANCAN_DNAMethyl_LUAD', 'LUAD'): {'mean': 46.27272727272727, 'std': 8.645449765795448}, ('_PANCAN_DNAMethyl_LUSC', 'LUSC'): {'mean': 52.63636363636363, 'std': 11.145914271995712}, ('_PANCAN_mirna_BRCA', 'BRCA'): {'mean': 23.727272727272727, 'std': 12.849413917238751}, ('_PANCAN_mirna_HNSC', 'HNSC'): {'mean': 51.90909090909091, 'std': 3.146557872704872}, ('_PANCAN_mirna_LAML', 'LAML'): {'mean': 59.18181818181818, 'std': 17.07107165948599}, ('_PANCAN_mirna_LUAD', 'LUAD'): {'mean': 33.81818181818182, 'std': 10.81932767040861}, ('_PANCAN_mirna_LUSC', 'LUSC'): {'mean': 48.72727272727273, 'std': 18.106206415033537}, ('_PANCAN_mirna_OV', 'OV'): {'mean': 26.818181818181817, 'std': 4.782889613805784}, ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML'): {'mean': 68.27272727272727, 'std': 16.298861695283613}, ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC'): {'mean': 52.54545454545455, 'std': 2.7090298955911534}, ('alcohol_history_documented', 'ESCA'): {'mean': 62.27272727272727, 'std': 4.614028196527098}, ('alcohol_history_documented', 'HNSC'): {'mean': 65.36363636363636, 'std': 1.9198829165402616}, ('alcohol_history_documented', 'PAAD'): {'mean': 51.54545454545455, 'std': 5.158640348767677}, ('anatomic_neoplasm_subdivision', 'OV'): {'mean': 64.18181818181819, 'std': 6.307545003164238}, ('anatomic_neoplasm_subdivision', 'PAAD'): {'mean': 71.45454545454545, 'std': 4.053363056292355}, ('animal_insect_allergy_history', 'GBMLGG'): {'mean': 89.9090909090909, 'std': 3.5790944881871867}, ('animal_insect_allergy_history', 'LGG'): {'mean': 89.9090909090909, 'std': 3.5790944881871867}, ('antireflux_treatment', 'STAD'): {'mean': 75.63636363636364, 'std': 2.185602778212965}, ('asthma_history', 'GBMLGG'): {'mean': 92.9090909090909, 'std': 1.2398346997259868}, ('asthma_history', 'LGG'): {'mean': 92.9090909090909, 'std': 1.2398346997259868}, ('barretts_esophagus', 'STAD'): {'mean': 88.27272727272727, 'std': 2.631657241114569}, ('biochemical_recurrence', 'PRAD'): {'mean': 81.81818181818181, 'std': 3.1857119032609513}, ('birth_control_pill_history_usage_category', 'CESC'): {'mean': 45.54545454545455, 'std': 11.656942031281668}, ('clinical_M', 'KIRP'): {'mean': 68.81818181818181, 'std': 4.238742991590826}, ('clinical_stage', 'KIRP'): {'mean': 61.18181818181818, 'std': 4.406981688560299}, ('colon_polyps_present', 'COAD'): {'mean': 56.45454545454545, 'std': 5.211240235624187}, ('colon_polyps_present', 'COADREAD'): {'mean': 60.27272727272727, 'std': 4.672762645780508}, ('family_history_of_cancer', 'GBMLGG'): {'mean': 51.63636363636363, 'std': 3.6994527180683288}, ('family_history_of_cancer', 'LGG'): {'mean': 51.63636363636363, 'std': 3.6994527180683288}, ('family_history_of_primary_brain_tumor', 'GBMLGG'): {'mean': 94.27272727272727, 'std': 1.54277843167974}, ('family_history_of_primary_brain_tumor', 'LGG'): {'mean': 94.27272727272727, 'std': 1.54277843167974}, ('family_history_of_stomach_cancer', 'STAD'): {'mean': 88.81818181818181, 'std': 3.9038019188334343}, ('gender', 'BLCA'): {'mean': 79.72727272727273, 'std': 2.596883064924673}, ('gender', 'BRCA'): {'mean': 99.0, 'std': 0.0}, ('gender', 'COAD'): {'mean': 91.18181818181819, 'std': 3.9501019968000786}, ('gender', 'COADREAD'): {'mean': 84.36363636363636, 'std': 6.691551352573798}, ('gender', 'ESCA'): {'mean': 83.54545454545455, 'std': 1.1570838237598051}, ('gender', 'GBM'): {'mean': 75.81818181818181, 'std': 6.027485256091191}, ('gender', 'GBMLGG'): {'mean': 84.45454545454545, 'std': 6.110551773384022}, ('gender', 'HNSC'): {'mean': 76.72727272727273, 'std': 4.53453233212361}, ('gender', 'KIRP'): {'mean': 80.27272727272727, 'std': 3.9563736630888986}, ('gender', 'LAML'): {'mean': 80.9090909090909, 'std': 9.129916314010027}, ('gender', 'LGG'): {'mean': 87.18181818181819, 'std': 7.170877742878187}, ('gender', 'LIHC'): {'mean': 86.81818181818181, 'std': 5.077905470461433}, ('gender', 'LUAD'): {'mean': 85.36363636363636, 'std': 6.049383548950591}, ('gender', 'LUNG'): {'mean': 86.18181818181819, 'std': 4.876999484000603}, ('gender', 'LUSC'): {'mean': 80.36363636363636, 'std': 5.157038037702849}, ('gender', 'PAAD'): {'mean': 87.9090909090909, 'std': 3.6792920626996475}, ('gender', 'PCPG'): {'mean': 81.54545454545455, 'std': 4.979295977319692}, ('gender', 'SARC'): {'mean': 71.9090909090909, 'std': 6.444935227187671}, ('gender', 'SKCM'): {'mean': 74.81818181818181, 'std': 2.7242407388675813}, ('gender', 'STAD'): {'mean': 80.45454545454545, 'std': 4.8685192141803215}, ('gender', 'THCA'): {'mean': 91.0909090909091, 'std': 2.5745095015465997}, ('histological_type', 'ESCA'): {'mean': 97.0, 'std': 0.8528028654224418}, ('histological_type', 'LGG'): {'mean': 52.81818181818182, 'std': 5.843694065450254}, ('histological_type', 'PCPG'): {'mean': 81.0909090909091, 'std': 2.906248611481051}, ('histological_type', 'PRAD'): {'mean': 97.0, 'std': 0.0}, ('histological_type', 'UCEC'): {'mean': 68.27272727272727, 'std': 3.620419591225432}, ('lymphatic_invasion', 'COAD'): {'mean': 56.90909090909091, 'std': 2.9373626220733655}, ('lymphatic_invasion', 'COADREAD'): {'mean': 60.81818181818182, 'std': 3.9730497060749377}, ('lymphovascular_invasion_present', 'BLCA'): {'mean': 52.90909090909091, 'std': 2.968150494057184}, ('lymphovascular_invasion_present', 'HNSC'): {'mean': 61.18181818181818, 'std': 5.457574916292232}, ('menopause_status', 'BRCA'): {'mean': 59.0, 'std': 18.2208671582886}, ('mental_status_changes', 'GBMLGG'): {'mean': 65.54545454545455, 'std': 3.1439302665073785}, ('mental_status_changes', 'LGG'): {'mean': 65.54545454545455, 'std': 3.1439302665073785}, ('metastatic_diagnosis', 'SARC'): {'mean': 56.27272727272727, 'std': 3.4136333373670147}, ('oct_embedded', 'BLCA'): {'mean': 80.36363636363636, 'std': 2.9317300902903813}, ('oct_embedded', 'BRCA'): {'mean': 58.45454545454545, 'std': 3.1439302665073785}, ('oct_embedded', 'CESC'): {'mean': 56.0, 'std': 2.5584085962673244}, ('oct_embedded', 'ESCA'): {'mean': 68.0909090909091, 'std': 2.9986222731764802}, ('oct_embedded', 'GBMLGG'): {'mean': 58.72727272727273, 'std': 2.6998622554395446}, ('oct_embedded', 'HNSC'): {'mean': 49.45454545454545, 'std': 2.3496087242400154}, ('oct_embedded', 'KIRP'): {'mean': 58.72727272727273, 'std': 2.894851514198302}, ('oct_embedded', 'LGG'): {'mean': 47.18181818181818, 'std': 4.152068089697902}, ('oct_embedded', 'LIHC'): {'mean': 61.27272727272727, 'std': 3.839765833080521}, ('oct_embedded', 'LUNG'): {'mean': 66.54545454545455, 'std': 3.08555686335948}, ('oct_embedded', 'PRAD'): {'mean': 76.72727272727273, 'std': 3.4401614415317274}, ('oct_embedded', 'SARC'): {'mean': 76.54545454545455, 'std': 6.651912047246079}, ('oct_embedded', 'SKCM'): {'mean': 75.63636363636364, 'std': 3.5744733184250004}, ('oct_embedded', 'STAD'): {'mean': 59.27272727272727, 'std': 2.9875775863918155}, ('oct_embedded', 'THCA'): {'mean': 63.54545454545455, 'std': 4.20743144196587}, ('oct_embedded', 'UCEC'): {'mean': 57.45454545454545, 'std': 4.356054018440341}, ('synchronous_colon_cancer_present', 'COAD'): {'mean': 92.45454545454545, 'std': 1.970862126243527}, ('synchronous_colon_cancer_present', 'COADREAD'): {'mean': 95.9090909090909, 'std': 0.2874797872880344}, ('tumor_tissue_site', 'COADREAD'): {'mean': 70.27272727272727, 'std': 2.831347545890443}, ('tumor_tissue_site', 'GBMLGG'): {'mean': 90.63636363636364, 'std': 6.944883365376101}, ('tumor_tissue_site', 'PCPG'): {'mean': 86.0, 'std': 1.8090680674665818}, ('venous_invasion', 'COAD'): {'mean': 70.45454545454545, 'std': 3.3402132856134243}, ('venous_invasion', 'COADREAD'): {'mean': 74.72727272727273, 'std': 3.4401614415317274}, ('white_cell_count_result', 'KIRP'): {'mean': 61.81818181818182, 'std': 5.1666444503210265}}\n"
     ]
    }
   ],
   "source": [
    "LR_dict= measure_mean_std(avg_lr_acc)\n",
    "print(LR_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T02:12:21.697668Z",
     "start_time": "2019-07-21T02:12:21.629637Z"
    }
   },
   "outputs": [],
   "source": [
    "result_dataframe = pd.DataFrame.from_dict(LR_dict, orient='index')\n",
    "result_dataframe.to_csv(os.path.join(path,'LogisticRegression_result_10seeds.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T01:48:11.071246Z",
     "start_time": "2019-07-21T01:48:05.137009Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38.0, 38.0, 33.0, 98.0, 49.0, 45.0, 55.00000000000001, 83.0, 75.0, 75.0, 76.0, 57.99999999999999, 77.0, 62.0, 55.00000000000001, 84.0, 65.0, 75.0, 61.0, 63.0, 59.0, 55.00000000000001, 60.0, 52.0, 63.0, 52.0, 61.0, 97.0, 81.0, 30.0, 41.0, 43.0, 38.0, 32.0, 38.0, 35.0, 31.0, 56.99999999999999, 28.000000000000004, 60.0, 52.0, 73.0, 67.0, 61.0, 73.0, 77.0, 95.0, 95.0, 79.0, 94.0, 94.0, 91.0, 87.0, 55.00000000000001, 75.0, 66.0, 66.0, 71.0, 62.0, 62.0, 96.0, 96.0, 95.0, 73.0, 99.0, 54.0, 54.0, 84.0, 65.0, 56.99999999999999, 73.0, 73.0, 54.0, 55.00000000000001, 66.0, 54.0, 60.0, 74.0, 55.00000000000001, 55.00000000000001, 55.00000000000001, 62.0, 65.0, 73.0, 49.0, 38.0, 82.0, 97.0, 62.0, 69.0, 69.0, 55.00000000000001, 64.0, 69.0, 75.0, 75.0, 67.0, 85.0, 62.0, 55.00000000000001, 57.99999999999999, 63.0, 51.0, 59.0, 60.0, 65.0, 69.0, 89.0, 85.0, 80.0, 60.0, 57.99999999999999, 65.0, 95.0, 96.0, 76.0, 76.0, 82.0, 77.0, 77.0, 74.0]\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 3600x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_visualization(Maj_dict, list(Maj_dict.keys()), \"Majority\", color='#332288')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T01:48:04.726358Z",
     "start_time": "2019-07-21T01:47:57.483024Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[60.63636363636363, 60.63636363636363, 62.36363636363637, 98.0, 40.90909090909091, 69.0, 55.45454545454545, 75.45454545454545, 68.63636363636364, 71.36363636363636, 65.36363636363636, 54.54545454545455, 66.18181818181819, 73.18181818181819, 57.09090909090909, 84.27272727272727, 52.72727272727273, 70.81818181818181, 57.81818181818182, 55.54545454545455, 58.18181818181818, 55.72727272727273, 54.45454545454545, 56.0, 54.81818181818182, 59.18181818181818, 56.90909090909091, 95.9090909090909, 78.54545454545455, 47.63636363636363, 62.36363636363637, 46.27272727272727, 52.63636363636363, 23.727272727272727, 51.90909090909091, 59.18181818181818, 33.81818181818182, 48.72727272727273, 26.818181818181817, 68.27272727272727, 52.54545454545455, 62.27272727272727, 65.36363636363636, 51.54545454545455, 64.18181818181819, 71.45454545454545, 89.9090909090909, 89.9090909090909, 75.63636363636364, 92.9090909090909, 92.9090909090909, 88.27272727272727, 81.81818181818181, 45.54545454545455, 68.81818181818181, 61.18181818181818, 56.45454545454545, 60.27272727272727, 51.63636363636363, 51.63636363636363, 94.27272727272727, 94.27272727272727, 88.81818181818181, 79.72727272727273, 99.0, 91.18181818181819, 84.36363636363636, 83.54545454545455, 75.81818181818181, 84.45454545454545, 76.72727272727273, 80.27272727272727, 80.9090909090909, 87.18181818181819, 86.81818181818181, 85.36363636363636, 86.18181818181819, 80.36363636363636, 87.9090909090909, 81.54545454545455, 71.9090909090909, 74.81818181818181, 80.45454545454545, 91.0909090909091, 97.0, 52.81818181818182, 81.0909090909091, 97.0, 68.27272727272727, 56.90909090909091, 60.81818181818182, 52.90909090909091, 61.18181818181818, 59.0, 65.54545454545455, 65.54545454545455, 56.27272727272727, 80.36363636363636, 58.45454545454545, 56.0, 68.0909090909091, 58.72727272727273, 49.45454545454545, 58.72727272727273, 47.18181818181818, 61.27272727272727, 66.54545454545455, 76.72727272727273, 76.54545454545455, 75.63636363636364, 59.27272727272727, 63.54545454545455, 57.45454545454545, 92.45454545454545, 95.9090909090909, 70.27272727272727, 90.63636363636364, 86.0, 70.45454545454545, 74.72727272727273, 61.81818181818182]\n"
     ]
    },
    {
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7iNba/5pj1k3Sj/sHJfm9JGcm+fsFdFotsoxHJ3ly+qDVqyd5YGvtgjll7Ugf2HDz9PrN9ZL8QpK/GoOR5pFZSfZPr7s9NsmHkvxDa+3dc8o7MMmRG3XEqvqJkf1nST7UWvvMHDKXsV2vluRm6RNI/jXJG5N8dl77zsg8Nv2YuHuSX0vy6dbaH8wpq9IHxt45vX3jA0k+31o7a055ByY5YuNYr6qfTH8A0f9M32/mUXfcrbX2raq6fnpn4DfSzzvPaq19ew55Byf5dmvtnJF5avrDXP65tfbKqfM2ZT85fYD8HZK8eY7f4y6ttW9X1Q+mD8r71yTntdZeMI+8kbnnqK8dkuSn05/oeE56m8rk9+Kb6lSPSnL9JN9K8juttY9MnTeTu7Dr8Ti/7WitfX7cy10nycPT61X3nTpvZG4cjyekD3T+5yRfTvKWje09YdaBSY5urf3F+Pzw9Lrj2Uk+PI82x3HvdkmSjXvSM9L3099PP8edO3Hekenf4f+u/nb1O6bXjb/dWnvclFkzmbPHxqPT21QvTN+mH51D3sFJrtla++Px+fHp947nJfnVObXjbJxvDk0/31yS/j2+o7X2D1Pnjcwdoy3+TkkOTh9E+uwkv9da+/LEWVdPclBr7c/HvfH+6ffGRye505zujfdvfRJQqk/guFl6e8MfttbeN1XOZeReL/2YODX92H9Hks/Nqd/o0PR71X+qPsDi8ekDuy9I8rzW2nfmkLkxkPqk9MmHP5jkleltY1+aQ97Vk1yjtfaR6gOqDkifgHCrJHdsrX1lyn1nHP8/keTXNo69qrpr+mTSjyX59SnvAcbx8OL0drc/qj6x447pD6+6SpKnTdlWNdr8n5bep/GB1to3Rh/ZT6S35364tfZ7U+WNzCPTj/O3zix7ZpK7JXlma+0Ppuwbq0sf/vnJJB8ZbeQ/mN6X8x9J3jN1n8NoL354+vnzL8eyh6fXr/4xybunPj5Gv8Zdk3xwHAe7pE8I2DXJn6e3rU7WXj3OMReln1+2pV+rbpfeNv7x1trXp8oaeXulT+L6P+l9DP9W/SEuj05yaJKPJvntCfebXZI8M/36dHpr7cJRV71Z+iDLD7TWzpgia1PuAa21b4y/H5V+T/7o8edjU/enbjqn7pXeX/xj6ef0H259gvCUeQs9x43MRe+rV00fr/GimWUPSN9/X9paO3PivGVs059J77e93+iT2ye9Pv7S9PuNyfsdR3vq10Yd8Zj04/71Sf6stfaY8TNTXjsOTHLIRvvQaOe4eZI/Ti/j30+Rsylz0WU8KH0y3odaa1+rPiDwheOf/yz9+njRFFkzmUe0Ma6oqq6d5Kgkr02/x/nZsXzS8THjd+6TXt85PsnfJvnjedw3zuSdlH7NPzV9cu5vzGOfGVn7Jjk3/Xq/PX3S+jHpbdX/s7X2zTlkbvTHHZA+Ru7x6WOrHjqPe9W6dIzDLdMfIHVKkgtaaw8f/z71mKqrJrl3a+014/Nt0q+P90ny/Nba70+VNZN5TJILW2tfrj7495rp9fGvtdYeMXXepuxHjrz90u/h3jCnnP1GO/WO9Pv+U9Mf0PfIJGfP4bjfaFM5OslT0usBeyd57kZda+K8g5LccqNtoaruneSW6W3H72+t/eEcMjfKeGz6m7J+Of38/aYkn5m63jgy90hvfzs6fQLJsenjYz/W5jBWdaad6tZJfjjJQekP6Xji1FkzmVdprf3HaG94cvp57vwk723zGeOw0LyRuS39eLht+vHx0ST/1Fp755zyDkqvV31mfH5B+ksBvprkl1tr/2cOeeeNeuot0u+pdqT3qU4+Vm3cb+yxUY6qekX6vvqx9PvzyfuNR7vKwa21vxj1/z2S/Hr6/fKzW2tfnDhvod/hyFjo8T/6/A7YOJdV1a2SvCzJb6Ufj/Oo/x+W5LobdYvRB3h0+n3AS6auV402zgMXXMZKr2PcIckTknw4yT9OfV88k7dRbzw5vX/zC0m+3lp7+TzyRubBrT88LuOcc0J6n8qjW2sfnENddaNv/Jbp1+J/S69HPm+qjMvI3JbkkPR5Bqen1zf+OH1uzKT3jJtyH5He3/D3Sf6utfb2OeXMtsUdmeRH0/fXxyX56NT9OKMP5zattV8fn++X5FrpdfLXzqm+sU96f+a+6f0pb06f3/Sx9PbrqeviCx/DtXStNX+26J/0g+LpSa4+8/lp6Q0rT04fBDFVVqU/GfM30wenzv7bc9MvlD8wPm+bMHfv9MHU95hZdrv0mcHvTHLUHLbrS5L8ryS7ziw7Osnn0is6U2btM8pyn5lldxrb9FVJbjCnfee30wcYzS47OckXk9xz4zufMO8m6QN/Tp9Zdlj6hKBfS3KLict37/SJojfftPztSf5o5vOUZTwsyQtmPt84fYD8H6YPsJ76OzwwydvSB61s7KN3TPK+qffT8fu3pzeIvTbJXWaW32rswy9KnwwwZeZhSX5mgdv0RumDbx83s2yP9MrVH6UPupo688fTO8JPGp93pE+sfOs4x022j47fv2eS18yex9M7j5+R5DeSXH/ivP3TB21eY3w+JH1AzplJfmnq7TkyrprkjJnPu41954tJnjqHvI3z+D1nlp2c5HnpA4+uN3He7unXxYelTyDdWP7sJG9J8kPj89T7zkLPqzP76sNnls1zX73nOL89cGbZ9UfeK5Jca+K8SvLEJB+cPXemd5B9KMlzpsxb0ne4V/rAzQenN1plnN+el+Tl6YM5pt6mu4zzy7Nnlh+Z3gDxyUxf39h7lGchZRy//8HpN6cb9e5d0usA70xv6Jw0b2TcM33CyC02Lf+N9IaHqfOOyMy1eCx7fPr18i4b3/eEeQcnuV+Sfcbnw5K8YXyHPzr1+XRJZTwqySnj79vHf98yzjfXm0Pegen3o9efWfaocZ59UiauM85kvii9IyDpg+Mfml6nOn0OedvSG99+Lv0hEhvLbzX2nWckOXQOZVzYdk3v1HjExu8d59kXp9+nPji9Y2ke3+PTM9oU0t/O/YRxjjt1Tnk/m97xkPQGzoend1o/beq8kfHYJD+f5LYzy249x/1mn/R2nNuMz3ul3/e8Psl7M9PuMVHe9vQHuPxykqvObOc7p088mHy7pl/rXzXzeY/0jrl/Sx8ENHXe3knO2jgW09vibjHKd8bUeTPf22uTPGDTfvPT6W0qx0+ct9s4v83Wqa6TPjjmvUlOnkMZl3E9fvjYT64zs2xHkt9N73icOu8q6U9xv+1M1inpnZ1vzsz93QRZNXO+uf3M8luM7/b0JIdNXL7d0yc2Pi2jDWAs/8n0a8ePz+Gc88gkf7rxHaYPPrz6OC4mb3MYx8bLk/z0zLLrpg+Qe2/6xJIp8yrJ/dPbAGbbHE5KbwN4afpEzykz9xr76ebzzbPS77UmPd+M339Q+j3UdTaO9fSnkP5G+jXliInz7pV+b7z5/uaMJJ+Y3f4T5e2aXod64cyya6TXp94z+91OvE0fmP4wl6QPeHhNevvGfTNhv9H4/dvG8fjuJDcay3Yf55zXjX11+8SZG31jJ84se1D69fLpUx8b4/ffPsm/Z6ZtfCx/Zfob3vaceN/ZJ/1a+IJN59Vbjm19v43tP+E2/cn0Os1JM8uvM859z5rDefzU9GvVPZLsMrMez05/aOXuE+ddO71N5TGz31P6fdXHk1x74m26x9gfX5jvrv9fd2zTx028z+zIpRObn71xPI5/u+M4D9x3ysyZffK16ZPFjhrLtqdfH1+V5NZTZY7f+4pRviNnlj8gya/OHBdTbdON69Jt0/ve75tkt7Fsz/H9viyjDjtVZvrgppelv115Yx32Sm+je30m7jse2/XM9Enbs8s3+uNOHp+n7P+/Tfo59e6blr8hvR130jaH9GvjaYs6x43j8RfG713Ivpp+3fhw+nVjdozDXdL7Pn944syFbdNceo3YdZwDXpfvPo/fJH3w8Z2n3G/G735O+hiRvWaW7ZX+gIXXTZy10W/0so1z51h+h1Hup2SMC5g492fSH8I79zKO3/1D6deNR2XU9dP7OR6dXq+6xZT76vhdr89MXX8su1r6xLxnzaGMh8+eU9Pf0PXQcU69/xzyDk6fKLb3+HxU+mScl20+z06Ut9v4Dp+Q0R4/lj9unBPm0cexe/pA8VM3LX9J+kPdrjs+T1k3/ngurdvsGOvw8STvmvm5Kct4bPpDlJ4zs2yX9PucP0ly0zl8l09Of0DVVWeW7Z1+3Zi8nTN9csF7Zj4fnl6X/Ez6RMep83bJaFvYtPzxY1v/4MT7zV7pbfEbY+525NLJ6h/J6IeYuIx3S2/HedjMsmunXzNemenHVOw98u6aS++Bd0+/13h1Zu5BJsy8Svp4nNlxo3cZy165cfxPmLdfvru9cZ/0vvgPpU+snLR8I2OPUZYnziy70TjPvifTj3HYyHvCIvLG7z8g/bq0cV49Kr1N4E3pk2Snzttoq3p1vvt+/IfT75mfmzG+Y6K8Hen9Nb+US8eNHDqO0XcleezE5dsl/X7xuRnjRsby+6Xfhz8qo14wce7909tVbrpp+VszM1Yl09z/7xjf4WsyxsHN8zscv3sZx/8Tx/a7Ucb9U/o91S+P73jStrHx++86jvX7ziy7Vvo93NsyfXv808exvpAy5tIH4h82Pl89fYz6W9IfAjb19twYG7sxh+PQ9DaPd2cO91Mjo5L8zubfn96f+sVMXB9Pn8B1Vi4dp7Zf+hjOt6U/rHYeZdw//aVYx4/P+6SPiXnF2Kd2mzjv4PQ2qd3H52uMc/iH57TfbE9vH/rZfHf7xo+n33PcdXyeqp5auXTM9mkzy49Ov497V6avU+2V3vf3yFw6juuA9OvlKzLGsE2Yty29b/wVWdAYrlX4sy1sZUen3+w8pKqu3vqs4Nekz5I9Jr0zYCrV+pMaX57kwVV1jzHTOq2156efeN5Y/W1Il4zZ9FcssM8ePze9cnXf6m+RSutvAHhbkn9I8pjxxNDJtNZOT5/wc+ZYj+2ttS+kTz68VfWn9U5le/rM/5tV1V1G/u+mN0R8LcnJU5dvuHeS61bV85M+a7619uH0m4BfrKrbtHEGnMjfpF8k96n+5qO0/sSRD6Z/vydV1fYp9pvxu89Kvzn9+araczx9JK21Byf5l6p6+/g8ZRkvTnLNqvrF8bs/lT5Y9UXp++mPTJiV9CerfS39Fdn7t9a+0PrT+J+a5PrjyYCTaf3JAh9NnyB74xqvyG39SeQfTu94uW7yn0/TmMLFSU6oqteOrLls06o6fuzzf5G+39xyPGklrb/h5A1J3p/kx8fTCKfI3H0cd7+a3vHw1Kraq7X2ndafRPg/0t96+hNT5I3MPdKf3v7h9HPZfZKk9ac4vDP9Rv1h40k6k2j9Ked/keT1VXVMa+0rrbU/Sm90PKCqnjtV1kzm/0lyeFX95vj8rbHv3CnJk6rqsRNHbkt/u+GtxhNdMs6nZ6V3Yp9S/Um2V9h4ktPFSX4lvdPvNlW128h8QZLPJzltPJlo0te7j/Pqk9LPq3st4Lx6Ufrg29vMc1+tqqPGU4b+OL0D5ZjxVJC01v46fdL8t9LftDTJ+W08WeX30juqP5XkmVW1Y9Q3/jm94/x+NfEr0Bf5HY4nx52XXr4bJPnBUa/7l/SG1v+b/t3ufkWzZuzV+tuMXpR+Hn90krTWvtRae2N6g+vTqz9lehKjnvr59CdxLqKMaf0JQ29Ncvqod3971FOfkeTb1Z/uPolxfNy8tfbe9Driy8e+s2Osy33S953nT5U5bE/ygKp6alVtH1mvSe+M+Omqusk4x011L3ud9Hr+vcbTsv4tvdHoX9IbQW40Uc6sjTI+bUFlPCbJC6vq7qNOl9af4vqpJC8ex+yU14190idv3LWqbjjy3ph+Tj0ufTDwZHXG8Xv2SL9GPn98j/+QPgHgbUmuU1UPmSJrQ+tPMj0zvT5+o+pPP9+oG5+V/qS1jWvHVN/jvunb9W6L2K4j68bpx8Yx4/rwzPQBziemv1Vmyu/xgPTG1KOT/GxVVWvtT9MHjpyVvj9N/fbRq6R3Ij1/3FP9Xfr3+vYkN6iqU6cKqqojxvf21vS3fs3uN59I72w5IZfeU02y37T+1O3fTPKCqjqxtXbeuO/5qbEeL984D11Ro262b/p9/ueSPHZcG7+eZKMd5+rVnxA4mdbfZHKrqvrl8fmC1t8ac+8kL6mqH5sqq/oTuY9N71h5VVUd31r7xthXn5pkR1W96L/8JZfP9vRteJequlvyn/vN29OPyUeNe4VJtNa+lb5PXrWqHjOWfTb9WHxf+nno2KnyhhunD65e2PW49bcAvTjJL4179B3jnvxOSS6p/rbeKV2S3qbyiKq6xcj6UHrH4zeSPGHj/u6KqP407pa+f/xrkh+YOd/8aXqb43XSH3oy5fnmwvQ26UOT3Hq0QaT1pxz/TXonywEjc6r2vzend6Q+u6p2b61d1Fr7p/SJiLtU1YOnyEn+82mVlT7o8PCqOm2sw2fSt+kHktxjqvuN6u3Be428P09y7XGtTGvt7PRzbaVfO6bcpuelD4S7c1XdfSz7RPoEub9OP9/sN0XWjG+m7xsPT29brdafVP/C9PrGDyeTlvE9ubTdeO+Ze+OHJPliVb1jfJ7i3rhafyLtb6S3iz9p/O7Pp1+fz0pyx6q61hXN2uT66cfcvarq8NbfXvn09LeA3TL9nDuJca2/JL2Mf5Tk1HEeujC9/eZt6YOE7zhV5rBb+mCDU6q/RTattY1649Hp5ZyybpzWn479wPRzzn413kTYWntServSO8e1ZJK2sVGPe0aS76Q/jX9j+Z+kD5J5XlXdsE30loXW+xffm36t/+rM8s+mn3P2TX+4wxUyrrn7j+Pjnennub9q/UnSNdbjzekDSSa7jxu/+2/TH6x26ySH1KV9nBv3Ve8a9wVTbdML0gfI/kP6k6M3lm9cO+5bVTeb6Hxz7fQ6xUHpE/6/mV7f38j8SHpf3MM22gAmyDy4+puV/iR90Mr+GW8DHm0Pb0lvH3/AWHaFMqs/OX6jfMek95/uPn73u9LPq4+qquOmaOOoquskeU1VvSX9O3xXkj9v/a3O1Vo7P7298fj068okxnb66/Tj4B0b221co/80fZtec6zjVH3xF6efb25WVafNHBtvTy/jq6vqWlMcG1V1TFXdvrX28fTBaf+jqvatS9sbT0sv589Oca9al7a7fS3JG9PPZ3M7x43MXVt/A+6r0ycZzXtfvWb6RLVj0wf9Hp5+rGRkfjB9ANQbxvd4RY/FhW7TUb5nVdW9Rr3qp5O8aFM5Pp0+ueLY8f9McWwcUVU3aK39TPr98buq22Ucj7dLcovqb5S9wqo/9f8a6df4c5LcdNyDpPU3x70/va9lsuN/Q2vtOentHGfOs4wzeX+YXt84KL3/La337bw5ffzDj41lk43jaK09OskXavSDjTL+7/SHuj60+psepnRxetvfq0f+J9Pv0V+Sfu8/dRvnTdMfWHHPqjqo9bfwPD3JV9L7qX5gyrDRbvQr6W3Ed6xL+6pfm14vfvSoU03S51BV+477i5emnz9PmblWnZ7ez/HG6v2sk4xVG3XSTyT5cFUdOtpwLmyt3SbJEVPeN85k/mN6H/HVqr8he+PY+JX0/vF7brTvTJj5C+mD8s+o3le9o/W+z9um15cn66se9acvJzm3qj428v911CVPTu8zm6xONX7/t9MHHt+lqh40s9+8Jr096fXjnnmq/ea89GP9SVX1w2O/+Uz6RJVPJXlZTTfeaKM940Pp7TiH1Rg7Me57PpD+1pXJ2v+qap+xf3w8/eEYJ47z6YXpbXHfSZ/wPLVd0tuIb11Vd07+s35zVvo18y410Xic8bvPSb/2v6/6WNxvtj4e75Qku1XVpG90G+ve0r+zE2q0Z7bej3Nm+vf7gOp9E1O5ZOTdYEF5SW/HuTC9TeWIca369fS6z9HV39QzierjUo9O71P5XHp9ZqN9/PfSxyFdJb2N/ArXq8Zx8J30ful/SfLEcU36cnod9e3p3+1Nr0jOprxvp7cr7JLk5hvHQGvt3enH6M3SH6A/Vd34mKq6a+tvUXtdehn3nLk3eHh6m/mbxucres+xcU/120n+Ln38z9y+ww3j+P+t9OP/2Hke/1V1ZPV+oleljzm6e3ofY417qpekt6feb8LMq1XVCa21D6TXL25Vl/Y5fC79HLAxRn7K7fpz4/feM3Mu47BPen38eVV1SOt9Ru9Ob6e+ZlU9bKqgcQz83/Sxsb9UVUe21r482jyekuTQqnr2VHkbxjF2v/Qx988e67Kttfab6Q87eFP1sV5T1VX/I/3c8oqquk5r7ZzWx3A+Lb0/9aUT5cw6Mn0M0IOr98d/M72d9ffTz2/3mvK+OL2MSfL2qtqttfb51tr708dU3H7i69RGW9zT0vu/HzNTT/3V9Aeu/MI4Xqdqp27pD/p4c5Lj69Kx8V9Ib6f6X+l1rZrw2D8vvb3mOuljKncd91mvyqX3qlONqd47fdzPG9LHcd+4FjOGa/naCsxE9Ofy/0mfhf+kfPeTeXZLf1LWiyfKOC59pv+Nx+ebZLwRJzMzjdMPztdlgtnH6Z0mv59Ln6x+tyTHbvqZW6Q/JfTY8flyz1xPn8BUm5YdM/P3jTcR3CX9CZN1RfI25RyU/gTiIzctv016Y87GUwKuUN7mMo79ZPZp5xtPk7x/+tMJr9BTFjPzxojx390zniC3aT1+MP0ke+QVzDsgm55inpmn4eXSt55cJX0y63ETfX/Hpb8h41rpHSuvzKYnDad3cr48/ZXoVzhz0+9+ejY9hXvsO78wRRnT39x2UvrAiSM3ll3G9/iYJL81h/IdOs9tmv6kqk+lN3LcbmPZZZTvFmOb7nVF8sbvunZ6x+kTM/OE400/sy395ue5E23Ha6Z3Ut8pvYP8h5JcbdPP3DC9I/AKvxFoZDx4/M5DxnnlWpt+5qYjb7KnSGxsz/H3N+bSNxBuHP83TJ/odcsJso4e2/Oq6QPz7vP/s3fe0VpU5xr/7UPvvYmC9EMRRSwoioqKvYMFCzYUKzYUKyr2Agpiw4I19koksSRRE2vsPSbxXm9MT+5NVaMy94/n3cw++5tDPLNHTdbKtxaLM3vmm/fb7S3PW3bMxxBAfgt5NauUMR2OEjjPQErrxuTyIZTFtwPXVjSe/dDJFCcFY9kquF85X7W1syMmg7/KtWpj+iwCFvojIKBr/E4UQP4UFVbnQ4DmHr7PQbvXN0YgcHlwIp1vYg7rkQP3KLvegVx+eJ2gD3IqH1rRePZHAc6+gvLQAp7TA+lwO6SsG/tuaxTY5Pszwa/Tr6KPRDoZkhE9CtomU1HVWvLq+McA61tb15Ce/T8GVSZqWwXd4P0DkB7cK9qPh6GKoMknnhmN6SixYmuU3NQtuN8e2Rpnp9KK6Po1sjoKpu7zFfZxIKoS2w6BtXehAJxmwTOLge9U2Ud77xAEbMb6+X4oaKWSNYN08RNszY5AgXf9g/utkR5yFhXoccF7Pf/sTF5FLq7Q9XK8Vyug29fGtW/UXtm42pieZGtlXVScIuY5p1HhCUtIBl+KbI7hyDHfN7jfGZ1KciJyaFVZYXmw8bp+QVsbm8M5FY2p1zkWItncpZF1sw/wEl9NReCdqZWNw1EA7ToVvH8YwnBmGH/rF/MxW1NnEFSXq7iPtxnfc+RyaiKSZ8knZdjaOw04ya4PLBjTMUhPraTCMnlFTG83TqIWG1uH4OTqlP1h47c/Of63FrW2+ECCU1BJxAARluNPqtqGr1geowTnscg55qtVdwru+7UzDp2gkVxJEvHu8cbr2iD7YkD0zKY2rkkVVpEefDvCqXrYviviN3sgp2RyxUNkS6zm32U8oH+8PpDOkWyrWr9GIh3c75Hu0TPNbI4rqTIj5RoAACAASURBVM5p4/oQwm/7owSq/tEzg5BNtUk83iXojUAnRDyPbJhh5KfWhvN4AFaYq4I+DkInAOxof29GrS1eGb/x3w/msCXSceJxnYBO5xifSKsfwvW/Ttt4ECqmMs6uR2H4UPS7LqKiE5aQn6GH/T0R8dQQH2+Fkp8vTu2fvW+YrYn9EVbVk1q90c/t6RXRXBXhtB2M3kxqdY4pyB5P1qlsz++K9FMvH/16CXWO1ghfGl4BvRlRW3NPL2o/ENnjLRLoDUW4TcdwzmJ6iK9eQ6I8RvrnfajY4MO233x15RYhXcQL7yJdNg5G1YxvQPKvd3CvLurnmcCuifRWRwWqOgR9W7Fmomf3RNhH8ily6JTf5dbPtVfSxwuQnyOV3wxDyf+zyXGjcB35veFQ0GMSPoZs/qcRD+tue2X1grU62+il+jfr0QlYh9t4vUfkqw362Af5XgYk0OvGlzzBgNzGSfKp2rsG2z7cPOjL0IJxPRrpmEknSVCMN4brJtz/5xLo6Qnr9ELkK7rW962gf5XwOHvXEJT8t4Ptv696rQ5HBSMOCca08PRdxAv3i3/Lv/KYWv9esN++dXSvRXS9NsJeSu/FqJ8vIsxxHWtbLbjfLHhuIcFJaCXpdUZJDE8hXKE1uX0Vn1z7HNXIjT7I5t872HuhLV51H1cD1ozaugR/e57aEsmXA1Lo2bvaENn0BHp+0MdxKMiySwU0u2A4CsIfLqcWV9kb2f/JpzoiTKWT/b09tZhKJyTHzkilFbyvD7nfux/FvurLgHsqolmPEos9LrwhxbFq1wKXVUCveXR9CHnMml8zzVGCwN4V0OuH4l5mkJ/24fXxsH9jqtiL9q42RLKChvGGfj9OQH6V5JPkkd1xFrmsuiSYRz+u6yCdaq0K6MV7fwgFWAbCXJbEz5egt5rtQY/dTKQWNx6IdPHx8e8oOZ4PIhxnADqhukv8XoS1PkM1eqO35Wba9W7kOo5fM20QDz81lV4B/Z7I1o/jcSaieBx/smyVvqMDCvZ/TxQ8v2VFNIaiRK7tkd44jlo8bjjCx9etso+2br5yejZm9fZ3H+QH7x/cb2nraS7V6DjDkU4112ivRnGM40lUcHqV0bseJR2MR4m3vaJnuqGTl6dVQG8IkgeTEc43GOPhNJQbZwNLK1orw5DPexYwwdp6BPf9/mhJdEp4SXpD0WEKB6AYh9XI8c7K5zDsQ3A9DfMrfhX7H+k3LyFcz9MJZbHXz9dH9tDICmh2Af4LnRK3prUVxcftRTV+nFXs9++J6b3hHv8q+hjRH4SS4foHba2R3+gsKjgp0/bjBajYWCcUyzA0emY9pI8nYcbRPHYjP5GvDUHceDCu+yFennTKIhF+iGKPYh1nTaO1ZgqtfzKPYdxIcxTnVZWPowf5qeKtUCzAoOiZSTaP/SugNwT5VLa0617kpx3GcWN3kCgbkY96W+Tj8Lp4hwJ62yLsr4rY+La2Tr1dsx61MZVdkX/ghAroDUc675PIFu1IsU/1K4nh+qb/feM/4D//mjhhUt5mIoD1CCIwKtgkHZAjrQrHykso+G3zRmh5ZWdVpJxUwQiOQw6rFwiOiy8QLBcBVyXSWg1VVTw5FkbUgjxDUHBF6aB8E06nG7NpY22t/JhGjOcU5OhMAlaM5hPWx47RvViRHYKA+Y4J9NqjysKP2FzGwU3xPJ6MggFLOR6RgvNbZMgdjpwLHmiI57AVAnInV7BOR5gAmUUtSBXO41BUiaA+kd5gWzsXInC4ZXAvNOZ6IFB5p0R6w4EfI7DtSXTCwAoHUcF6vTJ1XI2PdIr6VhPoUNWY2rtOQIbOX4Ad/XgSgRm2L06sYM08h5TRsdG92GHVFQXSblfBPL6KwOMYFIv7eDJwZyK9YUbvAVQ9aha5AyKcw04I4N2/gjkMDcWW0b3YKT8d2CWRXr3N48nY0dTkgSrNon6eYXuo0PHahDl8BTmGCg20gPe0Q8ZVUvKI0XwdOawfMx5wEpExZ89WwlfJHasn+zkK9n8sq5LWKgLa3wT2Wskz4TzOqWit+v4cBVwftIc8vA45ss4hwdHxDc2hX6tTaSRoMtiTlQRz2bu2QXrj37DkykbWzb4kOh75mgPWkKP4F0gn3CG6F/evp81zEvCIHEbvxPuDAj0N8fIH4t9WguaqWDJBuC7jdWN/zw7nuSQ9b+OcS+5w61ywTrsie2S3CtbpYHRK1C0oWLN/OLZfUR9fRoFVPlGjJsnB/r4C2CKR3urAvuG7CQqARP2bj3TZVMfRCJufkz3PifdhsL7uB3auYA73DPq0UtmOKpOn7kfvHL8T8fANg3vxukkeV8TjXkbg5kRrax/SDP5eBhyR0r/gXQ4lyF+zknlcEzm2kwDkonmjONC5H+Jvqbr4IJqmc5xewX4chpymNc79iFZLW19JSTJG700k/1eaNIkSd14gMSkP4Ranoyqxp9HQOdbAJqcgWDCB7v4oqLAxGdUeOXwOq4CWLx5zB3YaZkgzonsWcFMF9F5A9rEvmtAYrrI3qqKbhP/Z2nkc2YzeYRUGAVYqj8kxjluR3fFjlPBUkySCcJVvY7wwsY8vILvQ4w1FPK4OYSrnJdAaYX06nCh4I5zP4PoUYFIFY/p9G6vnkO7r57JZSNfW7Y1YMZeEPj6P5PpzqILkWAoSYJCT+Wlg0wr6+DrS31aaNIkcq0+QEMxNjqnshPSA7wNnRs+Ea2ceK5ExX5JmvdGcb2voVAqCx+z6bGBJCr2A5kKkT8wnwGmIcEDkmL+AkkF5NLSNH+Wf28YLgSkV9O95pP9vZ22N8dRdba0mBR2TJ+SfSB6sXmTjtEUnWuyTSG8EkomHU4txxn3sbuOequPUo5NFTvd7m9yvEuvGZ9tcpuJxb9g+u8/m6QwK9FEUfHAJCQHr1oe7Eb5xF5K3axQ84+dyDDArgV5zFCz9MfKzzY3WSszHe6IErFJr1dboj1FAzEBgK1QN/3vkfrIVOC7COGek7A1bM2/ZmpmHMNrrMZlcsD8OAI5OoOdsLJej5PdTiZKYCbBjpG8cQUIiZ/DeAda/2cjGnxjd9/M4iMSiHOR8dR8CmUeOj3lafg1tRAKuamvndVbCt6L9fw5R4cwm0uuCgpevDNquAuYXPFuHgsgOoWRQvq2J22yNNpq8HPVxT6LiICXoetzoRFSQIpb5K2wOpI+fT0LCCgpqfgfDVeK1GbW1tvW8b+K6eQMlMB5qv/8P5AmPsd2YxOPsHcMR3ng8QTJu0ZhWtFa7In1jarymGnl+VxKS8r/uMUWBam9Qi1Fv0FhfUXHVMWXoBe8YaGvV45y+PzUFom0ObiTShUrSXYRwqKsJMIyCdTOXCEdPWKuLUcGNhxDeUZRAktxHhJV8H51Qux+1fo5Y99iUxCB5FP/zFpKJI6N7sa7ax/qYWpSzHuk1l5AHVhfFOAyz+R6aSG8Y8v2dENArwlR6Ih0sFRv3SRX3ouT4a4L97+NGQvv/QhKDZMmx6mkF+z1eN/1sf5Qu6GZzeANKyHkUBft2iOj4/3ciERs3em8iPOgG4PcI46tJArbrCzCdPYHmKsAH9q44/i/eG31RbFxqMoe3HY+K1320N9ojX08qVjUY2W/H23WLmB4NbZzzCGznEvSGIZ46F/OzB3Ri7O8k4KIKxvPHKOalCNOM8b9ZwPQKaL6E9P9CbCzYG/1R8PWXKnDxT+bxRFsX3u739kYRNn5b3Pcm0huKkgxrdOyIVgtkw1VRBHg4isGbTuDfaOTZI1HsVWry6CVI1/HF8bycitdNMj17zzAUU3k2uc9xRXxs8FxPxHunJtIbivTHRn3sEd2bSfAB2hw+j3DjK1Dx3zGNjOm6yE9QWv6TF8g4llr9piiGcwHpPHw1GtGNo+fqjP4pWAxdSXo+pvJIahON6gr6mDSHwTpdhJLCH0Knb9fsjar2P8Jv3o7HtOA5L6uOIbCBEmlfhnTk18mLGRbFx94CHJy4Vl+zNfg2Siw6L7gfY47JfUR41+o0jDdeITeCtlWQnpDqb/S+qmOpLfwX0uuMYjhL4xsRzScRXvIbe+/GjdAdhmzL0n5ccrlxJZKzqzVCqy2y/Y+soI8+pmqerYs4qdrzoNZIT52ZSM/LqRVjGazLeN0sJN1n7O2bWfxzLK6LjWvpIiDkGO4Ntu5/B5wc3I9l1aWYDp1I82Hk//8u4q+d7V6LkC6y4e4gwW60dfoaSmDugXydi1byfHIM17/av2/8B/znXxMmKw+smo0SfBajxBVfaamBAoIqnp1ASQcSCpZ8m8g5g4K2ehc834bEgPXgXS3IK8m9DmwU9c0zu1bIoEtxPPRGCs6N6Fjuo6kFPEKlbkdKgqvImXMOcgLORUEOcVJOXdDP1kiBTM3oXh1VcngWGQQHYJndjfRxJglOeZTgeDcyCg+zdbobkdMjWLPdSDx9ABlUi61/V6Gqre2iZ0IBsjNpQQCrmACJHVaNKeczbd+WSui0/f8WCnA8DgUVPY6B7jFdVPHohrJrBwnkHwIH2nUHlPH/MVEwQzCu40gIyrX1/g5SdhZRa9DF4G7pMaUh2NcdASsHoqOddwvHNPi/I6pMULZCZg9bn7HDakcs6ZbaQKdRJBjliC8vo7aCdI1BHvw9mwIe/yXp9UcKo69Ut5ut00LHCaoyeQNpjtUOyFA9OGhrFOCzvX8VJSvlo4Djt6l1rDaoyhPsizYk8HEkN+YTBRI31kcEhJ5EQhAAOVi9e7ROr0Tge818kchXkSH3bsG49gn+jgGdlLW6I1aFlgIDLh5nBPSsmzCmnaN91snWUaOBRYjnlkrK+4bmsBkCxg6N24vGlQqCuaJ3Ho6cjn8CDgrXTfTcaZQEV/lmAtZWQ3LxEqTHXYaqyMXOOA+UjSEdzN0fWFg0ftFzvq9Dgc0b40tfgl4LpO9/igUUkDs56wro7U1CNSDk1H+TfxKAFozptiRWdCV35p6KHB23Wl93j56rso9vEQXIxXNEQ2C1dB9RItG3kI1zBtL/48IjK5LmkCxNDTrsZXtjpYBpMKabIdAqJSh/Q+vjfvH7G1k7W5Nw+jB5cNVMBASeguyPxnTxpHFFumrR+2M+6vu3QfxsSbp+jrohvbHRZC10stV5lLep+iCwdKWVb4PfNIkg2bskzb2xILvgvfFeDAHdbUhwdNi7Lgf+YfvyIoSbeFA1xo16I/lZ+jQwlHx6Qvw7VjKuW5Pm6PQ89XSE3yxF2FGhYxcVAbis6DeVpH8rCpRpTB+vR8HlpU8fsv3/GtI5dkIYzp/9HqfWVnVIxykbeNxYAYDwVIc4ueJQomr2TaTpnYDTaHj6RxxUVYk8Jk/G2ytom4WcC7sWrVukV21Rdu0gHeltauVx+6hvIR88jxK6I7I3nidyzBpP2S64DvnNViScrkZeHGc6SiQZg2yNd6hNCGpmz+xPyZM5bA6fI5D9iKc/QX7SWjym65Jmx7VA+OaRUfvKMJXDiJy+TaDXEjk3f0ReTGEd5ChrDONch4RAZ6RT/Ro4xq7XsnW7TfRcJfwmmMvXEQ/fGdn299BIEizCxk5KoNWYbXxK0X4j3TZenWKeOqJozdj1AaQ5Oj1P3ZeGBRwanCAR7JEtSEus6I2cqXtH7ZPIHbpxMMc4Eio6k/vG4nHtFfwd4nEDSEjKRzz85WjtTETO/jOL9gDi4zuSUNARVYy/C+mgZyLefgBBcjcN5fEWpNk4U4H3UZLWI0iHOYLaAIs6+ze2zN5AAcS/IbClgjG7Gfl1ihKBRlAyWAXZR28SYGsIf5yF7PGiBJ0Wtv9bUV7+90MBOecirOpVFBA0rpHnx5edQ5SYsWowR9chG+AghH9PiNeN9W0ryvuNmyF9+PCCtTQv+D1xYZ4dEsb0RKIAlKL14ukiLG79MrTsHV1QQbhzyIs4zCYIkin4ziBKVJFHdngdeRDaXIpP4ooLjw0g4cQaxMdrsDHb440VdjsBCxIsSXMycHW8PgqeC09YnFiS54xAusb2UfsRKMEiTrB2JPA4e0cvo3lg1L4hxcnjVazVAcAj4TsbeS7cj5PK8JxvaEzHYcVugr1wGMLm7glpBX93J/GUbGTr1iT9rmRMN0PJh03mceHYINt4KYbN00giIOKnKX7jelQUJ9Rxjka4yvSi8UvpY/COucAvkS3xEJJZQwiKyAXP9idBbtg7WiIMYBnyPZ5IFOhLQztuVxIKHZInyEyjuEhNHONwHPL9lt0fvgDwNBoW/msMU9mBhMQK8iIn0+16HNJvCrFcG/9jYt7QRJotkN0W+xxX5h8/vyxNcizuaMS7z0PBzidSnGg1EumUpYry2pg+Q5Dwar/h+0SnqNJQj1u3sTH4knQ7Gt2rUQzXmUSFxWnIczZC/r+yelx3Wzv/tOiote9q45qSzLk+kk2/QHEoR1N7IqfXN+oQXyqFxyP7YkVyTLge7f8Yp2qLeHypk/lQXMyTREl9KHktxMvCORxBmr7RAvn/Y8yxsaIDPRFunxLD2Qzh78tRPOjFRDY4De3/tihhqPSpTsZv/oGK/5xAwxPkYx4+AtlEKT6VVkgexnbVyjDHY/knyYIroedsjJbbeN6DbJiW0TOV0LPv1yP9cb9G7sexceOQHzdl/x+JYYjBe4uwBr83J1ASq0JxIC8SFC63vTKvaPzt/50oWXQA2RtFPtxNiYpukifL7UFCQT571/Y0LG6+UnmAcNypIR9qAq2eWPJ/1L41+amccRGA0nNo3/c49bFIX7oK6Y97U4BHVbT/pxAkhBeNFQ11461J01NDm2NthG/sY/2eGNIj56sDKRn/j/TU5zDdE+keWyC8qvDktgr62Ar5if4bYTl7RPdjeTyBhLgRhGc+S638b8wmH4v0m5SiA96O28f6OwLpFI0W+0bJdKWS1sjjVI5DhVyuQwcCTCrqK9KJridN/nv8fxaSVYtQLoc/JS/2N4wiLTfG21QrlVPB9c42DqXkFOLjRVjcOjRSIMrGv2yeyghUxOWAoG000pFPjZ4NsbiUAkD+8I9D7F2bIbvjGaKTR5GsaoMwmFLrBuUaLEJJhz72figqJBufDB7GqZSO4fpX/PeN/4D//PuSE5UDRztH7fNRcleNcmECZ9PGBMyXoLk50Sl7xjh9lbcBQbvfJG0oaVghJ4d3EDVDgSOHAdvRSCKg/VsrZrpNpNsGGf8HIIE5A1V2uhw5pzpGdFuWFR72/XURoHIkAhSesb9rAFtjduNIB6zbIMfxpgjgn44qdB1Nw6CuUPEr7UCy70+wtbKq9eEGpAyciALaQ1quDD0aGqAHYscYI2P1HiQ4dyNwsLISQ6+JtEeTO6wanPy1kt84tgxdJCCfpzawYiFBIFDQNxf8xjIOsg6IrywK5icMvHuXgpMbbJ2lGOQtkBPgZuSofgc5yreOnmse/N3kMUWK5/PIgBtsbVfb9XoouHKncCw93ZS9aHvh9rAPwMHA5+jkzKJjiNsn0myBAkd6xGNn10UVwdegvII8yNZHWDXiAQoqKNBQKS/r5Ghl63M6Cl7eJ7gXn8gX/r025Y2544lOSzH6z1EQeGy/Y4OyY2rvuAk4JO6HXRcFrPWgfNJhe6QcX1twbwfEc0ZF7cl8FVX/mxu1HY7k5ZRG6KWs1aOARxu5149aY6AuYc10REbH91AwYHdr3w7x8ZVVeG7y/v8G57A5OgFky6J3FdEgLZhrIIHuhEDTg9BJUX8kCLigoSOgX5l1wzcQsBa8YxECEzoiXfw2VG11daJj6pEML5vk7AOKpgOX+3kt6E/vqK0ufq4E7V1sDK9HMnkpsAkNT1oKdcftKZmUg2TAddHvXxmwOpQowKWJ9Hzy+JSgrR9K7FpCI87FxD5uDNxR1J9Gnu+Nqsg3/2fPruQd2yL7Yg558PrWFJ9E1AYlJaTIxv7kp8atKKISr+fgujWyw8oE5AwhTy7YGCUbTwtoe72uVwHdsiDgUOAnBIA30il2QTZWzZpMHVdkc3wrpFfwTFzxLCWQsxfS5cLTjOcAxxbQCotylErmQLKgjfGX+dQWcAjp1QXPl1o3wbtmIdu0KCmuMBGGdLmxDQJ1B1t/5yGnR6foOT/29aRhKjdjtmr8HgTwxid196U8fxtseyDU+zujRI5ryKusxnx9XBmayIYbH62XSUarbUyLXN8YUXZMbS/+DLODg/U/Efg/GteN10yguSfmdAzedxjwW+C0lfzOsjpHS2QnxsVqdjc+0CH8LfZ3aXmMArlfAs4oeO9xyFnXqeB7zRL3xnnU2o6HITxiRLRmkoJkkYP8zqjteFQU4FtEwcjBM2XlVA/gV8DsqL0FwnOXUlDxG/H+MgHy3YEPscAYGuJC5yL8qGaPI7mRgqn4U7mG+/5F98Mgfb936hNpjkNFIy5FQUzT7DcUvtP2UwoWtxqyNa7GAqqQnVpTkKYifjMA+ICGQcetEI+7F8M+Cr63SVP7iWzjh2jcNn6Ur8Y23gW4JHyP7f3fARc08p0hZdcN0uHvorZ41J7GhzwGEfK+AaQFVgwh18W9PnEospl/TLGvqnvRPm0CzTMxPTFoOxDpWUWBxy2QzC6Dq7S19Xhjwb2J6MSVDaL2Brh8U9dq8Hc3VBzTB1QcgLDj21Bwfk3F/BL02tCwQN5NwFH290mIv7+O+M+Y6Ltli/8Mt/14ZvwuhJs+QIEsTuhjC1QM49vkvM3P0WDE92J8J2Xfdyawp1HwmMdy9wT+jgK67yUPXEkqUIH07D+iALkdbN12RXJxJ6QHXEXiaVEFdJsj3HH9oO0ghOFebuupKEk2Rac6F7iwaJ4IArqD8W9GOX2jDbmvrQfyZV6AZOSPKFmMbiX0eqAA9UsRntfL9slcu+f3yASby9LJqQW01wVujdpOQDL6dIKkNRrKjybbqsG87APc0Mg8DidIhif3/5fVN45AAc4e6wxPrJxje7HGJ53IB0YCN0dtsxAmOI8gKTe4X3at+iR/v2b86QYhdjqEIBCwAp7zTYzp5gQ+HKS/XIYwwWeAK1L6tBK6+5PbxzGeMZoC31uZtYrshksJgjKRrL8Ixaospti32qzMXrTv+pNO50XtdTbH99JIYGrJPoZ2Wzfr24Z2vRQFst+DbKDY1kr1N7RAdtWeNtY7Gr1FKMjS8/sGp7uUpNXGxi5OyjmcQH+l4YnOjvJxIy2BBynGVE6jOLGynpKn5CA9/j3gnKi9J0r0u5FiX3W7ov3SRNrXYMlj1CbhNCgGYv+3pxxfXR3hJ4dE7Xsi3HPncL0E9PpRwh63OXyePG7EkfP2emQ71pxOb2so6QRppMtdjhIAeiH5vBS4E2F9vaM+Nk+hiXTWO+y31/ifiILy7e/RpGM5s1Cc2G5Ix/kIFc0ZHT4XPF+Wr54EnBu1HYcwrJFRH8MimWV1nDboNKUwcWsG4m/fo5GiFaQVjqlDxSq2jecqfHc0h4Mq2P/jbRwPR76NH1pfi4q5VBGPszFK7BmD7J2LkB+pwUmSwboZRFpRjmYI7+tn17Ec7BrStf9HJvZxNPI77mZ7ZCmytQoPyUihZ/27gVoc51CEb8T7wiHemOqPm48VOi6416tg/XakPD7eFiXZ3Y0lWKAY3LNoRL5TEIfUBHpDgMVR2/EogeVWiu2NUnLRvut935tjMQcF63Rtiu3/svF/qxL4/u3/I5BN9QOCE88qmsNBKI45Tqw8wvjAOtFvqWr/T0U6cLN4rRAUM6AhXy3rUx2K5PBYW7NtEFa2L8KOwhMBQ3pty9C0Nfdd4N6ovSXCVG+hkYINZfvofzsqVP0ysCXidXOwAqDRc468sELZGIcuKM7H+/XieQzt5SpiY3sjH+fpUXs/xPcuidpDG6fMPA5FGH98MM5JKMFrQLhmgj6m+Mb9CXlxcYpzURHLmqK0Ng+blplHZC/eiGHiQftMgnyZeM5QnkrZeRyL6f9B2wkIU51DgMPH+7EErVZIj/L8O7Q3RiNMueb0baSvl+U3qyD/UFw4oiPCVm+huOjQENIKOUxCettshK8eipIAG7Unyozpv/K/Ov7z+Xf5TEPM7AEA51xLgCzLjkUA76HOORd+IcuyPwA/zGzlftmPcy5cF/2dc62cc3XOuT7IyT8fMd4JAa3M/v84y7K/NrVzzjl/ksMy59z2KLB/EVJ8/oiyuy93zk3Ismy5p2l0X8uy7PMm0lvVObeBc65rlmUfI5D1OKO1DCls6yFH+XXOuVaA7+M/siz7rIn0OjnnWjjn6rIsexEBK22zLDsHMbg5wM3Oucucc+vYd1yWZcuzLHsuy7J/NIWefb+Pc25d+80fo2pn1yIn9b3IMbkzMNc5d5s994X/fpZlnzaR3mDn3Ezn3BjnXMssy55Cztt+KEhvE2T8TEJO+1UDWlkJeoOAg5xzo+0dN6A52xP4C5rDV5HwvcnGoy5Yq8ubQq/gsyoCiRuMm/221YLfFdJ5vSTdmcgJfpu9v7W9+yjkdJln11n4P/BW/Nu+zCfLsr8g5Xhd59yW9r7MOdcMKevvEcxf8L2Psyz7e1PpOeea2Xr/DPGziciA29ro3Oacm+Oc28w51yLa72XGdGukzE8HTnDOHYkcRycggGwKcL9zbteQf2ZZ9nmZvRh8egCDfB+cc92RAtQeGeX3OeeaRzT/WnL/t7Pvf4bA+Z18HzyPd851QXvG0/Lr542m8rjgHT9DjpRtnXOnOeemIifA6wXP+nl7p8w6dc4NRcDU+CzLFiPgb1Pn3D72/i+yLMucc+s45wbb387uvWx8scxnADLGcfpsjpI5bwCOds5tFfXziyzLni0hN3o658ba5ZvIKAYp5th+BJjunOsd0fyd7eMmf0yGP4z2/GTnXOfg3sPA31BgR/idKvhqLxTUhOkcE1CS3FnAgnBcy65V59zqzrlt7fJh4AOTWX5MW9i93VA1khUfk8dl18zfkD7xKtKhznHObYn4TT0yaIj1OKPb5P3/Tc2h8eaXEF9b8S7j8d2BI5xzzUIaWZa93fWLtQAAIABJREFUnWXZ35pKyznXEVWof9w5N8s5t3uWZQ+i4907IzDyQufcIb5/gf74YUke1xE55QYGv6PO9O2rsOTb+Etl+uic6++c29Q519+aZiFHWVvkaFnfaC0CznPOdQ74219Kyo0hqBoPSM5v75wb6+WGc6653RuFrdmgj8ubqosbzVbB5QeIv52TZdkMpLd+BzjTOXe80fki6OfSLMs+aSI9v8eaAWs553r63x88M8Y51y6SxT9B+k+TP8ZXpiHg5jn/O7Is+xABD58g+yr8Tp3RLdNHLxv+iskN23dZ8MwYm29/7bIs+zVKHPg8fPZL0GsbXH4XOR9fzbLsOFSN9GZgiXPuROdc14Dex1mWLSurb9inB7Clc66/rcFwHvtSu04/sd/WVNu4LXJIfcf2xNMowHKBc25/v/5NH1iC9mpIt4xu7FCF3Tq0N/y6WI505cWIDzT4lB3XYH9/inTVAfa+FXvOOTfWOdc32hu/RBXnmzqmzvpzM9KBz3Gyy/+G9v1JzrlxES0vp/6M5GeTPs65emABcm5MQXrHbOecl1mZ6YqjnXMDbV4zk/1l1k0f59xRdvkYwhm6B/f9mE9zzm0Sf7+kbGzr92SWZcuQTTw5y7LtkS5+DFrHxzjnxttzXgf/SVP5uPXR66cvAps457oEcsPzo82RbA7791FT+ZvRbIZk1X+hxA3PU/4PgcddkS2yYs0EcuO5MjSRI2wWwmt2NL7+JAqKOTakZX97XvRuGdlon37I5vi1XbcwO/F7yIY9LJL9nv7rCTRXRc4SQHseYTdTganOuRnxF7Ise6+MzuFJIEfCowHNw1CQ9SpIj2tblTzOsux/0bwNcs6NghwPz7JsHrJDphZ874uEMQXZ+v8DK3jfNsjW+B7wmHNuUNYQ41wOvJKVsI9RH4Y558YFbT6B7AZgndAu8J8ycso+dch5PNQ5tzqs0I0/Q9hfW6K9b/R+k5XAjbMs+z2aw22dc52N1zS3e6eiAPptC77355K6cWf7/nJUBXwXu/7M7BvnnGsPjPd2ZCCr3m0qTSf7f4zpbc8hPfFjhHkci4JJ/9GYzVhS3/Dj9z8Ip/0Lsi1OQs7IZwtoJfEb+/3rIf1mBWaRCRN+Bu2NUQXfIcuyJ5vaz6zWNu4S3HvYfsPB0XeqwDf6Icc8WZYtd86theThzsAkb99EdN9P4Kl1aM895RvMFj4f6ASc75zrFPHUD5DeVfbTDxjn5AP4wgmXGo1w+IeAqwN9x9P8fUlZ7D+hLe6cfEhnoICEA51zG0b0Psuy7NEy42pr7R7gC+fcPs65HsG976EA0gOi78S4/Jf6mJ56nXNuun3/D6gYwKkmj6cj39EpSP/wPocm0QnoDUV2y3bOudWs+T6gq9nI+yKH+eko6LLB+CXsjfdQUPqqzrnLonf9HNlR7Uu+u+Zj8uheZDsc5pxbI5ijnyJ9cmrIV8v2zebQFzX0uNAHwL7OufVQEOBpiA/8wz9Tdg6Dz29RH1dB8v4QFBwLKlxzHQr22Dvkf2U/gdz4HPgpsH6g8z+L/MWnoAKLveLvl7A3ejvnRtrlOxgeb3zVBbSnmn4X7sMvmqpvOOdGoMIJy5xze2ZZ9juU0PgHFAh8fSbspBDDLflphvo2DO29f6A9vyY6oXe52Yu3Ad/JsuzTCtaN/3wC9HTOtTf7rQtaSzOxIKoCe6PJtqrxmR3t8hVggnNui2AePR4/Co2Dp+M/pXTxLMsWof3wX0640ccuxyFfBT7PCnzSZfhA0IflQL1zblVrb42S1Y5HVdY3CWxoT6/MWq0HTnbCbn6L5muBf1+wN/oBmznn2ti9pLXzdY2pEy6+pelPPwJaOueOsXd9gLDj/0b85nPXEF+u6vMJsLtzrpvp4M1dHjuzhv1r8Cm5VvdFuv5i59zBzrkdUYG8N5A98Cqwn3Nu64jWF03di8HnT4hnf+Hkf/DxRsttjgfa76r5lODjg4ELnHNrmQz5GNmIa9sj/ZH8fBYlejWQHSXtjdVs/XQyXeB+FAz4KbIn26FiAEcgjKwFEPK4sr7xj4H/JbChnHNTjM6pzrlL7bnlkc5YNm6kGcJUlgX0Dkf6fz807q0j/v0uwijLfDqgpMPmtjf9O3+LbLg1CXDP4P7fshK+auMDG9nlX1GcAcByk1l+P+5udkioA/y1DOaAfMK/Al4y/u37cAcKIj/SrldgOPb/h2XscbNRjgfGOucONJn3Dyf8712E44wo+N7yMuvUOdfPObe5E77/OeLnU5C9+CTaFz1RUafFzrkVvs5MPqMyNP08dbH3t4v3tc3fpkYnlFNvNNWOc871cs6tbe9ajnTSo7Msuxf55juiwpsLnXMXB8/5cS3LVx3SF70/fGPk27kdrachmeF8WY73/bQET/X6Zx1KzNoyuN0SnaB4MLC2U1xng09WPsYBxCffQ7iD/w2+v+1QrEr7iOf8rOT+7+jy2LEfoSJVbbIsOw0VPzobuN05d4FzbkxAr2w8TlsnfA+ESw1ESUY7ID1kJnCPc+5s59w6tlf9uvlZke7zZfrofzMK0C/CHDsizMX7iz3PeatEH7vYuNZlWfY60hs3z7LsYhSrejBwg3NugXNuZ6Pj+9hkev5j/VuOYiv8b5mMEo/bAs/ab/L7I7N9/0pT9Van+F+PJ96HcMD+wX2vr09F+yf8nX9uKh8P5vDvWZbdiey1HZxzd6DTz69tTL6b3GjyurFPe2Bj3zfTg0cj39/3gZ1DGWb0SslFJxzsULNtPgI2cM6NsnUa6sbDCGzHgG4Z/wYIT93CObd6JpuxPZqz3ZG+OMM1jE0oNYfBx9uqS23vedxhEfA+Sj5qIC/s/ybv/2iMfoF4d0vrZ+hT3cxZTFvEV8tiqvuRF988FuHTM5H8fQrF49/onNssovf3kjS/QL7/D51zhwbv+wfSlQehE5drPmXoOcXiO/vt8xHe77IsG4p02HnOuQecc+Odc6tk+edTyvn/O5qe1MX6Mdz0iOV+jp1i49YN+pUUG2ufNghr/KMTtuvf/SGSlVNd4BsMeVDJedwNWM104RVxXVmWXYj4+kJXHIOf4hvfCxUXf9Boelv1VKRXTXcNc1q877fJuTH23c9Q7OSKeGazyfdAfG2ZPRfP2ZsJ8/gJ0MPWkXPyQ/ZCdmMHtP+LsLgy9sanaP9v4Zzb1973mZM/53XyQwfi732ewG/aIttiiF8ztk7+jOKbulAcU/l+U2WVc667c25N+/6jKH60B8Jyj0CJiIU+VftOWbnxL/n5TxLgv89nDkqEecw519EWqQeLP0In6NUwtBLGYw/gcBNIP0dg2CrGrH+Pgg5uQMrO8EBZTvpkWfYbdCzvaiib/GTk7HgIZTnfgsCOq10Q7GTfbapAHoaS09YHRhizecvePwNVPLgsy7INkLIzJ0twrhi9W5FS5Q3uD5ABvjqqWOudql0RCJoEyBvNB4HNnTnAsyy7C4Gp5yCDdW6WZRMRUH55WVpGb4S9uwUQBp//Ah07/CKqyHceUqLPzATSl6VXj4z8VkCnQMifhJINnwduy7JsH7R3Nsmy7FeNGVpNoDvMOXcEQJZljwA/cc4d7yy42uXOhg1RIFBs6JQ1kA8Hfuacu8MpgeyTYP/fhIDeou+VVTrIsuxGlBg3zykR0BszXyAgq/S7w48pp6ciRaZ1piTZ+WgMP0cGyDyk9OyHDILwdzYlAcg7qq9A674VCk7bDClxQ1FVxe+ipLmyYF9Is69Tsg9Zlr2CHORXWF9/Dyww5eledCx5mwpo1gMPOksoRuDNSOfc+vY7/D5YDxnJ3RLpDXbOneuc28s5t1am4MyDEZB6DbBVlmW/cZGD038S1unGKHjiLFujtyBlclPn3DT7bRsgw3V1o1WWj7d15ixFgadDnXMD7H1PoRMprkXBZZ1K9iek1wIlNB9r/PV7wGnOuW3DvegUSLIdOt0olaYHFbC+/BglOWzunAv79LLdS/445wY5JWqAksc2dQLDl6OggAONF12CJZYl0KpHsn+oGeX/i/TgqeQBVJ85BVgdaPeTPs65oU7BzMcj+T8bASuvooCr7ZHT4QIXBeSVpPdNzGFvp6IK3lD7GQouWuGcsj0+APHVpHk0mq3NSJyPgrbqUGDVmQjgnGb8djIw3wn4rcLm+VoC1gIdZ00EADgkD19AgNy9wJVZlk1CAY8XZFn2fxXojfchpxiZCjksAo5xSnrySU7rIZ01JSE+pHm+yx0Kr6LTjc51SlDbFumnTwMHu7y4Qlk+PggF/nVA6+ZnBMFFWII1qjp8SIU63GcoePQle+86ARj2IVpXu4drtKy+6hRYsZdT4NYfkAN5J5MXoXN8dcRvPZBdNlh1KHCtc24H25dfIOBqNwOstkCBB8chB6RPukxaq865852c4T9GgRw7uTzB0NummwC7hnzRaDd5Hg0AegKrWumc29D2yA6oSM22NvZLUEX7D8v2L6CZIVvuPCQjJka6+N+ADV0UWF3mY/ttllNRnt8jfjam4NGRyOHTwP4vqcfVWX+2RcB0Z+Ap59wBqDrYMWheWxTx76xcgPzOyNY/FFUePwgFp5zsLPjZqQDB/dQmcpbZ/wOQ028mCt7qhYJ+utk7PzddfVcUzJL0MZ56C3LyDbDms4E2xj9PQMHHZ6N+N3D6N5XvOOeGIzDVJ029gBy6OzoldC433rM+AlqTg47td36BEu9/gYoLjA542B+Rrjfe2cfay8qN9vb985Fefi3SA+YhbOcwFPjcr5HfmlJY4TGj+ZRzbiu/5o2nfYTm769x30rw8c6BLfgoAuUnZPq8BByaZdnjKDGvTRX6lMsDFz5F+naYBPsWCmA5065r9Mam7sdQnmdKUP8twoiG2zLxfPQNhHMmfwK7EbRWd3N5gYH30CmWJ6MK6yPj7zdl7TgFcW3jlLz8RySv1jWeTpZlc01etkEO9KoC1j2OOx/hGJe43DHvMiVXfEYeHFQVzX1RNfz7XZ4I6G3h/0EyJPlj+vi3nXNbWNO9QIjtfGHzuQ7icT2K3/Sl6Q1HwZuLUcLxOmbPPIiCDV9EOlZlH9PjrnHOzXbObZplmcdv/4p43RFZln3kGsdxSvFW+97jCIPfxTm3b3DvE5QIMd5ZYauytCLbeDEaQ28bh8mpVdrGXVxeoOkRYBXn3CT7Da+iU/p+hIKQm1XEU70+/w/gl8BGAd97BeGdZyD7rSgIsKk8tbfL/QxPoPGbZbbAr9HJp79C2NyvaQSvbiLN1i7H27+DEla62rp4BFXkvgDZdx0roLfiN2dZ9i3EV7cCtnLyl/nP22hdpdLzSUffRgGHnva1SDb9EFXOPjfLsv/OsmyTTNh5Cr07EM74ZKYkYJCvaDLipVdnWXZTpoCL4zP5zcrSW9UpiNHr/+8g/b+DUzFMvw9GIDwgJTDV01whh7Msex75F9sh+zBMMv4Q+FEFWNxwhOHcCTxseBWZirm+iIoBPZhl2TzTkffKsuzlRJrtnXMdTB5NR/JjAOKvHyEfygxkN96FEmaScE6TG7OdEhpAyZVrIr2jVaYCWB8ijHVNlGyRQm842hejTRb9AM3hHFgRIOrtjSkk+qqM3hIUvLkE4WGbmb5zA8LMBjrndvX0U+j5j/HO76L5G4Mw6Y+Rb3ykUxDpt4CZWZY9GGNVTf0YH683DOV3aG+Mtf35J8THH0L8p54KfA4oue9s59weiAdcDBzgnNvc5vEzs43PINEf5+TDCfn4Gaiq+zPOuX5ZHqT5OfAnpwLIqWPaFbjY1tB7iLd4DPMTlMy5DOEt/UmUjYHc+D3wW1uLO6Eg8euN7hc2ppejYLjSvPXrHlPTw+9D9vaZKFbkPBRP4RNwfm97/wLg21n5IOqQ7lDn3FHOOV/o5w4kP252zvXIFAS33PSgk0nncb2c/GIXIHsC5FesR/EkFyDs6l4kSz5KoRd+bL+fjuyLKSigu0Uwb08gPSfpY3vibvTbnY3h39Fpxyc45/4PWJJl2aVZll2K/Mi/SKTp43/WBMaYbfoKwsKORWO5MMuyrZDP7rBMRSNSsGqfCNMG8fINgttfoFNVuwPbuMBn5j8l9HEf5/Ax4gMTg9vvoGJOZyFe0zf+flay6EimBNybkf/oUGfBljbGryNcoNB2bOrH+MCdKGYKtI7mOOf28nMV7MddUWxS8ieTL/pRhB1v5homUdytn+aq8MWHfqAfIoz6VOfcgb7Z/vdznPxxuf9vFDDAKa7pd0iHPBDZWVdnWbYZiiM7NlOx0ZS9MRz5+rpkWfY20h0XOksUC2y8dZCvKk6SL1NY5QGkL65l77gGeM85dzkK7j49y7I9UOGMlII4K3Bj+/wB+cU7ZMKu/wudKDkb4dgbxd8v0b/Vke9vWKZkxZsQdjLS3rcgU6HuAQiLSylo5mn2cs55Xd9jmhc654ZmDZMaR6EC7FXY40ORj2MKDeMqx5jc3BfJsM1RwnOSr9rWzRJgD+fcqtafw5DePwGdzn0E8pd/QQV8zmg+7PJE5ztQ0boJ0ABzXBedgFiTYN1EekORTbgreeHU51HRwTEovvIUNI9/RthK0sc19CE2R3F4/vNrYLcsy3ZBcxvKTaBUjPNwtP8nGG/5HcLBd3Tm+zObY22UgNis0Zd9eXr3OecedCq42RvZrjejwlgPZln2kT2bjMU7FeNcz3jMKwjPWc8pbvxT4KAsy36A5EZHqsP/10CFG3ZH+/9i4FGnRMBQNz4D+ChRZvR0zi2zPv0YFcXbyTnXPVNSyLFZlr2JkruSY1TCT5Zl8xGWsggYlQXFcdH+/KSieRyK/NHHuvxQlQwVIWqZ5T7VDRCe89sKaPYym+00dDCOx42moLi1CSjh6hbE75JlB6zQUx9CeNgYZ0Xbg3uPUZF94xTjcCnS3bplSj6/FcVztUPxeOcg3+N0ouSjErr4CIQb9cqy7OcoMe0wlxdZ8/xvI+Svb1CwJkuL4f4A6fljgcm2pjC97ueIN1SiOxq98xGve9o51zPLsk8Dfe1BlBtQ48ssamvCZy4qRvEDs8PD3JhfodyYIppNlRuhnOqOYm7956/AxCzLhgNdXFR0sCQ9X+hoQ+TvawdsYHzzz8BpWZZ9G+mrw6ggbjz4rT9ACZ1XuDwR0I/hn6kAi48+P0NYTntUUL1lMGc/RTZeFTGVwxF2cbVz7nGn+O0foJjtPyG/0adQHYb7L//J/gWOI/zPv+J/1B4n2ho55B8jODodZUKfhZS50sdi27u2QcFNx9j1HBQI2D94ZjwS1jVHSZeg15HguFYkIH3g3YUomPvb2JHSiKGn0BuAQLB9Cu7thJTGU4K2wiO6m0BvOGLS+wPdonv3I8Z6QtDW6DGkTaA5CCkw+xXc2xMpU9MrXKe9kdN/r0bu3w08UCG9XsgJtXfBvdWQQ/CaquYwmsuXUTBMe5R0MAW4Ejnh/HNr25xvnkivVQEPWIYMjeZB234o8Cpp/yNFYi+i45ORIfwGAuBBiVevIwModUxHoEofR4GOirb27ZGC+r4fW6Qsd0/s34XA7KBtnvG7te23HIl4YHgsdsqY1qOA2Kv9ekDOh6tQAod/bj0EtmxTwZgOQ4kGh2NHJaOKVZcj42dvJEsmAu8C21ZA70VUWfg6lPTX1+4NRcEqJ6b2qxHa7WwOT0fAzvbWvi+qYDUPya+dE+nUI0PuNBTAPBzJ4pnAkGgenwHWr6h/o1Gw9jUoOH47ZOwfhJT0ieiEwJ0qoDUAJf2uEbUfioDkKcH6fRfYsKI+eodpP7u+GFVTqg+e2QDpHOMS6PRHgX372LU/kr6Lje8iBIAeav2rYkxHIFl8iq2fq5FTwe+PMQg4XoZOB6pCv/la59D2xqs2Pw/4d9reexRVrlsfBXe/VdG41qNquFPtejYKfFgTAfOXI92xj93vnEhvVQQG19l1C6N1vY23b98MBe10TaTXH+mpexbcWw/pqQtSx7FgrT4H7GHXddafNYATEbB7tu3XnwI7VETzZeRgXDtob4cCSf6Agg19e/NEesMQvzwM08dtbz6D7JpO1rah7Y9NKuhj2+h6bZQQNxcFWPn2qSggL5XecNtnhwJdrG0ycnbsEjy3vq2xzSqg9xqSSfXRve8hoHhW0Namgj7Wo2Dmk/3eRnrNdUjv8ft+fRuLLRLphfpgB6PrK/9NsPYJyJ5bjukbpOmO8brpgUDppeGcIV15AZGtUILeMKTnh3tjX8TbdwV6W9tGtjeS7JuA5mLEry9GQDnIrpqL7IzHkY3QLpVeQHeC9fUmlBDXB4GotyPdY3cUrLdrRfTaIVl/P9Lb2iHg8SqkE0xD/HXHCmiNQPr/9HCvITv9ceDvKJmrcJ2VoDcY6f/7Ru37INtqPkq02LnCPraLrgcarbMIdB9bv/Ps75S9ONzG7nZMRiL7vI/17QWEO/wvFchFo9mJiFca3T8Dk4K2iQgT6JDYx3oEgi9A+k4H5PSfF/Gb9akO/xuMbKht7PpolAgwOuwLwhy+T4ARlKQ3EDlMe0ftlyKcarRdjzH+sFYFfRyG5JLHMQYh23EWpm9Y+4aIz5WmSW6Ln4LJepSoshRhLL5/E+y5LSvo3yBk57cK2vojp87dwEBr28TWTb8K6O2EyYqg/Sakb3jZtR6SXSMq6ONQcp7a2tq6IH14EUpU74uCK9/DcIjEefTBmi2Mxrzg/npIPl6VOp7BO+sRBnU60r0XoqquoGIRlxi9PlXQs/fGmHgPhGdeS4DRowIBSzA/QEla3jYeHbUfYmtnd7uu2jb+rs3fACT3j7PfsXU0n88Bm1ZAc4jxlh3tegbSMdamIW48HsmsQRWtm6tRsQ+HZP0VyC73OMu61setKuij1xuPRnJyY+M1ewA9g+fGIvxxbCK9AQgjGh+1740CEfcL1s576HSAFHotUTDu9Ki9mf0/Bfhu0N6CNNnfBsmIQ6P25vb/RLvv8eRUv1hblETwQ2RLjQnujUL850wkE1+hGp1xVaRD7IUKm/p2X+zzLFScZrTNYbLNiHjWEY2M6TjgznAOK+jjcKRD3IyKboL01CtQkFULJKfWSKVVQPMoAp0QJancgk7FHIWKObyVuv+Rzvgaqti8Yi0iveCnNpfHIx70Turasb3xADrl3rcdiORhB7vuhApZLgB6JNIbZmMW2jGnIrv8Jtsv7W2t3gVsZ8+k+v6HoUJbp2D8C2FIf0AnAPhx3gjZO0m4UUR7IpJFk5H8mmF78HLjA+9XMI99UND9RUR6L8Ljfmn7YwiS/8m+uGB/XIjkYzfr48soGN7rqhsh2TgxkVZ7ZKeGulOd9auzjfEyxMtfIxGL/7rH1Pb+W5jPEsn9O5E9sxOyaR60ffg+1dni9Uj/vxBhxU9be1eEb3yAMPITEA+qQv9/GvmGRtoYno7kZTvESy9AQYFgNkkizf72+9sFbc1RQOwivzaRjvMWgcwuSa8z0hsPitq9fDwZWBT8jjrS9Y7+SCYUxcZMtLk7LmhL4qn2jkGIf3vbeytkp3rMw/PVNWzvrlbBWr0C2RhrIR18PrU+wQn2O1avoH/jorY1rc9XYn4do/c8QSxZAs1htg+n23ULhFdNQn7UGSj5Z6Kt1VTZ0RXoGLWdjOzUrTEMFdmp9yCdITUWZyFR3ALCUH5OrvtsiOTk+LK0gnf3s71RFFc1AQWLnxe0VbE3hiO+eiI5rjLC+M1iLB4O4XGvE9jMJekNQf6/ovi/HWz/H2TXyTFAthfvRHy1JeJjC2x/dg2e8zGV6yT2z8f+nEbux13f1uk55Dx8ExQLUYUtXo+w0ieRXjjK2s9FcnGSrdON7Lkq/P8+rvIAIn0b6TfLaeg3bpVIbwTSLQ4m8Jcgn8rtSEeeHrRXYct5zHFGwF/6IF3rcoRFdkF87j3MDqhgHqcRxaGi2JXlWFywtVURqzoU6VS72fV6qKjK/tFzGyLdZ2givUE2j/tE7esgvOVChKvujnhhqn7s4xun2Fq9ijyOsh2yUe8mwl0S6A1DMnYOuV14uPVtV3K/uPc1JPtwC+ZpIcIf2yD99VdID7iYRN2Yhj6hW1Dx/dYId7zR6K1i98dZHyel9Mne1QHDvoK2SxFvH0WOy+2IEspakaCrksc3nI7k1VXBvaX27ypUtOInVBOL49fOLGCktd2B+FsXJDPOJZIZlJeNg5AN05UcV+yCMNxrgRnWtjbS4ZIwXHvXCCSjjqYhBjgO2bG/AY4K2kvHGwdj+iMCDBDhjfNs/QyytvVsnpP0G3tXXyI7G+njNyJsp97a1rWxGJhIry21fuprkP3aI2jbGcXLtUzZG/au5tF1SxvPH4Rzhvjuuch2TNHHhyD+5WO4R6I4zth2XRvxpMEVrJs3bRz/jnj3JBRfsCU5v6kEi0P85jRq7bjNULzBtIDeO8C6FazTfrZ2fF+a2bheibCyNta+KUrcS8VVPQbl/WI3AbcG9zdHutVZqXvi3+nfN/4D/vOvkYkRWPUSCqQaHbQ3t03yuF2PoBqHVegc3g4ZqT4R8ExkoN6KgN2fkQhyBr/9WSQkw4SYh4xWO6RIjklh4BHN/VGVr7AtNPZPQ86dpGBje1czpEAdFbX7hMY1kZOwo3++oj7OIAJroj7e6e9XMa4ImF8c9NmF70ZJVQtIVIwDeuugCriEdILrTRHAkSSIo3f2QqDjHiFN5HSZigzX55FS+w7pSUcDbG/MpTZg7bvAXfb3erb/k4K5bI1cggK2v4UA5LbkINw0pGzMtnGowinf3fhKY8mj12DOlgpoDUMBYbPs//nBvQuRM3VkVevF3jsUKXJ7R+09bN5uQc7AJfZc6ppxtsfuRZVwIHcA9rM1dSRKIl+GlFWvEJU15PoiJdUHHI5Bxsbg4BkPTF5U0bj2Jjdk6pB8ug4pxw+RO1cOsr04ORifJveTPDnmIBom/E1EQPW1KAhwKnI8pjo6vUHcPKD/CjKgeiMj8nwURL4kdQ4DuqcioO91BFrvG9ybZvS9+nWeAAAgAElEQVTnIKCwEudq8P5z0AkZvZCyfomt0eNQIMJPSU9WHU+QOIXk0lnIATESyeO5Ns+bpY4p+RHn+wdt3ZCM+B61fH3tsrS+qTm0uXqfHNi8CJ2A5e/PRIDHcygBInmtkgM5BxA4MFFA7p2YIwUFfXkHa11ZukgP/boD1iajipsrfjMNAdD9jWbr1H3vxwfJo58HbU8CZ3jaCISYY3ty0wrmsb3RmBa1O2TnnAksC9qbJ9LrSX6y6Io1YX/7U8cfQw7z9yraH30RGDadwLmPwKILEEC1KnnwaBJgjfjLyxQHOUxCIOetSG4lg8e2/r5DbfCot3F2A26qag7tHd2RzegDb1fsbeS8udj263wqSDpG9s0LKNluDWs7y/b7zsge3sja1yfnhSlrtRcKoDqBwLmHgg0PRYVxRiLdqorgqi7IOVbkAJ+EdI3vIX3jnYr2Rr2N3SzkPLkHyYitgmdG23r9LQkFAOxdnQkcOagi5e2If59lY94WOeVWJMyWnUdqk1Ta2rp9DgWRtkDA44XImbNFBeumNdL/D4navS65odHvlUorePfpBLq97ZfdUcLVzta3xxHP2TaVLrJlnkA6YydyfXkw0sXnIl6/vu2NVJ46GOmf0+29ByNZOYegaANKXllKNY6cbrYGbyUPdPJ8bioC5tdCyTlvkx506IMc9iFI8rH2U1ExhwVI9v+MxIADe/dwW4vH+Dmy+bwWOQGmIHtnc6TPVuHMvQXpx6+hZIDxwb0L7f40qtPjvK56EEGCDyqMsQhhLTvanP4kZe0gXbyxIK41bUyfR1hHcgCwvbcVkv8fodM49gvudbe9eANy7L5Y0Zg+gE4UfBHJ5KHBvSVINm6NMMBU2e8QpnkjVhzO2jrZ/uuD7JBbkXxchslG0njcTNtnQ+26HQpu3pDcHhiFdMjhFYxpFxTkd7Zd90G21E7BM71QgMcdVBPk1A3x1QXIFu8ctO+HMJ1tEKb0FulBTt42fgPpVeFa3Q/ZxmdSnW08wtbgPgTJjggXPBHhuIvt76p8Kp6nziR3WLe2ebse8ZkBKGhmRWG3BHrDEG+Og6o6INm/BNkj86m2sNKzyM8xLGifYnvkdMRvd0I8tYrCajNt7fwEYQCHBfd2Nf5wnq3TKtZOayRzfeB4jW/IxnVOKq3gfTeRF+KKgyzWQoFkSUkqwfvaILnwBCrU8AukE4+z+/XGZz6jAp3Rvr+1zeHzNldXktupayD5fzPV6TctkN20adEcIvzjaSzRoQJ69QhPOdp4zN00DMK5GsnGsIBs6piuinwXe0Xt3k7eGwWrefyviv2/DzqpBYQnjLU1tAWysQ5DOuRFqWuHXCZtjwJvZ9n1bORHehvpr7vYmk4tUtESBdx+jnjcMTaX5yA9eHXjNWcgmd08pX/R2nkDyYe66N4e1td7kLxKTq5CusRGUdvbiL/uYuMwFsnGQ8mTElN0qg4Iw70KBaefhfmG7P6Z5Dw+yddg72sd/D3Y5vBaVPh4d2QrPmFjWlVxnM5IbvjkwsNtPT0JnGttA5A9MPjfcEwnodMg17TrOmQP+MSDLkiX24bcD5C6N1ax9X9s0HYPQcIGsgFOQjpmku/I9vvb1PqNmyOeFhY6qUIP9/bEEpur7yD9aeOA7hzkxznGxqKKtdoTi2Ow61g+bo3s2aTCmNE7pxDIW2pjY85Bdk9VhaPrkZ52BnmiQ0fbl48grKEvill5LXWPIP3/BXv/rUjv3wDZUguRrtqDijAVpKc+gJJgjiHAHRHPOx3pVdORLVTFuumG9MWwOPT95MVdN0Z63LcQvpsa49DL5vApJJMGBvdmI7tqrI3pm1Rjx01GJxi9Y33ZCtPlrH/vIZvqhSroBTQXR3sj9DnOQDE5lRTjQ7LqMUx3JMc3O6P4GH8q3022N3apgObhwKlRmw9C7oRk2REp6yV4r7f/pxEUhUa49Dxkh+xv4/pT0n04A5GNHcuN1ojHnE6ux71IYryRvXsQ0gd90csF6ORdf/9oZGd9n0D/TxlbJI/uAo6M2r0tN9bWjE+cSy2O2cboHRi1+/W6FdJvOoftCfQc4mnfIrc3HLJthqGDDg5FfPcxpEOm8rg6pJseH7X7Me2F+NDwKsbU3jHc9seR5L629ta365GuMwol575NNX6VqcDFwfU4W6Nb2t/b27jP8fTKjGkwh98Hngvaj0R8rVnQNhnZAn0T+zYM4U/7FtzbA+l3L9oeXZEMkUizP1FyFuIx30cYq7P9uDvidR7fKbtOW0XXi1HiT0vbh4tsrSyggiROo9HXxnUp8kd3Du7Nw3AWJJffJl2H64F46pl23RVhAWEcwLa2bo/EClWXHVP7ri+u0iCmwv72fvm28b0Ees2QPFqOsLiF5LylFdLTF9q/l6gGU+2MdLj9o3bPx2fSsLBaqvzvj+JtfKF6X4yrDyqqcI7N83XI31iFPG6NMMXlKLYo9KuMRtjRUQhDeyV1fyD87z0kO2I+cB3wQ/t7bSoqrIT8/68jvDi0wR3SiZ+063qqiY33h/8cT67ntEL+4wdtHkfZnnwztY9Ivr9PngS7CTppsDu5b/oeJCOrSgA+HJ1e/AOEFw3HilSgOOffoyTLH1ZErw2SEy8ZH9gouDcE2RkXIrv4ZaqRVccjHDosrnY7QUIjSoxdSGJhzH+nf9/4D/jPv0YmRgz7I6QwvosMxVHBfV/p7HWqqQByNQrY9JUNN0aJQD6ZZC2krO6FGbSkVzp6DjmJ2huzDpMd70aKR1hduoqAtb2Ay+3vuNrJGghAXkziSS5RP9ayv1tE94YYUz2p4rVzEHBZI30cYEz9GhKrHATv3Bg5kLySGAJH3ZFh9SaBwz6R3gh0hDrUAlVjEBh3GYkOuYjmasg49Nnp+9o6WYIUqs7IwNyMCpwPyBD4CXLevIYcm2Fw7IMIAHwpdf8H79zS1uMg23uXoWSj7nZ/D3QMcDKQY9/vjRz9zQiCw4P/R9g6HUZaVZV+tv48LxuKQJ0Vpx4io38Z1VbIPS3e2yiI8hZyJ/nGSIH0BlAVPO4CZBB3QE6pW5Hj7FzyoLJuwbymzuOrNKxofI/tiwPJHUlDqOB0POMnH6NTWnZGgfB1yODYBZ10+hB5IJ6vClQWdOiCjKoYWN0dGUDdkAx70MY3JYjTA33/BRxgbc1Q8M0tKJj6mmAO68ir51Wxblqh5LszkNPmEcTz1rP7uyL+UEUg91AECoVBXKeTJwK2QPx0ie3N0oacrZmeSL6/hOTDUluntyHA6u7U8Sug2w0BtF2j9i6It3o+mlxwIHh3y69xDh3Sl97377f2hxD/aRG0dSCXnSk029nc1QCddv9MBI6vTyRPEmi252sKWAt+8z5YMiW1QTnDkMN1GYknHAbrsS2Sx7fanngQmFvVumyEbg8UsOJP34v72d7W1h4V0VsFuD5Yu1ORffUo0jU6IR1oa/LqtalrZ1NbFy8j+bCE3MYaigDQW1EATRWBh0OBpcH17gjUuA3p/Z0R/9upij4imbEUO32D2uDRwSiocmqF66YDkvf+lMPpdv0jpAN0s3EfSx5Em9LHQ9HJm/fantsN2TSvIH67P/BrGp6CUMWp3D9FDtQlSF8dQm5jHYJk2S+pJriqAw2rYh2IdI1XUeBVMyQ/x5NXCUzpXwuk08yM2k+yvvYL2tqSXgWsNwIy78ECuG19TEP692IUwLaKrekq+riUhnrx/YjfjLW1dETZd/8TuteTB8nF+3GgrandqOA0l2CtzLU1uwAFUX2AAqD96UqhLpDKU0fbfvsNAv0XkhcFWRU5Am+x/VMFTz2ZoKiStW1i4xyffuid5ql9rLN9MA/Z+deh5EMvKycjR89vyYuelNU52iGdbVrUvj9yInVAQTnzkLNg0wrGtD9ymMbB46tb32cinepHSD6nOqt8sPRAFFwwG+nJDyPnhneUnY5syyr04+42d1Oj9k2RY3kIwgjusTWb6jw+ATgrajsS6VD+xIi+SF8dUMU6tXecjOTE5jan55IX3umIdJxPsWC1CvbGOJuz6QhfvZ7gBGXjB/+oaA59EaxTkZ3fAxUbuR3pBFeT430dsAC5hL3YjzzY72hbn8NsL/7O5vJ+xBuGESR0VDCPZyPb0AdUX470nfOR3dPO1mxSNe6or+8ix/ypSJ9ax9ZMC2QDPYgCTCuZS1QgIrSN7yD3Z+xCdbZxW4TbHBC1H2R02iN96gJrq+JU1dVQIEfMUz1e5E88fNb6nspT62wfxoFjxyHd0TuRJ9ue9X6QKnhqLHd3QLjRCCSjHkcYS6pvLDxJ+Rxbp/shnv0IwvubowSk8HSXKvjqQixw3L/TxnwQwnEmknhyRPDeZkjWhidE19m91ij5+FSqOf3bYxxrIN/GMGS3XosCt861tnEknjZUQPt0Wxfr2hp9AgXF9EG26pWkJ8Z2C/p4LnYKSDCmdfbMDkiX3SiFnr23MyrG54s2dUc6+KVIF/aBzjcivHNFsc5EuuuRnzjoacwA/o+GCR4dScSqMV8wwm1+jnSZG20vPmVtlwfPN0uk18bef7xdb42CuB5Evut6JKNmI4wjORk/GNNzkM5/H7KvnkP2TA8kI2+lwmrV1q+4WO0B1v9e5EGO25Dblil8/DBkJ4616weR/e356O5V9S2g2dz2/inWn72RfnUfVtzRxrqKZINBKMn/BJRw1xLJ51MQn+uG8Mb1kD3g/YEpxTj9WJ5t6+VFZNedhYIOH6OgOFhiP5t9HWOK7EVfJGkv2wtrIZ/001QUy9AI7TURljON3G66HWEecxBva1sBHS8zzsL4eME9Z/dfISoimUDX28arI/3wAmTTPYd4W1+7fwqS10knj5LjRl3sfRsG93w/B9p+uQgLcq5obHfAitJRG48zCp10cA0VnHZuv/8danGVAda/7WyM77C9mVoEuCPS/8MinA8jLKUd0t9uR3re41QQIG80TkCn/eyN9KklyI/SBgWwn4uwuiqK4/i1epX1rYfxmgXRcz6wu30FNB0qnvAh4t03A4cH948w/vBB6t4I3tmNHCs+EfH0F8mLDY5EPDB5TAOaOwLXheMc3BuNdJ0lJGLxwTs7IPzG4w1HIL3mcWSjt0e6znByfDd1XE8ArgnXSHCvo+3/+0g/AagTkkuxnboVsmu6WH8vR/y2igJnhwDzorbjUOyPLwjUFekKq6aOJ7JjDjF+EvLwbyGdJjxYon0VezF4393ACPs75uP+lNCjUukE71yCxRRQm6zeGcmpg+LfUpKWt1vOQjFOnZGecZvN5bxgz3Qmx65T98Zi8pMi4zEdjvT1U1NoBO/rg2KX47iqUYinb0Qeh3AP6UVH/EEbO9k7hyK7+B5kb7wJnFbhevFzMtzef6Fd+5PbnkFYwy62H5N5qq2LON7wSFTUwfvKN0FYTpK9Ebz/YOB/yOMXH0D8bDzyzR1HBbqUvbs/ijM+icA+RP6Fp8jxgc1R0pHfI1Xwm0dRDMejKDklLHYwF/HAd6hA/iMcaj7C3X38whUIyznb7leViO/14pMIfCjW1jL4+yYkz6qwc7xuNgrpMMfav+eRfF7X7k81mlXFG7e1frSmmI9vgGTX6hWN7fr2+31i5X2YfhU8MxphBR6jr2KtTkEY3yLkj3+C3Ee1ivVxxYmciWu1mfXxYZTDcQoNiysuRLjc61RXzG0k8N8Id3jb1s6awf2LkS7+Fuk+1T4EybHRbxiE8g0eQrLqPqpJVvOHmUwhlyM3kPtPPDY+iQrim4J33oswgPmIh99LrmOtg+Kud66IXitbn09ZP3+B9OFN7P4ApHN9TjresAqyt9vbWrkW+R4OQT7cNxBfvwXJ68p8qv8O/77xH/Cff41MjATWQ7ZoRxljuwMpBq2QMXc+1QDWR6OAog9REPXDCJw+CYGAR1e5MZDh8iINT6t7GzkcriQHj5ch8KWSUwDtnTuiwMn+dt2cXCnZFRnpoyqkdwNW0cGumyEFpD0ydCaQ6HQsoLk3wQlqKKDC9/FABJxXUT06TL67DgE3HuD1CvleCDxKWj82Tz7AbhXkdDs4HFf7f3tj7v0rGsshSND3sLk8DQVR3Gt78jwUkFNlwqE/ve1C5HjcEAXhvIuAAQ/KX0W6cdyVhscn30/uSN4XVSB4ESnn46kgcSxYi74SUKf4nTbeU2z/1yf2cXtkiG+AkoF+YPN3n61bX/FnHhUGHdhaOdn3zej7E1VvrXjN9EABDi0Qv74fKd8PI3BqTRS0ckpF9PoTVKFGoMa9yFH2DgqwvBIlsl+BgRAV0b4Qyat5yOi4Dp0m4xPnpiEHbBXASifkOAmPrd7f9sUfyB2vVVTH8TxzB3v3XrZOLrX2AUgmLyHxVJyAZnfyBI7WtoZ89bEhNs532PyOIA9QTK3kdqO9+8e2DycjvWA2MlB727OpVdxaI7m0EMneGeSJTqHh+AzVBXGElaLupADgR8b5pVXQC97pULDBaV/1HAY0u9h+W4oM02m2dt8wfvMYwWkrFdBri3haA6cQDWXHqUh37JhIawB5kvgovuKANeQ09oCXd/KtOB2KXP/Z2eglyUV711DkdDvA+tsNgeQfRs+NM97QsYp1E8zlneQAcnii23Ck+8wgMTEf6TjDkM79KyQ/nkQOgCsQsPp3KpTHEf3jkbNvDAJyn0aO3UE255cV8Ygm0uiJdIDmNp83I2DjXsRrpyMwKzlotID2AwSJDuTBo/0QGL8r1QSP9kZBt87W6Nm2J+9DTrJTEY+rrJCD0W2G5O4VyLa4CwFWfyF3wleSHBPRvQDxug4oKPAWJLuGIMfRgSRWx7Q143nMq7Yfn7Uxvcj4wudUEKhaQHsR+SkZbYP2W4Ab7e+qeM1A2xPLkB012/bgSwh07WVjew5RtckStHzwextkayxG+nhYgXQzZNvMrqh/7cnl/2MECYbBfnQo8Og0LCkogV4nZH9ORLbAfchOfARhLM2Rg/A7VFvkwMv7aYivTrC9+Eub0zFIj7ySxAqAAc2jMAcxDZMCpiLHYOsq6BTQPQnx7hYoOPY2o7eJzfc4qklyrEM2Tj25DN4DJR29RxTEVlHfJpEXqvI0ffD4/OC5buQO/LJOgCG2Ftsie+cmrLCArZ/lNs43Il6bHCBj3+8P3B/1cbrR+3YRnRSaSF54vb/OeMwyW7+/oUL5ZGManjT2BLlT8UIUGLDU1uo4Kjh1JKC9CuKrnt4ONqaLEf9uSQWOXGQrPWzvOxjh8e/b3O1tv+MGYGFFY1qHZNKb5DbasciB+raNc1vkkLuSaoJjeyKdyWMPJ5OflPUawgAvRbrkEwQn2lXU58k2rn0RTrwM6SC7IL19fxIS5JAO2sf+boFk34l2PZiGtvFIKiqMZe+4BTn5vcyaguyP9whOkqtwLDfBgmFpmJDzJxqe8NCFxGTV4F3nEpwCiAJwnkE2yAMk6lIF9HqRF3LxfZyOcMafkBc2SS46gGzGi1EgQHPbm+FJmcuR3HwVBXx0SaFXQH82sjX6Ru0bIH0ytXJ8T2RTeX/CbrZmJ0bPbYP0u9QE5xqMFNlskxBfex/xu/sRpjsoeC7lVKVxwfUOGL9GWMbfkT7wG5Tc2TWR3nDEJ32wxFR7/0Y0DKoah3wSq1WxVuydc5FfbBjSFW9C/O8x4HF7pjkW2FERzcnAU8H1AKM7yH7D4orodEf2og9mWoDk04NIh2uBdMWlVOBrQLJ9CPIHh4VjtkS8rdGAuZL04oD0TZH+vwDZNHvbuHrcs1OFc+hsf+8atO2FAq0uQPJ4SEW0VkFytz1KTrkOK5AVPLMLSvTek3T8vy9KpPDJI92RzV+PsLd3bR/eivQQn6ybKhe3RfLhfYSjPGNzeBSyW68kKHRUwbgegwoYrIfs38nIZ9sj6NNcohN7E2l6+dsN+XG+kjFF/OzHKKDYFxU9CNkYb5Pz7EpOb2vkN0xAsvFAch/VGdbPx5F+3DllvQbjNJ/8FIA4GH8Awl4uIBHDsfcNxuJ7kDw83/MBhOMsRzrIDbaW+ifOpT/91p/8cymS+T2j53zxumQ9FQU2bo7sOH+KmvcvhvE/UxHPqarIyTbUBv2egOKsDkG4clsUz+VPdiw7ri3tPQeiBBk/h7OwxI2gn90oiLkoQTNM8rmKXOe5yvbH08gm2Yw8piu1MN48zFeC7P3fEZ2kjPj9pFR6YR8Rzjkb2YgjEO+5B9keftyTCrkQJHLb9UXk+FF/VEzyMWRXTaGigmMBvfVQoTxfFKcZOY/fGfnjqvA5DsR8wkgmXY8wjweQznMA4rWVxsUF/bg+uK4L+jgL8Z3kU0CQnfoIlvhmbdOBT2x/fhX+lCOB84O5WxfZpzOQD+f/2XvPeC2rK//7yzkcem9SpXdQugUVsGONvfeu2LAgitJEBAQUURCxK/aGihpj771r1MQkk5gymfxnJlOf//NMPs+L39rsdV/nBg733sBkMi/uD+cq7HXttvYqv7VWcqBhcb2hpHtnIH/4WOvjvyL57Q0kVyZXcCvzDffgAtKIuMrmSD/eMxdda/NJYsXKeu5XjexWV5ABO2a85QFbl5ORLvxzYhKCPsZ3rkmlZfTaur+XUmonqnb9nIB8ELtnojsGS5BJtI9NQvLGYrcn65OePCbINSfY9a0IH7aaGHTQHekhyYkVkC76HXCxXfdDwQ6v2p7ogWwt5yCcTC7d6mZcIgFk63gJJar7eY6+ubZ7EgNUzkY26vcoxTpPNNqXIl9ojoTKv7A+fYj8NYsRNuch26NZguMczXD+n4RktoD1+TvEgybaPjkH8zem9tPa2BbhbRagM/I7xMdfQpiOz5E/IDWRa5AhLiRiVYuFYwIG6gFc1awK6fWzvRgSwq+tzGtj/CekP96IbC3Bdp8jkWNXa3usexZ4Twt0fj1Ium+8B9L/uyK734MoyKmYSLYzeXGxQb7vbmMckjUtQXLdnciv243E4DFHqxphb45Hct1ExEfvcvQvJI+fOsxVI3QGTkU2urm2NhehZCet7ZtyVFbcAXigcG8y8I/Il7Q2AJ90+3h3ol58OMKMHWHj9xYZ+fc66N9JLDywE0qk+g7S58aRoeAI0lN9op+PEK/rjM6vb63fQ5BPZZvEPtUg3vwSOrNaIB38ZYTfDHr37sh3nEVG/mv6bfEP+N+fmwwdGs3d9VDgZff3L1EGsMeQcFCr8tpG0utGzM5yFlLehiFhYy4ylr2Bso2fT6bskUZvFnISjUbC2wpkBLkVuNu9NzKRTn+k3E919+YiY0oPd28HJCCkArm7GFMLoJe9kSC8T+G9ccaIcgRV9LU5O8vdew54sfDeWAQEHpzYR2/YCMra/khwm0oUnEciYTUV4DwICdzPEEGjE2xd+kDAMTaHu6bQc+31sz1wjF1PRALADXaghMPscTaNwWpf2/PNkIPhN8jB/DBwj3svxWH9GqXlcUchpWMYMsydjA7IWRjAIgPNa4mOm1uRwBiq4wQw0gHIsVWxs9PGLAguxyIQ8AdEEEANAjstzT131v5FGJjZrnu471kFHJaR1vZIiAsKVT8iSC6M6YmI96U6VpsjQfvXuGpRiJ/9BRd8g8AA2YKqXbtzjH4nWyfvIp7XxL4vRybHekhQ/ZCoCDc3HlCFBPTPSQw2cmv1XSyjGgIf/Ae1efggxGOTx5QY9HczUdkehATzM5ECEABzM4EJGWi2Q4J4H2R0vBydGxcjQ0cwlP9AIsjJ0RxnPOeacnOFlI21c5xhTOdjwbaIb95CoWIbAlkkZ1WjDMgOyTybcg6bFa7bIQPOxzaHgccNQUklcgQA9UWyYgOk8AcjRpA1qpCTOoC7kxw5NoafUmpUvZxNC1ibaDSPsP6cgWTk3dw7o5DymhwEjEAbXyDnggeotUVGlpAxdxsEtkiWcRD/3CqMEzr77y7z3jgkZ3VOpNfA9uO1CDzRDQEP5iGAXDDc3IYBTDL0sYvfZzavy+3vhkjPWonk1zNJBI/aOK5EQI0OtlfmWT97Ew2uS3EVOxP61976GIIzJiMDXP/Ce7shg3USwMH+bzXiMattPXZHRvlZyOEaDL13kiGLPDqP/Z6oQbLTDKTrdLV+jyvORQLNvpRmVG2HHB0NbK3+xvbll+gs65BCE/GY82ztjLQ+Xoocmlu5vTGffNXG1wKlkE63zD0L58Z2OLk51w+BKS8wvnIhCk79I7GSRT/SA45rbI7mhD6hQJIvC+81RE6tHI7jgUj2DgkpQjWFkgxq6Ox6nAwAUtv/F9ocDrd7IagpOAh2RLaqLIBVnMMLZXR91l3/E5KzvsAFcibsjfZEJ8YJ6PwLDrPQdgPkfEyuxlug7cFVz9t8drW+PWr79UXyALnq2Z58ngiwbGR7o76trffIlFTJ0T0FWO2uA/hhWxTglQvk0B8FaRzj7k1AmVv3RzrXVHRO3kGGwCpHpx/wQmFNTUZ8fZ6tnZwBskcgQGo3u25DlLWuIBMA2Mb0Q1zFMWQnOh3Zqr62+Z2OZJIB7r2NXqvWZlF/CuD8CcimM8X+vp0yTt4K+/gmpcHUHYkVFYO8cQDSn1ODAHojx1+QM14n2oYvs3ntk2utWLtBLnsYq2rs6P1fIpAynNfJAMAy39DDxq81kkd+Z/3/BUoktVXiOn0fZ3tHOs9HyN/hdeMZZNCNw5pDes4aYoKxBsTg+G72XclgwwLdY4Hn3XU/pD8OQiCyxTnpGY3FlFYeGUaU49ZglfEyjus2yCYfZI0AiG+LQNwvUbBPJK4dn9W4Izovpti/IeB6EongOJyDn1I5517bn7sjW+TOSBZJyjjs9sGjtjbG2FhOQ7rw8chONwH5VFIrKvZHfrAFSC8NvO1ga/9nwAWh/2Sw/dn6/wyXxACBRF6wb3nPzeFE0sE/Yc2cWLg/3fbhGUjP2sP6nCNrdBcc2NXm7y+Y7cbdfy0nvyHqFA2RTuEz4wffzp5Id0yujmHtLUP6fZDLOxee74WAlhUDq4jA3veIVT13RdkoD4MAACAASURBVHrM2Y7ODSgIaG3iqgSaWyP9bUdcdT8ExAl260bo7Eiqbrieb7gYyYjBVtSB6BNYnLo3rJ0qBMh5xfZmUyIvDcGdYTwPIo+N8wIkS40hyjdnIvn7S8dz+pABjEtpRb79kP92BxR8MAnJxs8C/4UwDw1zzSWS257CgjgKz4YhPWhcIo3WuOSaRPv7GZtiTG1vfI0lGfNjhYDU35OpqkmBbqfimjc+cD86f4e7+wNJ9zf0xJKqImDsc8SEFNVuXxxFvkoVwU/kE7gdavtxVwQ8vBzJVmXXVYV0Tza6O6KgqjUIXxQqHYRAvRz+hgG2fk4iAjYXIf9NJ/feWKOZhDcq0J4ArLK/6yOb9S1ItnoGVyUsZe0iu/EKdF50R3LwE0gXfo1oD8gSJEuhohfSWS8hylJfID/uWUi2TK5Yi3SYd4zP9HH3FyLdOPgidrK1NT7XPFq7g5FMENboz2wdPWTflZQIzNbp2xQSlyFb37k2pmcj/rsvGTBOlPpOfSDOa36vI3v8Nxn3/5HIXjsS6RgHILm1HVG+WQackoFWb1szAVzdBJ2DtxTe2x75XrMkyLC+POF4Tkvb9x2RDHRPcR9loLkPAlGHgNtWRD3rUsRjcwSotChcd7e1+QLiq8GuNBb5l7IFAbo1exBKCrJn4fkEXLKqSvuL5N/eRN/soTa2EwrfsavxgDYZ+hYqGE5y93oSk60Fv8MRxnuSbMfGcz4h4sSGId3/1MJ74xBvCoHzOdbQdsAT7robwnQNRfLxte5ZavBYM8RDbyQGqjcu8z0vk5jkzPrRGWFV/p6oM/ZD51KR7+QsrjIbyaeBhw4i2v+XZ96H1yAeHvbHOSjQKFSOCufJRBKTq9tePNz+3hediYcgH+AFSJZahewez7MJkoEQ8feD7fo7YlLe90nUy5Ftb3jh3kgb5x9wFTqRPpAD/9cN2VPqo7PhA/fMF445hQwJson2Ma9v7INsKwOQfHUWChi9jQxnMZKX3sJwL0hOXYFhVSg9T64jPQHwQKN3FTo/apBM+hNML7b3xlGIQUig2ZDayWKW2xi2Q/rcDGS3+jXpumpvZEMJ59FAlDBqIJJzvkU8/C5kj0u2WaFzcALRB98X2frrIz73C+QHfALZq3IlORwGPGZ/VyG7wFxkw30QC/TO0L8B9v0LiYkMj0d2418R+U4uW2o7hPHp5u61RnbNg5C+cToxga7HJVXK4wbb3mvp1sTFSP5vbOtmEuKpt1Oq51XiN+6LzoiuyM74OOKzTZG+cR9lYmFS18xf22+Lf8D//txkSKl5n2jQaIkMq+ciJ/25dn9v8lRyu8naDQE4M4zhdLbr4ch49gYZsqojg+7BROV3hjEbH9BUg4SBpIwD1lZgrHPQgfux3W+BgGI/oMP6Clx53ER6HyIldBUSVBtYP29CAkhvdBh/k0rP0fzU+vhbSh3yaxCQZSoS0r/L0MdWRqtYxr2Bze1ttqYWI4dAUjQ+UTg8xRj5b4hOlgkINPIAEsi/IYNj1doO4DcPHqtVWQxVOvkMMw4m0Otp+/o4pEQGAWsKcpz/1O3/riRWdEEC+euYkur7iBSp/wDOcPcbp9BzY/ohMkh5AXIpUuzCveG48tEJ/XsGAVVDQO7htif2I/LYfREQohn5jORBwGmKBJslZdbMG6lrxtoKSkwNMmQuR7ytUeG9nW0/JvFxJDg+hZzlPVAQnK/o+gaFzBkZ+tgHGds6Fe4vs3XSBCnGOTMce2fjPAQ4DE7xcF4OsftZsrkgPv0FUWGdAPyZQrVfMmY7QsroHATWCMCqM1D2+PPXNy4V0GqEADE3ojN4BFKopiJZZ4D1+T4Ezq84eIyCgRQ5HOfbL8xjZ6TMfU5iRVVHp8b22i3IWNsYOXRvsf3ZBIECviW9ilM3xKv3xVVztWenbaI57IDO3Km4sxbxzzOR7FNLdkukWQ8BRY+362vQWR94eOC3OyEjaFLQATo7vqBQrQE54L9mEwDWHI1dEUjmUGJyh2+Qcj4DyXA5wGP1bE1eXLjvs8XehxycH5ABzImMNa8hGeAa+4aGtv/usf3YEBnkvyKDfGx0x6BsWDMpk3kfOau+IjHrkBvXo9A5GEDU9ZEscg/S74KzfGcygKusrVCxdlGRF9jzIKemJlUZaGviTVTBcCgyKK1CRtT9jReMt3WbJXjMaPdARuJVlMmkivjq1xn62MnmabXxgnAuNkBn5c1kqsRboHs4cmTsYtfNbY8Gu0BYNzuQL1v1VkgvXUIZgJgb0xzBo/WR4TRUVeuDQCJFPnswAj4nAeSQTjWwcG8HpO/fgHhcPxR4lM1xZHtibdVt68cbuIBHu5/DmRqAXOe5e01tnzyD5IAuSM5L5qmUJqlqi+wK91EAbKHM+Z+Q56wq65i1vfE4OqsvsnudyQAAQsFaK4mA49uQs7+he2ccco53Sp1LJFd5AHDQ645Ces7XxMyyHQNPSqTpgw3OsXEcYtchkGMwcuhkk3Fcf58HDnX3AujxQGSXS01W0x/Z+Hx27DCus1HW0bNTaNRhXF+k1CYXAjdHIWdHSpKjep6ercP5SM7p6d7bAZ3ZOXh4f2SLKsqNw5Ej7h8wO5XdTwVVBHDck1jmaLvfBp3Dv8OqZmScv97IVnKKXTdEyQaKgYg7I9m1YjuVa2sBZkdB52QIBAyB/ucjp2cuIF47VL2kp13fZfs/AOWnojPLg51TeVxvJGucQald9VrkqP6UyOM6kWDPsXX6HnBS+HbHX05BAJasunHx/6Pg8Z9i+jDRvjkY8b4sVcAcH2iJznxfmS+A4fZFPC9L0DFR525j4zyt8HwE0hGSEg6uY1yvs/0S9kZw+O+IEr0lBQHa2vk98LC75wF4f8LJWznWDgIvPOaufSXD2ShZ3vvIj3VgCj3r37vIxtAbyTqB9/RCFaveR3bxVzLQG4Ay0U9BAIPHkXwfMqcvB57KsU4KND8IewHZaQKQYlt0blyZmeaNlFan6IRsUs0RkGIBOocfJVaQT62M8wHynxzo7l+MfIEhcd3OSPdP5jeUVsKuRmfV8fYNp7lnoykDaq+AXlOcvRvZje5H8kYABLVCduOPyZes5hliYFojYiBgqAi4HwK05ADHHYr0/hW2zye7Z8MRMOgGMiQ2LEM7nB0/QnaVPShN4jgGySFZEjnanrgQyXMDkGx1ofGA5GRx66A5BdkZQoWjHZGcOrc4DhloXYDOveDDORHZwULlo85G/w7Sk+O2L64/FLT1tFu7IUFfMt4AnfV/j3SakwvPxm+KMUUYipW+TZxeiAKe/kCmBA7WZjN0/v2Hzed4oo41Btk7TiJjUgxkp7nH/u6O8BNXUBpwuT2ynSfLVOjs+B0wxa5rHC+4xvo+OZVOcS24cTwG2aMGI5vyMiSTPEw+u1FnpNOc5Gnb39MR1mkh8qsk43/K0B9FKXC8KRH7dC7S8ZJAnTZ2H1l7Pexec5QQ5FfYmUymBO5G71l0VpxBDDLqhmwt/wpc6N6v5YuogGYrJIcevY7nK5GPdSLSRVITVvQjBsUfTgzkOsba/xVmb7T7qYnjBiC/WKj+WY8ou12CeO6JZf5fiuzYBgUABDB80OdCwMyfbG/Mtb2RxT/u6J+MdIJaiTdR8MNnuGROFdLoj+wz1yN5ONjjW9n9FVh1XPIl5fA8ZjXwpLsORRtGIx2gQw5aBZpXIdlioLu3Iy7BdCLNDrYeLkf278DXOyMb1SrK+MYS12pbCrYgZOu4GukdFxj/2ZM8ck5YN48gnhZsZGeh8+QgpDePtesc66YPsjmGs6ohkluLwWo7G81U7EhfdG74IgoNkVxzi43rcGSL/zZ1TMvQb43sxd5vFQLK9kI+zyTMoe3zYPNrimTx5ZQmdmptY5okcyCeXYP0xpCwYiSqGOUDAV/C4QHJ4Hck+jQG2tydR6mfakcUmJR0TpWhuwgl4QhJfybZOGbBNDg6R6GzKhTjOBb54w6z63Y2tovJk3C8n43hhYX7RyO+/QtKdfTUalWNkM72B6N7onvWC51RcymtEpyEp7J/f0SpjfMBCgm5kf3qs9S1g3jqL7FEJ4Vn9yL7xyXuXpZgJ2trRugD4rPXIf4WcOQ7IN6b5MdB+JQvgCML90Mg4AMoWH4YOo9znBuDEabiaWRf9Ekr1yB53I9rKk8NyfFDsv0gg5yAePa3RGxcS/Lpql8DhwSaiJ9fi2zXH2PnCEqWl1Mv746qfobK6vWJOtyR5Dmntrb1d5xde1/HoUg3Pox8OONByI5wN+LfM4lYtSVI9z/FvZ/kh3dz+A4uwYHdPxhhGn+B88eRp4jL1cBM+7szSpL1OJLdmiO7471kTPz71/jb4h/wv7/ChCgC1mc2Oh4dkFe5d3JmPLsdZcAIgQ2zkAO0VrR4Cl1iMNciDORo9y+yjRkquwxFgmRS1mPkbPi4cADeixNQkYHnQmRoGZfSR2OsnyKDcTtkIAvgoxbIeXyfjfVT5HECdkKG1CmOzjs4gCwCkU9Fkc+7ZaDZHCmni3AALnsWlJGJSEEelkIPCRE/Bm5z9863OQtO+S5IiNubxBLHjkYL24N3uXsvAZe5660QsOJrEg1ktna+QELN28gReD0KIBmPDBFnpdAo0OuMDIun23UDZHwPgMdR9h0hGDiHotoKARiO8m3a/TFIgfsMgZE+IWbuqiQDwCDb+ydQAEvaPrwHGYz2NVo5SlV3p7QSpy9B/g0yVJ+EeE4WcDxSaO6wfgSg+mikDMy39ROMH1mCKozmq0SDZjebNx8I+CmuSkcGmo8gZeYVZBDwjrEVtgdbu3upxvGQDTfws95IcVxFBB5tRyHLfAJNX/njAlsfwYm8FzLIZ6niUKRn1zsi3rMAOZb7IiW1Zbn3E2mPQwaNa9FZMgwBHKYRnVhVJFTItTn8ikIWWuvnNci508D2ygPETGi5gAA1CATX266bIGVxFeKr75GnVP0hSDZ83frls6cNtPUZHFi5wH99bX0uRzztCSTbBXn1ZCTjZKsAYO3eQ2lQ+nLb9z2Jmdc+I/0sDtXxPkWVzIrA36yANeRUGVe49xUyphxk63QkCpw/g1itJ4dDdwVWwYDamaQ6o2CDpygEISeM68co689gdIZcaM8a2jp6xO6/RzrwsBsy7odzuA8CjV9JlHO6Eqs8JfNxR7sNMdvvPnavHzImTc84f60pdQA2R4EqS4hZG9uhxBI5AoCC8WgPpKctwAw3SA6fgcBdr9nvgNS+IsPqSbYP6iGD+SRkwApVVrohXvjT1D5ae00RkOsH2+93YoZdBKaYYvTH2zfl1MePQcHb492++RNwc0YaHYrXyEC2yK3XLigT2NfkDeTshQDwp9lcHor0/8XGF0L181QgR2Oj8xtbLz774FB0Xi4iY3UlHEANnb8/JgIPGiEdslbV0wR6DZAzbJJbm82M9hAka72OZJwfk85TeyDH8e1If+ppe+Vkuxf245EogCTH/m9PNFiPwFX/sPXyOZEHeTk6B3+9EjngGqGzYqXROx05c35BHkdOU+QA/BwZrpsSg/D6IVmgBAiZSK8TMUOsH7PLbX5H2Ts7kAlYTalTutrG9GwEnPeBgGOQjJyU6ZxYrW4VpRXWgwP0IFx1TjLIx5TqoAGg3hXxt1sLfXyfBCAH4tknU8hAjZxgC5BT8AwU8PQz8iVx+AirrkShOqTR85XIUtdpyJ58MtGe60HGpwPf+XWV2kdrZyqyVbVBTr/VwHXueTvr/6ck2nHdOpkBzHD3q6hdEfBiEpN/FWj/BJeJFsk5XxJtq1dZH3NUVhuIQH8LkI/hOyRbBb3xbUzGIdHeiPSWz4mB4fXQGRiySQ83egF0lGPvdw77AQe4RU7qT9FZ3A3ZIHLx1Ebu72okD5yGAA4+yDLw1NRqdV0olaPC2t0Rycu3ILngICQ35rCNtSCehcFmvCuyGV3j9sYYxOdzgeNmIpB80WkdKpL0yLV2QjvU9jn4SuQNkF0pR/Xf24Ev3PVIJF91IILm6iO9PMUOV8++exUuMNyeXY7AK8F2czeSXXPIba2QrH2ju/cUBtxCoNJ7iZXeUisqhCDUmei8bYJ490NIh/wJsVpVcxKrVNv/7YfsVMcV7oe9ciXSo45CungyiBudhzOpnfCjLTqjH0Xn5tVkSFZla+MHa/NUIn+9EvGa0ba+xqFgyCR9A8kY/ezv+3HJzIiBgI8SQU7JAQ6u/SeRz28M0tWWYwBduzebTOAm62etCm3IBnA/AiLujvy4X5OxgoTR2QrJMk8SKwKej2y8OSocrS84LgTkn4/sn9kSKjpaUxAALwQdnohkn1qBaglrtaHN120UsBrW1x8T5ZtjyOD7t/9/Kzo/PkJyxpVEG+vp9jxndZMDgDvs76LfKthxjyd/5a9JSJ64hZhsKATmDEEBwReQXgEs+FGPBu509/czfvA4sqcch3hqDj4eKnKsQQFNvex+kOf2AV5y76cGqnWzNRh8UfWQvf9ZJKeH6uodkZ4Qvid1re5AaYLxPZGedQ2SdUahc+ssDDOTQpNo+/M2lUuRDXKIuzfW1tYuldKydpojf8lpZZ41sTF/GNN7MqybQSiY4Fhb+8spxXKdSKx8WIWrMJdIt42tlzaUVq3zvo+bEXg1Fcg9EPkRpiDb1C1IJw5y8Gpgqv1dU/yOCui1A/4TA28jOeNxYsWqTkjOm5hKq0C3E0pu/CDRT+3Bx2ORzHUaMYA9ZW/0QWduVzduxyMQ8ni77oxAwd+Q7sfthHhbsA2PsHkMVVaaI7/SPBQQHEDQlZ7HHpMSzsNqZL950v4Oibo/JF1PbW/f3dl/t+3R6RguEGEbf566LxzdwQgfugbx0ReRX6CxjenZSLfKcU7VQ3rodcg/NLTwvDU6Fx9E+t4a0u1xvdGZFBIOX4sCjMOcHmlz+rDd/1EKPdfPOdZeE7teDSxw72yFbI5fpIytWycLgdmOfieEdxhg/95o6/ZBMuBjKE3kEnBVO6PzywcCbo9kk9TkMV1QIvEPkG844BxOQ7rUyXa9B9LPk31V9v9X4Wx71A4EDEnt+ifS6YBkY8+z66MzfxmS0Seg4JGfkrcKoE9QdQOlgYBnI36TLRAQnfknIH0jJHU6EuGpD6G2DyQVp/4JsnN8D9zrng21vR/sSMkVzlzb01Cg+rFG4xabv4a290Py4Ry2+GCv2pPSxGO9iP6yk22v5CgcM8j29EvIx7ANpYlcBiHcb057fIlshs7CnyHdpz/CAH5rdD8gDx/fHcPgYT4AR78ayVTP4eTUDGv1MyR3T0BBnd9i5way49zn3q9PmqwacFtrk6oY3SrEy78FLnXv59gXg5Bv4XhHM+jhAf84KxdNnK/Bzd2B6CzxfuodEU4th71qPHB9+H6j7XXII5Cd5wzSE+NubXv6RLsebXvhNnT2tke2nWAny4H97414aQgcrcEFAyP57plUOq69MG9XA1e7+x2JgYA9kW9pCpmD4//aflv8A/7WfyjQqXXh3l1IuQqH9VyUqXvtobIJ6L3k6E0H/o3EsrGu/b5IAS9WjwuC4+XIYXYqUpAPzEBzMFJizsAcHkhoXIgUu60olLVPpHe+HUhBWeyGDvwdKAWuVRENv6kH1nZI4T7L9XElArFchpwrSeWNHa1OSOhojBxv5yIj9aGUGuO2y7humiJn0TwigGMuEgreQobz5EO4QLOR7bMLkbJ4BHIezS281w8dngH4WKnRoQ06dEMWgIZIWFyGAY/QAb2kkvbXQbO/HYRXGr2HsCoVxIw2K1DAWi5gVSNkUAjO8FD29yXjN82QwjwAc3ZWMqbEylunFu4fQhReD7Y5/QWmsGfYiyOR8utLfgdFpAUymIWMrlkCZGxv/AWBUlYiIMU+SJGchRTLKuREDkJdpeu0rc1RB+AV3xYSLD/HjEp2LznrmGtrb2TU2A0FAs7DMjzY88XI0VI/ZR5tjV5rvyaFZwNt/r5A58jHpFc5rRVwaH8XAwH3RYpjJ9LP//UFyM0lOjhWIENkcgZJygcdzicGAg5HisBM8gEd5gK/xlVXsfu7IX7XJaxrv5Zz/xwPCE6xbsRA0hxK8jxb/zsiA85qBEQKVRefyTGHBZqzkAO3KQKK3otAloOQ8fUoMoBVUWB1AMJcT+0MS4uR4f/HyDiRZMhBPO4rZGA8AMnFR+ICAZGx4w4yANZsL55g8xayUj1p+2B3dDZmceA6mh3d3zdSWqXGg1cPR+dWMNin8NWGRut1onw8DMk1Pdx7jZCu0KVSmsTqgl+hs/EZJKvugsAbN9h1PyQrLCETWLVw3Qo5GZ/BQHO2nkL2ulRjTlPb+1dRPhBwsV23RwbkHEEA84E/uuvRSI4sniVtycBXkRz6us3jHcTAu7FI9r8DGa4aIVkoCeTs5wXJUZeiqhFjUZDO7UgfqEGyc44sZ60oVNlCjs0/Y7oNMuae578vpX9IDr+ncL8DMojNQcCEagSY2SuFXoFGMM51JYItGyNd/QkEAnyKTEH5SH/7AcnD79pe3wnJiV1s/yzFHK8JdLoQ+ff6AgEbI701uaoCkS9PR3poU+vv/chJvxKBYxvYL8d+7IWAFX+P7AvfoCROs5HD6H7E1xtSBlhSIc2OSOb+FToXX0FnY43N4yNufHMlcfD8tHPh2YX2HQuJlWxyOQFvRHLV3Ui2CRXs97KxzVL9D50ZzxArYod92QDpHu8ivr5Wpkqk2c7W6A6F+90Rb1uD5MdpZMg6jnjq3USbymnIWbZj4b3VSAfJ4ehojByc04vryPbNq7Z2H0G6cpKNE+nEdyL7ZtGe2xPpAPda/5IqjlibzVBg75FIdrrb9kJ3984gWzfJgdU2ng9YH4JMOgCdu75SzV22ZnNkq+xIlAuvs9/bwMLCe1UI+BRAyJXqGx1tbTZDAOrL7H5wRNagM+ojErPGO5ptsKzqyI66deH5nQgEEQJVemWg2RzpMqcW7j0BLLfrSZjDLnU/InDYCygArxOSKRYV3lmK+E6SzSjMPzoPf4xLZGT/1kd+jbdRAMlL5OGpbREfH1e43xn5A1YjXXIGmSo5IJ7zGuWrUnSwtbMMS0yWad1cixIZNio82xMlcfgaAWa+SB1X5Bu7kpiobjjwf6gdCHgtAo41SFk7CBh7Ck4nRAmU7iq8tzOyfyTJqa69alxCChvffzbaX9u6CjprjrNxGTF5URN3/0GUHKsayRxZqo1Z27ORvrqn7fN5hecnIJtxs5Q+ojNpNbKVTES881Pkgzga8fAFOLBVYr/CWTgLV3XQ7k02HhSAwTOQ7pwj4Wh9JFddi86kCYXnjW3/nI5sZ9tloNnE1sjPbG3egHjafrb/biDqXd0y0JuFEsf0Q7JaraoCtp4eopAAIYFm0BX3INpsRqJKta/ZejoSZ7fLQHOW7YlatnZbszOQnpoM5mbdAYfFQMBmdp1acWBDwXEvIvlgOLK7tk+hZ+2uK+jwGaLf7WSkl4/POI9jkP5/AwWZG8nF12ekFfjOmci30oTI+z5BQLnJuAq9CbS6WNu9kL/kH4hJXKrdnjkSB1BN2fuhbfd3X2QLG2jXDyF/8TJ0Ho/C5P8Eem1t/kYaX1lp94PsOADZVx5EtqskrIH939ZI9g2gyittXRb9ZLX0ngrptUG+949tvloguT9gGw61Z8mYH0ezk/GUzujsuBgFbT5ke3IB0qm2ykizHeKpIRmvB8pPQQDvm5BO+XPyJKtrgnTk4FMp+lm7Ilvyc0g+T/XhvA/8xN2bRqnPf2skxyUFOBT619j2ycvExIZhf4TglSCrBgxUpfp4IyRXnFa4vwTh15rZfnw117qx9u+2cWth87m08PxU+4bk4GokowWfdDPjP48SbY1hLXXCEo5koFkf6YZ/QfLVq0iGG4KwDT8nJow/gej3SFmvo5D8MpXob1+JfDrH4wJHEumEQLXVmC2jMI41SCZ/xv79kDz6/2B0Fs2lIBsazUPR+XUZ+TFy59qa6WTj+xTSwccgue5M8upyWyPb+NWsp+oWsUJPpX7jahvPNUTbeDUKvulJDFIJCXnaVUrP0e2AZJxwLs9Bdrnryrw7COdnqpBewBudhuT8fug8vA8Fq60kYi3ru3WcWjjiAczO4ca7Pgoi+TtkF7+DDAHAjsaTiOdcZHN6q/V1NsI3haSYJYG0FdBpTyxacCcWLOLGbiTymYWkx00qoVOgGRJvHuK/m5iAfBGyxd9BHltcSdAh5QMBw/44jzIVXjeSXlfceWfr5STbI6HS4nFIpjucPHbcjuicD/7EBkgHH+/euQbDP2YYU69vVNv6DHjUlSio6kWEcdibgo5ZIc0WyJe4q62Tl8P4uneuQLx2PunB8c2Qv9QHVT+KAgHDWmpq/ZxVCY0yNNsin1DAGtdDuv7HyA84xO73RbpmqPSaun7OXNfaQGdlD6Q77pRCx7U5B4c3tnvdkTx1BuJLfyRPotpqG7833Ji+BFzs3pllPKdBKj1rr4ntv2fcvdcprTR+FYq9qCZPbMxrWGIT3yYKsPwDwqjcTqaEPNb2/sjeVku+J+o0h5MHb3QILqGw3euGdNQQPDobyXXJsSM2hqdb/wKu/0lKkxqPsXFtk4FeV2Jxo7OIAds+Ke9UpIf0IVN8w1/zb4t/wN/yDx2Q1yAjVTGbwV2O4Z6ADIFJwmMd6L3prmeQAVRpbZ2Dq6Jm985HxsCg8F+Jy1hD5UKHBy4Ps36dhQSbb5BS/JQdYC9igMdM/ZyPHIsNEVj0DyjLwJvG+PYhj7DqheMdkYN4EjLK/cqY3Ec2vm8iYbbiPiJj9AcocOp6ZNhtYXO4hKg0boeCR1MzR4SAwybEgMMFSDB+DwGBQyayzxHYMoezur+tl71tDs9HxsfnCu+NxsDHGWj2xWVPt3vV1v7tSKgbjZTLVOeKBzcOQIrwHzHno3vWBhlDts80pochQX+6zeGXtjYvRVks7iFTphrkqH4eV04YZaf5PVKyglBwtQmicwAAIABJREFUCDEDYYqBoy9RaBxj+8SXig8Bvx1wVWMSaXYkGsSn2nj2QbztasRHVyMg+SWV0nH0QnDc1dbf1RTAE0iQ/DkwP8c8Fui3RiCfAJY5Ghl3ViHHY0PSK8eGDNu7IR53JaWAlaAMbGN7MgnkTJmAQ0oNKxdYn8MazZl1eH0BcsG424NEJ6e1ub6gw2uJQYcTEPA5KYCc0mDK4OAsmSvrYy2Ax6b6pez1dawbn1XtYMxZiwzw/4lkxb9Djvlkh5yt937uuhU6m5ogI8qvkZH3S8o4JRLo7oDkmRPRWVwrkAkZfFoTK9tUuh+7ozN3sLt3EpIHDidmsW2GZJBUsEp/dP7uhuSbW5HMNse9c5DROpJ8MurjwJP290Cbx9ML7+yC5MZko6NrcwwyWi1AMt15KEDnl8iwO5PEjMNGJ1Q0H42AwG8jI8tHRv9tJMfebOsmOZub7ckPKVRrtn1yAjK6tkcyx2+RsSnVYNXA2rsBZfv0Z3Ez24vB8BoMIDnAzksxIIDtyx+QXvWojWmuBCBhHvvb3K1Ajs3ViNc8hYAdLyNnctYgbmQgf5cILvyVjfVzyKGbw0DW2MbsEmpXrD7GaA5AQUDfkilpDZLVnqJQXRCd+69QBuSdcVyryv3t127qWiWe8w2xqnV2fScCOT9r8zuRPEEOZyDjcUja4AMBByEH5MzUfjl6gxCgqIXxgFtR1ZrVyO4wEvHTK9z/SRnPbkTHZm8bwxMREOEQZEd5AcnkL5AhW2WB/u4IBDAY6eZf2FwehoCCbyLAV045yzvFkpNv1YHeYQgM1AidS8uQ4/gUJMdej9NpK6QRwDDtkB3wfkrBDmHNbo309SSZyv5vFZIRlyAn0ajC8xZI15iKnLpJWccRqOE4CrYLBKT6LLRv9/ZjPaCSjaAZ9Lc9kFP6EvfM87vtbQ33r7SPNndBD97P9sXZRAd89nVqe/5gXLA6Cqa+G8nlPdz9O/0YJ9LdxWiERCDTgH9BvPYx2xtzKQMyr4DWAJRMaS7SBdoQg30GuPcmIJkjuYIMAqKvRnrGDRSCOuydGgQoT86sjGTG2QiIOgrJwbUCi5Fd5TucvSqRbjUCGYWkfwGI1NLoHIvsV38y3lPRGjY6IXixs43tF1iCM/feUASCyKEbb4V4XAvkM3mECCqoJvoiuiDeXnFltUI/OyCgzxxqBzc3QzbCC5HummTnRAC4UEFhfyQ7nuyeh6DVYEPLARwLCSlPtb1xIqWVD4M9bgyS/wem0ES68GxisFo4C2sFAqLzKskhj/Sar9A5/H+BM93cvohlIEcJSH5PelW1cgGHryL7wlvoXGmP7BFLSQ9waEes6HMPLomhWy9jiYGIycARDDDurq9CgeuPFt7bCSXI6J5hDt+lNLi5LSZTEPXu3ZH+mgUcY21OI1YXCJm4X0S2vzeIANXu9m9qxvG7kGzVysb1OmoHAg4kj17cCbjF/m6DZMOHEQ89HvmvP0P6xuck6hs2Z8GfssD6+g4Ckc9AcmuoqnwSGcAqZb6hj81fCBgPgNHzyCCjlunnPGRHWSffJI9tbH0BhyEQ8AUy+XCt3fUFx91NDEDKcf7XKejQrk8lf8W6EUjXv97TR+f+ZRnaDwF5IXFENQoGOhLx+E9sHc1C51lqdbNQiWMVsvvtgXTj13E6HcIafEU+XaOv7bWgD4fq0XNsjj9A8sEJSC/vnEivHpLRrkO8fCHS+eun8rP10OyDgKrbFe7XCgRE/rmkRI62pz9DMv1YpDd9gKvQa+8di2xK7VJ4jVs/72NnE5JnnrD9OZRoX7ydTIHcjvZNSKYKQQXeF7kjsl8fS7o87u2bH2IBCHYvyMXtkf+seY61av+OQfbh4LOdgvSrF9C5uQ3iucm+VXTuLyKC4m8w2sEvEHSM3W3fNCp+bwU0GyPsSDgjvT9+ue39aqT/DErsX8PC9W1IninujYE2zkn0rK0BSF5dbmNbhfTkq5D/JiTm2x3xuBy2sdZIz++DbACTkY3lMmK1uq+QX2xYBnoeszEK8dVpyMfyA8KwfIx8AM+krBf7vy3s377IVnSde9bQ/d0P8cHuiWu0PRHDNNT23Xy3ZoO9IdsZguz/w9x1P2LS+Ka2VpYhvOM0Mvj/bb1MIyaE7GLr9mqcbkpMytGQPInAuiD992qkh5wP/L/GF763tRsq2uQ4q96wNjvaOC9B/LSne29XZMtK0q2Q3+hl25P7ICzed4if/QjJ41OAFe7/ZEkAjgIBVuPsGMRzpSWyZe8ODE/cH1vjMGi2Nl6yvycgWflDhFP9mgy4KhSQd6+N4WOUSZyK8Aj/hGzLKUmH2iNZohrppEuQfFx2vMigw7k+lgQdUjsQ8I+U2l9S+Oo1Nk8+4V8VsgMuIWK6TiOffjwE2arOI+o7tyIbwAxbpz0RTi3Jb4x46DVEXFENOhNDst8vbc9cZPszRwBgFdI7zkHn/VwiH2/v1kpykKq10xf5UPoV7s9FdvJtHM2h5ElWuZWtiZsQhnKA9TXo/BcgnWR0pj72BK6xv5sjvdUnpgly6qkUEp1k2JN3AFNCW0ReuysxocxZZMBxW1u9UHzDTOvngsLzbZA/sksmeo3QuXCf7clHsEqL7p0z7XmSP47I0y4yvhOSDlW5NdrDxnZv0s+pjkg/a4dkjrtxMr6byylkKATg+rAnVh0SZ4+2/fdTdD5NJEMRB9d2V5unlRSKxdjztgjDkQNvPNjm7yRkf5tS5p0uCHM5Mlcf/5p/W/wD/tZ/yCi0CB3MxcC8Z5HwVYU5YTcxvTXA+YV7OUAAV2DGDTusRiAD70KUQWaUPeueQhMpOS/YYRFAazsgwe4r3MGPBJQeif0agIwLV7l784F/Bz6y6+ZIMFkUDo4MNF9A4NGgjO5l4/g9MQNnFVJikzLyICPUhyigoB0KZgxgx2okZFyLBNpfk16Np1zAYUtbr28BZ7h3sxnMrZ8f2+ERxrApAlgtIGZ6GWHjnCSwEoFjLayvocSxzyC9BgV3tiKxQpat9xuQYhEAHQOQ8rrQvTcBHcg5wLH9kDITKvANRELdFbjgW1vLpybS8tWaVmFVGux6TyTA7oAMSbmyqjdDfOwvRKfDdrZ+L3Lv7WV7NskY79pbggVr2fVSaz8YAsciMPA7JAZyE4E9u9o+WGrrYx9UJacNUoQaIyE5V5aTIJRXOfoLkSPiU6QUHG5rekwirUbIGRcc77sg3nMVpQFXu5Ano/o6Aw5x2cXR2fI94n85HPJ1DZBLcuSWobu+oMOHiU6zisEVlFZV9MapadbPAMwbjJzMObKqJAf0biS9QciQ+hoCpFQhPvo6OoPfwQDIKFA2B3B0oO23i4igpkbIyLIC8bpAczR5ALndiQ6pHZHj6t+N7yxAjvl5yKBzQuY+lmQUQkbHu5ADNwCrkoLVEHjsA2KQVk/b6+8QZeHA9w4ikxHH2qtGGSlvs3k8DinnS1HwyI9QwFGOTLW9kIE6yFMjkJz4ovW/EzI+nICcLElnBzqDfokFMaHz6S1kFKy2cV+IHC9/ITGQs0B7AgqGO7lwvyMyJIesg1mC5KytBgiUuxSBqXxmuXOBgzPQ6Gtr5Bh3byk6l95Fzrf+KMDhaTIYVpDc/aSbx8HIWXUYkovbIUDOPahCbi6DbnXh+mxbPz+nVJ7LZjxCjq87kJxfUs0AAauCw7dfJnqh7S7IvnBT4fk5REDy5jrXqorfl6Oftj+mYTojsgGcjmSf1aTr/52JFfbORc6awOuqiQ6AUSjpUPLeRzrVu8C5dl1j+2FiuLZ/zzC+k8MxPhGdj0fa9TYow/rZ7p2+KOgghyOnLaWBNwMRuDmcv7+1/f+Z8aJsVTI2Zn0l/v/uCLi9s7v3GAI8d0Pn4wokk7xNYjZn2++/wyr/IiDAPBQI6IO7JqCzJIfdrzVyYnSyPs1F4Iog46ytJJ9pTgYie8OZRHCOPxNPRbasccVnCTR7IcdGsHuNQzrUJYX3hlOo2lUhvb2Roy1UOTwM6cBnu/2xBwqo6p7aRxvTT5DNJuioQTYdi5xX55Kh+p+1OQDZMq9H+s62yJ5zt83dtgjoMRfJcjnGtC8CMx5bWJct7Tvm2TyPRTL6QYn02hGrRI1G+sWnSM542X4PIN34LjIE/9tebIoS412LAAchMcYutm53t/luggUHJNJsjiXls7k72j0LwXrnYtUzSQDk2HffjYJQQyLB9ujcmEkE4m9v+zXHuumMbAsPofO+KZLZHqEUkL8bkkOqSD83WiPnZg/kV5hte6EYCJgrGUdLW5d/j+mhKCP4U8Ap7r297V7rDH0M2aMPsevjkM3zRMzGY/fHFfudsHY+RufGWh7n+MBwBHCcnErL2utj9EJw8342j2Md7eeRXedXRN9DpT6V9QUcPgI8UXg/KVjN6L2J5ME+iKc/S+0KK4fa/mxEoYpNBTQHIbvCm1jCDbt/KTovx9n1SKSnpyaqDHN4il3XINmmmMxlB3R+5rAbtyVWbLkclzzSxjwEzj5AlNXXVs1JoHsZspfcbnuhBTEQcHd7Z0dbW8lVR5C/68dYtXoU8LwQ6ckBNNfT9s34RFr1bN4WYXKw9e13CNh8jdFegWwSqTaqspXx7FlIbjgzhcZG9HMRkv8HEf0L422vticNrLrOgEO/HhHvPY2MoCNrd33BcZdnprWhinzFyk6V8vF1VVUciUCqK5A8OQLxplpg5I2k5wPyPiDa48YZL/ges4shW0tSZTWUbORjYiXeg5Ctdhvk7/uTrdm5ZKpu7Pr5ObKZtHFrsw0KPPpnShO7JAUBI33jfKLPdAay6fyrjfMnKKDkbSQDdE7Zi66PX9leC8k4fJDKlchHF86XVL2xP9KpDnf3xqAguaOR7OETLSUlOHJ9fA2Tb+xeo+J+I57JyTZV25P3uet5WFIquw4+yR6YfTfD2plva3Uwwt9cTcGfiWT0u0gPkA/0TkUg+SHWvzX27yAkl1+F7HFJVZUKtGchW8AEpDPfgOSCFsSKR5+RWOkI6SwhMcW9uCR4RDDuNGKl3taJ9PojX8l1Bb5yg+2ZYDcejwJak/AN1tZA2xvn2Pq/jSgvNkFn5R3IF/cNhp9LpNkY2RdmIZluGNLPQ5WabojHL0O8Z3wivYCNm0/EHI2zNfR7DGRs892WRP84skP9kajH9ULnxXWF90ZjZ1qGMR1s/bnKrrdH9pz5RLluD6Sv5/DFB/zfOZTqxnciH4r3/w8lQwVA2x+fIb3tIyLOsRvCis219bMLqs6btFZZd8DhY0gu6GH3D0DndlKVM2urn7V9bOF+J6M9075jeyQbJPlybUxfpzSZUQ12bhD53F7IntWMdDtOX8SvQ9K741EC1QPCOnJjkbxWra0WyFd8o7v3AqUVbHvauKb6VHxA3lmIz31j++UyZLO6Atk49yG9iEsVSr5xMzoT6yO/8WKk1wTf30R0jrQk3a6y3qBDSoPvr0wdU9dWAyTrv4irSGvfcQ8Ziw3YPhuBZIy+RvdCpNN9jwLX3zZ+8BnpBQf6EzGMPmiyH7Iz/pnSamfJeFVrew6yyTdHcQDfIHvDE4iPvoHk8y+RzSVF/++P+Nskoo/c6xvXIpvAMEpxiSl2qoHWhxEIh7vQ5uvuwnuXI4xTjsrKrVBw7yK7nopsDhPdOyPtO5KxsSigKvhPD0R4uK3c8xBf8RqS61L1uPaU2nJ6oQIDnxXeG4H4X45kFa0oDVDbE/HUt8vM98Ek4n9sz79E9BGdbfszBAIGebwfeeSbAYg/P4V08h2Q3rjGngVdZHt7nmpz7IXkt55If/slsRpmDdH2dzuJcQaOZjd0JgV8cTvE797E4YuJGOikYHyEswtyxbbo3PjW5vE1G+sHkS1rCZliAP4n/Lb4B/yt/igFpwQgxzmUCgUXUQCVbgZ6x2ei146YxelAYLl71sIxumU5+kjMcnQUCuR6jWjk6I0ExzPJAP63NvshQ+KlSPH35U2nISU8W+ZPa3cgAqGchgTTe9yzoQiYfxYZDmJrs6ExzZfdva/tsLqD6AC9ACnrB9t1RRmkWX/AYT2bv/lImcyZ9b8xUlZPKfOsOVL8r0VCzseUqUi0kfT62/gdipSaZyitABQO5fPIE+QQsuMtQAr+IkzIR0LBcmRY2QWBng/MQDPMZTDgVFEm0p8oIFec0REZ+ucTy7gH43Hrwns7IXBFFtChtXkxEqr+hQj0D4GAxyOj3S/JkM3B0WyIDGR3EoXzJYj/1QLEVrpXqB0cN8H4wX84PvAKyiDxNXkEZB/M5Z1EPW3t/p7SQOAcmYdrECD3lsBbkOJxPTDDrdN/JB0AWNeAw52RYpW8VtkCAXLW3mapyseGqypejpSBxYh/5wDktEZGseTKRXWkF4LjD0GBP28TnfK7oGz10zLT7InOjVoODOTU/C2WsTsjzZA99iKi8r293fsKGakvRbLO3NS5XF8f3TsnGO850nhFqnHst9QG3nVDcsay1P1QhmZzSo0q1QhofBMytg5DBoDHkWEuyO0pBrlQwW0VytS0t90fhc6tW8hQ+a8M3f2AfyACSYbbvpni3mlMBoe8teUzRe2EALMn+/FDxtfzM9Bqi0t2YPdCIOASomMwOMeTnFVIx/kMyVYvEQE/1TZ/b+WeP0f7GCSr+Xn8jnhuhrGtVclmI+n0wFXEw4FTkEPuOaKTM0tFRd+W/T0O6adrAwHt/sVE+SOF57SjNIv62rFDgL+7rN/jEI8dl3Ees1QwrQOdhuXmBck0f0Sym3esJAHWrI0LbV+0Nz4wCRkbd3Dv7IUcAzkcOX2Mvxzu+lyr0jeSBz4nQ0Cea3MCchCFQJmhyMh6YZl3U6uOfILsNy8SkwFMR86pr4GL7V4NidmjyWyj2Yg+fmx7/i1M90e6+F0I+B94Tj2cEzSBZkdbEz8Ad9m9Vsj58KBdb2N75ZBM/RyMAI6zjQcNQGfhHGKw7M4o03JSMIf15V0sOM7db1a4Pst4XOsUnuraOxg5Ur9z+3IcchiHLJ07IPtAjgDZRihRwxXErPhHoPP/cGQ7+xar+JRIqxtWiXo974xFwWoX4gJ0KqQ3wOhNQSCKf0YyyI6Ir15aeD/L3rU1Ma1wL5z1bZDd7EEECMghG++GZO+QyGUECsZZgewPuyJw3JnkSeTSCCUZmYl0gWFIz/geOSOXIFvOG0gvyAFy6m9tnmx7/1jE20qqYGC2Vfu7IlnBrZvJCHDzr5jjFAXKrbb9EiqC75Np3bRCuuNvkH5YHzneZyG7RiN0Tv6BDKBKN65XouCUTsi3MQvpwqGK3U6Ip2ZJ5mRtf2ZrJVRVPgABWfez9fTL9fGJjaTX3PbBQhSYWmV75QaibjXa+EMqOK4RkoFPKtwvBnONROCc7qQD8icjGafGfh/aGv0S2ajaIHn8MaJNuVIbbl0CDp8DHsk0dwHceIK7V43OpieRLjkAAdV+SSKA2+2Jj5DvbxQCNPkEFdORjfUiZAcMCTNSeHiYw/r2ewaYV1jDpxu9AzLQC0FcC5DeVoV8VjcU3huDbIPJWZVdmz1RsNhl6NwaSQwEnI7O619giRAy0ayH5Iqb7LqN8YN7KGQaTxlX+/+tjLdc6u7NRT667rn6ZO3Wqoxnfa1nfbyZCPJKAnDWsZ+Lkc2vFQo6+DXpPo66Bhzugs6QpMzxbObguHV8w/qCDmtlI8+1dtyz/sbf3rF3kny41A7IOxABuLohUNmblAKtc+hSgaeGZCbNke04BBbtjIKRTiOxYryj2RoX4Ojuh0SdM4Bb3bquTukrMeDwTCI4tofN7UokL/e093YjQ6K69fSxeeH6amQ7b5zYxwAA/hkFuRfpxi/bPCZXU3fttkNg3KBzN0Rn8r7unVYI+/Q5GeQO1+5TWFUxN45vEpO67YH4alIScKIPcC7SN65BetVyZFPZw97bBfnkU3Eqgd61Rm+x8YURyMYwr/B+2xR61kZV4foyxL/3Qnx+GbLzPIF0rxwJMp619dgVYZmWUDuo8nAke6Xym/5IPrsMBU8+htNhUHDeR0jveZM8yThbIJ9jkKVa2npcgM7kBvabj2xZIflKDj/H/sTKf41s7dyDzqoO7r2OKTRtrb6JzpCzKPXpDEF4malkqlJj7Y5HWJj/Bzjd7oVAwBAUsD3yPeydiWYVAjlPB9rbvR2RHXcq4m/fkMf+18v22LFlnrVEtvglqXQK7fZAttTT7Pp4JIdvhzBrLW1/rkJJAdZiDiukt66Aw85IlitJMki+atWTqW3PDDbHbkb3XuTvCUmtKu1jD3QWBxtAQ+S/bV94bwIZgqpdeyF5zFvEYMPjkXwa+PY4ZFfJkjze0X4PpxsjHv9amfcqHdNyAXmnI7/VcwgbNAPx1BvI5ItD9syTkA2wGgVrnmtrdTyyHX9HBlvcOvpYLuhwHyS3B7xO6hkZgkMb2Ni9QGlFwANsbHPoHCFB1oNIftwB2bCW254Z697disTEEUjHf4cC1piY3Og04F77uyRRRmIfP0W29pAApBWSP36CsBatEc8bSHrhmHX1seiLW4wwR8mFXIh2x/3dvTFIXpxMAUdJhsqKrq3WyP41266nIRnnKSRP/pR8mOo3kC+jj/XvEZQkz8tS2yHsTKtEegOQbfphJG8Hu1Q3lCRrml2HYLUJmfr4CuLXU5G9PeBxHyEmsBuN/Fc5AivDOfUeUSc/C4ehQrr4H0lPHNHT1krwFV+B5I4aJNc8YOtmGtKxDkikF/AUkzFcIbLX/hqn4yC+9yX5Ko5/js75t4kyVRcUYHmLrdGhSL9Msjda23sgXSqM60B0btyHbFd72T45O8ea+Z/02+If8Lf2Q4aM4AAvBuYtRspxJ2J20vF/TfSs7SokJC5FGWKbIAfy9YX3RqDsJKmR3G2M2Sxz9z41+hcgJagvOswuIR2s0geX5QyBp5YggH4QjOcgJ3Wu6l9tjUmHiooNkXAxx/rUCgFll6PDs1EivWAEH48UmZnoYJ5vzPxAu97G5jdUsqtUqaprwOFkpLBny/5vY+crqRUrgjRCAvT7pAcdlKs42B0BSeYQsxGMRsacpP1INI6fb9cDkDPeKzd9kDHw/yMPyKncXL6Ky96CFKpQdSh1TH0Q1xHIifEcUdhojQzjn5AHiOcB3O2Q4nYKMmYEA+oYZMD5C4mGavu/awO5XJ//ESmvoSLgQiR8JRlU1zGuoWrVBOMBc4kBV+1JzI5n7ZQL5vJVYo4HfuqusxjkXF9HEAPiqpBgGTLW/JbEKqflxpR1Bxz+mTzC8ZYIkNtsQYfUvariaARWG5ZhDjsi49hcLKNrrnW4DnqBn/psZkcj4FgDxEvvJ2auyQIeMRpeptoOKZBHIYP8qcSgnBxVVdYXdBgCq8s9S5nLcn28FMnH3d39k0kEViEH6me2Dz7BVRdz/T8XVbTIlVGtOXIQnUZpIGB9dCbf5u5VkSHQCRkdf0qUJeYg43FHxO/62965iQxOOWIWw/DvHqhyZABQDkcOwtm51g6SgYe6cfOBgN8heaA74uVfkJDkwNqtRufQPGonN2iInCn3IwPae+SRU79wc3go0jO2c+8sRXpXckY1ay+cGWEsD0HGqqPteltkbPTZc5MqHdg++C1whx9r9/ciMgFkrb3eRF2pGAh4F9JXd0MGso9JNKza2gzgTS/zh3OxCXIEPICcdckAedvnt/hvyDV+66DXBhn7tvPrwK2Nc8nsWHFrZxoKoK6H5OBzELCxBwIf/BYzgmagdx5yrvSw69WUJh9qjxxkPyW90slWFIIIjB/8HKsKiuS3HyicYwk0+yB9MMjZtwIvunX8HHCNXScHkCO9/34SAwk3kmZPJG8HnhZsYEORnvoY8JB7P1sQLeLhF9h6fcSNwdUIWPVHMug4BZrb2p4IYODBCLx2qe3Ln5NHz+kOPOeuj0PgsUexoFH3LEsCANfeFOvTR5RWy1iFnIG5+hh4dgMKgaHI3noPCjZcC+JKmUcsY7y73hfpkdf4PYMCAVNl4zZIRpvr7s0BrnS077C1lMWm6uhcCqywv4sgxPbInnQNGZNzIODENCLgeUd0/p9KBjBlGXo7I+DEOXY9AoEApxH18XrkqToYbJynEGXyHkb/VhQ4W03M5L57Aq3WCHjjbYtnI70u0N4Knck/kBH8a20fiHjqTQh0HAIBr0L66p/Iz1MHIxB5V7vuhwK7pqJg3O9T+Q2lCZy2RXaiw5D+upfd3w+dG1lsnAX6LZAs092uq209XYtsVT/kGFekO60hBhpcgGThj4FHC+8m6TnIhxPoXIeA1S8DC+xeFxTYNaXw/3IEq20o4PA1YJsEOiGQaTZwRZnnjZCv6Al0Lj6D6aiJ/WuCzr1H3b3tEb9u4+5NIU/VmOIcPoh07oWF9xogwOo2qX10ba4N4rI5G4J8cE/b3gzZ3XPocG2I/KUKBajcj86nW5BNpSWSHb8nUebAArko1YmfRT7AVe6bbrQxb1AJnTJ0g47YnIJciHT/Z7DANdICYzZUGS98x0pchcdcvzr081Vkm08CVZdbq+5eMeDwN2QIHGUzBsf5tVrm/qaqyLfetePeC/pBcmJCygfkrSLqcQcgO2OOJJWep4aEG+2N17xCYuW9DdDuCDzpro9HwNGXED/tiGS47TLQWlcwXj3ES2cinSfJVryRfTy98G5qlZMettfOQD7wJ3BJW+2dnW3tnE5eH+5SxK+72R4pVuDsg3SsXBUkvZ34psAT0Hk5BwFxDydDZTWEM/hHYgKsvtb+XsbjpiL7wyM2tqng0SK9PkYvVBgeiQDJV7n/k5oco72tyw6F+1MRrmmEXQ+2Oe5h1ymBY58jW1EA4bdASSoDnqsF0lO/I1GusrZ+TvTzN0F89ujCe7chnSoHFiec+5OQr2Z32xu3IblmFeLz1cinOyADzcYF2qGSy1Qbg21tjC8lQ8V6W6u/Aa62667Ijnwpposg3eNmdH5mSz6KdNLrURLekCSzF8JFnXZFAAAgAElEQVSTPYsCSZJtf8U5pYA5QPajZagad5YgTqRTXFegcQE6l1shm0DAlWWxU6OAlOeQDleDeMwzyC5+B5KrtkK8PtghUoLj1hVw2MnW6hKEq8rqI0BnfQgULdocWyJZ4UYSbGNlxnSMjeljuLPR5vIQHL4xde1YG41s7G43HjPcjfPDyFbwLemJnDogedTjG55HGCofDPwqhWpSiXR9QF4N4ueTED7OJx/ImiAcycu+eEtTo/sA8G9k0uHK9HF9QYdJlU7dHNbz347sc9fb2uyHzpLPyRBU7fb/iXZ9BvKBtUTyzWLra86ESo2JSUWqEC5nGfLZHmZ038Zkmwz0SgLy3Lg2QXrH8QgPnEUm3kAfv6bgC8eqhCbSG4h8qn+hcCYg2e06dFb1d/8nRb5pTyGhh43zL4GZdt3V9syRxECyFJq9kc22OH6HIn4+EwWu7YswMqn2/wG2bo6w8b0c2VlCoG8vYvLD18ljcxxka/9H1q+biDb+prb3H0S6wDs5aFrbzdBZ+y6yZYbkCmcZnYtsfyYnx7U9/grODoWC/gKGeyQ6r48g4jkrlW86IMzGCcV2kD38t9bvecgPl6QzWrs9KPXNnIVk4bBuutq9NSg5ZnJCPkd7P3RWhEDObZGN48Qc6+R/6m+Lf8Df2g8Jv08TDylvSNoHCZBvGWPNYbDerPRc2+2QgBGqU2xljPRG5CA/BglgqU65BkjZuAZFVU80proCGeCuwaoQIsUyWaBDwuq/EQ3ibyDH6hqk+IcMYAvIAOZGwncTpDRdiwTvJ5FydQISVu9CQt0elHEQbCS9UK1qsl3vYuvoffdOfaOZI/q/rgGHQ5EzokemNdrK/X0/LkCM6NQegAxn9YhO0UoP5fVVHOyOlNTHkcL+FXkcZDXIafssUbi5FQlTk40H1EOG1aRMdeuYyxlIIZ9VeK8bCiaZkDKmhX6OIFYaa0LMpvS2jW2O8eyDhMX9MYcJEkr3R0aWP1NaEXDX1P4RA7k80OkR5Ly9GRmugpPwRhKrKqxjXH1w3J7IuDOHTNkcWX8wVw3x/FqGzo6sWXIL3xKM11XIyfI0iVVO6zCmmyLgcEsEyG22oEPqXlVxFzJkVLS2BiBeurON6yXUdlrtQ76y6p6fLgSm2/UpCDASAmYuQfJUQ9KdcSEQfVcUkLYTAlo9hBTYnyAl8kDk1ErKOOToFgPytkeOnMORQrsdcqiem4FWXfp4JfmcG+1R5q0AlhxsfSkGAvZCQM4cVVXD2jkdAXKOo1A9BTmOQ0BCMm+zdi5CRoVA/wMEMnwQGayqiSDkpH2CAA7nEzMNViPH5pMoCUfo2yjkiOydqY+TbJ0EnukDAbdDctxtZMjmigGzkYFlNeJvZYExyBgZ9JIUPn4O8Bd3/SGSS98Cnnb3l2PVuhP72BqdCcEoVIXOwh+jimC+IuD3JGboR8E4J9rfNchY5iurN3bvvU4iOMa1OxVVaekRaLtn29vzJ5HMnJrNvR2wNdK9pyNj387ueUP3dz0yJFZxa+9Z4L4cY7YBegHoP8f23EhqO5KGI7B1cvW/MvSrcUEpyEF3JnJ6/it55LitbB02RU6pldb+7MJ7raz/26bQtLVwJuLXIfvfk0gvn2BjGfbjEDJVjkRBjm8DI92955BDvgo5IW7OQcu1f6mt1VrOKDZBcgfkBPgKnf9hfa4igsWHILl4ZAZafdDZGPSZkYif9kU2gYfculmeY62u4ztKKrtbHxcjJ1IqeLwDEfT6MLL9vWK8YClyJn1ApkzcRmdrXOIy5OSci5wOHxIT5uyGAg8OTenjBr7FJ8nZn5hYKtXJ2cLWz9M2hmuQzeEe+/cu8un+NcieuBDpjaHKx9UYYM6uD7K9klT919pqR7TT7ENpEgAvy12GnJMlZ0oCXe+Ia1F4touN7fmY7px5rYzEJd1AwZshqHNw8fsqpNEA2dzOKfOsLwKQfYrOlndJB6vWIBvAAixo3fb9W0hWPgbJ/DXEpB0pe6OPrYmGdj0EORoHIZ72FDEQcDGJldzW8x2NfLsIILMAVZFN5an9kJ42FskcDRA/PQ0ldfwcA6fZntzNf0vGPoZzK+zFaqTLvo4LOqywj1sRq1JeiQAcHyHA4WVEO+/F7v9UzAMorY4VAh5mIHBTV/feychuXp/0oKONDjjMMGdXEgONi7YGLycHnTaHLn44Ojsm2fXlSLb40voezuMkvXgj5nAnJMe2yziuPohra3e/xr5nEZZ0NQOtTgjU/BGSKZogu8IcZPO7CMmLo5ANNvhBUvjqLCSHd7a1/zhWeQfZAUL16rZksq36eS1cex/5EtKrjde1Mt4Yu5cF3LSR/ZxBrPKaq5LDJg04ZAsEx7m1urkq8tV17Yy3/dMucR+uLyDvZSLAqoPxgB6boH/XozP+Y6KdLFsyHNfP0HYINHjN+M4NSE//F8TjppDHxlkuGG+5jetRiO9da+Oeo6rahvp4KEoQvLv7PykyR0uElQiBBE0QKPUJagcbjgO2z9DHTliQuF3PA/4TlxzL7vdHdvngk8yVrKKYMDrwmioke2SrrIbO958U9sm57nlzG48knMp66C0u0BuD7B09c4wn8gHcggLE2hWezQJW55izMDbIjnp+mblribBkq5Ae+RYZqjnb/5+G+Fqw9y1GfttbEQ8MYODeqfSQzPaKa/NUhE95ovDeI9SulFPpmdzCeMzebpwfRbr4fMRLmyP/3yoyYLkQ31yA8HDbIN31PsTDHwXutvd2Id3f2BuHeUN4w0lIX/wDETPXB8nPh7lxyKofF+cJnVU5MBwtEf+aiGSKECz6CLLHf2rreHt0frQnTwDwTkb3LIRj+pQYKNcD8fZL7DrYXyoeV+oWcNgRYUmTKoAZvXZEGfswSisp1yPKcpdbf8N1pf0rN6afhzF17zVBPLxfhrXTCfOzEZNknoKCDD8h+qZORVWHk23jyGZyB7JNBd1xlj17G1etksSCI2VoFwPymhk/WIECgOql7o0N0A+6VlPksxuXOod16GP2oEObw9ttndSysyFZ6jGkU+UK/jkG8bfd3TiuIiYe2xkFIq3FzSXQaocwflXoTL4RyQE+gOsHdL5cR6aEg5QPyFuOAvIORWfxGYgPtk+cww318UyEbcy2B43fvIIw+dsg+aaYVGEnJMsmz6O1N8P296jC/daIv07K1T/X9vHE5BFVlPr9xiJs17O2Xvez+ylzeT/wQYHGCiQbBPtAd+RvSAritraqkZ74ubt3gtFs4u7tg3yqSVhuzM7ork+3Pk5DiccDVv48pMOtPacqpNcRq0CHcI63omDHUxBGrH2lfVkPzeGUJhQurpsdkN3hTKLPNVW/2QXZw0929x5D/L0vkjWaoGQSyQkOit9MxBwGfj4K2eLOJ2Pyj/9Jvy3+AX8rP1z1BqRorCIKO54ZNTZGGxT3SpnOZqW3jm9oRanw2MYYzgJ0kKZmyGuFALFDrQ/TkOP2cffOSGSAyGFU7UwUEE9HWX7ewQU4IePDfYX/l3IYt0LGoa2RkHEJCqb0mU9HIuBFUvU/1165CmC7IkdrKAHcGwFnk4zjbOaAwwLd+cDldj3T+tuq8N44ZPxIDnRgHRUH3b5saGtsNBkcqwXai4jBG1+hAMAVKGvcY5QGRFbKczY4l3Z/ByS0Zgc6hfVr/wZBoDPReZUq5ExCQuFqpExNQoFBr9r87Y0z/AeaCWPqA7kWITD3Gkoz5q9ECnmnTTGejo4PjpuIHOS1MqImrJsNBXONs3FPNshtxLfVIwYMbQoj7iYJONyIMc0ZILdZgw6pe1XFfyJPJY7+yOkVAPC1Kicgg9mVyJCbahgvx08XIWX/XQoyDXlKuTdC8tlkW5OLkHL+BNHAOBB4x/7OkQm4TgF5bgySyrnXsY/9bYw7pNBy9GZjmSrd/XUFAiYHHxCz8J1n1ycQAwF98M808hlVOyMnYBUKZHwKycchO1YHJEud6ec9gV49JIc+iow1/ZEDYKE93xllcQ4ybHLFOutjcySTXoTOo5DhsJqY8Si8l1R1yObxJuAsu94anf9XIl4Tzo/x6DxOMnRQChKfjcBar2Nyv91/F5gX5iDDmNa3sZpmfdgNyY3X2fN9USBgznkcCPy9G9f6yOnoAwH3sb4PzUDPj+sM4HfEJAAN3Dx2sHUUMrCnnI1nobOwFwI9z0Qgo13ce/sCD9vfyc4qxLdnur97Fp4fj8uYnUirLQKH9LfrK1Cw2khKjZ29Ecg7a4b1wrd4Q2Q74GLyGONDBZwHbR5bWNvv4HRvFJxXS3+tgN5WyLjfAvHUlQjMMMe9cyAy9B5Xrv8V0gxBjuchA/lIWysvE+XKjihzbN8Ma8evj9Mp6BTISX9yat9cex2IgTaTUJDvUOQMf43SiguLSdSrjKe8iXTRecgI3tr6Otv2zr0YsIrIf7LrOOXWCALJDMywdq5HMmNzWx+XIVDp1kSgyCLyOVVbowCDP6GkDgHU8SgKiNkFBR2GtdNmc47r+u7Vsa0aZCO62XjPqUj+uBeXQM3WVo5zsQ3iZ4Nszc5FdrkFCMCxVeH9HPJ4PQQ2WIqcx6FC1orCe9shmaDiCll1ma/CvhhHxszD66Dved/Otj+yJDmwNpcRHZ41hWdtkEzbmoSgHJvDIGvXILvGDUjn+dDmdxKyp7xCnooD9ZEc+hfEoy9Ccv4xtmZbo7PrJ/ZuPfetm2z/u+/rTpSFUnjqDOvjGiRTnWxtr7T5Oxwlednb/Z9N2kc3llVk8FWh7OariIGj+9m9lsSg9fMxXTVTH8pVx7revqMDsnt8hUtGWOlYsfkDDoO/YiqukplvG50lAWiYwzbW2rW9BwIdPWT9HILAnTOQrJUcxFHHORxr9LPYVIvzWpjjYgWJJNCob8f42O+RveYCJA/MQXaGlsi+eTuJlWMpDeSaj/Scl3BAVaRPPQU8mHtM1zcGmdurS2W835GxEkcl/cxFt9gOmQMOCzxukwfHWVtbKuiwLmvn16Rn/69rQF7oX5aK3Ovo33Rky21P5oSfrp8LEYi0MZIfr0SZ6pvaeyHhcqpNtS4Bh39GfrEe5PGpbkwfc1Qbqod0t1lAL3e/EfIVPwGcmnMeHY2ngGfc9dXIHx+AnONs/WYNAKjDd1WRGFhB+SDHV5Ct/A1cpexM37whesVgsRyV4zsRA7Xa2Jq9lwIwFiWt6J6pn/WQrDHerkMyucDbgo7RgUQbTpF/Id/Yl0ieeg+BgQ9G2KCH/f6vlKb7/3MRMD3YF09AwTgB9DsO6Rw9U+hYW0EWPxfJwBPQeRz8OMF/fRWyL6dWVm+HMEyNjc9MQz4d78Ppi2TzZGycffNnKMj4cmKFlaeRH6UPkpmn2P2S5GeVrtNK3klYq01tHE+366WIvz5JtOl0sDVURQYZwNo5EukY44nJfp7G6VFIH7+d9KrcGxtwmMMfH/q4FPk3WyAc59zCe9uhs2rwJhzTfdx7E5B8lxyEgOzvv0f2t70RvqEdCqIeZfTfJ1Z0TS2q0B5Vp6xGuOKSebR32iJeuzy1f3UZc/u3ma3VpGIcG0G3XNDcJtEfXR+zBB0W5vAM5P8/zPWpYeH9HFXO/f4/02juAZxNISAHnY+pRVX8XhyD9J1zUALSdkSM3BKcDpfYx7oEHf4WBQo1JjGx4kb0cTEWGJ9hLbaxfeb94LsjXeqowrs55rG99a0x0psWUrArIt/4ghz9K7Q7G7i9cG+trIpLXJm6duz/t7C9EAoonYOSqv8E4f4vRhia+jnoWRvdkG58i11PQkUGPkQJ1U9CyeuSsNzonPqd9e9olPRwINJvGttY/wzzNRKLOqXYqg6wvTfBro9DctUPjk6WIDWiX6yvzVcrt1ZCcaORZMBulqFdhWwmIWnkSSjYfw3SyT9AeJaA0d9U51Q91+dRWHLc//2VGast/QF/Cz/kCHsQq+Ji955HAWNFh/yNJCodm5teXb+pcJ3j0OiBhIz5COjXHjmt5xArcA1BILYcIIBLjKn2susjjIkPc+/sjQShBqn0rL1OyOCwEoHlOiOg09qKX0iI/ignU2fd1armIaPKZyRWcXR0NkvA4XroHoEO/+fsenvk6AxO+dRKLnWpODiQDOV4rc0+yNg/DwWRNLD5uxb4LyJovb7dzwI62sBcXmHXQ1HgwybJdurHNXN7PnD6MsRPd0Pgv+lIqAyg3/1JDHC2dsoFcpULqqxCQbJZwWob+LYqMgRWl1s3lA/mGo2U1mRHZ13XR+41VBd6ZAo4rOOY5gyQ2+xBh66fm6Oq4gAEQP8nokGjrCEaGV1Xrut54hzuhoA5i9172TKNOZq3AofbvWaFd0Yiw3HHDOu0LgF5A1DgUZZKThvRx1fIUx2rgdFbDpxm9wJIZSCSbS7PNYeFPq5AINXgBFhMdPAMwjkEM9AM52JLoz8VGXH6uXemBx6RSKsdAjE2R8aOO5GD4a7Ce3sB/44ZyjZBHyejQMAR7p09kE6Sy6gzDgVUHWnXPZFRZbp7/mvyVI5fBrzpri9CgOB27t4xWFBuBnoBANTL5nIycto8WXjvMGSkS050QHRkDEFgnODwr0ZOpKUoq9z3gT9sgnENMqMHzIxHGS1zVVYdbPN3mV33QQ6AebZntrc+Hp2DntHYn/VUa0MgoQ/Ik5GvPjovFhNBVtORw3qMXU9A5/YuqfQ28tvWAnJJPyO7IGDFddZeNxQstxwlUxiCDOWp2fF8wGFvSgMOR9s7Ye8cSJ5M7kWaLa1vL6KzI8jDIaCrZQaaQQfoWLjvbWPjkdMsS1V142u3EzOAT0ZBTp8R7UlhbHPpVcOMnz2AnB3vIHl8DTq7WiDQ/PBc635L/Ixnzl7Hs+E2BtmcADaG/wfZWG5EvP2C8A3IBviF7dtKHUfNyRDsXiHtAag6XDg3irbbkcZvemWg1cVoTUdyTRNkv/0Ql2GU/KD81kiOucquW9g6uRU5AY9FIJ0sNrn/zj/yJDhr4fjqo7iEJzjQlo1rK/u70r0xwPbds+jsD/r9pcaz/bppmePMcO31RcDNx1Gg4XtIpnnR1m4jpIuMykVzM66DYubtr4nVqe5AsmKoNHoUrqp0Ttop72wkzXOQntaszLMR6GzOFTy+oepYr9h450rIs7kDDheg4IIqBNa+vvDezshHVavicgU0ByJAxYsISLEA6QI72fVVm2Ld1GEOX7Y53HdTrNd1jX+ufiLwYnN3PRudw0OQX/XXSJasj2T1JOC4Wzs+6GgGsuN0KbzbmkQ5lYx20szrJktlvC3dzzp8V9aAw3XwuOzBceFby6zVTRp0uIXWzuYOyNtQ/1aTIbnhevp5yTqej0bnY2qS47oG480D9tgCffySTLqx8ejrkW2jkbvfGOnEPyZvUgUPer0Jl2AA6ZAvoMQ8b5Av0WlFvDVlX1I+yPG/iDbOhpW2/d+BnrX5NDEh1dbIf7s2ENDW6vskBh1RWo16MZIdAyC12tZOIwRob5pCy9ocgOx+j9j6D8HvJyMb0t6F93P5OL0t8wJcUBFKXvUe8g28nmNvEKsOhiD5UxFovFh1cG+ku+ewHTVGvpoHUALuNsZbHyImGtwT+RuS/bjW3pnIZzoVYXEWoKRZ99vzYdbvHpXyCmuntVv7mw2HgnzVhyD9PySkaF14ZzsKgWOp34iwjSehc7EG2VLOQzLAnki3+5h0fbWSgMNcAdahj8HmuDUKNlhk6+lQpD/mwgGub0x3J1asT/IbFWg+CvwjCnR+BNlYphBxDxejoOTUAGA/jxOIQWQlQY72bksy+XHq8F1rk1UlttOEKBtvVhzaRvQxyfZQZg7ro8CfxSjRWDiXJyJ/SovUvbgOmuciXv5Loh85C37b0V2793EJ4tzzscjfkCyLF/q4voC867EKZ5u5jzmqqoc+LsKwtkQdfAIKBDwyY98CvWXIltkY6XTXIdk00D4aya01qfsW+cNCBeptkJwz2j0P++PUHPwNyRxticUZmqBz/i0UMNcJxR/8CGEPkv3GyObYBItdsPafQbL+GwhjuDcKmvuYDIHV6Dx4FNn3rzBauyGc5Tn2zs0o2evaeUyZT9uDhyN9vMbG+liE286Jh22F4kWGoUDtN3D4V9eXw1E8S4OUfllbXVDC9IMxTIiN58uoOnZI4twVJcvIaufYwLf9tzo7/zv+tvgH/K38EMjxeeBsd+95ovK4IzLm5lIANiu9LTiuvZFBJURrd0WZbGYgoecDTJnNRG8GAviHaOvTge+QI2AbpOBkzQJqfTqNmPWrNzLgLELK1jtkCMhbD/1itaonMo/pZgk4XA/d3nbdBBmV7kPK8RsYsLrSw4SNqzj4EA5sXSG9/siwPw0Z419EgasjiKDZZ9lEVeM2MJf32f6YmNB+skBW4ZjOw0Bxdm8RCrAYYXt/ElJWq4rfmjiW6wqqvJwMWfgqGddNMfbrWTc+mOugFPpI8D6DTBla/4eMaXKAnKO12YIO1/ENm6qqYiekgB6EgoDWAmDX1SaZzqsyc1gPGXDnoSyPyY6qDdF095siw/mnZAg4KqybDQXkvUomR87m7mOBXvcy63UIcgYkZ+PcwNqp4f9n76zD7Squ/v+ZCPGQhBgQIgRiEIIFSLAQgiQEdw3u3gLF3Yq7pQ3uLi3SliIVykvxtrQU+lLvr/bWhXZ+f3zX5szdOTfce2bO7OQm+3nOc7fdWXtm1sysWWt915Iiay4y2H2HhPI/MsSda3OAQ86GxyF5cTgyjL1FmsjD6yFj4pFIxhqDnG+PYX5nkiSginId7XootayHQ60vf0ki8FhAd+2QP5CC8EkEDvw5iZyArIy7gTuD6zOtTn2M7vcoGa8jaA1B+7R5aJ8zFBmKrqUETiWRgdzKKpS23e1v6AjwAwR8jJI32tCuZ6A9eDfkLPCJM1fCeo4O502kFzgDrf9/bkIdl7ExfvQC3jkvdg4I5s4uxj8jS+16L4p2+BFBVu4GaSV3emngGwaH/I8y8xyBAF2/wPZUsf1I64DDG2L7rC007Xq4zQF3EgRYSkxzFgoscCdSWBfOeKGifHsMMJ+I5unG+8Vcsz/am09IxWPWX1tSc4hbC8kWOwKTbKw8Rc3QE+3UifZV45rRT59CN3QCHFnnm+Ygp4oUTk7L0NKR6hzMWd3G4X1IpiqixjcscyDD2Bn2/UsH93PqPEZRig6LnIBnIB1OSrlxOJLbisjQRaTVIvNQU5zakVPBgOC6FwIkn2/tH5utpg+R2a4boJnUSbuNNJdCoJwj7XoN5PC4Z+m96Ugf2bDuEeni3kJr0nZoP/4IcLI9Pw2tXZulagu01m6NBUtDstoPbHyOtW95FAP+peBXm7+iosE30K4nAMcH926zth0ATLExMbP0f7HRqmeSIEtDO+mGOtpytpH+Np+/l2LdWFBb0XL9OptE8hWfDqz4Ok0CHKL906rIxvAkcmzcAwXiSLEOj0U2vUPQXmYLJMN9Gek3NkX68RPKfbuo9GGK+aMBmuORzet24Fq71w3twx+3tl2eBNl/W+Od4N6VyFa0Ssz8UqIzCDn2NMXuFck3ZxEpa1RVzyp4NaS7gDkuKTjOysgGOqyId6oC5C2ofleTWAfwKfUcjHwtvkc6G05WMF5FdWyVnt3vgXQuayXuy7rAYmQDvBTpcWeHzxqkszy1YHFZ5jzmBzkWwKZOSI+ULDNeFfRaoVnYVUegffj3kW7wHdLIj18EvmrnU5CtagcCp3/krP4CJZ1SA7TGIjvi0cjf7gIEGv+MPT8K2ZWTjomAfjlTdAjaOAj4FzUn8xTrYznr4D5Wvyml91La43oiR+7Cxrks0gncifbn34id45CNamowxx2D9B1rIH3RRWhPVfggzhdUpp30uiP999m01Pnnyg69FNKflLN+9kNBnN+kCX6jSMe6THDdC9l370H2x1T71SyAwzbWcajV8SqbH5LYcdrRptEBa9CaPyy4fgjJxMOQfPwSCsxZ2FxHZOjHXRK2YVZAHgKpnIYAE7lkjUpAh6U+7IxsH0chHcA0pDP+EWkBXWWavZA+6UZk30gSvLkO3RZj0e4NpxZwMJmPMxkBeRXWcajV8RQbMy7g4WlI9t+rGXxj1/2RrvFipPssbGNJ1g0E4Px9MZcZrdMJ5EVk343Oco5s0C+gdeHXNodORLLA41h2cGr7qxSZlSdQyzL8JpIfu6G91lcxrErwfhRN45Ehdt4PAX9PQfby8xBo7b7g/aSZXCnZA6kFIr0D2DcRjTFIHr7Qyt8O+dxuS83vYIqNxegAh0hv/BbaVz0M/D9MP4Z08U8C+9f5v0Ztqsl83pf8rE2r/oDF4RcsTMMIsmLYvacR0vp/SeSQl5MeEh47x5YTWd8CkBfW+1ykONo6xbfR0nC8XOnZgSjl6Y9JvIkLaHQtXY+2yf5nJATkteE7kmWrWlA70yTA4ae1r9F1KCJBocyKGR85Mw6ORY6NZeeba5ECt4gAch1SjjVls/Mpfdmw8ojMIK6gTd9ATgVv0DID18UIuNk0Zx1aB3JdhpTkKTKqZG/XdvJNCzBXg2UORQCYC5ESt6nZbxe1Nk3xfQvg1eSgwzZ8S5J1yvjmIIKoN1af16hFjQqNK60aXhL34UxkIF8pE/84ZMR9hISGo3p8E9xvCiCvijougGYhOycHc9ajE7T3eGpBJVLyaFeCKO7I4eBolAXgJyTMAIwMUqdQU+ysjsBkJ9Iy+2BS5fYC6vg6ikDWtDnOeLTgmVFIgZYk80CJR8pj8VSkVP0RBgBM2J7LIUDMyKBex2BK+bDuzWjP4LxL0Q7UIlqlWBcX1K6nWbv+LgPfFArc0UjpGe08Zry/XeneDJtDy3UtxmES51zmX2vDbN1n2XwT1aYIKHItJUeGqn4lfh1pc13qCPL1AIdHIgfhZgEByzSHI2f8O1PTRE53j6F99xloP7wzNSBgUsMrLXVHy5aeHYuykUZnqkR71R8g8MbHmMM20jP8GNjPrmU++kIAACAASURBVLulqiO1jL+nAavW49N6181o29L9gWjPHg1WR7LSm8hI9GJw/zKkRymcf0YnrNc+KMr4tnXmueTghjZ+07ooWEe0U2U9PrK/n8jDyDHmahJlxv0U+qkznHSzeeUgWjrhNG0/YTx/QEo+bAfttWx87IpsAnsjHcOpyDC5GUGWrAZpjAI+JAiwYe08HekWd0cOD5dZ20cDMIM5dS6S06bY/VUQsOmogn8Sz6knIRkm1Ds0ZU61Or5tNH+GgfHt2R1Y5nO7TrY2IgP41QhsX2T6zeXo2Codm3M3yfE9tOLEnrp+5AEcDg/ud0WOJJfbWIy2iyHnkF9jAfGC+8sgOfE6u94VBQgakYmXkvUhFYDVEKj5BaTDGIOcY8IsHzeiwBFhlsBmZzm71Ob0ZKAjq8cd1JGfaLItrNl8U0U9q+DVBbUXTQLHWRnZQYcV8U5WQF5b69ds3gnu90COlqmCnGUF41VRx0+j19bnTah/J2rBkGJ140chPU2o92+6g35r45wayPEVtAdKNf6z0qtHs/RsLxTAasNUPIScVa+zOp1k6+WZCNQ1CwUAifI5QrrZdykFEUM+CHdRsy+ebfSigGMNfmOR0Talb1y9rIOvA9ObWI/ymrWstevfiPT/o6b7+yrwteD+55AOeX27HhKMlehA5cjufRbSgXcpPZsPZJ2JX2aiYCtNt41TWzd7oWCOGyYuPwvg8FO+oTzXNnU9aUabIiDHy0gPfklw/1mkM+uBZJ+1m8EvrfTjETan75qg/KyAPGo2qGOQn9F8diES+PxVWcc29uGRyGfsrzRhT7UAmjel4Jt2fEdPZMONBuR8Wh3tXlMAeQtZHYt5bjqJgdx1+KYvsjtchXyPUgHVizpsgPAanRAo8EoU7PxK5DPzHpG+cUj/96rxRTeb069BSU7Wsjn8ZWBewnYci3wo56BgbhNRcLcrEBC4yAh4W7lNGqQ3AdmnXqBmsxmMAI6n27joY21c2CKbKd8UMuIAZOtdNWHZ45HvcWGX3gnJ4A8jH47o/U3Qpi9ifgV2bzUUlP5zdr0Jwjt8JgG9ItjoYfXacsmvwXat+gMWlx8tN8qOlg5r95Lesbrp9JDweAqwDRVEIP6UbxtCLcNb8jat08a7kiHNaVgXW7hWiqkj7VBY5J5saTLgMFf9yJBx0P73euAHwXX34PxpatlrupI5cn9sX5IZxGU0hyMg8XF2PQaB/nanlr76TKQYSxoht5XvSQ4CqqJdc/KN/d8466MNUXSKE4DBpXdmpRoTi0ObtoFGUtBhFeuUzZOHochm40rPLqQVIGCmPuxEwoiKbaTZmVo01BwgzqyAvNx1LNc3Z1/mrA9StpxKuqjDYdkFyLqYb1ZD+5uTSawsb2MdkwQdaQPtor5LpaTH/PvG0AnoOJoExmV+Q+cIW6e/QJCtJ0W92vkdOdr1GDKB4wOa3YJvaVSu6mzf/lXkKL6mjYOuNganLajtm1HXUjsPj6WDlP6HI+XppAW8V3UApGYDZEchB4+m7nOaTRM5rn4JuDe4dwTaj+9Ok5xxyv1TGv+fIT6S48rIiLK/XZ8NHEzNEDAJgXCOa+2bImhPQgFpDqUUrZEmZI5ox3dFg/+pBVba265fBjYOnp9nz1dM0a7BN3dFBpQRpecbI53D8o3SiGzTpuvjaOmskj3DZILvX8bWxnUQOGUn5nf4GdYEupORMfpIMjqKBf01ARhr530QaPRZa4PHidjHITllV+BrzJ+NrrvN4QUIqFvxHZH1Whnt8Q+w6xOtbQs97koIPHd6+J2J2nQUyjZ6IKV9DGkNxisiJ8eD7XoScpDdhhog9yab45KDca3fji6XjXR1I3LwbxPqVEl2rDZ8V9OBIzZOy3ayJEFCkXPD48BZ5bZG0Y2foJbZfWgkrcr6kIxgNRQR+2/AGXY9EAGtL7NvKPpuHnKi6ZyKb+rxBE0AHVEL7tMfZdsYWXq+MzWQalS/V8w32eoZlLlYACvrlUVi0OGism5ElLNQ1q+1fm7G+li63xQwXhV1zPjdDfFQAtmjCwKM3Vae17CMeVW0BaUg7x2JXhO+P5yvC3BoF5Qt4yqTbx4gTebI/YCvICfqsk7sNOD54HrFRukkapdkIPl6ZVIDOg9o5nxTojmYSAAw8vf5LpaFyvpzh+D54chRfWMS6BrL7Yj8VpYvPZ+MnPObFnj8U75vuVQ809b+bDYtWuobkwMOF6ZfyjZFuvFvo2BOg4H3CfT9aD9+T4mnc+iNeyO7ZFQmJzID8hCg6CrkO+YQKKecjXOGfU8SO1XuOrajD3she9LGixrftIOuC+lnotk0QN7CUscyvWbxTrlOJPbzDPgyDL41AmUlngscjwVza7SOyFf1NQIbht0fjgB5V9l1X+A7KeQOq8OPsWCO1HSMKyB/+EvsehiyH0XRRHLUd1BGxbVQIPPChjMAeBD4PAn8jBrhOZqAnaGmBy94aBTyz9mUWnDgmP1NN+AjzI/C1qvCF2U14A9Y4A+juWaK9kTy7/PAHnXeGZi6HReHX9GwS44KDudcZ+/9f4Jr55vYIanpOeeWQkbcQcBz3vvnS897e+//0vAH16fZ1DZq71H+noXt+xZ0OOf6o4XxLu/9XzLwn/Pe+7bQSfkt7aHb7MM519V7/2/nXCfAo6hV//Le/zbF9znnngL+jJzW/u2cW8p7/y/n3G4IpHpAfC3y9qVzbhwSSC9A0QDWRBEifhO8Mwv4wHv/g0bp1KE7GwnapyKh9X6UscWhNN0Peu+fds5dDtzhvX89Fe02fFsn5JD3u4gysrdr7jnAOTcWuBtFN7/HOefQpupPwTuFo/d/gIu89/+NoNfh27S9h7V5P+/9Hxqhn3udKtFeBUXImoA2xK8Hz85DjhVbeu8/jKSTvQ8XAb7pjMC6v2yUfgXzTXZZY2GSb1o7nHNdvPcf5/hG59zqwH+99281k04dup/UEWBh7Yv2Hs65TuGamIvPnHOjUDP+JKKMId77X9t5i/1v1Ue9doX2800h46f+vk+h2c17/087dwgMMwwZA09BmX+2R45xf878bS3aNUF5g5Dx7dvhOu+c2xXo672/JRGd7t77f6QoqxlHFd+XmqZzrhsC+30OON97f4fdPx4ZC87w3v8qFb1P+ZYkfGp7wZsRaGIzk5veQcaO8cC13vurnHPrAvchQ+9HsXQD+v2QcbGT9/5nwf21kIPA/d77b0fSKGScLt77j+O+uM00eyHA6Lve+8Pt3g+RY1Af4GLv/YvOuStQ1NF1Y3nV+nJlBH6f5b3/U7jeWh/uB1wfK9+0py1zy5YJx0YvpF/7d4LPagu9ZRGoai4Cxg4H/um9/yB4ZzoyjF3rvf9lYvrrowzZXyjpHDZDosVXUtILyg95NDwv9Jx9yrzcAI1lUPCrjYCXirnbnk1HmTM3CXU7jR4m01yLDNDTbE59C+kBJwA3e+8/b/qBx5BuNUoHUNC1eW4isD7wuPf+F8Hz1VH03Ie89y8noLc3CpiyKdJpPg/8BgFZ30f6sPecc3cC13jvX4mlGdBeHxl1H/De/zy4PxwBA9/03t8RMxc453p47/+e5ovbRG8QAuCfm3pst0Ivqayb6mhtPogssxOwKuKNv3jvjw2eDUEAwZ1jZZvcfRjQLfQH/VEAnJvDva9zbmdU7y+n7Hfn3LnAHijo1hkoi/TpSAZx3vsZzrkuyKn6eylofsr3JNunB/Pphkjm38t7/4fSO/uiyNk7hfNQA7Qq4Rujna2eVlY2Xl1Y5zhI51PR0deNCufUhZZ3II3uvoK+zN6mzrnlkU7/lznpl/vFObdsyL9O9uUHgGN8yf+pQXqLQ19WNibD+bqs/3DyZ/NefjMpbIEnokAxjyDQ3z/s/lgUsHsb7/1fY2k554ahoELfzq2naevhnFs+gcwRZQNu5//0AL6I+GEPu/cu2v//F3jYe3+79fEM5ND+x/Z+W73vdM5tAaztvT+//P3OudEoc8zXvPcvpKCX+t1PKacn8A/v/X8XJl5NsQ4HZWWtYxVtanL/V4Avee9Pt3vvIFDgn4Crvfc/cc49D/zee79js7/JvqHox9g9R1/gXARKuQ/ZMV4MdQvOuRnAKkjXGu3n7JwbgIJvTUHt+pjdD9es7ZBt9Qwf4ftnZWWvYxu/a76x2Gy+Tsg3C+X8BotHHas4Uq4drZUPzfEncvKxuQTp/Z/z3v8weLYiCnq6q/f+pVR6OaP5TeAU7/08J71uJ9MnjUIBpbf13n/fBX4mDdLqj+wz3/WmL3bOvYFsuB+gBA8/Q77d79o3NexL4wLMy8IwNpr5Dc65aQhAeZyXncahADn/cs7NBV713t+UYL7pgUCNf7DzQQhw+17wzkxkG/yMz2Tr7ShHp6o/oKMeTkbjBR7lCTVSEZCbXiebLK9HqYV/XHq+KXCwCZpRh3NugJNDURbHWlMGtbVN/YKu20FzoHNujUb+t0F6Q1GGppHAAU7G8mYK2f0RP/QuBKYFvZ/qW9pLt9lHoGD8r9fxC+/9b+1ZIwqubiGfeu+3Qhk67zAlZyHQLAX8yTnXKbYNcvalk8L0LuB27/1LKBL4Tb6lg9MyKMLDDiZQRh3OuX5OzqdPIkXgYcgZ8G1TZuyKHGVmA3jvj/eRAMCiDdvSNwH/xAAAq2jXrHOAEyDvCWA02giAnBpaOIlZO76EoqLH0OvwbWo028ur3ptDQnvp516njOZw59w2AN77d9Ec8B5wtHNuzeI97/1pwKPA8pH0qujDRYFv/uPN4Nrg2ph7vskua1REs5CP20zLm7E1h/zuvX/DxzvIR9WxgXlueefckW3Zc6Q4nJz62rrH+W/pOotiy3v/oY8AANpxvXPuHiuvMKR8su6mHi+x7doA3ywDHOGcm9yuD404nHPjgcecc884525HTumnA0cheetmlFFiVQSOiaW3nHNuZFvfj1E4BjSLtaor8Dvv/T2+JQCwEzJ2TnVyWo+l1w840Tm3cWxZbaTXvb3/4+MBTlXQLPpxXefcLsjA+hBylN3RObeH0bkcgQKjAIDFutGWIxGfdrVyzgD+5pw7H0Wn/rL3fhuUuWp359wWXgCOST4RANB0CX2AV5HTys9K8+kHwL+AtSPpDASeds718TIUZVkjvfd/RbL/SOfc1s65x5ChbDfgdeBzzrm+3vvjkKEsmldtX/8eytq4a/Eo+KZXjPaGkbT6A8c758a05f0EsvFA59yYtq63icZGX5S9eJv2jMvI4x8ok9KKKEDe37z3H5Tq/X0E9Nw6BcGwbO/9N7z3F3rvfxPMfb0R2Gq9FPTqHSF/FOfOjH92/efye205TL4pyv0dMhC/DGzonNsreLU7ysoXDfhyCqrgkUzzW+fcZUj/97TNqfsj/c32ph9Y3ScAAILaxwlYOBfNNb8ovfI+8Be0jjV82NjAC0g5F81zXwW+6b3fDslt3VBwLLz3e/m0AMAxKCPGs977n5d4+CMUubfQwTQKAOwN3OUEgM1yeO//H7Krft4J+FD+pq6paFn51zoBj5t+tEdfWW8+aJDmMOdcZ1dzJPg+cA3Qxzl3ZfBNE4A/orERdeTsw6BMZ7LNhihz6iV19r69gDOcHKqj1kfTN2wJ4OXEOQ+17cfe+zne+4+895sBSznnRnvvP/YRAMB28k6yQD02n85Ac/Y1vgSMs3duRXuCGZG0svNNQDtbPXPyau45zmg2zKuNznUded2AyubUhXZ9LI6CXxrlmwrqmJ137NgBuC+cTxpp7/YcNs9559xE59w051x/XwKw2l79UmAtpwBTjdJa3glg+N9m16sKelXRLB/hfF3Ya4q9h/f+k2BBjYxHJz+1T/zdvPefR7qA7YFpTs7zIH+EPwCpnOjXBK5zzm0c7r1L31apr6ePC64wxDm3ko3FdttrGtA5OK/AMbcDf3fOHeScewbtVbcBnga2dc6NsT4+1EcCAIvvdPI7uAzpVMP7ne38x8APkf6o4cN0qts72R3a9G0x9Izm0iio+kzbzzUdHFfw/afxTew6HNDMXces9IrD9hdfBVZwzq1huvFvoeDn/ZHuDO/9JijDXMNHg/0Yqz/ugvTsmyCw9p3e+49cS9tDb5QttOF1vzis734P/ArJyYc753YCrVlF/b33jyKbStQezo5sdYwdiw2ux1n5poqxuKSO1dNLuHbUldG8HTFlt3Z42S3ORj6pOzmzBzoB/j5APqyFD14SvZzRnIHsp5/1shF9bPunPyL55vf2bsMAQDv+BHwD2canO+eeAF5BPioDgCO9glTPAe7xcQDAPsimupt9+ycA5sg6NHw0c3x6778O7Ij0AXsbrWJu+T9qtrjYb1gL+d4eiDKK/8MrMGXYrl9H69RJkbQWu2MJCDDhYUqO66Cl4NZR6AV0nSlyBnvv/+m9v92Exy7Baz1QFKR2O36VaHVGE/SOzrn1gvuu9F6SidaUOC8759bM1abWbtsDc5xz6wT3m1XHccjo93MUzWUpFNk8fGeWvZeCXnYgRxV0i/5pqzCegN4otPidZXUtyp+JNuJ323vroGx2X/I1p5xGaWZrU5cZxGU0CyDXYaZ4vBN4EoH+nnFy+vs3EiyXdc71jp0jXAcHxxnN3GNxWeAeJBReBLzknFvR1q352tcE2kca3SAvDm1qNHMCgLOuU1ZedzTeH3XOXe2cux4BGl5EzodHOecmFO9770/yERH5K+rDDs03Ri/3fLO49OPSwKUmH+eQ4ZZ2zg1uNp0yTTLW0Y5/AzsBn6mngEypSHIy/L3knBubcY8z0GUEqhWHV9CGIc65S4J7hRNJZ5uLkoBKjG+ytWuw3x4CbO6cW7WZ9IzmWOBh+x2C1sSpKBjQf7z31yNHxFdQhLX5nBEbOHYDbnaKglt8R1MVq8YX2yIH+fudcxuHNI2HvoOU1VEAEyeAyj+BvwGzFzROUtTbZQYcVkizGN+bo0ink1B2pwOAj4DbgAOdgUm89/8bSa8fcJtTtqamH865lYALnXMzvMAihyNFeC+U3Qnv/TNIdi1k5GTRXE2X8GdkRFrHyYjziQ7Ey1HgZiDKwdMrSNIHwPPOuV6pjFGtHc65kc65g51zw733tyHeOQs5yB9h33QaqtdU+7f36hbWvmNAcP4MisAbrleFs9pN3vvrImktj7I37umcGxFZVluOw+03vrjR7Dkc+CsyRk0G5pt3XIIAda3Q/AbKpNYZ2NU5NyowCHbyciQ9DTlYRB02zrztg69zzl3lTE8eGKf/giJ2buIU3TX6aIvMFOpSGpGjnQCAr9g+fKZzrp+XM/lTSO6YZvfXQNFsH/CRUUCdcysDlzjnZnk55RyMshv3pubY9CLwLNL1QALgYUB/IqrLy14Of+EzZ315HXIEapTGWGCec+5o59wyXgD4R5HN5g6j82/gXqC7c657qrHqdKyA1t5/AD+F+Q3m3vv7EcCk4cPa6iHgFBfYjoJv6TP/fzV+BPL4yahevUrPd8act1LsDXwHB444OU0/C7wAnOScW8N7/2/v/ZvAFSjY4RnOuanA5cB1NmZjaGbtw+KwOTwXiGss0lHPtj0O3vvzgBOBzZwFNXECefUjYq6xcqoCjhTHSsiGvIx9zyd2aldzrrzUZL2Gjqr4pnQ0vZ5WRocFVlY0z3XYdcPKq2pO7dDrI+Tvy9z0guMGJPdfUNAI9sVRgJjWDpvnZiM78nbA121NLB/fB77n4xxWc4Mcs4Mqc9J0NRtKuwO5N0hvCLJRP+mcW6vYZ3vvL0S6q52B8U7B8T8P3Oq9/3sK2t77x4HzUTDA3rZ/K9p3ir2TKmv0sGIvlUF/U/TfNsB5TnaVuo7VCfenKyIb3Nre+y+jffHWwH+o7f9vRHrOIiBwkgBAzrlewC7IF/DrzrlNnHPXO+cGmT2pAALe5b2fG0luKgL/zCr2wc3sTye97f+hYFAbAhvUeafnfP/YOL0qwCq565iVnpW3glNAvB7e+xMRQORO4I/e+4O8gCNHAZNML4f3/tUFFPlp9Krox+yAPC+/tM2QfeoV5G+4havZpv5byNHe++O89/fF0MtZx4r6MDcYt4qxuKSOiY+KeLU/fDLGcwUAmVlce+/fBq5CgQV2cs6NszlgMrInpwjIOdT0jgXNdxGA7CDn3Gft3j+BsUg/EOWTY+vUlkBfJLf9BzgP+Lv3/hDv/beBK4HVrL6/8d6/FkMTzaM/QwFhwvYtZNYiQHgKH47+TraibIdzbpxz7jQX2FK8slFvh4KQzPECc24AzEL74qgAR06+DS8DqwFXo0yNvwnLdfLz+DvSff40ooqL5bEEBJjw8Ip2M8k5d4ddJ9l4Lyz0ArreOTcLeNg5d41TNoBPIivZ+ZNIWIiKwurl8HM/cpBbzzbK4QSwhqtF7o0+vEAbtwMPOucm5GhTa7eXgJ8AmxaCVFDH1cPrmMNlzlblKgByVEHXVZNx8C/I4LcRAqgd5pzbAsB7vwXQzTn3HaRAP957/1wMsZxt6jKDuIzmBOT4cj9yKHrfyr4fOa3siuagrZDS9Yve+79E0uzQ4DijWQXoeDawv/f+Ee/9RcBzwAPOuZHl8RkoPJ6y63aN3cWhTa28nADgKrIqDvTK5nEMWov/irIcX4IyShROMWe7BIbHivqwQ/ON0cs93ywW/WhHN5T1+xinjGCtfV8KRUcX4FxgLxcEOcggW3VHhr0cdVzZOXcSmld2Q9nTBkEL+X8rLPJninnOK2rpQ8ATth7WXYdStbN983aoH9dvrfwUdSuVtxSA9346AnOFz2YDbzrnuvl0Ec7+DzlXPuHk+N+0drX15zJgKFLg/g34bemdzZ1zuyceLzsCc733N3tF/78FOaV74EjnnPPev+29fwhYxXv/Yix9Lyf1rwKnBX1ajI3VnHM9Ysqvd5gi82SUMa4bku1aZAf1CuhwY7FPaJBOfzSPj0X7tO8hw1z4zlbOuYONZiw4PivgsAqazpyYzZDRE9gTOMp7fyqwNwIireW9fwS4Ccl40YfNq68jYN7Y8vOUfGrr+v3IWPyxjbufo+xjfwBOdTqmAtsCr9k3JpnrnHPjnXPdrG+/jQCWvYo9j+2xOnnvP/DeX9EgjU8cCr33hwFfAEaU3jnQObe1nafgnXEIMLUicqrGy2n6AqCfM/2Kk05ueWT0iTF0FKDJKQjwdIpzbm+0jmzk5CBLDI16h/f+HVTP36EABOE3be2k3015XGJ0tnGWETSYw1d16QE5nY3Xb0Fg7R+Wnm8KHGLzbyytlZxzBdjvX8CFaA7vimS5XZ1lsi0Mvd77H9q6FnXYONscgVRvBYYDZzpzGLM5wHnvfwgc4uWk09DhnBvunDvX6OYIHtEL+BhYHTncPe+cWxvx0T0oANqhaP9zgvf+qZg5wGT9+xBA/C/Wbr9H2VR/ifb9XW1O3QXxVbI51Y4fozl9oiuBc4M59Vfe+6saKdxJx3k3CnT0NZNh8N7fhOweFwHjnHMboTnvMe/9PxLIHYXM5L33P0XZ3IYCU1zgqFvU0c4fC/+3nfQK3vwS0t3+vvR8B+CwBqrSGr3cmdw6NHCkIItki3+idfYJ59z+zrn1bP26GAU3eAE41Xv/eOT4z9qHdY4cYLUxaO9/lff+yGCMdffeX4r2IC8653ZHNo5Tbbw2fOTmnUCmGuaUKftGBFi72Tk30QfZpMt92OBcUwnf5K5n6eiQwMoqeLUjrxsVjo0Ovz5W0JfZx2MgN37svb8Y+JwPAn046Vkecs5tkoJeifYwFOhsEyRHdkL6ufK3veq9/1Ikudwgx+ygylw0nXMDgNudsg7mCo7/G7RHHIl45iznXJFF/SLguyhY1lzgJO/9k5Gyak9nthQnXdSHwAFe/iiFXmUIcKVz7oDGqzXfkTXroO2tn0H7gIOcZdsMaI8Pr2OOYP/fE9P1e++fQAFHfg/s7Jxbyjm3CnKO/3Es7ZAHvPd/RQ73t6K1citgFAos1ymlnsELOPp1FMioGItFm06zOiY5nIJtXeCUIedq4B3kXxG+MwP1b4rAWFWAVXLXMSs9K6/QjU8H1gbw3p+M/HR6ulpw7JEIxPGnOsW0h172foT8gDz4ZM6cDHzeC+B7IsqsuINzbhej9XE4X8SsH7nqWNFYzA3GrWIsLqkjyetYBa92Ba5xzhUBBloE/CyfJ6DXHfk4P+WcO9/VgisWgdVWRoHA9kIBXM+PsRkZzR7A/6AAGVc7y5htOtydEc8c4AQavxm40iuobaP0Ctv4hsgH5WMkez8J/MrJtwMU9LAXaUCOznjnUeCbBPtEe745CkYYjVWxefkgBNhcM7jflMAYwTEdteNFzrm7nHwABnjvn0f+XZc55y5DNqQTfRz43yHe2NMJUHk30ltv6JwbE+giPrFFeu9/Xegbm1D3jnt475f8EvyAzsH5XcCKpef7ACvbeadFjV6p7HXQpD4WoebfBnoHz7skoDESGfhnoIifSwPLlt5ZFaG5Z9i1i6A3ABhWtCuwBzCx9M5+CEyVsi1d0Uco2nC5juPRwjgrAa1xyBnmj0DPBfEGMA0prBrmHeOP14Ddi7oCfUvvLIOQ+qek4tPcdJEzw27I+ecYoEdKHmmFZiekvLkY2BJFdtobOTSeDSxv790AbL0otam150HAGsG9i4z+yIJ+2Bal/2/3PIAEwqeBA0v3dwT2s/MdkCD5IbBVo7SCsscBX0bC6qbACcDg0juzgHGJeKaKds09Frsi556Hyu1m47NuXZe06cLDq2Rep6yc8QhosFZQlw+AKTY3TEAKs2dQBPtJkfSq6MMOzTdV1HFx6cdS2csBewGjSvd3A/ZORGMgAsaNRkaxvSntK9AeIXoOD8rrFJx3R/J+M+s4Hu2jDgXWt3tL1XnvOuC2lH1o5e7H/HucE4C9EtJY1vppNAKonIzAIuE7ayWkNxCYHFx3Dc5d6d0bkJNjLM3Opes5wGrNalfjm9eRc/omdq9b6Z0uxlefTzn+gXOQU/UtQAAAIABJREFUI9cnbWvzz25lHk01NpHBsQ8mCwT3V0QAgOj9fx2a+6OIvNsipe4Iu9+nFV5qiLbx62eBR4Dx5fKQPLszAnz2i6xTf6QrWh0pv/cDVii9sxVwcMJ2rILmjcDqwfWVwJnYvhw5dX0HOXuk4tFw7TgYGFN6vjMKShLNp2jP8Q6wb+n+ekhPNghl6rwbeIsEuqMSnbWQkWMuMggOsja+NtVcg5wI9qUmN95HTTcX7qe2R8bIVRLQHGX12rt0f6T93RXpCD6HnLqi9SpWbgFS3wbpcp5Gep17gMvL/BVJq5hXJhr/l9euXsiJ7UKge2K+6cb8sv9EBO5cJ3U9bc55qFx3O98Crf/LR9Lpav31H7ROzEQZHPdAwbnWRFmqzqAkSyZs1+ORLnwblIVwuN3vvyAeaIBOdxsfVzWjHq3Q3Al4HAFu90J7yfts3A+wOWKjBHQGIzvGvqX769v81hcZzx+095LMqcF43BAZUjcy3p2H5tYo/izR6o0c/8p13BbY3M4PB15FmU1nxfBLnTpuhOT/OUgu2B7pXKaX56FE9NYE3gCGluuB9BKvAjslpDvD+m3LBbzzWWBOQp55qt44t3HxrRT8g+27rc8uwNbD4PnOwEw7b8Y69SbSeYxBtrDvI4DaWCTzrJGCZs4+LNVxGLW91L4o+NhEu647Lto7JgNa51Da86L141lgObs+CznGbN0Irap5x8qabXPLVcB5du9oBM5Pxi9V8E0V9czJqyV6WeY4K68qXu2Q60bu+lVVzyr4poI6VjEeQxlgWj2a9nwf459uKegG5fZANpTzEAhplN2fSeDvlKKOwXXZ/2cs0uFssijSq6iORfDIJ4GBbfmmWHr2dzrSDa2C7MU/RfvFg5DuY38S7FWNVn/gi9ScjuvaT5Hu7PRU/Gpl7oCcrHvTck81JRUNK69sjxtaej7O5qLNEtAaQX3d3ypIFzkN2SEvAd4FZiegWcxv01Cgui3t+lDMvwDZzh4Blk7Upp/Icfa3d+n5UOS/cmj4fiTNQk9zE7BtcD/0Yd0O2TmXiaTVF8nCuyE9yl6YPix4Zwby00s5JrLVsSJ6Y5DNYc/S/aXt79nI3rAv8k3YNpJeJf1o5XZCfhMH2/WyaO5+GNgleM/VO4+gezjwMjDArpdDcsed2N58UapjFX1YEc3cY3FJHRPXseL5ZlVk4zhiAe+k1DfsDDxvfXU10h+tbc+WQ3bH9zEZJ9HcdiyyET9o5V9LzddyDPKp/j2RNgdq69TupftLoznvdORLcSzyodkmQd1cQKNLcD+UXzc1uqn2xiujNfcISngDlDUv2f4mKHcs8qseiQCjF9n1BHu+NvB3YLsUfIN8GI8BLg344loUrHIZtN+ak7qei9uv8g/oCL9gEuhXuh8ukOcgh8+eixq9OvQnI8PxViiKxEi7v3b5G8vnbSx/PNqQX4+M/ifVWwSRA8bJmPEjoj7jkFPPg8j43aeVekxA0X+2Ssw3E1Dk77F2HS4e/Wwi3D+S1rK26G2PFFWvYcDR1vonpp5UAOSogi6ZQQ71+gg5wfU2XvkZEuoeAO6ox8cLc5uSGcQVlN0PAXyWDe4djoypt2DgXwQKnJKgTTs0OC4335TKWgVFUrqVkiEcGXTeI4FD3uLSpjl5lczrVFC/bwCHh+2JZJwPgB3tujD2rLwI9mGH5psq6rgY9eMY5MDZK7hXBh11Rk7I84h3rB6PnFXPR4qVcczvWLEhUqqMKHgrQR3PRRkqrqJkqG5CHZexsbFHmUZwHu497sMcdSPqtzOlYDF16Gxs/BUNzLN+fNHadTCwAvPLxmORYjmFMq6zzSPXAOst4L1iHl8DmB5JcyxSpt6KFLmrBeWH+7gk7YqU1d+mtB+sN8YRcO4RYIPYtg3K3IsABEANmDPIvivaAFCH5oX268T8oJXdgZdoxUGoHTTKjio7oX3di9Tkj91s7M8H1I2kvRHKVvMEBl4rjcmByBC4aiSdbIDDKmlauaOAr9v5lsjYUYBFx6HI7n0S0SrqUnZSCcf/NCTXTk1AbzJwZ+neZ5Eh5WwEmBmC9FlbJObVKdZ249CacgJyIJ9r478AWqYwHK2C5JyPCIDddd47gDTrx27ABaV7x9v8UsiweyAjUyqAzCrImWt26f5eSA75Q+y4r0Nza6QbnhLcC+WOschQNziSTug4ui2t7NPQmn1/yjoGZT+COTWU+8rGypwENNZAhqrvIMPY6Witfwk5/023vpxP9oqkO9D+nm60vkINADgb7Z+j16rSPNbNeKNsIN+dGtgqpcF6JLID9Efr/y+RLPChtfmQRHRWB+4t3fuMzT0XoIADS6N1OPWcOhvtBQ5EAMNdEED3DptXhyWiMwTpOPsG9w6zOfZWYHu7dzAJnYCtzC0RmOtstBa/gnQ9OyIn1k0T09sIOVQVuptwfiv0O1NIG3zgUASe2tOu6zoEJKLVIYEj9doJOBXY3MbEj4Dj0Lw+Fxhd/tZFpQ+tzNxgtdMI9JzWr19F+oWXqa0pI2LbNDfvBGWuimTtoQjo/yy14C3HoQwVfVL2Z26+qaKeFfBqFYDcrLyam3dytmkV9ctdz4r5Jje4sgremY3221ci+XHDOu9MJk2Q7KIve2O6KKRL/i41UM5UFIQgKghoiV4WkGNuelXRtPK6IL+plUv3DyEBaKwVmqsgHUChw30fBQC6H+nGUgdV2gXt3e4M7pX14ysRbx/vSS1I5epI39Gn9M4QtKc7IFHdCr6ZjeT8egHAeiGAZXRQTmAzasENC9qfsXn7ZKO1KbIJzCx/SwTd2UgXN8fmt8NL/fsmti9PQKtTQPNZggCgtNwfb450vCnGf0FzS+RH9QxB4B1a6pauAHaNpJcVrFJRHbPSs3KOBU4o3Tsa2Rg2tOvzkb9MtG68in4s0W8qIC+YY6Yifdv6yF5+JtIh90FAj6dpnu9os+tYxVjMDVarYiwuqWP6Ouam1xOzzaDASiMJdJkBvQdiadXpxxHIhlLsaa4GfotsALsiP53V7Fkqv9WZyCe9DwrocCnKuHgM0tUvTRrb+HHAZ8JvR/qHB5Bc4xA25k0SJHIJ6G6N7G8PUwePgvwcriHe3yiUf0dSmqeRfH4FFmwkVf8F5d8KfNHONwD+hdaMG5CPU/eEbdob+XQdg2yA0+3+dcg29lNgh5T1Wxx/lX9AR/mhyML3IqeY3Vt55zzMEWlRolce0MC6KH30W9hG0SaAu4h3HBmAHPKL7FvrI0PH2NJ7xYLWCzgsgt4IZHifY9dzgSfrvFcoIbYg0abcypuJImG/ZQvVqkWbB4vYaiwgSkAbaGTNVkUFQI4q6JIf5DAKCeJ7ow1bMfZOQhGqfgAcZfeGEYByF5U2tf/PAuKy8voF53cTKIiR0Lgicha5kci5LSi3Q4PjquAbYDiBIyhyPj4ZGa3WLL17MYkc4ztym+bmVarJqrgc2ozuYdfdUKSRIkrNtlb/nct0GqRXRR92aL6poo6LUT92QXL9n9C8eWLpeagkG4yMniMj+eZNShEH67zXBwFnTo6pn5U1FjkaHI+MHdcjZ+PJRRukrGNA88ngurX+K6KuR2WXtjr9CzmLf5YAsEJtb1P8PQAz+kbQG4P2VQtUCCNQ25HAIZH0hiIg2vLI0fgiWoIc5stOTyuR69vZh28hxePWSBH2ZRTVNQTMFnvVFO06FHjq0+oQ9GUSoFNQ7mAk01xYur+x8VY0kKs8FpAcfnP4nJZyQKsAoXbS3cT6bhcUBfw546MVkKL6E/BPwvYsnJxOR/v/J4J5xwW8kyQSKJkAh1XQrMM3rwIP2/mRaB17Ejl17Zi4H2chJ5g7kbNIrzKvonW7YdABtbVgovFmbyu/P1qTtrG/n7X3CiNTKkPORJSJ57Ol+6vb/PchcFoCOiHfz0OBjYqsNKG8UYCtU4E590M61U72G4MMO6cg+bRwbhgc265B/c5E8s5J1HG6QfLIuQn5dByKZF4EHBsKDAq/yc6TOHXTEuD0OsE6X+rLnVPQs7LCtfcg4PQyf6WiVdQDZR271MbH0tavdwTtnMrJIQwcd4WNvX7WttfZs02QLjCF7r8bAvh1QY5yR7Xy3nXA85G0RiN7xiEE8j9ag1+xcVLMbctiEUgjaRZz5Bg0py5t88/SCPy3BcqmfErIsyl4yOj0RIbjwdbO36AWpb870klGZTmlBgzphGSMgie7IQD+KARuvqk0dlIGWbuQYC+ADLsP2PlRWCbQhPRWAv4GzAvuhfNbkv6zv1myY5X+v8MBR9B+6kZky7iM2rq0g81n7wPHFnUkbYajKvowC4gL2aBWsvNTgGdKbV7IqvcCu4U8lKCOTecdm78ODa43QvqxTZGDSqEjK/pzxKLIN1XUMzevlmjmBuTmBo51yHWjyvpVwTu5+KbKOlZEbxjSGQ1COsC3CfZRqXilRHM74AW039gXyf+Po73WpSTKAhbQywZyrIJeTprBvFMGwoW6xt3RPm94JK0xNqZPRfriAjS6JwIC/i/mjGz3J8bQa6UuKyL79FeBi4L7yYKpWXmVZB1EAc7fwoLu1BvvyO6wRwJau6DAo0UQs+WR7u8AtP8/yO4PaO1bGqC5FOYzafS/ifwROqP9zaWYT0sMPQK9IsrW8n1q63AfajqQrsF70xLyz2Zo73Yg0ondAuwVPJ/PNtcgnexgldx1rJDeWcDlwfVkNJdfifbsBaBkgf4IC2M/Uls3sgLykM3kdeQ/9gLSg01FoJzXgDdIBHLIXccqxmJV4z/nWFxSx6aM/yp4dRPkk7M90skvb/dD+a4vwgZE4Q2Qnr9r6d6NVvZAtNc5C+11fkoJjNggzRY+GnbvVgQUH4UCH1yLgORvk85H9grgsuB6A2SXPgvprdZBtqzodSqgMRXp34ahIGvfI0i+Rc1mlCrAyRrIjhsmrCnm917I/+lzCegMRME9Vgju9UdBwLZHe7qD0bpxIAGIM7ZdkX3vbhQksgACXofJpcj+OT5VHy7Ov8o/oCP8bKJ5FwnGT9tk1z14/km0/EWRnpW1BTLMHW2T3W7IOWdNtIF9G3PUSUBrV2yzbddfoI6CiAUoe9pBaxoto+8MBe5ZwPvJojnZRP5TBKqcgQw6j2JCOOaMFEkja7YqKgByVEGX/CCHCWjRvQhFp7oFbcB7WPk/JwIMW3WbUgGICznZhA4+5yBHmP6l9zZAG4MoxbGV1eHBcRWMxe5IWfxfpEC53sbn+kiJO48EDmOLU5vm5lWqyaq4DFL2P4852hr9G0rvbYfW6aisAxX1YYfmmyrquLj0Y/C/ByEnvA0Q0Oc6tOnvW+fdhudZpDg6A3P0W0BdwuxjdZ2S20FzAgKFbFO6fxrwC+pExI+sY+EUMww5FxR1CRWN46ljKI/pRxSp+k6kQHoCyT1X0NLwVyiSUkQBPBI4qVRua/LxOOKCnAwC/g9FpRyBFFfn2fgIFVNbAnfFtGNQ1mg0x+1Xun+Ete/adfq14XalFmFrKeSANzl4VrTvJGDderwSW9+wLggUV0Q5PhHN8z8qj6FIWlNpCbx/ik9RbDZSx6DtpqCsP6cikOM5Vs95yFD/NOb8k6Iti3KQvFqUuwK1LCerp6pj8L9ZAYc5aQb9uDUB6AcZVx+x837IUWVi+D8J6jgZeAytj2eg9XFnas7VKQK49EdAsXFWj29i2ZqQnqiYH/ZDBp6eqeoXfMMs4GtI3zifAz6aE79IhEEw6McVg/ZbHUUYnhNcpwLFDkRZ9zoB69nYK8CFPYJ2vRCLhBw5Bov6hfrNw5E+Z6OAdiEXHA/ckbAP10Rz2dooQ+9Xgf8HjKlXt0brSusAp6G0XBOjdbildt0AOaccgRxHByA93Yat0UkxTpChanW0Xpxj9+Yzvibqw62QDPBjJNOtjeTJ7yAgx7dJ68R5JNIvvkMgX9R57yosAmkDNMZbfS5Ba++PbB7ra8+/BVxv56mc1fsjvc0kBKj+BjWwcSdqQdb2RPrb3il4JaBflH8r2m+8EIzD7RCQLVZO7Y3WpL0DWvNCvrW/66P9yHwZ0Buk22J8oTXxyuB6JAq2lMowXoz/8UEbDgN+TyA3puKdoLws2bGC+nVU4Mg4G/8nof3EI0he3Mye3wg8kbLvKujDSsBqaC98ifFOJ7R2XFV6bx00x0ZnOKqAdyYgGeYIu14eOTb9iFp22plIFukfjKXYcZE7M162elbFq/a3EvBYDl7NzTtVtWkFY6NDr49V1HEhGI89kN7oPJtzRtn9mSQCGpXojUP6jbWRPPw0Crg8Gskkh2GB5RLVLyvIMTe9iuo42+acS4N74XjsgQIhjImgMR7Zw05C2T1uQv54RdCKx7HglMG4STkPFGC7InP7OARgOwfJCDcTrP+JaGbJOlgq7wrkGzfA+vXBYPyl0KkOQjJNJwS+uwsLlmHPi/35ida2KfuwAN7dhmxxX8eyVqJgbmsH78asw0PQulsEbNgF7btXQCDWpwmc1st1jOUha9tTsOB3yDdgfySj7lKPTmR9s4LjKqpjFnpIN765ne8EXBM860dNN34TCbJxVtmPZADkEfhLIL3cLdbGWxjtAdRkruHUAQYt7HWssg8r4pusY39JHZtWxyp49WngL9Tm2HqBqveiFV+gNtJYBbgPBb+9hMDPBmX7/QtBhlcig41bGeORDPooCmK6rd0fhexVH9EyQEZUEFdkU5xh51shW20IwivsyFdgQc4i6ZXl3g2QfLoz0qGOLNoheCd2LS7WhY2RX9oDaN2dVv4uZG89OrKOE9CacDuyvZ8d8OnVwN8Jsn6X26RBmmEbDUHZsW9D8nAnZIf8Agn9jJb8/BIQYFTjGdoZOd7MQs5Ar2JgEQIEbfn/FhV69ncV5GBwCkJyv4kM53shp4O7sChoEbTmcwwJ6M/DAE5IIbdiwj5cmgDcgzY3b2PGcGyznppv7O8awIPB/ZEIhPAwNZTzIpMBjAqAHFXQJT/IYQByVC8cObohJfUNmLLTFserF8U2JTOIq1THDZEwtStSED9t1+siRepGaHO8eQJ6HRocl5tvrIyB9ncTJKxeiJSeTyCl8a3UHEpWTkCvw7dpFbxq/5Mzq2JPZMCZhTYcX0ZAkgtK741GG5CoTA4V9WGH55sK5pvFpR+70zJTy4PUsnOfgRQR30GReuab7xr9DgQsOqMV3lguOE/lPH4K8N/gukdwfgVSWtVVADbAq+OQ4XgFBOZ6Dbi4XCdkLD+dyIAjaI7rXZSNgCNH2/W2aA17DBk4o7NGl2hfQWA0Kj0bQm0PlIpfH7G6FOvxYLRmXIyUj+si+SA6aqzROw7tAQahPXk4Vi4nyLaQgNY45NgwwfpxLnJeL0d3m42Ur8kdZOrw6FAUnetaq+9WqfoTyVIXIjnrQpv7ptp551Q8E9Bbx+qxi113R86VZxZ1oiZnpqY9D7ixVPfXkIK+b8LxkRVwWAVN5Bz2BgaOC+4/Bbycst+CsgdZX90b3DvC+Gn3VGMRycZnIuBWT+SM9zu07ymAlBugNXmTRDSLOXplG++9bA66F+11hpTe383mxJ6R9LZE0apfQlnBuiCQ7odoTv8FDYKN6tDcEs2tc+z6S0gHEOoj10Vr55SENF9GBpYb7d5n0Nq1KTUgYDe7H52VE60hRTTzeQgIsLe17UXFs1Q/Fgxwmk3izF9W7gwbd/saL74SzDefs3dSAQ/mKwftEyeh9fna1PUzGqPRurgSykZwDpK1xltf9sEyySWgVYzHnsi4+UNqkfJDeafo66EN0umDnG8PLN17NBgfR1JzJk/Vh2ONNy9AdoCdgD8YfxZjsJhTpydu02nUwBwnAX/GAJZIBniXUkCpiLbdBwVw3BDJNu/YuBxKTZf8OpZxISGvTgWmI1l8KFqbi8Ag69n4HJGQ3rZonr4WRZFdDq1dPwPOSlk3o5c1OxYdEDiCZMSlrL+OKj07BTmwLIdsnbeTHoibrQ+pDqzWz3jmRLR/WhXpO59Ejit7Id1qNHA8J+/Y/xTz9PY2zo+36xOQPHUgkqneJG2QmtxjP2s9K+TVbOCx3LxaIe/kBuRlzxqZs55V8U1FfVnFeOxNLbPaNcj3ocg0NBXpBlKD1Ycj+8qTwb2JKKNbdEb1VmjmBjlmpZebJvKnegsFbXkaeDF4FgIBG87Kh/ZPX6GkL0F6zneNdz8DvNCk9lwfyWonAb8GjrP7o1Aw27dIlKkynKvIlHXQyhyN9gXnAC+iANxnINvGEyRwVDc6q6D16Uy7vhn5iaxIDaS3LtLVResbg/ltPAYwQI7Of6TmYL0BApgm0Y8h2X8M8lmbiNbdryKdxmFIJ/8FLNhKk3j2cGvDIovicjYX3EnagIPZwSq565iLnrXlbkiXsimyRb0HXFh6b02kp09mP87Rj2QG5KF18DUssArS1V5l/fUNav4bM0kkV+WuY+4+XBhoWhnZxv6SOi66oOoSTYdsuY+ioBHzBVS39xqypdr/jkPy4L7ITrwdsuFcYs+PxoJi23UX4pMOTUD4iSOQb9hZyF58jD2fBzwVvB8FHrN23Abt3WYYX34LyTahL9caSJacHEmvG5LRlkN70a2QDvVNZOPsb+9thmTJKL/REu2NkEy8utE/HAFWp6VqTytjONLz7WvXk1Fw7C8gu80gtCcvgjmnDI6xIbXAn4ORDHKXtWcXpD9KjgVYnH+Vf0BH+CHB7Qc24Q62e1vbwEkS6bQqejYB/IjAeRI5j3wJOSd3SjCRj0EOTgWYsRBMCwXr520BG4OMvKtE0luWOg4LyEDW0xaxTjbZ302i7H9BvYqoOz3RxuCo4J0zkbHzQVpxlGsjrezZqqysbECOKuhSDThmZUpOxcar66HI+yNsnN5azAeLSpuSGcRVh35XpMgYbdc9keLjTiRMvgBsl4hOhwXH5eYbK2s8UmiuZdezrK5TkNJlgvXlM8A/iDQcLQ5tmptXqW6d6oYMABfb9RBkrL46eGdjpPiIdsLN3YcdnW+qbNfFoB/HIOXCbEzBhyJInoQUx+8ixdIcpBiI2pSjzX4RBfNA4OzgmaPmAPU5miMDXIOigBUKnCLC4W4kysKD1qo3kHKscIgZaXTDqLVrW/tuGUlvHHA/AmsMs3uTUPbojdDe8TAEODyTIGNeBM1hxVyJlDlXEzj5UtvPHUcaYEMBfumEIo7di2Tz64yHB6MIVvcix6Micm4qw98l1sYtMoshUNljKehYP76OwDBLB/34P2i/X2SNXx85x8xMUbcFtRPzA3A7L+j9dtIcg4yKg5DhcWM0572OotYlccYP6HVHStwPkaNBMTaHIUeSos1T1K3gj3WRYXWC0XkAi4yHlNXzaE7Qk3lkABzmookcX4oog53Rnm17tIebaeNzA3v+IgvIYhXxDd3QWvgDgmi4KIPbzTQIjGmF1iS0fhTG2gORMv4BBDz6IZbJKiHNLZE+bB4ydEy077gDrR9Dgne3IHJ9RuvfAwigM9Pa8Dg0x09A0WuTAACNXnekWzyN2vr0LNrHXYP2dz8mnWPVaijy9qZo/X8KeMyenYMyHoVZAmN1q52QDLUPktX2KD1f274nxfrfHoBTagfS8Tb+Nw7uDbDx8gDK5D4gksZw4MiwbVtp7zXRmjU+hl4r3zAJ6aUKJ7VRwHPAPSnnt6Avi+wG3ZCh/HVqAc/qBuOjnXM6mrvvouZgUAANl0a2h70Q6PF31gcpjY6roEwuK9h1AWR+CEUhfY/EkUeRzP1jzHiL1sGzbVychebbZPO4lb8NNQfcHkg+vR9lIHiFBDrOEs0pyIH6QeR8tCWa795Ga8f3STSnGr0RaI3vbXPOy9RsYysb74yO4R0qyI4V0OrowJEbsIjXtIzifB/az3U2Po3aN1bVh1QHViv2yH1oGey0KwrecjnSkUfpGyrmna1tnrkW6fmPQs7P2yAnqxuoZXht1ImzsrGfs54V82p28FgOXq2Sd3K06UIyNjr0+lhRHasYj9shm/srSK+yBXKSvQK4FMnIyeRGozkZ2R0PRXuprag5Ip5FguwRVlZWkGNuelXRtDInIt1JmGHkS8DXgusUvjg9kD9BAYTvGjy7EdnEOiOfgNTBgMeiPeludj0J6VILIGBnAhkvEc2sWQeRzuYm4DS73gwYa+fDjfbyiWgVQZvOojavzUN6pIfRPvLHJNiLB+NiOlo3foX8KFdAOqr3kX/D26TTNYaBmq5CslsRiKvIjjOpSeN/KnAw0veNRzbGi9B6sTKyrYxLQbNEv+lgldx1rKpNkU/MftSC8g43Pr0c+ejsivRzybJ/5uhHMgPyaGkX/gG1+bvQNe5k1xsgG856CWhmBx3m7MOqaFY9vy2p46IJqi616zgCDAPaWz1j5ysCO4bvR9A7Hzi2dG8EkmkOQT4W/49EOhybY24gCJBhvLIl0lttiuSd75LIj9No9EP+aVfb9YYoyMEZyEdmBzSvRq1T1OxSByB920dFPyLZ/zVkp9oZ7VOTrIvUdLhPAn+jZtcYhWzwD5LQLwaBN+eW7q2A5OECQHoukgFS442uR7Jxsf9eFvmsvUTiff+Sn7V51R+wqP1o6ch1ANpErYoErItsYl0HOVlGTwK56ZVor2gT+/vAV4L7/ZHRflACGiOA3yDFyqWUgIB2fiCKchRtrEYL8Mf2/a1lTpyLNjivkDA9tpW9BVIwnIIiVk1FjkAX2iL5FgKZnGLfuBTtd6rImq2K6oAcWemSH8hVgEX7IgGgGBuFUNAFKTqPRIJQCoVDtjYlM4irLf1bat/lsEgOifqzQ4LjcvONlTEWKTMOL/XZtlbXYiNVGLRjnVM7fJvm5lUqygBaat8fUnMkXxbNs5eiaCvfJtIAUEUfdnS+qaKOi0s/2jz2GpJzBpa+5T0U7Th0hF46kt54JGfvgoyY45DjUTkrwDr23pgEdezO/Abh65BBo19wb3s0p3clzqmqD3Js2KvOs+WsvZ9EiqV3ivkosg+bbvKjAAAgAElEQVRfRfum0KFyKApq8jtaKuq6xtAL+vF/kDK1GzUQ4lEE+yzkIP8a8YD8MUgpVSjh+lr7nYki8d6IFHKDUBCZqEz1QT/2Lt27DDmqrkoNDLcNUh53K/NZO+n1RPveIoNToQvogiLz340yOjyDAKaplLktgvCU7y/g/2KjyK1m/H9WnWfrIKXuXdYPKSLxjTB6fZDzxjMIwNITyXWvYcDgVD/jjVfR/v7rCOBQ0HoEOc6nyOSQHXCYmyawltEqDDdnIIDBCzbm7wSuSNx/YR13MX7pg5zXHqVlsKwRKWlbmT1K3zEO6ZC2pOZklcohZzlkpN7IrvdERqOVrH/vIVHWMSu/LwKqvRvc2xw5BZ1EwgBHpTZcipIcgwwhx6L1a1qj7YoFSrPzLkg+vr70znNo39qFBPJNqewCxNXd5pprkAGwm43F90kLOMoNcOpkbXwl2kseQH1w3uUEDoIN0loN7RdDR8PWgIB9EvNomJnyZiSfF0ErDkc6yVMS884sZEy9DcuCieSr19B6+RMiQDk2b/Y1vn+NlnNnkV3wKGqR+gc2SutTvqMI+lGM09EIHLsptaAWSYIAIF3RbdgaX+rX2cjpcb1UNMt8Sk3H2RlYBtl4lktBL+DVpY0n17c6z0E2lek2VpenFvQl1Vo1yua2fZBesAjqNh+wLIJGtuxYdHDgiJU1MKjHHbQMwFWMyfWB2+18qUWpD+vQzgLiqkO3RQTzcr+RNohLzsyKzuaal6npcFcBfk7NMd5Rm/Ni9v5V8k22eubkVSoGj+Xi1Zy8U0WbVjE2quSdjHyTtY4LwXgchzKCr43W/acRUGY00sUfRm0fknJ9nAvMs/MT0f7qHCSP/5QgsEwCWllBjrnpVUhzKrKh3A6sFNz/OvDNBOX3pyaT3ollybDrwsfgNEzfSGK9sZW5E/JBmEvNEXhVZJc7uQn0smQdpKV82h3tgW9ADtyFbLobCjqQOlhNJ0qZBZFu90Ck71y//I0RtKYY76+FgJX3Go3O1tbrU/MvSbUXXwPpawfY+LsM2eC6INBREpBjifbWaH080eaBo2x8Xo30PG+QwK8yqGN2sEquOlZFL6C7DEE2IyTzHIn0reeRIHhMzn4MaOUE5BW6t5VR8K1/oUxj/dCa8Rzyl0kSGKuiOmYfi1WN/5xjcUkdOw6o2mjORGvu/yC//97I/jgXyTfvAJskojUPOKmoMzU5dToG8kJ7qujxH9C8G5hj54Xs1hsFxT3dri8lvZ2zDy2Dp66G9o53IDBkVHBsNFdfRU1W+z3yRRkbvFMEWniQNP5GBZ+GGQ2fIMgyjvbGR5HG37jor82xDJEEOncUaOUHyG9mJgmy/wZ1XBcLmop0jj+iplPZB+0HGk6KteS3gD6o+gMWxZ9Noj+0SfYZtFAegRy7XrV7hbI+xeYxKz0rpydyblzTJvGfooiu3VHkrA/DCTCCziS02Z9sk/Xl1MBOxaR0LAIKbBJTR7TYnmZ0bkGOv8OC58WE9D2kyN08cZtujJxgptoCcg9avMZbW9+MKTlQFJZ2p5Ilc7YqKgJyVEg3F8hhrNVhJyR4PAWcHzwvxsbRmNC1KLUpmUFc7fiupNkw6ODguNx8Y/SWQ9kF9rDrbkj5XyhQt0Xr5c7lfm2kfxeHNs3Nq1SQVREpNFco3TsaODG4HooMR/8FZjXKMxX2YYfmmyrquBj1Yy+0lziodL+Y17ZFoKMkzkZI0fY6cGBYHtpz/Awp5Q5DkXm/RxpwzDAko51FSfmFgGPv2/lYpPBIYeQYhBT9RTsehNar54q5x/p2HWpRTxudc3og8N3+pfvFmrUFWhv7hfcj6zca7UP3Kt1fGzn+XIwiR+2CDB1RIEcr+3YbG99FAVs2RJmx90BG6lPQGjKOwMkxol2XRwbcJ5GCMQSLXo7WqUH2Hd/D1o6I+hVj4d5gvB2NHBD+FzjV7vVG+8f5gudE0t3caB1KoJQOyw/atRsJnKytrLuMl3rbdRhpeVnkQB9Fi5aO95dRU9iegJyd7kOyRzKHA+Rs0M/4Z2nkHPMtakFGuqG9ZZHZMYXeKAvgsEqaaJ18Ayn8eyAw3Or2bD1r46GkmedCJflPkB7pHaSfWhM5qn2NOmDv1L8U/FGnzKWoReDrhxxH5tHSCHEqcI6dD0lAswXICTnIfIWW2XFnIXDgyAzt2iVxe26MdJszkc52KpIrVgveO6sZPGN8/y5mrLWxsg9y4t7f5qQk8w2ZAU4B3xTrRDcENrwK0wOE/Wl8Gw0IRk5V3wFOCO6VQRXRGQdKdZyNZKirEIhqG2RIvR3Nr28BuyN5MglQ1nj2MePfIkNl4fR/IHIoa9iwiuTrLxkfDrR6hGDDou7HABfZeVRWzLa2d7N/yIHqMKTXLea9iURmqlwY6on2iI8gGXiO3Rti887dwO4p62B8OtHmnjuQg0WR7WAzJM+NSEAva3YsOj5wpNCpHoecVMch3UJZ97ATkse7E7k25+7DkFfJDOJqyzfVO18UeKfON1yHgnEUc+kOSDdweKLyK+Gb3PUM+LBDAiur4NXcvFNBm1Y1p3bo9bGivqwS6DwcBTJ5Mrg3EemLZqTim3rfi/SKN1DTxX0WySY3U9MNptAfZQU55qaXk2bAexOs7EHGr3chPfmKwbuTI+s0FsmmByL70bHIJrdK6b1dgEsStmVRxzBQywbISfaTIJ02TpIFcg3qnC3rIDCDmrzRDck516F51iE7RLRj9ad8Q7P1C4cAXwiu5yB72D4ksp+U6G2L7KtFIJyByFZ1MbI1DibQlUXQ6Ruc90a+lQORrfF1pEcueHk4lskx0RyXC3CYtY5VtukCvqlz6TolED9XP2YF5AV0N0J29w1sLvsNpgtD9tvp1OTHWN14VXXMDlTNQbPqsbikjh0DVI1kqoeogZ0eRDrxwoa1I7BuJI1hGDgLrf+XENhpkSy1JvAiwnhEyzxIv1/4LZ0MHGbnnQKe2Qz4lp0nD5BRql+yzPRBuX2Q3/EoNFePRrLpxViQGOQ/14ME2fFK7TYPyW0z7N5jwHPBuz0S0FsR2adHocAiP6EWALMrNd+HL9IcYOwHmJxq966ye4ejQLVTm8Uzi/uv8g9Y1H5IsPoKtY3VNOTEsaddD6LmYJlik5OVXkC3BwI1HWvXfRHw733kgJBMMUctNf06RvNyDEhlk3pPbHGMrSMSuLsio+JdSNGwAi0dVk5PWb+g3N0R6GEKigRQOG4WkexT1C1btioqAHJUQZf8IIfxSEDdL+D7EcjIcj61COCTkUP7tEWpTckM4krx/w3S7NDguNx8Y3SWQcr+54Fz7d5DwA2l97ZDwPUoR9XFoU1z8yrVZFXsY3xyK4oYX2xqpiCHtTCiy7LEb8ar6MMOzTdV1HFx6Uej59DcVkQZ7VJ6vpaNoaixEZR3EPC5ghYyNO6CogItgzK63YEUAlsV3xhJcxkEHJuL5KkTCSJhGg//HYE7YoFcy1HLVHclUrx9CylxLkIKnpexyHwJ+/FWa0PH/IabfihL1gEkyABoZe4BXBJcr2f8upmdz0YBT84kkSEX7UfPt/FxCNqTvmO/wWjffBoRmWrq0HzWeOdZJOcfHDw7FzmOfD+WV+37C3ljrtXpuzb+T0ZAhF9jytbUP6T4fxsp5x4zursGzz/hK+One2jAIYCanLSG8UjhvP1FFPSknKGnmA+XjaxfmJnyEOD+0vXLpDE4dgvOiz3+bUh5/BK1bDgzscw1ifqvCsBhFTTHoTVpUxsf+5R4+JOxGElneHDe0/qwAMSsgTLVFdk6dyVBhLzcP+TUszGwM1obbkYgshcxEI69dyBweao+DPrqPLRmjEB6jVuBC4N3mpIFrMlt2hUZ9p4FPqJmLDoK+BWSdXZC8vPGiWiWwXF7I11SKOM8Y+27fBPqnA3ghAC/z6L14hS7dxEyfK4bvNfL+KshWaDM58jB8VXqAAFpuS5eSDxwZSvrv1URePRxZOgdj4DWN1DLiPoiQXCCCJqDkfPmncG9o43Wjta3hfN3I07VhX7zgKCckciucQsCN3RGjjMf0gRdfK5fMB5XoObIuT+yO6xk16sbH6+UkmYFdV0T2aqmoKy8b1NzeBiC9NkTE9LbAsmD66H9445Ib3QLApW+S9qso1kyudHxgSNjjDfmBPc6o/XwMbQ/HWc0f0ICGS53H7ZCu9lgteTOLwsh74Tz6dJ2fjLSSxUBLNZGurFpiyrfVFXPjLyaHTyWm1dz804VbZqzflXVswq+qaCOlQGd0X5/IHKOfw7teYo57iwMiJSATgh83wTZHUfY9R203M+dYHw7HdNDRtLOBnKsgl5OmtTWpC2Rbnwu0vVtZt9wm/VftE4VyaFvIz1K4WfXF+lPrkZ7jr5or/ojIm1GdejPQvqha5Fjem8kJ1+OwIiDmtSXTc06GI4ptFZsTMvAuz1RgJDXsGw2CWhm3RdTWgeQf+M9BBlNUMDO+ygFlE9AewzSUY2069FIT9TJeOlyTEcYSaeH9dGhdt0F2WvvRPawgv7MFHWkArBKBXXMSq+KXxX9GNBrOiAP2U7ODK73Bq4LrjcE/goc0qT2zVHHKsZibjBu9rG4pI4dD1SNbOKXU7L1obX/cdKsxeOR78KpKLDaOmhfvjdBcEhkK3qGNLai8SjY7xXWhlsAvwQ2K723GdJVdYlt01R90iDtwxFodHXkS3a+/U5GgUKS2VStn36CfIzPRfuNAmD5DeDrKdoD7XHeQNkaN7V7JyH/6VWC94pM1in9OUeh/VURuHkStUCxc5CNMyqL45Lfp/RB1R+wKP2QIXNvJFyFkar3QhvmaARwlfSs7AnUwGmrIeVKAUDshYSFu4P3G3ECGIccNA8kiNhkz9YFLkBG5H2QU0nXSHpDkcGkJy0VgT0QEPB6ZIRcz+gXSqaGM0eE30rNAbAAprwe3NsSOcfGZjfImq2KCoAcVdAlP8ihB3IIOaDOsxFIAHkEOel8jwTZKnK2KZlBXFZWf+TcWzjG5Yi026HBcbn5xsrviQBcs9Da+GXg/4ALSu+NRsrOdmdSXdzaNDevUk1WxQkI2L8qclR7CDmrHY8Af4daHecz9jUyV1TUhx2ab6qo4+LSjwWfo/n1NQyAZPc7Bc+mIPDTtEhaS9nfg5Fs1Q8p5R5CwJFf0FKB3rDDcT3aKKvJEUieOgDJUrdSU/x9ljQgoNOsLyeitWRfpNRYNmiDC7E5L1E/9kGOlIXC3wW/zmjveCoJDXJo7n4MGR7vsLH/NWSgPy1h3crO+P2QPP6o1W8/5LhRREKLVqyW6O1nPDsW7RU/svrOtLY9AlN+xvAqChZzK7BL0L77W9/2tHtHYxk0E9exL8qoNAZlPHsDGeMfppZRqlizlzb+3jiC3jZoP3yv/S60+1+0skNngSE0kA3MxnwRaGhZpE8539rVofVpTvD+0UhGmdJoP1q5eyJF5nSkKO+ClLm/oBbcZWOkWI0Cq1IB4LAKmgG97taHhVJ8hvHqvnZ9ErB5Ilo30tJx40qks+lh15ug7GAt9EsN0upP4kh77aC9NnLC+xk1EPJgFPjrFpsH3iRBdtyA5ibG/+vauDgPyTlrIQeZS+292HbtA/TJ2JbFHDkUOeM8jAxhRQaVPdFe9g4SgRyo6RvXQYaVIuL+rtZv2yPd1VcIMhEmoJcN4BTQnowFajBeecPGpTP6l9HS8LtUZB3XRY4Vk+26AAJ+Jni3kFP7obVro0bpFeUhWX9NBJJ/kZqMPKzgMzS/vhrOUZFtuxSSi98jcMBFjpW3EKHnsLLvxByqS89WRjaBN5Fx/BXSODj3p5S1IecPOTV/aPPZ9XbvYqvjg8a720fSGGh8EO2w3CD9IWjtfzq4d7rVbTW7ThJ0xMoaimSMjYN7XWy+OR2t/9GyePH/ZM7kRgcEjlDbh56LZTIvPe+ObI6PogjZT6WoYxV9SMu1sekgLhQM9noiA6QsrLxTojsLAQtuBS62e9chHcQNSF4tbNaLFN9UUc/cvBrQzQYeq4JXq+CdzG1a5djocOtjlX1ZBb2A7lxgnp2fiAIQnYP2PD8lQYAcJFusY+crIB3nA2j//VnklP8SQRZHJKdcgekOI2hnATlWRS8XTYIMIkgv9Qq2HiE96u+QznYNJD+OjKTnjDePCe/Z36WRHeVu4Jv22yZ8J0F9C6fuETb/fEBN/7+zjcck2fiCemXJOmjffwKyMbxq93YA/kQtiOIMq2OUrgrt/wel7Jt20J6OHMe3t+ur0P50eyRTfc147PbEdIcjO/ypSE/+BNKX7WPtEe2zFvDMOihAVZE1cjdk09kp4KMfYs7dEfSqAKvkrmNWelZWTwKf15R8uDD0I5kBeWjdmITkiwvs3nS77k4tUNztyOYxdBGsYxVjMTffVDEWl9Sxg4Cqg7YtxvswJE+dReC3hXzkougin9d3CYI12/2dkO3xbCTHbYX8xqPsDVb2WOS/tVvp/t4oSPUctKfcCOFIYvXHvYPzLHJc0IfrIxtHL2QbewrJb8tYuz4R26ZIZpsa0NwdOCfg4c2MZws/9hRjcTCSC+eU2xXZUH6B7NQXIxtWah1HDxTA/XJks/0a0jUUAZeyySWL66/yD1hUfghZfR4SmHdGivlD7NlqyOgfBTqogh6K0F4oFkah7BvvGc1JyGl0x+D9XsBvsWjgDdAbaYvVBfb7eXkyQ4vlvcDfyotaA/RWRYboZ9Fm+wxaOq/1RoqGLyMEexIHp2Ai3wYtwitYX96AgGO9kWD1DpFOQGTOVkUFQI4q6FJNFqB+SIlaRBgsBMiiv7pZf0+m5mgVY+TI1qZkBnFZWUPRhuZC4BgSpE5uA80ODY7LzTcBzW5o/iwM4kPQvH518M7GSHEe61S9uLRpTgBwFVkVx6Csu0eW7u+MZIFfILngVRJEVayoDzs031RRx8WoHwehCE5FtM+9kTPnVLsuxt9spICIWj+R0uEKahnybkMy8JPImaIr2oc8ReDEHUlzOeScUshSo5DTxgpI8fIj5ABwN3LUbSFzRdI+D8mK8xkykcPqm8Rnjh6M1qEiQ/ROaB7dpMQv061+0Vl5EFjsQmrKo1tsrD9OLePRCJShK4WRYxQKgLEDAaAI7Ufvsb4reDVVhsNBKMthASCbhCJzFbz7I+REcidaP5LwDZIn5iCHv70oBftBCvt3iv5NUM8yuHKAjZnn0V68F3K4eJCag8DSCJTQMO+i9f9has45Y5Fj1cF2/Ti19aXR9b8Lkgm3RQbwk9DatC/wBeRQOY8gyJL93+FEOligPf4vUBT5IrrZKsgo/037lu8Bs2P7j4yAwwppdipdz0S6qlXtehpyjN0jllYd2qOoRd7bEgUEmmHX44AvEQkyQ2vfyZQymNYZn6kz1HySxcx48xEUNKfIzNkbOecdS1pgRSekA9wEgaxexbJ0UgMCpgis0BvJ+nMwJ+dmtOP/Z++8460qsnz/BS45XQQUEeEiQUCQoIDkKEkkiJizGMDUBsyomAMGVNRWWxFz7FbbnO02tDm0oXXs7umZnu7pme7pmfdm3ryZN37eH79V7Lqbc1HOrlPFvdw/zueevc++tXZVrapa6bdWvl0kz+yDzpEjkePRgfNbonM7qJPD1sQdCCj2z946XICAgW8SIMGBR6/iAKc66E4CrvN52PhnHHLMhUzMNQc5yJeivdxVM94F7d9nes9WI5vEhDLotMKSeiFZY47d623z5gL0/4AcZS3tejLF7HKOX8cbn45HMsBiZOv09fGaAON5MyY3kJPVjG4rFKgWInN0c3TOXoDnaM+3WcG9oD+yL0wwvnwauN2NJQp03LnoO6Cz+HoUXNiyaHubSLvG/u6L5IBl3m8X29ppF/J9kH1uLQo8bkIGyC+ciGsjNCtaHcvNGQ0YOGJtrCBz8rfI/dbd+94hFM2Yc+jRigZWszZuQfroBnZMAgJwE/POIBQwMtXWxe3ADfbbSFSZZ5PP3s2Jb1L0MyavptjjUvBqTN5JNaax+peyn7H5JnYfU9DLXW9rY9jZrk9DfpBbyXStQlVukax9IrITf4Zsmm1QktcH0Nn5H2xYwSKEX7fiIMeU9GLQRPrDmVjyG7tXS9ZBCfqutu8hfBxNkB1jsl3XSkaJJVxAdpyt/N8Cjek2wFHIbv0O5vcg868ErQJI5KqDyHb7Vzw5Bumv/wpchUDX0wrSaGX7yUo83TDkPG2E9lhkGzsHVeJaau9zCvJ7/Bz5AHdH+19VEV61vz2MbzrYfL6AzqnONranBeyfO+/7ITnxv1ASsGpkw34ByZNfEMDHYX+jgVVi9zERvY7W7h4UPGM3x3kkASDPoz0HxTGcbtf3IV/jSAR+vg9LWFuf+phiLSaiGXstNvaxAYCqc3TnofiQh1Bs3EDgSmQDHRGCltE5BLjAzSneXo58YgejGNY1bh4prsediiXBJEvw5mTkeUiGexrJ0AuK0ERJXM9jQ8BhDDlurvHOVLvuiJKdPE7mx2pV9H1szIaTgfymIZ+tDxh9hrCJuIYDD3nXed4Zg8CIxwLjAvTRrYs+6Mxqb+3fgM7ENiix0hWVntfGj81J6heoLx+kRP3SDo8qFBz4C1uUrxIom3NMerZpz0MVDva3Q7EFCpg7DVXI+DXwNrUz8relzMzuKED1Ae/6KNvoxnr3xiBjq8sIVGTT+REGALBNdTUKWPWBgLOA/yGAkJOjPRUFwbrKFC2QsHE2Muq8QEHjMZGrVZEAyJGCLvFBDtXe9/vxqhiQCVgDCZtRNfaYRgNxWVsD0H45wdb+cryy2N68Bqu8QAMHx6Xgmxztnta2c1ZtiwAkq5BB9W1sT20c082HV0lTVbEf8A1ZFr7mSK6p8p4Zj2SCf8GqPNSzOWzQfJOij1vQPA5AGbIeQoH/g1DA+kokIy+093CZnELoHE0R8O+HwI52r1fuGee8ClK5BxkzX0TJU1wgw4kIsPIZlukVyVeFZAEbv21z9y5BTr8hdt0FgZC+pLhzbEekPz2CgjZ62/2lSH9ciAwu4+w6VJbzdigY5gYyA1/r3DOj0X4fIpBiNAI13Gjjtj8ZSKUFMlS/UO5aqIPm/kh/PIzMMHcQOrd+g2UFt/tFs9X28Pkd1lc2vN7otzK+OSkE35SgPwWdjSORYa4zkqWa277wE2+8m6Hg7qIAwKYISLind+9g4MbAfRtrvPF32P5JtncejoIc/g9hgyrdmXg52reXe/PaHNlB9iYzIheVi6MADlPQtLXhHJkDbNxc4qrlKDjIBzoHmcf8nGAV3ez78cC9SPf4gEDVXJFh/BJkdG+V+y0YsCnfR2QDu8Z4cxKSiU+3Oe5CoIrDJegfjmSPdzA5ADm0Dg9M5xDklJrPhmDSoMFN1uYCFEg1267bIWDx9SgJwscErviIdOMvyIK1f4BkAue86UqW2CpEsGoUgFOO5o5I598FyT1beb9dAcwJPKZ9kYzcF9nKv0GOcqfjjPTGtyWymU8uk1YVAgH/0vjDVVLtggI6drV+/8jRDNjPGSgo7wikCx+KAgEWo+ychSo5oKA0t9c8Clzs/daM7Lw8CLOFBuSZ3iggdQl2bni/Fbbd1EGzKbIZvYmAlK4SQSu7frwCNI9Dek117v4EcjpWQJptbc85x673RgHAx3jPFK4C6vGOk8NbIJ3ufO+ZMbY2CieP8ehFrY7l0W9QwBF0PrikhWcBz3m/VXn9XEKmZxQN/E82h0QEcZHJvp1QUoWa3O+LyeSQIIGeMXnH/r8ZOgd/h5LDNLV7vWy/uYdwyX9S8k20fqbg1RzdKOCxmLyakndijGnq/sXkndh7XIo+puAd+z4F2fl72fXdmI3Mrpcj/+tUAlS3tnk6Felut+d+a4r23IuQLadNUVq564qCHGPTS9THlsh+sR2Zv+YB4F7vmQPJ7A9FaPmJKK5FdiO3xpsZv7RCsXmFqkSWGlMs+RWy53yE9HIn441B+9EOoehauzGrDrr95Uxbb1fb+nT3d0W+l4mB6E1GSYCOIwe0w5KOhf6g4gM3kNmJ+iM7+XHeMx2RnfADwiQ7m48SZb6C/A4Tvd8mIntZkCIHuXa/QnEUxyKwo0t0NhDt387vWUSuigpWSdHHBGPqkpuelOcX75lCZ+HmMo9EAuR59Oah+FFXKfZMu38liq14l/BVlWKBDqPPYUK+ibb2G/tY/0HVOdpTUAxFbxRj9TO7vyuSDS5EsRYhzoyLgDty99Ynq8DsSPnfCtK8jlwxKI+mA8VVkZ0zRc6qjjZvV2G2zNzvVUVp1EG3J/LFDbTrPsgH2QH5c59Ftu1QdtUOSIabj3zGp6IENRON7gdYEvKCdFrY337Ij1LtzZ3DG+xCIJk/R3u+9eM+cgnyEVj3/VJz3PipzCf5C2yuH+/w6EhWYWEmGSigA1JU7wDO8v6v3IDcqPS8/29vi+7PWDCn91sXFAj0OLDU7rUoSG8wctR29zadJSiYzDkJB+KB4woeHqvJqlU0R8r49SjQqhlyYl9KpqyHDCJdhgSNQSjD8qN2iHW0Tbd9QZ6JWq2KBECOFHSJD3JohZTDs+16JQqSzwdzTELCZJf6NqZee7FAXDuifc0pM03IBYfYGKxAoNzCQhxbBjgu9lrsTM5QiwKdTveuuyFg/LdYEGDR8WzIYxqbV0lTVdGBKL4hczQ+BdxS4tnWmMxVz+awQfNNij5uQfM4AO1f+yH59FyyTFIDkEz+CZI3/CCIcmXV1mTVGpohA9Jao+0AHu2RseNDwidVucjG1AWl7472oPO9Z4o6xlvaON6CVTHxfrsEBf+2QoaxJeSyDpdBrw9y7B1i15cjo5Qz8OyHKhw+bPcLZeOy/60mAxK0RQC1W9w72P1OKPD4I8KBDjsiEP7hCNR1HQoCOMN+74KCWEI6clxFvtVIf2sD7GDr4RTHy0XH1P7/UiSvdsjRPwwFIjjA7CFkFR6DZORGhrdPyZxGLshija3Fr8lVj4HPrAYAACAASURBVKKMQGuP3iDgCI9HnycDkc1E9oaOeEbrAPN4NwIbHYYX5OE9s8T1O9CYDsSyVqN97WMM+GDjXQg0mu+f/Y0COIxNEwWoDkXnxnEoSPV1YIity5vYsKJUqLWxJ7DCu/8a8GP7Xo2Sy4QMAKhGMlaP3P1dbByCZKrMtT0XOcH8xEOjkZ1sLfCPwJgAdNyYDkPnfGebw3eBfe23oQQMVvH4tDlyyvXK/T4JOJ/ceV0Gna5kFWKrUXKoHsazI41PmyMdaBUB9MYS79DW5mt7r983IPC8Oz+CrH0iApw8vtkZJZBw2f7PxSrUoEzWtZLJBaDnZ60cA3xo10uRrWFe7v+6Urx67Cgk47/t3euGbIL3oCqAMwOObVN01q5FZ9YodFZ1s987oASEZctVCLD1GFkyvuHozD8w99xUFDizbbm06pjHISi4oXvu92FItipUibsUTe96D3RWLSZzjLdGNpBdAtIdh0C/eb2jp+03B7v5DkXTozHEeMZlBV5kvHpcSJronHoT6VKT0fnxNyjQeIW9Q7Cqo0SqjuXxaYMFjqCkIVehM7Gprf/rcs9NQMEBO9a3OfToRQVxebwzAdk3N7BjIp3nrfzeUF94J3ftMrbv793rjXSCYfWYb6L3Myavpt7jYvBqbN5JNaap1kYq3onBN7H7mIBeK2CUR3OltX03AoxNQLbqXt7/XITk8yBAK7TXLUOyyAqy4FTf5rqOnO1lU8fUvlcc5BibXkKafmWKBehcOtCuX0MJ6s5C/qrCwaMoxu4l+z4GxeLshRf7hvSt18glXAhAewFK9j/RrqehpDzHogpynxAYPGJ0Kl51kNr2lO28+68Dt9r3sYRLjuHzzQA21I1HIjmncFB1niayU79te5gDxvZHyQbP956/BLMfF6TbA/lqBiF9/AjklxttdH8egm+QLcp//4OBNd71BODf8ZIABRzbWOC4qH1MMabIrrca+d+aoORbPXPPTEcAwXb1cR49elEBeQi88SqWVBzYzWj/wHvG2cxDJTqL3ceocxiLZsr9rbGPFVv/0XnV2joS+RcXonhyl6C7hdEdWLD93m5NI9/YA3iFDMgSVywhgF/M453p9n0P5P9vU4LmhQRKWksmN25lY9kr9/sMVCWwEDalDtr9kH6xEJ2XzyP/3BSbx8IgOa9/rrL4EhSvNR0lHVqK9u9nCJBsGPmnz0b+r7ZINlwfG+a9zz5Ih2sRal0g2f8l5HPcH/lsHd5pZwQKLByn1vjZhDlJ/QKb24faFeJGosoDN6GDcYAtTpepshMSou8DDqoP9KydWsA6JOi/gjLzdKM2Yry5LdYLC9Crtg28CXKAP4QXWGXPrCQHxsm/56b20f7ORdnoXTBaFTLg3IYFU5MZPYvQ62Mbtm9QnI0M5Z+gzPFHoUCu3qF4lUjVqkgA5EhBlzQVeZrbev8hsK+tkWftejd0YI4nUCWHmGNKAhAX2je/Av5Kls2sZCAKcu7cXtfvm9jPBg2OS7AW29sYrjW6LuB+jK2FHb1ntwVGN47pZsmrUasqem12QqCRpxAg5fwSv1cFoJNiDrcEvom932wR82jtPQi86107mbgDWVBAW2TQdsaJcuWbHsBvkTFlPzK5+woENBiKDA274wHHyqVn/7uxinyD7foco9+yXDol6E5Ajr9VbOh0fMzNa8G+NUHBWpeh6jtufpoh3bE3mWGlBZIvC1cAsrX/T8B7KNDXGQOPsj47YNfuKLtU4Xn0/x/JwK/Y9+Foj/nMePlCwmSM3lhFPhdIfTEWJBCQb1og3fAlt/68Ob0bOztDfxC46DVguF0vQsZOlwClhiwgYH223gL0phlv/DNwDAJVHoYCES9FSVaCZQHzeKcJMhZfjznjUID8IPt+AQpKChGUNwPpH+8i43QXJJ9+inS63+MBrgLQiwk4jEbTxq2lfe+Fgn8ckOJcdG7eimTx80P10aM/G51HU3L3nwJ+HphWUxvHrymxbyJZ9XIMSFOQ1jZI3nCAzVuRAb4lcuzcgyqf1SAg55SiND3aU23t32F97YZsKuuQTrA+4UDAcd3R2l1v5/N+H42CZcrmV7R3L0N7WSu0Z7+JzuK13prPy3ShzkWXXKEK2VbPyI33W+ic3CoEPe96Cgrc3IcKApys3floP70LnZGXGf8uQ/abnxKmUrVvN15N5tQ8ALjKvs9AzqrCe3iO5mAk5ztw2jNklc+GI9tkkL28xFyebGvyHSywEdn9RwSitwvSG/dFOs/BNmfnoCC13QlUbTxHdycUVNmnxG/tkNy6PBAtH5BzMmaPQokNXkV7aWv/2UB0+xuvDCrVtq3PhwOP63Dkr3KJ8AbaHrPMo1k4uNGjNxCdEQejAMu7UfXa9iiw42QsWD3E2BK5OhYNHzhSTVZduBna656xPWBvtL9+RdgM5zGr8SUBq1m701HyljoTJyCgxaEh+hiLdzx605GsvxSdj+OA3wD7eM8W1v1T8E2Kfqbi1Vh7XApeTcg7sQF5qapGNujzMdFcxlyPNciXeh+SE9siPWA7FLx6C/Af5BLjUSz2oGn+O7IHzEayyHJkj7wU6QKjgS8pAyxPZJBjbHoJabr1OBnZppqjgNzbyYCAByNb8tSAa+MxWwtNgTOMP89Hes8cVJ0vaCIndOa/i8Ug2JrZHul3FyD/VZA+euMateogsuO8g3yBVyO7eCujcx/yA8wJQMf1byZWpT4/bjbeKzGgYyB6W3v39kX2qj2xhO42l9ND8o21OwDPJm18cycmO2IJkIrwDbK3DbU17/yYU+26FVl8zjpU1KFbwT6lAMfF7mNUeh7drZDf7Yd49nZqx/4uQHtg0biKlMDRFIC8jggA7BKbtkEJun4FXGb3giXjitHHRGsxNhg3+lps7GNF+phkvyGTAbazv8cCL6Nqgz3tngOTFU6uhPzsf0G29iZInlqB53tD8t2HeODAgrwzD+nC05E/6i1kr2rlPTcCxQiFqFjnxrQjXqwmtXW7adb34EnOrP3LkSy1l12fSgE8TB39m4fiClwhrCNRnMxMu25PCf91mTT7I7/bZSiuYAGKlZ9P5u8cg3ThaQH7uDU6C89B+uJbmG6BzqxWZLGBjQDASJ/kL7A5fZDCvxhl/98VBXLOQM7w921h/h5VsnPgkk7IibXJh2Vseo6m932kfXa2d3mMTEhdn5HHNvkPkLC5qVWAdrLN7EUUPNnDPu+iA8sFpx6FBVkUnMMBtrnd7G0wx9gGO8577inMoBKAZhUCbf0jCqByWc2boMPLBcsNAX5JWOd4rGpVqYAcMYGOSarjWZvNkfDkAm7bIEDwveiwLFyNJ/aYEhnE5bXzIRK2z0T7qNsHSo4bBQOPaODguNh8Y+0MQufDYFsXj6Iz5BTr07HW/w2c4uWsjy1hTFPwqtdelHOqBN1Oxiu/whxYdn8iciSFyHAY/WzcEvgmQR8b/Dx6bXZEcsUauz7O5vMlBFY5hUBBlbYGn0JAwBetv/chp+4LKFudq6LTuyhNvrsi39P2zFgkrwfZ4zwau6HAglpAQBToWLjamNfedkhGvBjtpycB/42qEP0a6UHrdZFANB9HoJtTbRxvQxUeLkJgEre/F3Y6+v+PdKlqo3UkkvFcwPECAmVzZeMV+a5HwXEdgPsJl8zFBai0QAbiF3L056EggKBVXJBstRMyiF/g3V9oc3kqgarxWbujkSw11Hj1QQSwbIEM2jPxzuhKfIxXrkM2ld+TBeqfFYKHkB3nEeOTHdGZuRIB2lwSpxB6TlTAYWyayKZyr42fy6T8Kh6YwcZ4AgJ5FLblWHtu/2qGzoaFSPaZbXuPSyb1eoh5LPEOB6H9vKW3L7hx3wE4OQCNvVCGw45ob7scAXB+jM7HO1AwV5uitHLv3wGd+W4MVwB/T3ZW9CYDIAWtroLOqaPse9Pcc8dgVasK0GqDZLcrkAy8Awp+m2S/74r0jdaBx3Q20qPOQ0CqrREAcTUCxn+C7I43UsBJ5tGbaWvyLAQUnYrsZYuoAMDJ2mtrvLmrXU9GTr9LPJptQtFG+9uHeKAK5CC7F8kHX2HguFB9tXn8HRkYvw0Ckb6MnJyPEy4AyM3lHOBc+34SCgJ0/DoMOQEnBaDn9rFBZBUp2yN54Hm0zz5BoKQRHt0hSEa9eiNj0I2wMrmrSHkGOiPOtbmcjs7LfQL2rwkK9nvL1kf7/O8+/xak1YIskLGd7TcvoX3NBY0ejBLoFQaq5/rYBwWIn2f3tkE+sHUUSIZZB72oldyMZkMHjrj13x4vczLyeVxjnzVY8o+iNGPPobeXRAWreW0ei3RyFxRfMlimnvLOLHQuLUR7uasYsztKSLR/URqp+CZFP1Pxauw9LjavpuCdmGOaeG006PMxUR+jA52RPfG/gNtzvzU13roIxRkVsjtgMUWu7RK/VyE99lqb57l2vztlViEnMsgxNr1UNO3/F6Cz0cmHrZBN/DbCV3H1QTAu+X6VvcNqZOt42OOZkMlcBiJb5hHIJvcTVDkuWFKsEuMareogiqV4CwGQTkO2PwcEbI5052DJo4xHfknOFp2b4wOBJYHozUE26otcm6iy4h1oP632ng3lF/MTVt6L9HIXyH0Gnl8n4LjOQf7c0+36PhQkPxLZzu7DbHVF+kcCcFzMPqaiR6aPz0J76HPA3vnf7fu1mA+5ns5jxQF53lrsj/a2Ziim+xms2hey795AoKRqMfuYYg5T8k2stdjYxwYFqvZ9Ko8imaYDknfWIvlmOvKz1pmsaxPouT18PPL3NUUxFNcZveuQHPkrwiZVrUY+jOvtegJK6nAeKuK0wPoYkuaeKG7sMSzRcO735mhvDYLnyI9xbn53Q7pPMFo2hh+SVaNsYX/n2tgGTYxpbQ9Efm+XcGRve4fHbG0ETXJClhh+JEoa8z4wwH6bbGO6QaLOxk/lP8lfYHP7ICHub5AyPMC7vyMKGn0QBV108n4rErAajR4KEHnPDozBCMi0DjlzT0bC40+QofPPbvNByvkmByAj58LHSAEebBv18fZbL+BJZDhagwIrCm06trG9jwIarkZBx93skDgKGTX2R0LBF4TNcniU9Wc8CgK4ADja+30synxetI/Rq1V57aYCclScLonAMXW8izPiNEVCbXcCVHKJOaZEBnHZ/3WzdTjcu3e57Qk1+bbZMBiwXLoNFhwXm2+szf7onDo+d38xUjb+ATmt3iUMiKvBj2lsXiXhOVXiXToi4MiTyEDQHyk8IR0c0c/Ghsg3qce1Ic8jCkw/iMyZ2hEZGX4GvGF0t0OBSD8Ehhaktx1wjX3vjjKp3m+8cxwKJP/S1v/bBAqQN3obq8j3YwTMaYIZ6QvQ6YbJFrn7uyEwxxqky41AjuzdC9LriwKLnRN6O6RDPYb0jhq7Pw/pQYUDOYw/r/SuHwJetu9TbCzfR2CyL/CCMMqk19Xef4MK2yho7VvgTO9eyKyK31WR7zIklxfKOlZXH9E+ex2Sbfojg/ynBDAcu37Y33EooGs7ZKD7hNr66t5F14bXluvbocA93v3FaN9bSpmZonN0/ODXFqXewb6PRM756d69wjyEdMgrgT9iVbfQ/n298U3/UHxqbUcBHMam6fFLaySzXeD99jiyUfmBHK38/ytAdxcETHFzd56t+ddsXu8Brg05nkZnoK37Fmh/f9J7BzcWoYO5t0Jn0/52vQ9ZJantUFKrQvt4jt4eKODoDWrrAOegc2NAKFpe252973sD15XisyJ8mttXdkJBE5dT25Y7H+2voeXG3ZF9dTzShz+xeWyPwNUXIJ12MjpDilbmno308pm2Du+z+wuQHBkM4JSj2xIB45d695aiM/JcPMB8me13wQP0I7lxsX13Drlm6Lw6gwCZ6nP0ByC5yQEAd0B2xg4IdPhzAlbIMhrzkA7s5MjWaC+/A8nHofXjJnV8dzbOIFlOczTbILvN83jVKXzagce0Bwo26IkCrL5Gsvl5yAcxEwvUCTWWdn0ACjaYQS5pQ76P5YyvzdEMtIceAzxu989FgAAHzp2EZOdglWO9d7gW+chcRvPOyJdzDzk7U6AxrXh1LBo4cKSu8bX1nufLZvnn6sMceu3GBKs5mbAHma/oMCRHuUCSkslbCo5vCiDXmUgmnoxXJcd+mx5ir0nJNzH7mYhXk4DHYvFqCt6JOaaJ99QGfz7G7mOq9Wi0u6DK7Rcjf2tbu+8nPFuHJc0uk0YVsg/d5N0rFcy5vuJj/pky6UYDOaagl5DmVkj372E0RgCH2G+LbH3WBOZT37ZYlfutBZnMFQrI1R/pM+2QDeVVZFOpQvbUk4vyZwnaFa86CLT0vrdD58QoJH+8ixLxvoR05uFF+lOCdlvby/rZnE1BSZWc/hgsuaG1NxHZ2HZCYIpfYNWIgCUIoLd1YJpzkK3mFuSHnIb8nU8i2e4bwlernWdzdh8CH5xp969EgLJ3CWs3igrGS9THqPSs7d1R3PES5OO4DS+hErl9r77MI7X31GiAPATYeBvZax9C/v9TUTJel+wwVNGRVH1MsRZjg3FTrMXGPtZjULVHcxzSjUd691qj2Jx7bMyD+Y7wkqt593ohrMHtKH51ut0P6Vdpj/mn7XpnFE9xD4pzCIk5GIv21R7I3/A5nm5BZjduWZRWHfR9u/UI5GcJ7Ys7AiUYGYL045+iM6QdsusGTzZsdF3chOOj3tbHaWR25BBzuCPSyR1IfT9kezgfOB0BAIOOaeNnE+Yn9QtsDp/cQm+JAhw+ApaXehYFQZ1dX+jl2rsLKYdrvEXZG2Wt3c82+OkEyHQM7IsHorJ2X8GCK5EAOx44miwbUbkGh1bIiHm8d281cLh3vQcKQn4YL/NJoHGtRoEcDjh5GnLK3Yec1YdSUCAncrUqEgE5UtAlEchhI+8TOpt6tDElMojL2m6OAsMeJRdQiATTkkDAgOPb4MBxsfnG/q8fOp8WevN6GrUDvMejvf1fKFjmfEsY09i8SpoKoE6RqSv4pRMKjP0A+AMFs46nmMOGzjcp+rilzCNSxL9AyTG+BQ6w++2R8fhenwYBsoCjpCM/xyopIyfjGuSMcxVcBqG9dloIHs3R31hFvlMC0bgG6Wc7lPhtMDJ0/AI5sgsZVm0OP0F76gdkVf66I+DYjXiZzQjkOEbB6F9jwTB27wXgRe+6t413IUcH0nePRmfuIo8ffcf8auCIkH3022LjFfkuDECnrj76doFVyHj8NAEMx3gOaAQ6eBMvwxjSbd4lcBUX++sqxQxFQT5+Rd611s9xBWm1QMbLvgiotj+5YCZK7J+UCEgus48OLNITOVXXkdk5RiDdsm/AsY0KOExBEwWMPAH81fjE8dFj6MwKEoyTo9kK2eIORg6cWVhwo+1vb6GzOdTeuguSiW+3cexKtpeH3NvygLUWSF9egwKPHP8egIBlCwPSHooqby5EQWJX++0jJ/n0QLTcehyDghzOtrlsjs7MxSF51GgNsXXfynjjUlsPu9k4V6LKWWuUSGEAAhe9g+ytbwKHec+NRmd3iErZp6D9dTaS9Xt5v80lAMApN4c7Wf86oKDcH5LZV8cgR9n9FNhXjS9WoCC4lnbvdmB17rmdqB3UFnLP6YmAoyeiANl3kE/AAfRCJwBrhRy2g1Gw3HQExu2LHLpTHL+E7GeJ99ggODcQ30xAQZQT0b53J9pbCyVu+A6a7jzcwfaD92wvWIwCgS4iTKIBR28iSqJyKLJtLERBBlMJHOBo9PqgZDF/BA717p+LHLyrUVLJ3fz3LNjHAVjVWLu+ymh0s+suBMo6bn+jVMfKjw0NDDhSzjgUXf+x57AE/WggLmtzrq331Vh2bHR+/JHwAdXRQUdkZ/HZyHfzJlnF6D2oLeOE2GtiV8aL2s/YvJpij0vIqw3y3Ei9NmLyTky+STmXkce0af67jetsdG4tRza7S1HA42iUFLBogrX+yHZ7Zal3KTUmBJCZiQByTEkvUR87ozNqFUpQ8yPgT0jvaErBxEab8B4V0YOpDR55GNn/nL98ErINBrGN5ehWtOog0rsPQpX2pgIvI5tVC+AKYJE9dzoCrRWuAlJij1uHko49ZPzzDPKnBPUZIRvSoUjvn4HkuMUIhHCejUVQ2wOyjb9ue+kj6HweiWxJKxGQM0jiSI9mO+STHmnXu6H4xh94zziQZQh7TgqwSuw+RqXn8ezZWNJPFMdxBPKr+DJdEP089jwSEZBn9PqQ2fvOR3tdtf22O/KvTAzMp7H7mGItxuabFGuxsY/1HFRNptuciuTi9khXfhFLloH23CDxoyXo1/LtxvqwkdiJct+HDZMYjre9bjHyvdfY/YGlaJVDt9T/5N/Du7+D970sOZLMruL4pj/C5XyGCo7MQTJ5oST8RfofsO2WSFf7JXCCd38mioFaAUyq9Hs0fjYyR6lfIPWH2sGFNQiY1gaBgF4BLrLf+pNl6j0eOZU32XAUm55HywdRXIgCgad49/YCri7xf0Wdc9va3xZIWH6BDIEc0qhaZQdGe29zXUkucztS2ENkHd0Wq4bh3VuEAmV624a+FAFYrqWgYZXI1apIAORISdfaiwFy2CDgt9KfmGNKZBBXjvZOyKi4lpwjHAnnv8LL+F6QVoMGx8XmGzc2CKj1DRZgiAypt5R4tjVeddzGMd08eJU0FUC3Qk5aFxxWl2LaCTmwChnHE81hg+abFH3cguaxLwqAX2LX+yPF3FVwaIMCPO8oSqsE7VZoD3fJFboj4MHtmKHce7aIPB61Il+Oxs0o0LikIxOdaw4QVO7aqEHB/EfZ9SFGc7TxSQekI15N5bLVv4MXHI+cqq+X6m9BOu2RznsdMv7lA+ZWICNZiIzKqSrybbSP3nPtio4pMkA/CfSz6+HWl0dyz02z+9sTIDO2/Z2BHNLLEMDgWuQYPxBleXsFyVcPFqFp/D8POeB+R1bVrE4DL4GC5ZGR+kbjyZ2RHnINClhxzsD2AehEBxymoGltDkUBYTuh4Irnsaqy9vtTBMxyam0OQKC8aei8PMT7bSZekqdA9MagoJQBtscst37ejlcZt8ja9+fOvk9Atj63Pg4x3j0I6T8nYzaOonStjU4oc7yrNtIN6VirMBB7ntcC0JxqYzgPgTifRYFH95NVJi6y1/TEZCUUEPsPyBF/M5IxtkGgn2ttbkNVqsxnpW+Nzq/ngJ3t3lOYswzt+1sDPQvSnYSCji5DTtXXsCA/FKh+TKg14dGcb/24wfabRShQ5X0k9/yt7QtrKW6Ta4WqX65EGUd3sHE83n4fiwClu4Tup7Vfjc7EexB4bSskn58Val34baDg5vuBB1DA85V4yToC9Sk4EO170p1rPLIEyTL7IP3mbtsTggXEejTnoLN+O7ueiclWKDBvg8RkBenNQkDtlUjm/wXSARbZ+gyeUMXono5k/qOo7YSfhQDIwejaPH5m6/tVssy0F6OkAIXBfyXGtOLVscjkqYYKHAlasWRznMPcPKYAcQ1GlVq7IZnjeUy/QPLbv+H5QesR77gxHYUq0XS09p8l84uPR36cYIHxMfkmRT9j82rsPS4Fr8bmnRRjGrN/qfqZgm9S9TEivS7e91LguyokI1+L9DiX7KQ7Fi8U4B36ID21JBCQzAbYjjJlSSKDHGPTS9RHx6t9MYAfsgWuIatMNxYlBCoUP+b1J5Wu6sAj2yD9/zVkL2ttv31EQdtGiXGNVnXQ+vIHdE6M9u6fjHTzg5BPaXwAWq5/k5FvYZZdH4sFUyOb2I8xu3VBei4BneOhKpu3B7BkeEjvfwjzuwTkm14I+HeV419kB7iZCiaoR3Lq22SJftoAP0By6mX+eASgFR2sEruPKeh5dJchm59LXtXd3uMeTCeoj/NIRECet+cMRHrcHGSzdn7W8ZgPJ/DcRQUdpliLiWjGXvuNfQzcx5j0vPXv/Hs72Di+gXycI1Ds4y6B6EW3rYbiu+9Jq6Xtmd2RjL8Hio35GJON7bndUcKMzqH6h+wcZyA5fINYDaTzbJCQvAC9OWjvPs9r3yX7HI5iDYLGN6T6IP3mDBQTMzP1+zR+cvOT+gWSdl5Zjc5DStQUVGXhAxRIOR0FdLyHHObvIwNHc5SBdcjmTq8E/WFkwSLXoWwRznm0H3ImtQ51OJa43xp4zr6PRw7d6iK0fHrkMsPbIeKyZI7Cc1wH4p9ngV9416ORYvVHYJl3v5DRgcjVqkgA5EhBl/ggh07AMQQI7N0cx5TIIC5rpydeVg+0x56FAsVG5J69gjAGxwYNjovNN7n/dRXbnkJG8PNL/F5Vbvtb2pjG5FUSVFX0aFyGANtdvuM5V02mCeUlcUgxhw2ab1L0cUuZR/vf84F3HP+jffUJZLC6HBkCO9ja3angPHZDzjYfSP0cCqxwASTbooxc68pZg3XQjVaRz2vXT+hyK5I5+nprZTLKsFpdZP6srUPR+bQb0gl/btePGd2RNva3EqAiFxk4zgeQPIcCDG/y7r0GvBViPH0+RxVqTkB66j5kut04BPIKEhxH5Ip837OPs1HmrA4B10cVAqSssushyEF9Se65rQLO5VR0VoxFOvFy5MA6ATkbX0P74FSU5bloUMduwL8iwOPOdTzj1mZHG/cOBWlOQHacfiiw4RZkzO6HnPJ3I3tPKOd4FMDhZkBzNKqG5YIsuiFZ/R4qU8mlFXLkLrXr6eicPMyuzwBmBKQ3BO3Vp+XuDwP2RMF55wag0wmd872Qk+q3yCl2F3C4PXOQrb/9vP8rV8Zpi+nYKDisBlXi+09grN3viiqPrSZwJnfjzTuwwELv/kFkyVxCVMX7NdI3ViLgaB+UCXQdsuN2s/VSGPyDtyejs+FiBOTsanvLT9HePgo5x4IFHSGZ9QXjnc4oyOFK+20SkuuCZqpHIPRXjZdORU7VNmSJ5PZCuqbTRWrKpOOf8wNsXZyP5MWxCGjxINrXQ4J/N6iAh85nt9cNQ/b/0NUxJwN7IwdkCwSUc4kGqLMUJQAAIABJREFUByJ5smzHKrLFXVSqn5X+IFmuDZLVtkaJTt4gA6u2svktpN+UoDsG7a/jvHttkYz6GDqzZgemeRkegBo4CXjYvp+AV2m5AuO8k+1xyxFYdSCWTCYgjRFIF90WBaj+xcbSBXNeQcAs59ZmtEpuNFDgCDqPbiJQIP/mOofeuMYCcfUGjvWuJ1pfp6GArh3svgPK9qpvvJObp8dRVZq7kNw6G8k4LyB/+dxQ9GLyTYp+xuZVj24KQG70KqAxeSfmmKboX+x+Juab2ODKWGNahWzevo24lL7j/m6ff6bgXLYlS55UEghIZgOsRknINqniApFBjrHppaLptT0PyTVrUTKlXt5vc5E+XuhsJEsc65LUR9FVc3zaB53JeyC7Sl/7bRzyv27n/08A2lGqDnp9bG7z9zVWBYSsct5JyE4fTJaz/r2HfGUfUzsGbx+7t7Agje7e9z1QrObBmF0BJVY4HNl3f4YlWwvYx9bIL3SFzeUk77dbbc20C8yr/W29NLOxfQaLpUQy6w2ET8gXBawSu48pxtSjORb5O8chu835yEbdHvmrniVgsqpE81hxQB7ZnunAP60QEO+PZACjaSieOiSgMhXoMDpQNQbNFGuxsY8NA1TtjesM5CNajuSPdsDW9tuOKDagUHJcvNjT0OtsIzTbed9jxKk7ne1IpD/+jkymugX5w/qiWNLPCOuPm43kxklG59YSz/hxI+dRQMZCcuPHyG/8dygOpxrJxpOQTzWE7a8TAXBDBd/BrZPuyOd/Rci5a/wEmKPUL5C08wo0ugkFNL1gB2V3FJR7BQpG2AYFXfhV88oCIcSm5/2/W4guoHGwXa9BQTEXoYDk+QHG1NEag7KM70aG4G5vG97ByJBTOKjSo7cNOYEbGTwvsXH/LQEcx2xYqeEh4CPv+gTg3fz7FaAXtVoViYAcsekSH+TQDQFtL0MGsODKWuoxtbajgLisrVZIyP4WZcK6CQVzjUPC5J2EN8I1aHBcKr4p0ZdjkeI2yrs/0ejWR15NsRZjAoBTVFXcGqtMhYIAT2QjAbBke3xZYINEc9ig+SZFH7eUeczRcQ6il8gySPZHwdbL7Low4AjpNo8iMH4zlHnzKnRWPwGssee6Ez4gt+IV+Uq0mQcCrkWOusnIwFPI6ei13Q7pFquRAclVNKpBuuNyuy4sV5KB436EgkeqbB4vtN/fAq73ng9WzdmfG2qD5CYjsNhvgEUBaUWryLcJffwaD5ATsK9dEUjOVeUchgK6rwlNy9q/3Po0yva+nnbfOc/aIx39fTYxEKfEOLpKdTsgme52rNon0r2qvWc7on2v7IQgXlunoUD1CQhk3Mub194E1D+IDDhMQdPaama8eh8CN7e1305Be98GQO8yaTXNXc9GOoezj01GFcAOCDWeHq05yHG8lhJZ01GA0B2ESXiyEjmob8IcfSjQai0ZEPAwwoDj2iAw/hPGLw74t8T2HgcQ3BroHXA8HYDqfOORM8jt4fb7D/CASuXwp/f9FRQA18au+xuPPowc9S3KpePRaI4y65+NbJwfosC7NUgWaI0CdZ833i18NpLtbX2Afyc791vaenwL2ZHfI4BDB08fQ3JiJ+PVIxGQa4MgB3uP9RUQC/RxsONDJJPehnSf7e1deoXYbxBA7njvuq5q9WOtXwuK0sy1O4csCPifgCO93xbYWi2UHMPG63O8atGxPmRVjtaiKoqvkWXkX4AcyMGd2ciWe4mbU7JEDp1QRYLdAtDwwapNbO1f513XIDlug/2uQmO9KwKu3ID29BB+Fb+P7ZBsOhXpw9sgOeNzPMBhkfkkJ+cTt5JbgwWOIPnwbkrYMCiY5GMzm8NoYDVkj/sn4Di73g7pGl9jVYyM9mO272yQzGZz5R1qA0R2RrLOQKQ7PY1sR529fc6BuuvV2k/RzxS86tGMCh6LwaupeCfmmKbsXwreicU3KfuYYEz7275SsgpfKb4hTHWFPVFw84NkyZv6IJ/H9bn/6Wj3J2wiraggx9j0UtH02h6Ngri7oji4z5FPaVcsiTwBkjhaHy9iI4ljK7Cv+YHjDyFZ7Q3g92SB4w48Unb1xjpoR6k66PVxFqr41x7Z+j7D4iuQ7aaGzB8fQsZpAdxrtPbBzivMNo1iH+cVoYdksx8azwxD9ttz7d4VSK7ajey8XByYb4YgWa2/9fcMpG9N8J4NHe/kA0cfsv6dipKgrUCFJILp/6QBHEbpYyp6RnNPZDs+3db+CcjWeL3x60fAXvVtHokIyLP9ZEf7PgfJGRfbfC5A/v/L7PvHBIinjt3HFHOYiqa1GW0tNvaxYYCqPdpTkN9ksu0FD5HZ/2ci33GhPQD5/+4BVpTqd/57oH61RzLifnXRDEyvGvkym6HY7b8gWc6PwV+NfIKPkFV5DlKsChXh6gvMR8nVnT7j9l1XOKIjSuwwrgDNlshXMxydIW+gffsFFDM3GKscWbBvVeisX+nzfX7MQs3p92kHxY5eiPS5jRbraPzE+yR/gdQfZHS4GjlUXOadHsgIsiz3bCgjRxR6ZIExfibpy+1QcVkjV6Ms3S7wKYRCPg8pq2ejYLujbVNqg4Tj32ABeoHmcJ5tpo+jwFUHOpyFEOXvEyAbLwoM+TUS9I/w7q8BPrbvzYEHEJiscBYHIlarIgGQIwVd4oMcBiChdAJS1pZjRj/vmTkEzMKTai6t3YqDuMhKJ0/x1uSJKLvYIzZ/PyZgZnzSAzlijGsyvsm9R0cUBPgkMNTG/kMCVXFq6GMak1dJUFXRa+8msowxTt7ZYI+mdvbPx9nECiSJ5rBB802KPm4p85jne/u+CngHT/ZAAKiLCAvkuAWdvy/iAYyQEf1l4LZQ/SvRx4pV5KOOoP4c/R8ig84fMKdKEZr+/yNg0THICTnX+/0kBBhpRgAgp7Xpg+PWgw7tt87IqbvB/h5wTv0+H490qn8PNaYlaESpyBezj177PcmAFF2Q/u2AwLsgA/aOBWm1pnZVoR6o+tZjtuf0st8OAY7y/u9sCmYrQ/r/U7b2d0X76ynonDrX1ourllFt+9ImBf9shPbeKHjM7+NBSL8MDRiNCTiMQtP2kgH2fS6y3zyC9u8LUED5CTamrwJjAvStB9nZMMDm0AG5lqOAOWf4nxqCV7zx7IdAqW2RHeIBpOdsk3t+P6TvtClK074fj5LlLLDrahvvB/31GKiP44F/Bu63ayeXH2nvMKkCfOrbOJchMNdEbx7dfJ8C3F2Qpi+3fQg86l0PQM6XskDNddAbhOyYr2DOWgSsvgIFj7cxfgoZOD4DVf26Hjnlqv3+I1kuREB+M6R/T0N6/m1I978LyczOkTsd6Rw1dt0mv2bK4JlpKDncCyiYow+y/91i4xrSHrczyvB5qnevVGBsO7KzKtT50cL2mRrk6HwDAUmaoAC51VhCvnJoUjtAtCXKjt8z98z+ZICVUHqGX+HQAWTOAP4XlrQNOZU/owJZjq39vREYvoN3bxwBwH85OmPRWbQ1Oj8+As6w33bDO5tjfND5PDVEP715nIn2bhfsdCaw0r7PR4GVhWUcj16sSm4NGjhibblzthMKNqrJ/b4Y88GFWP8J5jAJiMsb14UoWOsUu16ObJ9L0Dn2MeFt4xXlHbJqPO5c2AXpiy6hSyd0Zq4jSwQSSieOVhkvdj9T8CoJ97gYvJqCd2KPaYq1kZp3YvBN4rlMtR5LVuHzvjt9vB22LxakNxPFHPUFrkR2hpPst35I53F2pg4oKLqsJGDEBzlGpZeQZhWKyRlJVp1jJvLX3o1sK23ydAvSO4lcfAjysQSLT8u1PQ4lN5ti1/ORbHApsrN+QjjwiO9fiFZ1ENmOvsQLlkY210+QbeUPIceXTI66y9b+q2T+jnkEShxj/1+N/AqfAXvbvRHIN34VOi+rMHtkiPG0dmaTAbbesrXQFukB6whoU/VoOuBoN5Rc7WXMDohA8vsAEwPSSwGOi93HKPSobY9qh+yqXdB++iHSSXz/YMj1X/F5JDIgD9mpT0SJA/ZBYJD9jcYqW/9DkB9+JQXsqan6GHsOU9OMvfYb+1j/QdXUlncPM1pj8XRHZFMZ4Ma1yB5g/z8YJVU9biPPhK6meASSZzbATZDZJEPJNu1tr+uNfAp9kF5+BSbXYBXeCZjokCwh/HVIBn+RTC5eiMCkftzoSxRLHD0Q2f7aIp30F2S+jn9DcmWwRHlGYyVwHLnk8MguGGr+WpFV3uzPRnxtyO9YMkl/4yfNJ/kLJO28BWqg7PEPowzL29q945BCWRVwsUShZxuqy7rfyzaX2d7vq5CBbme7DgWSaYKCM+5Gjo09kNK6DZnCsRpTngPRHIbQ252RA/lToL03zt+UOsjKpDUBBfu9ikCca1Cw8/a2oT9pz12MZ3goSDNKtSoSATli0yU+yGFHWwP7e/3tkHumMxJWzyaQgzzFXObarRiICwlTL2EZE5CC/GsEvG2NguZuRFnj/pMAAXlsAeC4BGvRBYeWdB54e98HyHDsgkhCZz5pMGOaileJWFXRG9cqdNae4N8v8axfyv0lNtHokWgOGzzfJNhvtoh5LNGuD1K7DQEtqm0uvyBcsJNP52okK3fJPdONQLLxRmgHr8iHdJgzgGHfg/4qYKab/0D9c/pTG+SMuAY5W8ejs2pGwLEsBTqck3umI1blKRS973iPY8kMgiGDVUoBAScTuCJfqj6SZQD9AviB3etsfbvBrtsHoNMTGY3vwLKpIcf122TO8qFIP58ZcP76I3DMPsiG8jVKtFQFHICCHl0G4JYow9zkgjTHoOCCQShj9avI9rA1Cgj4hAAVskrQrTjgMCZNm6NrkSPT8ekMZPx/zOZxEcoe92ioMUV751Dk2D0O6fuvIyfukSi4unnuf0I4x2cZ/9+JnLZD7D3uBpbigZqQs76wXc5/b3R+/YXMsVyNgpDKqqZWio6NaScUyPUL4OrccwtCrn9vXH+OgmFusXun2jxOI3NatbT7ISoe+jLxe8CD3nXZwM0SdJyO3A/4E/Aj77ddyDJkFq466LW7KwoAHGXXa5B9pbP/TgHp9QH+B2XEd8Fbe6GgpzuRs/UzLFN9CPpoD78VOWt3Qra/VfYu3dE5FsQu7tEcjvbR5fn5te8bZHctuu/YHtPX+GQFOqvc+t/H3qnO5Dnfo/2WKBimCgUVnFDHc2uAV0KOp7U7A+lWk+26A3J8foZA5J8SoMLBRugPQw76/e37CJQ4YlRAGmNQxdFHkP42CwUafIrOji8IV3Ws3CoJRQOAp6HA0cnevVnGrxfZuinbAV+CXpTqWN46bpDAkRzNCci2sYENA+3hbxGwwknEOYwOVsvR39PW/o3In3ECkt3moeRGN5OdjfUKOIJk/58iParG9rMxQDv7/Vi0l19c3/gmRT9T8CqR97hUvBqTd1KMacz+pepnCr5J2McU67EtWbX2kkBAagdWvkIY//yByJayp7U523j4RPvdvVMTpFsWSiZVV9+876FBjlHpxaaJ7BuvkcVuXYkl30Jn510ESMrj8WmzUvft+/5or+tZlF4J+hcggKqLBdwK6eh3Egg8kutntKqDtraaG70D7N5eyK6yh+1DBxM2ccxA4CD7fijwV8wXhva4Lwmgi3v0WiL/yZPAe97vQ41PryEX3xWAdj+UBGwciic7FZ3D/dAeehYFEyluZGwvQfFVb5HZ5saTCyoPQDM2GC9qH2PSQ37v94Fj7boK2f/usT2gxu7PxhJ31qd5JAEgz/5/O1Td9OdkVU07IhDSrRioJD/n9amPseYwJc3Ya7+xj/UfVJ2jOQvFhxyMEiq/R1aUZBZKAlrIH4fifJzNpgeyGfXJPbMAeDhw39YnVUVAuF6532fY3hPM3+i1vQzpAMOQjHWJfc5CRXRC2o77oH20P/LZ/BuWDBeBOr8kA3G2QnidSQXotUR+jBPsugbZ5fogH9wdBPJvUFuXqSFXHdbG91rCgVR7oiTDDyIbZq8i7TV+4n6Sv0DSzotpX7XvO6EAwJcRCvoTAgZWxqKHDv1fAEch4b+3HUq34GXfQY7jO/ECAANsBq664PUo0PgNoLfdm219Lhzc4LXRDhmpj7YxfNuj56oS9A9Azzd8zbLN/GTk9DwVVf/4MTLuvFgBPq14tSqPTjQgR2y6xAc5DEAOt7+SZTArGcCEAo9vr+v3zXFMSQDiQkawN7Cqqd47zEeBaovs2gX+BQmkZAsBx0Vci3lndV3rohNSDmY1junmx6sl+lHxcypHcyAKkDvEH4M63u1lyqzqEnMOtyS+iT2uW8A8ft9qdc8jhX1OCLp10LnN1n3oYOroFfkQqOk+ZGgsCSKgxNlShGZdbZFVjrvf+hd0DnO0fNDhPhXqT52AG+8Zf26DVuUrMa7BKvKl6KPXXlMEEBmAMn+9iFUiQllB/85+CwU4XI6q8Nzo3TsF2RmeQ06IkNkqh6DgtAu9e4ci+WOSXbf0futIQUMyMhD/MzKi/sGuhyIn4DM2xg50GEJ2jA44jEnT+O9mFBTrVxs9Dp2VHezaZcwrsha7kAX/9UL6h2v3XOAGm8dvyZ3RAfrZHYEcneH9QOCfEEBnGtrLe4Sk6dH27VdnoERHrnp24b3U22+mIduY2ze7IPDGxchu9haBg+TRvva50a5BMtbj9tuFyO7pVwkMdnZQ2270BfCTgG27Me2LOf+Qs+VPwNnecyMpWMU1R7cLcoCty92/2mhvFYqWPx8I5PtvZPablshevNw+04vwja33cxxNZJ/+M5ldbigCya1BjsEgTs78+yKA5buUAAJSOzj2MsrUCTzeGWq0Otq6/5Ys8d8oBFYbHaCPx6Pgwo22h+yqwYIckB56F9l56+9zc1EQgMtQWuTc2Ak4ZiNzOhPJWT9BNoYFofjG5m4ZCgBsguSb21EQQnMUmNSv1HsVoD0HJVE5iu/QdSiYudb61AzJiIf484gCO+ZZfwvpOCSq5GZtNGjgiNfHO9mInRb5Wg6tp3OYAqzWxObw55jehPai3wMne8+4yvX1ogoY0iNWe+9/Iln129OQn/xE5NN9Ffl2nqTMZDWp+CZ2PxPzajTwWGRebdDnRsr+xeadmHyTcn3EpueN657IBvYgBqJEQY4vAdfn/scl5CzXH+fP5VZIFm6P9P9J9ts9wH8j273P55sss3r0ooAcY9PbDGi2QLq309fWIHvZTGQHDFZZHemGq4FV3j3fltMagYP7B1wb7bx716DA8W2Ktv8dtKNVHczRPQyBVp4nSz60ztZoiHPRjelU20P+iALyt0d2479BieQ+Jey5safNXSeUbOhBvCB8FFwdzNfp0e0D3Ovdb2Hz+Da5iusF6bk17WzirZA9449Y7AYZcLR7IJqxwXhR+5iAnn8ufoklMQX2Q/upS8g5HsUmBtlXE8xjTECe76vtgmzhf4dnG7N9aFy5NFL3McUcxqYZey029rFhgKpzdHdFevFY5KNaS5Z4dDzyRRaOxwGmIJ/CQgRA26BSK5JBfkTBBOclxrQjteVhX3eahva/woA8j944lGS9LUrC+5SNc2eUHPfJUH30aI8ALkfJRvrYeP/WxrOWbox0nN4BaE5FZ+4uKK7qShRr9PcYNodAdhUELByAFRnLjXdbBBw/M+B4ngj8P2onqW2ae8btTR2AJSHns/FTYO5Sv0DUznrBcd69e4Cn7Htf24CeIUB59QT0+qGgm4Ny97siYfJmZFTpi2VaDzi2Y5Fg3AcZAdaXp0Zgua8Imx13JjqAZyIh5y2yTG7TbRwKG1lQQMg5CEjpAsj2QcaAE5BhowoZyM8iMHDUe4+KVavK0YkO5IhBl/hArm1tfhai0sbvkzkA66oKEjqYs2JjSmQQl7XVHQUZuyxjLZFx0VUEnG/7zGJ/3jc25ps4lg0OHBebb3J0nLO6y3c8t778OGUEcW5JY+rRig7mtrajnFM5mhNRAOLBuft+dZJrMWdIfZjDLYlvYo9rQ51HNq1a3dUUNDzw/cB4NyNDR59yaBTsY5CKfGTGi22RYe5i6g6OdWdVFQWMkHW9L7UNOUvJsuUGC8qpg9ZxCFCxb+C2Z6Cz+Vh/f6a2sdOv5NQ2BN3v6GuQinyp+mjtzEWO8KexjMbIMfg8cJZPM9R4IuOmA6ee5t0fgGwQvfP/U5B2a2QDeAYFALjxPgplzu3srd0Q89gV7ddT7XoRAq6Mt+tOZDaIEMEOUQGHKWgi2awa7avPASO83x7CbFRFaaH9+F4E9HMVzV6ldhBHb2Sr+oWb4wL0WpABGKuRDnIn3pmA7EoX2vfgAUG5tenbIFegJEdtCZd0aCZygO2NgnCuRuflVuj8fwIDBxak05wMJFKFguJvyj3zArIFVBEgkOu7+Mr7HiQIwNtvXOXIn6EqQGNQYMDvgJUV6IvLoHqo8cceud9voKAOVaKPHb3vg1HCrGPseicsa30Aei2R488FMbe1/ezH3jMjkHNsYOA+jkbnxki7dkDAU/N8ZHvFJlerL0F7AgJWHuXduwoF/1+J9LtCFfK8/rVB9vevyIJHfTnc2c2DVKjIvcN1SA5u5u0LQwgEVkVn+kforG+1kec6oKSEIc//+Si54OcYeArpP4eg4M79KzCeA1BQ7wrkJL8Ds696z/gBwDeHWKPIn+IAwI6H+uHpmOWOKZGrY9HAgSN10D0WgYwPtOuSQSQF2k9R4SwJiKvEe6xBSSLd/raXjfWywHRiAkduIktEkQ8WOQAl5XgUncmTUaXuTa6wnIJvEvVziwBWxuTV2LwTe0xTrY2UvBODbxLNZer1ONPGri/SL74FTrLf+qHEvQPsugMCsJRV+YDa+vGvMb0bgdNWI/l4DkpCWLhClkcvCsgxNr1UNK0NV0GlKUra5AeR3ogCoEMmjhuOwG8HIvDv695vvswasrLaXKTHPYD2mm3Ruf8aXpBu6A+Rqg6WoLs1CqoeZNdjUKXVzgFpjEG2jF2QrvqAzWkzFFQ+jiwuKMS5MQnFNE707nVEZ+PTgcevVuJIZKv6JZbEyu4da2vjHjwbWpn0umOJxNC++SDycc5FYNFbUazOAry4zoJ9jA2Oi9rHFGNqtJyO2A/JOf+FZP5q23NeQD6zLwgj5yQBOdr3igPyvLU4HO3drp8rkFw1Dp2PXxCgonGiPqYA/0ajmXAtNvYxMO+k4FWPdhdki7/frpsjOWQN8n+8RkGfSo7es8D/JqtsvEG8BnAQBeTvEu3tiXz8j1EiSZT1+QYK+se99uYiXdXFVHREss3jZHEVhRMAe/SGet+HoaTuq1BsxzZIN3Z6ahMK2q6RP2VfsliAH5DF39SgRMuFig15tNxZNQkl+n0YJcOf7D3j1s/WwIkh6Nn39igRx6VIt+jo5tP+OrnEJY0JltSl8VOQb1K/QJROamNxi3AkCnRu6/3+ABYMhAxohbK5xKbntXsMcJl3PRQF5I9FSsESlFXic8JmHdsRKfkO9DcIlYx90TahLwgIcLKN9REsEBiB715HzrEjkcIcQsEZgAAM5yLD0c3eb4vQYXg8GUrfbXSFDLnUAXShgtWqSvBvVCBHDLrEA3J1Q4Gow717lyMgYE1+ztjQORjSCVmxMSUSiMv+tzMKgHuFLGvjo/6atHsLkMJciaDKBgWOS8E3lHZW13n+kQmtRTOON9gxrYNWJQHAm8U5laM5xdb9Ugz04PMQFuBan+awofHN5tLHhjqPRKxWx6aB8X6EVSCpT33M/f8k4CSbw1uQw3pwKbq2htbyHWdNHXRqOQBLvb/3TFWedoH+fRc4rp31P0iAvLU5E4EcZiOj35N4IEO8MxgZj+7P7+0VGtdgVQdj9tFrczDKNHw6Cj64Hws2QEbr1wiT2cyN13jr31i7nmF0j0WBSBcSoLKSR28CSq7iqqrdiXTx7b1nC2eqy9Geg4IO3kUAufZ23wXkFq7+k+tjNMBhCprWRg9kC5uC9tUfIsfV3ggoU8vxGaB/rW39XeD99jiq4OSv+UJOByRvTgIWo/P+VmQTeB243HtuCVb9MMR4eu2WrA5H7UDBHULxK9Lp7kf2sqnIvrgOOce62TPOGVAkWKWFjetI22+OQ7bNL/HkCxT8dFCRfuX4phsbcShS+xwOFVS5K9JXd0SBqgcj+Xt7JDf+Cwo+CAGwaGpr8WNgmt3bF51bG9htA+43C6yPPyWzHY8G/gPp619QMFGd9esHZEHMbwL32Pe2xrcPes+HBnDMMf5ciip/zrL7u9g6OdN7tho5rws7da39vyeX5AzYA53XDpBY7h63/sywvy1R9YQPycAHJfeYADS3JzuHjrB9pq9dD7Mx7Bto/nraujsInVUlZfp8nyguN45A/pMxyJfyKSZ7I93rcMIEOHehtq/qa7JKrgOBU5H+NsrvFzqvXyVcZcfjgN+Qyam7YkHlgdqPUh2LBg4cyfM72l9dsOphqIL6kI2tgQLrP0WFsyggrhLjuj2ZzHSW0XDrdFcU4DG5YN+i8461VYXk/BNy95vlnm2GgoQ+o4D9KBHfpOhngwVWpuDVmLyTYkxTrI3Y/UzBNwn6mBTobO0diHwaeyIf/WwEtDzRfneJHZogm9mYMmi09L53R/EUE3LPnIDAY1/h2eSK9peIIMcU9BL1cVtkpz4Rqz6C9qIjvWfcPhRirxuCfDd+8p2ngZe966CxOCjx3pvIrroC7TcnoKDZa1HweItA/Vvvq/HuRak6uJF3mo7Oj2DB8dbuMdQGjB6K7EWHUED+3gi9c7HEhtROTLMVsiGNCETHTxx5G7If9wB2sLV5HbIrf4rs5Dfh7Ytl0Gtm6+8lVNzgZ8D+SC5YhWwPQ1AyoMLAURIBDiP3MSq9EvQnovNvPJLf/oQljEKyz1QyHb0+zWN0QJ61Px3ZUh+1ce2B/OFXAv8X2RuDJI6P2cdEcxgbjBt9LTb2sUGBqvN2/QOAv2AVVb37XciSaAaR5YCLkG/6Ccz2V+K5YLIO8qW+jfaYaCqsAAAgAElEQVS3c5F/qo33u7P9ly1v5Oj1tLlyfo0+yN/ZARXjeBbp5qES1TZDiYWf9O7tgnSODZIdBqK5DMlxbyK72xF2XWdCyYL0JqJ4m2G2ZpYhm87k/DwGojfTeOVwux6D9O+z7bcryRK9BPNvNn4C8kzqF6h4ByX0PoeM3k2QQPcuUhqdQWxnFHD04/pGL0f7KOSw7mEbzSMoq/nnwBH2TGcCOFb9gw4FHb4P/NS71xkZBWdQR6BwmXRb2qb2EV7VPQTGuxAZeQqXVkVG8a/JMrVXIyPYHt4ze9h4n0xBQw6Rq1WRCMiRim6u/UpWHGyOAn4exQym3m+XUQcQsL6MKQlAXCjT+A1I+N8GZar7V+DS3HN9UEBbsIxjJd6lwYDjEu4BGzirS7VJ7eD4x9lEQ/aWNKaxeJXNoKriRt5tMAokvwbLtlJf5rCh802KPm5J8+jtXVGq1VEmGK8+9THXXl+k13VEwSK3IuOqy7LqZzp6kTIq5lDbARilcpxHc1NBh0X5tQMySPe3/n6E9KjHyIKQ3Zi6jMOTCvYxdtXBaH30aI5Aer8L3qhBesfdZMlqOhahkaM3BzmkDgX+DOxn96ciI+7fYntcIHpzkQ61BDnC90XgrnUoWH37ULQ8mruiDOPTULD6ddS25yzCKo4GHNOKAw5T0MyttR5IRlyHnJudkQH5l8avwRJHGb1RyKHyVwTSdmv9MaRTNs+/Y0GeeQE5j13l+q1RlbzbbB/4mMDyMXJ0nEsWfJcPOG6avxeIbmcUKPYm2jv7ooqSFxIIWIVsK4uQI+F3ZNVaT0CZQPdBgTGfUHAf9WjOR07V51HSgw1AQGRySTtyNp8yabZF2X//DHSye9sh2+ax7pkKzOGJwDveuO6NgDlBA7is7d2N1ta2Fn9N5kgaihyghasOIqfiO2QBXG0QeOmH3lg/DvzErkOCcfuis6ovMA/4BmXmdhXPRpJVsWiJzsvJZdJyMs5Ao9cEBY59ReCKUR7NOTa2d2FBvUgfeB+dj78lAIg7R3MP48mHsAqgqOLxg0ju+QhYGJjmOuA/gSUbeSaYgxzZOO8CnvXurbC+7WzXhZJiWRtVyK5a43gI+Yr86hj9UcXae8iCHKptDRUKOi7xPhciO8MtSD8uGjiSpJIbDRw44tGfi3SW1a4vNp5/xEuAWE/nMDqIy2tvDtKb1gJX2L016Jy6GcmQE4rM52bAOwOtj4f4Y1viuXFYRZn6wDex+5mKV4kIHovNq6l4J9aYpl4bEfuZbI+LuT4S0XP6xigb5+YI2PQUmf54D/DfyFfggzE3WXZFMvEJXn+6Ak94v7f22yZLJhFqPisOckxJLxZNj2+cvjQa+ZFuQTaPQ4FzQ8xZCdpjbY2sw4uBQ3vcmxWg1wPtpfd49+YjOc4lsSlsL8rRjF51sK41ZvvCUWSJjwolj8j9HYXAd8O8Zx6y8Q6ie+Ton0Wm47g9ZgRK0FU4cXyO1gy0V89BMYd3I/lwGyT/X49iSCcRoMIisi2ejApEnG/3Ohov3UrOX1TuPJIQHBerjynooSR153vXBwNrvOsJKDnPMYH4M+U8RgHkeftMWwQscvbNq5CPrBey2S4nUCWnmH1MMYep+CbyWmzsYwMBVXt7wHiUSHIKiumYhXyMof0Mjt4ALD7Wrm8GnrPvO5D5j4LEjHnX441fFqPkFDV2f2D+HQPR74dAjguRDfl5hIuZgvAUhZJh58Z0J7SXbwXcCzzgPXM+8rdskDi3AL1BeIkhUDLFG5Ge8y0BE45Z+y6O6KcoierWdt0b4REeIVz1RtfHEeh8OgPFT6y1+6NRjMxvMN8x8ru+TCC/fOMnIO+kfoGKdk6b6QfknOBIeXoVBeM2M6a9EMtkX1/olaDfDmWLeMX+ujKy45HgulVgehPIHPH9kTFnVQXncwhSfrdFB/45+Y07f7AVoNUHGaUuJ6s4eAMKkLvYDrCWKNhxp0A0o1SrIhGQIxXdEu1XuiLPTqgKx1pyBinjnV8RoBJHqjElEojLa6clEoSdQ3wbJDBe7z0zCSlAISpHNGhwXEK+qdNZvZE+uuD4sjKObwFjmgLolLyq4kZotkZy0CTKAP8kmsMGzzex+7ilzGOu3UnEqVaXEowXpY8l6LZDSSLcOtgBOZMvIgPMVKOzquxMR6SpjhcFHJffO2y9dEf6ag9kpPsFMlg5J3lHlLE+RJbjio9ryj5aWzXGg3eTBW33Qka5h5BxNZSOXIMyx/VDIIfPUbD80fZ7awIYVq2tZsiQ+CgKYtsPOcBd5vZWyP4QRBf36LZDmf+e9+gcjewAR/trIT/3ZdKLCjhMQRMveMjWxBE2d7va2rutAvM4FFUB2AnZkZ7HqvDZ708RIJsznlwFXIICco4B+nv8dDpyKhVOVlXHOxwCPFrHb/4etxd1yCjf0b47N8Za38ajfW5H4B37rR8CzReuVpUb127IlvkYApK5gJwDkX0uGHgUJXJ7B2X5PAeB8s7Dkyeorae+R5kBSHkeQFkyH0HnsluHJyMwQDMCBR4hm/Ui7/oYdE66aqD7UoHMjahq1BgUFPcGkj2+QcncmpXDlxvh9R1tvZ9j162RrcjZrtsRKFGdtzbc3z7Wzw/teilyAs7L/V9Xistx05Ft8Sm0n05FcurnWFKAgPM3EslRk8iS7zmH3xJ0PgYD/1u7/ZEMPgGBjp8Gbrffaqz/DiQXErCyFNkfH0CB5PlqEf6e+hzmPC+TVo393Rf5jJZ5v12MzrB2ofqH5PkasgDHJgic7jvJB2D2ahTsfTOBKgBam36A+Cjk43HJVYLbVL3fQlbHatDAkRJ0ByOfSTfjh+fJ9OCTUQXp9vkx35znsA7aFQdx5doZhJKYTUWy6u3ADfbbSCSLBzmPU/GOR38iSvhxcO5+qL0tGd/E7GcsXk21x6Xg1Vi8k2pMY6+NhP2MCVaN2seEY+r0jVkoeYtLZNIU2ZEOQQCW2win/zv7QjdMx0Y21Qu8Z3YH7kQyaqgA4Fggx6j0UtG0/52N9pajkQ+pObJRr0UJDv7HeDvUHA5CenhXFCx/r79W7JmgQA5rswey9X1E7aqUT1CZRG5Rqg5647ot0kXr9CfgVTyhoC6AZOJlWPC97TUrUBD5rijI+HZgXQXGdijwT8BByAewm/FqYcAh8g9NRHavDsj+PgDZ4z9FduR7gV29/5mAki+Wvb/6fIBsnFejIPzB3v2XsP090DjGBuNF7WMKesabD2NJ8W2dPGx86mxT64B/wGIT6tM8evtNVECe9eVDZJ++0Lt/ua29XoSzw0fvY+y1mIpv7HuU/a2xj/UfVJ1rZzKyuV9r47gS2T5nI90nNBBwNvKDvYfk8HZIZrsd2eV/SZjkmC2R37Y78uPugcB/H6OEES756O7oLKlIYRW0l95JlhT7VLy9NuCY/ppMb+yE5KlXkJ76ATA6AJ28XvyW0eho97dHcuMbhAfktfbuPQm85l33QXpAyOq4o5Av2lUYbouAgHd6z/Twvg8mcMKTxk+guUz9AhXrmAxSl5NlTq6yDW80ciAPQk7kO4B/BKbXJ3ol6Dfz6HbL/TbGFmiwCgDWbnuU9dMZIgfZhn5jYDpNkEJzEgr+GYKCZNYiI8uO/rMB6Q5GxvBVyAH/NTJcvWjv8QEFM/+SoFqV/X8SIEcsusQHOfTEC+xBAvlZth5G5J69goBZlCOOaXQQV258v8KyQyMD5E9tbQ5DwciFM9azhYDjYvJNifa+r7O6EzLsbnLQw5YwpjF5lUTnVIpPinXRUPkm5bg29Hks0WbFq9V5tCaRBowXs4/DUPDEKOScm4EM8n6WpcGur8iwVOTsSFE5Lhro0NqYggJURiL9sTOS3Zoj/fEnZICZZuj8LgoAjDquKfqY60MvpO+fS2aArCFsprPmXrujyEAOeyOQw9EBaLUkyz7dxf6uJcts7MZwIQIdhQ6qdCDNkcD/IgM3NkdglRvxDJ0B6EUFHCak+TDwpXfdE1Vwfgk5JILLjMgO9zi1wWTfIDmoVSAavhPgGuOTSShY5XQb6y4ENMZ7tIcAh3nX9wEn557x9Zw3KQZW3xM5yJejzJzHIRvobcjJ8SvCgfHcuE5DmUC7oiROq8mcEC2RjlJnopcy6E5BttO5yKEzDQGqbsQLirPxfLHc8fT6NxO4FAUB7YDkj2uRQ/AgJHsUBlb5Y4POwVuoHaR2EcomOb7U/xTs4wgyQFF7FAw4xK4fsnEsHKji0dsZ6Tv9UWXOs+1+a+Pf20PwaI7mXONNd0YeAFxl32cgcOeMUHSt3bFI/hxi62MO0kMG25j/lgCZVY3W1qhqoV/h4EQEBlqEB1INtA6bosRfbyL5tKvdb2XXjwceSzePg5HTtqddn270hnnP5G1HRfbUtii4yIFV90b6zTHeM33LbX8jdPugPXyl6z9KVPFEHc+XkyBrg2Cp3D5Ulw2irMBRNmL/I6dvE646VoMFjiBd91jveiJwJjoT3/bou728VwC+TFaNL9d2xUFc9v5dUIXjJ7CKzUiXuxXJqSGqf24WFfK895mCgtaWEu582iz4ptL9TMGrsfa4VLyagncij2nKCqcN9nxMMZcJeKel9707qvg9IffMCUg//oraOmWRQOf1Oj1K9HMzAgQ68MN9qDLIRxSsHu2/K5FAjrHpJeqjm8PhSA8/3ebxKrwAdQRQ3yUgvVnoPLwd6Va7I3vjXch21SfEeG7kPboiH+qdyEY22NbGroHpRK06aG0/543jdiWecbpxYfsqsm/8BiXj+hOSo1oBpyA/zs+RXr678VWw6nweL41Fe96dKDnY3IBjuROyhzVB/qrt0NlUjfynv7J+bY3O5YkUSCTvrf/hyPfXyq5XoDjVccgu8AUB7NXU1vVjAQ6T9DEWvRztOQg0crpd32d8OhLZHO8LseekmEdrMxogz9odYHvbXkim+SFwuPf71fW1jynXYiyaKdZiYx8r18eYvOq16xKZutjwEcAFmN6MQHPBEmQineZRrPIe8hPdR5awZhFhwGoupuJIFIvyOzId8hYk4/S1/n1GID9u7h38hCKOj3czekEActbmzsb/rpBTT6S7Vhkv/YiCxX+orRfviHRSN5532xh3zj9PQZucN24u+c01GK4Iyf0v5Oc81NyhRAOfIX91G7vfBsXlPBaif42fOJ/kL1DRzmmR32zfr7RN9TMkaE1CCvowAinksemVoJ/PJt0aCQJBqpx5m043sgDyDggc5/o9BAUjBEf92sF0PHKIDUZC8YPA2SE3uRzNISgo5u+9DbbKNsLCVcesvSjVqkgHOIxKl/hVh1qh8tDfAtfbfA6wtXckOqALZ4ndHObS2ogB4uoMbJ+7dyJm5LDrbshQ9i2BSoBbGw0ZHLdZgLn4Dmc1MnReS8GsJw19TGPyKpErgMb8pJjDLYFvYvdxS5vHXLtRqtVZO9HAeDH7mOdxlMjhcuvDIqTblKzgjlVe20R6KavjRQHHkZ3poxDg0DmoTrL7a5CO+rWj679jmX2LOq6x+0gdziBvvnZAgU2XECj5j9fHudY353SYR7b/TUHOs0KV49A+Og9VpDsA+MDun40AebvZ9TgEzClcxc0fQ2SreZYM+DcK+CtwlF03J6efFKQZFXAYk6bHN7u4MUNVld71njkGOVqDVOTy2m1mn662JkeSVVY7BWU+3CEgvbnI6TDDuzca2QXWogRgY0LRs/Y7kYHyVqMqa/sCp/njYH/dPl722YHsHXcip9xMlEipm/cukwkQyJWjuQCdT+7cb4dsN9cjPfFjCtocPT7t5N1zWTjn2vWFCBDogEHVyGlf9CweizKLz0NA++tRANsOSId6m6xyZNmBAF4fZyKQUTt05l9NduYPR86cwg7HHO1ZCHg7iuycug0lxZqM9JHdAtKbh4K13FkxCAUgnmHXrSuwFmfYOpzl3ZuP5MZLUQDgCH8uAtBshgLE/jcZQK4aOaxPdtcB+9gC2TV/Bezn3V9u8xlE38+PD8pW+zpyTjvZozVKeBB6v5lv+81TyMfggqvOQFXPRnjPVtt6CZHIYYjtZafa9SJkyz7OrkNUVnN7QA+yfXt766urCNjU3mO49/wm8yu1nd+zbQ0sdjxSYo6b+X8D9LXildxo4MARa3MQqoTh+HA7pD997fHQbKQ7dirCMynm8Hu8Q0VAXCX4fzw6I/b37vVGMkioarWbRYU8j+ZgJPtfA5wVsN3kfBOjnyXoNBhgZSpejcE7qcY0Vv9S9jM238TuYwJ626BgeBeQ2hUvSQRZYKlLfLJtXTxVJv2+KKaiGu2flyF7Uleku56J6Vzl0iQyyDE2vUR97O597490xgPtegiy515BiaT4ZdLzbTdbIxl1sl1PAv6MEjsNR/bAmhD8+R3vtB1KmPkN8plNsvuFdTmPRrSqgzZv7yBb4s3IplKrOj21YwDeoxhgbTAC/y7y+Gi9LmL3OiIb4QdUJsmas1dtg2KTasrl0Rx/7on25zZIz9/LvvdE+3UrtPc9g4EDAvZpOoprfNTWeg+bxyuB/4vsHEGSnNvfFOC4KH1MRc9ozkM2/ftQoqwz7f6VyB73LmHjf6POIxEBecju1Qv4PVlMc1eUhO9mvERIgecwSh9TzGFCvom2Fhv72OBA1U1RcuEPgLu8+7ujPbZj/j0L0uuI9JpP8GJskO/hCUzvCkCnGvmIm9n4/QX5pvxCSquR/vwIBXWq7zO/9n0E8qsWTqyQa7cv8jkcZXzzvq2VSfZ7q/z/bCKtTsj/3AH5olah+J+Z3jNrUSxJ1yK06qA/GiX4nI9i4K4HltpvbwCvhqSJZO+VKGHFJBR/vxe1/XHBq6o3fir3Sf4CFe2cAiieRRlknkYO1R4osOzm+k7vO96lCXK2vkpWPavcja7K+94LCauHkjly2yCl/Ca7LnxgkQkAw6hd2rQ3CrK+H6GuawiUwZG6wWL9gfNRMPDI/DsWnKOo1apIBOSITZd4IAdXoWIKyqh2mfHnk0iIWgv8GGUHqBMYUB/GNNdmxUBcZBni1yLjn3v/MSjg1xdYtyVMdozNBchRcXBcSr7JtV+ns9r6GSRwraGNaWxeJcE5leKTYl00ZL5JNa4NfR5z7UWtVufRjQk4rHgf8c4aFCC/wv62sHu7W7t/CzxbdPxytKNVjsvzJJFAhyhg4zVguF0vQsbGZXZdg2XlRXtTiGDnqBX5YvUROaKXuXcv8bvbf/oiJ0Cw6jHI6fARtY2cM5A+vgrZPkaW4rUyaLkAgr8AC+2eA65+hkAOnxKgAngd9BchA/xhdj0S+B/MyBqIRgrAYQqas1EQjJ/1+34EmDkM+JIwelxnDAyGAt9+hPayvsYvdyMn60HoTCkEArK1OMfWc3MEht8ZVabbAwV29LW1vzfhwPB+pbPTbD6bG89egRwD/4olx7Fn2yEnSJE9zu1t11jf3iALiJnpfg/Qv65k8lQ1Cp7qgfbpkQiE1NzGflWoPQAF5TyHADFurz4T2SP2RckAxnjPj6FY9a+mHo2LvPunIbtRE2R/vMLGu225tLy290B7+J523QkBAX+EZLhPiq6LEjS3R87T9RUH7O80FFzxWdE59NpsguTRN8kAd07uHmg8uyJQv7rgBbgh+XexfXeyYzNkiz/DX4+Bx7c9snc+7d07CYF111fmK7Ntt9eMt3U3HsmNi5Gdc7H3bE2g/jiaE4w3R9v1TLRv700WeBzUQY1k4J9gfgUkR15NJoNchoEO0Vl6NhZoWia94WhPc7bogbYenNy4D4EqgHg05yNZ9SfAD6zP3ZFOuioQjSpkrz0fnZPvo3PjBvts58bQ/vq68VV4Ab0F36Pildy8eWtwwBGPLxeigKNT7Ho5snssQfv4xwQINkw5h9/xDkFBXN4eN93Ww1IU1DQO6VH7eM+GqKay2VXI8+i1RvLpJMJmrU7ONzH6WYJOvQZWbg682pDOjZT9i9nP1HwTey4jrsetULxNNzIbwCvABd4zriJB8xA8RHY+jkG2onuRHaUDsqteQaBk40QGOcaml5DmLY7vEKDpGSQnOpl/EEqsdC0F5X10rp6Jl7gM2fu6etdHA1c7ng7BO9/z3boi+W4NMK2CNGJUHZyGkmPOQ4Hbrsrqel+K/e2IAILlxqo5HjkO+W0uwpLKIJvY/wHO956/hDL1ZG+v2ZU6EgqWWgew6TFjri3gcOAOYF+7dwKywTm73NXIv/ElgYCcXj/bItDkGLu+CvlUeyHb9XICBnOTBqwSpY+pxtRotOP/s3eecXpUR7r/KyCN4igHJJRzRAGhgBgJlLNEziIjMhYZhBCYjMAGC0TOWGDLgDGYnMHYJIOxMTisd/F6vbt3k/duvPd674enjvpM651B8/Z5T2tG/aF+8/bpnq4+uU5VPVXShzmb21QEBjzbeyYY+CByP0YD5KXbBu0Tv8HWTmRXOs7mZ7BgnDHrmEcfxuaZ81ws6tgEQNUe71bIvngnSRDAEch+FMKHepv/qf3ti4JvXI4HakR2wFB21Q5Izz8QZXMbjGTm60iAce2Qnqp1AH47LDPhBeOlfN8YP8jpQejceg0KTufwOFfiBZDIWL/O9s7+Nja6I53blXhyN9qTMweqRsEhpnv1PAzLGmt9NhfZid0ZL0hQVY/fHBuj6xDuZz6yiR9KAB11QfEp9w+oeAW1oM223+5QuRoB2YJEG43Nb0cXVqSMG+j+p9T/7QCv1sjoPxgZ5OcjVPV9tgA5R9ELgH/HA+iUWbf2JMonl8L1HWo7N0y0sscJAzjs5S2adQEBx9jCfnsInql3x8hWlQuQIyZf4oNjRtp3OoeQRQgIOA1tyKPQhvk88J8EjKySR1+WeHdwEJe12TqbbxOR8P8SytrQGzgFgQO3EzhK1b+BvJs0OG5nGTcpPrlHx22MbZrHWCXCPpUH5dGHu8K4iV3HXaUf2d6ZoaLZ6rz/jQY4jFlHdMb5NdobeiIn+G+hfelGkkAP7ZGS52myAxujZo5L/X9scNwYYDTwb9R2GlmJFKxr0/3d2No1ch2nW73OxoAqJZ5xa3vQDA4o2vDh9tuPKr3Y5k9IA8DuyPD2hrVfD5I1dAlSeDpwQKhIZ6OQ8cSNo2UILHeyN67mheCV4ltxwGFePJHDxqeYg4a1sdsnzkHOGyEiALZEzkTXkGTGm4cMHN9DUfMOQE5HW4HFAXiuAoaiPb052qM+Q4abq5CTx5uh56Hxno+cbP4ZOcX18+7tj8AXNyBjlgMplq2DsHXnr6w/D0VRMg+0ey6DTWbwmH3vqQjIVYX2oHeQ7Ho/2pf/Grg69X/lOuS5ud4ZOTZOQUbG79vv8cAlyPi31Pu/soHqHk8nWyxHDmu+IewVLPq3zZlrMd1oxrb9NiaXkTj8dbS6XkCJqPwB+rQT2ofb2zxtbeW9U3/L7cMuyPjlslINRUDOarv2A9kNBqYEqNNuSFc1wKvP3ZgO0ntuNLX3yko5WbdHkWN/jmwObxAOHDsPZXE4DvgnFASwEzK0voKXETBgfRYjGe4Cq8ulyPA4B507Dq5EWyIbzo9InPFaIUP5prqeb+D7W2FnFOuzy9B5cDIJ4OoolJ339ArUbx8k73dF69xv7BvaI7Duc9QRYKIMXsMQoOlNt65YPb+OgE99rczVuxqBE4PqjqhQJrcSfJokcATJU99F5+L/RA6rnZB8/CSyiy2t6zsaUx9+xTcEBXGh7Lg/RefEZ4E7rXwuCqx6WFYeeY+dvGlnGDc51bvRAyvzHqtNbd/Iq36x65nnuIndl5XmR+3gKrfaXj8cnYu/g3SRh9k+tjxw3dz+uA7JhTcgZ8uOSA+wkYz6cOMTFeQYm19ePO2dg4CH7HdLFGjoaRKZfzR16LEbyKc1ss/0IdH3bwEe8Z45ArjbH9cZeTbzeLf0y0o82w/ZXb6Bl0kmJFGBrINeHYfb38FIVv2UJADYUhSEqLNdOx+ABtuNPH49vLJDkA/lUpKz8zAC6qoQoOInpGyWftuR2B9aBRo/F9g6tsiuT0B6QHc9liSoXaj5uATpxt/GHMit3Omu+5MhaFSJfswDrBKljnnx895fjXQ5U+26LbILfg5ckx6/jaEf0+OcCgPyvPrNRjrGQ6yuJ1rfTfD47p6VX851jDYX85r/MediUcemBaou8S1V9g2vWlu/jAU+D1THZchu9TjyGx+JdPLrCAAaq4f/qeg8taetNVcZXYQCdfQJWMcFSM65DAsQn3quufdsZh8Za9NtAenQWcbJbeNQMML9ArZlC+Sf/io6X/WxPtxAIBBeqm4TSPAq+yMbmQ8a/SEZglLWwXeo93s/q996a9slwFuY7bWgxkW5f0BFK1da6TcNOa8ER4/H5IcUf6vwHBn9BZTaB9cskZw6AEcjpc1fkwDzjsScK5FDwGOYc3nGeo1Dzhtr0UFmDyt/BXjefo9HisfMUcBsAb8WKTNdBpO6gIBjCaCsqqc/K5qtyt6TC5AjFl8igRxss3+bJFqz47UcAQEPsGunGAyaBTDPvky9OxiICyn13ifl+IIcjS4D/oCcrN7Di+4WoA67FDhuZxg3KT6NOjpuzDbNe6wSaZ/Kg/KYF7vCuIndrk21H8khWx2RAYd51NHeOx3JF9sil6Kz2w3I0aCr9+w9eNlPMvCMlh2PZH2OAo7z1pAZCDjSx/ruEyz7mN0/EAM6BOzLWBn5otaR2kCGmQhceC51AwHd2WM3AmWsBm4BHkyVTUjN2xAOK92RA0drBP57BoG3mtm8DKZc9b8XKTTvRUpdd6Y7AfgTX7G2l8E3OuAwJ55d0Bp6JXA1AgQ8RQI+rPOcXgavEchJ7SHgJq/8NORM1tGuqwLy7ILWz8Ps+mCS6Od9rH27BW7T8cjYNxTtXQ+jM/Jg75mRyHG/7DOVN04GIgcZB/obhoBxL6B95DMCADk9vm0RIP865HQ0CJ0rauz+ZKQLCHJeBGYhR/xbvLL11n7OwcKNnawOgK5Nl4ow03MAACAASURBVGBBFJAj3GYU5GhvG8c/ozYoMPMabu36htePbo8YUOobA/ZnNxs/J3hlM9B+EqoPr0GOaN2Ro+ODxsMB9PZBhs5WAetVZXN8A4oIOgjtVafb/ekowMSkkO1Zz/e0Q2vr5yQA+bLHDTLQViPw7Ui0P3xMArbsCBxO+GwDfZFOvh8yIv/K5sdlSJ6ZT2ADa4r/scBdXhu6c0d7MuxX1p7zkEx4MvCUlV+KQMfOCagG6amC6lHQXjXf+nEJyvq9EjmS3WLzNGgEYJsffwPc75VNRAFW7iDJQNKJMh1Hd/Cbcg84FphPxcEGSN6uRkb+5VY2GtkCz/Ge2S007zz7MBYhcPEQJH+8h5d9G4GdK6JHjTF2diZqauMmb4q1xhmvXMdqU9s38qpf7HrmOW5i92Wl+dkeNcJktJuwbNjorLXR9rEFodrXZIoqJIOvsLKxyPZwFzoXdCJMgIOoIMfY/PLi6fFugWSbe+26FdLdvEI4R3Xf320F0ocdYdcu4/lFSDef2Yk7xXsZOnPfSeKw3jz1jLMVDKKCPg7GI3jWQWAhAqmMRGdgl73xRHSe/Rm1A2Sdm4U3Cqj+GtIZn2BlRyPd/AGEtzcMwpO/EdBgVh196AJaZQIEWbu9igIOvYjZ3YDjbU4Gz6qO1vAHkO/oGWjfP9a7vxGYEZBfdHBcDnWMxo9EdzwM6XJaIP/bH5IEjFuI1vhgIJJY/ejVLxogz6vfByh42qvAVVZ+FspEFkx/m3MdY8/F2GDcqHO/qGPTAVVTt99/KxSQ8HZgg1ee1Q44G+ndByIQ4JtWPhmt31cg/EWQ85T9nYHsqu2QnPGM8euKZMangZUB23Qh8umuQevrnSWe8f0NL6OBSZaQrc/hUjqg4BfDkb/j3lav9shn5nUCZVVOfUMPlHTo+yRAwFuQvbFjiD70eHW0eiy3eq1FtrF9jfeHBMDiuHGDbMZvA9/wyudaO1+D/LeD+jcUFI9y/4BoFdVkWWUbSEUjVlaKn7eQ742cDm9AEbK3lHjGLawdkbE5iyPAZGT0e8wttla+giRC1/L0N2Tgdy3wf0ilwkbC+fPA7whwQMaAX8jh4BtIwVAvELDCY7Qi2apK8MkFyFFJvkQEOaAMFf+KlxEDORy5jIDLUVT8g9LfEXrNyasvU7wyg7iQM+NvMAEUOfqcS23H532QM/c/UYEIIOxC4LidYdzkQU2lTfMcq0Tap3IaH9Hnxa4wbmK3a1PrRyJnqyMfwGHsOrYhcV7cDTkW/AsGHkEOu3sjhdwtSBnoQB2ZQF1EzBznvbvi4DhqR28dgc6ni72yGmRsDZ7hJFa75lFHZPR7CoENetlcGYQcYs7EovR6z/sG600ECpph8/Ip4BK7diCH0BHPuiDHgoF23R8ZOm8D/hGLlBuAj1ubF1s7tkVOHd8C1tq9ochIPyEUP/sdBXCYB88U/2oU1fQh5NzRFUUiXBdyzBivFjbmb0O6oonevccJkDnS1ka/TVuRON0cTLJHHo4AM0GMKt5YrUJy3PPevbFI17gR6G9l+yEDdoMj9CEj0e72ezQ6e7+MwP9u3HRG699+JFkeQ7braCQvXYsX8AvpVz4hY5Yzrz2dPvUulLVqrffMNWi/rw48J2ajjG0zvLIa4HK0zr1K4hSUqU3t71C0b7REeul7MB0KMgx+iPaT4Dpx7xsmAX9n/Xme9WEmgxwyvn3TGz9nksgwX0NA8g0kkZaDBONLjdMRyIlyPZI/piMD9mNoD8uccTTVjvXKLzZWN9u4Las/0/+HMrbei4CcA6zsMMI6Grn6dbG/g9C69r6N3YNQgLUrqbB+Hhnmz7L+u9bWhVAyx2AEhP8jcIxXfilyVv0m0q848HEou9EsBOZqj+TH29z6g9bZJzB5KxC/+SRy4iDgf+Ht+bYeuIzjTodeU+F+bfQBx1J8YgEqNtn4cXv/KuDP2NkxJsXqwwp9u5sbDph+MXKOeYdE3lqMBcjw/6exjp2dhRrzuNkZKdYaZ7xyHatNbd/Iq36x65nnuIndl6H5eXvVNJQ9/hGkI+6IZNTrCBCI+yu+4TpqB4qYhs7N60myvIdwko0NcozKL0eeTg/dAtkTH7Dr1kgfmDl4DLXPN4uRLWclAho6IOBRyA9uv8D1G4bAagejQGO/IpV9j9q6+B+Wuw559YyaddDGyOck/k1t0fn4GJubd2A6Du8bs+is9kXn7tEIbPdjYL3dOwGtQz3KfX+pMYrsej+2MXIdsBX5eB2X6sNqpJurycizNwIAjEB6q7PQedxlADwJL7tLgDo2RzaUvwZut7LuKJnD7aR8LQPxjA3Gi1rHPNrUeCyxsXM1smlMRXbN3yI7/e8Jm+koD+BoFECevbcF8qXujQKOpYPxnJV1vu8EdcwDOBYTHJvXXCzq2IhB1d47FyC7Ql3yVBuS4BJrAvE8Hp23VyKQlfN1aIXsu6GDYy8BfkkiA1ejoKNPYYH3CBQcl0QO3IjOHcuR7cEFWXDyVEvvW95saL8iWf9IGzNtrexFdLbZgnx+fo0yEVZhyXiy1q+Ob+mOsvA+Yf3Xl0DnY689e9vfE5B9bw6Sk9egfeuHWAKiQPzc35HWrtd5z9yObDbBExwVFI9y/4BoFdUG2gc7WJFyOGks/GzT+BZeNCMUScaPYO0rHd7EHJ8ayMdNfuckO9gWuI1YpHikNG5DoqALpViZjhDhv0hvCkgIGpaVn/XPo8Ajdr07cvYtCQT02rQ9FUxBzE6gHKeCQI5K86XCIAfkwNgHHdyutLKtmFDuPbfC6tmgDIM7Y5tG+P5mKPL2b0gcGJ8BNpd4tg3QuYLfssuA4xr7uNkZKWab5jlWY+1TTb0Pd6VxE7tdm1o/EilbHZHBeHnU0f5/idVnGfAzK9sLGf8cMM5lHvNBCA3OjOf63/7mkR0vBjiuuY0Fp2ibgIy53009t7+V70EAZ+6Y7ZpjHWus7/4vUnC+hRSrF9n1mUDfVHtUozPOrMBjaQJyWn0CRY4OAnKwd09DzhSL0Fl8Colytwty9AiddWiBXw+kLF6KnFSeR3qIzBGViQw4zIvnDnzLZGQAnR/4/X2R4n+2jf07UFCgA43nl8CYjDxaeb9nIkOVy/p3tLXrkcg4cQ6BA46hyIovoIyO9yOgRTu7dzEC6Z1o19XUkSV0B/iMAb6HjCc/sDEyHO25txIYoJ7iPRY5MVUh8M/VtsZNRQay72NBwLK2q60vt3r9tD/K/ne298yQCtTxKGQE3BMZdN5DDgCDkN7WreUhdMbOALcJGW6W2lrgQKOfE3ANr+Mb3J400sbUxQRyyEPOU6PtdzoC/0ok29wMzAlUF7eOjSEx3O6OQKTr0J5fhXTVgwLzXIrAYm2/4vm21tcNdjj0eC0CLrXfZ6E9ssau90QG5ZrA42SRrTF97Ho+Jlsh2XwrFXY89r6lJTqPHEQZNpSvePf5yGB7Ip5MimSRQwiUwcF77zQkt872ym5C+/4CJM+VzOheJr/ZyCF2X69sEJLPry7xfCu35jUVopECR7z5vweJo/1FNvdcNtzJKFvFrLzbubGQ165TUIToauRM/RyJLWkftB8H2atij52Cdi2KtcYZr11irMZs012hnnmOm9h9GZofiW7MBfu+ATlcdrQ23UiZ+vASvPwsR/uic3ANCuLiwDhDkf7jLTICHTx+UUCOsfnlxbPEN/hAwBeAx/1vC8RjhY3ThXZdhfScd1GhQBXAOKQb87PDHGPym9Nt+E7OL5PRTkakrIN+36AgWRtsLTgf2Y7vJQn8tc1xPEufItvKbtaGY1GmvA/Q+fsZ5BPYDNMNBOrD0SggXTVwqPXRchurq5DuyIE5O6E1MHOmemRXfY8kg007pM/9MQGcuEv1o127YFiT7borCtB3D4HO38QH40WtYx5t6vEaTBIUaz2y73Wye3MRGLlBSQ12ln609+cByGuG9sKnkb90fytfghc0Lsvallcdc+rDaDzzmotFHZsUqHo08ucetwPPLuErbC9f1Y8kto1T0Pr9ImanQfaqb5IhaVMdvPuhgLQuW+xgZM/tiOSf52wcBQmuSKI3/gYCTr6E2VGtjsdQG6fyMmXKVchfrQuS9Ucj2fFyEv+xfVFAztaVGD+pb+mOZORnMbt8gHe6cbMMycGuHY+3dp1v1x1I9PMhAsfWIFvjKTYvByN/mNvRefIdAoPVC4pPuX9A5gokA3YIX+Hgk2Vi5MUv9b7maLP/NzzFhi3m96MDtH9gfZEylA5eHRcgpdEFyADfAR3Eb0BKgdcxgTlQm05BB2+HFj8eGZLHIOe/m0K2KRLCn8RS1Nr1LSj6kFtMm6c2qxjOck1aOV5pvlQI5IAca25Fjio9kRPHv5ByarDNsjmew3pjb9MI398ZAQGfQcrc9SXuB03/Xc+37DLguMY+bnZGitmmeY7VWPtUU+/DXWncxG7Xxt6P5JStjrhgvDwz8v0I+Hc8p1uSjDVfSz1bloKMnLLjkQPoEDlOjwButOuxKFrlVannugTglVe7Rqtj6n1O8bYWmIjOMFtR0JN/R46lLsNEJ6TYbrBidUfGOTKc9SU5q4cykB2DIhg/gowBX6AzwQZSzhwBeW4Clthvl8WtFTK0HkNY5/gogMO8edYzZiYhY+uKEH1IbWeVvsig8aCteV0RKO9Tq2smoBM6g36OgXuA36FAUg9gkSqRwepe4NAKjNPRyInKZXK90NaAS5He7B0UyfpFAgTKQfLLv7n10+bEOGQEuHtH1okd5NMPuNd+z0Byx+PGZzjStVxpdR1BhmiV6f+x9vrc1pxmVsf9kG7n3FD9R6J7cvrZmdaXbyIjyxFW52DRv43PECTjdASuQHtCW6vnRCQPVMyQQ+1AatuNlyxta/3VEgF9z0iVpx3jQo1V14/72/x/0fgPRvrjzZijbAXaciFax+vUW5LIfO3IEKETGRw/ItkX25AEyXvC7i0LXL9pNhf97JjtkOz6PeT4sDAwz70aUo8Qa4H3rtForzoPGbFHEjjAgcfrMJS57RivrJetqa+E7Eubf7diThtIn+CcYQeis9VQby2sWDDOXYUI7/y/CJ0r7sei/yI5+Sm0L/8asy0W/degdp1jbfgfSG7sb+v6D2wv+dCtuY117BRUUKWoGKsFlUPFuGlwezVDeqK7SHQ1Y1FW7LvQmb0TFgApIN/lSF/zPHLcPAsFH78TeAMFrhmEQIknBOAXDeSYB7+8eJb4Bh8I+Dphs5x1MdmpL7IVTcSCgAMHIF3VgArUqS06vz2LAmY43cCJ6MzqMtoHAY8RMeugvWMyylTVAQVXexbpN4fYGnC4PZdVh+tsf64OLW293kIS8H8r0o8FzTqC9KaPID1ga698Nso4Otf7ppsIFOzU3nkJCoblAkouR3qO0NljZiMd8SFIn3IiWkedHrkrloU8BD/vOgbgMJc6xuJXgu9IZOdbhPS6buzsQyC5Jo9+9HkTEZDnvW86Onu74L8zSQWxakx1zHMuxuKZx1ws6li5OsYcqx6v3kgGfg9oX8f3OJmqCi8TWpn9uAjJMr2R/P0jEuzGHOvTBeXw+Ar+QxHWwYEMX0D+f7ORLbBsObEEr8EouPowJIv/iSQw7XQUPNL5lFWhYHI1DeRRhYE2kU1hPpKlNmOBFezeQnSezBzklJQNt57nuhP4vIH2o49IAm84P5UlwE9C1C/FbxbS/5+KdNPX2fjsjvxxngBWhh6nBcWn3D8gSCU00X9uC+gVlIgU4y3kHbCowY2Fn71nNElU3q8ZfxdFZp5tYi4iSFukAKjJwG+BLTrzEJL7XStrixQD38aUg4H6cLHV6QZb1JyC4XgkKH9AwAg5Ht+eSLnhAwFvRgqIjt5zDgAY/FCQJ5GTcrzSfKkAyAFFHLidxCjeEwlTfhbOGhSlLlPGgZ2xTSN8f2fkEPc5MMUr3xcpcrtH/JZdBhzX2MfNzkgx23RXGqtNtQ/z4JnXuIndro25H4mYrY78AIdRM/KleJ+ElBqP4UX4Qsqrf0ZRc8t2ICeHzHHkBI7z3t/dxo2Tk/dExvKbAvLIJSNf5DpuB+RByrlPgFV23QpFsN1EAvLaDQWyaWiW8xbe72nAaXW1fYmysgxW1DZu7o7J+OisdTI6i08EDm9ofXaQf0ukHD/LXbvvCc3L3hsNcBiTJyUU5KXGBDJgBctyZu+Z5v3uiwxGjyInlk5YhMBAvDYAf0TZxyZa2TJkyHFAwNUEPv8jvduFwN9687wlcgi6BjmPTEYguh9SZrTKFM9jEMDpAzxdovH5Zqg2tXf+FouqjoCigxHY+UEEBOyFHBBDOcj0834fgiIqzrbr3RA4bmIgXn4kx3uR7nQU0h91tXsDrZ0zO8d5/Foaj40I6P8OyX65N15Wy4A8pyN9zbx6ng0azAntHX+JOf7531PXdUZ+05BT6gikl78Mya+D0R52L+Gd1ZqjPf0AdK5YDFyPwNzbHCztbycbS1PK5FWFdP5j0Ho9BzmsDUEg4NlufQvcrmdhgRSsvu4s0hmBZKcGbtNmKHvrFX77pZ6pWMZTe/9kBAa6FcmTmbKblHh/N+z8aXX9ghTgn8RuFLIv1wFn2m/Xj5OR3FHxiLwFZeq7UUgntJ+tb3cDt9q9vWwNCi6LN0Witjw8DjnAjET7/bPI3tnV1qIBBJaNCyqooIIKKqgcQs5/d3gy5DTkp7OeJNtDFmf1dt7vzkin65w7D0X6gDnINjET6XFdluksQU6ighxj84vNk+T8P5o6dDRUKIizyU/voTP4vchZ/O+Q03xzoGeo9rS/k5Ff3Fhr47uRPrCv96zL9tLcvmNWRt5Rsw5avRagzHRLrczpjocj38BMemM80ALSZ3wbOArTKyJ91bHWzm8GnhtDvN8rbD6cguyZ/REgaGnqf0IHddwXuBrpay9GZ4PQ5/8lSBe0GgUAc/qVs4DfEygAGDmAVWLXMQ9+eAAU9xcFbfojia1sf+SH2GhBjqlviAbI83hWI4Dzu7YOBQGO5FHHPPowr3ETeS4WdWwCoGq2tw3NQz4Ap5PShbN9trqy5yeyzX4B7OWVtUG224ft/YtC9WUJ/tciG5LzHVmL2T4C85lovK5HNrHZKGDmPaSCnFm7DiyDxzAUPPF2JKN2QzrVtei8ug+y424lLABwMZIV67QLEdDPyHvnccjmPpYEmPc4sgWuBvYOyKsZyqR4kl33QoF4bnb1ow7AbEGNj3L/gMwV0KR4GhlQetnEWE/tA7lbyKtRKu2yN82Y/KjtlHcg8KJ3faEtrHcjh6dV3r2hZHPKbY8OpcMQ4PEn6ID8FhaJl0ThECJK9nB0iOmLFA9fIkH8SLu/O0m63CCR423z6WC/u6ODuAMC9kGOc04Z2QEpJArDZyMiKgByQIrTL4Dldt3bNuQbkSPwu6SURwU1qH2rEfD3aWC8rUHBo47v4LcU4LiCGgUVY7WgcqgYNzs/ESFbnf1vnmC8WHV08v8MFD3KZTP4NvCk/d4XKZGqAvVfzOx4eYAOXZv28/h2A/4CuMHry8eA4SHaNId2jVpHdD79XwhcdHbq3lzgY0pEwva+s0cD+fVCisaBdn0psLqe552OoyUZnbqRjuET4/kXJNkFJwIvhBovqfYZinQ4rYEpKMDPMrs3HTlXhI5yHhVwGJsnctZah5dtqK72r+u6TL7fAX7pXfdD5/6XrS93CzVu7PfpKKuSc6zqhPbOx7CIh4Ha041V5wBQjYKO3Y0HfLR7rVCUxw8Jn03uRBRJfhTS0V1AgEyD9m5fx/kqAnO1tethKODZd5DjfNmgNeTY46JRzrf5vQXpGhwY6HnqAa9lrOdspBsaBHwfOXI4cMwKa9/MQdWobax6GUXJ/wGKrDrAq/9rhHfGWYRkiqOBf8TTSaf7G+lUDwox/+19+yLHjaNKtUfGd/cHLnHfj4yZ/+CN0/G27m2yMRsEXFlirbwUgWLfREbWBxCIZFvmQxIHwAZlHUitby2QPLzFxur1SPd/e8jxUuIbDkRGXT/43gwCg/9SPGcj3f52c8Ebq51tHQoGmk3xGYIAV5nriUC/q+23m+dvoDV8GMp88CEwP+D3uzVnCNoL2yKZ7l2kg6+yOfIBgWWqgsKRzftuKIvJ99FZsoWtf3ciR5XMstSuQrb3Pgz0sutJyOHHOVN3RrakBzEwRKj9sKCCCiqooIJ2lDw5bpidpzohu9AG4Di7NxT56rxFRsAKcvb9EDjYrtujjGnLvWc2Yn45dj0EBR0MEmSJCoMc8+YXg6c3bpagM/gk6rAp4GV9K5dn6rzR036PR+dvB4ibbnUOKq8ivcpHSAf3DDobNzP5+F5Md+1/K+Ywm5FvtKyDJDri1ugM+TJwvJXNRYDLTLoqa5c7kG5qT6QPu9TKrkNnjqnozPgOcFBGfn4wjs5Id3K1V7bK+JyDbCG9vO8M7tTt8e1nvC/GgpAFfHcLpB/qjfwq3/PHJwKR1ATkFxWMl1Mdo/BDPq/D7fciZFv4urXxCltvrrHfH+PtmY2xH1P8gwPy2MG9BuiB7MjDGvJ/O0Md8+7D2Dxjz/2ijk0CVO3bxzbYGlqFsvB+EwUF75x6thM685Tl/09im1mL1vAOKDnNS8Dd7hkqlOSEEoF5kWz1cwIGHcCzOyOZbj3yJ+uO7BIDML8xMspVyN59NfAfwEavfBgCrD2I1nF35glhB1yC/FTm1/VOEptRR7LJ4LWCKlu9HrA+OwXJBNcSyNbv8XNBadcgv5Eedt0L4YAGV2KMFpQf5f4BmT5ei/PVKIr0ICsbhIzX16DDsg/Ie6nchTwmP0wxY79HoQPxQOCe1HNr0Ca5wK7LjlqbXtDs+/sghcJI26ReR0Jr7xCLqserJYogtTcSBroB5yOHoNUEiiDlLXRLkXF8C3C+lXVDzrMP2rUz1DWzxb+iB5+CKkNkBDmgCBRpxd6ZbtzYdS8bT3/GIjmEnB9NjTzBpuR6hZR0xyIDwd+QAI+LNi2ooIIKKmiXISqcrS7FKwoYL+c6LkPBWc4D3gYOtfKnkPPjbwhoVLF3VzxznMcrGjjOe9dSZBz/DAOtmez8K5IsEh0qUNeY7Rqtjuj8/T2UCe8+pIReDvS3+7MRkOWU1P+VNUdQJMongdOM93ossxKeIwU6D/vO8fcB3TLUc7CtOYNRtr+P0Vm8mZW9hxS+IZS4/vn/x+i8/xDSo+xv8/4OpGgNGT0uGuAwD572ztFIX3Ma8Afg0hLP+OPm1gxj1dVxEglgdAvwnvfMydaXewasow+UuQABnZyhvpPNz3Gh+Nl7l1vdnkQOOCORbmwTMCP17AoCZudLvftEmx+/JDBQDk+/h9bXrd71CKtvJmMHit7+O2SEu8fmwBEoQNRaW2+OR3qcrhVov5PQGrsCyRz9rbwtAupPT4+xDLwmIucq985jbLxcjxyOPiVgoCpru87IObO/1fHHpPTE1I6q+gYwOXAbz0YB5NbgZXoM8N7W1qYuS1M7FDTuiVSbbyAwuNrqdAIC+Pa2NWCU3ZuC9PHOYa0a7ZdlAQBRluEDkYGxlfF1kXhHIvk8+NzwvmNPJH8fZr8n2lgtK6NhPfUcjxcBF63lG6kt5/h2nNcow0Gv3LlMmXYcm4crgEdQ5sbXrK4HoHVvndXnOCTDdgmx3hjvRQjI7NbXtsh28zw60/2EACDngsJTegygADhfAId5ZQOR43MweWpXIGQX/gE6pw5A541pJJGUT0Hnja/n/a0FFVRQQQXtuoT0DZ96cttZyKnyTnRm+wz5PK2jRBCyMvgdZLKGy06xBgU6mmzX+yE9TpX3P53K5BUb5BiVX2yeeMHfbEy8Qz1+UtTW/91GBnAcsuG8i4LkXYnpU+zeEhSMaEm57/fe1YUk41YVOgPPtuuu6Lx6JtIRPE54PW60rIP2/tE2LrpZWSsEPngDONjqOc7/xgx17GT1+TlwoJVNBC5DwIeByJbVJQs/+2YHNB6D1pgalDxhnffcAwiQs0e5daqjD3uQCihaV12sD0KdyZshYMPTKHBUfytfgvmMhuhHe0d0sErsOsbiZ215JgLfHmx8DkNnyRttfoxFoOMNBPQ5jNGPO/qdBALkoXX7APs9GzhjR78rA8/cQId5zMWceMae+0UdGymoOsVzLvL/GYdsmpus/GCU5e1UElm1NbJrzSqn7+yvC+I6CNnd3wbORbLOS5jMHKoP6/sW+z0R+Vxklo9T/fg48LRXNgnpPu+lMqDjcUjevRELUm/lE1AwhyCBYrz33oJsbl0QiPQxzDZFbb+YTgjMWlZ2Pm/cLEJ+OJfZdXMSuXwCOotPDFi/pcjPoB86B1yP5JBuyBfnbbxzR0FNg3L/gAZ/8PYGq6G2cN9EEul4MHKgG2LX1UhB0WBkbg78hqMUpiNtA3rGJubrCGR0OTJa74kMracjIGBm5QNyhDsbbZKtEXjqSVvU9kLOlSGyHLhFbjCe0xJwKHCf/d4HKSIzO1VArWjjc5Cj0SDbQP4MXGP3eiDl2Ji6/r+gXYdQxIatSNF4q7fJTwN+4c8FJMQGS8nbVInto+PWFTGuMxLGF8T6toIKKqigggrKkzz5OFq2Oo93FDBezDqm6tHO6laNDCvvYkZUj+dY/xsz1i9qdjyPf8XBcV4dmyPD9AikmHsJWOvV+Uu7FxLIFSsjX/Q6erxPs/G5G3LuvguLRGbzcAopIFBGfvsgB/JDkOHhfGSE70sqUw7SCbxExui16Mz9NQQ++DGJDmUBUqyGOP+3IYlA3QMZGsaj6HRzge9a3ftZ3472+z7juIkCOMyDp8dviM25w73rT7HsWVbmK8hfJLuD1UKrzwyv7NtIt7MagdWCn8epHVXxAgR4dOMlqJ4I7Y0/snH6OPCilQ9GjgB3oX0sqzOO68d6vx8ZBoIaVrx3+0DA94HHvOu2gXhMRMCYZ72yZUiXe5GtN31C8CrB+0wb9y+SGHJXIQNWsHGD9LbnoAiZM6ysSJQuhQAAIABJREFUE9JZfQM5jbigcaEMxy6o081oz3qTBBx7IB4wliSq6r4VaucxtsbdBFyU8V19kT7cZWl6B3jYfrez9cYfp0GDHKA96S9RsIovbM47I+B8ZARc7D2/DUxaBi8H4loJ/D0W+d/urUBOlcsC1Gk0cLJ3nbavzEdy1pMoO0BQ4BhyqFyP9sbvILDcaSiqaq3gZCR7VZaIrouQ/vjEutZOj1+IjLUuw+ZdwKte+TSbd3vbdbB1DjmqfoycNi9Be+8zSNfbFcnFI0v1d0H5EsnePwfZONYg+WIGOlMd7D0bVNfQVAmdMb7p2hft/S5o7LnobHomAuO+hoAXT4fePwoqqKCCCiqoLnJnG/vd2fYhB/I5FJ0X5yAd2kwU6Hw2Cnw2NANfP3v5SpM1FiO95uVIt7gR6XiW2HMh7A6xQY5R+cXiaWPlZpJgBn2QjqoL0hm7M40D0Lm+dnrjLODDvZFevDsKOvILdN6fbOP0ecKcVduhAFF7kOiPn8bOgzaGFwC32HVo/V9eWQcfQk7MDnzn/KB+gmUGyfh+d+Zojc6HTwPve/fHI8DTTUDHEPxQgK+/QGfTAVY+A9k7bka2lFcJrDNGuvjn0dm4Ll+nIEkO6nj3dBRM/VS7nonW7qD6NyKDVfKoY0x+tp6egwC5662s2trzTrxEISHbtdL9SA6APPvfDcCvkZ6qzjlOsm9l4ZVLHWP14c7C094Xbe4XdWycoGrvHU4GvQ7ZqxchYNMA75mV1MYkdKWMgJIkMs48JIOfZ3VqT5JdbTjy5xgSqi1LlNWVaGVQul0y1HE0kpW7IHlqi/fMehRcIaiPk9eXbdA5dTNwtPXrGQSQU9NthRKAvYbkufVIBn+K2sFQ3Pmm7GRj9p7FaK+ahHyLHrd3N0cBLL4gLIhzmq0x072ypegM/i7yQzgwZB8WtHNQ7h/QoI+tjZC93iZhP7RJXolFj7Fn2nj/N4kMAMCI/IYjB5jj67j3JjKy3ooUA3vZvbMpM02nV8fxSDl0JzIgX2nltwPPoijaKwP25UKkqPoMKaO6oIPyp9bOvwDmBOAzxPppMtqollhdlyBg5RTkLOtSEGc2xBfU+All4FyHnIomIiXYS8hhtTeK4Ho/JQzjVEDR0ZSIJDpuvRlEMOUYXpSFggoqqKCCCmrKRKRsdZ78nwfgsOJ1tPPar5FSpRVS0D+AFIFvkICOFhM4ixJxM8flAjq0c9R1dkbsZ2VTkMPxRXYd1MgZs11j1pEkG954r+xuYCoyCn6OzsbfR4b63QPw9M//HZGC8RF0Dv+p8XsTnX1cdO5OKEppJkWnvaszAhj8A4lidyo6mw8K8P6uCGizwOZ/F6tPa7tfjZyOzg40VvIAHOYJcpyLsrW/iUAyDvwzCAFYLvf+xwGAsirIh9n4dGDtURjAAhnOryKggjzdVtQGAq4D/gnpljI5qQG7Uzui4VEIRLXS2tbpGttaP5el86uD995YdrgdGRdZxk497/SBgJ9h8keG97UhkSn2QsaivYE/+vMdZcq61bVvJcjWoY9I9qfZVsfgQY7Qvn8VchwbW4l+8+b/UJKMsbeioGo97XqK1XlPu26H9pGKOOek+r092svaZHjPcOT4dq5dt0VGwDu8+jxFIieHdKoYi8CMDljdGwUadHrqtZh+HM+htkxerVC20QHIgfRtJG80Q0bHbxIg2jja639q863OswSSQ9pj8k3AMTsWyb9ur6gBjkXyx78BF3rPVqF9tOy9CoHfXkV7xLWUiIhLbcDh7YRxsuxgbfwOcJZXfhve/hKAj1sDpiI9+f7AB9bODyMZMXiAk4LCEpKNHQD4WeBOK5+LAMGH5fl9jZFsrjk5u3nq3uEoAOlWZFuahXQRQYIdFFRQQQUVVFB9ZOeXD0myZLVHerfl3jMbnTxg10NQZoyygwF5cmMv7HxmssZvkdNsK2SPOJ4kaEW5oIOoIMfY/HLk2QXpE/ub/F9l42Km98w0493RrjPr/xDAcAzS5yxE9qP56Bz+EDpztc0yZlL8uiG93LlWx5OR3sbp4pYimbkDGX1UyCHroDcXx6Csm04HvwmtBf1IggOOCMhvKQL5dUbn/ceA73jP7Vnu2KyD7yhkT/wUaOXmDdLxfAcFHMpsT03xnILOw1PrecbPjnlkSP723mrgSOTI/W2rf5CggyV4RQXj5VHHSvOjtp2hG9p/v8Tbb5FeJVjwz9j9SFxAnlvPeiIgw0/T73fPpebijWQ4j8esYx59uBPxjD33izo2Mn4kMkdX+3sZ8gF6icRX5gjgpMD1mo1sC7OQfPM4iZ1+vq0PQWQOr44LUHDayyjhA2Pr3A4Ffd1BvgvRuW2CXXdGsuKrCDfzYX3rX5k80/pUF/jwfuBvgYUBeXVBmBjn57MAk4PR2edVEjtVexR8Oat/Q2sUsH0CCVD1BRSQshuS1YNmVkTnxGvttx80vwrZBffwx1lBTYdy/4AGf7CUQz9HTpu3A0+gw9YYZKy+2QauE/6yOuZE4YecKT4hcVppjhw5RnnPXEbi9BjMYIQO/09gh1VkHP8WcCFyBOiJORyFWASQkfgHtrj0R8awS5BScBSKyp0pw4DxGYWURF8jcfbdDTlzbHWbBVJ4/Ddywswcbaygxk3IyfB94PRU+UE2B/+AnHPfw5R2BX1lm5aKjlunko+A0bELKqigggoqaGclcshW570vFuAwj4x8+yIH3++SGFTXAH8FzLXrGhQhdEKAOuaZOS42OG6MjZvz0fnx20BvuzcNGZGDACvyatdYdQRG2hw4i8SBoxlwOnKo/oQEjLAH0Ddgmy5HBrLpdj3F5ua5yOmnNRZ5FDlhPEoABT2JzmQUUiRfjrLyfEQ45XhzZJC7GYs+DdwD3EcSZGQN0gE0yzJuiAw4zIunx3svFB1vGooGeAVy7HCGlcFunNi3vQTMCsC3CzKOX2n830R71erUuAqxN+5FCSADtYGAmcGq9p59kIH/a3a9BCn/3yBxOjrY5l+rQDxdW10IPJKum/ecO48H4VvP9/hAwMyODjbvv0TGKAf+mYbW9DO854JFjqyn7Xojo9H99jdENs66In32RHvWd/Ey8QWqj9s3ZiFg2vs2VlshR7w3UKTMn+JF5Ef7ytiQ3xKhz4bbHLzErtug6Ny32XV7DOQYkHcbBBT5ANkYnGNgf6RzbOvPwyzrHNLFDzE+6xDI0QG5D0aGyCDrKXIovBs5NGyhjiBgaT51jfGG9CUCNf4XCcDJX7+Hoiiyt+FlHqCBIGskfzqH172Q3L3Krkci4OY9wJTUGKu2dg8ZSb6N9d9dCJy7LwoiUXZWw3T/YJkivPIbSLK2bEAy5JRQdSqoMoT2/iG2nr+Hl2EEOY9ntsXtKoTOEC1RhpozUuUtUs+2QPvmzwm8RxdUUEEFFVRQfYR8Gr7w5NQ1SI/jAn7th4KRVHn/0ykA36XoXPW8nTv6mPzxBXBQoLpFBTnG5pdTHf1zZxcEinsVnU2XIN3tRSgw/efeeaAlAlrNytCfQ61uHez6epKgldcgx+7MQDV737YzL3Lmvsfq1AFlGvkSnXE+J4DTMTlmHbS5+DFyPP4eMM1r3+/anAyZAKDG+O3rlVUb/2cD8knrEXoCFyNbh9Onuqw8HUv9TwP59QSO8a6XATfZb99H1Onm/fP/G5RxztrR70X+V2OBYVnrWQ+PioBVYtcxrzYl0atMQOCKKrteh4DGM5BN5TO8QKGNqB+jAvK89pyO9HtdkZ3vNUz3SArcQBLgtKxMtbHrGLsPY/Pcmda3oo6NZ9yk2xYFF9mK9uFZKHDrEXZvb5R0aP9Q/Oz3ahQgYzqeXhXhHkaQ2MZD+aq4oBg1yHZ0Z4lnfJnjMjJkjkb+N5+RBPnshwJmtERnjnsIk5HbD1RRsq1IcB3DAvdhGyR3b0bgSvcthyCfnBXes1PJ6EOG7EST0HlgKAIVOjngT8h+FtwnHgUc/l6qbCoB7EUF7dyU+wd85QcqO5zvSHA+cJ13fS6K1rsbdTjs7Mz8vPceBvwriYLjh8BG++0WnlOBTRVo4xoUwdk5OrRCaPx7kHIlpKNqJxQV99ckCOoRKBrPBgIoGO2dXZBQcWyq3LXljUjhuQIpH4JmASmocZJtvL/BlF42z8+ltnPaPshp5p+wbJwF7VDbbhcdt9TaQu3o2E9hkeULKqigggoqqCkRkbPVkQ8YL5eMfEgh9RCKyPUUcmochhwfP0SBVj4jYOYoImbH884zscFxE5GB+Cy7HoDOig+58YMBxxpju8asI3KA+RlwSIl71UjZ+HjAerUnUSyOQGANZxTvhcAqg5HS/Mx0mxLojO7Grf0dihTHF+NlOMr4bgfyq0GGvbeRQn4oAgf9CBkHf0UYA0A0wGGePD2+ryPjiZsLU+xbNpPSi6H1PnPmSntXNXCczcNFyLh7KrAu1Lg0Pi1QxpYz3XWJNggRTbE/cKA3Vr9n9WmBgMffsjG7P4ENciR7+0JgQ13tYH87oexSfcrk5WcAqHMsUFvXUq4jh1tX+iInpl+m7k+1sXteyDFT31iyv20QqNo5H5VbvwHe/C6551k7r0POZGUb++p490wUOGEpMkzdjjlvIgDbSgzEGWrNiUXeOB2H9MjDUBTOi70+/Ai4OzDf5iiD6o9RdPxVSDd9oK15Y5Fxt2ug+o1HxulqZOD8M0kAgim21oSO4vog8J/ACfU84+ZK68C81yAgoIta668z7ZAcWZYRGZ0zbgUGuDa29eUN75lhKNjhw5gMZWvqa1TA2IrkvJXWjy9gclWgdy9FcunNJBGcb7F1wDkDl50tpqDKkTf/nTPqxbauvENik1uMBVXw/6egHW7jkSgT9tH1tSFy7Myc0aWgggoqqKCCdoTwsoebjPgXtucPQEGjXkL6uN+QALmCBMhGILTP7PwxFzgH6Yxa2bd8iZzoQ+jHooIcY/OLyROdcZbYOJlqZ5khCBz3faTH3gsFk7yRRB/p5M2SQV92ZKza31ZIj+mC1W+yc8Z8pMOuM+NamfxmYYEcrM63oIDyLUxum4sB5gLxjZZ10OO5J9Lj9kDZQP4GZax3upuOJGeCULrjS0kCGqZBpd8GJgbswyU2Fm8i0ZddgWx/ByG9zoBA9ZqJgkb1sLkyD3g59cx0K3ff0sm+ocHnfxsjB9jv2XhBR0q1xVeV7Wib7sBzwcAqOdQxKr8S75gD/B7Z3r5A+uv2CJD7X0iPsyAjjzz6MTogz96xCPkd+Blq77b3Hmz3Rnj8XqTMTE4x65hnH8bimcdcLOpYkTrmCnIkySw42ytbRRL44F1gaQhe9u4FSOY+Cp2r3vfm/wKEKQkWTNVbSzYieXw5ClTp1hpnT3EyRzUKnNvgIKt+nxivq4ETkZ3xA7Rv1dj9qvT/ZKhjDdJP71B2aDL6ASFb+zz73cb683bMXowCgSxItX/WhGOtUWBjd+YYgHwBBqPAAPcSMIhjqqy5jdP7kA1yDgrMXdEszgXlT7l/wFd+IExGBukudr0KKYx6eM9sxZxIGxu/FO/z0KH/LeDrqXtdkCNt0KicJIrAOcC/Y46ICPxUQwUMVAh5fy+KHOsUDKOQk+4OLfI7wKMz8IB3nXbgWoOUA5/gRTkKsWEV1DgJKcaPRYrv/lb2DLC5xLNtgM55f3NjIOqJjlviWT9SxcuFEFJQQQUVVFBTJSJmqyM/MF7MOg5CDrfO0XY0yrB2PnJu2A9FjdoPGZBdZo4Qyqpo2fE8nlHBcfa+ASafPeS1c3/gG8DjNrZCRqvNo12j1BE5VDxov/1okU65uJ/x7Jp1jKJz8XoSpfR4pBBeiIzjz9g8HYccHUJnOSqpgNyR58rktz864y9H+oU7raw1cug43a0/GflEBRzmwdMbj+OQwaYDMnDemhrLVxHBsdn7nsnIADG/AjyOBrbWcc8Hx60qNY53kEcN2gcPs+vZNlZXI3DKZmSs+gEGAAwxP5AO4yWUWfBN4D9QJs6VaA/t7j3bCe0nNRl5LjdeLyDg0XaOYV67tqeMyO7euJiA9sFeaL94z7vXHa19QZy4vPdOrWuuleqzDGPmNuQs+VVAwN4E3hftvRcAl9vvKhs3r1j/Bo9UGZtQFPefkDgcjkIOIhfYdZuAY6c5tY2rtwHH2+/TUUDA54wyR1a1985EdowTvbIbUFaq65FTZTDDuMdjDTKmbkERT5un7vtr6vOU6aDnzce9gJNIshwfA/wz5miI9JLNkHz5e0zvWybP1khm/Lr7BuRosMV7ZgSJU+du1haZdJzUo4dG55xDCRCs0mvTLjZGl6Gzx+PACWi9vgMFlTkw9NgpKDt5fTgFyWnVKPDpc8CVdm8flOEkGGh0VySk8/gUOKpUHxRUUEEFFVRQTPJkgF5AG/s9Fzn9zUN6zRnA8VgQkCx7Ftv73gwFnvOuh6PzubMD9ApRRyKCHGPzy6mOLdA5+EPgj1iQD6RjOAfp5bfLaBygnk5X3RzpqO/x7n0LBZBfnoVHCZ5LkS/eHK9spvG7MMQYTbcPkbIOpnj3QXqy/ZAOdSjyzfuJjaVKZIy7iOSM7LIeTkS2lZBBHBdjwWisL18g8Tc8H+lzQmQ48202nZDu3dndnkD6m7EIkPQbEnBsW/u+skBH9o4NyJ77MfUEbCLRqzQasErsOubIz+3H7VBSCpeJ8waks+6PdEvnkTHpQM79GBWQh2ydT5Fki/UBx5cgXZXbF1shu0TNzl7HPPowr3ETcy4WdQxfxzzXG+89a0gCubby2nF3dAYaEoofskU/i+zwrYH7Mf9xpFf9BbAoRL08ntX29xsowOBLXp1WIpuHb1d5mfKCDrh9aj4KoNAFBcX+HnAAAqxfCZwWuH7DkU3BjaPtfG68+rUlAMASOAIFxXRAwHYoOMcHWHD10OPU3rUfAv9Psrpcj2xlvyc5H5e7hncs9c1e27VANsF7UHb3oOO0oJ2Tcv+AHfpITcCfoejfHZBgd6otuBPR4TgYKC82vxTvo5GCZYhXNh05W/WrEE+nhJgL/AMpo1VAPv7CMwNlBLyWJJJ8u4C8+iLBaqrP36grOpQ3x6Iqh1zIC2q8hA6OxyJn2J8C60vcD+pQvasQOx4dtzNyKCtbOVZQQQUVVFBBOzsRMVsdEcF4edTRzmufAn+LFHDz7CzwgP09CDk8BldwkE92vDzAce682B8ZOC8lUUQOIIDD8U7QrtHqaGP0ee/aPycPRM44rxMuk9ruRstsXq5DUdYOQeCGMzAQQsA6jkcO8F29smapZ0KCRp3C+ioSsEozFIjkZeTQEYyfvT8K4DBPnsg55lMSQ7XLDucDAYPOxfr6GCnLnwZWlBpTZb53LLWz4DwKnFNqrCIn+nfIeFa1PvsYONyuZ6Po6sd4z7jAZCHqOAQ5b1Uhfee+SO/4AAKmPENiSOqMDEtZ6zgLORd1Q0b4fwAuwwMCptr1fcqUQZAx/ncYwN/KtiCnihpk8N8j8HhcgWSZWaXGSqp+rcmo70TyzSfUAwTEc8ALMW68d61EctRIr+xVlA0tc+bo2ERtR7xBNqcdUMz12UgEtA6ScRTo4P3u6/1eCtzpXR+NgFarA9Z3EjIsbk6VL0bG6r1C9CHJPjwGAWRdkIrz0bq9p/dMOgBY1vVmMTrHbEby460os8HRwP8llXGAMjOcpt4x2NaWDd54+jHw/Tqe75mR3xDghq94xnd+yiTzIFvUhdh+j5wqaqx9XUZAtx41mvm/KxE6ez+FQP8PoDPOQhRk4EW0h2XWNRS0TY77EulYKmK/LaigggoqqKAdJTtjvIACbaxDYKBZyAHxoIB8djN5u5e9/2akY3wFWOM9dwNwtv3OKqPGBjlG5ZcXT3tHH5MP3wKO9Mq7AWuRA3SnrH3ovbc30gOdic6FLU1OPd57pn2o+tl7OiFbigNrzEZ65M7o/LOJQDYVrx9nESHroMdvoLVtK7s+G7jUfh+IbB7DQtSxxDeMB/4eOBLpH6ciX72swT99u0lLBNiciALVvIHAou+T2Iw6Zh03SI83C+lQR6N19QAbI6fYM99EwMrn8UCcNkdHlsnX6at6omB/P/Xu+frG5iR6lc4o8UHbMnnGBsdFrWMebeq9cwkChL4NXOGVX4vs4v0J5HeYQz/mAsizdz1qbesD5vfwvwFvL20sdYzdh7F55jUXizqGHzt5jNXUe09Cek0/kdOs+r6lTD7dEMjv23a9G/LB2WT9+zqBAysie8ONyJdqIvAnLLAjklV/iQUZRLLWd8pdc+wdy6wfl3l1dP02DoHkys7cmuLl1uVjkD3udkrI+h7/Tuhc2WCZyuPV080tJIf/iQQIOM++YXzgPhyBfG+cPHo2SRCJASjwSlbwf3t09j7NK6slL/u/CehrUNDOTbl/QJ0flkzKzvZ3vi0wS5EiYhMyjL9BgOi4sfmleM5AIMN97PpU20z2QA5JHxEgYs1XfIsTSBaijIC9yRjBaQd4Tkfo9RvRYTprxKgeyLmxp12vRwrO/nbtNouD0cG8qpL1K6hxEhK0T0FgX9+JbF8U1aV7Xt/W2ImviI6LlJ43EzjjaUEFFVRQQQXtDERO2eqICziMXkeklFpjdbwNKd4ORAr379oza5DDetcs9SvBewBxs+NVHBxH3RmG3HlxEHLmviorr7zaNY862vefZr/bInDMOd791vZ3JQK09A3A051/B6OIbVtIgWGRLuIzwhjgnEw/GwFyXrLxObPEM372r3kE0j0g0O/9eBl+rH53UCILWcZ6xgQc5sFzDAqMM9QbR4OQ7uZD4LYK1K9eEBNyvuhb1/0y+HZGOr+PkI5oNVLQn+s9kwarNDiqYqnvRXvgJyRAwBosY166LTLUr7n113dQlHgffLSBJBCAA5I3Q44IITJJzkZGsSXIKLY/0nN+C+k63XpbTQbQIZI5PiUBo43CshojXeNThDfG9UGGKjcWh5NypqS2sWoLZWYdS82JR6kDCJjiV5Ohbm4uTkSyWl9kULwaOTdMtjZ/zdr2kpBtW2lCEU0fxpxAUDT8Z0hA/36bDsbTCWbg2Q54hMRg+xlay0+2Ofoj4Djv+ZNszi4uZx3w+nAkAo45sOMXGHirgu27HO0bz6CIrudb+QVofZvoPdsJyeyZ1lSr3x0kBvCx6Ixzrl2fQ7LWZcoAYn/7euNnD6ury3bQHBnOJ/jfF6htu6PsjUfUcd+PkNs6I699kFPBtxCAdIGVt0Rr+ZNUIOtoQZnHiL9fjLM+HIkcgZ9FDvhdbc4MIKA8VdA2ufkO4CbMuaOgggoqqKCCYpPJ/58hMM5ck4U3Iz3qSgRa7xlQRj0Q+EfkT+EAacuM521IL/AFlq07EM8oIMe8+MXk6Z1ZnHNsC5RJ+vskwLW+yJ8rhJ46rRPeG+k5NiOd1TEYYK1ShPx8fkISFOsJ4EW7F9T/h0hZB712XYjOo6+jTCOzrY1/h4JyfUwqkFXAujod33TkX3mftXPW4J9VJIHgxiObSRU647xDosv5A9JRtQ6xvqHz91EINPk7krPTEhuvp3jt7r5hGyApYz9ORwDrrsg/9TWSrJl7pJ51GYAa7KBPPoDD2HWMyi/FewRaZ1ahAJx3AMd69zcCMwKM1dxAjvauigLy/Hnm9dlVCEztfIInIz1qH+//gvk5R6hjHnMxNhg3+lws6tg0QNVeW+2JzhhV9s7r0TrQD+nh3yfMmpq24R6OzjoHpsq7kfiuhAzGORHZZ69Htinna3EPqWBuNn4apJ9HQUb2tN8d0FljONL57418qdojefV1TAYL0abUDnCyEJ0VjyU5hzRje1t8lqzK6fNUL2ST/xdr4y/LnXtfwfdU4C4s8ChKPnYXgfApJMDJo5FMekKp8UsJ2xQB98aCdk7K/QNKflQyGJ2ziBPgXNTzlXbdhtRG2Rj4pXgvRQbyc20RcI6B5wB/hxQ6mbNWlFpYSzzjNu0gznGl3l2ifB8CZFVARs3foc3vM+RstMAWvQu9jcw5Oc6v5BguqHGTCRXHI+ff8cjJ6SMCAYB3ZaKe6LhIwdwp728sqKCCCiqooNBE5Gx15APGyzMjXxWKHHuRyf7zkEF1K2bAJXwWoKjZ8bz3VhIc1xMpqEpGhPXqPAQpz4aUyyuvds2rjkiR+lvgPLtegEABZ3jPTEAO3iEAOO78PwVFxeuK5O/7kbK8CzJGfkZAgAwyctxt688Am5MbqQ0EdAAWl1VuYkD+45BR9QTkjDvU+jFTZLU6eFUccJgnTwRuuB+tqZfb2vMyyoDWkQBGlRS/OUgZf0w9z6SNMA3es7y5MQ7p4bojMPkBwHVIr/QveHsVMnx8QHawykSUlctl3VuAdI6H2fV+IeaDN8ecUaU/MlhfgRmGUKasjfZ7u+x1GerY2StrZeuBi4h7BdK5ugxhnVA05LLa1d7RBQUSugIB1d5Ejj9H2f1q//vK5NGGJGBcPwTqehuBq7+BMvL+kQSgmzZW1WRoz754+xACsNUCAqb4vYWBoTLUdyHSRa+zerlxeyHwLsp2NhKtR9eVO2byIgRO/4HN/V7Ag0hX7MD4+yCHklaB+LUFDkXAqdloP56N9I23IFng9tT/HEcZ2YC9cTMHOeI+Y3N/P3Q2+AUWSKIC7drO6jjOrmej/d/ZVK4BJtnv5sDFlOGAaHxc9NLx9ncLcI33zArkuOqvbSEcAZcjA/iTKIprOxSM8Cngxgq1a7W3/ixC61wVtQFfmSPkeu8abuuIyx5/CpKjXJTcbdFjC9p5iO0BzpOA75E4pHW2df1BLDNtiDlR0Hb90AbJjDXUYX8tqKCCCiqooJCUPoshHdhz3vVwdF51QTFCgY6c7rTKZI4vSXQNnVFwnruR3iGkzjE2yDEqv5g8Sc6OS0mCuBxsZQtREIm7UHD8sQHrtxDZa05CAel3Qzqr+1FWmf+Hgr6HOL/5AXKm2u+WKEjNZLseZHUPGjieiFkH7f17IT3HSGQHPAHpiieic+RmKuwf8g5LAAAgAElEQVQbR21wQFdggN8PZb6zJbItfIpAjC5QXTcE+pmMzj73EF5PvRL4bxSQ0+kg2lh73o0CZWUC/pXgucjmgW9HuRvpFg+2e25MdUI2nwY7yHtzIw9wXJQ65sivOVrT/hrT9SH945EIeHxKwPGSF6gyKiAP2TLfRLr4y1HSga1o37gD6TqXB27TKHXMYy7mwdPeEW0uFnVscqDquchm+wySBwYh28f1wHtIVl2ZhUfqu/dBev/ZyA7ubLiZedTDe7z3e0+U8OhGtH/0RL4WbuyUJXsgmftI5BfigHcvIpvNFuBOG6MXoDOek7lCyOSLkL/9k0btbE7cgmwPbb1nQ9iMB1P6PNXCxvARZLShlhg3o6gdAHMt8lPbDPwZCyKZkVd7dLZ3/gSHoPNaXUBAZzOqokh2tEtQ7h9Q54fJQP0pdmDzFqGZwK+AkxozP3t3O8xZCzmqfkhtYfFYAhzIvUVnGdpkB9XzrHMkaVbuYu7xG4xF4K7nmXojve8gv9bIEdZFVT4eZfpYhJRXV9sCfx8CXAY5BBTUuIlEGVXSYQkp4o61efk3wEIrL4zk2du+iI5bUEEFFVTQLkVEzFZHTmC8yHUcYDJ+B8xIi5QfZ6IsB87huA+wm/3OZHCsozxmdryKgw6R8utRpOAc/BXfESJSZB4Z+aLWMfXeMQhI9DVkSFqKwBQ/QMrOXxLwrIocUO/EMo1Z2RlWtgopsJ2TTghFbnOrx/8GeljZSAQc2YTn7E8Ao2o937EQRXJ7GZ3lKhLIhYiAwxg8SXQ07YAO9vtYa8dltvYcC5xZgXqNBv4SOA1Fb94u8je1o0feSgYDMgK/vwj8M8oA2M+7tz8ystyAnKyaof1tfLn8vHH5S+A8lFlpovctvwWODNCOfUmc+0cigJoDHO6BDNSbkQ5yJjAtcD8uRZEVn8HWV5v/W5FB4iWfJwre1aA1gNpGxI72+xhk/FiEDJCnAusC1ms2cjI4ERkcq5Gh8UkSYMy+CBToA/LeIJuxymWpfAJ4xCu/38aMn7Wuk7Vv2fzsPSOM52Abm3+H9KkOENTD5uB8Kx8TcgxViuy7v+nGEJIX3Rj9GjJ0bkCOiL/BMp8F5N8OAY1/iJe5AYEDr0bA470D8ZqOgIVjkaF4EZIzxiAHwN+RCsgVsI4/wpx80fp5IbCprufL5LO3rSknosAJ/ZDD413A0fbMeOS0Gsy4iRwA3rU15nobJ5eh88ce6GxVMsBEBp4zkcF/E1ovR1q9tsluBIyQa++ZgpwrH/XKTrS+DTovCgpL1AY4D0Bn8mkkEYJPsTmT2emgoIIKKqigggrKn5Ce4mgU2GQWCo7TBgWFWOM9dwNwtv3OHMCF5Exeg2wMbZCO6gvsvI/0RyH8f6KCHGPzy4un9+75SE891MbP/8MyVSEH5Dsw35iMfJyefQLSxZ2PwDA34AG3UHCeSaHqZ+9chnQcW0w+nuTdW4H8xiri1E2krIPoTHobAh05P78BCBh3qD/Oyp2L/v8iAEzJAGal3p913UFn1C+Bd72yXkiH8zDy4QoCcEx/PzqTn4ucuMdaWV9k8xwRsA+bIV3bU9iZHi8wFQIc3kESZK0VsrnWZOAZGxwXtY558EtdO/2iAxx3RQG/7iFAZtUc+7HigDxqyw+jEMhnArL1b0W2lR5IDjmSAAGOY9cxzz6MzTP2XCzq2HRA1faeYUiGc7bU6+y9DqDWmyQQeAgfh1nIhnuz1WsDsqksRPa44DIjAqY9DjztlU1COt57CSgbI2xFF2RHGY3sR5djgDi03j2GBcsMxHM4Om9Mt7o+goI3NkdgvE0kGZeboXNl1nEzGfhh6hu2nacC1s3JxgtsfPwI+cA53609gKloP8mcdRAFGz3M1ppVVlYKCOhn43SBSUr6QxXUtCj3D6jzwzSxV9uisxo5x51H4mwR1FksNj/j2RI5HD1qk36AlS+mNkI4xGa1L1JkuIx47Uhl3KK2U9XpmMNsA/m4RW4xQsPfZQv6dtGMPH4dgFEZ63cDcK53vQrPAQYJB0NIHKAKINcuTGwfIbeuTJWdkQNZ4egQvg+K6LgFFVRQQQXtUkSkbHVEBOPlUUekBLoeRU66CylQZ3r8T0FKo8MJ49wQPXMckcFx1AYSzEQgsXOpGyTnDMq7lXNmzKNdc6rjEKSoPYVE+d0POQCcadfVwFFIge6MgqEMVocBfwLOT5WvRZlAegTg4c7/LtNIK3TOetErG41Aq64NOqCzetZsVWl9hh/hrC3mBB2yTUt8QxTAYaV5ev243NaW57EzsDcX9kbAgKCOFTZPJmFgVbv+FLjEe9ZXWL9Itoic4xF4aSgyPDyMgByDvWdGokjPQc6p9r6fImDVCgR0/A9gut1fRHbDRhXKrPWejf/BCAR4IwnYaSgCPl6MgQIC9mNnJFdMQXv09+33eLRPv42XAYDyolX6kepfRwbAs1PPTEY6yBAB1XyngyeBfwOOSN9Hss6nwGI3XhEgMAsAcKbNt90R+Pb/WH1dGzxKIvu0B94vdwyxvaPKMAR4et+uLwL+iwQI2BEzFIYYQ7EIycSjS40/FOF9LTLwzgnEb9v+6K1hB9jcWJ169gTg2gA8WyDnwv9NInd3Qsbcc9x1Bdv4WBsbe9v1XGTYbe/NlxA2jjuQg+oRdt0dOSC/g84cXxDWIacLljkdWIJsRisRKPAWBKwOZhw3nvuhaMpLkXP3Byjo4R+QDNvae7aaMiPkeuO0J5Z9Eu3J91M7u+IaAgFVCwo6TkoBnAfZ9bnIceNM5Gz4Gomc1yHvby+ooIIKKqiggrIT0vf/I8oC7mTwZSj4z20ooM0XmO4hAL+0o+M+3r3jUFD1M01mzeSwSmSQY2x+efFM8V+NdDbLjOcq4D+B40r1exnv3937PczGqTvDjUW6qesocQYvl2fqHRPtnNQNBY/6G5sXs9AZ6lrs3JiVnzc3omQdTH8vCuj0HDorum+5FOkEtwWPCcB3BbJr7JsqL5WpvhXZs2ONQfrNPW1+/JAkyMkEpDMbF6hujudCpNM7D+m/+gFfRz6dJ1ob9wnBs8Q3PIp0Dtuy/JBkVmqV+s6yAMHkBFaJWcc82tT+zrZ5dwjyxT0R2SAm2P2ueOti1vEaox+JDMhjex/O8Qi80MauO6PgRqtDtGMedYzdh3nz9N4dZe4XdQxfx5zGanMkE1+OcAfHeveuQnryYGB8e+9QZBd3yWEmGv8z7PogAuFGvHEwGu0XXZANZYv3zHoUSCJTAHBkNx5nvwciG8fF6Ly4p/fcQjwbZ8B27YvW9fZe2XMoMGczUsB4yrCxeO3pgsa3Rn4Ep3rP3ACcFahOvl1mOAq+6+yODyFbctf084Q537RFNsZngAOs7BDrzzWpZ6uRL05NyD4taOel3D9g24ckk3ISOoyvQNFN30TOgcci9HOQhTw2vxTPntjhECHiPwQOtut98KJlZeDVGzktOZ6HowPqPsgJ8SXkGDDG7vuRY1/Hi9K/g/zcYtrMFrmXkdJqGTrcdPaeTaOOX8UUIA3k2YkkyvkBSFE0yLt/DFI4BoumUlDTIZIIud2+4jk/O2Yw5WpBBRVUUEEFFdS0icjZ6lK8YwEOc6kjMr69jpR9pyJwxbkkETlPReCHzEYV4mfHiw2OG4aUtxvs/NYaGaXvsn4cnnreP8dtoszItTHbNcc6rgT+HkXAew8Z3o9DAXo+xTLZV5JQlqFfk1LyAf0DvNvpGuZbO7lMNT1t/v2ARLnZzvu/mdQRMbgBvAciJfg23UKpb0v3aQZ+0QGHsXlaP76P1uvNKDOVy6o0Aa25QUAV3tiZi5xw3kTgjeFWPghlBrzcbw+05jXY0OLxq0IOOc9798YindVGNy8Q+OL3BIqwjvbIoTb2P7SyKxCYfa/0d5bx/rHWNt1t7j1vdR2InJo22r1+GDgvRL08/rPQeneLV7Ye6SSd85PL2tfgOlLbGD8H7fmDkIHxz8ANdm88cghakXVeICPjWO+956E9a7ONISfT7I70n8tT/9+x3HFqv4cjffVCmxv9ke74BTxgOtJTDcUz2JU5N5YCF3nlx2EZ3JATy+uuL62sLHB8HmRt1BLpxM9IlafBgGVnGK2D9zIke7+O9uMeyP7wJLWN12ciY2GItbwD2n+f9crOAu5DIMGSgSYC1Xeg8fqZrT1/QaDM49S2qRxk69r7eEEF0T6xpyvLuAY4frOAt9D5pjVyGp1h925GzkjbBT7MWNeRtq5MSpXNsHHyNElE2eYIHJglkvNyBGz8CZLlZiJ57j7gpkqNl4KCjZf6AM6HIyfErch5ZRbKUhs023lBBRVUUEEFFRSXSPSlVcD3UJYsly26M3Jivxu4FS8YTwZ+e5Do97ojYJXL+lcDnIzADXOQ7jVIlgXigxyj8suLZ4p/N6RHcWec76AgRH3Irkvd7J3NqozPx974HYV0Yzfj+ZEFrFd7k4H3R2fHMQiE94qVhbbHRck6SG2d6jprw75Ih3QHCv6xikABsjy+g5B9wwEa+pPyJ6S2TeU+MtjmkD7sr0gAVG1tTrj+e4qAmTGNx1JrtwVI//Y8cszvjQJHvU943Xg15quGbJ1fA3ra9WSbk328/wuityIuOC5KHfNsU2vLDxC4+lXgKis/C9kYgmY5jdGP5ADIs/c6H87uts48jHRUzi/4VMxmFYBXLnWM1Yd58cxzLhZ1bDKgaj+BkQtqsL/33HVkxFSk+DZH9s0PgQe88rnINled/sYAPF12QSfndEZAwFdR0NgPCRCQD/nHnIfwIT9GMvJAJNfcgTAkuyHdcWYAoNeHVfbezgifMs975kQsUHaodkWy2wPIZtwByeWbrN7BzlNWn5tRoIg2NjZ/hidzo+CKzxEoS6XXpu1JfPEOQHulywh4JMoc6eZkNfK7CJ7wrKCdl3L/gFofo0n4AYniaBiJwDXQ7gWJ5pIHvxTPzxDQcHeEsn4BReP/BYaQD8BrBHIcaYucQu5EKPnj0eH/GmojuzvhZc9rAJ9qtOHvZ9fdkTPnWbaJuAik80v830vlLDrI+P0jEuWRE9C/jpyRnVBwOxmzDBbUdIjSEXKH1vO8G0eNxsGpoIIKKqigggrKn4ifrW4AkcF4OdSxc4r3OcBJdn0o8O/IyfpB5AA8ICO/6Jnj7P9jgw5rUHaj/2vnw7eQM/BFdn0mFlSF2oFjXqGBgWPyatfYdfT4trexeTlSBE5E0bmuQMCnPwPrs/ZhHbx9MMlq4JcEym6U4jMFOfkvRM5FDuhYjRweXqAE0CIQ34+pAwTr9WPmzDxEBhzG4okcRLZ618dbuy5HDiNHA/+A9FYdSZTHoYwce6F9axqKeHgFcBN2PkeRnve1362Q7mhWBn4LbTyOQkr4+STG44vt/SfadXVda1LGOp8MfN1+L7PvyTQv0dp4P4ou3gMZx29FQc4cEPA6BO74HMvwGKAuzvCwN3LKucvWgrXeM9cgY0d1ueOG7Y3xS5FBfjHa8ycB/wRcaff7+N+XoX4DkPPiJgSkHmblt6O1rQdyOjqcJAp52Wud1577o8ANbp//FnCC/T4dBTobGXhcLkGOagu8sllWz1vQWjvV/87GSEiP/Jd4jiLp+oSsH9rzPwKmonX1RQz4h2SDZ9E63Nr6NlhmRSR/PIY5qiDQT2bn3x3k3RLJsgcRKHOcNz+WI4fKIXa9HgVV2B0FWLgscF2mIcDdbK/sJuvLBcgJIKQDcDOSM9SXwGklnmmOHL0v8MqyRMgdiOwrI23cXIv25l5ofd9CxojDBVWGqB/g3CL1bAtb639OYFtnQQUVVFBBBRUUlzw5rsZk7jYos/i2AONIL9A8/T8ZeF4LjPeur0T6zXtRQPX7gNvtXmZ/CuKDHKPyi80T6WTa2u9FSFdzgfcNtwOH2Rlnc0h5EQHHHrLfLZHd5mkSff9oAuvfSICNLuvJRZijMXCU3avTN6hMntGyDto7ZiLH8YORA/BNSJ/kAsq8iwUEJEAgIHSe6IP8/k5GesatwL9imSOpbVN5lWwZgEZQO4PaIKQn7ojOq28RwJ+S2vaTaqQDG478KN9Aus43sWwuWMC+EH1o71lm738b2Y/2tXa9Hznm/4IAoEPID6wSq4558XNjH9nge6N1dBtY1u6flWU+5NmPRALkUdqH0/kYr0W6qXORverXeHq6ALxjgg7z6MNc5n/MuVjUsWmAqj2eC2weXoDs1R2QDeA6AgY3KMG/FZKT7wTOt7IR1r89A/Mah+ScPe26H7JvtESBD+8BlgWs19XAfwAbvfJhyA/pQRRk1oGfQ8ipK1EghU0owPBMhFVZi+wen+KBAgPwm4pk36OQjHg5siGPRuepbxDuDNcZ2fT62/jojuTwK/GSXyE/r0zBsFN8l6MAv8+is0ZnJK8+CRxiz/S2v81RgoCKBJEpaOel3D9g24cok8HPSAy5u5NE4V6JnA5WNFZ+9t5RSLEwEgmOHyM0bltbYGeRRJrOEq22hff7XuTk4JD3bkMeYwvrXnbdBkWvKQeQ1wU5LDyJDP0dbOH5xFtkplt9R9h1B6QoKIffMASkPNavLzocPIA2/5OtPf+AZTssqKD/+Z//gRIRckvNN2pHq3qKwIJdQQUVVFBBBRXUtIlI2eqIDMbLqY5t7T3rvLIDkIJ1CnLC/RpSbDyG55hQJr/omePICXRo75hmbbgWGa1vRsrqv0TgyqtIIj67TO4NChyTV7vmUMdu6WvgCKT0X2ZlrZEy8h4CRFar51t8Q/aJwO+ALhnfOQRPcYgcjVwWrua2zjxOkjFnbOA6+WDgCxFApk094+bHZMx6SETAYWyeSAf0nHfdFRk9p9j1FuRIVvbcq4Nvc7R3/IIEtDXF1obNJdaCKrJFjx6NAHfOeeRCWwMuRYryd5AO6UUCRx9PfcdhSGe1Hunjxlt5Jv0f0sfda2NmJJILNiO9nDPeTCHj3ljHON3mkIYcjr4LnO09kylbrb3DGeO72dhpY3wW2f1bgf8mfDauk5Bx7AZq79F3IOPYXwEHBODjDJzzbL75gKPTkaFqjY3hYJGjSQAkjyAH0vYouukGu16GwLkLQ7ZrnkSSifeoUn0QmNdi4BHvepqNmb3Q/tjTuxcUKG/vbIdkLj+jRJAAZ1aHHTZEh2hf67uPvHXTycFnIOe8T0LMxxTPw9D56hivrBfaP15pSBvsSPvgBaIDTkNOI0u951wglzPQuajBzpx4cgNyoOyCZN4+Xtmrbh3Hi3Rc0M5J7ADA2cpmUATKLKigggoqqKBGTdR2jv0tnt4SBeT6FdKv/iHk2dHe3w85c7ZHusGLSECH84BvlyOf1lPHGiKAHGPzi83T3v0U0gtPQefhs5BjqgvcthrZIX6N5xuXtZ72jhYIEHOvXbdCOo5XQoyXOnh2RrayeXZ9MNKJr0VnyprA/KJlHfTGzuXARfa7JcoI+LBdjwW+ifSCIQLVjUY2vmoU0Ohl5IjcAulU15L4W3VC+t4G21RSPPsh4OSZKOjJT2wcL3FtnrU9ka55H/s9AukceqP17X2bg52A39h1q6ztmZrTo2xuTEAAy60oU08PWxuOJNHPh5iLsQCHUeuYZ5t6fJvZnHva2ri/lS+hdsC1RtGP5ATIo4QPp3fvECR33EeAbMN51TFWH+Y0bnKdi0UdmwyoeiGS1eYjm9WPkL2qLQKy3YQB8ytBSDZYgXTjbyOZJ4htjNr+GkOsPiciGe4Da9sa9x2Bx844koyKp3rlE1DA9cyYCqgFHP0uOuMci2zWE5B8ehHy9QmSNd74DURYlbV23QuBSK8isXfUiQsok2cL5JP2Kgoe0QedPTZgwVQDj8upJMFGbkP+DLvZeD0EndF7pv4ncxD5ghof5f4B2z5EiofXkMPjNTZI/4QOydOBGfZcqEkZm9/uSIHyGolz4XRbzM8K2I5uYZ2AGWqN7y9IojzNQ5E/l6b+t+yoR2jDvxdF+R6CUP8OyX0JUio5x6BmSOGxVxl8nLPPQ17ZScgwv7ctdEcgxcYzBBY6Cmq8RD0Rcks860erehnLQFBQQQUVVFBBBRVUHxE5W53HKwoYL686IkfmFUixeJ5Xfgtyzr0gK48UvxriZseLCo6jBMgFBVD5BFhl162sPzdhQDU7a91HGYFcYrdrHnVEjtS/sn5bAvTw+ulwdF5e7T2f2QGA5PzfB8tGVeq+/e4bYG7MxQL/2PVs6589vWde/P/snXe4XVXx9z+TCgmQAknoHUIJhA6hhxaSkFBFECnSpBfpICC9iFTpIAgiRQGRIkgvAiIiCCggCuJPRbDgq4goMO8f39mclcNN4J697jkkrvU889yz9153z159rZn5ztADHsaQoPooJN+YFQEqTmPKKFxpv7mbGuc4OgM4bAtPpgQ03Q/ck1xfgNaPccgoaJVM7Vf11WWRMmxWJB87L8mzGlrHshmpIwXR4cCfknHeBxkBnYI8rq+EjON/QIvCcaZ0xtUloAcZBe0cZcwWlQvNo2/G3FV5p+4b/eeRarzmasPk+osIYHRwjL9+wHpRjwd39T/d4NWsjN+fBFCIlCp7IyduV5MpihpTzpnzoMiDlyGHCgvG/f5IaTWyZhkH05DTDkCgzQnpO5Gc80BknJcFcJTwr6J7fj36zg+jXn8InDyttp+eCa1bv0PAyvkzvrd5fCyNlNUjaegBTiVDVN6kf0zTAA2thRchWX0u/YYhI5jjp/YNH/ddn5DP/Gk/jPnmLAR42hMZV10TY2cJGsY6OYyq5qABoN4BGeOu35QnSwSApC0nojX3a8CaUc97oj3PFkn+gWgv1G0nCwhEvRXSo6wW714YAam3peE4cnuS81ahTz/RRoBzoUKFChUqVKj9BMyXnCmGISPACiy2TuyVZwc2QLLlLIaVXZxx7kQylJmTe2ORsW6OiHxtBTm2m18HeW4E3BP7/ypqmyED2ZuSb5on/cYMfCv5ZW8kO/5mXPdHcpyVcvBJ+C2Q/N4BeCE542yLjNY3zsyz7VEH492fR07G0mhjj6BoeYbsBM8G5szAawt09v4iUzqVGYvsDTeM6z7IOL8lvVETz8HAMdFHN0M6ly/TAD7mOPfPjYzvvxfjboG4vwiSo/RFdqQn5eirUYZvVW2CItTcTEP2MCTqc6dMfaTtYJUOlLGt/D7mW1YHniLAFdF3fkVNG8NOtGP8fzsBeVO14aRJvkgm52btLGOHxmK7wbhtH4uljDMeqBrtD49B9jLjkQOAvRAQcV2kW822p2oe98n9fkhOfyFwXHI/hx3HOASOG0pDN70liix3ArB3rvI1lxNhLbZBe5wdgBWRo8HhGXmtFu9M620bdFbMBvxr4rkMsr15iMacPiz67Jk02Vdk5Dscndm+TwMIeC7aN86Wa1wEr02RjdHWCABYRY+vzjm19/uFZgzqHOPGJLcADRTzyUiAtG1cH02CQp6e+KU8k+vJ6CC5Ow1l8ZpoM5DT8GA88AohAIx7FyNl4MxIkb5i9Y3U9LQUC+AvkWHVDQh8twja+OyHvJyv09QO3TYIoLGB2hQZVn4BLfhXoU36LcDnkvxZFPKFZizik3vIHYKEkrWFVYUKFSpUqFChGZ9of7S6ToDx2l3GOQiAG1IYj0PCnMPj3mjgxiR/TiVAWyLHxf+vQ/vAcYOBPyPF9wFNzzZEXrl27eL/qnNcLYFgO+q1U2VEyuOfR/kOR+fvNZFAtzcS0F0FfDFTH62+dzI6g4+cWr6k37QcbSjhNxOKZjQ5fh+PPAFuhAzxfz61b6nBcyMEbl4VCd+vQnKGvwJHNv3PkDrjMd7RVsBhO3kmdTowuXcncH/8nhR1/BIZgWrJu5+jYaw2OMZLCgSsHXUo7atJfR2PwFxjmvL2Q0Cyp2hxzYp3bIKE+6OQQL5ZefyRsRdt2qpSLgWrLYwAAOfFuFgy7vdFssBVM7fj/MnvzyJjsrEJzw2BFTLw+YgyPmnb7dH68QyweVf1UqPfTETAuHOQk7NVkZJ3BySH/AZdgK67yWs2BJ6cOynbjQQAlgY4sNoD9U+/MUPdDkV7iO3jWz5LQ068IppDh+fi92mjGKcXI0XgERnfOwHpGs5AysaLgsd2aB37DTWB1Uk/nYSM4KYJXEZ79wvJq3cYi+bujzgXobFODUFngn41+CxXfXe02beQDmWvKP9XyRBxGBhBGGmgs8YDSHG8GzI82AKtEeN6qD+Oj/ePifnlN4RxKgJAX0myN6UGyDLG+4vAX2g4i9k++siFUbev0EOK+UI9R/QQwLlQoUKFChUq1HlCzkRGJ9cnIDngN5BdzhXAhfGstmw83cMjI+C1k+trCQdKcWY8m3DI3er5kTaDHNvNr4M8U2PuteKMcwGJoyhkfPxAnfb7mG9IgYA/BG7oCV5Ih3Q1cq5UnSEPQTqV6gzdJzdv2hB1MPn+lZHMYaEo72UIOLoUOrf+LCm7UeMsHu9IHXFthmzz9kBGzgugM2xzwIGhGfrqh+1FQ162HAqsUNupUhPf7ZGD0WuSe/MhOfkF6NycDTiK9Hy3xRywAJJxjCFk9Og8vkMGPh0Dx7WrjJ3iN43vGIRAMY+jdfI5whnh9NSOdAiQF+/7WBvO5utPexk71IYdGf/tHIuljPnL2ME6rdb8JWNcDkFReR+Ke73j94/JGAEQOeM4YWpzCsJVbBZ1smcmnpORLnNyXPelsU9eFu1z1stcv8066lkRCPFK5DS3dpTDpA3HIJ3D96MsG9A4+3weYUpGUD+qcsVvGaS3HYIwMMeic2mlGx9GTRu1T/Atw4ADYqwsicCcS2Ss00o3Pg6dox6i4bRi85iPaumpC81Y1FnmDfT2SUhI1ZfGAXxVFL0uGwCnnfySQTkBKeWPi0VrK2RIsithuEsGI6eE7xIxeS4f14vTOPBfHs9yC1bOoCHgmwchu2/takJtlTcSaDxZLUJI2f8QcHpc9yc8qtflVWjGJz7GQy7aTJ5FxtDuhQoVKh2FYQAAACAASURBVFSoUKEZm2hjtDraDMbrUBnnREawz8bZaQskiN8ERck4MPLdTRg61OTXkeh4Ca+2gA6RQuqmeP8VSHC0KQ3B0VgkqNuj6f9aAo91ol7bXcamd2yCzsLzRZ+tvGRvhwTp25M3ytmaSMk/Kq5nJhTuNDngiX6zN930fBbjfq34vT46m38WeVReG4Fk9kPzzx3AlrnKFzxXiPeuktwbg4A6P0aRc+ao2hAB6bp9jkvqq22Aw07wjHeMQ/P4yYRRR9Tx95I8uT2Aj4oyLhbXiyDwWn9kOHJB5n6zKXAdcsS1IRLCH4rmmTWa8m5GjUhyMUa2RsqNXwOLTCNvbaOjpN9siIB/eyBZ4HzICPCIzPPMAsBuSd95Mep2FxpRwe4ijJ4y8JuWMj4FP/ahab7LwHsj5OV0peg7d8b9CWjv8SiwVSZeI5BS84txfRLwGI05e3U0r86Zo3xNddcXKTt/yJQRaicihe4mufrPp5XQejkLMris7RE0+swvYy64BQEAV0Agrq8jBWhtxWrwGh/z6VTXuqQfDaSGl9xkvhlNRP+L68NiTPTtgucgBKRrSabKlJFV70X7qL5IVlt5OF0+6ruuwxFDa8A1aP/wQJR1y5iDjo7y7Bz8huaab4J/f2ScuhTSdTyMnEe+TBg4khhyZmjHWYDvovUqPUeOQXP6V2mKelho+iF6COBcqFChQoUKFeo8IePGu2I/tyg691cgso0Q6KCW0+941+zIIdW8SOb4ApLhXko4pkBgq/virFE5sKmzV203yLGt/DpUxtRAdmFk9Lt4nHd2YUrHYD1tIJsCAR/MxS8p46zxd0CcZy6Oej0POLuHyrRA8rvHow4C66HokJcCb0Zbjo59/73IZq7Ss+SIADQERRo8Obm3BZKJHYjkVBVQwGhBp4LmtH264t+Ub3U0922Wud9UgIOtgCOj78wXz5ZFMp7lMvAbDpyT1NV+wMJxfRCSrx4c/ehlMtmm0V6wSlvL2Ik6/aTjKr5tGWDx7vzfp6Edm/i2BZDXBd9p2nBOj2XsRBu2i2en5rdSxhkLVI30bo/RwDjMg2TXsyEHCN8kk9PheP/SSL++7CfIuwkf44BxGv87J7GPQPvwu1DEuD7IDmBPdK5bC+2Pa+9zaOxxRk1t/kI6lgHEOpWpTleOOh0d18cgZwrr07BryhatDunGfoH0qa8jZxHLB99LyKgb/wTfMgzZHdxBcr7K8N6NkZ52qWiza2IsrIB0uM/xP6DDLdQ96hxjTaxPI0HVYcgrxqBYOEcjr+PZOmy7+QXPDZFSdXnkifPM4Pc5Gt5yPvRik4nn/OiQuh9SVv84FuoKPJddkIOEKd9MrldGRjN3IeV4DmPKtYG3kEFc5Q13SaYUUu2IjOX6TG1BK1SoIqbhIRcJIgd3+hsLFSpUqFChQp9+ogPR6mgjGK9TZYz3XA/8I85NP0LgjSuQsPFWBERahBpgmODT9shxdBB0iIBgj8e7Kk+uP492XRABSddo9f2drNcOlHFY9P/+CIA2R7TXXPH89ygC2YsIlDhXjrGR8N8WgQuWB/aJcXETDUVjapD/CK1FjpsZ+E68+1lg9bi/DQI8jU14zJ62YYby9UPKlHe6ai9gMRSFbFxyr38Nfm0BHHaSJzKeeCra7yrkpGpCPPsxDQ/g2eRU8b75Yi44HEWOvDdoM6TQqT0eE15rIMXRCGRMdXfcXwQpAi6N/poTyLEG8p74EMl6mTxPIzkORkD+On11HJpDN0Uyv8vi/sjoM8eSSemADF9eRTLGy6MPbYdknAdF2XZBxmQ5PXJ2qYzP2W5d8PwiMtaYjPYdC8b9vvG3NuiQKQ2rto9xsQNar85C8+yxaM3aNHP5VqVhmNYXGZLdTSMK2kn0ULSzGZmiz1xOAtpGDvNSYPVsmXj1QvvALZGieCKKPDApmWPSuean1I8+uFH0yVvRfuAotM86lYZn/pTn3bQOjq/e0z+5dzsyqrCgsWTU49DwgnspERU37o9BQNlV43qeTPyajYpmQgYOHxrEovXxDznmVBr76olRzsFov/ogcEY8W4QaYNFCnx4iM8C5UKFChQoVKtQ56mLfWDkamzm5NxY5B5uUgx8yWP0aAlDdigBkFufWc5Lz5HeAlTKWtS0gx07xayfPZP+/HpKpXBvni/WQzOgeZBuTS2b0YbQKphL9InddJu8dj2RRZ1a8keH655BzyQ+YhiF0izzbGnUQycYuJGT66Cz+ZxqRTuam4fiwVQBgf2Dr+D0q+sc6SL6YOo/5ZvSn+TKUa1l0rj8oufcRWXSMlwpwkUvfMAGBNVaI6zWQfeUpyLHTuSRn8bp8kSF8Fc2lOSrPZ2MuuIL6UU47CVZpSxk7wQ/JbLaM32NJHNY15ftIP2ml73SyHZu+o22AvKb3T9WGc3opYyfasFP9ps1jsZRxBgBVN/FfHIG5Vmu6fx06h/wW2Dwjv7mQXdVPiAhqzeOdhm5iJuC0Fvn0RdHvlqDhMOVutM+4DgHVXkbYlZloOM7NsW9cBzlt+ERyflrco6ffGjzfBb6c9KUvo33jhjnKlfCaB52nFkDrxQs0AnAtjRwfZwEB0tjXT9NWAtkJLZixjCORM8o1knv90f70IuRAfnz6jYUKuXt7QYDJAJklBt8eMeCfSBaSStixaPo/0wm/vkxpzHEGMujaOCbZBZNnW1PD03gXZZwNgd/6IqOq65Ax1dCYXI+MfLUMuRJ+o5GR6oJIIHgVcGw8Wy6ul8zYd0Yg4eb9yLhhY6Y0CFibMOpsZ58uNH0TxUNuoUKFChUqVKgG0f5odZ0AHLa1jF3wvzbOFrMhoNPJwA+A/xffNXcGHu2OjtdWcBwy7P0cU3ocvgxYDQnMXox+9H2kxK5dp+2u106UESnhn0Jgo+tpRMs7ATkgehY4OO4NZSqGEN3kWfWBVZGyfHl0nnkeRY4ZF/xXSv5nMDL2aCVyZMVvDeRp+Lq0jRAI8Q0yGBt1wbMC3cyEwEd30FC29E7ynYVAZXVlHW0FHHaCJ5LffB84Jq5nBXYDrkzyrJq5HQfS8Mr9heiLk5GQ/gvAfhl4zQ3slVxvjzw5b45kcQvF/QFIsTXVSH0tlnFVZMC1GgKRfY/wJBnflsquqvHYbUBuE++DkDJgHaS0qrxV94pvWSZHGRN+KyDDqjuSe5MJWQraF2QByDTxnZoyPjdIdV0EkDkBAabvSep0cpSxX8Y+M3dybzIy6PpCXE9Ce6w10v/JVM4LkOy0MtycCcmQX0Ry6qI0aq1e10JAtRtIFKwoOkZliFhbv5FcfxntjR9GQLxvIoO1Dz3woz1YS2t/E69l0B6niji8TszdzwJvE2eQpD/9hNaiOA+jYUC5MdrDHZw8vwPt3/oiHc/KmdtwVgSsfBTYP7l/Acn6kpHfBARK3zOue0X5lo557+tk0Bs18XueJMofMrT4EXAzMhwfk7uchQoVKlSoUKFChbpP6dkTef9fO7m+FsnFK/nG2YTzmJpnjtnjXDEUyTGOjXNi5RBjKJKrXkKmSA5dnHN6HOTYTn6d4hnvXBvZqa2CZI8bIH3DCsjZycPAvLnKh2QYzwErMvWIbpWct1fNvloZZ68S58RJcW47B+lVKgDeomSKHJOUs21RB6Oe+iD921PoHF6VbS/gPTLKcJFzr1eQMfWCcX8NFHnkrKjv+3PxjPcvj+w2Uyenqa2lNf+u03fi/5eMua2S2w5DesdV0Bn9BWByxnrtg/Qa+zbd792UN5dT1XaD8dpaxk7UabzrOATYeGZaYyCZn6YL4Ogn+I62AfKa+LbNhrOnytiJNmwnzw6OxVLG6RhU3QXvMcBNyXWqR52HjLiR5HojpL/dhwgMQ2OPkTo5rKXDRaCtocjp4NJIx/IVGk4d1kY6l1o2Bk08R8Y7K+B67y7yVGUcQE19J9onbh6/x6GzwC7J8+MInVKm8vULOjj65ROErh/Zrs0EzJSJV+pU8dKu6jLJm1tXvSJwVXI9U9PzLM5GC8141D5GjQGyAUKnLgW8hpDbM8ezdWKyHTEd8lsMCRe2JIwykYDscmQ8Unni/zywY+a6nYgO3ecCuzc9Ww55/d0gI7/JSNhwI/IAdGZMQvciD+SvkMFbNfIUvzgNBP6ayDDmq8gYYDzybroAOnCVUKeFuk0UD7mFChUqVKhQoRpE+6LVdQyM18YyLoYM8K9C0WgWiPs3IQ/DlWHz0Dh/1AJRNPFuS+S44NVOcNySUa79aSg3DQk4vxVlrIBq85HBAKDd9dqJMkZffYkAZ8UY/G7y/F7golbbbSo8q/4/LuaClWg4AqqiUy2PPIRVnmxnjm9pxSD/Q8E3MCT4PQ6c2ZRvQ/J7AJyEZCnfR+CKUcgL4XcJcFO08cDoUy0ZyidlbBvgsBM8E96jol6fZErA7GNkNP5Pvn1TtEbcBWwc9yqDlVWRHKe2IyckK7oX+FJcb4Lm1YdoAAC3RvKr2kCuJt7jkJyxcjA2F3BolHuX6LPVNwyO76oTyXU9JN88FBmJPUYj8ugEEiVLzXLNDGwTv1dGkSNXBV4nAa8jGeh5VRl7guhhgwM0n9+NvPIPQGvGBQnvF8irkJ+I5KYXE4YMaH08H4FIZ+2pugxeZyOg04iE93k554D/RYrxcTXai6yGFLu/JpwQZnj/WLT/3zzmmQ0Jr6ZoL/U4YYyL9pk/oca+HK1D8yAvrpfEvdQYbzHkBfgCEsUjLYCskVL8SOTJdFOkwN0VRac9uXo/ilx3bQ+24cyxVlwaY2JttB+vdb7pgs9CMa8citalysHCqXH9f2SMAhr1e3OsEb2QruzQmNsHI0PHdXuqXgsVKlSoUKFChQp9ckJgvNOAeZGs44U4o15Kw/7oauRwZCCN6BI5IkecBtyCADGLIPufr9OQww1FctZa0RVoM8ix3fw6xTP+v5Id3wX8myRiG3AAjQgdtSKOM6WR9sJIxrDiNPJXBsdD0BlylhZ4Lk7DWdPcyFFVFdl8VuQs50wkU+7b9L85xkdbog7SkKlWUU36oDPbWUjuUD3fB9iobrma+umvkQF3v7g3EBmUfwfNOTns/5qN8VdC8ouPAAGZ0hj/FGpElEzqbVVkZ7gWcDySO/yERmTF4V19Z80yL4lkxztMox5q9xs6AFZpZxk7wS/piyOQDP7p5FnvNB9TznNnEOvz9NSOU/mmjgRVoI02nDnL2Ik27GS/aeNYLGXMXMZPw3yD9sF3EVHN4t6GhNPRHGWMvxMRIO0UpI/fFOE7dqex36rytqzDjXdXNioLId1xpfNYLsk3Hu23Jmaqx+rbd0R78gvpItIhU+6r7qNmYCfgMyT4EGB9ZHuwZw/0lWXQmakfOgu/RDilRXvJXwLLZ+a5CUkwqq76ZFKns5FJh4TOOU+T7Lujzxyd8ixUqJnay6zhdWiDuN4qJqAdkBJ7ik48vfBDi/4TwIEkgi9knPMPGijrlVEo2/UylnF9ZAg8GhnKvkGEpI2J7l5gs5o8BhDCHLTZvp0Q5KAN8QXAzuhwszR50PizIUHDb5AR8MLIwOBgJPjcHQk8N4n889XlWahQoUKFChUqVKhQK0QbotUFn7aA8TpRRqRQfA44Kvb6N6NIeRPj+Y1BWZS4dCg6XsKrHeC4eeKs+Nkung0KfjdkLldb67VDZTTkffdlGmC7vsgxz8JxvRdwevyuG6EuNa6fEwGb1q2+Jf7OjLyuvUTiHCe+dfFWyhh/10fA3EqmMTuSfZyEIvU8RhfC5JrlXQnJTdaI+eBSFHFwTuRo6VYSYxFqGADE/7cFcNgJnkk7LhE8hiAB8jFI8TAmxuwvyBCpson3OCTwnyd4/Z1QlKE15EFqyuOQsc1W8XsdBK7eC4FXbkbGaotFP86mVEn4z468p6/fdH9OtI49SkNm1T/67ro12nHF4DcagYDuAE6OZ2ui+TUnWO0sBL57ikYUsDHRrqlisFvRaVv8lqwGB0mdLoXm8rOa+tWTCIzzODAhI7/BaP+0QdTlN5I23CrGynx1+U3lG1LjlHOjL30ZKeoKADBPHa+D9uqPoaiA1R62rjHXmsjY4BC0zh8DzBHPxiEl58Qk//LA6pnKtCcCAi4f132SZwORUrzb+4wu+IyJct1MIzreAsB1aM8xKO5ljQDYxXfMgnRGz0XZsjhVTOaAkQiMt21czxZ95pKE/yLp/2Tg3Svq8DJkWHE+Wi9P7sm6LFSoUKFChQoVKtQ9QnKgOZGjsfOQDGHhuH8lMlatnLl8B1gpA8/hwDkJ/wNoyDaXBY4OvsvFvboyuLaCHNvNr4M8p4hUF7/vBe5NrvcOvh8x7u4mryFIXlTJg+dB59+hyJi8MoYdFn8rEM1g5Di/JTs5ZIu2Fg2nasciec46cT0gzj3nEeflDOOj7VEH430TEeDwIiRD6oPkYadFn/pIhLw6/Sa5HoGM1R+j4dCs6rOz5eKHgHhrE+d7GkDAg5K8Vd3WisaT8Ky+vxeS990MbIcM9s8B9q5bvo/5jrWRnGH7abVBBj5tBeN1oozt4pf0ndWRffHsSJ7yAA2Z3HxNeav+WsseuJPtOJXvmeGDKuQuYyfasFP9pp1jv5QxP98Orxv9kUPF09GZY2zUc04nBxsiXd+yCINwftyvHAHuRWO/1x9F7V63RV6LI/3NhcjJwBwIDHgQ0m2uiWxJbiSDrjpZe+akEQxrPNqrfoHkjJGUcRAtOo5O+M5FYz++JTrnVBEBN442nJcMzpST66FIB7YJcub6JNoXfy34Z4ni3MTzXHQGGIr25tcDq3RRp4PJFC07adPtkf3fYQh79HSOPlNoxqb2MGl0/DuBtwlDP3QYXw9F4zuHMKyou4C0kx86DD8D7NR0fyw6CExERolXxSQ/qWbZ+ia/ByGE+tIxkT+KDOWeB46NPAvWKWPwuApF/5sZLXo/IgyL497uhKeljH1mIDI0+CYycrwMCadeAM6LPIfH8yyCnEKFChUqVKhQoUKFPo7oYLS6eG+PAw7bXUYEQnmOKYWMA1CUiitoRFW/L85ydc+LnYgc13bQYbz7qvidGv9XQqT1kOes2evWaQfrta1lTN4/BAlRb4sxsHX0z8pIfRHgT8hYp46gcxACwlUK997oDFxdV8565gGWI/F0Rn3DnHEIHLYVMqo4EwHIhqB56PvAFhnqslfT9VbAFcn11kiWMiLaMUtUpXh3WwGHneCJZEUvxxh8AVg3+ux5SLlzFxHJsc4YQUL9G5PrXZCAfFOkHN8B+EuMm9nI4MgJKWVfowGmGIu81u+EZEoXoXXsNjKBcZr4D0Rr4uC4rpQqlSFAanw1lPBM2I33L5i8ax60xp9avTvG6PeQ0uZJEgBwzXJVa/y8CHD0QtPz1aIPH5KDXze+K7cyfgMEED0ryjN/8qw/Ut5kcwAQY+/EGItD0Hw+GgGCvxZ5sgMqmXJtTH9/Hik5a0fjLDRFfa+CDMoOIgHx13jfMkhJ/Lm4ngvtx0+K64NoOD808kSqXRnJ+leP6x2Bt2g4PugTvEahqHULZKy7SxFIvALCzYf2G6eT0csp4eV3Ks8GoOinWYw4k3rdAK3Dz0YbVmWcNcp4Yw5+U/mG0cg7b+XUcWzU89A6faZQoUKFChUqVKhQHkLyrm/G/mxuJB96kTAmjPvfAC4hIoBn5H0B4XyqeW8Y55GTIs8sdc8btBHk2G5+neKZ8N4YOXI/kYj0BzyCDGZ3R3LrWo7jk744P3LashoCUd1JYkyMnLwcRwN41XJUlSbew4A/09AV7Rt1vE5cDyCc1dfk07GogzScby2BDH+fj/uDok+dQ8gh6/bV+LsJCqpwJg3g3fHIIdhngLsJe8NM/XQCOhfvCbwJbBz3V0SyucOTvLn6zcYIxHlkxS95tiw6o9dyxvkJv2Mscra2J4kMsgf4tBWM14kytotf9NeXm+a3y5Asfut4VkUGHRzjpVZ//TS0Y6Fs/aftbdipftPOsV/KOP3za+IxF9JzXovAc7UwFcl7Kx3nabHHmICwDgsmeTYnccCLzmMtty2KUncy8A6h74v7iyNHK1ehs1UF2MthlzMB+BnSE38P6ay3RgC2PUii0sY69SHeo7t1Gb8XQvvGnWjsG7dE+/NKT1834njKr3/SlpvQCIo1EtlcpTqsXE4AKoc4J6MzwF3obP4NZH+QRkSvnJxkWfuT9w5D+uTb0Dl8Us4yFpoxqWdf3jg8Dkzu3QXc11W+5t+fdn7x/4sBlzbdOwgdFq9Bxn/DkfHMYnV4IkT4+sjgZW1kTDkcCd1upmG4dRnafCySqR0PRh7px8f1nsjQuBIITo7nA3NOOAgVvyMSLp6AFNa3osW/MrJaqCf7cKFChQoVKlSoUKFCFdH+aHVtBxx2oIy9kHL6dhrOWz70XhtnqlRYVtcAoBOR49oOjot3bQTc1VUbIUHdLsgAOAfgsO312u4ydsF7SLz/aaTwGxj3+1XlzsBjMDIemYtGFLW7gBOTPGOQ0XxtfknfHIZADEsgIOUvYh46P77FaAAe68hw+gNbx+/RCJSyMor8kwIaryGDEJUOAA47wTNpx+FIKV0JwXdGRg/LIHnLMciAZJ66bRn//yRwZ3I9OxJSV97xrkOgsmE5yhjvXB855qoAMmMRmGLHJM/QTOWr1qbhNJyPXQtck+RZE62bgzLwOwiBRitlygkISDmq6Xvmp2n9zFDG5REgZ84o40+SZ8NivE630ePi+z+cZxAQ8HF6YK2I94+JfnoM8CqSdVbeMldA4P9sXuMTnh8xDGuek6p2r9t3ZnTqqn6mVmdIbn8LsBuJUrAFnjMjZeZPkaFfFUlhgRiTA4g9x7S+p5s8JyKDvIuQrP88BNzeAXiPAAIm+bsFbv4E/Eejc8HXaChc5yfZE2TgsSjw1Y/Jk9ZrbfAhAv5fhc5Zo9DZ6gAazhtnJaKr1OCxNPDFT9A/q31d8R5bqFChQoUKFSr0KSJkpHpL7L8XQaCcrwPLxPOhSI68VCZ+hhx8nAjs23Q/la+OpiawijaDHNvNr1M8E94LAg8jmeN10XcqoNwdwO9pOCHp2yKP9Iw0FPgiinqxADLK/RlwRJxzXiQcVUUfu48Wo6p08R3HI2c01Xlxr3j/2Iz12faogwnvNZBDt4lITrVg0sYzE4CjTLwmRruNQmDRH9KQLx6KAE3Zzo1RhqfQmXwyisjzH2DLeL4yAcajZjSehOd6SNe5aoyNh4iIgwjE+gw9ED1mGt8zCjl6OhM4ogf5tBWM14ky9iQ/tA4OQWvyWnEvnQOPCp7VPNcPzcHrzCjtWGj6bcNO9Zt2jv1SxumXH9PQX9DYd+XAjVQOMY5B+/N7aGA2tgN276HyLYucRZwB7JXcXx44kNDtZuI1EumNVkcOR69BuvFeUcbzCXujWNd2pZt2FsjZx7oIl7IUsAXCkJyJ9FYzRb5rkL6h1r6Yxnl3bmTDcDeyVRsd1/cQTg57qP2GorPaEXG9MQ2w/wLo7FPtlWdBth25wP9d6R970bAFLPrbQtOknmegAXFjTADrx70fALcneXICx9rCLxlkKyLl+AJx3R8pdddFyv4zq0kvA8/+CPz3IBLWrBT3ByKjv80QQv66avGqwWsICTobGTfejoQOcyHPSq+ikLy/pslrTos8F0WhcQfSMJycGxnHXEwDhDg/LQqoChUqVKhQoUKFChVqhWh/tLq2gvE6UcaExzzIqOEkmox8kQepe0iULDV5tTs6XlvBcUgItXfSdk8ABybPq8hxmyMPYbkAh22r106VcSrfMgh5U7uHJMpjWvYW39ufhvB0BPKq9m0URWYIAlJdjgTYTwObZyiLNV3Pic7oj8b3LIK8uZ1AEuGsLk8ka3gFGRsshAzhz0Zz3zYIBPUyNYXjtBlw2CmeXXzDtwjZWFwfS0Qbim86DUVYqgNU6ZP8vh+4J7m+ACkFxkUfXqUH+uo4NJdWQMB1kOHKYXGdLcoRkok9ApyCgGMDY/zfAXwpxmNtz+oJv9mRQU6lrDkcGcxkUxZ1wXMCkvetkty7DoGO1kFGDfP1FP+eJKQ0mQP4F/DdpmdnoPU6KxAw5s7HgM8k9XsfAlVVis0sc2oT37OA4+J3VwqkbFHV/lcoxv95wPaEU7ou5qMKIDyWFgHP0U8XRkrE2ZBy9Spk0DkIAbl/Sk2vqgm/aq9mSP6+dlwvE3NO5ajiQGDD6ht7sJ5XRlEjLiSTg8Om9w8Dnge2m8rzqg0H0OLaiJx6VJ5pDYEa3wHmintrorPVEeRxODAk1p8tmYYeCoHYjyeDHqdQoUKFChUqVKhQfYr92Tnx2xCAqgI3LYvscM4hHEaQyF8yfsOSwG+ZUh+Q3VEM7Qc5tpVfu3km57jl4/x5ZNKnzgreS8W9HwAP1ODVBwH9JiIdwFFIZnwAMjaeJ85xhyDZynpN31gbKMeUUUGOBF5Pxsp+ZIyqGO9sV9TBZpnCBlG2n9KIFLNu9JtaDgCZEtzbJ/rmCtG2DyGno0/ScBY3W1ff2A1+A2iAGBdD5+QFkNOqn8X9PYEPaALiRf13G4SAjOA/DBwRfWMpYEMkT/1CtOOekWfJOmVssV5mRobj61Rt3EN82grG60QZe5of0mVsgtbEyj64ihJa2bNW/S0ruPvT0I6Fpt827FS/aefYL2X89PNL5se5prWm81EHJC3pHRJ+GyH7qQrA9jdCD4CcAvyCRGeesf6qdWJmZNtwEdIBroj2kcMz85sX7Q9nSe7difTURpNtDN3UcxAANGSfcic6L1ZOovdB+o69kf3PtwjHJxnKdQo6T/WNetwH4XLGI2zM7WmZc/XTpO02jrY7LOlTn0U2CJsleVejBceVyTuXRDrAuT6uTxUq9EmoFz2YzGw1pMz8NvAPYHMz293dxwPDzOwuAHf36YmfmQ0FrjKzud39p+hQvIqZzeru7wI7u/sDwNvIgM3q8AueFu/+FZrIn0YAOdz97bjeFPgqMmT5VQ1ecyCjwkfM/pB7vQAAIABJREFU7FQzWxlN2N9CgLxR7n4eAjk+g0IC39ly4cSzd7zvNGRkfJKZDXf3P6BIJ48BW5nZ5939NXf/bx1+JZVUUkkllVRSSSWV9EmTmfVCCqLfImP/an/+L2Sg2g95P8Xd10NRulo+c5jZIkhAdbq7n+TulyCvTb9Ae+LF3X1LZPD5HTP78LzRKt92lzHl6+6/R0KdIah8yyVl6g/8wt3/U5dXpNkQqAp3f7/i4+5uZgshENLySBiW45w6H1JqXh9nHuDDuv07Upb/wcxmT9uxRpoJOMjMDom2OwZYycz2BXD3d81seeTl+V13/78MPKG99drWMsbYIG2/KkUbXodAXOeY2frJs1bHYm8kiN7WzLZCAs5HkcHGdqgeV0DggLeA/d395rr9J9pqdTPbO2Qdr6Nx3y9kEYYMEG5w93/U4ZXyRGf994D3gd/Huy8G/glMQqDDL7n7czXZ/QeY1cxeQW32IyQ4fh2YYGbbmNlKqO7/VpNXR3hWfcDMRpjZvHH7D8AKZjZ3XD8AvAHg7s+gCGSnRxu3xNPd3zOzgfHOscB7ZnZ/ZPkBMnw6D7jW3Z9ohU8TPzezFcxsRTNb1N3vQnKkw81sW3d/EDgVeQnE3T+owzPhvRJaq3ZHbbsq8I67b4AidP4/BEj+Xqvj0cxmMbPF4/fqaIw/ClwX4/JUJJu70cxG1S7UR/kvjkChk9z9CTNbysyWdvdtkEOyL6H+8rvcvNuR3P0Dd/8zAuROMrNNkmcHI3DegpnZ9kHtuI+ZDXT3O5Dsc5/4DnLMqV30uXuRgeFH1iMz6x1r9SAz29LM+tblP6OmZF5dEhng/gOtw4fHftyTPFW9DkbrV7dk12bWK+a4D9z9N2jN/Yy734QcHuwCXI9k/8e5+19qlm2gmQ2NMoyOfjIIKVRx92fRmrV2lO0sd7+7+sY6vIN/l/3O3X+C1o6/oX1IlhT9fYi7v4mMU5c2s5mqfV7kSdvwNqSI7XaKOfJaM5sfKXYPQvqNa2IeeASBAJcGcpw3ZkXGmjMDV4Zup6vvegPN4XcCmFmfDLxLKqmkkkoqqaSSSmoxxf6sb5y7HTg3zgK4+8+RE75/Arub2SwILJP7G36JHJ0cambbxz2Pc0ItWzIzG25m58Tl4chx1Bzu/mvk7PBPwK5mtpy7/xXYyd1/Mb3w6xRP+FCOOxY5wdoNONbMxkafOgEBsPaKvOOBf8b5pCV2wG/ivd8DbnH3l9H59H4EJnvX3b/q7ge7+33xf5V+4M+tMG3SeX1Q9Ud3PxkBHZ81s0Xc/Vx3f7LFsnWZ4tx4AXCfmS3sso37AY16/lfUQV0+bmYbm9kZZjYJ2R2eCgwG5jWzLVH9Xhd6iJaSmc2EbAkxs9HIQP5gdO4+EskBP4PsDy8ws/5I/lHHtnJB4EtmdhwCM/d299/G/Xsjz6/RWfnf6T+6+5vu/lp3mEUZVwdGmNkEVMarUXCFfYEt3P0KpFva0Mzmi/kvm73qJ0nu/o67/9PdH3T3d3qQz3NI93gM8KiZzdxTvLrg3ZYy5uSXyPYGJTKVV4DFEXDjg9ANnGlm83hDX13Nc6/XLEaXqZPtWFKe1Ik27FS/aefYL2X89POLPc4kBCD7oZkdNJWsvSLvLKGraOm8E+9YC0XA+7q7/9uF3dgFnXOuQvuRw9z93mm8qtvJZOv0QXzHOwiodi8CyN0BvBx79Do8qnVqptCrvI2cOa+eZLsReC+Ocv+X/p93wwbAzNII579CTip+Ur0bnXGeQo4WzgOud/cf1yhbep46Eumi53b369z960gnPxE5zV0TAXGzpOg365vZRtF2DwI3oP3qwZFtOHCoT6n7f8Ldf9Yiv01RhPgvAaeZ2SrN+UJP9UHo8OZroWgl/Y+lCl2a/8UyMLoBeNTdDw2DoDWRgcOe7v5PMxvj7o9Np/xOQSj4bYBtERL428Bj7v7HMNb5GvDluotHKNfdzDZGgr4XkbHRpsDj7n55CP4WBP7g7n+t/qcFXr1iEjkReVP/DxIeTUZGBrsir0ffdvfv1ylXF7zXQIZxJyOg4zrIuOkBd3/BzHZDUQCOc4EDSyqppJJKKqmkkkoqqS3JzOZBDkf+jpxu/Cx5tiXyWjnBa4LVQpl5PDIw3s3d/5CcB2ZF3odedxmTYmYr5VJytquMXfCtyjcP8iT7FlJuLoAMZPd3gTxaff8CwCbufr6ZDUAgmGvd/ax43t8FHNsceBf4uWcCx5nZRsBB7j4urj88p5nAceshT2Db5jrjmIAh30TAtMtRNPkvA28ihf1GSMh5S00+nazXdpVxKHAuioLzeirI7SLfVsBTOcajmS2NjBmWAXZx9zvMbDgyzF8TuNvdb6jLp4nnmgh89yICrVyMhJ2nItDh7AiMd3sGXtWYXwkZG/QGNkPRjj7n7r8xs4WRonWQu7/Vqoyjie9SyIPcu8AK7v4fE7BjI2AVJNC+MKeso908zWwy8lD3FyScvy6u/4qiEI0Fjq47Npp4jkNt9xfgG+7+spndAfzH3TeLPPO4++8zteN4ND4uB/ZH3qKfiu+4EDjG3b9Vh8dU+K6KAM1/QiCuz7r7K2a2lGcw4AoeCwKXoOh/k4Gt3P15MzsbecreLGSOhwE/cgFJsqWYyyqgUx9gLdR3vuvuV5vZIHf/e452bFdqmm/mQ/P0b81sM2QMtE2Oea0LfiOR1/FfIK/4+yEjvAPc/e0wSPqruz+ekfcYNGfeaQL33IMif56X5PkQAIjAqwfmkpXPqClk/OejyAo/MBnMTUL96ayQWfdxAaIHoUgIX3b3hz/h+2f1AIKa2byJgnYSMsTbPa53QB7I73D3KzOUa1UEoL4TKfjHI4cOuwMPu/tVUdZTgB1dRpBZUqyNi4fitI+7v9dFnsHu/lYmfmshPcrMSNfwFuGtNubxyqt7NTZuAr7ySduwiVfVFwYio9jeKAIoyIh0IQTu/EfmMl4FbA3s4+6XTSVPNf5ncvd/d5WnpJJKKqmkkkoqqaT2pNiD9ga+AvypOrdVhoWJ/Hg08LZnABx9zPeMRRHITwZu924CcKbx3guA80O2MYVc1cyWQbZWQ9DZ5F9dyV0/zfw6yHMUkjNc5+73mdkucX2Au99vMt4d7gFyysBvHhRB7V/ARZXczQSW2RFYH/gc8A93f78mr0quMsibgG9p/ZrZl5Gt3D11+HXBP+VxJKrX1UNevR+yR8ylj1sCGVI/jGRG/0Qyxx2ANRDA6PKQRbQsiwsZ0SHIweD7SN74q2i/C4OnI+DoN9z9R7UKxoeODr+GHI+e5O6nx/y2GTq7vgJ8BslXn6orazQBF/dEUf+WQoDbB81sGAJw7onq9xxgX3d/sUbxSpoBU+hVDkGRj+5GzuL2R3Lyd5Gc/IicepWSSiqppBk1hR78DAR06oVk8t92968leVJd1QPAF9z96Ro89wT6uvu5ZtYPeD/ePzdypjJL6K/r7jmqveoo4Pmu3mUC6vVFEfleapVX0zs3B/YAXgYuQ2C485G9zNvI8eiX3P2HGXidCiyB9o5j0N5qceBSd3/GzEYgm5zZ3f3NDHX6kfNUei4OXcsAYIlWdDYfw3s7pCve2N1/GLz2RHig5j5bt5wLBK8JaN89Geka/191hrIpHVXeg/a0dR1llzSDpx6JBBiHxffRYXUbM1vF3d92GW6OIFDIuYwM2sXPPoo8vg95croMeTmaAHzfzM5FxqqneAb0eExmk5E37spj7cPIE9BqZnYhUti/5/Ig1ZK3mhAGfdNkXHg0Mvp7Exms7YUWj/eBzZFX2WF1y2aJB7EQJpyPQiYfBXwHGUDfYmZHo6gkx3sBAJZUUkkllVRSSSWV1MZkbYxWFwrGC5ESbG8zW77a24eB8E3A6BBekVHh2JYyWhcRJ+K8Y8H/RBTN4jQkBDnIawAAI7U7ctwCZrZ3XD4CDDGzA4OXh0ISYDngj8B2dc44Zraomd1mZnuY2RIhCNocGRjs6O63IsDR9ejc+Hl3vyVp21ZT2+q1U2WM8/XvgMvMbI6pGWlEviur8dgq3+T/XkIC2ydQpJqhLi9x30MRANe3RqS3llPFz+RUaD5gD3ffAsk6tgDWRWfyQxA4LwcAsHeMg/FoPps9BOBnIIX8TWa2NQI8z+9hHF/DyKEq40pIWTsRGTU9ZPLk/EtkxPJ5BMb9ft1+0wmewW855JluMgL3jHdFVNoL1fWLyElWjvFf8VwdrRv3I2dO+5rZBHefAMxlZg9E1j9Cfc/KoTg6BdXpr5Bc80dmtnqsFfsg2VHtlLTjgiZw3LsIFHwRsK4LODIWeZHsMupSd5O7v4oARAcgUPXzcf8AFDXzHhOg8jTPAABMyjjYzGaLuexpYDG0fm2G+tKC8R1/j7/TBQAQPlx3JyDQ+HLAA2a2qbt/D60ht5rAVjn5TUaO6vZFnj6Hon7zNnCJRURAzw8A3Ao4w8yORwqqo5AHdDNFmUuVRzchgHsBAH58+gXyqronfBhF9XtIZn6Ymc3iAn0NjvtHfVJFZCgULzI5vgO428xOMrMvIo+xy5jZzsH3KqT0m2hmE61mVA6Xd9Y/o755ssvI91XkDHAPM7sRyeYv9owAwEjrIIA63jUAsJfLAUDtSHVmth6KWPEg2kN9HemKlgOOMDmtcG8YHNwBHNuKMjnOM++ZnIB8He2l/ovmA9B6/DrSH/VCEWRzpceQPmoDU6TcKfpH0/i/xQQ6L6mkkkoqqaSSSiqpQyn2oO+hs+rBJqcfKfivMnp8xnsYABh87keOQZYDDjCzI+q8L86hfZBjofWCxwdxvyrbs2ivfKYrqkrL4Lh28+sUz+DbF8n+VgJGmlk/d7+ccJhlZuu7+19C/lhbTm1mA0JvszIy5N7aQv6P9AOPAbu7+1ueDwA4DnjQFBUmtSdLIwKe6O73ZJKpti3qYFKv8wIrogighyDj+PcRWPQKV2S+z7n7D+JbWpbFxVxzLzAIeMfdfxWP+iCZxwHAbcANXhMAmPT991HfuBDZNE6Mee9mpAP4PQKtPhX56xrjv4v0QosAPwVeMznpeRPpGb+Bgjpc4gUAWBJT2qqanFUdjUC/WyPnnKsiWeAVyD53p5x6lZJKKqmkGTWZ2VwIRD0YOTv5JbAT2kMeGXlSAOCNCKDfMgAw0vvAJDMb7u7/ifevC8zn7q9XZ6q6+s3Yq66DHLouOpU8/3VFjn4JuraP+iQp2TcOQoC8y4AnkROJf8a9fmjdOtBrAABtSlzMEQiXMtzlcONB4A1gl9BdnRnP3oR6NhxTO0/FvrF6779ckaIfrv6vxWKmdToizjnXICDed00RAd8Gfo5sdKZwNtLdcsa5rfrdH7XZz5Eud1O0t/gbsIopGnE6Lr6D2rQAAEv6+OTu2QghmBdEh6q5kHfVLyGDkY2REckvkffz6Y5f8LwAWDp+92p6Ngx55V8dWDbuWQaeg5CnkZHIwGllZKgyFzLKuwhFYMhRvlORMdoQZKSyb1xX5ZkJCZRWzsBrcWS8dRiwWNzbEh3C50aL1heRMeK1CM2dtc8WKlSoUKFChQoVKvRJqNrXo2guFyCvuHMj70cvAOMy8enVxOckpHyv+H8OKQanuzIiBy17ocgf0+I/NwJarJ+xbKPi3PilOF9NQobAtwWvF4BNM/EaiaLRHRLXGyODjn2TPMsDz+coIwLDvYk8f/0Egal2RtHxnkMRJXtqXLSlXttdRmA4cE7VL5Hyb7Fp5O8df/vW4Fn1/0VjDPZGQtuzkDMc0Dl9HDBPxrJOBp6Ndrw0ub9/nMMnZ+IzV/J7oej/K8b1kjHPzYZAXHcDEzPwrNplPPAasEpc9wKORWCnrRHgYYFM5Ww7z4T3MiiC087A48DCcX9UTj4JvwURYO2YuJ4V2A0BYqs8q2bmOSuS9a2FIrqBgKofkMipyCCLi/dMRoqOSia2DzJgWRsBEZ/JMUbS743yfQGBHLdtynccsGamslVzziSkwLkOGd+keVaKuSHLHqcThNbkx2Pe2QD4AwI6b520cbbyBZ87Y77eHK1Rs8WzJZEMd5keKOOjKGLrnMjr+XdRRMm3gJWSvLMg0O5anW6bTyslY2NhYKmk3l5C3kirfMsAi8bvfig66Trd5DUAyfi/hyK1zh5/b0X7qGtQtNj0f3YG5s5QvhHI6/7XkAx+qSTPYLQuL5X+TwaefZJ7VyGnDc1zYO/kG64FBtfguyQydFyx6d4aSIl6K4qeCVqnd6k7NuLd3wY2SO7dHmXphfaVtdflpE5HAashxwkgg9FbmfL8WNXpoKiPMv4LFSpUqFChQoU+RURDvrl90/1eHfiWmeP8sw5y3F33fUsiZ007JPes7hnj08KvnTzjnaPjbNEbOQI7H8nIqj3/rjn2+0wpM7odOd6r5CjjkfOUS5HMLLeMYzkkV1k9/ZamPH3SvxnKOaiLZ72S319Oz3gZyjgB6U2eBO5N7q+KdAFnx1isNQc0nRsXibo9C4HwZolnyyN93LIZ+82qSIcxb1zvFv1ozeC1Nw09aC55w5A4886JwFznVH0oni9E6Cd6cj4oNH0QsoX9FjBnXI8Gbq7WvehPzyPj/I5/b6FChQp92ql5bUWOm78ba/6wuDca6VQXiuvBSN/abZ1nsv4vh3R8M8XcfTqyWZk/9jhPAmtkLuvI2BtvGde9u8hT7c0HAP1q8lsN4TeOS+5tg/R/G2Yu27RwMcvEd/yETDZVybu7PE/1YH+dhOw07op945yxd/07ws78DlivJo/eKHLjGtH3L4y96jXIjmxk5NsQ2VZV+p3B6IyVxRag0P8G9cxLhXSuQF1zIW+rf0OGXGPifraB2g5+SKjSB0Ux2Lfpfo8K4NAB5B5k7PNt5KXmtygqxhTf2OL7mw0c9wcWies5kMHjzcDaGcu0BDIkOgwJGS6gsejfhQy4Dkryz9STdVyoUKFChQoVKlSoUEVMRXkIU4DUzkPOK14mA1hlKnx6DHDYiTIiZynfRp5FF5lKnkr516/5Xjd5LYqAaHsQzkSQwO8JYL+4HgRsj5SuK6Xlz1DWdoIOZ0HCvq8gcNwKSKl6PIq+9QGKaJKDV0fqtZ1lTHh+RNDZVTmY0lj9FmBEDZ6bIAH4N1EUroURaOVsFLXpJWD5jGUciYAH6yAl/BPAl5PnBwKjM/DpjQwMqj4zIMp0BPIW9zCSC3yu6kN1+g2dARx2gmezUmVJ4AGkNJk77m2AAFDz1R2HXfAfhUAvT6b9JPiPycmrC95fBE6M35ORsD6bMU68dzWkRKn67SAky5oUbXhV1Y456hZ5OTyZMNZCTr9+E/PC+sizYw4+qRHTBgiYujCKGvcB8NV4NjrG5Wa5ytgu6mJsjER7kCdRZOMDkTJni6n9Tw3eQ5HS6MgYCxUYd20EFKttRNnEbwXgKRR9rvnZWkjp+U0aRiwjyDCvz+iEomA+HnPqpWhPMwCB5i/vIn+vdB3oJq+ByCneD4DPJPe3iTnh72QCVNPY52+KgL8ViPFYZHQ8d/TVYzLxWxTYJX6viPQA28X1rsDRTfnTPdW9tKiLQPqNmdB+9XfA3lNps5uAw5J7/TOU+ZiYS5dvun8/cFPmfrppzOGVUe6hcf8wtC6ukOQdjADfRXlcqFChQoUKFSr0KSTkDOR3KOrQ/J3+nsxlayvIsd38epJncoZLHafchpwO9o0z4znRf/o0/19N3uOQfmMxJNd9H/hCPFsOuBgYn7ke+yLHN+8An+2qLMm5cQiyoZujZt2Oi3PVLM3t1cV1q7LqVBY3Ks5wSwTPpwhZXDwfw1ScaLbIu3JUt3xcD0B6j/uQvPEWAgiVid96yAHXpcCfgeXi/i5IzvJb8husT0TymwuQvqgv0jmchvQPP5/R5tVC9QkFrLgNBftYAIECxwAD4/leJGCEQoUKFSrUNSV7qg2Bw5GuYSakUzkPnW+GR55Zkv/biBpYhOD3auyrnqNhU3E6Aqo9BGzeA+XcETnlvJCGU4WpOTm8D1iyBq8xwIvIIe9Po3z949nnUXCsEXQBROwuP6aOi2nei8+WfmPG+m3LGQ45xvgl0oFviPTFFyFbltVRdMVaGJmkXyyJdEX/R0MX95kYF5cie6rngUnJ/04G1s1Z5kIzPtX7ZymGK1TqashA9AvIgGvXJF9/5I38JuLA2spE0G5+U/mGHkceJxP5EjSAcauhKCBj43p9dAgZkGOyY9pI7jmAg5BHp0F1y4sMYl6lYVg0KNpw/7heKdq2MlCptVAVKlSoUKFChQoVKvRJiTZHq6MzYLyOlREZpF+CPNVODQhYeXLtO7X6+QQ82x05rq3gOJqU23Fm2w4p3yfHvf7B7/Ic/abd9drBMk5V0NlF3ubIKnWE1SORMHgEAh+8CAyIZwsjb3nZFOQxvm9E5/zBcW80Ah+emItPwq8vihz3jbjeNdpyYtT3gTRkBC3LHGgz4LBTPBPeE1AUp72C7y5I+bBF/H4O2CRTG6ayqlHI2GZuBHi4CCkjFgF+UdVFTxGwLQI2HRtlHJ2rThMeGwGXIcOqg5DDqjtjrBpdGGLV4DUGKXDOCz77IIDKWGTE8ggJOKgGn2Yvx5Ni3E9E4JAVkVO1E+L5PLnrtYf7RepAYHHkebzaU2wJXB2/V4t6zQaGQ+tRv6ArEIC0iqK2dk+OCxT165Gq/CRRaZERy+XU9I4/o1O0WzWmhwE/QjqAocBWaO+6ZMx7rwFL15kDkv/tR2MvsSVS5u7UlHdX4NSMZV07+mc1b1ZjZF+kGP854cU2A6/RSMm5c9TrXshI82sIzP17mjyq0jDk7Hb0iqReP4zQjPZPVzKlIrVvUuaz64yPhOesyb2vojl9rqa8K7fKpwu+A5EjhypS7dio183j+hQazgh6IWDyurn4FypUqFChQoUKFcpPSNZyMZIlHdHp78lctraCHNvNryd4kjgpaTpv7E44w0OyiDOQEfKQzOXZKc50k5HMegvg38DOTflyRXJLjae/hBybjG3KmxpVf+R5C7x7POogH5XFHYfsDSv90AikQ/p6D/TJJZCRcwUAXBjJbmdDANJHyCQ3jvcvGX1x7bjeC/grDSDgSBJnNZl4jkFOv5ZADnGeT/rIXvE9m+Wu20LTJ/HRABn70XDgdhBymHUwsAPSgdaaYwoVKlTof4ViH/ws0gNcj/QMQxG+4XIkh+9LvkjAi8ecvUJcn4act6ZBpIZl4lXtVeekgWUYj7AWX6Bhy2F81G6k5QjdwMrIsWClxzkmeK5PAwiYzZFDvK+tEfmm0o969AyHcCk/SK5HokjrWWxwYp99cfQXQ7Y4/6DhVHgAiqp4JLIj2bDd9VxoxqNqkup2MrMlUNjW/dz9PjNbCnmofRNtjitj1bki3xMICb00mvDf9W4wbze/j/mWtdGkepq7X53c7+XuH2TiMQkZMz2MFsFz3P1X8WxjJMw51N3vqMnHkMHaV4A/uft51f20vsxsGFq0fluTXy93/8DMjkaCzHPc/VEz+wrwhrtfYGZzII9D33b38+vwK6mkkkoqqaSSSiqppO4kM1sdGd8/Adzq7r/uIk+1p+3n7v9J73WT1whk8HuPu7/UxXNzdzezuZHnrFvc/d4WitX83naWcXFkCPs0Urj9DUU4PAIJA+9y9xeT/L3d/X0zG4ycoHzF3d9soYyzoOhJlUfVb6OI8T8FdkPK1ePc/bjuvnsq/DZHBuJ/R2V8EBn8v4zOjue4+6WZeA1FUQUfQOemJ9z9jaizCcgL2EPufmXk7+vu/20+47XIuy312skyJt+wJDICONrdr4p7H3m/mQ1BArxj3f3hGvyGIxmHI+9t27r7b8xsPXe/L8nXchm7OOdvhuQl30Zj8W9mtgLqy9sAv6kr40h5xpz3XeBJdz8wybMS8nh2mLv/sA6/eF9fNM8c4+47m9muSFj+fQSw2hdFyjskV59pJ89kbVgSuAb1vzmREntHFBloNFKw3Ojud2cs53gke3scCcr3QMLrnZBR0AsIrHJ/qzyrdSB+d7nuxFy0NTJcedzdb22xSOk7q3qdBfgXUtIcgZQ5p6P5fF3Uf++uyy/hOxKBDY+JetsEteEzcb8v8kD8eqZ5/BSkYNgJGeL0R44GvuHud5jZeQicM9LdX6nDq53JzGYHDkXOCxZBwNT/h4yd7kDtdxzyQr4yAnk/UIPfcKTQ+2PIcHcB/hT8eyOl48+Ad1FfPczdb2uVX8K36qejEajqRXf/nZl9F3gPgY0/SPZxG6H91+ru/qe6/GfEFH3nLOA8d/+Jmc2JHDtsEuNudtR3XnX3M8ysj7u/l4HvZDRnz4Ha6D7kaXQntOe/IvLth5xobN3inDo/sIe7HxnXX0T7uEvQnDYBjZXdkAOL/8T+o85+Y1GkZH/MzJZHyuoD3f0KM+sF7B9Zj0L1flyio7gEAXbv7ybPamxMRE4j/gjcjACdewDLA3e6+02RfyCKTv59d3+2lXImvDdB9fcuAqj/DLXteLSf+32d90+F50AEljzZ3W81s37IUHY+d9+7q/zu/nbu7yippJJKKqmkkkoqKW8ys5nRvnhFJA99p8OflC2Z2SgkH3sbeNPdT5mR+OXkGTLwC5Bs4ydIRnQuchr3FJLL7eLuD8VZYMGu9Et1U9htXY0cxv3IzL6DnNctCLxeye9qvL86x01Cjqp6ozPOr81sT+SY6+upHC5k8TcAx9eUxfdFzgXPR854ru9Cdl7pqoYA3wG2cfc/t8DrFGQbtx2S4e6GDIAvdvefhxzih+jc/0KrZeqC7/wICPcHJDfeCJ2VL3X328xsDnf/cwZZdS/kfOY0ZLx9HjrXvxfteB6KSv94zSJ1xXsNZGzdCzgatdGrZraYu/8qacNseqOSpu9kZhcA57v78826BzP7LALLLo7sVbPpAEoqqaSSZqRkZvMAo9z9rrg+De19z4jrcxF4bR0z2xp4zt1/kYFvL6TTPAw5qDgr0aOchPaO2+XcT8W7JyAC9eIAAAAgAElEQVS7pQo3sR1yrromsge6yt3/FXkHo+iEh7n7I93kk9pUrIP2hye4+4mhPzkKRen+FrIvy763sTbgYj6Gf9YzXHLemMnd/21m/ZH+72Z3vyDyfBX4P3c/p+73x/uGIyeOiyMdzrLI5ml/d78y7DvecPe/5OBXUkl9Wvmn6IgXI0PGyhjtJXc/Jp6/ig7/NyLvyn9y9/8zs4uB9939359mfh+XQpiyL3BVGAfd7u6v5ZroTEZ3X0GCjt3RojU4DHGeQQf1w7wmABAgFoP3zOwa4E4z+7u7XxWT34cLi7dgeNucwmjibDP7krufYGYHAAeZ2VbAKsiTOyFo+DJatEsqqaSSSiqppJJKKqnHU2XQ63JQ0RspAPuY2c3eBJILo+o+7v6fUBZ6i8bAiyDhUD8ze78LPh4ClT+Y2cFeA4zXwTLOhQBbE1G0k7UQKPA38Wycmb0d57dKITcIRXU/vjvnkEppGd//TzO7BymO1wbmdfeJIdj5FQHkaKE8U0t3I4FUV+C42YGLzWxuzwM6nAl4Bzm8+TVwqpntgYSM1yMQwCZm1t/dL3b3/8KHZ79upw7Va1vL2FVy91+a2fbABXE0vjoRvFYCw97I89oJ3TU6aHrHBwj8NwEZAox3AUvWAM4ys89WAus6ZQx+E9GYfBoZL7yHzuIfmNk97v6UmY1193+0yifK18vdPwieI5B3vz+aALNXm9nl7r6LyeHTVxDgtxYAsKpTFyD0HWAxMzvLBTi8LPKshDy5Hgb1+0wneEadrgEcggB3N4SxyAEo2tE+LsOVD4EqOcqJAEcHIw+Aj5rZzgh0dRgCyLwZeVo2OjIZLm1kZg8hMMqyZnadT6mM7+Xu/wS+0fR9OcbHZASM7QXs6u6HmtkJ7v6PkNVtjzxYZknx3YMR4HA34H6XEc77SInUG7iwGo815vHhwFHuvj/yLrgfigD6Z+AdM/sNsFCMz8HIQ/j0BAA0InowAm4ujqJhvWhmuyHA6utoXd4YGXE8UJPfSYCb2ZXIOd2FCAh/E5JR74XWxGEIgPVwKmdtNSX99CtoL/W+mT3r7luZgIA3m9lmyZh5CXmSLADAqSR3/4uZvQkcZmYnufvPzOw+4BAzO93d/2RmT6H5qA9ar2ulmE+OQx5NRyDnHDO7QHIzATuY2d1oXv0ArZEt8XX318zsBjOb391fQ6C4tRAo/3wEulsbReh+Nvm/OuUcCbxlZoOiPjcEfhjXZ5vZ2dGXfwccY2YXuwCXvZHTw24bVcb7xqNxvjeaU68C9nL3C81sf2CymT3i7m+4+9tmdqrXN1pdGc2rh6F6XRdFCD8PnQFuMrO13f3dOnyaU3z/JahMb7j7j83sp8AyFmD2OM9V+5QCACyppJJKKqmkkkqaDpI3QH8PdvRDeiC5+3NhH9QbWNHMZvYeBDm2m18uniH3esvMngUORE5UPosic5yMjH6fQ3ZxD7l0RnVkcYOA/7r7v0wGzmsBbwFfddltvQrMb2azAn9BEcezODpJZNXHoEgqpwAPmNmGcY7rh+zJfhrf5Ei3dGp3ZfEwhTzeQp/wDZOh9i5xrro/yZs6q7weOKk7Z9UuZHH7A3O4+0tmdjVyALaLmV0Z5+aVoi1zpv+HHJ2tgmQAZyJ51TLI+PkvUEveWMm5BrmcCx6Gyros6v9PRDv2RkC92qkL2drMwBXA7xHQ8B0zWxfY2WSXWOm1CgDwfzyFPLc3coq3Hooa+UHc7+Xu77v79ZG3b8wRJZVUUkklNaWYN1cH9ov58jbkcGD2Ko+772dm3zGzwe5+Qw6esZZbrPXnIPuGpc1sfXe/192PCv3NkLr8mniPpKFz+DHSOVyL9IB9UV0MBf4VdbMVcLh3EwAIU+j/h7v7zaEL/JqZ/dHdLwdONLPjgD/21N7GexgX8wn4Zz3DRZ1uDGxrZi8hxyrnIRu1C5FzkU2Rc86WU7S9uWxj3jCznVCfGejuN0Vb3mlmCwOfQU4kCwiwpDzJuxk6EE1arwJnxnVfhFTdMq4NeXe5obvv/jTw6+a3jULgxDOBI2q8px8wa/yeBYWsXxZ54vkpMgy+Eh3ElyIJI5u5PGsjgdH2Tfd7ZeRxKvKEPwQZFh2AvHBvlrRv7yR/CXVaqFChQoUKFSpUqEcJGYvfgoxx50TOKBZGkan2Q9Fw0vy94+9gZDw7rJv8+iS/10LGtwcj49up5o+9cp/u8OpUGZveNQZ4FBnJr4CirdyIvGX9CxnS90/43Y8Udt3hMRSB0C5FkeqGJ+/7HAKL7JTk7xt/Wz5vIKXtFNcItHExMDnu9UegkcuBiRn77CbArcB8yGnMnQh4sF3UxfbAUhn4tL1e213GT/AdY4HfIUP9+Zue9UZAmlbfPQkB8b6CPLetCvwcnZMPR+fzSRnLsjCKAHoMcA5SkA9E4MObERiwpTmmic+c1XejKDjPIeDUiXFvDiQXuDKu56rTb0hkFghMMVfC5wfA5XG9BJKrbJqhjG3n2cR/JPAKcElybziaX++Iuad3Tp7B41vA+sn1sSjaICj64Gkoal7/Ft8/EEVN+ykCAHe5Lkbeam3MIjcClkPgmDWRl8OHov0qhdbzOcZj9b1oHp054X19NUbi3ibAMhnb7gJg6bT/Jt+yffSdZxB4bopv/TQTUix+E60Lc0ef/C2wRjyfFclUj+2qHWrwnQ0p+25L340iAr5Q8c9c1l7IuOkOZHQHAstfjrybgqLJLTe9tF+niSll0CegfcfyKFrk8cBjyEHfK8AGGflOBK5JrscArwXf/sCItN0zle9etI/qi/YvI+L+8ihi5ujMdTsE6XQmxvVKyNBqr6Z815Nhjxz1dgjSnUwCHo62e7lqO2Ifl2tsAPMC1wHXJvcmoz3OPHE9MgevqfBfCBmSPot0Lq8AE3qKX6FChQoVKlSoUKFChXqWQsZxNaGLQdG+b0XOmkCOhvZDupXXgCE1+c2MdEa7IaDYc3HG+DaKPGLIIPaSOFttlvxvq3Lc+RA4rro+EdnGbYYiZZweZ8fF43mzPH6mFvlWMrBJUZ7LCbkjkvvfgpwopf8zBDl/XKtFnh+RxSXPlkF6sQuQ7Kq2HJcp5dVVefvQkAMuh2SuueUbD6ColScHvzORjHjNtJ9kPItvDJwRbdkP6VJ+jfQrW0Y/3iRXGQvNWIQA1b9Fjg6re9aUp8hUCxUqVGgahBybfg5FvFsP2R88gxxXDI195bPN+7gWeVV7mo2RjvqweP+sSB94GjCuB8s6b/CdJbl3J9qnG3KYneZvST+e/P9nkJx/07heHzng3rPNbZwFF9NpQo5THkd68EeQTc5opFu9DDibvLY4cye/N4998hZxvXLsl9frdL0UmrGoF91I4XXor+hAPMzMVkdCiKfd/Ub40IPKM8gb8oKBcm0ptZtfd5O7P4cOlMcAj5rZzN19R3i9GQusYfKifjHyyP88OhQf5EKGv4a8VvfzCB8bZc+W3P0hFL3iZDPb08zmj/u1kNxmNjwQ+CCP3PcCQ939LWSoczxCW6/l7v/1xANv7jKWVFJJJZVUUkkllVRSF6mKVncUUnLei5RwabS6eWEKD6BVtLrvePei1S0O3Ghmx5kiNz2BjDZHAhPDm1Sav7e7vxceR8+mde9V7SzjFN/o7o8hD6A7Agu6ImRti4R0VwCPuvu7poiDZwHHePe9Y6WR40YB95nZmshg/nokjFvPzL4Y31Q3Ot5Q4DEzu9TMNjGz4S6vorcjT82bmdlO7v6uu1+NogDd3up51cyGmdloM+tvZr2QsOo14D13vwm15XPobHo0cI+7/6IVXk2pbfXawTJOM7k8AI9HSvIDzOyI5Nn7ca7tdjKzRZE84X4Ehr0IGTjsEFkGoIhut9aUq1j8XQQJOs919+OR0PYN5O3sIQTo/KW3FvGzOW0I7BReznZHnpy3j3snx1jZBZjHzJZ29z9Cy/1mTmRsgCkC0L3A9WZ2YvDZHsmTrnRFU9zN3W+pWadt59nEv7e7v4gUHuuY2UEA7v4G8ph9QMw9daMbVX1nRLU+IG+OK5jZ3HH9AOpHuPszSGZ3urcY8cgVqej3SKnye+SxupKdffhdTWvjWaZopN0t33xmtk78XoQAUrj7I+6+FwKQXICAgL8Fts4xHt3dzWwSAolcYWaHuvvTyMhpQTM7I+riNk+ictXhGd4vKy/HeMPLMXF9NQLPjHV5l6wdWbFdyd3/guSlVwD/RH3we8COZrasK4ri7cC8ZjZT3bIl9fZfYNfgvYqZzRvteznar11rZkPTvtsiv5lM3jZBQPx3kIJ1QNz7NVovVwRw9/WiP00X7dfJFO31vpnNAeDuR6P18BjgfdSOl6O90C7ufk8dXk23Xo37I01Rjf8/e+cdbkV1veF3YcMuYu+9YtTYS8TeQI2xocauiAUx9t5QUYMVxRYr0dhQUbFGURM1sZufvffeMBoL6Pf749sHhuu9yD0z91yB/T6Pj5w5c8+aPWfOzN5rrW+tR3CS5fTp/j2qc2O9fvnCfH6K9DnrYKHcINxd8OOIWAvP5w5O9/BSNLmvfIGTOc+IiA0kPY7XIueEK8mSYg9zYeFsWXvf4znNcFxcZV9JF+H795UR0VnuhFjlb0M4AL9YRPwhffYt+PyumF6/VJGtnxt3x9bzgD1xImcPSbe3lb1MJpPJZDKZTCbTtiQfx3u4Q10n7K+9GzghIlZNMZpzJW2B4za7lLT3LV5TbIN9HGdIOhsX4fsYiw2vkNQT6Crp5gp8RpMDW0fECel1X9zl72Cc3HwI9j8+GO788XaTY/6uHqPJH1frOngOLqZ2f0QsJul8XFTpwOTL6ZDGWVfXwbH54grn7/9w55EzJP23Xj9uRMwTEfsWbHSojTf9f2TavipOku9bxr/RxPYquOhoL0Ynq49M26bCSezT1/avYi0eEYvh5G1h33hfXKRrAE6q3hb7OG6ryhefmbCQ9AKO3xwSETukbVaZjEc+8Uwmk2kPCvfJ4TgOdx2ew3XC+Q274LjKQOCIpvO4ekhzuI1w/HsQLh5xNs55+DOO5WwQEZ1b/pRxpxAb7xjOXfoGFz9etbDbYJy/IknvFv+u3vh4RMwe7qx4PY5v9I2IzSXdCxwF7JPikK3S+9SLKtDFtDcRMT8uun19ioNvCSyEixF/KWl34IAysf+ImCOs+SEiNgBui4hbI6K7pJtw7ts+EdFD0mPA0ZLuq2B4mcwoYlznrulGeRa+8D8Jt908FHhI0paF/ZbASaSvSnqr7gNrsL32JCJ+i50rc2HR33Vp+4U42ehEfC52SgHztj6eLlgM+A3wiaR+FXzmQOA8Sc+FxZ0/Fd6bCScEr4MX5V/lRVUmk8lkMplMppGkgNXp2GkzDAcB5sFdK2bGC/QTZLHaDLhr1tFqpVgtCQ5qXZr+jDsB/hl3rZgO+AC4UdK7MaYY76Zk//5f8xjT372KO+M8Lemswnvr4QqdAyT9pcnf1YQRs8hClnrG1x0nwe6NKyn1xOKqwbgzVzfgCVUgHAsLYO7EgoNbgD/iYOfzOPl4C9zF6SFJF5a0tThwFfA9FsWdK+kfEdEXVzudBAfj+4fFibPIwqNKaMR5be8xjuMxTpmOYzng0ZQkUe9nLYcd4FdIGhgR0wE9sNPx6CQGqOKYa7+rDfDveyTwLu4Y9UVKvj8Qd6/boV6hQQu2dwU2wIkduyZ7c+Eqa4MlHZic2SNK2tkBV4q+FdgMB/s/xh3drpR0RETMjMUV+0t6roy99rLZzDF0SAkcS+AgyzWSTmwDO5tiX9xnuCvoNen151ggvBa+ZoeUtFO7VldKtmbClfg2wsLw/6T77me1AEp63gwGjpeLWrXKHr4+P0jjAgdRfgucLumetN8ZWMixvlJBrjrHN8oPFhHr4mfhH/Dv/kTgVEmHR8SKOGB2pKSX67XXwjEsjp9bR0u6Mm0bbxMbImIWfJ76pHH0AW6R9HpELIXPbzf8jN4cX0dDK7K9IX4W7o6fW5cAH2EB7Htpn7lqwb+StroB82FB7OGSloiIPjjR6aDkZ90EV+bfCfi23sSxiZH0fOyDEwxfwgL5Prgw38lV+uEjYuP0uZOTEu/wfOoJ4BNcDKCHpEdL2pkZdxl9O12rWwIvSuqf3r8d32d3xZ3Cp0rBx0qIiN/h9c1gSS9FxDY4QL+npHsiYgXcRfmeiJgcd3H4qoS9jfFa5hNJ56cg+BW4yEpnPLc6vy2excn+TDixcUXgKbzGGoznWpWc13TOZpcFhuOyf4yP9/VMJpPJZDKZTGZipRkfx37AbZJeS2uObbGYrL+kh9LfHIXXVgfVabPoq/od7kr3LHCoXFCJiLgRF3dfs8p1RrhQ5mXAMElHRURH4HxcMH9yHFcZLOnfJe3Mjbt9nZRen4j9qAsA+wJPYt/OypJejoh5isnqEdFRdYoO09+3uS8uIn4D3ABcKOn0tG2MfLy0bRocU3m9qu8yIlbDMc0OuGhjD0lvRsR82E82bxUxnILfeC6gKz59V6d45ybJfj9JwyNickk/lLWZmfAJF8a6Esd2hqoCoUomk8lMyBSex8tgsX8tbrxd+u84HOuYEc9RX6lizhEucngojk8viIsNXI7nx0fjIn2zS3qlpc+ow+bmOO/nVdwtbhqs7bgC6yn2xZqWu0vYKM7F5wf2wXPxv8pFcLfAc+Pekv4WLnL4WZlxTWykWPGfsPBvrxRPnRnH4t7C66+6832SjbWxUPJ+LFA9CQtGlwL+IWlQRGyJBZVbqFAANJOpinEWAQJExCk4OXUnnDy2O7AGcKKkJ8IdAf4CbCfpydIH12B7jab4oIuIU3HA+hKcKPpW2n4pTjS8WVYHN+rYKklwTE6MSfCD/iNJA2rbiw/5dIOdSuOpkDOTyWQymUwmM34REZ3kDhXFbWviKqDHSboxJab+ASey3i53cpsMuAi4VK2sAFqw0yjBYcPHGBYsXoYdJzMAc2Kn2NOS3kqBlQuAMyVdUPi7nwUmx8HWzMAcuHvICOxUPB6vFz+IiPfw+mobLLw8TanjWBVEY8RxC+MORr0l3RURp+Pg6Zbp/XuBVyT1qucctmCzoee1PcbY3oQ7Q90OTCZp7bRtGuz76IEFZl9WIeQICw6Pw87FL3GVvEdwR8DhETEvFgtUIqps4rTeHjvhLwPul/RZSr54HPt2Xqnomm2I4LC9bTZzDDWheBdccXF9Sa9X+PnL4OfF5vi63CcJkGbBYrkFgGckPVRRIGcD/OzpIemRiJgdPx9/h8e3Ea7k/EZ6Nl6Hq1fX+yyeBAdursWFt+7DFQGnAe6SqysSEYuqRCensHD5HCzY+jAs2HobFwI7BAtx/47Fz0dFxHRlBDG/cCxrYAHyqXLVw9r28fLeGmMvOLYIPrdz4PH+s6LrdAUsWt+1Ni9L9+8LsDD2eFUg/ivYmxQHj5bC3ehuiIhpsY+8D34e7wLsLenOquxODKR73I3AHniusyIwbZpvnIg7AG9Xxe8xIpbHz99jcUeFD/D97nfAwngdcL6kO0ramQJf9/PgueiRyc4euGvtKZK+iogHgPclbVvGXjP2VwEuxsH+OYCLJV0XEVvh9cCOteu0ot/j/Hicl+Lvq4OkHimmNA+ea+yj8kL1JYHVlQp7NBPXmAMXAOmDu3OeLOmBKu6tKbayA7CQpGNqz/4m+/xsWyaTyWQymUwmkxm/+AUfR7Go+va448lhwNVyl47W2qolcS9FKgyPC+9fhNeu10j6Ju27tCroHF+wPZdcAHMxnOv3gKQjI6If7rKyIbC7KijkFBEL4nX/kLSemgKYFfgbsHNKTn8WFyObv2wScAvH0Oa+uIhYFieJXy/pz00/v0leYu27b/WavJm18LrAX3H3ytUlfZvij7vjtfjwKsaXbG2MCzd9DQyXtE7avhL2WwdwOPD9+OjjzLQP0QYNMjKZTGZCJsVxz8c5DpviWMSDwPqM7ipdyhef7NTmK4tjoeG0QEccz90TeBnnWE0BbKwKxHEFm9PjuNu1OF68Py7CORIXqpgL6zjuKWGrI+5k+DiOYyyGY+5zpG03SvouIq4ClgXWkPRpvfYmFpqscabC18m0OIbaCRfNeKGWC1XFGifFb9YBdsPxxe5p+05YDPhvSZdGiUL4mcwv8YsiwGi+6tBQSa8mZ0MPHLB+AKu6+5VZkDfaXntRuOksDXyHHxTCauAHsXK8E74hvS0nAI+3VVyjhSpH4+t4MplMJpPJZDLjL9HgbnXRPmK89uzItw+jhRvd0n8r4e5GLwGzYPHTQ/V8frLR8M5x0XhxXGCn4pHA1pKeTNfE3VgI8HpE7A3MJ+mQihKNG3pe22OM7UHhd7Uyvm7eSE7GO3CxnJ3TftMAnSS9U5HdjtinchTQVdJTYdHlmcB/cPJBJcH4JskExSSDPbCT81bgQUmfRsQUSt3cStpsD8Fhw2wWrpslgZFqRogWo4WA00j6ul5bLdhfClgF+6t6YjHM6xHRpZ4Eo1+w1RkL/Y5TEt+l7bPhQNLOWFRxW0rYuQF37Lu/pN3psHBjc/y7eBRXBZwNJwjdU4XvKiUyLYXH8TkOTA3Cz/fbI2IAPsdLSHqtjK1xOJbxvspxenY0W3AMRldTTz7XryS9UYXN9HvcDVhM0sHhjmOTSvohIqbCyWsnqZqOozV70+BrdFvg33he+IHcCXRd7Df+VNLDZW1ObISLC/aQtG9YbDk3cAIuVPFkRCyiCjpypnvp/sBrkk5O2/pjUdfv0+vKxL9hId56ODh8t9wdb17cffA1PD8dHhErqIJOdYVrtROwJvB1unfuirvF3ioLAXvg3+uwiuwtiiv/Ti9Xwp0OCxCHS+qZfjuzyp0z6r6Pp3ENw50bh6qFLhApbrUFrvY6RCWqADfz2WvhmNGWkt5v8l5tHtAJB7PPVe56kMlkMplMJpPJjDeMzcdRXMdEk6LqUWfHs8Kaam3s03wYx2tOwmKu83CRwyuVhIBVkHwo0wLv4HzAAWlddxkuqHZ6WtfMJumFCu02pOvgLxxD5b64Zq6P5fG4rlMTIWBh3TgD7qRztKSRddrdEFgX52vehQtk9sbizd/geNlhkm4rMbymPvgu2KdxIPAuzqe8V9LB6f1VgM+q8OFkJj6iogYZmUwmMyGT5nHT4xyS0yXdm+YEB+FO1XeGRU/PSXq8Ipsb405/e6f8hjmBs4FdgUVxN76Tm4udl7C5Mi78PZOkY9O2Hnj+dIhKCP8KNjqkf+6IdTCLY0Hgh7gb4PzA68ALeKwD2nKeOqERERvhYvw3YWHehrhYxCZYwHmWShRwb8be/Ph6BOcZnCfp3PReLUfmKEnvVWUzk2lKh1/aQU78nCwilkyLyAGSXk3vfYpbrT4GnIKDyENrSRf10Gh77UFasCrddK4GNsbOlRE4YWRV7AB4Hgerv4fRSSzjI8lRswNwSETskLaNShBsz2PLZDKZTCaTyUx0CAeKvgCWjoi7I2KziJg3OW/2Bw6OiF5N/q6WXN4aAeAMwCsRcXlE7D/qACxcOBA4NiJ2l/SDpGsk7VNb48gdpA5VfV2OGjnGBSNiu5Rsj6TzgGdx0OQxLIZ5HDuu+gLvqpwAcGFgCHC4pFVw4K9Psn008Bnurt4/rb0+V3kB4OI4qHkBDtiultannwN3RMT/4aTxY7Bo5kKV7DqY1ks34wD8CeGObpvj77ZW0ewuYKeIWKCMLWif89roMbYXaf2/KTAAWBq4KPkDugOzR8T1ab+vVVIAWPSPpET1c7Cf4diIWFzSK8AB2Ik9cxlbyd6MKelEtbV9Si6o/fti3B1vG2DNsMizVHe8gtCnaOcqfP12B7pGxEzpXM4j6SVV04mnoTYL182lwHQt7PNjREwq6euImCTc3a4umvGtjcTFt/YA/iALANcF/hIRc1fsi/sOBzieSMcyVe0YJF0EbFBIHpka6KWSAkAAWXRzA67oeDD+XZyJ7z/vpH3qFY7MEhFnp5dHAPcAM0j6KSUTvA7MHxGb4865v1UbCwABZAHORrhr1v4RcXhb26wamZE44HhQROxY2w5j/F6fUQUCwOJn4+ti/ohYIH2XP6Rkrt8C26taAeDGwIm4sMBmwJK4e+Rk4UrySLpFWQA4TtSui4hYMiIWwp0ONo2IDSSNTNfKSGARgAqTx2bAwuJl0lwLSQcB04U7HwD8tyJbSHoEF+L7FOgREQvKSaIHYzHykSkBsLQAMNlTuMPpY7jqfu+0/VLcfXCbiNgurXOGlXl2FH4b6+Ln76nAVmmMX+Fqw7NFxOA0p3qtdowlhjgtXsdMCVweFvv9jLQuGIqFlhunNWCrKVynS0fECemzh+HxHpjmUbV9a4mc0+Og9lPKAsBMJpPJZDKZTGa8Ymw+jiZ+3k8kvVXwedQ190+fuwbOUdsKdxjsh/1hU+Nk6+1wwfpKkYvRbQgcHRG95KTtXYAdI6KfpC9UrQBwrrS23w37pU9K/vIPcReZa3EnwjZNrK7aF1dYG68UEWuEi/w8DuwFbB0RBya7PyW/cU0AOBi4Q/ULABfDYlXh77EvjpUNwPmN2wIHy0Xcyqz9ZwSuDBeHA1+nXYBaEbyNsC/+3DTORyr04WQmMiR9m3xIDygLADOZTGYMirFxueD528D0aX5xJ+4IfFCKm1+l6gSAi+CC5n+S9FQ6hvdw/OYqHNe9WRUIAAv++FVw06b1gO4RsW64qPE1WFR2TkTMWjIO3znZmBF3OFwXx1VGphjG5cCTwBJ4fnVtW89TJyTCQtH98Vzx78CXwFvpGhqMY2ZV2psEd2lfJv0eDgDWDRc4r+XIZAFgps0ZayfAdJMbl6pDnXAiyxtN32vVwTTYXqOJiI7JqUBEzIMfSjsCy+PK/OtK+iQtnhfD1WrqSfj91RITQMXxTCaTyWQymcz4TzSgW12yMz2uNPoWTgKeE1dTfToFbdfCwrIzJV1Q+Lsquro1qiPfZcDfcDXV/6R13T64anSPHpwAACAASURBVNVvcNXY/uEOWZL0bgl77dEdb2Gc1Ntb0l0RcTowr6Qt0/v34o5fvaqw14z9TrhLZG9gGmBpSd9EqvYbEdOrZDe39jivTey3+Rjbk3QfuAInVWyGA/JbSnovOQj/Duwv6ZmSdmoJAN1wcaHJceKGgMNwsPx4Sc9GxFSS/lfS3lTp838ETkjfVbFKb/HfewKPSPpPCXsz4g5DY7OzG7A+cD1OmP+xzLXaaJtNPndBHEDZR9KTLexf7AB0CNBPJbpJhUVH62ARw+U4eWM3HGzphLvkVVHNeVTXWez7+jEi/gb8JGn7tM/qWCy/M+7mVrfvLyImkzSidr6aeX86/IzcEyewDKviHhcRA3HVv+didPXt2th3wMKxtXEHxJvS3zTEzxkTSJXjlLg2EHc2HVTYXsXzf1RSFTAfFo19CPTChdteAoZjoe6+kv5Zxl4T27X54U6S/pW2TYU7AX6PO2TuJumWqmxODISF1UcB+0n6V0RshxMOB+HKqhcDPSU9WrHdlXBl2sdxR8dvcBGE9SS9XqWtgs2lcaLclPg+9HqKR3SuBc4rsrMoFquehYtjDASekbR/en9X4ElJT1dkbzV8rz4JmAxXG34aB/3fjIhpgQWrspdsXglsjX/nf2lhn9rzeH7gwzL31IhYHxcVWR6L5J/GAeu5gSP0804O1+P5XWX3oEwmk8lkMplMJtN42tLHUfyciLgL6AosrFSQLlzAchpJJ0ZEZ0mfjfXDWme3C17P/FPSfyNiReA+4ABJF6X8uE5yUZsq7LVL18FxOK7KfHHJh3sG7ohzArCD3IVnOezjuFLSKWnfGYDrgL6tzT8s+MbmwteMJF0dTpTfBDee6CdpeNTZnbIFu/1wLGN7XFhpD2AqXHzzP2GB4N04llWqUGUmk8lkMpmf0yTnYEXsjz8CmAI/j99McY/9cByrriIDLdheBRcW+EN6PYVSE6Ww0GtKSa9WFU+NiBWA00i5GhFxDJ5/DMbz1+8jYjZJH1Zg6xSsC9kexwCWwIUpL062Z8WxyM6yjmS80ca0JxExefrnfjhutDmwraTXIuIPuNhqrYB2FfYWw7Ha73Ae2cmSboiIDXBuw22SzqnCVibzS4xVBDhqJyd23onbwl+ZtjV7g6nixtNoe40gORVOwZ0N70uJGwcDr+IkgJ0kvRwRm+HEuI8LfztejHFcSU6e3jjh4RNJ/dr5kDKZTCaTyWQyEzhJTLES8FxNaBMRf8Edst7BQceHgJlwQvehkt6vyHajBIcNHWNyst0JnCjp2ibvTQ/8A3hR0tb12mjBbifg98AWwLHAgjghf/MUbFwQd1pfBXizpACoXcVxheOYHotxtgQOVEEsVqGDs2HntQX7bT7G9iIipsEJHC9jsdiuyTndDXhMrei8OQ62uuHA/55YcPwjFnWNSNu74PP8XXNiqFbamhTfz9bEDun+GotYrqSthgoO28NmWBB3Mu5yNzL5kc4AeqQEmVrCf2dJn8WYHYBuxgKA++uwWwvkLI6LVQ3GwY1ZGC1cXRpXJhws6Z6KfH8b4XvrP4COWJgzBPgBC2N3xOK4m0va6YQFEvtJer6lazIsBNwUeEklO2SlZ0ezRc5gjI51kwLTSfp8fL/PtSfRBgXHCr+v9bAwbBAOGp2NhdWz4nvfSOAiJRFnCXvz4t/Z0GS3P/A+cC6+Z2+IuyyfjEXeX6eAYL5uxkIhwTGA+bGwel8lYXVETIErux4AfARcV/a7HMuxdAX2BubB8/IrNLr7d5t8hyl43R3fz/urwm6j6ZzOjOcaMwI7S3onIuYDLsRzxT0rsDM3/s4OTTb74/O4gKQPwoLx3fAc61q1gagyIvbChU06AX/GHfd+KrxfFORdg4Wkdd2HImIp/DzsKxdt6AosgK/RBdL2WiJnR/wMPaC1iZyZTCaTyWQymUzm10kb+Thqvr9pJf03bbsXQNI66XWtqOOOQIcK/Maj1roR0Tt99pXAw8nPuQMumrerpMvL2GrGds0XsCr2mR4j6YLka70OuF1SqW587Ulad9+Ii9Usgbs4zo0TnQcnX8Dkkh5Kfo8hwCn1+I2TvZrg8GtgeOGaWQnoAQRwOPB9yVjcLFi02Set//sAt6QY3G+wj3om4HJJT0WFosNMJpPJZDI/JwmaTsHdzIYm//dZOEb2Ey5scHzVMZU0JxgEnCXpjrRtPVw075SK8mKKc9WuOPenr1wQI3D8emEcU/p7GZvNzHH+BNwkN8BamtFznOeANXCeTGnB4cRCiqnshoslX4Lz5daSi3Evj6+l7VRBccxwsZGZcFzmf/i7/C8WyfbGRZY3AD6W9ERZe5nMuDBOIkBo+6pD7W2vrYmIbfBD4TyczDQUuAdYDZg+JZOthBfou2oCr1YTE0jF8Uwmk8lkMpnMr59ofLe6hgsOGz3GZHNlYG9JO0ahu1IhqLw2FlT0BT6vMsE5Gtg5LhokjisEp1vqVDUDFiDshJ2Q99Y3orEeQ5ue11/DGBtJuADOtJIeiYgjcAGgLSU9nBzK52OBVxnh2DzAmpKuTOvss4ABwEL4e/wKWBSvvX/EXSxfKTUwxvidT4ZFhpsA79G8EHBSWdQ2FTCXpJfrsNcwwWF72UyO4/nTy29wR6UrgHNwF6Xvwx2Q1gPOkPRV+s0MBo5ViQ5A6XMPBq6WdF24mvL+uPvZvpI+rX2P9dpoYm95/EzcDtgGj2n1dH/og8f/mqRhVQhkIuJ4LMDdWdJLLX1vkToGpn9X0Umu2SJnMFoImKmGqKjgWETMU0uwS/fUC7D49ZaIWAZ3cv2XpMsiYmqgoyzKLXWdpt/ET3ge8XlErInv5cOBe7EAcHv8eyw1f5tYCHdyPQc4SNKH4c7OZ+Fg3/Cmc5HCs6otRXkr4nvrE7iya92dW5t87qh7VzPvrYzn44NUQYeDpucnItbBorzbcXXTjyJiAbwu2Qt4oYJ7+DL4mfhuek5cBcwObJLmqWsAPfG99o0ytpK92hynC54Pvy/p7Yg4BBd1ORp3O1SMKci/EYvXWy3IC3eGng14HQtEezaZbyyM1xor4468X6XtC6pCcWcmk8lkMplMJpNpf6rycTT5zA3TZz4FnJl8Gf8EOuMctR7AOSpZjCvZqq2p1gGWkDvxHYh91DfKHet+i9fHV0m6q6zNgu2Gdh1sBMmnPovccWdh3HGkAzAHMFDSsuHiNecBv5d0S+FvZ8bdcsZZTNpkLdoFOBV3NHkXeBC4V9LB6f1VgM/q8fe3YHsgcJ6k55r6ZlOCdw9coOdQ4H/NxZgymUwmk8lUQ0ScCdwvaUhEdJT0XURMiwtazgW8IenfVcdUwkUMegFzYoHVP3G87ABJd1doZzU8x7opLHg8Hc+TL0nvHw9cL+nZCmz90hxnTVyM40RJQ8ram5BpJkY1I869Oxj4D47T3AtMivMPjirOj+uwV5wbTynp23B3wT2B6XGeRldcaGRgW8YYM5nmGGcRILRN1aFfk722JCIWwqLGp7Gz5h7c9ePOtO0F3B3kuHwjz2QymUwmk8lkqiEa3K0u2keM114d+dbHlag2SK+LFbPmB9bGzqptVVFXxSb2G9Y5LtpeHNc0Ub0lgcqMeLxPSnq8Xnu/cCxtcl5/TWNsSwpJDqthZ/E8eDxfYLHTRrj67h+BQyTdVsLWzMC8uMtZX0mXpmu1M3A10F3SxxHxKfA2sFwVv4vCGGcCvkq/ga7AVrhz1SixHPY71brjXI2vqVaJEAr2GiI4bC+bBdv98f2zK77vdMfV/97FVZX3Sckyk2NH9qn1CA6a2FwUP0fukdQzbZsl2VsUi3NHVpVYES6CNT/uvHUqsI1c8XAJSc9XYaMZm4fh3+L2akYIWBByVFpFOiawIme/ZqKCgmMpEHexUkXIiDgDi6lPTYGdrrgT2AaSvqju6CHcNfZ23JHzSixy+lHSW+GK51cAm0p6p0q7EzIR0Q9YCtgFXxun4S51j8vC6tVxVc7TlLoh1GnnZ/OjluZM6Ro6ALgNrwm+r9du+rwlgEUk3RwtiLUjYgZJX5axkz6ndp9cC1gReAa4H69xeuJ4xx1pntdR0ncl7dWer1MD1+LvcOP09kD8HNkqJXZWMsaC7c2A4/Gz/2vgCUmnRcSheF5wmEZ3lJwBz+2OVAlBfvqsvbBYdWW5s8Go7zSdh5uwGLiSBMtMJpPJZDKZTCbz66QKH0fhs+bDHTDOxr6x94ELJL0cEbfjRO41JL0WYyky00qbG2Of3yEa3cHlIGBxnJC7Oo4bPVo2ltMkLtXQroONIK37e+H4wgq4MOebEbEt9vcflGJ1PYGLyiTHNxPDOR7YGdhC0uMRMStwK74m9y03sjHsBr7ejwM+kjSgsJ3C97s08I2kV6uynclkMplM5uekZ/D12C/eL0YXel5U0ksNsD87zqvaGfgSC6xurdjGVjhmtL8sdFwHz18vkXR+RTbGaY6Ttk0nFwDOIrIWaJKfMQUwIl2X3YHfSTo05Twsh3O6npWLc9d1Tgtz44Nx57/LgBuw7mdmoCMwJRYELgvMrYqKf2Yy40qH1uwsaRhOWFsG2D8iDm+To2one1UTEbOEK9+SFqF/x4kH4C4SiwJr4QqvI3Ay3JDaTT6TyWQymUwmk8mUZm7gKUnXhjssAKMCg8NxtdH3I6Jz2Xl4WIx3Ha4QdbZSd6/kUBiEHVUvSuqftr+jarq5NHKM80bEPunlP4FOEfEn8DiTswW8hvsAiy3qFgAmERHFcdVIY7sGJ8ufnRxztfcqdYylRP8bcEegN3GXNQpCkVLOHEmf4y6Rf4mImVoShqT9LlcSx9X7fbbHeW30GNuL9DtYE1fePRwLOo7DlcBOxc7k54HdJd1W4jtcFCfff4jFtntHxG7pWv0GFxqaOiJWBS4C+lT1u0hj3BgYCtwQEZfjbpiDcRebI5KQ6ieNFgBej0U09QoAZ8KCwjvxfXYO4KCanYjokMQJI5O9G7BTvdU02mbtGqjdPyUdhCtU34TP2wDge6ALsGc6ntr9Z0+VFwBOkgI2GwJdw5W5kfQx0A8HP75XCQFgYYzzJYf599hpfgHuZPlGEpUcks57aZr+tiSdAtwMXJWCVD/V7oExWtgyA3Bjep5XgqQHsYD85IjYK9y9k5bugZn6kfStpK8lPVBvcpykvYHhEfFA2nQnMCNOTgMLVz/FnVVLE+44WrP9Ne6evBUWir8rCwDXw/eCY5UFgL9I8sefnV4eAdwNTCfpQ1yIrzd+TvXESYAPqYQAEEY9FzeKiAERsUNEzJ+2jXEfSveaB7DI62aVFAAmuuIK+Kh5AWAHSV8Wr7XWEhEzRsQ06T7ZHTgDBzz74OfE07i78aZA9/ScLCsAjPR8XR84F4viR+DnMbj74IfALWleW1lgNSy22wXYUVI3/KyaNSI2l3Qq7uRYe651SMdyrFopACw8G1eIiJ4RsWpKLugJDIuI36ZzMGnad35gCfwMzWQymUwmk8lkMhMwZX0chfXGsngtcYekG4D98HqmV7gY18a4Y8YlyW4VAsDJgd8D+0m6o+Dz7I/91H8H9pD0aNpeWgAYEetERO+UWP0kzsdbLe32HPBXHK8aH3kJ+AnH+e6XBYCBOwLOGREn41jEyZLuLhNPKcRwLo6I6XCM6Dpgl4j4jaSP8Np/jXBXxUqQGZnsHRQRO9a2wxiJ8s8oCwAzmUwmk2lz0jO4P7BuRGydYqqrAreGO9dVSjMx3Q8k3SVpWzynvLWqnJGImD1c+OJ64CCgb/L93wscBewTEXPV8mnK8EtznCb7ftXSe5lRgrwrI2KOlEtwG7B7uEjEv4DlImI5SS9JulrSRZIehvrPaWFufCHwLS5WORtwDLATMK+kv0laE+iqLADMtAOtvlHJ7U33xxfyw+EKRG1Go+1VRbrRPA8MiYgjk6Pjapy0cS/wMbA1sLykfpIGSLoP8o08k8lkMplMJpOpkOmAWQFS4uqoqkrhbnXz46o8U1QwD2+YGK8JjRxjR+DAiDhY0v/wOm35cIVV5K4qywInAt+rhMix4MiZLY3rZ+tXuePHELzWqrsTX8FmQ8VxLSSqdxrL/pPIXcgmq9duo89re4zxV8AawF2ShknaHXgQC4GXkTRY0s1lnY446f8KYCZgBtxVqHdYCPgB8ANwNBY93S/pHxU6x5fEv/EDsABxFuAyuZDTrbjy2dxp3+nSthOS8KFVpPtYQwSH7WUz2esODIyIC9O2g3ACzs3A85L64cJRf4fR9yhJ79Uzxib2f0wikZdwx79dIuKo9N7HqqDbUBrjprgK91ySngZOAD7Hz5BuWBRzo6RPy9orJAB1i4hjI+KUNMYT8Tm9PCIWT2OfNP1/evwdn1rFeS2i8bzI2cRA8f4o6XWgY0TcIldPfxHYKSKG4ufiJWUDORHROdkamRLVTk4CoHvwvXt7YNu030+4yvstFc8fJ0hkAfNkEbFkesaeK+m19N4ZOEj3Fe4+MEpYXQ+FpMrF8ff2XzznPiwiFkn3odo+RbHxJljQVsbmpGlM5wOvRMQfi+8XbP6UbA5K/2+tvUXxHGaOiJgDi/E2w0Ux5sLi9xOAp3BXicdVQTfVdO5Ww5V+r5L0g6RNganw7zCwUK93eh5XLayeFXdbBngI+ARYNx3b4ZKeSP/+CThb0v2tNVB7TuHz+1vggIgYgIsA7Ac8GkkIKPMssJKkt0qOLZPJZDKZTCaTyUzgpPXGWtgXvwdwbESsldbMffHaau+070bA15EKV9VDE7/KD9j/v3x6/X3aZ3HcQW6QpPuq8HEU/LhnAa+mbacDLwNbRcQV2G97rqS7xie/SiHO9yPwCC6+s3JEdEtrxJuAO3AH+/2VutXXGTNqGsO5F5gp+YUH4SI8u0XEsnKRpeUlvVhyiD8j+dZ3wIXidkjblL7n0on4mUwmk8lkWsVjwJm4yOmlwMXAnyT9X5kPLcQ4Zo/RhVN/Nn+JhEYXqShVHDv9e37gQGD7FB8eDByPCyBsm+JFXSW9W2XMoaU5TtPjy7SMRgvyzsf5S5cAk+OY3wrAG8AJETFNWVvNzI2HAbNLuhYXNTkHF2w8M9w5G1y8e7wrcJ4Z/4nxM5/u102Mbn97HE7+mxm4FFgYd4wYhJNHj8DOj5OTsyWTyWQymUwmk8mUJCLmBbpLOi8ipgLuB/4m6cz0/hSyWG1z3EnhP6qgI1+4U8WBkjZIr6PgvJkfWBsLZrZVie546fPaZYzps7tg8dFV2LmyBq6K9Qnucr4+cKikIRXY6oe7qe88NmFIEuT8kP4dzTkKx8HWjNhhc5CkD2vruhb22xJ4UqlbXRkiYiBwnqTnCmvJn42hSfL4FUBPufJqPTYbdl7T3zZ8jO1JRGyFu0adVFvrR8SDwBe4A+AnFdhYAzgWJ6jvKeneiFgbO+OPlXRzRCwCTFNLAKiK9LlHA7sodR2KiEeAy/A9YUZJnyQnZ0/gaUn/rtPWktiH0htXbb4a+FzSH8PdsX4PnCHptbDgcChwhEp0x2u0zYiYHXcbOxzYFxCwlaT/RcS5wHJY9PmjSnTjG4fjqP3+umCh3PqyGKqKz14ZO+W3lfRiWHA3O/aT7YcrcV8raWiZe00Tm91wUlMvXKFvOLBOGmNfoDuwuqRvIqITcCNwtFrZyamVxzQlFswsh5Oe6upWl6mOJs+5RfD961/p9e34d7dJumZ/C3wi6dmSz8SOeL7xPhZJXww8CsyHf3sX40S5Afjavbwtf/sTEum5MwnuwPuR3AGgtj2K87qW5nl12FwVV9w/Qu5wsDQW+c2Nu4O/mILJI9N1dAtwVGufGRGxEA4+XxIRy2FB2ruSroqI3XEAsm9h/+KcajBwvNyZdFzt1c7l2fha/TOwNL6XTo7naVsAi+Dr+UEsVv1ZN8J6iYhj8He5nKSnCtuHAV9I+kNVtpqxvQuwKvAXSf9Oz/+dgT2B/7U0lx3Hz64J1QN3GbxK0oPhCsrdgJGS+oe7rT8r6Z6qrtdMJpPJZDKZTCYzcZD8e/sB18iCu93S6/0lDQsXHZpFdRZRa2JrVDEu7Ku+ANgYx43ulvT3iFgRd5PZswqbBduT4+7xf0vjmkKjRYcr4TXre0oF+ccXCud0JVwA8DlJ70bEHtg33Q/H4jYHzi+zRi3Y/FkMp/DeUkAPnN94KF4Xt6Wfei1cTO5kYKikt9vKViaTyWQymbETEXPiouRSRUUAImITXHB4CuBiuYhD031qMY5pgMmTCKy1djoCKwOPA/MAiwELAHOkbTdK+i4irsIFHtcYW+5MWfIcp/VExCzAkZL6pJjKn4DBSsUSU87M1vg7XQdYVC5aUdZus3Pjwjx9ESzsvF3SI2XtZTL1klXEFZOcJVekm88JwF/x4vsznFA0L3Z4TIKdHwOUBYCZTCaTyWQymUyVNLJb3bwRsU96+U+gU0rYrFUhnSK9twwWOWyvkgLARCPHuFBE3BYRvSJiMbkLxeY46LeTpFux8O9aLGT5o6QhyQlTj7126Ryn0dWj/hIRM7WUaJv2u1xJAFhinBHu4vI5FogyDuK4Wqeq01srjmuP89roMbYHte8/IlaNiD+EO7o9hjtwbhYRv0nJ+p/gbk5HVmT6ZVyx+TXgu4iYNiUz7A+cHhG7S3q5CgFgjO6QWbvW/wfMiZ3hNa7GSes/Kokc03d8qeoUACZGYCHevyV9KWljYMGI6AncBxyXxHgBbItFvHULABttMyJWABYFrpZ0e7L1DXBtREwlaV8stvyhbGJF4VpdMtzZaQzS72+SdI9fRhUJABPTAU8A80XEgcB1uEL3y/j5sZOqFQBOjbtV7YDFhl8A3wFPJkf90Vho+U24q+IhuFtlmwkAASR9K+lrSQ8oCwDbneTD7RsRc6Yg0c3AWRFxZUR0T7/HDhHxkKThcnfXZ6F0l9qRuJDC3MDpOAGvJlZdDlfofxzfz59ry6SqCQ2Z2vk9KCJ2rG3HAusxdq/I7PO4S+xeydYz+Fr6BDg0IqaRBYAzpO1H1vnMmBpXo90VeBt3HdwlIk7HcYZe4WIApOP4MSxwvgE/t8ZZAJiIdC4HYYH6O8A7cheAGYH/k/Qm8DXulnumSgoAC8+padMYTsC/kb+EBfO1sa2FEx7bkvuBZ5PtU3B116vSPfyndBytuoYiYuqImDGtEZdOfz897hSLXEH5RWCN9Dw+UxYAjiFgzWQymUwmk8lkMpmxkfz5m+ICQ4uGCyBdgovHXRIR60j6TEmMV298o0Za42wMnAK8mNYvTwMf4RjWtXht+WdVJDos2G5Y18FGks7p2rhT/JbA0xGxjKSLcd5hf9wd8OV616g1xhbDqZ23tF69DhfG+29b+6okDcNr5WWA/SPi8La0l8lkMpnMxEjBH79IRMzdwj4dJL0n6QUlAWCU7FyX5mm9sHBrc2C7FDsu7lPMG/kHFvC11k4H4AdcgPMG3EH5ERxzeB3H43ZLcy7heHybCQAhz3HqQdbWTBYRS6b57lmS3qrNVVPs61BcwLG7SgoAxzY3Tv9Wsvsyzi3IAsBMu5JFgBUj6TPgPVz9fnpgCE6E3Qq3HF0ZJ+iOkPSRpNfa61gzmUwmk8lkMpkJEUkv4SBnj4g4ADtzrgH+GBaznQP8DXfsuLekuYaJ8Yo0eIxLASsBBwGDIqI/7j5yENAzIvZIyfGDkojl8XSM9YrxmjpyzpX0SnOB2hizy8kNETFra+1F+4kOW0xUbzrWQiL3TdSXyN3w81obSyPH2B6ksayLCwAtijs3rY+7ni2HO+ZdzugiQf+t11bBGT9dcmBugs9tT2CtiJg6OY974iTyUoSLG9Ucm5sCV0bEEcCPwEDggojoGRE7Y/HDW00/Q9KIVtpsuOCw0TYL3+Nq2Gd0CLBDOsdI2hoI4OYUXKmkqmK6VjcFLsWivOb2+THcserriJgkLJBrNYUxTpPO72PYWf5n4GPc2ekBYJ50n1DtGOuxV7SZPucbfF5H4N/glpI2xOLch9M+rxb+vF/67WQmEtL1MhkwKe7CeRSwuaSVcUBx3YhYRFI3YGS4m2UlpOfiw9h3PDWwe9p+E+4M+DtgL0kPK3UlzLSOlFS4A3BIROyQttW6c0fxdWsp3N8WiIglJH2Ju5ouFhHnpc9+FhfHOCndTyfHgeXj1EqxcbgYxyqyuHA54AwcyByI5xvv4qIAU+BrpxikPD0dQ2u7Ds6I5/xzYlH81Hj+Mlva5Xlg03B13OuAG1RBImd6TnUH/hoR14W7SJyJA/TXpuOp7ftYWXu/cCxv4A6Pe2IRew9Jt5f82C5YyLkHcHVEzIO/o5lqc2QcP5oUCy1rx1KVYDWTyWQymUwmk8lMwKS14NK4u8ip2J/ZBVgp+fsvx11Hfij+Xdk1R1rz7g4cBjwYEevjbnUv4kJH5wN/kHRrc7GIVtoa1XUwIvZOfscrgJmTj55w18ELcacXYPxbV4WT47fCa9E9cNzxvrAQ8BJgJ+zHuqesrbHFcNKx1PwozzTxp7YpybeyPx77wxExZaNsZzKZTCYzMVCIG18FdC6+V3j+/1SLFae48RjdgltLuNhfH9zp+KMUV9gZ2DrlIDRXOLq3pKdbaaczniPOCLyCc5sew7F+4dyNJ4ElcG7HtfXkGNRDnuOMO4VYV7MFxwtz/P9J+qQWCyuz5hiX/KZCXkOr8mAymbYgxrO17q+W+Hnb0f2A2+Tq8DMB2wNrAqc06oGRyWQymUwmk8lMLETEQrir0G3A/ZJeTImVNwB/lXROchRtirt0fyzp8ZqDoKTtLtiJdBVO6F4DJ5N/gqtIrQ8cKmlISTvtMsaImAbojoOm0+DgcV+cELsHMCtwvKTjSwyvZitwN5PjsONvQG170zE0cQDemI6hLuFYRAwEzpP0XM15+Qs2Z8DfeU9V0K0u3IFoIHCqpEGF7bWg9iS4uust9QhV2uu8NvncNh1je5Ecj4OAmyVdGxHz4aTxoZIGpt9PR2AVLATcUa6cY7lLzwAAIABJREFUW6+9jbC46TEsQHsxIvYEVsT3hruTAKrZ77cVdgIXNHoTJ41cge81ndJYdsaix7VxN6trJN1VYlyzyEJVUsBhK+AF4DJgNSzSuRAnqRyCHf6lBM7tYbNge3V8Xx2Cgwy7AUsDt0q6Le2zrKSnStoZFYyJiAWxEHUftdAhsvD774TH3E/SV3Xa3hTYFRcg213Sx+GOlf+NiN+mY9lD0kP1fH4LNrvi6/EDSfemZ2Rf/F3Oi4Wzd1ZxT8uMv6Tg3xnAn/D9eQ98rW4n6aFwF7Jz8PPysDY8jg64yEIf4Hm56xkRsTnwaplnRcZExFo4+fFk/Fx+u6LP/T1ObvwOB5Bvwp3j/o07DezWZP8OwKySPqjDVjfgS+BZScPDHWTvxvOzswrzqC1x0Hh9SR+Gu5FPqzor10ZEP2BJ3HFgZlwU5M/AIZLuiIiZga7AW6pIkJfGdjau2vo7POd4F6+vjkvb1lDq7FChzdkl3TKO+5ddV12Ik2N3lHRVOo9r4vvRB/g8H1x23ZjJZDKZTCaTyWQmDgprwlFrlYi4DTgXuBc4Hhd2uRn4R0poLb22aXoMuAjmHLiw2svA7Lgr4JFV2Ghib2Psr+4jd/mbC9gGJ3d/hTurHCTp1qpttzXJf9ABj28tnJQ+SNLIiNgrvV5dbVQ0aiwxnFIJ/5lMJpPJZH59RMRy2Pe+taSXk6+6swrFaZvEjY8ATpQ0vJV2xph3hgtG9ASGAddJ+iRGF7L4vaQ3Ui7OHdhX3qrCigU7p+D8pu1xbsMSwCLAxZKeCRfB/iSN+ZMq58eZagkXyLgTOFrSlWlbm39feW6cGR/IIsAKaS5xtPDeTLgazzrAtsBX+aGRyWQymUwmk8lUQ0qYvggYDnyBOww9D7yKF+ZnS7q4IlvtJcZr5BhnKiYNp/XMBljgOFTSLSm5eGucuHqjpKFV2E72xsmRkxyOg4Fj1couJ7XPpJ3FcYXPbzFRPQnkppW7zZSx0ZDzOhb7bT7GtiYipgLmlfRCRCyGf+N7p/9fIunbiFgKi8c2lvRlRHTE94zz5U4+rbVZu/ZWxIkbFwAbAd8DgyU9GO4+uiJwYE3YVsFYZ8BdqUbie9sladsu+H5wgKTna8dXwk5DBYftZTPZrYmMz8DncRtJd0fEvMDGwKq4o1Lp5P9wsaqTgV4pUWRRLHzqkYR4teuqs6TPmtzjbsb3uPvrtL0MFsMeCmyHq473BF7C5/di4LAqk3FSsOoO/PvYDXe/GhgRlwHCosvtJd2TAzmZiDgVB/92AGbCQrwpcZfc/0TEVriIQ2/ghyqCOc0FhcJC8hXwc+RdSYeXtZMZk3Cxjt7AN8AnkvrV8RmTAyNSYuXM+B65O/ARfk6sjzvWfQg8g5/RzzdNxCwxhk7AU1jEPTQilsdCwKPkjoC1/a4Frqx3Th4/L3L4J2CIpNfS+38EDgCOURKsV0VK2uyPi6hum7Ztis/zXpLei4hF5W7oVdkMfA9YSNIxzc1nys5xanbStTArXkutjAWUO0p6Pu0zAzAfvt88n59TmUwmk8lkMplM5peIiCmUiqREKryV/t0TFzs5PsVwTsJCwCMkfVGB3doaZzlgBI5XfYvXOW+kWFhXXJjr98AXFQoOJweuwQnrd+E1+SK4q/ozwEJ47f/c+LSuKpzTTpK+SP6iI7Cv+hpccEgRsS/wsqS72/BY2qSgUiaTyWQymfanOD8KF2zdD/g7MBcuqPATjpPd0iRufBPQV60s5FyY46wHLAdMgePya5PiKDjX4eOImEbS1+nv1ge+a20uTgsxjptkYeHSwGY4Jvgc9tUfKOnD1tjItA/tJcjLc+PMr50sAqyAcU0cTUkCU0l6q10ONJPJZDKZTCaTmUCJxnara5gYr4ndhowxImbEnUzux12qHk2OtxmwSGVd4EFJl6f9J5M0ouqg6lgcOZV3jmtvcVzh80snqo+DjXbtyNeIMbYl4W5qvXFF3i5YyLUu7k5zlqSnI+I3WGz1+4KzutW/j4hYGPhG0vsRMSfuNvqopEMiYjpgf2BGnKA/LCLmlvRORUOtHcN0WPQ6QtJGadv0QC/snN8M+LqCBPmGCA7by2bh99VZ0mdp2zH4nt5NrjA4H+5Ud78q6AAWrhw9f3r5DfA5FjueAzwp6fuIWA1YDzhD0ldp/LV73DhXVoyIuYEFJD2QfiNHAd9L6pXePwmL//bBFblnqCIZp3Be58RizU5yh6rlgXuwAPLadN/pKOnxem1lxn+aCf71wc+618Pi7T8A3YBbgM2x0KlUgYNw4Yj1JJ2fXjc3t5gU/z56p+N7pYzNzM+JiCmx7345/Bz9thV/2xkL/AZIeiwiZsOFQLrLHfc6Y4H+m5L6R8SkSp0VSh5z07jC7sDBwH6S7koB+keBP0kaEC5I8jcsLHuthN2xdseOiJ0Z3anv86qCq+k+vi2uyttX0o1p+83AFZJuqsJOM3bXwgmxW0p6v8l7xSrHu+Dkhx9a+fm159RmeHxHSXo1Io7FHYjXx0mqayp1A81kMplMJpPJZDKZXyL58AbiYliPYQHcObgA15PAv4Dd5OJxkwPzSXq5QvvdgNOAS4E/4rXp/em99XCX94PL+lWasdvQroONJJ3Tg4EXsR/3GHyOR2Af9kOFtXmbChzH9xhOJpPJZDKZlomIVYFJcX7Trrjo92nAC7gT8XuSBqd964obN7G3Fp6nnonj77PjQhHL4kKyT+M57Y9N4xF12htbI6elcIHzHXFXw9JFeTONo70EeXlunPk1k0WAFTKuiaOZTCaTyWQymUymPNFO3eoaLDhs+BgjYg68rvkaJ8L/EQt+nsfCxy3w+B+SdGEZW+NwLA3rHNfe4riCvboT1Vtho1078jVijG1JStzuA1wk6bC07STcwWVyYEHsOL6xpJ09cND/EbmT2wnYGb6LpH9ExNTAkcAMWDhSZSXnLrhC9Kv4XnAX8JykfdJ+02PB1ZtlbRZsN0Rw2F42I2JDLNz8ECfknIaLSa0NbJHEJB0lfVfWVhO7/ZONrljk1B1XOHwXOBx3lrozJQTdiO+B4yxyTsk3GwAfADXx0lHAb4HTJd2T9jsDd6tcX9L/KhhX7VrdEBiAxYX/wR0GP4qIFXDCUx9J55a1l5kw+IXg3yLAgTiR7FRJ/6wg2LgE8CxOhjs7bWtOCDgZMHVbPnsz9RMRpwPz4g6jT0XEaXgec1q63+wK/AY4CHeyq0qs/jssthss6aWI2AboB+wpdzVdAYuq70n38I6SvqrTVmu6Y8/RVDBXBWmdsxV+VjwFDMMJBttLeqyCz689N5bGz91j0vZDgVnw82NE2lascjwEr+3qmv+nNcbZwM6SnqkJRcNdnLfC87jja4kVmUwmk8lkMplMJjM2CgVbDsfrpz5AZ2Bx3O3krzhW9Iik09rA/iK4O90WOE51CDAVLlpza/JhPyzpzgpstVvXwUYSEatg/+YfsT96R0lLpjXpibgrz7GN9BuN7zGcTCaTyWQyzRPuLLwvsJOkf0fElJK+DRdYvRzYV9L9KeYwGMdBWhM3nhPoIumu9PpULJzqn16fAywtqWtEbA08K+n5CsbVYowDHLgp7DudXBw3azvGM9pLkJfnxplfK1kEWDFjSRxt07ajmUwmk8lkMpnMxEQ0uFtdO4nx2q0jX0R0B/YE9gZWAHoC/8OOvjtwp5wnqnDIjcOxNMyR097iuEaSK1bVR7jTTi/gS5xkcYekS9J7KwLfAR3kjoBV/BZnAf4PWFnSGxGxP7AOowUq0wCzq8LOURGxKa40/B4ezzBc1flOnFyxW0V2Gi44bC+RY7g75E3AbkAnfO3MJGmPiLgAC+ZWx2LEstdMbYxTSPo+besPLANsCiwPrIYF67dJ+nvhb+eU9F4dNifBovhrgbOA+4DD0ra7JN2b9ltU0ktlxtfE7krAlsDNuBPgylhgeYPcXXEF/D3eXZXNzPjJuAb/kjjoK0lvlLS3ALCQpLvDlUXvx+LwM2t2mwQcmwoSc+DxV0AUOsFGRF98Hz0GV8ndBHdSvQwLqvco3k8rsL0KcDEuMjIHcLGk6yJiK+A8nBB4Z9q3susl2rjIYUQsCayuVEikmd/CHDiRtA/wGnCy3GW2kthKRKyPO28uj+c4T+M53dzAESmRtiYAnAG4Hgv0WtMddx7cifaI9HpPXDzmIrxO3BgL1/cA5gF+kDuS5t99JpPJZDKZTCaTGSvhbvRnAQck39cBuGPLMXLRmplxF/KuOK6ztCooHFew3wmYEpgOmAk4Fxev2RuvjbcE7qsyNy7aqetgI4mI1fA57QAcDfSQ9GZEzAd8BMwr6cX2O8JMJpPJZDLjOxExi6SP07/3wTHrPnLh4ZWA84HjJN1S+JvZJX3QChuB54P74VyG2yKiD9BZqShf2u96HFOpPO+mrWMcmfYnC/IymdFkEWAbMLbE0Uwmk8lkMplMJlOeaGC3uvYS4zV4jDPjBOMXcUXVGYHjccL6BxHxHnAJsA1wO674Nc4Ov7I00pEzMYnjsoNs3ElO6ylw8v+bki6KiC2AnfD6/0ksAj6t6gJAEXEKTt74naS3w51jNsfVf8e58t442poW3+cOwOLDZXFnrEHAP/B9cIeqBMCNEhw22mZEdMC6opqwaCXcwbFXREwKzImrOJ8j6bGI6CLp2bJ2C/a742tkpKQ907YzgC7AdpI+bSJuGfXvEjanw8+lzYEzgUdx9fHZgCFyp6qyz8V58f3qFpwU8yIWbC2T3t8OJzi9DVxVCGbl4E4GaDn4B2NWAa3Azmb42bCtpNuTEHgYYwoBa5X7a4KjTjjR68B8vbY/BUH1qEIgEXEwsCruMPAm7q46Fa5Ue1+FNjthsdjX6d65K07qvDUJAXtgMWubdKaONipymMY1DJ+/oWqh82242MoWuLvikKqE3EmQexTQV9Kz4W4RC+A5zwJp+ylp34543nNAPXOtiFgG+DzN27pgYfyyWMD5Di4kc6Wk/6tgaJlMJpPJZDKZTGYiIvmKlwR2BATsAKwP9JP0cMHfcBbwtqQzKrI7PxYg7iB3TtkTF03sHxHbpmMYVMX6uGCzYV0HG0kzBXHWxR0c38OFc76NiDWB3YF9JA1vnyPNZDKZTCYzIZAKC+yP84quT9v2AY4AtsP5RzNIeiXFzDrUGzcOF9rtBmwPnI5jKTdhLcU9wEI476hbW+kq2irGkclkMr82Jm3vA5gQkTQsIjbCiaMLR8QEnTiayWQymUwmk8k0GknvR8QRuFvdVcDLOKmz2K1uCtw9oywdgW9xYPU14JSIqInxrgVGAt3DXZculDQiHWOpBO5GjTElxF8FfI+FE+emil+fA3eEOzydmYK5ZwGzNFIACFAQpz3QAFvPhrutTQIsFxFTTqjiuEae1wkBSd9FxJ3A+RHxT9x9bAQWOi0A9Ky4ynEHST9JOiwivgX+FRErSxqQhGSVXpcRMTm+D3QEvk2ilOexCHqVJGRZSdLIiuxNiwMOuzOm4PBtYDPg/ohYoirBYaNsJuHAmsA/ImIdLLC+B9g4IjaRdCvwVkR8jzsDPQY8V/+ofmZ/duAkXH1734gYCmwl6YCIOBe4NQkfRgVvygoA02d8FRE3AD8AB+MK3Wfi5Jx30j5lhU2dcLBoekmfpSDOoxFxkqQjJV0dEZNhIeCUhWPLgqoMAJJeiIgdgIEp32pQQaxbOvgX7gDWWdKQiNgDOC8iesvVTtcChkXET5LOTgl5k0oamQpLXIcT9vL1+isgifE2APpExPu4y+gZ+N55FO5Q95c2sLkJvnd+DnwI3CPp0oj4CdgmXTNXQ9sJnCU9mAoOXBnuODxU0tsVzHGmBR7H9+fLI2JfFTqtF+x/mp5dU+Nn56MqURE4rWVmS7avSHP9DpIeAB5Ic7pVgJUjYjpJX6U5Xw9Jr7XWlqQf5Y7Q90bEcFxEZSfcAfijiFgWF3L5a71jymQymUwmk8lkMhMXETELcKSkPtjntx8wo6TXIuJvabeDI6K/pIfS60+xX7ISJL0REd9gv2Nv4CdglRRL2Qf7H5+uaq2aCsl8jRPT58Q++JVx18FBEbEl7lYz3iVyp/X/hrjA6APAXcAp+LzOlYpJHQ8clgWAmUwmk8lk6qHJnGw4jjesEhEjJN0s6bxU0PLPwLqSXoFRMdVWx41r9iQNj4ibgclwvPgoXLziVCwOXAI4oq0EgNCmMY5MJpP5VZE7AbYhkbsqZDKZTCaTyWQylRHt2K0u3FVpTxxgXAHoyZhivG7AE2XFKo0eY0QsDAwFeku6KyJOB+aVtGV6/17gFbl7Va6MlZkoiYhFceef2yR9Eu4E9I6ka8Id3zoBs6vCTm4F26N+dxFxJE7y6CLpzYo+v9Z1aE1gCUkDI+J4YGlcZfi9cMehTYDdgBFVCMaS4BDcWXAXSS9FxFT43tpZ0tE1cUxZW+1hMyIOArYEpgf6SLo7IrZKtq4HngEuBHpJ+ldZewW7K2DRxEqSTk3brsOCi20k/S8iFpP0YgkbtW63zXYPDHcE7IafmX2BYRWLY6fGz90hkk6PiLmBfwGXSjo67TOHpPerspmZ8EiCvCtx5dGhVQUbI2J74AXgJUnfpN/9qbhC/W3hTmRPAIdLOj39TU0A2FcVd3fN1E+4k9uNwB54Pr4i7nDQKyJOBJbBnVW/qtDmorhL7FlYBDgQeEbS/un9XYEnJT1dlc1fOJ7Ku2NHxJW4c/K+LYkoY3R3zPmBD6uKqUTEXvjcrizpqeIzPz1bbkrH9XKdn1877ikkfZ+2DQX+izs6g4sEXIg7DN5WbkSZTCaTyWQymUxmYiIiBgLnSXquaawm3FF9J2Ad3HXlR9yR/OqyPusUsxou6YeIWBAXWDsqJXj/Cfs/n5R0Sxk7TWw2vOtgI4mIxYDLcQf6qbDY8VScIL8aEMAlku5oqwJAmUwmk8lkJlwK8f/VgFlwfP+2NHebC3gKeBXoBVwo6ZGK7C2D5zafAa/gYg7bAcfh2NiMFDoOtvUcpy1iHJlMJvNrokN7H8CEjKRvJX0t6YEsAMxkMplMJpPJZOon3K3uLuACnLS9WuocUetW93+4W90xuJPDhSXFcTNH/D979x0uSVV1cfi35g45DTmKCkgSRUBQDETJA0pScs5IZgiSg2Qkg0ieEVQkSM6ShA8FCRJFQBgEyUkUiev745yGcpxB5nbPTaz3eXi4t7q6d9Xc7q6qU2fvrfklTVCTfO6gdIp6z/ZFwJeAB4B9gX2A6zuQANjT+yjgW5Trwhfr4j2AqSXNVn+/EOjY5OaI/qJ+PloVh5ekfOavkLQS8FlKUhwunfpe7mQCYCt26/XrdxC2fwwcBczeoThddUB+WUpy8cP1oeMog/831psBPwaG2/53OwmAjX/TJYDNbL9D6ZB3uKSZbf+LUoVwNpVuep2oGN2jMRt/u+HAYODvwF31b3gpcDSwJuWGw/6dSABs7OM3gUsonffWl7QKgO3vUyaO/KZOEGonAXBKyvFo3pro8F/jijUh5grgdOCNTiQAjvKZ+CelauTyKh3WnqYk52wv6fC6ThIA42PZvhFYgZLItaOkPTv0uudSOl9eImkp278GdgdOkLSi7fspxST+BB8mJ19E6SqXBMC+ZVLgSts3UJLDTgYmkbSg7b0pSVwdOUdWMR0lAXAqYKTth4GNgXkknQpg+8yeSgCs8R6gTO7cF7hdpeBhu/4POAv4jqSFRj2ONBLphgCnANN2J0jj2LiwpC0kfcP2KZRk/Bvr3/E9SYPrup+nVCB+uxuxppU0a93u5SkdQHcFsL0SMDkfnRe8AKybBMCIiIiIiPik6jXjYMp9oqXgwzHj5njZS5Trjq1tv1qvV/dvZ8xa0qB6rXo5sI+kTVy6pc9MSTjE9jG297d9aXN72mX7r5TJ2j+ui5pdBw8EjrP9207GHNca16mzUBoJHG97GKVL/PuUMd2zbK9JKTp0FXzYjSciIiLiE6v3/5eh3P+fFzhK0qGUAnX3U+6PnQNc0G4CYCPecpT7XetQ7sWvXv//C0q3waG2X/R/dhwcp8bRPY6IiD4jnQAjIiIiIqJPUw93q6vJeOdSJoGOBE60faukgyiJP13AObXq6FTAdO0kVdSYvdKRryZ0fI8yCLcfJbloK2DVWsl1duB2StLhk52KG9GXNarVrUL5zB9g+2+SfgDMCHyHMuFiO9tndDDeFLZfH8M6o1Z37nZ1PEkz2H6u/jwZcCJlkP+y5uuqdLAaDDxj+5buxGrEbE3qX5YyqX8z2zfW79DtKBWqTwF+CGxj+5p24vVGzMbfcTLb/5A0NbATMCdl0s1D+qiL3qDWZJ1O3OSQ9C1gKCUJ8G5K18b5gctaiQaSFrB9TwdiHUCptr2RSzfF0R6TWvtaf+7EsXlJYEVKR86bJS1ISaq8wPZJdQLNXDVhJ+ITqTf8uiiTr/7gbhZxG/WzLGkXYBlKct8tklanFHnYxPZlrecAE1I6kf6tzV2JNjW+w79IuQbootyg3rx1fJB0FnCN7V92Mmbj96UpXcevpHzXPa9SmOMsYGvg4f42+a/x7zofJbHyWdsjJe0GfJtSTOW+uk7ruD0FZbLA/u0kx6oUbjia0gF4Gkpi/l6Ua58zgUVs391Yf2bbz4xljAmAXYBZKV1q9wJ+RukgeQNwmEvXipvrvq89xheLiIiIiIj4GPW+1dXAPraH12X/NbbY7nhj4zpuYtv/kvQZ4IvUYpiU5LyNgO+2JnF3inqh62BPkrQi8BNK57/XbS9dl38NWItSyG1P4O3ci4uIiIjuqPeexqMUGrjY9i/qvbCbgWtdihwi6TO2n+7AueMgyjnaucDRtm+oxfJ2BY6yfbWkDYEHbd/V5u5FRETD4N7egIiIiIiIiDGpg1Sj61Z3raTZbD9B6Vb3uQ7F+wIliaKZjLcDcKvtfSR9g5KMd1RNaniFUoG1nZg9uo9Ntl+V9Jsa+wzK5Nz5bf9T0vi2H5c055gSkyIGojrJ4ZvA/sD2reQM278CkPRLyvfCPB2MNxRYV9LfKIm317h0qWut84Gkwa5da2y/151YdSD+iDqJY42arPY6MLOkLkonvNZk/Wvb/ey3Eg7rpP7JKIl3O9ZkPNXv0AMkPUQZo9q4AwmHPR4TPvw7rgBsLun3wA2295Z0GLC3pN9RqmYvbvvR1nPaidlIrluN0jHqt7bflnQF5W/5g5pUcUknEgABbO8n6S3gXEnrji4RsMZ8tx5H3ulAAuCilC6YtwN7SvoSJcliZ+DU+pk4DvhbpxIr49OhkfR3c5uvY0nfpiTfPkDpqPoisK+k/W1fqFK1/83mc4C3gCQA9gH1b7gKpdPo9rbvkLQHsKuk6SndchegJI+3rZHwtiSlm+l9lGS19ynd6j6QdJXtJyQtZ/vfnYjb0+q/63eBA4BngDcl/dH2EfUa6AjKdc/d/qgD4CXAXrZ/N7bxGpNVBawCbFETcb8ErFR/P0olUX/q+pxBLp2dxyoBsO7f25JupCT9bkTp2nC6pOuAw4HdJR1he3FJC4/t60dERERERLTYfljS+sDJ9dJnRGsMrDk21+64WOP6eJs6tnoe5Vr4Jspk7hmBuSkJax1Rx6ynAS6j3Bf7q+0zJbW6Dh5v+5jG+v1i/K/5d6nj7dtSCtP8DbhF0pG2h9n+ff03eNndLFAVERERAR+eC74j6UnKvWJsvyVpLeA4SZPaftP20431x1rrfKye67wqaSQwRb1ne7WkGSj3V64Fzu3u3IaIiBizQb29AREREREREWNSB51+A/wYOFDSQsCqlAGrl+tq1wAbqnTK6LaPScabuvHaFwJvtBNnVD25j2OI/ypwAaVDzZPAHHX5O3WVju5vRD8xN6ULz+8kTSBpUP1vApcuensDS0iau91AtdLvIcCPKAkGa1IH5RvrdNUEwCmBq+rk9bFWB+KH1dc8py6+D5gP+HxNNvwKpSLxtN2J0djmVsLhBTX2P4BmwqHqeq2Ew190IAGwx2M2Yi8FHEn5Wy5D+T5fw/YewK2UTqtbtBIA24zVmmQzJYDtnYFjgIMlTWv7KUpHoj8AT3QwHjXeYZTj1rmS5qrvm666biuxZQhwUZ2s007suShJIrva3o7SufLLlC5LD1K6Y93R2LY+PwEoBo7WZ6Mmqv6UkgS4EXA25Zzx58DhNfn3V61k5F7a3BhFPWagYjbKef9WtlvfKRdSEjo3oiQdH2D7D23GnKreZH9fpQDAT4B/UYoLHArcS5lcuQowtCZT98sEQABJk1CS1DewvRLlczK9pFVtHw78kY+OzYMonRD3G9sEQEmTSJqqTladvx4LpgBWALB9P/AIsFg9Th1j+7o6UaGtRHXb/0fpxvESsJak2etxeBjwJWCvGvPOduJERERERETUccztgEMkbS1p1rq8Y13jJM0P7EcZ/x4GDAV2q9emh9jeCZi3w2OcE9p+AVgDuA3YVNIBlDG/rVSKd36oP4z/SZoKGF4nwEMZ858PmNT2m5Tr1cUlnQjl2rIT/6YRERHx6dO4VzWXpJkkTQDcDWzbmF80AzAZHSjk0CjGt5KkAySNDzwHLAzMUld7GHgeGJQEwIiIcSNJgBERERER0afVJLXfABdTutUdAqxs+/U6MfZxYE7bT7Rzs7M3k/F6Yh8bE527RhP/deCXwLmUCmBLNx7r8zdUI8aBLmr3Tdtv18/dV4H56mdpbmAq4LXuBmh8FueldDmbiTL4vmetyDdTa71GUtWvKJMtXh7ti34Ctp+nJE5NLekk22dQulEdXJPnzgFOsf1Yd2PUOD2WcNibMWty6MSUJPK1KIl5UwO3UI4Xa9g+hTJZ5rJOJADVGyvLU5Lwzpa0J+XYdQ3wG5VuiE8Cp9XEh24b5UbOfpIOU6lifTDluHW2pHnqe3Rw/f8UlOSZw92NzkrN2MAQSiLH5nXfL6ccKxelJIvcbfv37exjRHfVz8bClATgLW1vDuwQjxOTAAAgAElEQVQD/IXyXX42pavZW83n9Ma2xn9qTsarf5Mu4FXg8fp4Vz3+X257KWB92xe38x1ek5pHADPVY/xqwHcpRThmqdtwIHAPJfnwrkZRjv5seuCz9efbKMVWvgNge0/bf6w/fwAcZ/umbsSYDzhN0ubAeXUi7NHANJI2qOv8ldIBeKrWkzr1eazJoSdSkv63Uenm/jTwQ+AXtt/vRJyIiIiIiAjbN1ISyL4C7FjHBTtpCPC47bts3w7sBGwpaaXGvak/w38XDhtbdVxlFUohsdsoBXGuBZYG3mUcdB3sKbZfAZ6mXKtOTrn3dj6wsaQv1zH6VSjFatouNBgRERGfXvWcajlK5+bDgNOBS4FfA6dKOgM4CfhJLaDbqXgHA3+o9zGOpZy77VfjnQpckATAiIhxR5l3EBERERER/UFNKlibUg10F9v3NR5TpyZxqnTaWo1SUXVSYH7b/6zJeO9ImqImzXXcuNrHOtH5eEonpedqAsd/JRPW9dagJFXc1a2diBgA6o35O4HLKJPI5wTOAtazfbukSYEp2kxwms72C5IWp3T/mQRY0fYzklYDvg3sYfvt+r30K+Ag27d2I1YrkWsI8IHtN+rn/Vzgz7Z3lPR5Sre6F2z/qVPfq5KmpfzbPWV7W0lHAp+hTMT/ArC/7YvbjdPTMUf996ndjsan/Juub/tlSXdSkhD3t/23duKNEvvLlES4TSlJh4sA09jeXNJPgQUpSYnvduhvuBJwELAV5abN68DSNeHvIEpF8G/VY+WUwEXAPh7LTk41VvO9+nZNiP0KsCfwF9t71/WGUv6+bSU5RrRL0reA64Fjbe9RE8WXBDaxvW7vbl18HEmHUjq1bUxJwDuC8h13Vz32fgtYDjiinRvjdVJkFyWx71lK0uj8lO/S8SnJ96tTzjWOpySRbzNQbo5L2hj4BnC67d9LWobSXXFL4F81Qb/tcw5JpwKbUboOnlvPBZagTFj9O+VvPcz2Je3E+R/bsDDlmDgdcFQt5BIREREREdFxkiaiXGsuRJl8/db/eMonfd05KMXGDgIes/1vSXsDf7J9aSdiNGLND5xJuT4cn1JY6WbbrSJkH0ia2/YjnYw7LkmaDtjL9g51PGAH4FLbT9Qx3e8C0wBn276ndd+xN7c5IiIi+rd6TrUmcDXwGLAtpXDeBpSieDMC79i+u4P3/48BbrJ9iaQJ6znjZJR7H7MAf633Azo2jysiIv5TkgAjIiIiIqLPaNzY6xpdx4SaFLAqsCElGeaGcbQd4yzhsLf2sTHReSPbL33Meh/edMygXHwa6aPOe1NSOmQ+Q0kcO8z2FR14/UGUG/1/pXzPXAYMpwzKXwqMB5xGmSxweZ0scAZwbne+DxpJVSsDu1ISEO63fUhNBDwHeN32eu3u2yjxeizhsLeSHCUtSam8fRlwP/A+8AfKxJVngZ8B29t+oM04gyiFFV1//xqwse2tJA0GZqZUWzze9p2S5ms3ZiP2JJSklJ8Ac1AmrrxDuWG0QD2ezWH7MZXulgcD17pUJR/bWM336jDKv+Hdto9Q6c67E/Cc7V07sW8RnaLSQflkShXVUyV9k/K5WRn4e86l+o7RTMbbDrjC9uOSdqYkVf+ZcuzfHdjW9tVtxmyd+3+dksA9iFJk5DlJi1ESRjeS9A1KEtuRth9uJ2ZfUo+/q1D27QrgB5R/1ys78Nqt48b0wGLA14HFKYmAD9V1hlC6O79j+6EOHf/Hs/3uGB77OmV/Rwykv2NERERERAws9R7Yu7b/JWlFSkG6l20fpdJdcE5KN/cXKJ1d1nPpDNjJbViccn34/fr73MBVwA9b4/CN675+c69K0snASbYf1CgFOSV9CViLUthtd0pxnHSPj4iIiLFW78uOTyls/C9gReBlYGpge0pxvk1tP9XhuKJ0Gfyj7UMb90Dmsv3nTsaKiIgxSxJgRERERET0CerhbnW9kYzXC/s4uonOV9n+yxjWbyU/jXFia8RA1ZxI0PgsjA8IGGL7+fo5okMJa98HTqF0Hn2c0hVnSeBN4MxaOa+17oS2/z2WcT78fpH0HeCoGmstSqLW0baHSZoa+Dml6+B9Y3zBsdu3Hkk47K2YNe6iwInA7ZTkuMspyZqrAnsD7wEH2L6ozTgTUjoZ3QosDcwEXAfcSJkkc1ld73RKpewRnUqqbPw+NeWG0XnAsrZfkfQG8JDtrzfW6wImsf3GWMb7X+/Vw23vKWkRSnLgXrYf7e7+RYwLNSn4QuAO4A1K4vZlvbtVMTr/YzLeqsBslEq1V9i+vs1YUwEnALtR3hfPUCZQrmH7XknTAI9SJjkuDmzRieS4vqYmrC9CSVofafv3HXjN1vH/u5SiCnvXhPT9KFWPl6Ucn5ewfWC78Rpx5wXmtP0bSYM9mo6NkobYfq1TMSMiIiIiIjpJpYvgLynjmfdRuvGdRpkoPp7t1SStQ+ky+FlKZ/e2CuSMYTt6rOtgT6j3DrqA/YHnbZ/QWP7hPQWVbj3/tP1YL21qRERE9GONsfHWfIZZgQuAy1tj4ZKmpRR2vcT2neNgG75OOY871fb5tcjh2cDqtu/vdLyIiPhvSQKMiIiIiIg+Qz3Ura6nk/FGec0e7cg3uonOo3u9xiDhEErSzBa2n+9OzIi+rvF+n5xSbfe95vL68+g+J6P9rhiLuPMC0wO32367JhucB6xo+0aVrmvj2X6tzc/9qN9xKwMjKUkNuwE7AzdQBuZ3H9Mk9rGI1xsJhz0esxF7LuB0YN/6dxtK6fpzHzACmACY2PZT7Sbk1Xi7Uo5FUwA72L5W0prAFpRKi/cBpwJb2b6jnViNmIsDn6F0Mbuh3kA6CNiTMvlnZeBq27e0Ged/vVd3Aa4HTrS9t6TJxzbJMKJdKp3MZvT/qHgvaSngMEry2AF1Wb+pVD/Q/Y/JeBolGbCt4/0ocQ8Fvkj5Hp+Wch1wJLCb7avqzfjFgafGxc34niJpYcrn5BNN1OzQ8XEx4DjKddV9rfMZSdtREgGHUBLyL2wnzigxt6Z0Glx0DI+3rrfaOreKiIiIiIgYlyQtSxl7ewK4w/aZ9fr4XGAiYLU6uXxS22+2GavXuw72JEnzAFcD+9geXpe1XVwwIiIiopEAuCTwA8q53M31/9cD59v+cV13nBX+roVhV6Ccu90CfI1yz+OKcREvIiL+26De3oCIiIiIiPh0kzSdpOPqrz8CrgWm/Jj1u2y/I2k86N5NM9uvAE8Dp0uaZkyTfOt6Z7cSAFs36sZWb+yjisHAK8BS9XX+VwLgFJQuNkcnATAGIkmfkzRnfb9/l/JZPF3STwDq8kH1Z9cBbCRNKGmKDiQErAmsB3xd0gS2L6YkI9wgaXnb/2x1jmlnQsCo33HAFZROQxtTuqr9kZKstqNKxeV2EhunAoZLmqEumojSQXUeYDlgYWBzSYfbfhlYuQMJgD0esxFblKSCKYDNAWxfDvwGWJTyb/yq7afqY2115Ks/DgcGA38H7qrv0UuBoynvqe2A/TuYALgQJblwTsq/8za2R1LeJwcDlwA32r6lu8fFlk/wXr2L8l4dJmn2JABGL1kUuLwmHI2R7d9Skqw3lbRhXZbJXX2Ei/coExp3lbRBazkw6t+p3eS0Uc/9bwI+Y/sZl+4JhwM/ljTU9ou2L+jnCYCiHIO/Wn/vGs06/7Gsm9c3s0o6pLFoHsq/7Ts1Oe9iSedSOvNuAXzP9oXtHKtaz63XVdg+BfiLpPWaj9efu+r11hBgRP1/REREREREn9EY+74WOACYG/iqpMnqdfM6lDHAm+pT3moz3kSUsc11JS0CHAE8Byws6SLbhwLXUa7vNgC26c8JgAC2HwbWB3aTtH5d5nq/IfM0IyIiotvq+cQSlGK19wATAnsA36LcI99c0j513XGSAFhf+/16f3xxStHD1ZMAGBHRs3JxGRERERERvcr2C8B4kr5YJ4OeaPsvo5usqf/sVneBpOnHJlZvJOPV5/XYPjZijnGi86hxa7wpgYspiSRtdXaK6MNWB65X6Ry3DqWz2VHAtyWNgJIsC//1WRzOx3xXjElj4vi3JK0A/Bh4htINqNVB5iZKUlXbHYdG8x13HTDE9ge236JUAfy8SgfCIcCCth9rJ7mxJxMOeytm4+84BJjQ9u8pk1K6JB1ct+lK4JfAb92Bzj+thO06AecFyo2b24GfAnPbfhu4wfYylI5Ev+lQksPMwHjAhrb3Bb5LSVT5ge2NgZ9QOldeB90/Lnbzvfp4d/cvoh22z6Mk952p0iVztFQ6f/0OWJuPJstFHzOmyXjQucr8ozn3P9b2443X/zmlC+qRkqbp75MA6z4+DXxH0kyuXZVbGudUU0raWdL43YwzEjhfpTstlE4R0wIXAAJ+BjwLzG77EdtPNLZvrEiaQ9Km9Vi8ELCLpHXrw7cAn2++9ijnjRcCp7gWdoiIiIiIiOgL6njjB5K+JGk24F5gM2Be4PuSJgGwvTqwff35/TG+4CdQx/lOonSq2Qz4ie3jgLWAf0u6GPiF7V0oXd6vbideX1HvsW0HHCJp69Z1bDvj8BEREfHp1biPO4gyNn247VOBY4DjKEVjX6LcT257rk8j3pySPjOGdQbVwocP236ksX0REdED8oUbERERERG9RkWPdavrjWS8nt7HUX2Sic4qnTn2BQ6yfWs78SL6MttHAycCZwJv2r7C9gO2FwbmqAlHo/ssnmT7yW7Es0rHwZOB92py2EGUCeobSfoZcA5wsO1r20niqvFG/Y47wfZjjde9H5iD0n3wItsP1v0d67i9kXDYSzFbyXgrU7rvnSVpN9v3UipXf07SUVA6Atq+v7uxmmrMFYBzJO0OfN723pR93FvSNsBIlc6WH7Se051YjX1cnpK4dBJl4s/0Lp34lgXOk/TD+nm5qwP712Pv1YhOsH02cCAlEXCJUR+vx433VIoqLOzaDTT6po+ZjNd258ZPeu5f31NL236pv00CbFxDzC/pQADbN1KKGuyiWkClrtM8p7oYuMf2O92I2VXj3Es5Fl8E/JnSCXgp2ycDfwOG0pn7XpMAp0naBBgJ/APYWNLRQBewlaSlWiv7o6IqF5CiKhERERER0cc0xv+WAi6nFKv7DTALsCWloNEGjUTA+zoQs0e7DvY19Tp5BeArlGJ1e/byJkVEREQ/Vc/jlqUk+01NGZ+eyPY/KIUdJgHmtP1n2zd3YM6BJa1CKTg+dfOx1mvX+x5ddVlXTQrsV/c6IiL6syQBRkREREREr6k3+nqkW11vJeP15D5+zDZ8konOB9SbkhEDlqQpbR8BHEbpVvPlxsMfft4an//LgP1s39zNeNMBOwBDbV8n6avAOrYPBU6lJDpt79K5rq3kg4/7jmvs1whgGLCk7Ysbg/RjHbc3krh6OMmxNUnFKp0jDwI2Av4EHCbp0Pp3Ox74rKQ5u7tfY4i/FHAkcAiwDHCgpDVs7wHcCswObGH70XZj1X38GrA0ZR+PBF4HVpM0re07ga9Tui22raffqxHd0UhyWljS5pK+YXs4sAtwhhodAfWfhSMuonxPRB83ribjjeW5/7OdiNnTGhMOvgcsIOnXkvYC3gTeBd6H/y6qAuzdneuNxutMUOMvDUxA6fr7AfCCpCWBXwHD2pmsqtIBcNH6GgtROuAOrUmGy1ISDSeu8b9dn9M6rh0N/NgpqhIREREREX1MvY5bDFiRMnl8Q+BQSveYSYB9gHWAKTsRr95369Gug32R7QeAHSlFOG+XNFEvb1JERET0Q5IWANYDnrB9FGVew6mSJgdmBmYEmkUI27qfKmkhSmHMdW3fK2laSXM3X7sxbj8lZe7FZO3EjIiIsaPMnYmIiIiIiL6g3oA8GTi8Tv5vLW9VKO0CjgIubSdZTdI8wNXAPnUy94cxRllvSkoC4H6dmsjZU/v4MfHnoyQD/hN4sSYiRQxojc/XQpT3/69sXyVpGGWyw7HAU8AZwAa2b6oJYPsA19u+rZvxJgLeoyQSvkD53E0MLALcZHvrUZ/T/t6O+TsOOpdAVV+vi5Js97ztE0YXp06In9z2K+3uY0/GlDQVJblvV9vPqXQBHEmpjL0bJQnoeko32b0lTW77je7u2yixBwETArtSkolmpHQc/BXwTeAc2xc0bqx0dx8/S0luuJRSJOwR4A3bX6mPrwMsTNnvc2sCZr97r0a0Q9JQSlLPjZRKp89RJm0tTemY+QPbN9V1hwC/phRV+F2vbHB0Sz1ed1G+E//g0lm2U689pnP/fl0RV9KXgL0pXcQfqEmxswE71/8fZPuwuu6ElOT1ncf2mkrStMBEtkeqdKtdA3ikTnJA0pXAy8AmwJzAxDVxvZ19Wwl4DXjA9uuSFgaupXy2j22c561B+T5Ytp4rTABMZvulduJHRERERER0WusaVNI1wOLAF2w/XR/bEZjU9sGSprb9cgfiNbsOngXcDkxH6T74DGVM5UJguO1/thsvIiIiYqCq84emoNyvvdH2ypLGo9w/3huYBxiPcg/i4jZjfXgPWNKClEIN11Puj3+HUpDvRNuX6j+Lql9MuSeQguMRET0oSYAREREREdFn1A4Owymdj66wPbLxWBdlYuVrHYjTa8l4PbWPHxN/nE10juhrGp/plSgVd6ekDFDvY/saSdtRKtOdAfzS9u2N505q+81uxlsR+Ablcz4TsCcwoiYYzg9sCuxi+91O7OdotqNHkg56I4mrp2JKOhT4EqUz3it81HHoTNtXSjoB2AKY1/bjHYj3H8l1tRr2+JROUuvbflnSncB9lE6xf2sz3lcoyX9P1deeBfgDcJbtveo6G1ISAY+0/VQ78T5mOwZkgkz0f/Wc7ATKseGWmvS0EvCe7aMkbQE8Wr/XJwR+R0ly6kgX5xg4Pu7cv7+pn4sZgCcoSelbNL+vJX0BWJTSPXaPVoK8pNnH9lhZk+p2AWYFrgL2An4GbA7cABxm+w1JNwPP2l67IzvJh8Vg7gG2tX2FSifnaymdDE9urPcryqTVKzoVOyIiIiIiolMaY9WT2f5HXXYDfNhhHUnbUq7hNgAGuUPd+OqY3yrA+ZQugItRCi1tTBlnPQJYu90xzoiIiIiBqJVk1/h9BUqy3Qa2z28snw54v97rbbuQq6RvAIOBhyjF975POW97GFgSeMb2hXXdIXxUVD3FMSMieliSACMiIiIiok9RD3Wr681kvJ7ax4hPK0mT2P5n7ao2C3ABsLHtByXtAcxHmbR9ba12/Cfbv63PbSv5SNIylO6CmzeTCutjQykVj/exfWl3Y3zC7eiRpIPeSOIaVzHrjZK9bO9QEwu3p/zbPVYfP4LSNfJZYDVKAsKDbezKqPGXBFagdI+8H3ifkpi3ZY35M2B72w90KN4klMSKS2wfLekzwB2URMd96joz2X62E/E+ZjsGTIJMDAz1JudTwDHAE7b3qMu/R7npueqok+IkzWb7iR7f2OgXBtq5v6StKec6X7d9j6TBtt+rj01CmYzwQ9uPthlnUWAZYAHgWtunqHSyPRx4HDii1a2vAx0AR03G3wwYRjnuXlMrH/8B2Mn2CZJmBX5BmXTRdjGAiIiIiIiIcaF2Vd+OUujkmDpB/HfA1JRxj7WA423/pkPxerTrYERERMRAImlIa45SvR/+dcrY+L2SlqUk3W1s+4JxFP+HwA+BDW3/XtJEtt+qhfLOpoz73yRp/LotR9i+dVxsS0REfLwkAUZERERERJ/TU93qenNCbjryRYwbdfL5g8Aw27+uVeh+RalCd0dd5yzgy8B2rUS97lbHkzQHMLPtm2sC8VnAzfX/a1A6Aj4DnFqXnWP70k5U4/sE29brSdXjyriKKelk4KSaMNqatNKqmr0+sCCwFKUb38X1OZ2orLgocCJwOzAHcDmlQ+WqwN7Ae8ABti9qM86oSQ6LAftQOt+eIGlmSnXHn9revZ1YY7ldAypBJvqvmuhzGmUS3KTANsCttoerdHI9lHLz88W6/jj/Lo+Bob+e+zeOgQtTkvEesH177RZ7HLCU7bslDaYkr3+R0rH3m53oIitpEUr3vzmBTWw/XpPWTwIeAfbsYKeKbwPfBi60/WdJP6B85re0fV39NxhSfx4fmLDV7TAiIiIiIqKvkfQ5YATl2m0NSpGxn9p+VNKVwPzAYvU6azzb77YRq9e6DkZEREQMBJImBfYH/grcRykOexuwKHCg7fMlLQVcD6zV7AjYgdjT2X6h/rwtsCmwg+1bJX0NOIVyb/zSxnNmtP33Tm1DRESMnSQBRkRERETEp1p/nZAbEWMmaXXKwPjmti+SdDDwPHC17b/UBLIDgVcoHZ3a6R63BPAO8GDtRrMOsBXwAXAn8AIwF7AjgO03ezJpZCAnVXcyZu3810W5ufK87RMay2n9vWqSw+S2X+nU31HSXMDpwL62b6wdI1eh3OAZAUwATGz7qQ4lHC4JrAhcXpNXFwSOBi6wfZKkWYC5bN/QTpxubFeOx9ErGhPVZgF+Bxxr+1hJ0wOLATsAz1GSx4fZvqQXNzeix0laiXKcuAmYBvg7sBfwPeBMYBHbdzfWn9n2Mx2MPz+wJjARJVH/idqJb2rb93QoxqKUBOA/AjMBp9VJFWtSEg43sH11XTfJvxERERER0Sc1xjgWAIYAi9o+RNJ0wJ6AgdNtPyTpKmAi20t0KHaPdh2MiIiIGEgkTQysBiwMzAYcafuWOvdgbWBEHbNehlJQ4ZoOxf0cZR7DbbZ/XZdtC/wIWIdSPHZInWMhUswhIqJPSBJgRERERERERAwIdeBZtXvbKsC5lE5qj1K6nY1HSeRYEVgf2A/4ke1HuhuvTqqYAPgLZZLD5cBXgddqF5mvUTq8DbX9fHt72Lf1RhJXp2NKmofSwWgf28Prsv9IBOyk+tqLUBIPHrC9Tl2+IuXGyp2UhIf3OhSv2XHwC5T3688onZtOBc61fVxr25LkEAOZpMmAWWw/XLuNvUKZELcUsIDt12qH18kon5FX6rr5bMSA1zjHEfBTyvHhFklfAlYC3rN9lKSdKMev61oddMfR9iwMDAWmA46y/XgHXrO1j1MCSwBv1v3YBFgSuKxOqliLUiDgxnZjRkREREREjGu1ANj5wHXA6sDytfDYVMAhlOu5H9Z1Lwe2sT2yzZifo4e6DkZEREQMNK2x9XrfewVgF+Bu29vVx9cCtgR+ZvsXdVm371U1n1vHx7ekjL3f0irYIOlaSlGJ79h+o709jIiITksSYERERERERET0e6Mk5Nn2O5JWA86hTBq/E1gWWBC4AJiUkvy0lO0XxjLWJMBXaxe1pYGRwHzAYcButi+pk+aXBk6oyy7rzJ7GuCZpMeBk4HDbIxrLO5Lc0HivDgHetv2WpK9Qko/+Ynvvut5Q4Cnb97cbs77ex3UcPA34EjDY9u87ES+ir5M0M3AJcBulsuqWtu+XdBxlctrqtl/uzW2M6Gn1HGeC2vF2ftv3Sfol8Ffbe9Z1vgdsQumm/H5d1qnuuGOcDCrp65Tj1gjbD7cbq77mypSOFK8Az9lepS7fCFgZuND2eXVZEoAjIiIiIqJPkzQfsD3wS9u/lbRp/X3HOh44NTBdJ66perPrYERERMRA0TinGgpMD5xHSQRcFrjf9kl1vXWBR23f2aF436Qk/r1r+/Ja8G8WSlfnx4CtgFNt/1878SIiYtwY1NsbEBERERERERHRjsZg9XeBU4DzJM1p+yJgPeAiYFnbv7G9LzAtJQFw7bFNAKzeA7aWdDVl4vi0ti8GdgeOkbRSnSQ+E7CD7cta3eSi77N9C6Wr4yGStpY0a13eyQTAlYFLgbMk7Wb7XuAI4HOSjqrxLu9gAqAok3GmADZvvT5wMbAosA2lomQSAONTw/YzlMTYrYGbawLgIGA34C7g6jo5LuLTZD7gNEmbU86nZgWOBqaRtEFd56/AYGCq1pM6lAA4L6XLIJIGj/q47TuAIzqYADgXsAGwYf1vMknH1lhnA1cADzXiJwEwIiIiIiL6LEnjUQqnfBWYS9L4ts+gjF+fIWlp2y+3rqnaHa+uY5xLAtdSxhv3k7RkHW8/CJiYMuaI7RWAN1vjrBERERFR1HOq5YEfUwrVvQVcA1wPzCNpl7reue0mADbiLQOcAcwLHCXpUOBU4H5KAuI5wAVJAIyI6LvSCTAiIiIiIiIi+j1JKwIHAmsCPwVmAza0fbuk1YGfA5+z/bykKYCpbP+1jXiLUJK4fmd7jVaXOEmrUhIMN7V9abv7Fb2nVs7eDvgn8KLtQ9t4rQ+7CEr6DnAUsBqwFnAwpevgnvV9NQzYy/ajbW5/r3QcjOjrmt286mdiAcqEuJ1tn1mXTw7sDFyTm5zxaSPpVGAzYAPb50qaFlgC2An4O6Vz7DDbl3Q47tY15qJjeLx1rjXY9nttxBGlIMRJlETGjWw/LelzlIkOT9resruvHxERERER0ZPqNc6XgT0oBfF2Aj4P/BK43fb7kjYD/mz71g7G7bGugxEREREDVT2XOw24yPaVksaz/a6kyShF874DHGT7qQ7FGo8yb+Ji27+QNBFwM3Bt497xZ+qY+Yf30yIiom9JJ8CIiIiIiIiIGAgWonRz+jIwAXAB8GtJi9u+EPhsTQDssv16OwmA1d+A5YEZJR3fSvCqHQHXAP7R5utHL7P9ALAjsC9we70JMtYkTQUMlzRDXTQRpePQPMBywCKUzpIH2/4DJYG0UwmAPdpxMKI/qJ+NxSTtDnTZPotyI/UnklaTNDdwOKXjWBIA41Oh1QFC0vSUCsPHAjtJmtf2i7Z/DaxI6ebwPduXtNs1ohFzMIDtU4C/SFqv+Xj9uasmAA4BRtT/dyueixcoRSNeA5aVNL3tJynnknNLmjddnCMiIiIioq9qXE+pXuPcB0wGLAMcB7xOGaNerBZSOd32rZ26zunproMRERERA9h4wEzAdKMsnxK4iFI4tu0EQPhwbPwd4EnAddlblKK180uatC57urV+J7Bt6AIAACAASURBVOJGRETnJQkwIiIiIiIiIvqdxkSHeSSNDxwGjKQkbW1se0/gGeCI2vnvpfrUDzoR3/azNaFqKPA1SUdIWkHSncA9tdpxJjf0c7bfsv2m7ZvrTZDuvMYrwNPA6ZKmAa4AHgU2pnQAvAsYAQyTNLvtN7q7vZIG1ZiuHQcPAjYC/gQcJulQ238Ejgc+K2nO7saK6K8kLQacAsxKSSZa1/ZtlO/zwygVUK+2/a9e3MyIHtNIHP8uZbLoPbZ3AS4Dzpc0U/3cbG/7XtsPQfcnAEiaQ9KmNeZCwC6S1q0P30LpWPHh69cEwPdr4t+FwCm2XxvLmF013pKSdpe0PHAbcAKwNLCipBlsPwEsZ/uhTHCIiIiIiIi+SNIEjeuVSRsPXQp8zfa7wAHAu8D3KcmBQPsTuVXMDwynFFA6D5iPMj7eZfts4BDgnebzcn0VERERUTTmOMwuaZqalHcisIGkJWsXwG8A1wGz2H6+Q/HmqmP9EwB3A9tKmq2uNgPlnDFzGyIi+onBvb0BERERERERERFjq07kXoEyWX112/dLepWSCPhVSTMCDwI/s/1683ntxG1NlK8/d9l+VdJywMnA5sChrSSuTG74dJM0HaU64w7Aj4DtgSG2XwLekvQE8HlJqwJDgAVtP95GvKmA4yXtavs5Rt9x8HpJ79veuyZgdDvhMKI/kvQFYDdgO9u/lbQ6sHH9bv95vbE6ke2nm9/3EQNZqzsmsD+wke3HaqeIAyS9AvyScpw6oEMhJwFOk2RKouE/KJ/DBSlJ8ltJus32b+v2vS9pSuDXwP62b/2kgeqx8R3bb0oaSkmOPxPYgXJsPICSFLwz0CVpuO1/d2g/IyIiIiIiOqoWRzlZ0k+BO4E/Sjoe+DOlaModkm60fYukHwGfs/1qmzFbhWNa4yT3SWp2HTyA0nVwsKRbbZ/efF47sSMiIiIGksZ51fLAT4DXJA0HbqaMU58t6VJK4bqda9G6ttR4ywFnUxILBWxJ6Tx4qqSRwILAfrb/0W68iIjoGekEGBERERERERH9jqS5KFWFN64JgKKMc9wDrECZsH6B7f9rM06rOt4Q+M/Evjopvat2o9mgbstF6QAYALZfAMaT9MX6vjmhJla03h/3A3NQki4usv0gfPSe60a8Hus4GNEf1U6Z81Eqmn4fwPaFwBnAppI2sP2S7afrY5moFgOWpFklHdJYNA9wE/COpK2BiyWdS/l8bAF8z/aF7Zzj1A6Ai9q+D1iIMslhqO2TgWWBvwETAxMA367PkaTBwNHAj8cyAXAuynFvJkkzAasB3wWeBGYBuoADKeeOxwF31arLERERERERfY6kQXUc+n5gJ2Ba4AfAK5Rx8vWAB4CvA9h+x/ajbcbsta6DEREREQNNTchbGNgU+B6wDzA/ZXz8Rkry39nA2rav6MScg9rF+dvAmpQimU8C5wJnAZsBpwGb2r40cxwiIvoP5Zo7IiIiIiIiIvobSbMBu9neStJ4lDGOd2r3tX8BM9r+S5sxmtX4lgOOsP330a0HmdgQH6nviS5Kgt/ztk9oLP/wvVITGya3/Up3q2M3Ow7W198euML2Y/XxI4CngGcpCRCHtRIOIwa6xvf41JRuYP+o3+lDgcdtH1PXWwN4sibLRnwqSPoK8IrtkZLmA/YAFgBOoiSVLwYMt31/h+KtBLwGPGD79TrZ4VrgANvHNj6vawD7Asvafk7SBMBktZPuJ4nTOgYfRzn2HUmZSPE6MD5wDrA6MCdwPHALsI3t9zqxnxEREREREZ1WxzWOpXSEeVHSzsCSwL6275E0LbA2sDiwMDB/BzoADgFOBlpdB++jXEP9GbgbuIMyYfwWSeNTug62lXQYERERMZDV86v9gJVsz1mXLU3pqvw0cJ7tJzsUq4syHn4nZe7EisDLwNSUe8nfoJzLPdWJeBER0bPSCTAiIiIiIiIi+qMuYEVJK9h+tyYALglsYvvNDiYAfhs4gdKp7b8SAKtBdd1JJS2SKnnh4j1KJcVdJW3QWg7/kQz4Xu3g1+0k0p7uOBjRn9Tv5pWBK4ErJZ0CPAHcAMwuaY+63gVJAIxPi3rzH9v3AmdJuogyiXNDYKname9vlGTZjt1Dsn0F8BBwn6SVbN8JLAPsK2mb1nHQ9gXAw5Rugdh++5MmAFaqx+ARwA8pkyeerpNRpwLurxMp3gRuB45JAmBERERERPRltl8GngHOlDQlcCalqMqBkr5h+0XgRNurAxcBG7cTrze6DkZEREQMZJLmBFahFKl7WtKxALZvoJy/zQZ0ovPfh69h+y1K8h+UQniuY+0nUAo6TNduvIiI6B1JAoyIiIiIiIiIfqUm6P2FMrH7VEnbSlqb0vHloTZf+/OS5mokZC0FnGb71tak+ebguaTBtt+vlftuoHSaSkfAAMD2w8D6wG6S1q/LXBOT2h6XUzGYMgFnqfr6H4xyg2cEMAxY0vbF6VwZnyaS5gL2AbYEVgAmAbYArgGuB2aT9Pne28KIniWpq563TABge2lgAkrC3AfAC7Wowq+AYbbv60DM5jHpVeBg4CeSlqvJt98Bjpe0XV1/VmAW4JFuxJoKGCFpZuBBymf+H8AMdZWHgFUknQucD1xQj9URERERERF9jqTpJB1Xf92TMpYxVU3Q+wUlEXCYpG/a/qCu9xIwUxsxpwbOkTSt7UOBWykd47F9HmUiuSnXkD+sSYkRERERMQZ1jHx5YPFanG8HYHJJRwHYvg7YzfZf241T70EvCZwkaTdgRmAlYHVJe9V4LwIH1EJ9ERHRDyUJMCIiIiIiIiL6lUanmEuBTSidYr5JGRy/tM0OZ18DppA0Uf39OWBGSRPbfr8uW1jSV+s2vFcTAH8N7FoH7iM+ZPsWYDvgEElb1+QGGhNz2nntHus4GNFPfQA8C4y0/SawGbAYsBGlO+A+7d5UjegPJE0radaaALg8ZQLArgC2VwImB4YDg4EXgHVtX96J2K3OypJ+VAstnA7sC5wiaRnbdwOL8lHS33PACrYf70asV4CRwCnA28A8lKIRI2r36JeAuYCLgVVtX9n2DkZERERERIwjtl8AxpP0xTqmd0LrWqle3/wC+B2wl6QpJU0OTAyc3UbMHu06GBERETGQSRq/nsedCswj6fu2HwCOB2ZuFHx4td1YdSx+CeB04B5gQmAP4FvAcsDmkvap677bbryIiOg9yryfiIiIiIiIiOjrJA0aNWmqVc3ufy3rRqzJgduA9SgTyI8AfkrpKDMEOAvY1PY9dd1LgH1t39pO3BjYJM1HSQb8J/BiraTdyddfDDgZOLx2/2st/6/PTsRA1ahyujjwMuWm6WGU7+07bf9D0sbAhLZP6c1tjegptevfLsCswFXAXsDPgM0pXYwPs/2GpJuBZ22v3eH4iwKnAX+kdKM4zfb5ktakdJPYwPbVdd1uncdJmg7Yy/YONQF+J+CS1uRYSesBO1PO1zqS3BgRERERETEu1WubLmB/4HnbJ7SWN6+bJE0LTGz7qfr7+Lbf6Ua8Ua+rtgcut/24pGmAtYGlgKNs31afszcwxPau7exrRERExEAk6YvAUOBPtq+StA4wre3jJI0PzAe8Z/tPbcZp3RsbBGwIjGf7Z5ImoxRT3gLYAJgdmMH2ze3Ei4iI3je4tzcgIiIiIiIiImJUjcHqz1E60nwA/Lu5TqPbWVerS183J45PAsxt+4+SvkJJ0jqLMkF+TUolvvWA6Sidcg6yfU99+gLAnrbvGOudjE8V2w9I2pEyeWchSRPZfquDr3+LpO2A4ZImBa6wPTIJgPFpUo8by1O+t7eqn7tbgW2BuyW9CAwDtu7N7YzoSbbflnQjsAylC+ZZtk+XdB1wOLC7pCNsLy5p4U7EbJzHTQnMAOxk+zpJmwArS6ImAnZRCi60trVbhRxsvyCp1R3jQUnH2v6gtR22fy5pMHCkpDuAV3J8jIiIiIiIvqxeH70n6Vzgakmv2x5er7U+TASsnfloXP+MdQJgfZ1Rr6tOaF032X5J0i+A8SldB9cF3qfNroMRERERA80oY+MzAq8Bh0maA/gssISkq2w/Kumedosbw4f3xpYFpgSmBtaRNKIWxrwXmASY0/ZDwJ87UVQ5IiJ6VzoBRkRERERERESf0upcJmkVYHfgIeARSkeXx0ZZt8v2+zWR78u2/68b8aYHjgXeAT4PbGH7kZqwtT6wpu0nJM0MDLb9VAbHo68a1x0HI/qyelP1UmDvZiVTSd+lVDidE7jA9vW9tIkRvUbSIpTuf3MCm9RuDp+hdON7hFLU4P0OxlsZOAZ4BXjO9ip1+UbAysCFts+ry7p9XvVJu2PUZTPZfrZ7exQREREREdE7JC0GnAwcbntEY/mgThQ46emugxEREREDUSMBcBVgZ2Bt23+XNBewKDA/sClwHHAA8H4n5htIWgDYCTjB9p2SjgWmAn4IfIZS/HhD2w+3GysiIvqGJAFGRERERERERJ8gaULb/64/L0rp5DQU2BNYEriS0r3m0bpOKwFwCHAZsI3t+7sZezPKRPURtrdpLN+B0kFqXdt3dn/vInqOpImoHQeBP3Sy42BEX1aPB78G1rD9uqSJbf9L0iS2/9nsHBvxaSRpfkqX44mAk2qRg1mBqRtdjjsRZy7gYEqRhVcok1Xvs71jfXwT4G7b93Yw5jzA1cA+tofXZSnaEBERERERA4KkJYHhwCHAFbZHjoMYn+i6KtdaERERER9pFmaQtARljsNmtv8gaXLbbzTW3ZQy/+EH7RZTkNQFTAGMBG60vbKk8SgdCPcG5gHGoxSSuLidWBER0bcM6u0NiIiIiIiIiIiQNB2wtaTJ66IpgK0pSUzfolQhnh/YQdKXABoJgOcDe41tAmCtcNxyD7ANsICkYY3lpwGHAxOO9U5F9BLbb9l+0/bNSQCMTxPbrwHPA0fX3/9VJ8ldWI8vmaAWn2q27wMuAd4Ehkma3fbITiUAqpiOkgA4FTCyVhfeGJhH0ql1O87sZAJgfc2HKR2cd5O0fl3mul25FxYREREREf2a7RuBFYCvADtK2nMcxPhE11VJAIyIiIgoJE0FDJc0Q100E6Xz3uCa8HeVpIMlzQ1g+wxgSkoB5O7G7Kqv9b7tVyiF/5aR9H3b79Yx/y2A1YGVbF88yryIiIjo53LjMyIiIiIiIiL6gn8BlwOTSFrQ9tW27wJWAjav1en+CkwCvA0gaTLgIuBg27eMTbBWtWJJ35S0KiDbI4AdgTUkbStpTuBQ4Fe2b+3UjkZEROc1JqP9CPhA0m2StqJUXD3Z9hutSqwRnwa14u9/qZ2NrwJeBcbvUCzV17btF4CfAq8By0qa3vaTlOIOc0uad1xNOKjng9sBh0jaunY5JJ/9iIiIiIgYCGw/QBm/3he4XdJE4yBGrqsiIiIiPqGahPc0cLqkqYGXKEUbjqfkaJwMTE3pyIekWervj45trFocuVUoeTFJu0n6iu2rgFWAMySt0di2F2y/XH9OEYeIiAFE+V6PiIiIiIiIiN4kaZDtD2oCx0+A/2/vzqMsK8vz73+v6kZAFBoQnEdUJIoDKL9oHBCcUEAljkTEqGjQBIcgKE4YBzSIMgg44BBaNKI4BURFRQUn1AgqoGjQOIJDKyKD2PT1/rF3mXoRFLqq+1R3fT9rsarOPvuc+z5rUV2n9nmu514fOKbtV5O8C7gp8ErgMOCfxsXrJHkk8LPp2ytRdyfgUIaJUYcCT2v73iT3YJgAuD6wX9uTZvcKJUmr0/j75PkMH7b+qO1npsPfE25NWi2S/A1wx7YfTrK47fKrOWfJOD1ztrUWjYsOHghsB5wFfBb4W+AZwCnAyW0vSLJe28tnW/Na9HQXhkWrlwC/bHvQqq4pSZIkSWsT/66SJEm6Zkk2B17c9jnjpnf7AB9p+8MkNwcua7ts3HT4eIZ1CF8fz13S9jfXsd4NgAMZNk0+C3gr8AXg3sC/tT0+yQ7Ap4AntD1+jl6qJGkeMgQoSZIkSZImZsZEvhu2vXi8gH0Aw8S/twLfB94CbAa8re2H56jurRkuuD8ZuBXDxJoAB7c9euxj87bnz0U9SdLcmA6Or8TjDAFqwUiyN/Dktve+hvunN2C42oDgtayxCXBF298n2Zlhw4Z3AA8HvgO8AtiaIZB7EnBs2ytWptZK9rc+sAjYFjij7WWrq7YkSZIkrQ38u0qSJOmaJTkKOLLt2Vf97CrJIuBBwOuBl7T9yCxrXR/YDbgXcDuGNQ2fT7I78ERg6RgEfDAw1fYTs6knSZrfpibdgCRJkiRJWrjGAOAjgFOSHMiwoOBA4DJgL2Crtk9h2LHuw+PueHNR93+BJwAbAa9tuwXwr8CRSfZo+3sDgJI0f4wLzxiDS9sl2TXJJtO/F676+2H8gPVPDABqbTbj52AxQNujge8ledLM+8fvF40/R0uApePX61pvS2ApcLMkN2NYfPBI4IfALRgWif4b8A2GSc5fW50BQIC2l43v5z7nQlVJkiRJuu78u0qSJOnPZbAYWAbsAH/67OpP1+HbXskwUfmpbT8ymzUOY8DwUuAE4HPAJsBjxzrvAY4D9k7yxLantP3EXK2pkCTNT4YAJUmSJEnSxIwLx/cADgcuAp4EPBh46XjKXkk2bHsxrHyIY8bi+DsluW+Sddv+gCEEeO542s+AjwGG/yRpHkmyEfD2JPdMcg/gWGBf4GDgQWOoqTP+rV/U9sokGybZLck6E2xfWmWS3D7J08b//7cF/jXJP4x3fx64Lfzf+6cZPxtLGBYMHN32t9eh3vTihn2ALzKE/m4OvBbYkGEjh12AE4GHAG8ATm975qxfrCRJkiRJkiRJE9bBcobw3b5Jnjx9/CpBwNPbfnX6vpWplSRjwHBnYHfgZOAQYJ0kzx6f+z+BY4Dvz+xx5V6dJGlNsHjSDUiSJEmSpIUpyTbA3YAftH1PkhsDD2WYJLMY2A+4bdvfzbbWeNF9J+BNDCG/qSSHAr8HrkxyDHA/ht34vjBeUPfiuCTND4uBrwMHAOsBu7T9XpKXAo9g+Bz0M22Xzwg5bQR8Anhe2z9OrnVpldoAeFuSAv8FXAz84/ge6zzgn5J8oe1nYNh9OMnGwPuBA9uedh3rZfw5Wwp8iCEMeLe2FyS5P/Cttj8cN3n4IvDGcTGEJEmSJEmSJElrjbbnJtkDOGpcWrB0xoZ8U21XzEGNJnkY8GrggLaXJfkEwxCo7ZP8a9tD2h4321qSpDWHkwAlSZIkSdJql2R7hsXj9wP2SbJ12wsZdq87A3g0sFHb785RvS2BvYHd2j4Y+CDDhJpfA0cAXwP+ue0XwN3xJGk+SLIeQNtfM/x+eB+wLbD1eMohwC+BvwcefJUpZx8E9m37pdXfubRqjRMA7932LIafiTcAO7c9iuH9zU+A6wPrMrzXmjnF7xDg1dc1AJhkE2BpkpsDZzMEEC8GbjKecg6wa5LjgOOBD7Q992qfTJIkSZIkSZKkNVzbzwP/Arwmyd5JbjUen3UAEIbr+sBjgBe1PSnJOm0vYdgE83RgqyS3notakqQ1R1zTJkmSJEmSVqcxkPdGhgXoX0jyEuDxwBPbfjvJZsD12v50DmoFuAHwEoaQyP5tTxjvezOwTtunzTzfAKAkTV6SKeBRwM0Zgtp7Aq9j+LDzgcDr2n4uyfWBFwDHj7uu3oBhItrLVmLKmbRGSPII4LfAt9telORewCeBV7Q9dPr9TJLHAC8DHjJO61sXuGHbX61k3YOAOzP8HG7GEMg9GNiv7cnje7gHAP/b9quzfqGSJEmSJEmSJM1zSe7CEAa8BPhl24Pm6HmvB3yY4TOwd40hwD+OYcMLgI3HjZYlSQuIIUBJkiRJkrTaJFnEsHD8xcDH2+43Hj8AeCbDFJtvzUGd6cXv12t7RZKNgQOAFcBHx/DhjsDuwLPa/mG2NSVJc2sM+H0JuB1w37ZnJbkxQzjw4cARbT+VZGp6V9UkNwVu3PbMiTUurQbje5tvAM8edwC+J0MQ8CXjRMDp894HHNv2pJWssznw4rbPGTdXeB7wkbb/M97/JOD5DMHbE2f3qiRJkiRJkiRJWvMkWR9YBGwLnNH2spV4juk1DlsAF7X9VZKHA/sCr2x7apL7AO8Edmp7/ly+BknSmmHxpBuQJEmSJElrtxkXqzcH/tj2fUmuAB6aZO+2R7d9TZJ1gI3nouZYb1dgz3Ga1DuANzEEDV+V5GvAg4CXGwCUpPllxlTW+wDnAJcyTHM9q+2FY6hpEfD8JN8AfjM+bqrtz4GfT6h1aZWaObG47W+SvAp4Q5LlbT+R5EHAGUkWtT1i3A34FsB3VrZm218kWSfJndueneTQtiume2n77iSLgYOTfBlYNh3KlSRJkiRJkiRpIZgR+vvcyjx+xpqKhwFvAH6b5Njx+Y4G3pXko8COwPMNAErSwuUkQEmSJEmStMqNgbz9gWXAD4CDGcId9we+1/bQGef+aYH7LOo9AHgt8ASGqYPbtL1nkjsAewObAp9se9xs6kiS5laSxW2XJ7k3cATwz8C5wAnAt9s+N8ntgC2BM8fQn7RgJLkfcD/ghLbfTfJ44CDgmW1PSXIvYMn4/fWA9dr+biVrhSFweyBwYdsjpo9f9b1akpu1/dnKvzJJkiRJkiRJkhau8fr+fgzrG24JPIZhk7/jgCXARsDytmfNxZoKSdKaaWrSDUiSJEmSpLVbkrsD+wK7Ap8EdmCY0vRR4IvAXcdJNcAwxW8Oyt4YeAmwLXAX4LHj8V8w7Jz3I2CbJPeYg1qSpFma/j0wBgBvBxwGHN32y20vAvYCtht3Of0A8BsDgFpoxnDs0Qwh2DcleVzb9zFstHBckoe1/eoYAEzbK1Y2AAjDe7K2yxkWGOyb5MnTx8eA4MxzDQBKkiRJkiRJkrQSkiwBdgfu1va8tp9m+DzsTsAzGMJ/X297FszZmgpJ0hrIEKAkSZIkSZpzV1kYvgJYyhAC3B3YdVxQfutxEt8BbX80m1pJrnqNY13gNcCzgMe0/UGSXYBD2v4E+BDwW+AnK1tXkjSnXphkm/H7S4ALgX9KsjFA2x8AD2UIjz+v7Zcn06a0ek2/pxp/Fm7C8P//nsB7gV3GIOD7gX2AP0w/bi4XALQ9F9gD2C/JHjOf/2reg0mSJEmSJEmSpGspyR0Z1lL8B/DjJIcCjEHADwK3A3LNzyBJWkhiEFySJEmSJK0KSXYELgeuAF4LrA88tu1PkzwYeCXwqLYXzKLGem0vH7/fHrgZ8J22/53kQwwbIO0B/B1wCLBv24+N51+v7RUr/QIlSXNqnAD41rYPSrIRQ5h7A4bQ028m2500OeNGBm8ElgEXtN11PP4UYBfghLbvGY9lVe0AnOSBwLEMP5snzWYTB0mSJEmSJEmSFrpxI8B/YZgA+LQkdwGeDyxru+94ziZtl02yT0nS/OEOrZIkSZIkaU4kuUmSl8449EhgnbZfBT7NEAa8V5J/ZFjI/qpZBgA3As5OcvcktwLeDuwEPDfJ04HdgIuBI4DnMAYApyfWGACUpMmbOTm27fnAhkk+3vYi4ECG0NNbkiyZUIvSRCXZEngysOf43w1n7AL8LuAk4Jzp81dVAHB87lMZ3mvdneH91otWVS1JkiRJkiRJktZm46bFBd4CbJXkcW2/DRwO3DzJYeOpbpQpSfoTJwFKkiRJkqQ5keTuwMuAc9u+OMmbgVPanjDe/2zgpsBmwAfanjLbaTVJngO8BPgocHTbryV5GPB44PS2bx/P29gpUpI0f8ycxprkjsAmbb883j4RWG+cCLg5w++Wt7b95uQ6llavMSC7GXAksAnwlLY/TnIbhgUBP2z7zAn1tj6wCNgWOKPtZZPoQ5IkSZIkSZKkNVGSOwM7A99se3KS3YHN2h6W5HrAXYDlfjYmSboqQ4CSJEmSJGlOjBej7wy8APgucCVwKnBm20umg3hJptqumGWtP4UHk/wDsBR4att3JbkhcB+GqTlnt33NXNSUJM2NJJsC+zHsZLoF8Gbgd8B5wPFtTxyDgEva3jfJOm3/OLmOpdXnqhskJNkReBbwMeDEthcmuR3wTmBvhs0X/KBHkiRJkiRJkqR5bPr6f5KNGTbZ24Lh+v8xwK2B7YHd2543282UJUlrr8WTbkCSJEmSJK01btX2G0leB+wPPBbYA/jGeCH7xkl2afvj2RYaL46ng+OSXB94U5Jz234lyReAKeDH4/kGACVpHhinm63DcG36RcAdgUe3/W6SvYAHJTmv7c5JPp9ku7ZnTLJnaXVJsqjtlUkeCGwHnAV8lmFjhWcAK5Kc3Pb8JA9te/kE25UkSZIkSZIkSdfCjADgrsDzgSe2/VSSzwL3BhYxfGa2R5JXMHwuIEnSn3ESoCRJkiRJmpUkUwyBu/OB/2r77CR3A54NLG771PG827T94RzXnjkRcC/gNQxhktOd/idJ88s4AfANwPOA9YC9gKcy7Gr6hXGS6+HAhW1fOLlOpdUrySbAFW1/n2Rn4JXAO4CHA98BXgFszbAw4CTg2LZXTKpfSZIkSZIkSZL0181cs5Bke4bPwZ7e9owkG7b93YxznwbsDDzezwAkSddkatINSJIkSZKkNd6itsuBbYCHJHlD27OANwGbjpMBYZzKt7LG6VH/v++nJwKO378NeDnw8SRLZlNLkjT32v4auAB4J/B7YCnwYWDPJHdtezHwMYbfHeuNIXNprZZkS4afhZsluRmwG/BI4IfALRh2//034BvAYcDX/PBfkiRJkiRJkqT5bdwA8NgkNxkP3YzhM7LFY+Dv5CSvSnIngLZvBzYGHjiRhiVJawQXUUiSJEmSpJWWZGvg75PcuO2vgO2AxyY5uO03GUJ57wVoe+VK1tggyb3GwN8Dk9x9evrf+Lwzg4BHAVu1/a1TACVpfkiyeZLDxpsvBE4FbtT2fOAY4KfAMUleMt7/4baX+++41mYZLAb2Ab7IEPq7OfBaYEPgQGAXCpuDZwAAGExJREFU4ETgIQxTNE9ve+Yk+pUkSZIkSZIkSdde22UMGyUfk2RT4FfA3RmmAU4BRwGbAusAJLnFePu8iTQsSVojLJ50A5IkSZIkaY12D+BRwIokn237iySPB05Pcnnbl85BjXWAlyW5GLgz8MyrnjAjCDjV9sdJMjMoKEmanPF3wzpJ7tz27CSHTwf82n4ryR8Ydj/9f8Bz2p7uv+NaANJ2eZKlwIcYwoB3a3tBkvsD32r7w3E64BeBN46TlyVJkiRJkiRJ0jyVZHPgxW2fAxzAcP3/hm0/meRs4LK2y5LcEXgBw4aZMGyaef+2v5lI45KkNYKTACVJkiRJ0rU2PXEvyR3Hi9f/CRwJPBLYPsk6wMXAEcBpc1Gv7W+Bo4FHAGe0/XKSqXGCzsxrG1Ntr0yyBNg9yaLZ1pckzc6MaWfLgB0A2q4Yj09PcT2PYbfTfdqePh4zAKi1VpJNgKVJbg6cDWzA8P7pJuMp5wC7JjkOOB74QNtzJ9KsJEmSJEmSJEm61tr+ApjeHLPAEW1/ON73U+CiJA8FTgBe3vbr4301AChJ+msMAUqSJEmSpGslydQ4cW8n4P3As4H/ZphOcxLwMGAp8DHg/eNOdplFvYz1FgFnAI8Gtkvy0rYrxgvm1x/PXTwGADcae/lJ2ytn8XIlSXNg/MByOXAcsG+SJ08fh/8Ll7c9q+0PJteptPq0XQb8iGGTgz8AWwH/zBAM3Kntr4AtGSYEPrrtxybWrCRJkiRJkiRJulb+0uaY0+eM6xguAZ7a9iOzWVMhSVp4Fk+6AUmSJEmSNL8lWbftH8aL01sCLwd2A+4NBFin7XuSfBW4FcNOdl+A2U1yGgOADwGeAHyi7fuS7AqclORS4EzggCSPa/vrcQLgB4D9pydJSZLmh7bnJtkDOGrMeC+dEQScartiwi1Kq9w4RfnFbZ8DHAA8D7hl2/8BfprkRsCrkyxqeyLD+xpJkiRJkiRJkrQGGD/7Wp7kOODjSS5qe+y49iHTn43NXM8wmzUVkqSFJ/7ekCRJkiRJ1yTJpsDewGfbnj4uXt8d+DGwP7B72++PYb0vtb14Dmv/LXAIw2TB+wBfAo4CbggcDqwLvLXtB5NsAHweeG7b0+aqB0nS3EryQOBY4DXASW1/NOGWpNUqyVHAkW3Png7AzvzgP8lTGN5j3Q9YZkBWkiRJkiRJkqQ1T5L7M6xveF3bpTOOuzmmJGmlGQKUJEmSJEnXKMkdgGcBvwdOBM4BvgJsBtx4XLh+b+CVwDPanj9HdbdgCIkc1vb4JNsBewI/A94O/ArYsO2yGY+5c9uz56K+JGnVSXIX4F+AS4Bftj1owi1Jq1ySAIuAA4EL2x4xffyqu/wmuVnbn63+LiVJkiRJkiRJ0lxxc0xJ0lwzBChJkiRJkv6iJFsCewCLgbcyTOA7GXgz8FvgmcCBbT8yxzUPBTYAHtb20iTbAv8M/AB4fdtLx3MXt10+V7UlSatekvUZAlHbAme0vWzCLUmrRZKtgI8DL2177Hjsz4KAkiRJkiRJkiRpzefmmJKkuWQIUJIkSZIk/VVJ7gQ8CViHYRJfGSYEXgyc1vaU2Sxgn37sGP7bgGHi4M2BfYD1geeOQcB7AZc68U+SJK2pktwfOAp4XdulM45PtV0xuc4kSZIkSZIkSdJcc3NMSdJcMQQoSZIkSZKulRlBwCng3W3PmePn3xV4JfDfwK2B/YHfM0wavBHwjOnpf5IkSWuyJA8EjgVeA5zU9kcTbkmSJEmSJEmSJEmSNI9NTboBSZIkSZI0PyTZPMkdrun+tt8BlgLXA/ZMsvEc1r4tw2TB7YGPApsD3217LvAWhjDgFnNVT5IkaZLangrsBNwdeG6SF024JUmSJEmSJEmSJEnSPOYkQEmSJEmSRJJ1gX2AvwFeNwb+runcrYAr2543y5rpeGEiyabA3sAKYBfgH9qen+T+wJeBRW0vm009SZKk+SbJ+sAiYFvgDN/vSJIkSZIkSZIkSZKujiFASZIkSZIEwDgFcHfgRsAb256/CmulbZPsCFwGnAe8FtgGeFLbc8YA4JuB3f5SKFGSJEmSJEmSJEmSJEmSpLXZ1KQbkCRJkiRJk5Vk0fjtbYCtgPsCz09yp2s6N8kNkuywsjXHAOBOwJHADdr+CjgB+CnwD0leDhwN7GcAUJIkSZIkSZIkSZIkSZK0kDkJUJIkSZKkBWp6Gt/4/dbAR4HHAbdnCANuzIyJgEkWtb0yyRLgQ8Dz2p65krVvAHwYeFXbz844fn+GMOJNgC+1PW1mn5IkSZIkSZIkSZIkSZIkLTROApQkSZIkaQFKcitgzxlTADcFzmz71bbvBU4EtgAOSLIlwIwA4PHAy1c2ADhaxHBd4gdjP9cfj3+z7bFt/73taWNdA4CSJEmSJEmSJEmSJEmSpAXLEKAkSZIkSQtTgDOBTZJsAHwTuHGSJwG0PQP4PrCYIbA3Pb3vIwzT+z5/nYolGb9uOD7/RWP9g5Ns0PbSJA8APppk0yRes5AkSZIkSZIkSZIkSZIkCYib6UuSJEmStLAkSdsmWRc4Cfgy8HrgYcAOwM+ATwBHAU9v+/XxcTsBF7b975WstzPwJOBCYCnwa2AvYBfgHcDTgAPafnQOXqYkSZIkSZIkSZIkSZIkSWsFQ4CSJEmSJC0gMwJ5019vD7wR+CzwYWAzYH/gUuADbT80R3V3BA4BHgO8HbgcOIZhsuDjgT8wBAw/N93bXNSVJEmSJEmSJEmSJEmSJGlNt3jSDUiSJEmSpNVjRvBve+Dvk3wf+DzDBL7/ABYBb2z76CTrtv3DygbyktwauCdDsBDg78Y6dwDWBz4HPAtYAZzQ9tLpxxoAlCRJkiRJkiRJkiRJkiTp/0xNugFJkiRJkrR6jAHAHYG3Ad9jmPp3IHAv4KnALsD+SRa1/cP0Y1ay3PWBHwBL2l4JvBo4nyH4t1PblwEbAA8HNlrpFyVJkiRJkiRJkiRJkiRJ0lrOSYCSJEmSJK3FkmwObAl8pe0VwFbAa9q+M8mGwL2BJwGfAJ4B3HAM7a1svZsCOwAfA34HnJrkBODNwB+ATYBdk3wJuBg4qu3PV/oFSpIkSZIkSZIkSZIkSZK0lnMSoCRJkiRJa7c9GcJ990kyBawLPCPJVNvfAWcyTOK7ddtz254xy3r3AB4P7DKGCfcHdgaexhACfBXwZOD9wOFtvz7LepIkSZIkSZIkSZIkSZIkrdWcBChJkiRJ0trt9cBLgCcAy4GjgFsBb0vyTOBGwI2Zo2sEbT+W5PYM0/5u0PaoJC8CDh7rLwVOB27U9gdJ0rZzUVuSJEmSJEmSJEmSJEmSpLVRXGcnSZIkSdLaZ2a4LkmA/YA7AO8BfgK8ELg9cH3g1W0/NEd1dxqf+6fAdsBr2x6TZDvgIOCEtkddtUdJkiRJkiRJkiRJkiRJknT1DAFKkiRJkrSWmQ7XJXkAcDPgt21PTrIfcGvgvW1PT7IpsLjthbMN5I1Bw42B44GXt/1CkscAjwM+0/bNSe4JvAV4aduPzfZ1SpIkSZIkSZIkSZIkSZK0EExNugFJkiRJkjS3xgDgzsAhwHrAQUn2Ag4Gfg48M8kOwLK2F04/ZrY12y4DfsUwcZC2HwC+CLwqydPbfg34FLBzkkWzqSdJkiRJkiRJkiRJkiRJ0kJhCFCSJEmSpLVIkqkk6wFPAh4OXAJcCpw8BvVeBXwX+MVsg3/j9D+SLEmyZDx8KrDFOPUP4NPAWcAZ4+2fAYe3vXI2tSVJkiRJkiRJkiRJkiRJWigyy/V+kiRJkiRpHklyk7YXJPkPhsDdvYG92n4vyaOAH7Y9cw7qZJw4uAuwL8OEwS8CbwZeDdwSWA5sCzyn7SdnW1OSJEmSJEmSJEmSJEmSpIVo8aQbkCRJkiRJcyPJjYCTkzwSOB04GNhtDADeF3gtsMcsa0y1XTEGAB8EvBLYDXgicChwg7YvSLI1cE/gyLZfGh+b2U4flCRJkiRJkiRJkiRJkiRpoTEEKEmSJEnSGm5GuG4ZcCKwRdu3jaHANyf5ALALsG/br86izibA4Un2bXsBsD6wJ7AV8BBgO+DTSTZo+2LgWzMfbwBQkiRJkiRJkiRJkiRJkqTrLq6/kyRJkiRpzZbkDm2/N36/F/A04P5tr0jyYOAy4PK2X5vtNL4kBwFbA09hCB2uCywF3tH2Y0mOAPYaz/mftitm89okSZIkSZIkSZIkSZIkSVropibdgCRJkiRJWjkZbAAcmeQ9SXYA3g2cDDwaoO0pbU9v+7Xx9nUOACbZPMlh480DgFOAJW1XtL0MOB+4bZJHA0uAbdp+zwCgJEmSJEmSJEmSJEmSJEmzZwhQkiRJkqQ1TJKM327a9hLgkcDngYcAXwLuBewwV/Xa/gJYJ8mdxxDhEW2/P6OPbwG3Bw4EPtj2nKv0KUmSJEmSJEmSJEmSJEmSVlJWYgCAJEmSJEmasCQPB14PfAL4Ytv3j8d3Bf4OeDzwvLYfmmWdAIsYAn4Xtj1ixvE/TRZMshjYsO2yJFmZiYOSJEmSJEmSJEmSJEmSJOnPLZ50A5IkSZIk6dqZDtcl2QzYDXgBcCNg+yS3aPvGth9N8nHgXGDz2dYcw3zLkxwHfDzJRW2PHfvIdE9tlwPLZjxGkiRJkiRJkiRJkiRJkiTNgalJNyBJkiRJkq6dMXh3H+AxwJVtTwI+CHwKuF2S/cbzrgBuBzwyyaLpqX2zrH0usAewX5I9pvsZe/L6giRJkiRJkiRJkiRJkiRJq4iL9CRJkiRJWkMkuR+wFLgN8OQkO7e9GPgEcBqwRZLbjadfAryg7ZVzNZmv7eeBfwFek2TvJLcaj6+Yi+eXJEmSJEmSJEmSJEmSJEl/LnO0DlCSJEmSJK1CSe4AHAYc2fakJE8AXg3sM97eALhB2wtXQy93YQgDXgL8su1Bq7qmJEmSJEmSJEmSJEmSJEkL1eJJNyBJkiRJkq6VOwDrA09Icmrb/0yyAnhXkqe2/S+GUB5JMlfT/65O228neS6wCNg2yfptL1tV9SRJkiRJkiRJkiRJkiRJWsicBChJkiRJ0hogySLgAcCjgAuBQ9penuSJwAVtT51og5IkSZIkSZIkSZIkSZIkaZUwBChJkiRJ0jw3PdkvyWLgwcBDgN8Dr257+cxzJtmnJEmSJEmSJEmSJEmSJEmae1OTbkCSJEmSJP1l0+G+tsuBU4DPABsDt7jqOZIkSZIkSZIkSZIkSZIkae3iJEBJkiRJkuaJazvNb5wIuHHbX66GtiRJkiRJkiRJkiRJkiRJ0gQZApQkSZIkaR6YDgAmeSiwNTAFvLHtH696HgyT/65taFCSJEmSJEmSJEmSJEmSJK25pibdgCRJkiRJ+lOob0fgYODzwIuAlyW56t/uU+O5S4BDk6y7unuVJEmSJEmSJEmSJEmSJEmrjyFASZIkSZLmj12BfwLWA84D3tJ2xfSdSRa3vXIMAJ4AnND2D5NpVZIkSZIkSZIkSZIkSZIkrQ6LJ92AJEmSJEkLUZL1gC3anp1kC+AShuDfU4E7Abu3/UmSpwEr2r6z7fIkGwPHAwe2PW1iL0CSJEmSJEmSJEmSJEmSJK0WTgKUJEmSJGkybgM8IskRwLuBPwK/AHYEXtH2f5LcDXgu8BOAJNcD3gW82gCgJEmSJEmSJEmSJEmSJEkLQ9pOugdJkiRJkhacJBsALweeAxzWdr/x+AuB+wKXA7cCXtX2o+N9S4AN2v50Ml1LkiRJkiRJkiRJkiRJkqTVzRCgJEmSJEkTkGRTYD3gacAmwLlt3zLetw3DVMD1234vSeof8JIkSZIkSZIkSZIkSZIkLUhTk25AkiRJkqSFJskNgS8Dj237b8AZwL2SPDHJ3YHtgZ+3/R6AAUBJkiRJkiRJkiRJkiRJkhauxZNuQJIkSZKkhabtxUmeALwryfK2b0pyJfAo4FDgKW2vnGyXkiRJkiRJkiRJkiRJkiRpPojDBCRJkiRJWj2SbAMU+GbbK5PcFTgeOLjt25PcALhl23Mn2qgkSZIkSZIkSZIkSZIkSZo3pibdgCRJkiRJC8hDgX8H7ppkcdtvjrcPTfK8tr83AChJkiRJkiRJkiRJkiRJkmYyBChJkiRJ0iqSJOPXByTZq+1BwGnAy4Ctx9POB94OnDmZLiVJkiRJkiRJkiRJkiRJ0nyWtpPuQZIkSZKktVaSnYA3APu0PWU89nLgbsD/ArsBj2v7lSSpf6hLkiRJkiRJkiRJkiRJkqQZDAFKkiRJkrSKJLk+cDTwrranJrle2yvG+x4M3BT4WdtPTbJPSZIkSZIkSZIkSZIkSZI0fy2edAOSJEmSJK2t2l6aZBHwN8CpMwKAWwKntb18vO0EQEmSJEmSJEmSJEmSJEmSdLWmJt2AJEmSJElriyQZvy5JsmQ8/CngJkm2He/bFjgUuOX04wwASpIkSZIkSZIkSZIkSZKkaxLXGUqSJEmSNHvT0/yS7ALsC/wc+DJwJPAq4I7AFcBdgRe2/cjEmpUkSZIkSZIkSZIkSZIkSWuMxZNuQJIkSZKkNVmSqbYrxgDgg4BXArsBTwTeAGzQdv8ktwfuBPyo7TenQ4MTbF2SJEmSJEmSJEmSJEmSJK0BDAFKkiRJkrSSkmwCHJ5k37YXAOsDewJbAQ8BtgM+nWSDtgcA359+rAFASZIkSZIkSZIkSZIkSZJ0bUxNugFJkiRJktZUbZcBPwaOSXIj4CTgPOAfgde1/RpwLPD8JHdI4t/hkiRJkiRJkiRJkiRJkiTpOnHxoSRJkiRJ11GSzZMcNt48ADgFWNJ2RdvLgPOB2yZ5NLAE2Kbt99qumFDLkiRJkiRJkiRJkiRJkiRpDWUIUJIkSZKk66jtL4B1kty5bYEj2n4/ScZTvgXcHjgQ+GDbcwBm3C9JkiRJkiRJkiRJkiRJknStZFirKEmSJEmSro0xyLeIIeB3YdsjZhxnDAWSZDGwYdtlSVL/AJckSZIkSZIkSZIkSZIkSSth8aQbkCRJkiRpTTKG+ZYnOQ74eJKL2h7bthl1sBxYNuMxkiRJkiRJkiRJkiRJkiRJ15khQEmSJEmSVkLbc5PsARw15v6WzpgCONV2xYRblCRJkiRJkiRJkiRJkiRJa4E4jECSJEmSpJWX5IHAscBrgJPa/mjCLUmSJEmSJEmSJEmSJEmSpLWIIUBJkiRJkmYpyV2AfwEuAX7Z9qAJtyRJkiRJkiRJkiRJkiRJktYShgAlSZIkSZoDSdYHFgHbAme0vWzCLUmSJEmSJEmSJEmSJEmSpLWAIUBJkiRJkiRJkiRJkiRJkiRJkiRJkuapqUk3IEmSJEmSJEmSJEmSJEmSJEmSJEmSrp4hQEmSJEmSJEmSJEmSJEmSJEmSJEmS5ilDgJIkSZIkSZIkSZIkSZIkSZIkSZIkzVOGACVJkiRJkiRJkiRJkiRJkiRJkiRJmqcMAUqSJEmSJEmSJEm61pJsmuTM8b8Lkvx0xu3rXYfnOT3J3Vdlr5IkSZIkSZIkSdLaYPGkG5AkSZIkSZIkSZK05mj7a+DuAEkOBH7f9vUTbUqSJEmSJEmSJElaizkJUJIkSZIkSZIkSdKcSPJfSb6e5OwkTx+PLU6yNMm3knw7yT5XecyiJO9OcuBfO1eSJEmSJEmSJElaiJwEKEmSJEmSJEmSJGmu7Nl2WZLrA19LcgJwR+BGbbcGSLJkxvnrAO8Fvt72dUn+3184V5IkSZIkSZIkSVqQnAQoSZIkSZIkSZIkaa48L8lZwJeAWwBbAN8HtkxyeJKHAhfNOP8YxgDgePsvnStJkiRJkiRJkiQtSIYAJUmSJEmSJEmSJM1akgcB9wf+tu3dgG8C67X9NXBX4DTg2cBbZjzsi8COSdYF+CvnSpIkSZIkSZIkSQuSIUBJkiRJkiRJkiRJc2EjYFnby5LcGbgXQJLNgLR9P/AyYJsZj3kL8CngP5Ms/ivnSpIkSZIkSZIkSQvS4kk3IEmSJEmSJEmSJGmtcBLwjCTnAN8FvjIevyXw9iQBCuw/80Ft/z3Jq4F3AW/4S+dKkiRJkiRJkiRJC1HaTroHSZIkSZIkSZIkSZIkSZIkSZIkSZJ0NaYm3YAkSZIkSZIkSZIkSZIkSZIkSZIkSbp6hgAlSZIkSZIkSZIkSZIkSZIkSZIkSZqnDAFKkiRJkiRJkiRJkiRJkiRJkiRJkjRPGQKUJEmSJEmSJEmSJEmSJEmSJEmSJGmeMgQoSZIkSZIkSZIkSZIkSZIkSZIkSdI8ZQhQkiRJkiRJkiRJkiRJkiRJkiRJkqR5yhCgJEmSJEmSJEmSJEmSJEmSJEmSJEnz1P8HRzOP/WDtmrYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 3600x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_visualization(LR_dict, list(LR_dict.keys()), \"Logistic Regression\", '#AA4499')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T23:24:13.157127Z",
     "start_time": "2019-07-22T23:24:13.071278Z"
    },
    "code_folding": [
     1
    ]
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def model_visualization(model_dict, tasks, label=\"model_name\", color='#332288'):\n",
    "    means, stds = [], []\n",
    "    for task in tasks:\n",
    "        means.append(model_dict[task]['mean'])   \n",
    "        stds.append(model_dict[task]['std'])\n",
    "    print(means)\n",
    "    width = 0.5 # the width of the bars\n",
    "    colors = ['#332288', '#AA4499', '#44AA99']\n",
    "    #flatten_std = [item for sublist in stds for item in sublist]\n",
    "    x = np.arange(len(tasks))\n",
    "    new_x = [1.5*i for i in x]\n",
    "    fig, ax = plt.subplots(figsize=(50, 10))\n",
    "    plt.bar(new_x, means, color=color, alpha=0.5)\n",
    "    plt.xticks(new_x, tasks)\n",
    "    plt.title(\"{} Performance\".format(label))\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.xlabel('Tasks')\n",
    "    ax.set_xticklabels(tasks, rotation=45, ha=\"right\")\n",
    "    plt.xlim(min(new_x) - 2, max(new_x) + 2)\n",
    "    plt.ylim(0, 100)\n",
    "    plt.grid()\n",
    "    plt.errorbar(new_x, means, stds, linestyle='None', marker='o', color='r', alpha=0.6)\n",
    "    fig.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    #plt.savefig('/{}_result_plot_with_error_bars.png'.format(label))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T03:42:21.939893Z",
     "start_time": "2019-07-21T03:42:16.055505Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_visualization(Maj_dict,LR_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T23:31:00.495810Z",
     "start_time": "2019-07-22T23:31:00.447004Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def all_visualization(Ma_dic, LR_dic, MLP_dic):\n",
    "    \n",
    "    Mlp_means, Mlp_std = [], []\n",
    "    LR_means, LR_std = [], []\n",
    "    Maj_means, Maj_std = [], []\n",
    "    tasks = LR_dic.keys()\n",
    "    for task in tasks:\n",
    "        Maj_means.append(Ma_dic[task]['mean'])\n",
    "        Maj_std.append(Ma_dic[task]['std'])\n",
    "        LR_means.append(LR_dic[task]['mean'])\n",
    "        LR_std.append(LR_dic[task]['std'])\n",
    "        Mlp_means.append(MLP_dic[task]['mean'])\n",
    "        Mlp_std.append(MLP_dic[task]['std'])\n",
    "    \n",
    "    x = np.arange(len(tasks))\n",
    "    new_x = [1.5*i for i in x]\n",
    "    fig, ax = plt.subplots(figsize=(50, 10))\n",
    "    width = 0.4\n",
    "    colors = ['#332288', '#AA4499', '#44AA99']\n",
    "    ax.bar([p - width for p in new_x],\n",
    "            Maj_means,\n",
    "            width,\n",
    "            alpha=0.6,\n",
    "            color='#332288',\n",
    "            label='Majority')\n",
    "    ax.bar(new_x,\n",
    "            LR_means,\n",
    "            width,\n",
    "            alpha=0.6,\n",
    "            color='#AA4499',\n",
    "            label='LR')\n",
    "    ax.bar([p + width for p in new_x],\n",
    "            Mlp_means,\n",
    "            width,\n",
    "            alpha=0.6,\n",
    "            color='#44AA99',\n",
    "            label='MLP')\n",
    "    \n",
    "    ax.set_ylabel('Accuracy')\n",
    "    ax.set_title('Baseline Performances over clinical tasks')\n",
    "    ax.set_xticks([p  for p in new_x])\n",
    "    ax.set_xticklabels(tasks, rotation=45, ha=\"right\")\n",
    "    fig.tight_layout()\n",
    "\n",
    "    plt.legend(['Majority', 'LR', 'MLP'], loc='upper left')\n",
    "    plt.grid()\n",
    "    \n",
    "    # Error bars\n",
    "    x_e, y_e, e = [], [], []\n",
    "    for i in new_x:\n",
    "        x_e.append([i-width, i, i+width])\n",
    "\n",
    "    for i, task in enumerate(tasks):\n",
    "        y_e.append([Maj_means[i], LR_means[i], Mlp_means[i]])\n",
    "\n",
    "    for i, task in enumerate(tasks):\n",
    "        e.append([Maj_std[i], LR_std[i], Mlp_std[i]])\n",
    "\n",
    "    flatten_x = [item for sublist in x_e for item in sublist]\n",
    "    flatten_y = [item for sublist in y_e for item in sublist]\n",
    "    flatten_e = [item for sublist in e for item in sublist]\n",
    "\n",
    "\n",
    "    plt.xlim(min(x) - 2 * width, max(x) + width * 2)\n",
    "    plt.ylim(0, 100)\n",
    "\n",
    "    plt.errorbar(flatten_x, flatten_y, flatten_e, linestyle='None', marker='o', color='r', alpha=0.4)\n",
    "    plt.savefig(path + '/new_result_plot_with_error_bars.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T02:30:28.038484Z",
     "start_time": "2019-07-21T02:30:27.983629Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 17, 2: 6, 3: 3, 0: 24})"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collections.Counter(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T07:03:02.534197Z",
     "start_time": "2019-07-22T07:03:02.456545Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "class MultiLayerPerceptron(torch.nn.Module):\n",
    "    def __init__(self, seed, input_size, num_classes, num_layers, channels, learning_rate, batch_size, epochs, patience, weight_decay):\n",
    "        super(MultiLayerPerceptron, self).__init__()\n",
    "        self.input_size = input_size\n",
    "        self.learning_rate = learning_rate\n",
    "        self.batch_size = batch_size\n",
    "        self.epochs = epochs\n",
    "        self.patience = patience\n",
    "        self.weight_decay = weight_decay\n",
    "        self.num_layers = num_layers\n",
    "        self.channels = channels\n",
    "        self.output_size = num_classes\n",
    "        random.seed(seed)\n",
    "        torch.manual_seed(seed)\n",
    "        \n",
    "        nodes = []\n",
    "        nodes += channels\n",
    "        architecture = OrderedDict()\n",
    "        for i in range(self.num_layers):\n",
    "            architecture['fc' + str(i)] = nn.Linear(input_size, nodes[i])\n",
    "            architecture['relu' + str(i)] = torch.nn.ReLU()\n",
    "            input_size = nodes[i]\n",
    "\n",
    "        self.features = nn.Sequential(architecture)\n",
    "\n",
    "        self.classifier = nn.Linear(input_size, num_classes)\n",
    "    \n",
    "    @property\n",
    "    def criterion(self):\n",
    "        criterion = F.cross_entropy\n",
    "        return criterion\n",
    "    \n",
    "    @property\n",
    "    def optimizer(self):\n",
    "        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)\n",
    "        return optimizer\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, self.input_size)\n",
    "        features = self.features(x)\n",
    "        logits = self.classifier(features)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-21T18:00:35.415460Z",
     "start_time": "2019-07-21T17:57:14.355068Z"
    },
    "code_folding": [
     0
    ],
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Expression_Subtype', 'LUAD')\n",
      "('Expression_Subtype', 'LUAD')\n",
      "('Expression_Subtype', 'LUNG')\n",
      "('Expression_Subtype', 'LUNG')\n",
      "('GeneExp_Subtype', 'GBM')\n",
      "('GeneExp_Subtype', 'GBM')\n",
      "('Metastasis_nature2012', 'BRCA')\n",
      "('Metastasis_nature2012', 'BRCA')\n",
      "('Node_nature2012', 'BRCA')\n",
      "('Node_nature2012', 'BRCA')\n",
      "('PAM50Call_RNAseq', 'BRCA')\n",
      "('PAM50Call_RNAseq', 'BRCA')\n",
      "('_EVENT', 'ACC')\n",
      "('_EVENT', 'ACC')\n",
      "('_EVENT', 'BLCA')\n",
      "('_EVENT', 'BLCA')\n",
      "('_EVENT', 'BRCA')\n",
      "('_EVENT', 'BRCA')\n",
      "('_EVENT', 'CESC')\n",
      "('_EVENT', 'CESC')\n",
      "('_EVENT', 'CHOL')\n",
      "('_EVENT', 'CHOL')\n",
      "('_EVENT', 'COAD')\n",
      "('_EVENT', 'COAD')\n",
      "('_EVENT', 'COADREAD')\n",
      "('_EVENT', 'COADREAD')\n",
      "('_EVENT', 'ESCA')\n",
      "('_EVENT', 'ESCA')\n",
      "('_EVENT', 'GBM')\n",
      "('_EVENT', 'GBM')\n",
      "('_EVENT', 'GBMLGG')\n",
      "('_EVENT', 'GBMLGG')\n",
      "('_EVENT', 'HNSC')\n",
      "('_EVENT', 'HNSC')\n",
      "('_EVENT', 'KICH')\n",
      "('_EVENT', 'KICH')\n",
      "('_EVENT', 'KIRP')\n",
      "('_EVENT', 'KIRP')\n",
      "('_EVENT', 'LAML')\n",
      "('_EVENT', 'LAML')\n",
      "('_EVENT', 'LGG')\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-339-ddadab851f41>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtaskid\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtaskid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mtask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeta_dataloader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTCGA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTCGATask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtaskid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/PycharmProjects/gene-graph-conv/meta_dataloader/TCGA.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, task_id, data_dir, transform, target_transform, download, preloaded, gene_symbol_map_file)\u001b[0m\n\u001b[1;32m    160\u001b[0m             \u001b[0mhdf_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"TCGA_HiSeqV2.hdf5\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhdf_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dataset'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindices_to_load\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    163\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices_to_load\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/h5py/_hl/dataset.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0mmspace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_simple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    572\u001b[0m         \u001b[0mfspace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 573\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmspace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfspace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdxpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dxpl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m         \u001b[0;31m# Patch up the output for NumPy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for taskid in sorted(tasks.task_ids):\n",
    "    print(taskid)\n",
    "    task = meta_dataloader.TCGA.TCGATask(taskid)\n",
    "    print(task.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T07:14:00.567610Z",
     "start_time": "2019-07-22T07:14:00.502966Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "#After hyperparameter search (in another notebook)\n",
    "epochs = 250\n",
    "patience = 10\n",
    "batch_size = 32\n",
    "lr= 0.0001 # best_lr\n",
    "num_layer = 2 # best num_layer\n",
    "channels = [128,64] # best channels\n",
    "weight_decay = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T23:19:54.815168Z",
     "start_time": "2019-07-22T07:20:39.276860Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ('Expression_Subtype', 'LUAD') MLP Accuracy is: 67.0\n",
      "\n",
      " ('Expression_Subtype', 'LUAD') MLP Accuracy is: 68.0\n",
      "\n",
      " ('Expression_Subtype', 'LUAD') MLP Accuracy is: 71.0\n",
      "\n",
      " ('Expression_Subtype', 'LUAD') MLP Accuracy is: 66.0\n",
      "\n",
      " ('Expression_Subtype', 'LUAD') MLP Accuracy is: 67.0\n",
      "\n",
      " ('Expression_Subtype', 'LUAD') MLP Accuracy is: 70.0\n",
      "\n",
      " ('Expression_Subtype', 'LUAD') MLP Accuracy is: 69.0\n",
      "\n",
      " ('Expression_Subtype', 'LUAD') MLP Accuracy is: 69.0\n",
      "\n",
      " ('Expression_Subtype', 'LUAD') MLP Accuracy is: 66.0\n",
      "\n",
      " ('Expression_Subtype', 'LUAD') MLP Accuracy is: 69.0\n",
      "\n",
      " ('Expression_Subtype', 'LUNG') MLP Accuracy is: 67.0\n",
      "\n",
      " ('Expression_Subtype', 'LUNG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('Expression_Subtype', 'LUNG') MLP Accuracy is: 71.0\n",
      "\n",
      " ('Expression_Subtype', 'LUNG') MLP Accuracy is: 66.0\n",
      "\n",
      " ('Expression_Subtype', 'LUNG') MLP Accuracy is: 67.0\n",
      "\n",
      " ('Expression_Subtype', 'LUNG') MLP Accuracy is: 70.0\n",
      "\n",
      " ('Expression_Subtype', 'LUNG') MLP Accuracy is: 69.0\n",
      "\n",
      " ('Expression_Subtype', 'LUNG') MLP Accuracy is: 69.0\n",
      "\n",
      " ('Expression_Subtype', 'LUNG') MLP Accuracy is: 66.0\n",
      "\n",
      " ('Expression_Subtype', 'LUNG') MLP Accuracy is: 69.0\n",
      "\n",
      " ('GeneExp_Subtype', 'GBM') MLP Accuracy is: 75.0\n",
      "\n",
      " ('GeneExp_Subtype', 'GBM') MLP Accuracy is: 71.0\n",
      "\n",
      " ('GeneExp_Subtype', 'GBM') MLP Accuracy is: 71.0\n",
      "\n",
      " ('GeneExp_Subtype', 'GBM') MLP Accuracy is: 74.0\n",
      "\n",
      " ('GeneExp_Subtype', 'GBM') MLP Accuracy is: 74.0\n",
      "\n",
      " ('GeneExp_Subtype', 'GBM') MLP Accuracy is: 72.0\n",
      "\n",
      " ('GeneExp_Subtype', 'GBM') MLP Accuracy is: 73.0\n",
      "\n",
      " ('GeneExp_Subtype', 'GBM') MLP Accuracy is: 74.0\n",
      "\n",
      " ('GeneExp_Subtype', 'GBM') MLP Accuracy is: 75.0\n",
      "\n",
      " ('GeneExp_Subtype', 'GBM') MLP Accuracy is: 74.0\n",
      "\n",
      " ('Metastasis_nature2012', 'BRCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('Metastasis_nature2012', 'BRCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('Metastasis_nature2012', 'BRCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('Metastasis_nature2012', 'BRCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('Metastasis_nature2012', 'BRCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('Metastasis_nature2012', 'BRCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('Metastasis_nature2012', 'BRCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('Metastasis_nature2012', 'BRCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('Metastasis_nature2012', 'BRCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('Metastasis_nature2012', 'BRCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('Node_nature2012', 'BRCA') MLP Accuracy is: 45.0\n",
      "\n",
      " ('Node_nature2012', 'BRCA') MLP Accuracy is: 47.0\n",
      "\n",
      " ('Node_nature2012', 'BRCA') MLP Accuracy is: 47.0\n",
      "\n",
      " ('Node_nature2012', 'BRCA') MLP Accuracy is: 52.0\n",
      "\n",
      " ('Node_nature2012', 'BRCA') MLP Accuracy is: 45.0\n",
      "\n",
      " ('Node_nature2012', 'BRCA') MLP Accuracy is: 48.0\n",
      "\n",
      " ('Node_nature2012', 'BRCA') MLP Accuracy is: 47.0\n",
      "\n",
      " ('Node_nature2012', 'BRCA') MLP Accuracy is: 49.0\n",
      "\n",
      " ('Node_nature2012', 'BRCA') MLP Accuracy is: 44.0\n",
      "\n",
      " ('Node_nature2012', 'BRCA') MLP Accuracy is: 51.0\n",
      "\n",
      " ('PAM50Call_RNAseq', 'BRCA') MLP Accuracy is: 82.0\n",
      "\n",
      " ('PAM50Call_RNAseq', 'BRCA') MLP Accuracy is: 80.0\n",
      "\n",
      " ('PAM50Call_RNAseq', 'BRCA') MLP Accuracy is: 83.0\n",
      "\n",
      " ('PAM50Call_RNAseq', 'BRCA') MLP Accuracy is: 77.0\n",
      "\n",
      " ('PAM50Call_RNAseq', 'BRCA') MLP Accuracy is: 83.0\n",
      "\n",
      " ('PAM50Call_RNAseq', 'BRCA') MLP Accuracy is: 78.0\n",
      "\n",
      " ('PAM50Call_RNAseq', 'BRCA') MLP Accuracy is: 82.0\n",
      "\n",
      " ('PAM50Call_RNAseq', 'BRCA') MLP Accuracy is: 80.0\n",
      "\n",
      " ('PAM50Call_RNAseq', 'BRCA') MLP Accuracy is: 83.0\n",
      "\n",
      " ('PAM50Call_RNAseq', 'BRCA') MLP Accuracy is: 82.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('_EVENT', 'BLCA') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('_EVENT', 'BLCA') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'BLCA') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'BLCA') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'BLCA') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'BLCA') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'BLCA') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'BLCA') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'BLCA') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'BLCA') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'BRCA') MLP Accuracy is: 82.0\n",
      "\n",
      " ('_EVENT', 'BRCA') MLP Accuracy is: 82.0\n",
      "\n",
      " ('_EVENT', 'BRCA') MLP Accuracy is: 82.0\n",
      "\n",
      " ('_EVENT', 'BRCA') MLP Accuracy is: 82.0\n",
      "\n",
      " ('_EVENT', 'BRCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('_EVENT', 'BRCA') MLP Accuracy is: 82.0\n",
      "\n",
      " ('_EVENT', 'BRCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('_EVENT', 'BRCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('_EVENT', 'BRCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('_EVENT', 'BRCA') MLP Accuracy is: 83.0\n",
      "\n",
      " ('_EVENT', 'CESC') MLP Accuracy is: 70.0\n",
      "\n",
      " ('_EVENT', 'CESC') MLP Accuracy is: 66.0\n",
      "\n",
      " ('_EVENT', 'CESC') MLP Accuracy is: 68.0\n",
      "\n",
      " ('_EVENT', 'CESC') MLP Accuracy is: 68.0\n",
      "\n",
      " ('_EVENT', 'CESC') MLP Accuracy is: 69.0\n",
      "\n",
      " ('_EVENT', 'CESC') MLP Accuracy is: 69.0\n",
      "\n",
      " ('_EVENT', 'CESC') MLP Accuracy is: 68.0\n",
      "\n",
      " ('_EVENT', 'CESC') MLP Accuracy is: 70.0\n",
      "\n",
      " ('_EVENT', 'CESC') MLP Accuracy is: 69.0\n",
      "\n",
      " ('_EVENT', 'CESC') MLP Accuracy is: 69.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('_EVENT', 'COAD') MLP Accuracy is: 75.0\n",
      "\n",
      " ('_EVENT', 'COAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'COAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'COAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'COAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'COAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'COAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'COAD') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'COAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'COAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'COADREAD') MLP Accuracy is: 72.0\n",
      "\n",
      " ('_EVENT', 'COADREAD') MLP Accuracy is: 73.0\n",
      "\n",
      " ('_EVENT', 'COADREAD') MLP Accuracy is: 73.0\n",
      "\n",
      " ('_EVENT', 'COADREAD') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'COADREAD') MLP Accuracy is: 71.0\n",
      "\n",
      " ('_EVENT', 'COADREAD') MLP Accuracy is: 73.0\n",
      "\n",
      " ('_EVENT', 'COADREAD') MLP Accuracy is: 72.0\n",
      "\n",
      " ('_EVENT', 'COADREAD') MLP Accuracy is: 72.0\n",
      "\n",
      " ('_EVENT', 'COADREAD') MLP Accuracy is: 73.0\n",
      "\n",
      " ('_EVENT', 'COADREAD') MLP Accuracy is: 71.0\n",
      "\n",
      " ('_EVENT', 'ESCA') MLP Accuracy is: 51.0\n",
      "\n",
      " ('_EVENT', 'ESCA') MLP Accuracy is: 53.0\n",
      "\n",
      " ('_EVENT', 'ESCA') MLP Accuracy is: 51.0\n",
      "\n",
      " ('_EVENT', 'ESCA') MLP Accuracy is: 54.0\n",
      "\n",
      " ('_EVENT', 'ESCA') MLP Accuracy is: 52.0\n",
      "\n",
      " ('_EVENT', 'ESCA') MLP Accuracy is: 52.0\n",
      "\n",
      " ('_EVENT', 'ESCA') MLP Accuracy is: 53.0\n",
      "\n",
      " ('_EVENT', 'ESCA') MLP Accuracy is: 53.0\n",
      "\n",
      " ('_EVENT', 'ESCA') MLP Accuracy is: 52.0\n",
      "\n",
      " ('_EVENT', 'ESCA') MLP Accuracy is: 53.0\n",
      "\n",
      " ('_EVENT', 'GBM') MLP Accuracy is: 73.0\n",
      "\n",
      " ('_EVENT', 'GBM') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBM') MLP Accuracy is: 75.0\n",
      "\n",
      " ('_EVENT', 'GBM') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBM') MLP Accuracy is: 73.0\n",
      "\n",
      " ('_EVENT', 'GBM') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBM') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBM') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBM') MLP Accuracy is: 73.0\n",
      "\n",
      " ('_EVENT', 'GBM') MLP Accuracy is: 75.0\n",
      "\n",
      " ('_EVENT', 'GBMLGG') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBMLGG') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBMLGG') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBMLGG') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBMLGG') MLP Accuracy is: 75.0\n",
      "\n",
      " ('_EVENT', 'GBMLGG') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBMLGG') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBMLGG') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBMLGG') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'GBMLGG') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_EVENT', 'HNSC') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'HNSC') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'HNSC') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'HNSC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'HNSC') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'HNSC') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'HNSC') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'HNSC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'HNSC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'HNSC') MLP Accuracy is: 59.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('_EVENT', 'KIRP') MLP Accuracy is: 85.0\n",
      "\n",
      " ('_EVENT', 'KIRP') MLP Accuracy is: 86.0\n",
      "\n",
      " ('_EVENT', 'KIRP') MLP Accuracy is: 86.0\n",
      "\n",
      " ('_EVENT', 'KIRP') MLP Accuracy is: 86.0\n",
      "\n",
      " ('_EVENT', 'KIRP') MLP Accuracy is: 86.0\n",
      "\n",
      " ('_EVENT', 'KIRP') MLP Accuracy is: 86.0\n",
      "\n",
      " ('_EVENT', 'KIRP') MLP Accuracy is: 86.0\n",
      "\n",
      " ('_EVENT', 'KIRP') MLP Accuracy is: 86.0\n",
      "\n",
      " ('_EVENT', 'KIRP') MLP Accuracy is: 86.0\n",
      "\n",
      " ('_EVENT', 'KIRP') MLP Accuracy is: 86.0\n",
      "\n",
      " ('_EVENT', 'LAML') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'LAML') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'LAML') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'LAML') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'LAML') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'LAML') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'LAML') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('_EVENT', 'LAML') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'LAML') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'LAML') MLP Accuracy is: 59.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ('_EVENT', 'LGG') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'LGG') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'LGG') MLP Accuracy is: 75.0\n",
      "\n",
      " ('_EVENT', 'LGG') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'LGG') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'LGG') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'LGG') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'LGG') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'LGG') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'LGG') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_EVENT', 'LIHC') MLP Accuracy is: 63.0\n",
      "\n",
      " ('_EVENT', 'LIHC') MLP Accuracy is: 65.0\n",
      "\n",
      " ('_EVENT', 'LIHC') MLP Accuracy is: 63.0\n",
      "\n",
      " ('_EVENT', 'LIHC') MLP Accuracy is: 63.0\n",
      "\n",
      " ('_EVENT', 'LIHC') MLP Accuracy is: 63.0\n",
      "\n",
      " ('_EVENT', 'LIHC') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'LIHC') MLP Accuracy is: 63.0\n",
      "\n",
      " ('_EVENT', 'LIHC') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'LIHC') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'LIHC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'LUAD') MLP Accuracy is: 52.0\n",
      "\n",
      " ('_EVENT', 'LUAD') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'LUAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'LUAD') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'LUAD') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'LUAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'LUAD') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'LUAD') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'LUAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'LUAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'LUNG') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'LUNG') MLP Accuracy is: 64.0\n",
      "\n",
      " ('_EVENT', 'LUNG') MLP Accuracy is: 64.0\n",
      "\n",
      " ('_EVENT', 'LUNG') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'LUNG') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'LUNG') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'LUNG') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'LUNG') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'LUNG') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'LUNG') MLP Accuracy is: 63.0\n",
      "\n",
      " ('_EVENT', 'LUSC') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'LUSC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'LUSC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'LUSC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'LUSC') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'LUSC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'LUSC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'LUSC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'LUSC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'LUSC') MLP Accuracy is: 56.99999999999999\n",
      "Not enough number of samples\n",
      "\n",
      " ('_EVENT', 'OV') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'OV') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'OV') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'OV') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'OV') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'OV') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'OV') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'OV') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'OV') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'OV') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'PAAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'PAAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'PAAD') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'PAAD') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'PAAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'PAAD') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('_EVENT', 'PAAD') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'PAAD') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('_EVENT', 'PAAD') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('_EVENT', 'PAAD') MLP Accuracy is: 61.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('_EVENT', 'SARC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'SARC') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('_EVENT', 'SARC') MLP Accuracy is: 54.0\n",
      "\n",
      " ('_EVENT', 'SARC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'SARC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'SARC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'SARC') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('_EVENT', 'SARC') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('_EVENT', 'SARC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'SARC') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('_EVENT', 'SKCM') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'SKCM') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'SKCM') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'SKCM') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'SKCM') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'SKCM') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_EVENT', 'SKCM') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'SKCM') MLP Accuracy is: 63.0\n",
      "\n",
      " ('_EVENT', 'SKCM') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'SKCM') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_EVENT', 'STAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'STAD') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'STAD') MLP Accuracy is: 59.0\n",
      "\n",
      " ('_EVENT', 'STAD') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'STAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'STAD') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_EVENT', 'STAD') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_EVENT', 'STAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_EVENT', 'STAD') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('_EVENT', 'STAD') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('_EVENT', 'THCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('_EVENT', 'THCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('_EVENT', 'THCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('_EVENT', 'THCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('_EVENT', 'THCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('_EVENT', 'THCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('_EVENT', 'THCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('_EVENT', 'THCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('_EVENT', 'THCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('_EVENT', 'THCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('_EVENT', 'UCEC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('_EVENT', 'UCEC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('_EVENT', 'UCEC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('_EVENT', 'UCEC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('_EVENT', 'UCEC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('_EVENT', 'UCEC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('_EVENT', 'UCEC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('_EVENT', 'UCEC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('_EVENT', 'UCEC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('_EVENT', 'UCEC') MLP Accuracy is: 79.0\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "\n",
      " ('_PANCAN_DNAMethyl_BRCA', 'BRCA') MLP Accuracy is: 48.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_BRCA', 'BRCA') MLP Accuracy is: 51.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_BRCA', 'BRCA') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('_PANCAN_DNAMethyl_BRCA', 'BRCA') MLP Accuracy is: 51.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_BRCA', 'BRCA') MLP Accuracy is: 53.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_BRCA', 'BRCA') MLP Accuracy is: 53.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_BRCA', 'BRCA') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('_PANCAN_DNAMethyl_BRCA', 'BRCA') MLP Accuracy is: 53.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_BRCA', 'BRCA') MLP Accuracy is: 52.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_BRCA', 'BRCA') MLP Accuracy is: 52.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_HNSC', 'HNSC') MLP Accuracy is: 76.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_HNSC', 'HNSC') MLP Accuracy is: 75.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_HNSC', 'HNSC') MLP Accuracy is: 75.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_HNSC', 'HNSC') MLP Accuracy is: 77.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_HNSC', 'HNSC') MLP Accuracy is: 75.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_HNSC', 'HNSC') MLP Accuracy is: 75.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_HNSC', 'HNSC') MLP Accuracy is: 75.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_HNSC', 'HNSC') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_HNSC', 'HNSC') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_HNSC', 'HNSC') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUAD', 'LUAD') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUAD', 'LUAD') MLP Accuracy is: 51.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUAD', 'LUAD') MLP Accuracy is: 53.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUAD', 'LUAD') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUAD', 'LUAD') MLP Accuracy is: 53.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUAD', 'LUAD') MLP Accuracy is: 50.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUAD', 'LUAD') MLP Accuracy is: 54.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUAD', 'LUAD') MLP Accuracy is: 52.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUAD', 'LUAD') MLP Accuracy is: 52.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUAD', 'LUAD') MLP Accuracy is: 49.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUSC', 'LUSC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUSC', 'LUSC') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUSC', 'LUSC') MLP Accuracy is: 63.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUSC', 'LUSC') MLP Accuracy is: 63.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUSC', 'LUSC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUSC', 'LUSC') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUSC', 'LUSC') MLP Accuracy is: 65.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUSC', 'LUSC') MLP Accuracy is: 63.0\n",
      "\n",
      " ('_PANCAN_DNAMethyl_LUSC', 'LUSC') MLP Accuracy is: 57.99999999999999\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ('_PANCAN_DNAMethyl_LUSC', 'LUSC') MLP Accuracy is: 61.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('_PANCAN_mirna_BRCA', 'BRCA') MLP Accuracy is: 38.0\n",
      "\n",
      " ('_PANCAN_mirna_BRCA', 'BRCA') MLP Accuracy is: 39.0\n",
      "\n",
      " ('_PANCAN_mirna_BRCA', 'BRCA') MLP Accuracy is: 38.0\n",
      "\n",
      " ('_PANCAN_mirna_BRCA', 'BRCA') MLP Accuracy is: 39.0\n",
      "\n",
      " ('_PANCAN_mirna_BRCA', 'BRCA') MLP Accuracy is: 43.0\n",
      "\n",
      " ('_PANCAN_mirna_BRCA', 'BRCA') MLP Accuracy is: 43.0\n",
      "\n",
      " ('_PANCAN_mirna_BRCA', 'BRCA') MLP Accuracy is: 42.0\n",
      "\n",
      " ('_PANCAN_mirna_BRCA', 'BRCA') MLP Accuracy is: 43.0\n",
      "\n",
      " ('_PANCAN_mirna_BRCA', 'BRCA') MLP Accuracy is: 42.0\n",
      "\n",
      " ('_PANCAN_mirna_BRCA', 'BRCA') MLP Accuracy is: 43.0\n",
      "\n",
      " ('_PANCAN_mirna_HNSC', 'HNSC') MLP Accuracy is: 52.0\n",
      "\n",
      " ('_PANCAN_mirna_HNSC', 'HNSC') MLP Accuracy is: 54.0\n",
      "\n",
      " ('_PANCAN_mirna_HNSC', 'HNSC') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('_PANCAN_mirna_HNSC', 'HNSC') MLP Accuracy is: 54.0\n",
      "\n",
      " ('_PANCAN_mirna_HNSC', 'HNSC') MLP Accuracy is: 52.0\n",
      "\n",
      " ('_PANCAN_mirna_HNSC', 'HNSC') MLP Accuracy is: 53.0\n",
      "\n",
      " ('_PANCAN_mirna_HNSC', 'HNSC') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('_PANCAN_mirna_HNSC', 'HNSC') MLP Accuracy is: 53.0\n",
      "\n",
      " ('_PANCAN_mirna_HNSC', 'HNSC') MLP Accuracy is: 54.0\n",
      "\n",
      " ('_PANCAN_mirna_HNSC', 'HNSC') MLP Accuracy is: 53.0\n",
      "\n",
      " ('_PANCAN_mirna_LAML', 'LAML') MLP Accuracy is: 73.0\n",
      "\n",
      " ('_PANCAN_mirna_LAML', 'LAML') MLP Accuracy is: 72.0\n",
      "\n",
      " ('_PANCAN_mirna_LAML', 'LAML') MLP Accuracy is: 73.0\n",
      "\n",
      " ('_PANCAN_mirna_LAML', 'LAML') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_PANCAN_mirna_LAML', 'LAML') MLP Accuracy is: 72.0\n",
      "\n",
      " ('_PANCAN_mirna_LAML', 'LAML') MLP Accuracy is: 72.0\n",
      "\n",
      " ('_PANCAN_mirna_LAML', 'LAML') MLP Accuracy is: 73.0\n",
      "\n",
      " ('_PANCAN_mirna_LAML', 'LAML') MLP Accuracy is: 70.0\n",
      "\n",
      " ('_PANCAN_mirna_LAML', 'LAML') MLP Accuracy is: 74.0\n",
      "\n",
      " ('_PANCAN_mirna_LAML', 'LAML') MLP Accuracy is: 73.0\n",
      "\n",
      " ('_PANCAN_mirna_LUAD', 'LUAD') MLP Accuracy is: 40.0\n",
      "\n",
      " ('_PANCAN_mirna_LUAD', 'LUAD') MLP Accuracy is: 42.0\n",
      "\n",
      " ('_PANCAN_mirna_LUAD', 'LUAD') MLP Accuracy is: 39.0\n",
      "\n",
      " ('_PANCAN_mirna_LUAD', 'LUAD') MLP Accuracy is: 39.0\n",
      "\n",
      " ('_PANCAN_mirna_LUAD', 'LUAD') MLP Accuracy is: 42.0\n",
      "\n",
      " ('_PANCAN_mirna_LUAD', 'LUAD') MLP Accuracy is: 39.0\n",
      "\n",
      " ('_PANCAN_mirna_LUAD', 'LUAD') MLP Accuracy is: 39.0\n",
      "\n",
      " ('_PANCAN_mirna_LUAD', 'LUAD') MLP Accuracy is: 41.0\n",
      "\n",
      " ('_PANCAN_mirna_LUAD', 'LUAD') MLP Accuracy is: 39.0\n",
      "\n",
      " ('_PANCAN_mirna_LUAD', 'LUAD') MLP Accuracy is: 39.0\n",
      "\n",
      " ('_PANCAN_mirna_LUSC', 'LUSC') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_PANCAN_mirna_LUSC', 'LUSC') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_PANCAN_mirna_LUSC', 'LUSC') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_PANCAN_mirna_LUSC', 'LUSC') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_PANCAN_mirna_LUSC', 'LUSC') MLP Accuracy is: 63.0\n",
      "\n",
      " ('_PANCAN_mirna_LUSC', 'LUSC') MLP Accuracy is: 61.0\n",
      "\n",
      " ('_PANCAN_mirna_LUSC', 'LUSC') MLP Accuracy is: 62.0\n",
      "\n",
      " ('_PANCAN_mirna_LUSC', 'LUSC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('_PANCAN_mirna_LUSC', 'LUSC') MLP Accuracy is: 60.0\n",
      "\n",
      " ('_PANCAN_mirna_LUSC', 'LUSC') MLP Accuracy is: 64.0\n",
      "\n",
      " ('_PANCAN_mirna_OV', 'OV') MLP Accuracy is: 30.0\n",
      "\n",
      " ('_PANCAN_mirna_OV', 'OV') MLP Accuracy is: 30.0\n",
      "\n",
      " ('_PANCAN_mirna_OV', 'OV') MLP Accuracy is: 31.0\n",
      "\n",
      " ('_PANCAN_mirna_OV', 'OV') MLP Accuracy is: 33.0\n",
      "\n",
      " ('_PANCAN_mirna_OV', 'OV') MLP Accuracy is: 30.0\n",
      "\n",
      " ('_PANCAN_mirna_OV', 'OV') MLP Accuracy is: 33.0\n",
      "\n",
      " ('_PANCAN_mirna_OV', 'OV') MLP Accuracy is: 28.000000000000004\n",
      "\n",
      " ('_PANCAN_mirna_OV', 'OV') MLP Accuracy is: 32.0\n",
      "\n",
      " ('_PANCAN_mirna_OV', 'OV') MLP Accuracy is: 30.0\n",
      "\n",
      " ('_PANCAN_mirna_OV', 'OV') MLP Accuracy is: 32.0\n",
      "\n",
      " ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') MLP Accuracy is: 80.0\n",
      "\n",
      " ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') MLP Accuracy is: 80.0\n",
      "\n",
      " ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') MLP Accuracy is: 79.0\n",
      "\n",
      " ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') MLP Accuracy is: 79.0\n",
      "\n",
      " ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') MLP Accuracy is: 79.0\n",
      "\n",
      " ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') MLP Accuracy is: 79.0\n",
      "\n",
      " ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') MLP Accuracy is: 79.0\n",
      "\n",
      " ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') MLP Accuracy is: 79.0\n",
      "\n",
      " ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') MLP Accuracy is: 79.0\n",
      "\n",
      " ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML') MLP Accuracy is: 80.0\n",
      "\n",
      " ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('alcohol_history_documented', 'ESCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('alcohol_history_documented', 'ESCA') MLP Accuracy is: 68.0\n",
      "\n",
      " ('alcohol_history_documented', 'ESCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('alcohol_history_documented', 'ESCA') MLP Accuracy is: 66.0\n",
      "\n",
      " ('alcohol_history_documented', 'ESCA') MLP Accuracy is: 68.0\n",
      "\n",
      " ('alcohol_history_documented', 'ESCA') MLP Accuracy is: 68.0\n",
      "\n",
      " ('alcohol_history_documented', 'ESCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('alcohol_history_documented', 'ESCA') MLP Accuracy is: 68.0\n",
      "\n",
      " ('alcohol_history_documented', 'ESCA') MLP Accuracy is: 68.0\n",
      "\n",
      " ('alcohol_history_documented', 'ESCA') MLP Accuracy is: 71.0\n",
      "\n",
      " ('alcohol_history_documented', 'HNSC') MLP Accuracy is: 63.0\n",
      "\n",
      " ('alcohol_history_documented', 'HNSC') MLP Accuracy is: 66.0\n",
      "\n",
      " ('alcohol_history_documented', 'HNSC') MLP Accuracy is: 64.0\n",
      "\n",
      " ('alcohol_history_documented', 'HNSC') MLP Accuracy is: 66.0\n",
      "\n",
      " ('alcohol_history_documented', 'HNSC') MLP Accuracy is: 65.0\n",
      "\n",
      " ('alcohol_history_documented', 'HNSC') MLP Accuracy is: 66.0\n",
      "\n",
      " ('alcohol_history_documented', 'HNSC') MLP Accuracy is: 65.0\n",
      "\n",
      " ('alcohol_history_documented', 'HNSC') MLP Accuracy is: 65.0\n",
      "\n",
      " ('alcohol_history_documented', 'HNSC') MLP Accuracy is: 65.0\n",
      "\n",
      " ('alcohol_history_documented', 'HNSC') MLP Accuracy is: 66.0\n",
      "\n",
      " ('alcohol_history_documented', 'PAAD') MLP Accuracy is: 52.0\n",
      "\n",
      " ('alcohol_history_documented', 'PAAD') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('alcohol_history_documented', 'PAAD') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('alcohol_history_documented', 'PAAD') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('alcohol_history_documented', 'PAAD') MLP Accuracy is: 53.0\n",
      "\n",
      " ('alcohol_history_documented', 'PAAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('alcohol_history_documented', 'PAAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('alcohol_history_documented', 'PAAD') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('alcohol_history_documented', 'PAAD') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('alcohol_history_documented', 'PAAD') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'OV') MLP Accuracy is: 73.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'OV') MLP Accuracy is: 73.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'OV') MLP Accuracy is: 73.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'OV') MLP Accuracy is: 73.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'OV') MLP Accuracy is: 73.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'OV') MLP Accuracy is: 73.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'OV') MLP Accuracy is: 73.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'OV') MLP Accuracy is: 73.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'OV') MLP Accuracy is: 73.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'OV') MLP Accuracy is: 73.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'PAAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'PAAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'PAAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'PAAD') MLP Accuracy is: 76.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ('anatomic_neoplasm_subdivision', 'PAAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'PAAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'PAAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'PAAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'PAAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('anatomic_neoplasm_subdivision', 'PAAD') MLP Accuracy is: 75.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'GBMLGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'GBMLGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'GBMLGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'GBMLGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'GBMLGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'GBMLGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'GBMLGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'GBMLGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'GBMLGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'GBMLGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'LGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'LGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'LGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'LGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'LGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'LGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'LGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'LGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'LGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('animal_insect_allergy_history', 'LGG') MLP Accuracy is: 95.0\n",
      "\n",
      " ('antireflux_treatment', 'STAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('antireflux_treatment', 'STAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('antireflux_treatment', 'STAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('antireflux_treatment', 'STAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('antireflux_treatment', 'STAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('antireflux_treatment', 'STAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('antireflux_treatment', 'STAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('antireflux_treatment', 'STAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('antireflux_treatment', 'STAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('antireflux_treatment', 'STAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('asthma_history', 'GBMLGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'GBMLGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'GBMLGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'GBMLGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'GBMLGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'GBMLGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'GBMLGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'GBMLGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'GBMLGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'GBMLGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'LGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'LGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'LGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'LGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'LGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'LGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'LGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'LGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'LGG') MLP Accuracy is: 94.0\n",
      "\n",
      " ('asthma_history', 'LGG') MLP Accuracy is: 94.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('barretts_esophagus', 'STAD') MLP Accuracy is: 91.0\n",
      "\n",
      " ('barretts_esophagus', 'STAD') MLP Accuracy is: 91.0\n",
      "\n",
      " ('barretts_esophagus', 'STAD') MLP Accuracy is: 91.0\n",
      "\n",
      " ('barretts_esophagus', 'STAD') MLP Accuracy is: 91.0\n",
      "\n",
      " ('barretts_esophagus', 'STAD') MLP Accuracy is: 91.0\n",
      "\n",
      " ('barretts_esophagus', 'STAD') MLP Accuracy is: 91.0\n",
      "\n",
      " ('barretts_esophagus', 'STAD') MLP Accuracy is: 91.0\n",
      "\n",
      " ('barretts_esophagus', 'STAD') MLP Accuracy is: 91.0\n",
      "\n",
      " ('barretts_esophagus', 'STAD') MLP Accuracy is: 91.0\n",
      "\n",
      " ('barretts_esophagus', 'STAD') MLP Accuracy is: 91.0\n",
      "\n",
      " ('biochemical_recurrence', 'PRAD') MLP Accuracy is: 87.0\n",
      "\n",
      " ('biochemical_recurrence', 'PRAD') MLP Accuracy is: 87.0\n",
      "\n",
      " ('biochemical_recurrence', 'PRAD') MLP Accuracy is: 86.0\n",
      "\n",
      " ('biochemical_recurrence', 'PRAD') MLP Accuracy is: 87.0\n",
      "\n",
      " ('biochemical_recurrence', 'PRAD') MLP Accuracy is: 87.0\n",
      "\n",
      " ('biochemical_recurrence', 'PRAD') MLP Accuracy is: 87.0\n",
      "\n",
      " ('biochemical_recurrence', 'PRAD') MLP Accuracy is: 86.0\n",
      "\n",
      " ('biochemical_recurrence', 'PRAD') MLP Accuracy is: 86.0\n",
      "\n",
      " ('biochemical_recurrence', 'PRAD') MLP Accuracy is: 86.0\n",
      "\n",
      " ('biochemical_recurrence', 'PRAD') MLP Accuracy is: 87.0\n",
      "\n",
      " ('birth_control_pill_history_usage_category', 'CESC') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('birth_control_pill_history_usage_category', 'CESC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('birth_control_pill_history_usage_category', 'CESC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('birth_control_pill_history_usage_category', 'CESC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('birth_control_pill_history_usage_category', 'CESC') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('birth_control_pill_history_usage_category', 'CESC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('birth_control_pill_history_usage_category', 'CESC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('birth_control_pill_history_usage_category', 'CESC') MLP Accuracy is: 54.0\n",
      "\n",
      " ('birth_control_pill_history_usage_category', 'CESC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('birth_control_pill_history_usage_category', 'CESC') MLP Accuracy is: 56.99999999999999\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "\n",
      " ('clinical_M', 'KIRP') MLP Accuracy is: 73.0\n",
      "\n",
      " ('clinical_M', 'KIRP') MLP Accuracy is: 72.0\n",
      "\n",
      " ('clinical_M', 'KIRP') MLP Accuracy is: 72.0\n",
      "\n",
      " ('clinical_M', 'KIRP') MLP Accuracy is: 72.0\n",
      "\n",
      " ('clinical_M', 'KIRP') MLP Accuracy is: 73.0\n",
      "\n",
      " ('clinical_M', 'KIRP') MLP Accuracy is: 72.0\n",
      "\n",
      " ('clinical_M', 'KIRP') MLP Accuracy is: 73.0\n",
      "\n",
      " ('clinical_M', 'KIRP') MLP Accuracy is: 72.0\n",
      "\n",
      " ('clinical_M', 'KIRP') MLP Accuracy is: 73.0\n",
      "\n",
      " ('clinical_M', 'KIRP') MLP Accuracy is: 73.0\n",
      "\n",
      " ('clinical_stage', 'KIRP') MLP Accuracy is: 64.0\n",
      "\n",
      " ('clinical_stage', 'KIRP') MLP Accuracy is: 66.0\n",
      "\n",
      " ('clinical_stage', 'KIRP') MLP Accuracy is: 64.0\n",
      "\n",
      " ('clinical_stage', 'KIRP') MLP Accuracy is: 67.0\n",
      "\n",
      " ('clinical_stage', 'KIRP') MLP Accuracy is: 65.0\n",
      "\n",
      " ('clinical_stage', 'KIRP') MLP Accuracy is: 63.0\n",
      "\n",
      " ('clinical_stage', 'KIRP') MLP Accuracy is: 64.0\n",
      "\n",
      " ('clinical_stage', 'KIRP') MLP Accuracy is: 65.0\n",
      "\n",
      " ('clinical_stage', 'KIRP') MLP Accuracy is: 64.0\n",
      "\n",
      " ('clinical_stage', 'KIRP') MLP Accuracy is: 66.0\n",
      "\n",
      " ('colon_polyps_present', 'COAD') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('colon_polyps_present', 'COAD') MLP Accuracy is: 61.0\n",
      "\n",
      " ('colon_polyps_present', 'COAD') MLP Accuracy is: 61.0\n",
      "\n",
      " ('colon_polyps_present', 'COAD') MLP Accuracy is: 62.0\n",
      "\n",
      " ('colon_polyps_present', 'COAD') MLP Accuracy is: 61.0\n",
      "\n",
      " ('colon_polyps_present', 'COAD') MLP Accuracy is: 61.0\n",
      "\n",
      " ('colon_polyps_present', 'COAD') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('colon_polyps_present', 'COAD') MLP Accuracy is: 62.0\n",
      "\n",
      " ('colon_polyps_present', 'COAD') MLP Accuracy is: 62.0\n",
      "\n",
      " ('colon_polyps_present', 'COAD') MLP Accuracy is: 61.0\n",
      "\n",
      " ('colon_polyps_present', 'COADREAD') MLP Accuracy is: 70.0\n",
      "\n",
      " ('colon_polyps_present', 'COADREAD') MLP Accuracy is: 68.0\n",
      "\n",
      " ('colon_polyps_present', 'COADREAD') MLP Accuracy is: 68.0\n",
      "\n",
      " ('colon_polyps_present', 'COADREAD') MLP Accuracy is: 68.0\n",
      "\n",
      " ('colon_polyps_present', 'COADREAD') MLP Accuracy is: 69.0\n",
      "\n",
      " ('colon_polyps_present', 'COADREAD') MLP Accuracy is: 70.0\n",
      "\n",
      " ('colon_polyps_present', 'COADREAD') MLP Accuracy is: 70.0\n",
      "\n",
      " ('colon_polyps_present', 'COADREAD') MLP Accuracy is: 69.0\n",
      "\n",
      " ('colon_polyps_present', 'COADREAD') MLP Accuracy is: 69.0\n",
      "\n",
      " ('colon_polyps_present', 'COADREAD') MLP Accuracy is: 70.0\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "\n",
      " ('family_history_of_cancer', 'GBMLGG') MLP Accuracy is: 48.0\n",
      "\n",
      " ('family_history_of_cancer', 'GBMLGG') MLP Accuracy is: 50.0\n",
      "\n",
      " ('family_history_of_cancer', 'GBMLGG') MLP Accuracy is: 51.0\n",
      "\n",
      " ('family_history_of_cancer', 'GBMLGG') MLP Accuracy is: 52.0\n",
      "\n",
      " ('family_history_of_cancer', 'GBMLGG') MLP Accuracy is: 51.0\n",
      "\n",
      " ('family_history_of_cancer', 'GBMLGG') MLP Accuracy is: 52.0\n",
      "\n",
      " ('family_history_of_cancer', 'GBMLGG') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('family_history_of_cancer', 'GBMLGG') MLP Accuracy is: 51.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ('family_history_of_cancer', 'GBMLGG') MLP Accuracy is: 52.0\n",
      "\n",
      " ('family_history_of_cancer', 'GBMLGG') MLP Accuracy is: 50.0\n",
      "\n",
      " ('family_history_of_cancer', 'LGG') MLP Accuracy is: 48.0\n",
      "\n",
      " ('family_history_of_cancer', 'LGG') MLP Accuracy is: 50.0\n",
      "\n",
      " ('family_history_of_cancer', 'LGG') MLP Accuracy is: 51.0\n",
      "\n",
      " ('family_history_of_cancer', 'LGG') MLP Accuracy is: 52.0\n",
      "\n",
      " ('family_history_of_cancer', 'LGG') MLP Accuracy is: 51.0\n",
      "\n",
      " ('family_history_of_cancer', 'LGG') MLP Accuracy is: 52.0\n",
      "\n",
      " ('family_history_of_cancer', 'LGG') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('family_history_of_cancer', 'LGG') MLP Accuracy is: 51.0\n",
      "\n",
      " ('family_history_of_cancer', 'LGG') MLP Accuracy is: 52.0\n",
      "\n",
      " ('family_history_of_cancer', 'LGG') MLP Accuracy is: 50.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'LGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'LGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'LGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'LGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'LGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'LGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'LGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'LGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'LGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_primary_brain_tumor', 'LGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('family_history_of_stomach_cancer', 'STAD') MLP Accuracy is: 94.0\n",
      "\n",
      " ('family_history_of_stomach_cancer', 'STAD') MLP Accuracy is: 94.0\n",
      "\n",
      " ('family_history_of_stomach_cancer', 'STAD') MLP Accuracy is: 94.0\n",
      "\n",
      " ('family_history_of_stomach_cancer', 'STAD') MLP Accuracy is: 94.0\n",
      "\n",
      " ('family_history_of_stomach_cancer', 'STAD') MLP Accuracy is: 94.0\n",
      "\n",
      " ('family_history_of_stomach_cancer', 'STAD') MLP Accuracy is: 94.0\n",
      "\n",
      " ('family_history_of_stomach_cancer', 'STAD') MLP Accuracy is: 94.0\n",
      "\n",
      " ('family_history_of_stomach_cancer', 'STAD') MLP Accuracy is: 94.0\n",
      "\n",
      " ('family_history_of_stomach_cancer', 'STAD') MLP Accuracy is: 94.0\n",
      "\n",
      " ('family_history_of_stomach_cancer', 'STAD') MLP Accuracy is: 94.0\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "\n",
      " ('gender', 'BLCA') MLP Accuracy is: 78.0\n",
      "\n",
      " ('gender', 'BLCA') MLP Accuracy is: 79.0\n",
      "\n",
      " ('gender', 'BLCA') MLP Accuracy is: 79.0\n",
      "\n",
      " ('gender', 'BLCA') MLP Accuracy is: 79.0\n",
      "\n",
      " ('gender', 'BLCA') MLP Accuracy is: 78.0\n",
      "\n",
      " ('gender', 'BLCA') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'BLCA') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'BLCA') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'BLCA') MLP Accuracy is: 78.0\n",
      "\n",
      " ('gender', 'BLCA') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'BRCA') MLP Accuracy is: 99.0\n",
      "\n",
      " ('gender', 'BRCA') MLP Accuracy is: 99.0\n",
      "\n",
      " ('gender', 'BRCA') MLP Accuracy is: 99.0\n",
      "\n",
      " ('gender', 'BRCA') MLP Accuracy is: 99.0\n",
      "\n",
      " ('gender', 'BRCA') MLP Accuracy is: 99.0\n",
      "\n",
      " ('gender', 'BRCA') MLP Accuracy is: 99.0\n",
      "\n",
      " ('gender', 'BRCA') MLP Accuracy is: 99.0\n",
      "\n",
      " ('gender', 'BRCA') MLP Accuracy is: 99.0\n",
      "\n",
      " ('gender', 'BRCA') MLP Accuracy is: 99.0\n",
      "\n",
      " ('gender', 'BRCA') MLP Accuracy is: 99.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('gender', 'COAD') MLP Accuracy is: 86.0\n",
      "\n",
      " ('gender', 'COAD') MLP Accuracy is: 89.0\n",
      "\n",
      " ('gender', 'COAD') MLP Accuracy is: 87.0\n",
      "\n",
      " ('gender', 'COAD') MLP Accuracy is: 86.0\n",
      "\n",
      " ('gender', 'COAD') MLP Accuracy is: 89.0\n",
      "\n",
      " ('gender', 'COAD') MLP Accuracy is: 86.0\n",
      "\n",
      " ('gender', 'COAD') MLP Accuracy is: 85.0\n",
      "\n",
      " ('gender', 'COAD') MLP Accuracy is: 83.0\n",
      "\n",
      " ('gender', 'COAD') MLP Accuracy is: 89.0\n",
      "\n",
      " ('gender', 'COAD') MLP Accuracy is: 88.0\n",
      "\n",
      " ('gender', 'COADREAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'COADREAD') MLP Accuracy is: 79.0\n",
      "\n",
      " ('gender', 'COADREAD') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'COADREAD') MLP Accuracy is: 78.0\n",
      "\n",
      " ('gender', 'COADREAD') MLP Accuracy is: 79.0\n",
      "\n",
      " ('gender', 'COADREAD') MLP Accuracy is: 79.0\n",
      "\n",
      " ('gender', 'COADREAD') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'COADREAD') MLP Accuracy is: 78.0\n",
      "\n",
      " ('gender', 'COADREAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'COADREAD') MLP Accuracy is: 80.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('gender', 'ESCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'ESCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'ESCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'ESCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'ESCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'ESCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'ESCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'ESCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'ESCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'ESCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'GBM') MLP Accuracy is: 70.0\n",
      "\n",
      " ('gender', 'GBM') MLP Accuracy is: 70.0\n",
      "\n",
      " ('gender', 'GBM') MLP Accuracy is: 71.0\n",
      "\n",
      " ('gender', 'GBM') MLP Accuracy is: 71.0\n",
      "\n",
      " ('gender', 'GBM') MLP Accuracy is: 73.0\n",
      "\n",
      " ('gender', 'GBM') MLP Accuracy is: 71.0\n",
      "\n",
      " ('gender', 'GBM') MLP Accuracy is: 70.0\n",
      "\n",
      " ('gender', 'GBM') MLP Accuracy is: 71.0\n",
      "\n",
      " ('gender', 'GBM') MLP Accuracy is: 68.0\n",
      "\n",
      " ('gender', 'GBM') MLP Accuracy is: 73.0\n",
      "\n",
      " ('gender', 'GBMLGG') MLP Accuracy is: 83.0\n",
      "\n",
      " ('gender', 'GBMLGG') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'GBMLGG') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'GBMLGG') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'GBMLGG') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'GBMLGG') MLP Accuracy is: 79.0\n",
      "\n",
      " ('gender', 'GBMLGG') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'GBMLGG') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'GBMLGG') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'GBMLGG') MLP Accuracy is: 83.0\n",
      "\n",
      " ('gender', 'HNSC') MLP Accuracy is: 75.0\n",
      "\n",
      " ('gender', 'HNSC') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'HNSC') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'HNSC') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'HNSC') MLP Accuracy is: 75.0\n",
      "\n",
      " ('gender', 'HNSC') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'HNSC') MLP Accuracy is: 75.0\n",
      "\n",
      " ('gender', 'HNSC') MLP Accuracy is: 75.0\n",
      "\n",
      " ('gender', 'HNSC') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'HNSC') MLP Accuracy is: 76.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('gender', 'KIRP') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'KIRP') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'KIRP') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'KIRP') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'KIRP') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'KIRP') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'KIRP') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'KIRP') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'KIRP') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'KIRP') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'LAML') MLP Accuracy is: 51.0\n",
      "\n",
      " ('gender', 'LAML') MLP Accuracy is: 53.0\n",
      "\n",
      " ('gender', 'LAML') MLP Accuracy is: 53.0\n",
      "\n",
      " ('gender', 'LAML') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('gender', 'LAML') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('gender', 'LAML') MLP Accuracy is: 53.0\n",
      "\n",
      " ('gender', 'LAML') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('gender', 'LAML') MLP Accuracy is: 51.0\n",
      "\n",
      " ('gender', 'LAML') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('gender', 'LAML') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('gender', 'LGG') MLP Accuracy is: 79.0\n",
      "\n",
      " ('gender', 'LGG') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'LGG') MLP Accuracy is: 73.0\n",
      "\n",
      " ('gender', 'LGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('gender', 'LGG') MLP Accuracy is: 70.0\n",
      "\n",
      " ('gender', 'LGG') MLP Accuracy is: 78.0\n",
      "\n",
      " ('gender', 'LGG') MLP Accuracy is: 74.0\n",
      "\n",
      " ('gender', 'LGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('gender', 'LGG') MLP Accuracy is: 72.0\n",
      "\n",
      " ('gender', 'LGG') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'LIHC') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'LIHC') MLP Accuracy is: 79.0\n",
      "\n",
      " ('gender', 'LIHC') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'LIHC') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'LIHC') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'LIHC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'LIHC') MLP Accuracy is: 82.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ('gender', 'LIHC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'LIHC') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'LIHC') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'LUAD') MLP Accuracy is: 83.0\n",
      "\n",
      " ('gender', 'LUAD') MLP Accuracy is: 85.0\n",
      "\n",
      " ('gender', 'LUAD') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'LUAD') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'LUAD') MLP Accuracy is: 86.0\n",
      "\n",
      " ('gender', 'LUAD') MLP Accuracy is: 86.0\n",
      "\n",
      " ('gender', 'LUAD') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'LUAD') MLP Accuracy is: 89.0\n",
      "\n",
      " ('gender', 'LUAD') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'LUAD') MLP Accuracy is: 85.0\n",
      "\n",
      " ('gender', 'LUNG') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'LUNG') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'LUNG') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'LUNG') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'LUNG') MLP Accuracy is: 85.0\n",
      "\n",
      " ('gender', 'LUNG') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'LUNG') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'LUNG') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'LUNG') MLP Accuracy is: 83.0\n",
      "\n",
      " ('gender', 'LUNG') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'LUSC') MLP Accuracy is: 75.0\n",
      "\n",
      " ('gender', 'LUSC') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'LUSC') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'LUSC') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'LUSC') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'LUSC') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'LUSC') MLP Accuracy is: 74.0\n",
      "\n",
      " ('gender', 'LUSC') MLP Accuracy is: 74.0\n",
      "\n",
      " ('gender', 'LUSC') MLP Accuracy is: 75.0\n",
      "\n",
      " ('gender', 'LUSC') MLP Accuracy is: 73.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('gender', 'PAAD') MLP Accuracy is: 83.0\n",
      "\n",
      " ('gender', 'PAAD') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'PAAD') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'PAAD') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'PAAD') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'PAAD') MLP Accuracy is: 83.0\n",
      "\n",
      " ('gender', 'PAAD') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'PAAD') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'PAAD') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'PAAD') MLP Accuracy is: 83.0\n",
      "\n",
      " ('gender', 'PCPG') MLP Accuracy is: 73.0\n",
      "\n",
      " ('gender', 'PCPG') MLP Accuracy is: 73.0\n",
      "\n",
      " ('gender', 'PCPG') MLP Accuracy is: 74.0\n",
      "\n",
      " ('gender', 'PCPG') MLP Accuracy is: 73.0\n",
      "\n",
      " ('gender', 'PCPG') MLP Accuracy is: 78.0\n",
      "\n",
      " ('gender', 'PCPG') MLP Accuracy is: 73.0\n",
      "\n",
      " ('gender', 'PCPG') MLP Accuracy is: 75.0\n",
      "\n",
      " ('gender', 'PCPG') MLP Accuracy is: 78.0\n",
      "\n",
      " ('gender', 'PCPG') MLP Accuracy is: 71.0\n",
      "\n",
      " ('gender', 'PCPG') MLP Accuracy is: 76.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('gender', 'SARC') MLP Accuracy is: 71.0\n",
      "\n",
      " ('gender', 'SARC') MLP Accuracy is: 71.0\n",
      "\n",
      " ('gender', 'SARC') MLP Accuracy is: 71.0\n",
      "\n",
      " ('gender', 'SARC') MLP Accuracy is: 72.0\n",
      "\n",
      " ('gender', 'SARC') MLP Accuracy is: 72.0\n",
      "\n",
      " ('gender', 'SARC') MLP Accuracy is: 72.0\n",
      "\n",
      " ('gender', 'SARC') MLP Accuracy is: 72.0\n",
      "\n",
      " ('gender', 'SARC') MLP Accuracy is: 69.0\n",
      "\n",
      " ('gender', 'SARC') MLP Accuracy is: 72.0\n",
      "\n",
      " ('gender', 'SARC') MLP Accuracy is: 73.0\n",
      "\n",
      " ('gender', 'SKCM') MLP Accuracy is: 69.0\n",
      "\n",
      " ('gender', 'SKCM') MLP Accuracy is: 68.0\n",
      "\n",
      " ('gender', 'SKCM') MLP Accuracy is: 70.0\n",
      "\n",
      " ('gender', 'SKCM') MLP Accuracy is: 68.0\n",
      "\n",
      " ('gender', 'SKCM') MLP Accuracy is: 69.0\n",
      "\n",
      " ('gender', 'SKCM') MLP Accuracy is: 66.0\n",
      "\n",
      " ('gender', 'SKCM') MLP Accuracy is: 70.0\n",
      "\n",
      " ('gender', 'SKCM') MLP Accuracy is: 69.0\n",
      "\n",
      " ('gender', 'SKCM') MLP Accuracy is: 67.0\n",
      "\n",
      " ('gender', 'SKCM') MLP Accuracy is: 67.0\n",
      "\n",
      " ('gender', 'STAD') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'STAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'STAD') MLP Accuracy is: 79.0\n",
      "\n",
      " ('gender', 'STAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'STAD') MLP Accuracy is: 80.0\n",
      "\n",
      " ('gender', 'STAD') MLP Accuracy is: 78.0\n",
      "\n",
      " ('gender', 'STAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'STAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'STAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('gender', 'STAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('gender', 'THCA') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'THCA') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'THCA') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'THCA') MLP Accuracy is: 81.0\n",
      "\n",
      " ('gender', 'THCA') MLP Accuracy is: 83.0\n",
      "\n",
      " ('gender', 'THCA') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'THCA') MLP Accuracy is: 83.0\n",
      "\n",
      " ('gender', 'THCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('gender', 'THCA') MLP Accuracy is: 82.0\n",
      "\n",
      " ('gender', 'THCA') MLP Accuracy is: 84.0\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "\n",
      " ('histological_type', 'ESCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'ESCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('histological_type', 'ESCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('histological_type', 'ESCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('histological_type', 'ESCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('histological_type', 'ESCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'ESCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('histological_type', 'ESCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'ESCA') MLP Accuracy is: 98.0\n",
      "\n",
      " ('histological_type', 'ESCA') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'LGG') MLP Accuracy is: 52.0\n",
      "\n",
      " ('histological_type', 'LGG') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('histological_type', 'LGG') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('histological_type', 'LGG') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('histological_type', 'LGG') MLP Accuracy is: 53.0\n",
      "\n",
      " ('histological_type', 'LGG') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('histological_type', 'LGG') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('histological_type', 'LGG') MLP Accuracy is: 53.0\n",
      "\n",
      " ('histological_type', 'LGG') MLP Accuracy is: 54.0\n",
      "\n",
      " ('histological_type', 'LGG') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('histological_type', 'PCPG') MLP Accuracy is: 84.0\n",
      "\n",
      " ('histological_type', 'PCPG') MLP Accuracy is: 84.0\n",
      "\n",
      " ('histological_type', 'PCPG') MLP Accuracy is: 84.0\n",
      "\n",
      " ('histological_type', 'PCPG') MLP Accuracy is: 84.0\n",
      "\n",
      " ('histological_type', 'PCPG') MLP Accuracy is: 84.0\n",
      "\n",
      " ('histological_type', 'PCPG') MLP Accuracy is: 84.0\n",
      "\n",
      " ('histological_type', 'PCPG') MLP Accuracy is: 84.0\n",
      "\n",
      " ('histological_type', 'PCPG') MLP Accuracy is: 84.0\n",
      "\n",
      " ('histological_type', 'PCPG') MLP Accuracy is: 84.0\n",
      "\n",
      " ('histological_type', 'PCPG') MLP Accuracy is: 84.0\n",
      "\n",
      " ('histological_type', 'PRAD') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'PRAD') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'PRAD') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'PRAD') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'PRAD') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'PRAD') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'PRAD') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'PRAD') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'PRAD') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'PRAD') MLP Accuracy is: 97.0\n",
      "\n",
      " ('histological_type', 'UCEC') MLP Accuracy is: 71.0\n",
      "\n",
      " ('histological_type', 'UCEC') MLP Accuracy is: 71.0\n",
      "\n",
      " ('histological_type', 'UCEC') MLP Accuracy is: 71.0\n",
      "\n",
      " ('histological_type', 'UCEC') MLP Accuracy is: 71.0\n",
      "\n",
      " ('histological_type', 'UCEC') MLP Accuracy is: 71.0\n",
      "\n",
      " ('histological_type', 'UCEC') MLP Accuracy is: 72.0\n",
      "\n",
      " ('histological_type', 'UCEC') MLP Accuracy is: 73.0\n",
      "\n",
      " ('histological_type', 'UCEC') MLP Accuracy is: 71.0\n",
      "\n",
      " ('histological_type', 'UCEC') MLP Accuracy is: 71.0\n",
      "\n",
      " ('histological_type', 'UCEC') MLP Accuracy is: 72.0\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "\n",
      " ('lymphatic_invasion', 'COAD') MLP Accuracy is: 61.0\n",
      "\n",
      " ('lymphatic_invasion', 'COAD') MLP Accuracy is: 61.0\n",
      "\n",
      " ('lymphatic_invasion', 'COAD') MLP Accuracy is: 62.0\n",
      "\n",
      " ('lymphatic_invasion', 'COAD') MLP Accuracy is: 62.0\n",
      "\n",
      " ('lymphatic_invasion', 'COAD') MLP Accuracy is: 60.0\n",
      "\n",
      " ('lymphatic_invasion', 'COAD') MLP Accuracy is: 64.0\n",
      "\n",
      " ('lymphatic_invasion', 'COAD') MLP Accuracy is: 63.0\n",
      "\n",
      " ('lymphatic_invasion', 'COAD') MLP Accuracy is: 65.0\n",
      "\n",
      " ('lymphatic_invasion', 'COAD') MLP Accuracy is: 60.0\n",
      "\n",
      " ('lymphatic_invasion', 'COAD') MLP Accuracy is: 60.0\n",
      "\n",
      " ('lymphatic_invasion', 'COADREAD') MLP Accuracy is: 66.0\n",
      "\n",
      " ('lymphatic_invasion', 'COADREAD') MLP Accuracy is: 68.0\n",
      "\n",
      " ('lymphatic_invasion', 'COADREAD') MLP Accuracy is: 67.0\n",
      "\n",
      " ('lymphatic_invasion', 'COADREAD') MLP Accuracy is: 66.0\n",
      "\n",
      " ('lymphatic_invasion', 'COADREAD') MLP Accuracy is: 66.0\n",
      "\n",
      " ('lymphatic_invasion', 'COADREAD') MLP Accuracy is: 67.0\n",
      "\n",
      " ('lymphatic_invasion', 'COADREAD') MLP Accuracy is: 66.0\n",
      "\n",
      " ('lymphatic_invasion', 'COADREAD') MLP Accuracy is: 65.0\n",
      "\n",
      " ('lymphatic_invasion', 'COADREAD') MLP Accuracy is: 65.0\n",
      "\n",
      " ('lymphatic_invasion', 'COADREAD') MLP Accuracy is: 66.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "\n",
      " ('lymphovascular_invasion_present', 'BLCA') MLP Accuracy is: 52.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'BLCA') MLP Accuracy is: 53.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'BLCA') MLP Accuracy is: 52.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'BLCA') MLP Accuracy is: 53.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'BLCA') MLP Accuracy is: 52.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'BLCA') MLP Accuracy is: 54.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'BLCA') MLP Accuracy is: 52.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'BLCA') MLP Accuracy is: 53.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'BLCA') MLP Accuracy is: 52.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'BLCA') MLP Accuracy is: 54.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'HNSC') MLP Accuracy is: 60.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'HNSC') MLP Accuracy is: 64.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'HNSC') MLP Accuracy is: 61.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'HNSC') MLP Accuracy is: 63.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'HNSC') MLP Accuracy is: 62.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'HNSC') MLP Accuracy is: 63.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'HNSC') MLP Accuracy is: 61.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'HNSC') MLP Accuracy is: 62.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'HNSC') MLP Accuracy is: 61.0\n",
      "\n",
      " ('lymphovascular_invasion_present', 'HNSC') MLP Accuracy is: 62.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('menopause_status', 'BRCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('menopause_status', 'BRCA') MLP Accuracy is: 70.0\n",
      "\n",
      " ('menopause_status', 'BRCA') MLP Accuracy is: 70.0\n",
      "\n",
      " ('menopause_status', 'BRCA') MLP Accuracy is: 68.0\n",
      "\n",
      " ('menopause_status', 'BRCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('menopause_status', 'BRCA') MLP Accuracy is: 68.0\n",
      "\n",
      " ('menopause_status', 'BRCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('menopause_status', 'BRCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('menopause_status', 'BRCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('menopause_status', 'BRCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('mental_status_changes', 'GBMLGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'GBMLGG') MLP Accuracy is: 69.0\n",
      "\n",
      " ('mental_status_changes', 'GBMLGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'GBMLGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'GBMLGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'GBMLGG') MLP Accuracy is: 70.0\n",
      "\n",
      " ('mental_status_changes', 'GBMLGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'GBMLGG') MLP Accuracy is: 66.0\n",
      "\n",
      " ('mental_status_changes', 'GBMLGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'GBMLGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'LGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'LGG') MLP Accuracy is: 69.0\n",
      "\n",
      " ('mental_status_changes', 'LGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'LGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'LGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'LGG') MLP Accuracy is: 70.0\n",
      "\n",
      " ('mental_status_changes', 'LGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'LGG') MLP Accuracy is: 66.0\n",
      "\n",
      " ('mental_status_changes', 'LGG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('mental_status_changes', 'LGG') MLP Accuracy is: 68.0\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "\n",
      " ('metastatic_diagnosis', 'SARC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('metastatic_diagnosis', 'SARC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('metastatic_diagnosis', 'SARC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('metastatic_diagnosis', 'SARC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('metastatic_diagnosis', 'SARC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('metastatic_diagnosis', 'SARC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('metastatic_diagnosis', 'SARC') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('metastatic_diagnosis', 'SARC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('metastatic_diagnosis', 'SARC') MLP Accuracy is: 60.0\n",
      "\n",
      " ('metastatic_diagnosis', 'SARC') MLP Accuracy is: 60.0\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "\n",
      " ('oct_embedded', 'BLCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('oct_embedded', 'BLCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('oct_embedded', 'BLCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('oct_embedded', 'BLCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('oct_embedded', 'BLCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('oct_embedded', 'BLCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('oct_embedded', 'BLCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('oct_embedded', 'BLCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('oct_embedded', 'BLCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('oct_embedded', 'BLCA') MLP Accuracy is: 84.0\n",
      "\n",
      " ('oct_embedded', 'BRCA') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('oct_embedded', 'BRCA') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('oct_embedded', 'BRCA') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('oct_embedded', 'BRCA') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('oct_embedded', 'BRCA') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('oct_embedded', 'BRCA') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('oct_embedded', 'BRCA') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('oct_embedded', 'BRCA') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('oct_embedded', 'BRCA') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('oct_embedded', 'BRCA') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('oct_embedded', 'CESC') MLP Accuracy is: 53.0\n",
      "\n",
      " ('oct_embedded', 'CESC') MLP Accuracy is: 52.0\n",
      "\n",
      " ('oct_embedded', 'CESC') MLP Accuracy is: 53.0\n",
      "\n",
      " ('oct_embedded', 'CESC') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('oct_embedded', 'CESC') MLP Accuracy is: 53.0\n",
      "\n",
      " ('oct_embedded', 'CESC') MLP Accuracy is: 54.0\n",
      "\n",
      " ('oct_embedded', 'CESC') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('oct_embedded', 'CESC') MLP Accuracy is: 53.0\n",
      "\n",
      " ('oct_embedded', 'CESC') MLP Accuracy is: 52.0\n",
      "\n",
      " ('oct_embedded', 'CESC') MLP Accuracy is: 52.0\n",
      "\n",
      " ('oct_embedded', 'ESCA') MLP Accuracy is: 70.0\n",
      "\n",
      " ('oct_embedded', 'ESCA') MLP Accuracy is: 70.0\n",
      "\n",
      " ('oct_embedded', 'ESCA') MLP Accuracy is: 70.0\n",
      "\n",
      " ('oct_embedded', 'ESCA') MLP Accuracy is: 70.0\n",
      "\n",
      " ('oct_embedded', 'ESCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('oct_embedded', 'ESCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('oct_embedded', 'ESCA') MLP Accuracy is: 72.0\n",
      "\n",
      " ('oct_embedded', 'ESCA') MLP Accuracy is: 71.0\n",
      "\n",
      " ('oct_embedded', 'ESCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('oct_embedded', 'ESCA') MLP Accuracy is: 70.0\n",
      "\n",
      " ('oct_embedded', 'GBMLGG') MLP Accuracy is: 59.0\n",
      "\n",
      " ('oct_embedded', 'GBMLGG') MLP Accuracy is: 64.0\n",
      "\n",
      " ('oct_embedded', 'GBMLGG') MLP Accuracy is: 60.0\n",
      "\n",
      " ('oct_embedded', 'GBMLGG') MLP Accuracy is: 63.0\n",
      "\n",
      " ('oct_embedded', 'GBMLGG') MLP Accuracy is: 62.0\n",
      "\n",
      " ('oct_embedded', 'GBMLGG') MLP Accuracy is: 62.0\n",
      "\n",
      " ('oct_embedded', 'GBMLGG') MLP Accuracy is: 62.0\n",
      "\n",
      " ('oct_embedded', 'GBMLGG') MLP Accuracy is: 62.0\n",
      "\n",
      " ('oct_embedded', 'GBMLGG') MLP Accuracy is: 62.0\n",
      "\n",
      " ('oct_embedded', 'GBMLGG') MLP Accuracy is: 60.0\n",
      "\n",
      " ('oct_embedded', 'HNSC') MLP Accuracy is: 44.0\n",
      "\n",
      " ('oct_embedded', 'HNSC') MLP Accuracy is: 43.0\n",
      "\n",
      " ('oct_embedded', 'HNSC') MLP Accuracy is: 44.0\n",
      "\n",
      " ('oct_embedded', 'HNSC') MLP Accuracy is: 45.0\n",
      "\n",
      " ('oct_embedded', 'HNSC') MLP Accuracy is: 45.0\n",
      "\n",
      " ('oct_embedded', 'HNSC') MLP Accuracy is: 45.0\n",
      "\n",
      " ('oct_embedded', 'HNSC') MLP Accuracy is: 42.0\n",
      "\n",
      " ('oct_embedded', 'HNSC') MLP Accuracy is: 46.0\n",
      "\n",
      " ('oct_embedded', 'HNSC') MLP Accuracy is: 46.0\n",
      "\n",
      " ('oct_embedded', 'HNSC') MLP Accuracy is: 46.0\n",
      "\n",
      " ('oct_embedded', 'KIRP') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('oct_embedded', 'KIRP') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('oct_embedded', 'KIRP') MLP Accuracy is: 56.99999999999999\n",
      "\n",
      " ('oct_embedded', 'KIRP') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('oct_embedded', 'KIRP') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('oct_embedded', 'KIRP') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('oct_embedded', 'KIRP') MLP Accuracy is: 55.00000000000001\n",
      "\n",
      " ('oct_embedded', 'KIRP') MLP Accuracy is: 60.0\n",
      "\n",
      " ('oct_embedded', 'KIRP') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('oct_embedded', 'KIRP') MLP Accuracy is: 56.00000000000001\n",
      "\n",
      " ('oct_embedded', 'LGG') MLP Accuracy is: 47.0\n",
      "\n",
      " ('oct_embedded', 'LGG') MLP Accuracy is: 44.0\n",
      "\n",
      " ('oct_embedded', 'LGG') MLP Accuracy is: 45.0\n",
      "\n",
      " ('oct_embedded', 'LGG') MLP Accuracy is: 45.0\n",
      "\n",
      " ('oct_embedded', 'LGG') MLP Accuracy is: 48.0\n",
      "\n",
      " ('oct_embedded', 'LGG') MLP Accuracy is: 45.0\n",
      "\n",
      " ('oct_embedded', 'LGG') MLP Accuracy is: 45.0\n",
      "\n",
      " ('oct_embedded', 'LGG') MLP Accuracy is: 45.0\n",
      "\n",
      " ('oct_embedded', 'LGG') MLP Accuracy is: 42.0\n",
      "\n",
      " ('oct_embedded', 'LGG') MLP Accuracy is: 45.0\n",
      "\n",
      " ('oct_embedded', 'LIHC') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'LIHC') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'LIHC') MLP Accuracy is: 68.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ('oct_embedded', 'LIHC') MLP Accuracy is: 68.0\n",
      "\n",
      " ('oct_embedded', 'LIHC') MLP Accuracy is: 67.0\n",
      "\n",
      " ('oct_embedded', 'LIHC') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'LIHC') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'LIHC') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'LIHC') MLP Accuracy is: 67.0\n",
      "\n",
      " ('oct_embedded', 'LIHC') MLP Accuracy is: 66.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('oct_embedded', 'LUNG') MLP Accuracy is: 69.0\n",
      "\n",
      " ('oct_embedded', 'LUNG') MLP Accuracy is: 68.0\n",
      "\n",
      " ('oct_embedded', 'LUNG') MLP Accuracy is: 69.0\n",
      "\n",
      " ('oct_embedded', 'LUNG') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'LUNG') MLP Accuracy is: 69.0\n",
      "\n",
      " ('oct_embedded', 'LUNG') MLP Accuracy is: 69.0\n",
      "\n",
      " ('oct_embedded', 'LUNG') MLP Accuracy is: 69.0\n",
      "\n",
      " ('oct_embedded', 'LUNG') MLP Accuracy is: 70.0\n",
      "\n",
      " ('oct_embedded', 'LUNG') MLP Accuracy is: 69.0\n",
      "\n",
      " ('oct_embedded', 'LUNG') MLP Accuracy is: 68.0\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "\n",
      " ('oct_embedded', 'PRAD') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'PRAD') MLP Accuracy is: 79.0\n",
      "\n",
      " ('oct_embedded', 'PRAD') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'PRAD') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'PRAD') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'PRAD') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'PRAD') MLP Accuracy is: 79.0\n",
      "\n",
      " ('oct_embedded', 'PRAD') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'PRAD') MLP Accuracy is: 79.0\n",
      "\n",
      " ('oct_embedded', 'PRAD') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'SARC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'SARC') MLP Accuracy is: 82.0\n",
      "\n",
      " ('oct_embedded', 'SARC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'SARC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'SARC') MLP Accuracy is: 79.0\n",
      "\n",
      " ('oct_embedded', 'SARC') MLP Accuracy is: 83.0\n",
      "\n",
      " ('oct_embedded', 'SARC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'SARC') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'SARC') MLP Accuracy is: 79.0\n",
      "\n",
      " ('oct_embedded', 'SARC') MLP Accuracy is: 82.0\n",
      "\n",
      " ('oct_embedded', 'SKCM') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'SKCM') MLP Accuracy is: 79.0\n",
      "\n",
      " ('oct_embedded', 'SKCM') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'SKCM') MLP Accuracy is: 79.0\n",
      "\n",
      " ('oct_embedded', 'SKCM') MLP Accuracy is: 79.0\n",
      "\n",
      " ('oct_embedded', 'SKCM') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'SKCM') MLP Accuracy is: 79.0\n",
      "\n",
      " ('oct_embedded', 'SKCM') MLP Accuracy is: 79.0\n",
      "\n",
      " ('oct_embedded', 'SKCM') MLP Accuracy is: 79.0\n",
      "\n",
      " ('oct_embedded', 'SKCM') MLP Accuracy is: 80.0\n",
      "\n",
      " ('oct_embedded', 'STAD') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'STAD') MLP Accuracy is: 67.0\n",
      "\n",
      " ('oct_embedded', 'STAD') MLP Accuracy is: 67.0\n",
      "\n",
      " ('oct_embedded', 'STAD') MLP Accuracy is: 67.0\n",
      "\n",
      " ('oct_embedded', 'STAD') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'STAD') MLP Accuracy is: 67.0\n",
      "\n",
      " ('oct_embedded', 'STAD') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'STAD') MLP Accuracy is: 68.0\n",
      "\n",
      " ('oct_embedded', 'STAD') MLP Accuracy is: 65.0\n",
      "\n",
      " ('oct_embedded', 'STAD') MLP Accuracy is: 66.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('oct_embedded', 'THCA') MLP Accuracy is: 65.0\n",
      "\n",
      " ('oct_embedded', 'THCA') MLP Accuracy is: 65.0\n",
      "\n",
      " ('oct_embedded', 'THCA') MLP Accuracy is: 65.0\n",
      "\n",
      " ('oct_embedded', 'THCA') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'THCA') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'THCA') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'THCA') MLP Accuracy is: 66.0\n",
      "\n",
      " ('oct_embedded', 'THCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('oct_embedded', 'THCA') MLP Accuracy is: 69.0\n",
      "\n",
      " ('oct_embedded', 'THCA') MLP Accuracy is: 67.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('oct_embedded', 'UCEC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('oct_embedded', 'UCEC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('oct_embedded', 'UCEC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('oct_embedded', 'UCEC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('oct_embedded', 'UCEC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('oct_embedded', 'UCEC') MLP Accuracy is: 60.0\n",
      "\n",
      " ('oct_embedded', 'UCEC') MLP Accuracy is: 57.99999999999999\n",
      "\n",
      " ('oct_embedded', 'UCEC') MLP Accuracy is: 60.0\n",
      "\n",
      " ('oct_embedded', 'UCEC') MLP Accuracy is: 59.0\n",
      "\n",
      " ('oct_embedded', 'UCEC') MLP Accuracy is: 57.99999999999999\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COAD') MLP Accuracy is: 95.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COAD') MLP Accuracy is: 95.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COAD') MLP Accuracy is: 95.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COAD') MLP Accuracy is: 95.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COAD') MLP Accuracy is: 95.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COAD') MLP Accuracy is: 95.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COAD') MLP Accuracy is: 95.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COAD') MLP Accuracy is: 95.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COAD') MLP Accuracy is: 95.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COAD') MLP Accuracy is: 95.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COADREAD') MLP Accuracy is: 96.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COADREAD') MLP Accuracy is: 96.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COADREAD') MLP Accuracy is: 96.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COADREAD') MLP Accuracy is: 96.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COADREAD') MLP Accuracy is: 96.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COADREAD') MLP Accuracy is: 96.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COADREAD') MLP Accuracy is: 96.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COADREAD') MLP Accuracy is: 96.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COADREAD') MLP Accuracy is: 96.0\n",
      "\n",
      " ('synchronous_colon_cancer_present', 'COADREAD') MLP Accuracy is: 96.0\n",
      "\n",
      " ('tumor_tissue_site', 'COADREAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('tumor_tissue_site', 'COADREAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('tumor_tissue_site', 'COADREAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('tumor_tissue_site', 'COADREAD') MLP Accuracy is: 78.0\n",
      "\n",
      " ('tumor_tissue_site', 'COADREAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('tumor_tissue_site', 'COADREAD') MLP Accuracy is: 75.0\n",
      "\n",
      " ('tumor_tissue_site', 'COADREAD') MLP Accuracy is: 75.0\n",
      "\n",
      " ('tumor_tissue_site', 'COADREAD') MLP Accuracy is: 74.0\n",
      "\n",
      " ('tumor_tissue_site', 'COADREAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('tumor_tissue_site', 'COADREAD') MLP Accuracy is: 75.0\n",
      "\n",
      " ('tumor_tissue_site', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('tumor_tissue_site', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('tumor_tissue_site', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('tumor_tissue_site', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('tumor_tissue_site', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('tumor_tissue_site', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('tumor_tissue_site', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('tumor_tissue_site', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('tumor_tissue_site', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('tumor_tissue_site', 'GBMLGG') MLP Accuracy is: 96.0\n",
      "\n",
      " ('tumor_tissue_site', 'PCPG') MLP Accuracy is: 89.0\n",
      "\n",
      " ('tumor_tissue_site', 'PCPG') MLP Accuracy is: 90.0\n",
      "\n",
      " ('tumor_tissue_site', 'PCPG') MLP Accuracy is: 90.0\n",
      "\n",
      " ('tumor_tissue_site', 'PCPG') MLP Accuracy is: 90.0\n",
      "\n",
      " ('tumor_tissue_site', 'PCPG') MLP Accuracy is: 90.0\n",
      "\n",
      " ('tumor_tissue_site', 'PCPG') MLP Accuracy is: 90.0\n",
      "\n",
      " ('tumor_tissue_site', 'PCPG') MLP Accuracy is: 89.0\n",
      "\n",
      " ('tumor_tissue_site', 'PCPG') MLP Accuracy is: 90.0\n",
      "\n",
      " ('tumor_tissue_site', 'PCPG') MLP Accuracy is: 90.0\n",
      "\n",
      " ('tumor_tissue_site', 'PCPG') MLP Accuracy is: 89.0\n",
      "Not enough number of samples\n",
      "\n",
      " ('venous_invasion', 'COAD') MLP Accuracy is: 76.0\n",
      "\n",
      " ('venous_invasion', 'COAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('venous_invasion', 'COAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('venous_invasion', 'COAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('venous_invasion', 'COAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('venous_invasion', 'COAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('venous_invasion', 'COAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('venous_invasion', 'COAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('venous_invasion', 'COAD') MLP Accuracy is: 78.0\n",
      "\n",
      " ('venous_invasion', 'COAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('venous_invasion', 'COADREAD') MLP Accuracy is: 78.0\n",
      "\n",
      " ('venous_invasion', 'COADREAD') MLP Accuracy is: 78.0\n",
      "\n",
      " ('venous_invasion', 'COADREAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('venous_invasion', 'COADREAD') MLP Accuracy is: 78.0\n",
      "\n",
      " ('venous_invasion', 'COADREAD') MLP Accuracy is: 78.0\n",
      "\n",
      " ('venous_invasion', 'COADREAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('venous_invasion', 'COADREAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('venous_invasion', 'COADREAD') MLP Accuracy is: 77.0\n",
      "\n",
      " ('venous_invasion', 'COADREAD') MLP Accuracy is: 77.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ('venous_invasion', 'COADREAD') MLP Accuracy is: 77.0\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "Not enough number of samples\n",
      "\n",
      " ('white_cell_count_result', 'KIRP') MLP Accuracy is: 74.0\n",
      "\n",
      " ('white_cell_count_result', 'KIRP') MLP Accuracy is: 74.0\n",
      "\n",
      " ('white_cell_count_result', 'KIRP') MLP Accuracy is: 74.0\n",
      "\n",
      " ('white_cell_count_result', 'KIRP') MLP Accuracy is: 72.0\n",
      "\n",
      " ('white_cell_count_result', 'KIRP') MLP Accuracy is: 73.0\n",
      "\n",
      " ('white_cell_count_result', 'KIRP') MLP Accuracy is: 74.0\n",
      "\n",
      " ('white_cell_count_result', 'KIRP') MLP Accuracy is: 73.0\n",
      "\n",
      " ('white_cell_count_result', 'KIRP') MLP Accuracy is: 74.0\n",
      "\n",
      " ('white_cell_count_result', 'KIRP') MLP Accuracy is: 74.0\n",
      "\n",
      " ('white_cell_count_result', 'KIRP') MLP Accuracy is: 74.0\n"
     ]
    }
   ],
   "source": [
    "avg_mlp_acc = {}\n",
    "\n",
    "for taskid in sorted(tasks.task_ids):\n",
    "    task = meta_dataloader.TCGA.TCGATask(taskid)\n",
    "    input_size = task._samples.shape[1]\n",
    "    num_classes = len(collections.Counter(task._labels))\n",
    "    try:\n",
    "        train_set, test_set = load_sets(task, valid = False)\n",
    "        for seed in range(0,10):\n",
    "            MLP_model = MultiLayerPerceptron(seed, input_size, num_classes, num_layer, channels, lr, batch_size, epochs, patience, weight_decay)\n",
    "            trained_model, train_loss = train(MLP_model, train_set, taskid, False)\n",
    "            mlp_result = test(trained_model, test_set)\n",
    "            print(\"\\n {} MLP Accuracy is: {}\" .format(taskid, mlp_result))\n",
    "            if task.id in avg_mlp_acc.keys():\n",
    "                avg_mlp_acc[task.id].append(mlp_result)\n",
    "            else:\n",
    "                avg_mlp_acc[task.id] = [mlp_result]\n",
    "    except:\n",
    "        print(\"Not enough number of samples\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T23:23:25.358362Z",
     "start_time": "2019-07-22T23:23:25.304756Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('Expression_Subtype', 'LUAD'): [67.0, 68.0, 71.0, 66.0, 67.0, 70.0, 69.0, 69.0, 66.0, 69.0], ('Expression_Subtype', 'LUNG'): [67.0, 68.0, 71.0, 66.0, 67.0, 70.0, 69.0, 69.0, 66.0, 69.0], ('GeneExp_Subtype', 'GBM'): [75.0, 71.0, 71.0, 74.0, 74.0, 72.0, 73.0, 74.0, 75.0, 74.0], ('Metastasis_nature2012', 'BRCA'): [98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0], ('Node_nature2012', 'BRCA'): [45.0, 47.0, 47.0, 52.0, 45.0, 48.0, 47.0, 49.0, 44.0, 51.0], ('PAM50Call_RNAseq', 'BRCA'): [82.0, 80.0, 83.0, 77.0, 83.0, 78.0, 82.0, 80.0, 83.0, 82.0], ('_EVENT', 'BLCA'): [56.00000000000001, 60.0, 56.99999999999999, 60.0, 60.0, 59.0, 57.99999999999999, 61.0, 57.99999999999999, 56.99999999999999], ('_EVENT', 'BRCA'): [82.0, 82.0, 82.0, 82.0, 84.0, 82.0, 84.0, 84.0, 84.0, 83.0], ('_EVENT', 'CESC'): [70.0, 66.0, 68.0, 68.0, 69.0, 69.0, 68.0, 70.0, 69.0, 69.0], ('_EVENT', 'COAD'): [75.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 74.0, 76.0, 76.0], ('_EVENT', 'COADREAD'): [72.0, 73.0, 73.0, 74.0, 71.0, 73.0, 72.0, 72.0, 73.0, 71.0], ('_EVENT', 'ESCA'): [51.0, 53.0, 51.0, 54.0, 52.0, 52.0, 53.0, 53.0, 52.0, 53.0], ('_EVENT', 'GBM'): [73.0, 74.0, 75.0, 74.0, 73.0, 74.0, 74.0, 74.0, 73.0, 75.0], ('_EVENT', 'GBMLGG'): [74.0, 74.0, 74.0, 74.0, 75.0, 74.0, 74.0, 74.0, 74.0, 74.0], ('_EVENT', 'HNSC'): [60.0, 62.0, 61.0, 59.0, 60.0, 62.0, 61.0, 59.0, 57.99999999999999, 59.0], ('_EVENT', 'KIRP'): [85.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0], ('_EVENT', 'LAML'): [60.0, 61.0, 60.0, 61.0, 62.0, 62.0, 56.00000000000001, 60.0, 61.0, 59.0], ('_EVENT', 'LGG'): [76.0, 76.0, 75.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0], ('_EVENT', 'LIHC'): [63.0, 65.0, 63.0, 63.0, 63.0, 62.0, 63.0, 62.0, 61.0, 59.0], ('_EVENT', 'LUAD'): [52.0, 59.0, 56.99999999999999, 59.0, 59.0, 56.99999999999999, 60.0, 57.99999999999999, 56.99999999999999, 56.99999999999999], ('_EVENT', 'LUNG'): [61.0, 64.0, 64.0, 62.0, 61.0, 60.0, 61.0, 62.0, 62.0, 63.0], ('_EVENT', 'LUSC'): [60.0, 57.99999999999999, 57.99999999999999, 57.99999999999999, 60.0, 56.99999999999999, 56.99999999999999, 57.99999999999999, 57.99999999999999, 56.99999999999999], ('_EVENT', 'OV'): [57.99999999999999, 57.99999999999999, 59.0, 57.99999999999999, 59.0, 57.99999999999999, 57.99999999999999, 59.0, 57.99999999999999, 56.99999999999999], ('_EVENT', 'PAAD'): [56.99999999999999, 56.99999999999999, 57.99999999999999, 62.0, 56.99999999999999, 56.00000000000001, 61.0, 55.00000000000001, 56.00000000000001, 61.0], ('_EVENT', 'SARC'): [56.99999999999999, 55.00000000000001, 54.0, 59.0, 56.99999999999999, 59.0, 56.00000000000001, 55.00000000000001, 57.99999999999999, 55.00000000000001], ('_EVENT', 'SKCM'): [60.0, 61.0, 62.0, 62.0, 62.0, 62.0, 61.0, 63.0, 61.0, 60.0], ('_EVENT', 'STAD'): [56.99999999999999, 61.0, 59.0, 57.99999999999999, 56.99999999999999, 57.99999999999999, 61.0, 56.99999999999999, 56.00000000000001, 55.00000000000001], ('_EVENT', 'THCA'): [97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0], ('_EVENT', 'UCEC'): [80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 79.0], ('_PANCAN_DNAMethyl_BRCA', 'BRCA'): [48.0, 51.0, 55.00000000000001, 51.0, 53.0, 53.0, 55.00000000000001, 53.0, 52.0, 52.0], ('_PANCAN_DNAMethyl_HNSC', 'HNSC'): [76.0, 75.0, 75.0, 77.0, 75.0, 75.0, 75.0, 74.0, 74.0, 74.0], ('_PANCAN_DNAMethyl_LUAD', 'LUAD'): [55.00000000000001, 51.0, 53.0, 55.00000000000001, 53.0, 50.0, 54.0, 52.0, 52.0, 49.0], ('_PANCAN_DNAMethyl_LUSC', 'LUSC'): [56.99999999999999, 61.0, 63.0, 63.0, 56.99999999999999, 62.0, 65.0, 63.0, 57.99999999999999, 61.0], ('_PANCAN_mirna_BRCA', 'BRCA'): [38.0, 39.0, 38.0, 39.0, 43.0, 43.0, 42.0, 43.0, 42.0, 43.0], ('_PANCAN_mirna_HNSC', 'HNSC'): [52.0, 54.0, 55.00000000000001, 54.0, 52.0, 53.0, 56.00000000000001, 53.0, 54.0, 53.0], ('_PANCAN_mirna_LAML', 'LAML'): [73.0, 72.0, 73.0, 74.0, 72.0, 72.0, 73.0, 70.0, 74.0, 73.0], ('_PANCAN_mirna_LUAD', 'LUAD'): [40.0, 42.0, 39.0, 39.0, 42.0, 39.0, 39.0, 41.0, 39.0, 39.0], ('_PANCAN_mirna_LUSC', 'LUSC'): [62.0, 62.0, 62.0, 61.0, 63.0, 61.0, 62.0, 57.99999999999999, 60.0, 64.0], ('_PANCAN_mirna_OV', 'OV'): [30.0, 30.0, 31.0, 33.0, 30.0, 33.0, 28.000000000000004, 32.0, 30.0, 32.0], ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML'): [80.0, 80.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 80.0], ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC'): [56.00000000000001, 56.00000000000001, 56.99999999999999, 56.99999999999999, 55.00000000000001, 56.99999999999999, 55.00000000000001, 56.00000000000001, 56.99999999999999, 56.99999999999999], ('alcohol_history_documented', 'ESCA'): [69.0, 68.0, 69.0, 66.0, 68.0, 68.0, 69.0, 68.0, 68.0, 71.0], ('alcohol_history_documented', 'HNSC'): [63.0, 66.0, 64.0, 66.0, 65.0, 66.0, 65.0, 65.0, 65.0, 66.0], ('alcohol_history_documented', 'PAAD'): [52.0, 55.00000000000001, 55.00000000000001, 57.99999999999999, 53.0, 56.99999999999999, 56.99999999999999, 57.99999999999999, 55.00000000000001, 56.00000000000001], ('anatomic_neoplasm_subdivision', 'OV'): [73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0, 73.0], ('anatomic_neoplasm_subdivision', 'PAAD'): [77.0, 77.0, 77.0, 76.0, 77.0, 77.0, 77.0, 77.0, 77.0, 75.0], ('animal_insect_allergy_history', 'GBMLGG'): [95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0], ('animal_insect_allergy_history', 'LGG'): [95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0], ('antireflux_treatment', 'STAD'): [77.0, 76.0, 76.0, 76.0, 77.0, 77.0, 77.0, 76.0, 76.0, 77.0], ('asthma_history', 'GBMLGG'): [94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0], ('asthma_history', 'LGG'): [94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0], ('barretts_esophagus', 'STAD'): [91.0, 91.0, 91.0, 91.0, 91.0, 91.0, 91.0, 91.0, 91.0, 91.0], ('biochemical_recurrence', 'PRAD'): [87.0, 87.0, 86.0, 87.0, 87.0, 87.0, 86.0, 86.0, 86.0, 87.0], ('birth_control_pill_history_usage_category', 'CESC'): [56.00000000000001, 57.99999999999999, 56.99999999999999, 56.99999999999999, 56.00000000000001, 56.99999999999999, 56.99999999999999, 54.0, 57.99999999999999, 56.99999999999999], ('clinical_M', 'KIRP'): [73.0, 72.0, 72.0, 72.0, 73.0, 72.0, 73.0, 72.0, 73.0, 73.0], ('clinical_stage', 'KIRP'): [64.0, 66.0, 64.0, 67.0, 65.0, 63.0, 64.0, 65.0, 64.0, 66.0], ('colon_polyps_present', 'COAD'): [56.99999999999999, 61.0, 61.0, 62.0, 61.0, 61.0, 57.99999999999999, 62.0, 62.0, 61.0], ('colon_polyps_present', 'COADREAD'): [70.0, 68.0, 68.0, 68.0, 69.0, 70.0, 70.0, 69.0, 69.0, 70.0], ('family_history_of_cancer', 'GBMLGG'): [48.0, 50.0, 51.0, 52.0, 51.0, 52.0, 55.00000000000001, 51.0, 52.0, 50.0], ('family_history_of_cancer', 'LGG'): [48.0, 50.0, 51.0, 52.0, 51.0, 52.0, 55.00000000000001, 51.0, 52.0, 50.0], ('family_history_of_primary_brain_tumor', 'GBMLGG'): [96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0], ('family_history_of_primary_brain_tumor', 'LGG'): [96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0], ('family_history_of_stomach_cancer', 'STAD'): [94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0, 94.0], ('gender', 'BLCA'): [78.0, 79.0, 79.0, 79.0, 78.0, 77.0, 77.0, 77.0, 78.0, 77.0], ('gender', 'BRCA'): [99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0], ('gender', 'COAD'): [86.0, 89.0, 87.0, 86.0, 89.0, 86.0, 85.0, 83.0, 89.0, 88.0], ('gender', 'COADREAD'): [76.0, 79.0, 80.0, 78.0, 79.0, 79.0, 80.0, 78.0, 77.0, 80.0], ('gender', 'ESCA'): [84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0], ('gender', 'GBM'): [70.0, 70.0, 71.0, 71.0, 73.0, 71.0, 70.0, 71.0, 68.0, 73.0], ('gender', 'GBMLGG'): [83.0, 81.0, 80.0, 77.0, 77.0, 79.0, 80.0, 76.0, 81.0, 83.0], ('gender', 'HNSC'): [75.0, 76.0, 77.0, 76.0, 75.0, 77.0, 75.0, 75.0, 76.0, 76.0], ('gender', 'KIRP'): [81.0, 81.0, 80.0, 80.0, 81.0, 80.0, 81.0, 81.0, 81.0, 81.0], ('gender', 'LAML'): [51.0, 53.0, 53.0, 57.99999999999999, 55.00000000000001, 53.0, 56.00000000000001, 51.0, 56.00000000000001, 56.00000000000001], ('gender', 'LGG'): [79.0, 77.0, 73.0, 68.0, 70.0, 78.0, 74.0, 68.0, 72.0, 76.0], ('gender', 'LIHC'): [81.0, 79.0, 82.0, 84.0, 82.0, 80.0, 82.0, 80.0, 81.0, 82.0], ('gender', 'LUAD'): [83.0, 85.0, 82.0, 84.0, 86.0, 86.0, 82.0, 89.0, 84.0, 85.0], ('gender', 'LUNG'): [82.0, 84.0, 82.0, 80.0, 85.0, 81.0, 82.0, 81.0, 83.0, 80.0], ('gender', 'LUSC'): [75.0, 76.0, 76.0, 76.0, 76.0, 76.0, 74.0, 74.0, 75.0, 73.0], ('gender', 'PAAD'): [83.0, 81.0, 84.0, 80.0, 81.0, 83.0, 82.0, 82.0, 82.0, 83.0], ('gender', 'PCPG'): [73.0, 73.0, 74.0, 73.0, 78.0, 73.0, 75.0, 78.0, 71.0, 76.0], ('gender', 'SARC'): [71.0, 71.0, 71.0, 72.0, 72.0, 72.0, 72.0, 69.0, 72.0, 73.0], ('gender', 'SKCM'): [69.0, 68.0, 70.0, 68.0, 69.0, 66.0, 70.0, 69.0, 67.0, 67.0], ('gender', 'STAD'): [80.0, 77.0, 79.0, 76.0, 80.0, 78.0, 77.0, 76.0, 77.0, 76.0], ('gender', 'THCA'): [81.0, 81.0, 82.0, 81.0, 83.0, 82.0, 83.0, 84.0, 82.0, 84.0], ('histological_type', 'ESCA'): [97.0, 98.0, 98.0, 98.0, 98.0, 97.0, 98.0, 97.0, 98.0, 97.0], ('histological_type', 'LGG'): [52.0, 55.00000000000001, 55.00000000000001, 55.00000000000001, 53.0, 55.00000000000001, 55.00000000000001, 53.0, 54.0, 55.00000000000001], ('histological_type', 'PCPG'): [84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0], ('histological_type', 'PRAD'): [97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0, 97.0], ('histological_type', 'UCEC'): [71.0, 71.0, 71.0, 71.0, 71.0, 72.0, 73.0, 71.0, 71.0, 72.0], ('lymphatic_invasion', 'COAD'): [61.0, 61.0, 62.0, 62.0, 60.0, 64.0, 63.0, 65.0, 60.0, 60.0], ('lymphatic_invasion', 'COADREAD'): [66.0, 68.0, 67.0, 66.0, 66.0, 67.0, 66.0, 65.0, 65.0, 66.0], ('lymphovascular_invasion_present', 'BLCA'): [52.0, 53.0, 52.0, 53.0, 52.0, 54.0, 52.0, 53.0, 52.0, 54.0], ('lymphovascular_invasion_present', 'HNSC'): [60.0, 64.0, 61.0, 63.0, 62.0, 63.0, 61.0, 62.0, 61.0, 62.0], ('menopause_status', 'BRCA'): [69.0, 70.0, 70.0, 68.0, 69.0, 68.0, 69.0, 69.0, 69.0, 69.0], ('mental_status_changes', 'GBMLGG'): [68.0, 69.0, 68.0, 68.0, 68.0, 70.0, 68.0, 66.0, 68.0, 68.0], ('mental_status_changes', 'LGG'): [68.0, 69.0, 68.0, 68.0, 68.0, 70.0, 68.0, 66.0, 68.0, 68.0], ('metastatic_diagnosis', 'SARC'): [59.0, 57.99999999999999, 56.99999999999999, 59.0, 59.0, 59.0, 56.99999999999999, 59.0, 60.0, 60.0], ('oct_embedded', 'BLCA'): [84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0], ('oct_embedded', 'BRCA'): [56.99999999999999, 56.99999999999999, 56.99999999999999, 56.00000000000001, 56.00000000000001, 56.00000000000001, 56.00000000000001, 56.99999999999999, 56.00000000000001, 56.99999999999999], ('oct_embedded', 'CESC'): [53.0, 52.0, 53.0, 55.00000000000001, 53.0, 54.0, 55.00000000000001, 53.0, 52.0, 52.0], ('oct_embedded', 'ESCA'): [70.0, 70.0, 70.0, 70.0, 69.0, 69.0, 72.0, 71.0, 69.0, 70.0], ('oct_embedded', 'GBMLGG'): [59.0, 64.0, 60.0, 63.0, 62.0, 62.0, 62.0, 62.0, 62.0, 60.0], ('oct_embedded', 'HNSC'): [44.0, 43.0, 44.0, 45.0, 45.0, 45.0, 42.0, 46.0, 46.0, 46.0], ('oct_embedded', 'KIRP'): [56.00000000000001, 56.00000000000001, 56.99999999999999, 57.99999999999999, 56.00000000000001, 55.00000000000001, 55.00000000000001, 60.0, 56.00000000000001, 56.00000000000001], ('oct_embedded', 'LGG'): [47.0, 44.0, 45.0, 45.0, 48.0, 45.0, 45.0, 45.0, 42.0, 45.0], ('oct_embedded', 'LIHC'): [66.0, 66.0, 68.0, 68.0, 67.0, 66.0, 66.0, 66.0, 67.0, 66.0], ('oct_embedded', 'LUNG'): [69.0, 68.0, 69.0, 66.0, 69.0, 69.0, 69.0, 70.0, 69.0, 68.0], ('oct_embedded', 'PRAD'): [80.0, 79.0, 80.0, 80.0, 80.0, 80.0, 79.0, 80.0, 79.0, 80.0], ('oct_embedded', 'SARC'): [80.0, 82.0, 80.0, 80.0, 79.0, 83.0, 80.0, 80.0, 79.0, 82.0], ('oct_embedded', 'SKCM'): [80.0, 79.0, 80.0, 79.0, 79.0, 80.0, 79.0, 79.0, 79.0, 80.0], ('oct_embedded', 'STAD'): [66.0, 67.0, 67.0, 67.0, 66.0, 67.0, 66.0, 68.0, 65.0, 66.0], ('oct_embedded', 'THCA'): [65.0, 65.0, 65.0, 66.0, 66.0, 66.0, 66.0, 69.0, 69.0, 67.0], ('oct_embedded', 'UCEC'): [59.0, 59.0, 59.0, 57.99999999999999, 59.0, 60.0, 57.99999999999999, 60.0, 59.0, 57.99999999999999], ('synchronous_colon_cancer_present', 'COAD'): [95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0, 95.0], ('synchronous_colon_cancer_present', 'COADREAD'): [96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0], ('tumor_tissue_site', 'COADREAD'): [76.0, 76.0, 77.0, 78.0, 76.0, 75.0, 75.0, 74.0, 76.0, 75.0], ('tumor_tissue_site', 'GBMLGG'): [96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0, 96.0], ('tumor_tissue_site', 'PCPG'): [89.0, 90.0, 90.0, 90.0, 90.0, 90.0, 89.0, 90.0, 90.0, 89.0], ('venous_invasion', 'COAD'): [76.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 78.0, 77.0], ('venous_invasion', 'COADREAD'): [78.0, 78.0, 77.0, 78.0, 78.0, 77.0, 77.0, 77.0, 77.0, 77.0], ('white_cell_count_result', 'KIRP'): [74.0, 74.0, 74.0, 72.0, 73.0, 74.0, 73.0, 74.0, 74.0, 74.0]}\n"
     ]
    }
   ],
   "source": [
    "print(avg_mlp_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T23:23:29.065503Z",
     "start_time": "2019-07-22T23:23:29.013019Z"
    }
   },
   "outputs": [],
   "source": [
    "MLP_dict = measure_mean_std(avg_mlp_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T23:23:32.606223Z",
     "start_time": "2019-07-22T23:23:32.515575Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "path = '/Users/mandanasamiei/PycharmProjects/TCGA_Benchmark'\n",
    "result_dataframe = pd.DataFrame.from_dict(Maj_dict, orient='index')\n",
    "result_dataframe.to_csv(os.path.join(path,'MLP_result_10seeds.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T23:23:36.280885Z",
     "start_time": "2019-07-22T23:23:36.245020Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('Expression_Subtype', 'LUAD'): {'mean': 68.2, 'std': 1.5999999999999999}, ('Expression_Subtype', 'LUNG'): {'mean': 68.2, 'std': 1.5999999999999999}, ('GeneExp_Subtype', 'GBM'): {'mean': 73.3, 'std': 1.4177446878757824}, ('Metastasis_nature2012', 'BRCA'): {'mean': 98.0, 'std': 0.0}, ('Node_nature2012', 'BRCA'): {'mean': 47.5, 'std': 2.4596747752497685}, ('PAM50Call_RNAseq', 'BRCA'): {'mean': 81.0, 'std': 2.04939015319192}, ('_EVENT', 'BLCA'): {'mean': 58.6, 'std': 1.5620499351813317}, ('_EVENT', 'BRCA'): {'mean': 82.9, 'std': 0.9433981132056604}, ('_EVENT', 'CESC'): {'mean': 68.6, 'std': 1.1135528725660042}, ('_EVENT', 'COAD'): {'mean': 75.7, 'std': 0.6403124237432849}, ('_EVENT', 'COADREAD'): {'mean': 72.4, 'std': 0.9165151389911681}, ('_EVENT', 'ESCA'): {'mean': 52.4, 'std': 0.9165151389911679}, ('_EVENT', 'GBM'): {'mean': 73.9, 'std': 0.7000000000000001}, ('_EVENT', 'GBMLGG'): {'mean': 74.1, 'std': 0.30000000000000004}, ('_EVENT', 'HNSC'): {'mean': 60.1, 'std': 1.3000000000000012}, ('_EVENT', 'KIRP'): {'mean': 85.9, 'std': 0.30000000000000004}, ('_EVENT', 'LAML'): {'mean': 60.2, 'std': 1.6613247725836133}, ('_EVENT', 'LGG'): {'mean': 75.9, 'std': 0.30000000000000004}, ('_EVENT', 'LIHC'): {'mean': 62.4, 'std': 1.4966629547095764}, ('_EVENT', 'LUAD'): {'mean': 57.5, 'std': 2.109502310972899}, ('_EVENT', 'LUNG'): {'mean': 62.0, 'std': 1.2649110640673518}, ('_EVENT', 'LUSC'): {'mean': 58.1, 'std': 1.0440306508910577}, ('_EVENT', 'OV'): {'mean': 58.2, 'std': 0.6000000000000029}, ('_EVENT', 'PAAD'): {'mean': 58.0, 'std': 2.323790007724449}, ('_EVENT', 'SARC'): {'mean': 56.5, 'std': 1.68819430161341}, ('_EVENT', 'SKCM'): {'mean': 61.4, 'std': 0.9165151389911681}, ('_EVENT', 'STAD'): {'mean': 57.9, 'std': 1.8681541692269397}, ('_EVENT', 'THCA'): {'mean': 97.0, 'std': 0.0}, ('_EVENT', 'UCEC'): {'mean': 79.9, 'std': 0.3}, ('_PANCAN_DNAMethyl_BRCA', 'BRCA'): {'mean': 52.3, 'std': 1.9519221295943157}, ('_PANCAN_DNAMethyl_HNSC', 'HNSC'): {'mean': 75.0, 'std': 0.8944271909999159}, ('_PANCAN_DNAMethyl_LUAD', 'LUAD'): {'mean': 52.4, 'std': 1.9078784028338933}, ('_PANCAN_DNAMethyl_LUSC', 'LUSC'): {'mean': 61.0, 'std': 2.645751311064594}, ('_PANCAN_mirna_BRCA', 'BRCA'): {'mean': 41.0, 'std': 2.0976176963403033}, ('_PANCAN_mirna_HNSC', 'HNSC'): {'mean': 53.6, 'std': 1.2000000000000022}, ('_PANCAN_mirna_LAML', 'LAML'): {'mean': 72.6, 'std': 1.1135528725660042}, ('_PANCAN_mirna_LUAD', 'LUAD'): {'mean': 39.9, 'std': 1.2206555615733703}, ('_PANCAN_mirna_LUSC', 'LUSC'): {'mean': 61.5, 'std': 1.5652475842498543}, ('_PANCAN_mirna_OV', 'OV'): {'mean': 30.9, 'std': 1.5132745950421551}, ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML'): {'mean': 79.3, 'std': 0.45825756949558405}, ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC'): {'mean': 56.3, 'std': 0.781024967590659}, ('alcohol_history_documented', 'ESCA'): {'mean': 68.4, 'std': 1.2}, ('alcohol_history_documented', 'HNSC'): {'mean': 65.1, 'std': 0.9433981132056604}, ('alcohol_history_documented', 'PAAD'): {'mean': 55.6, 'std': 1.907878402833888}, ('anatomic_neoplasm_subdivision', 'OV'): {'mean': 73.0, 'std': 0.0}, ('anatomic_neoplasm_subdivision', 'PAAD'): {'mean': 76.7, 'std': 0.6403124237432849}, ('animal_insect_allergy_history', 'GBMLGG'): {'mean': 95.0, 'std': 0.0}, ('animal_insect_allergy_history', 'LGG'): {'mean': 95.0, 'std': 0.0}, ('antireflux_treatment', 'STAD'): {'mean': 76.5, 'std': 0.5}, ('asthma_history', 'GBMLGG'): {'mean': 94.0, 'std': 0.0}, ('asthma_history', 'LGG'): {'mean': 94.0, 'std': 0.0}, ('barretts_esophagus', 'STAD'): {'mean': 91.0, 'std': 0.0}, ('biochemical_recurrence', 'PRAD'): {'mean': 86.6, 'std': 0.4898979485566356}, ('birth_control_pill_history_usage_category', 'CESC'): {'mean': 56.7, 'std': 1.0999999999999965}, ('clinical_M', 'KIRP'): {'mean': 72.5, 'std': 0.5}, ('clinical_stage', 'KIRP'): {'mean': 64.8, 'std': 1.16619037896906}, ('colon_polyps_present', 'COAD'): {'mean': 60.6, 'std': 1.6248076809271947}, ('colon_polyps_present', 'COADREAD'): {'mean': 69.1, 'std': 0.8306623862918076}, ('family_history_of_cancer', 'GBMLGG'): {'mean': 51.2, 'std': 1.720465053408527}, ('family_history_of_cancer', 'LGG'): {'mean': 51.2, 'std': 1.720465053408527}, ('family_history_of_primary_brain_tumor', 'GBMLGG'): {'mean': 96.0, 'std': 0.0}, ('family_history_of_primary_brain_tumor', 'LGG'): {'mean': 96.0, 'std': 0.0}, ('family_history_of_stomach_cancer', 'STAD'): {'mean': 94.0, 'std': 0.0}, ('gender', 'BLCA'): {'mean': 77.9, 'std': 0.8306623862918076}, ('gender', 'BRCA'): {'mean': 99.0, 'std': 0.0}, ('gender', 'COAD'): {'mean': 86.8, 'std': 1.8867962264113207}, ('gender', 'COADREAD'): {'mean': 78.6, 'std': 1.2806248474865698}, ('gender', 'ESCA'): {'mean': 84.0, 'std': 0.0}, ('gender', 'GBM'): {'mean': 70.8, 'std': 1.4000000000000001}, ('gender', 'GBMLGG'): {'mean': 79.7, 'std': 2.325940669922601}, ('gender', 'HNSC'): {'mean': 75.8, 'std': 0.7483314773547882}, ('gender', 'KIRP'): {'mean': 80.7, 'std': 0.45825756949558394}, ('gender', 'LAML'): {'mean': 54.2, 'std': 2.2271057451320098}, ('gender', 'LGG'): {'mean': 73.5, 'std': 3.8013155617496426}, ('gender', 'LIHC'): {'mean': 81.3, 'std': 1.3453624047073711}, ('gender', 'LUAD'): {'mean': 84.6, 'std': 2.009975124224178}, ('gender', 'LUNG'): {'mean': 82.0, 'std': 1.5491933384829668}, ('gender', 'LUSC'): {'mean': 75.1, 'std': 1.044030650891055}, ('gender', 'PAAD'): {'mean': 82.1, 'std': 1.1357816691600546}, ('gender', 'PCPG'): {'mean': 74.4, 'std': 2.1999999999999997}, ('gender', 'SARC'): {'mean': 71.5, 'std': 1.02469507659596}, ('gender', 'SKCM'): {'mean': 68.3, 'std': 1.268857754044952}, ('gender', 'STAD'): {'mean': 77.6, 'std': 1.4966629547095764}, ('gender', 'THCA'): {'mean': 82.3, 'std': 1.1}, ('histological_type', 'ESCA'): {'mean': 97.6, 'std': 0.4898979485566356}, ('histological_type', 'LGG'): {'mean': 54.2, 'std': 1.0770329614269039}, ('histological_type', 'PCPG'): {'mean': 84.0, 'std': 0.0}, ('histological_type', 'PRAD'): {'mean': 97.0, 'std': 0.0}, ('histological_type', 'UCEC'): {'mean': 71.4, 'std': 0.66332495807108}, ('lymphatic_invasion', 'COAD'): {'mean': 61.8, 'std': 1.6613247725836147}, ('lymphatic_invasion', 'COADREAD'): {'mean': 66.2, 'std': 0.8717797887081347}, ('lymphovascular_invasion_present', 'BLCA'): {'mean': 52.7, 'std': 0.7810249675906654}, ('lymphovascular_invasion_present', 'HNSC'): {'mean': 61.9, 'std': 1.1357816691600546}, ('menopause_status', 'BRCA'): {'mean': 69.0, 'std': 0.6324555320336759}, ('mental_status_changes', 'GBMLGG'): {'mean': 68.1, 'std': 0.9433981132056604}, ('mental_status_changes', 'LGG'): {'mean': 68.1, 'std': 0.9433981132056604}, ('metastatic_diagnosis', 'SARC'): {'mean': 58.7, 'std': 1.0049875621120918}, ('oct_embedded', 'BLCA'): {'mean': 84.0, 'std': 0.0}, ('oct_embedded', 'BRCA'): {'mean': 56.5, 'std': 0.4999999999999929}, ('oct_embedded', 'CESC'): {'mean': 53.2, 'std': 1.0770329614269032}, ('oct_embedded', 'ESCA'): {'mean': 70.0, 'std': 0.8944271909999159}, ('oct_embedded', 'GBMLGG'): {'mean': 61.6, 'std': 1.4282856857085702}, ('oct_embedded', 'HNSC'): {'mean': 44.6, 'std': 1.2806248474865698}, ('oct_embedded', 'KIRP'): {'mean': 56.5, 'std': 1.4317821063276315}, ('oct_embedded', 'LGG'): {'mean': 45.1, 'std': 1.5132745950421556}, ('oct_embedded', 'LIHC'): {'mean': 66.6, 'std': 0.8}, ('oct_embedded', 'LUNG'): {'mean': 68.6, 'std': 1.0198039027185568}, ('oct_embedded', 'PRAD'): {'mean': 79.7, 'std': 0.45825756949558394}, ('oct_embedded', 'SARC'): {'mean': 80.5, 'std': 1.284523257866513}, ('oct_embedded', 'SKCM'): {'mean': 79.4, 'std': 0.4898979485566356}, ('oct_embedded', 'STAD'): {'mean': 66.5, 'std': 0.806225774829855}, ('oct_embedded', 'THCA'): {'mean': 66.4, 'std': 1.42828568570857}, ('oct_embedded', 'UCEC'): {'mean': 58.9, 'std': 0.7000000000000027}, ('synchronous_colon_cancer_present', 'COAD'): {'mean': 95.0, 'std': 0.0}, ('synchronous_colon_cancer_present', 'COADREAD'): {'mean': 96.0, 'std': 0.0}, ('tumor_tissue_site', 'COADREAD'): {'mean': 75.8, 'std': 1.0770329614269007}, ('tumor_tissue_site', 'GBMLGG'): {'mean': 96.0, 'std': 0.0}, ('tumor_tissue_site', 'PCPG'): {'mean': 89.7, 'std': 0.45825756949558405}, ('venous_invasion', 'COAD'): {'mean': 77.0, 'std': 0.4472135954999579}, ('venous_invasion', 'COADREAD'): {'mean': 77.4, 'std': 0.4898979485566356}, ('white_cell_count_result', 'KIRP'): {'mean': 73.6, 'std': 0.66332495807108}}\n",
      "121\n"
     ]
    }
   ],
   "source": [
    "print(MLP_dict)\n",
    "print(len(MLP_dict))#121"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T23:24:24.399323Z",
     "start_time": "2019-07-22T23:24:18.164656Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[68.2, 68.2, 73.3, 98.0, 47.5, 81.0, 58.6, 82.9, 68.6, 75.7, 72.4, 52.4, 73.9, 74.1, 60.1, 85.9, 60.2, 75.9, 62.4, 57.5, 62.0, 58.1, 58.2, 58.0, 56.5, 61.4, 57.9, 97.0, 79.9, 52.3, 75.0, 52.4, 61.0, 41.0, 53.6, 72.6, 39.9, 61.5, 30.9, 79.3, 56.3, 68.4, 65.1, 55.6, 73.0, 76.7, 95.0, 95.0, 76.5, 94.0, 94.0, 91.0, 86.6, 56.7, 72.5, 64.8, 60.6, 69.1, 51.2, 51.2, 96.0, 96.0, 94.0, 77.9, 99.0, 86.8, 78.6, 84.0, 70.8, 79.7, 75.8, 80.7, 54.2, 73.5, 81.3, 84.6, 82.0, 75.1, 82.1, 74.4, 71.5, 68.3, 77.6, 82.3, 97.6, 54.2, 84.0, 97.0, 71.4, 61.8, 66.2, 52.7, 61.9, 69.0, 68.1, 68.1, 58.7, 84.0, 56.5, 53.2, 70.0, 61.6, 44.6, 56.5, 45.1, 66.6, 68.6, 79.7, 80.5, 79.4, 66.5, 66.4, 58.9, 95.0, 96.0, 75.8, 96.0, 89.7, 77.0, 77.4, 73.6]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 3600x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_visualization(MLP_dict, list(MLP_dict.keys()), \"Multi Layer Perceptron\", '#44AA99')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T23:31:10.327316Z",
     "start_time": "2019-07-22T23:31:03.409563Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_visualization(Maj_dict, LR_dict, MLP_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T23:32:16.606641Z",
     "start_time": "2019-07-22T23:32:16.571866Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('Expression_Subtype', 'LUAD'): {'mean': 68.2, 'std': 1.5999999999999999}, ('Expression_Subtype', 'LUNG'): {'mean': 68.2, 'std': 1.5999999999999999}, ('GeneExp_Subtype', 'GBM'): {'mean': 73.3, 'std': 1.4177446878757824}, ('Metastasis_nature2012', 'BRCA'): {'mean': 98.0, 'std': 0.0}, ('Node_nature2012', 'BRCA'): {'mean': 47.5, 'std': 2.4596747752497685}, ('PAM50Call_RNAseq', 'BRCA'): {'mean': 81.0, 'std': 2.04939015319192}, ('_EVENT', 'BLCA'): {'mean': 58.6, 'std': 1.5620499351813317}, ('_EVENT', 'BRCA'): {'mean': 82.9, 'std': 0.9433981132056604}, ('_EVENT', 'CESC'): {'mean': 68.6, 'std': 1.1135528725660042}, ('_EVENT', 'COAD'): {'mean': 75.7, 'std': 0.6403124237432849}, ('_EVENT', 'COADREAD'): {'mean': 72.4, 'std': 0.9165151389911681}, ('_EVENT', 'ESCA'): {'mean': 52.4, 'std': 0.9165151389911679}, ('_EVENT', 'GBM'): {'mean': 73.9, 'std': 0.7000000000000001}, ('_EVENT', 'GBMLGG'): {'mean': 74.1, 'std': 0.30000000000000004}, ('_EVENT', 'HNSC'): {'mean': 60.1, 'std': 1.3000000000000012}, ('_EVENT', 'KIRP'): {'mean': 85.9, 'std': 0.30000000000000004}, ('_EVENT', 'LAML'): {'mean': 60.2, 'std': 1.6613247725836133}, ('_EVENT', 'LGG'): {'mean': 75.9, 'std': 0.30000000000000004}, ('_EVENT', 'LIHC'): {'mean': 62.4, 'std': 1.4966629547095764}, ('_EVENT', 'LUAD'): {'mean': 57.5, 'std': 2.109502310972899}, ('_EVENT', 'LUNG'): {'mean': 62.0, 'std': 1.2649110640673518}, ('_EVENT', 'LUSC'): {'mean': 58.1, 'std': 1.0440306508910577}, ('_EVENT', 'OV'): {'mean': 58.2, 'std': 0.6000000000000029}, ('_EVENT', 'PAAD'): {'mean': 58.0, 'std': 2.323790007724449}, ('_EVENT', 'SARC'): {'mean': 56.5, 'std': 1.68819430161341}, ('_EVENT', 'SKCM'): {'mean': 61.4, 'std': 0.9165151389911681}, ('_EVENT', 'STAD'): {'mean': 57.9, 'std': 1.8681541692269397}, ('_EVENT', 'THCA'): {'mean': 97.0, 'std': 0.0}, ('_EVENT', 'UCEC'): {'mean': 79.9, 'std': 0.3}, ('_PANCAN_DNAMethyl_BRCA', 'BRCA'): {'mean': 52.3, 'std': 1.9519221295943157}, ('_PANCAN_DNAMethyl_HNSC', 'HNSC'): {'mean': 75.0, 'std': 0.8944271909999159}, ('_PANCAN_DNAMethyl_LUAD', 'LUAD'): {'mean': 52.4, 'std': 1.9078784028338933}, ('_PANCAN_DNAMethyl_LUSC', 'LUSC'): {'mean': 61.0, 'std': 2.645751311064594}, ('_PANCAN_mirna_BRCA', 'BRCA'): {'mean': 41.0, 'std': 2.0976176963403033}, ('_PANCAN_mirna_HNSC', 'HNSC'): {'mean': 53.6, 'std': 1.2000000000000022}, ('_PANCAN_mirna_LAML', 'LAML'): {'mean': 72.6, 'std': 1.1135528725660042}, ('_PANCAN_mirna_LUAD', 'LUAD'): {'mean': 39.9, 'std': 1.2206555615733703}, ('_PANCAN_mirna_LUSC', 'LUSC'): {'mean': 61.5, 'std': 1.5652475842498543}, ('_PANCAN_mirna_OV', 'OV'): {'mean': 30.9, 'std': 1.5132745950421551}, ('acute_myeloid_leukemia_calgb_cytogenetics_risk_category', 'LAML'): {'mean': 79.3, 'std': 0.45825756949558405}, ('adjacent_hepatic_tissue_inflammation_extent_type', 'LIHC'): {'mean': 56.3, 'std': 0.781024967590659}, ('alcohol_history_documented', 'ESCA'): {'mean': 68.4, 'std': 1.2}, ('alcohol_history_documented', 'HNSC'): {'mean': 65.1, 'std': 0.9433981132056604}, ('alcohol_history_documented', 'PAAD'): {'mean': 55.6, 'std': 1.907878402833888}, ('anatomic_neoplasm_subdivision', 'OV'): {'mean': 73.0, 'std': 0.0}, ('anatomic_neoplasm_subdivision', 'PAAD'): {'mean': 76.7, 'std': 0.6403124237432849}, ('animal_insect_allergy_history', 'GBMLGG'): {'mean': 95.0, 'std': 0.0}, ('animal_insect_allergy_history', 'LGG'): {'mean': 95.0, 'std': 0.0}, ('antireflux_treatment', 'STAD'): {'mean': 76.5, 'std': 0.5}, ('asthma_history', 'GBMLGG'): {'mean': 94.0, 'std': 0.0}, ('asthma_history', 'LGG'): {'mean': 94.0, 'std': 0.0}, ('barretts_esophagus', 'STAD'): {'mean': 91.0, 'std': 0.0}, ('biochemical_recurrence', 'PRAD'): {'mean': 86.6, 'std': 0.4898979485566356}, ('birth_control_pill_history_usage_category', 'CESC'): {'mean': 56.7, 'std': 1.0999999999999965}, ('clinical_M', 'KIRP'): {'mean': 72.5, 'std': 0.5}, ('clinical_stage', 'KIRP'): {'mean': 64.8, 'std': 1.16619037896906}, ('colon_polyps_present', 'COAD'): {'mean': 60.6, 'std': 1.6248076809271947}, ('colon_polyps_present', 'COADREAD'): {'mean': 69.1, 'std': 0.8306623862918076}, ('family_history_of_cancer', 'GBMLGG'): {'mean': 51.2, 'std': 1.720465053408527}, ('family_history_of_cancer', 'LGG'): {'mean': 51.2, 'std': 1.720465053408527}, ('family_history_of_primary_brain_tumor', 'GBMLGG'): {'mean': 96.0, 'std': 0.0}, ('family_history_of_primary_brain_tumor', 'LGG'): {'mean': 96.0, 'std': 0.0}, ('family_history_of_stomach_cancer', 'STAD'): {'mean': 94.0, 'std': 0.0}, ('gender', 'BLCA'): {'mean': 77.9, 'std': 0.8306623862918076}, ('gender', 'BRCA'): {'mean': 99.0, 'std': 0.0}, ('gender', 'COAD'): {'mean': 86.8, 'std': 1.8867962264113207}, ('gender', 'COADREAD'): {'mean': 78.6, 'std': 1.2806248474865698}, ('gender', 'ESCA'): {'mean': 84.0, 'std': 0.0}, ('gender', 'GBM'): {'mean': 70.8, 'std': 1.4000000000000001}, ('gender', 'GBMLGG'): {'mean': 79.7, 'std': 2.325940669922601}, ('gender', 'HNSC'): {'mean': 75.8, 'std': 0.7483314773547882}, ('gender', 'KIRP'): {'mean': 80.7, 'std': 0.45825756949558394}, ('gender', 'LAML'): {'mean': 54.2, 'std': 2.2271057451320098}, ('gender', 'LGG'): {'mean': 73.5, 'std': 3.8013155617496426}, ('gender', 'LIHC'): {'mean': 81.3, 'std': 1.3453624047073711}, ('gender', 'LUAD'): {'mean': 84.6, 'std': 2.009975124224178}, ('gender', 'LUNG'): {'mean': 82.0, 'std': 1.5491933384829668}, ('gender', 'LUSC'): {'mean': 75.1, 'std': 1.044030650891055}, ('gender', 'PAAD'): {'mean': 82.1, 'std': 1.1357816691600546}, ('gender', 'PCPG'): {'mean': 74.4, 'std': 2.1999999999999997}, ('gender', 'SARC'): {'mean': 71.5, 'std': 1.02469507659596}, ('gender', 'SKCM'): {'mean': 68.3, 'std': 1.268857754044952}, ('gender', 'STAD'): {'mean': 77.6, 'std': 1.4966629547095764}, ('gender', 'THCA'): {'mean': 82.3, 'std': 1.1}, ('histological_type', 'ESCA'): {'mean': 97.6, 'std': 0.4898979485566356}, ('histological_type', 'LGG'): {'mean': 54.2, 'std': 1.0770329614269039}, ('histological_type', 'PCPG'): {'mean': 84.0, 'std': 0.0}, ('histological_type', 'PRAD'): {'mean': 97.0, 'std': 0.0}, ('histological_type', 'UCEC'): {'mean': 71.4, 'std': 0.66332495807108}, ('lymphatic_invasion', 'COAD'): {'mean': 61.8, 'std': 1.6613247725836147}, ('lymphatic_invasion', 'COADREAD'): {'mean': 66.2, 'std': 0.8717797887081347}, ('lymphovascular_invasion_present', 'BLCA'): {'mean': 52.7, 'std': 0.7810249675906654}, ('lymphovascular_invasion_present', 'HNSC'): {'mean': 61.9, 'std': 1.1357816691600546}, ('menopause_status', 'BRCA'): {'mean': 69.0, 'std': 0.6324555320336759}, ('mental_status_changes', 'GBMLGG'): {'mean': 68.1, 'std': 0.9433981132056604}, ('mental_status_changes', 'LGG'): {'mean': 68.1, 'std': 0.9433981132056604}, ('metastatic_diagnosis', 'SARC'): {'mean': 58.7, 'std': 1.0049875621120918}, ('oct_embedded', 'BLCA'): {'mean': 84.0, 'std': 0.0}, ('oct_embedded', 'BRCA'): {'mean': 56.5, 'std': 0.4999999999999929}, ('oct_embedded', 'CESC'): {'mean': 53.2, 'std': 1.0770329614269032}, ('oct_embedded', 'ESCA'): {'mean': 70.0, 'std': 0.8944271909999159}, ('oct_embedded', 'GBMLGG'): {'mean': 61.6, 'std': 1.4282856857085702}, ('oct_embedded', 'HNSC'): {'mean': 44.6, 'std': 1.2806248474865698}, ('oct_embedded', 'KIRP'): {'mean': 56.5, 'std': 1.4317821063276315}, ('oct_embedded', 'LGG'): {'mean': 45.1, 'std': 1.5132745950421556}, ('oct_embedded', 'LIHC'): {'mean': 66.6, 'std': 0.8}, ('oct_embedded', 'LUNG'): {'mean': 68.6, 'std': 1.0198039027185568}, ('oct_embedded', 'PRAD'): {'mean': 79.7, 'std': 0.45825756949558394}, ('oct_embedded', 'SARC'): {'mean': 80.5, 'std': 1.284523257866513}, ('oct_embedded', 'SKCM'): {'mean': 79.4, 'std': 0.4898979485566356}, ('oct_embedded', 'STAD'): {'mean': 66.5, 'std': 0.806225774829855}, ('oct_embedded', 'THCA'): {'mean': 66.4, 'std': 1.42828568570857}, ('oct_embedded', 'UCEC'): {'mean': 58.9, 'std': 0.7000000000000027}, ('synchronous_colon_cancer_present', 'COAD'): {'mean': 95.0, 'std': 0.0}, ('synchronous_colon_cancer_present', 'COADREAD'): {'mean': 96.0, 'std': 0.0}, ('tumor_tissue_site', 'COADREAD'): {'mean': 75.8, 'std': 1.0770329614269007}, ('tumor_tissue_site', 'GBMLGG'): {'mean': 96.0, 'std': 0.0}, ('tumor_tissue_site', 'PCPG'): {'mean': 89.7, 'std': 0.45825756949558405}, ('venous_invasion', 'COAD'): {'mean': 77.0, 'std': 0.4472135954999579}, ('venous_invasion', 'COADREAD'): {'mean': 77.4, 'std': 0.4898979485566356}, ('white_cell_count_result', 'KIRP'): {'mean': 73.6, 'std': 0.66332495807108}}\n"
     ]
    }
   ],
   "source": [
    "print(MLP_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-22T23:38:51.394089Z",
     "start_time": "2019-07-22T23:38:51.351289Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[66.18181818181819, 68.78512396694214, 71.19090909090909]\n",
      "[16.879739922010373, 15.918857150475835, 14.624783674320149]\n"
     ]
    }
   ],
   "source": [
    "mean_mean = []\n",
    "mean_std = []\n",
    "\n",
    "Mlp_means, Mlp_std = [], []\n",
    "LR_means, LR_std = [], []\n",
    "Maj_means, Maj_std = [], []\n",
    "tasks = Maj_dict.keys()\n",
    "\n",
    "for task in tasks:\n",
    "    Maj_means.append(Maj_dict[task]['mean'])\n",
    "    Maj_std.append(Maj_dict[task]['std'])\n",
    "    LR_means.append(LR_dict[task]['mean'])\n",
    "    LR_std.append(LR_dict[task]['std'])\n",
    "    Mlp_means.append(MLP_dict[task]['mean'])\n",
    "    Mlp_std.append(MLP_dict[task]['std'])\n",
    "\n",
    "mean_mean.append(np.mean(Maj_means))\n",
    "mean_mean.append(np.mean(LR_means))\n",
    "mean_mean.append(np.mean(Mlp_means))\n",
    "\n",
    "mean_std.append(np.std(Maj_means))\n",
    "mean_std.append(np.std(LR_means))\n",
    "mean_std.append(np.std(Mlp_means))\n",
    "\n",
    "print(mean_mean)\n",
    "print(mean_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-23T03:03:03.394925Z",
     "start_time": "2019-07-23T03:03:03.168263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barlist=plt.bar([1,2,3], mean_mean, alpha=0.7)\n",
    "barlist[0].set_color('#332288')\n",
    "barlist[1].set_color('#AA4499')\n",
    "barlist[2].set_color('#44AA99')\n",
    "plt.xticks([1, 2, 3], ['Majority','LR','MLP'])\n",
    "plt.title(\"Model Comparison\")\n",
    "plt.ylabel('Model Average Performance over all tasks')\n",
    "plt.xlabel('Model')\n",
    "plt.ylim(0,100)\n",
    "plt.errorbar([1,2,3], mean_mean, mean_std, linestyle='None', marker='o', color='r', alpha=0.7)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}