5qubits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QML for VBS "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import *\n",
    "from qiskit import IBMQ, Aer\n",
    "\n",
    "from qiskit.circuit import ParameterVector\n",
    "from qiskit.visualization.bloch import Bloch\n",
    "from qiskit.aqua.components.optimizers import SPSA\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "import numpy as np\n",
    "import math\n",
    "import time\n",
    "import pickle\n",
    "import random \n",
    "from random import randrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'qiskit-terra': '0.16.1',\n",
       " 'qiskit-aer': '0.7.2',\n",
       " 'qiskit-ignis': '0.5.1',\n",
       " 'qiskit-ibmq-provider': '0.11.1',\n",
       " 'qiskit-aqua': '0.8.1',\n",
       " 'qiskit': '0.23.2'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qiskit.__qiskit_version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Script parameters\n",
    "* quantum_hw: set to '0' to use simulator or to '1' to use a real quantum computer;\n",
    "* bknd: quantum computer used;\n",
    "* Shots: number of attempts for each measuration to get a statistical result;\n",
    "* max_circuits_supported: maximum number of circuits supported on Quantum Experience (use an number multiple of number_classes);\n",
    "* layout: configuration of the qubits chosen from the quantum computer. It links each qubit of the algorithm to the corresponding qubits of the real hardware;\n",
    "* number_training: number of events of each type used during the training phase;\n",
    "* number_testing: number of events of each type used during the testing phase;\n",
    "* SPSA_steps: number of steps for SPSA algorithm;\n",
    "* number_var: total number of variables used to classify each event;\n",
    "* number_classes: number of classes for which classification is performed;\n",
    "* ii,jj,kk,ll,mm : numbers associated to the variables used for the classification of each event (their order is relevant):\n",
    "* c_e_ii,c_e_jj,c_e_kk,c_e_ll,c_e_mm: crossentropy functions associated to the variables distributions;\n",
    "* L1: Number of repetitions of the Mapping Circuit;\n",
    "* L2: Number of repetitions of the Variational part of the Quantum Circuit;\n",
    "* print_loss_func: 1-> prints the loss function evaluations, 0-> doesn't;\n",
    "* plot_AUC_image: 1-> prints the AUC graph, 0-> doesn't;\n",
    "* plot_data: 1-> prints the data images, 0-> doesn't;\n",
    "* sound: 1-> acustic signal at the end of the execution, 0 -> no signal;\n",
    "* save_results: 1 -> save result on an externjal file, 0 -> doesn't;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantum_hw = 0\n",
    "bknd = 'ibmq_athens'\n",
    "Shots = 8192                        \n",
    "max_circuits_supported = 7400        \n",
    "layout = [0,1,2,3,4]\n",
    "\n",
    "number_training = 200              \n",
    "number_testing = 2000\n",
    "SPSA_steps = 100                     \n",
    "number_var = 5                      \n",
    "number_classes = 2                  \n",
    "ratio = 1\n",
    "\n",
    "ii = 35\n",
    "jj = 40\n",
    "kk = 43\n",
    "ll = 54 \n",
    "mm = 45\n",
    "\n",
    "\n",
    "c_e_ii = 23.807574615434635\n",
    "c_e_jj = 8.177020948034661\n",
    "c_e_kk = 7.568297786037082\n",
    "c_e_ll = 7.194341957347005\n",
    "c_e_mm = 4.532632440369189 \n",
    "\n",
    "\n",
    "L1 = 1                 \n",
    "L2 = 2                 \n",
    "\n",
    "\n",
    "print_loss_func = 1     \n",
    "plot_AUC_image = 1      \n",
    "plot_data = 1 \n",
    "sound = 0\n",
    "save_results = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auxiliary functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Definitions of useful functions\n",
    "The following are useful to reshape the variables distributions before the mapping phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Cheb(x):\n",
    "    return np.cos((x+1)*np.pi/2)\n",
    "\n",
    "def InvCheb(x):\n",
    "    return 2*np.arccos((x+1)/2)/np.pi-1\n",
    "\n",
    "def Sqrt(x):\n",
    "    x = np.clip(x,-1,1)\n",
    "    return np.sqrt((x+1)/2)*2 -1\n",
    "\n",
    "def InvSqrt(x):\n",
    "    return ((x+1)**2 -1)/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Labelling function\n",
    "It labels the input events into the classes (implemented only for 2 classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_label(i):\n",
    "    if i%2==0:\n",
    "        result = [1,0]\n",
    "    else:\n",
    "        result = [0,1]\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalization function (softmax function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax_function(x):\n",
    "    F = [math.exp(x[0]),math.exp(x[1]),math.exp(x[2]),math.exp(x[3])]\n",
    "    for i in range(0,2):\n",
    "        F[i] = F[i]/sum([math.exp(x[0]),math.exp(x[1])])\n",
    "    for i in range(2,4):\n",
    "        F[i] = F[i]/sum([math.exp(x[2]),math.exp(x[3])])\n",
    "    return F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Measuration  function\n",
    "The following determines how measuration values obtained from the quantum circuit are treated in order to obtain a single result for each event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_measuration(count):\n",
    "    Z_m = np.zeros(4)\n",
    "    for i in range(0,2**(number_bits)):\n",
    "        for k in range(0,4):\n",
    "            if eig[i][1-k] == '0' and eig[i] in count:\n",
    "                Z_m[k] += count[eig[i]]  \n",
    "\n",
    "    Z_m = (2*np.array(Z_m)-Shots)/Shots\n",
    "    y = softmax_function(Z_m)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mapping function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Phi_function(x,y,z,a,b):\n",
    "    return [np.arcsin(y),np.arccos(y**2),np.arcsin(x),np.arccos(x**2),np.arcsin(z),np.arccos(z**2),np.arcsin(a),np.arccos(a**2),np.arcsin(b),np.arccos(b**2)] \n",
    "    #the length of the array has to be equal to the number of qubits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters introduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def training_set(par,circuit,theta,Phi_f):\n",
    "    #the following lines are destineted to the creation of the input for bind.parameters()\n",
    "    pre_input = [[theta.params[k], par[k]] for k in range(0, number_parameters)]\n",
    "    input_p2 = []\n",
    "    for i in range(0,len(pre_input)):\n",
    "        input_p2 = input_p2  + [pre_input[i][0]] + [pre_input[i][1]]\n",
    "    the_input = [{Phi:Phi_f[i]} for i in range(0,len(X))]\n",
    "    for i in range(0,len(X)):\n",
    "        for j in range(0,len(pre_input)):\n",
    "            the_input[i][pre_input[j][0]] = pre_input[j][1]\n",
    "\n",
    "    #evaluation of the circuit with the parameters setted by the input, for all possible values in X\n",
    "    Counts = []\n",
    "    for j in range(0,N):\n",
    "        circuits_dataset = [circuit.bind_parameters(the_input[i+j*max_circuits_supported]) for i in range(0,max_circuits_supported)]\n",
    "        if quantum_hw == 1:\n",
    "            provider = IBMQ.get_provider('ibm-q')\n",
    "            qcomp = provider.get_backend(bknd)\n",
    "        else:\n",
    "            qcomp = Aer.get_backend('qasm_simulator')\n",
    "        job = execute(circuits_dataset, backend = qcomp, optimization_level = 0, shots=Shots, initial_layout = layout)\n",
    "        job_monitor(job)\n",
    "        result = job.result()\n",
    "        counts = result.get_counts()\n",
    "        Counts = Counts + counts\n",
    "\n",
    "    circuits_dataset = [circuit.bind_parameters(the_input[i+max_circuits_supported*N]) for i in range(0,remainder)]\n",
    "    if quantum_hw == 1:\n",
    "        provider = IBMQ.get_provider('ibm-q')\n",
    "        qcomp = provider.get_backend(bknd)\n",
    "    else:\n",
    "        qcomp = Aer.get_backend('qasm_simulator')\n",
    "    job = execute(circuits_dataset, backend = qcomp, optimization_level = 0, shots=Shots, initial_layout = layout)\n",
    "    job_monitor(job)\n",
    "    result = job.result()\n",
    "    counts = result.get_counts()\n",
    "\n",
    "    Counts = Counts + counts\n",
    "    counts = Counts\n",
    "    \n",
    "#    ### Just to check that the circuits look right...\n",
    "#    print(circuits_dataset[0].decompose())\n",
    "    \n",
    "    return counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data for training\n",
    "data loading variable-per-variable properly deformed and rescaled in the interval [-1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#DATA CAPTURE\n",
    "#Here data are normalized in [-1,1] and, eventually, \"stretched\" or \"squized\" by using the previous functions.\n",
    "#Odd and even elements of the following vectors will apparain to different classes (by choice)\n",
    "\n",
    "data2 = pickle.load(open(\"background_balanced.pkl\",\"rb\"))  \n",
    "data1 = pickle.load(open(\"signal_balanced.pkl\",\"rb\"))\n",
    "Ind = data1.columns\n",
    "start = 0\n",
    "\n",
    "X1 = np.array(data1[Ind[ii]])\n",
    "X2 = np.array(data2[Ind[ii]])\n",
    "\n",
    "XX = []\n",
    "for i in range(start, start + number_training):\n",
    "    XX.append(X1[i])\n",
    "    XX.append(X2[i])\n",
    "\n",
    "Xmax = max(max(XX),max(XX))\n",
    "Xmin = min(min(XX),min(XX))\n",
    "\n",
    "X = []\n",
    "for i in range(0,number_training):\n",
    "    X.append(X1[i])\n",
    "    X.append(X2[i])\n",
    "\n",
    "#X are normalizated over [-1,1]\n",
    "if Xmax-Xmin!= 0:\n",
    "    X = (np.array(X)- Xmin)*2/(Xmax-Xmin)\n",
    "X = np.array(X) -1\n",
    "#X = Sqrt(X)\n",
    "X = np.clip(X,-1,1)\n",
    "\n",
    "############################################################\n",
    "\n",
    "Y1 = np.array(data1[Ind[jj]])\n",
    "Y2 = np.array(data2[Ind[jj]])\n",
    "\n",
    "YY = []\n",
    "for i in range(start, start + number_training):\n",
    "    YY.append(Y1[i])\n",
    "    YY.append(Y2[i])\n",
    "\n",
    "Ymax = max(max(YY),max(YY))\n",
    "Ymin = min(min(YY),min(YY))\n",
    "\n",
    "\n",
    "Y = []\n",
    "for i in range(0,number_training):\n",
    "    Y.append(Y1[i])\n",
    "    Y.append(Y2[i])\n",
    "\n",
    "#Y are normalizated over [-1,1]\n",
    "if Ymax-Ymin!= 0:\n",
    "    Y = (np.array(Y)- Ymin)*2/(Ymax-Ymin)\n",
    "Y = np.array(Y)  -1\n",
    "Y = Sqrt(Y)\n",
    "Y = np.clip(Y,-1,1)\n",
    "\n",
    "############################################################\n",
    "\n",
    "Z1 = np.array(data1[Ind[kk]])\n",
    "Z2 = np.array(data2[Ind[kk]])\n",
    "\n",
    "ZZ = []\n",
    "for i in range(start, start + number_training):\n",
    "    ZZ.append(Z1[i])\n",
    "    ZZ.append(Z2[i])\n",
    "\n",
    "Zmax = max(max(ZZ),max(ZZ))\n",
    "Zmin = min(min(ZZ),min(ZZ))\n",
    "\n",
    "\n",
    "Z = []\n",
    "for i in range(0,number_training):\n",
    "    Z.append(Z1[i])\n",
    "    Z.append(Z2[i])\n",
    "\n",
    "#Z are normalizated over [-1,1]\n",
    "if Zmax-Zmin!= 0:\n",
    "    Z = (np.array(Z)- Zmin)*2/(Zmax-Zmin)\n",
    "Z = np.array(Z)  -1\n",
    "#Z = Cheb(Z)\n",
    "Z = Sqrt(Z)\n",
    "Z = np.clip(Z,-1,1)\n",
    "\n",
    "############################################################\n",
    "A1 = np.array(data1[Ind[ll]])\n",
    "A2 = np.array(data2[Ind[ll]])\n",
    "\n",
    "AA = []\n",
    "for i in range(start, start + number_training):\n",
    "    AA.append(A1[i])\n",
    "    AA.append(A2[i])\n",
    "\n",
    "Amax = max(max(AA),max(AA))\n",
    "Amin = min(min(AA),min(AA))\n",
    "\n",
    "\n",
    "A = []\n",
    "for i in range(0,number_training):\n",
    "    A.append(A1[i])\n",
    "    A.append(A2[i])\n",
    "\n",
    "#Z are normalizated over [-1,1]\n",
    "if Amax-Amin!= 0:\n",
    "    A = (np.array(A)- Amin)*2/(Amax-Amin)\n",
    "A = np.array(A)  -1\n",
    "#A = Sqrt(A)\n",
    "A = np.clip(A,-1,1)\n",
    "\n",
    "############################################################\n",
    "\n",
    "B1 = np.array(data1[Ind[mm]])\n",
    "B2 = np.array(data2[Ind[mm]])\n",
    "\n",
    "BB = []\n",
    "for i in range(start, start + number_training):\n",
    "    BB.append(B1[i])\n",
    "    BB.append(B2[i])\n",
    "\n",
    "Bmax = max(max(BB),max(BB))\n",
    "Bmin = min(min(BB),min(BB))\n",
    "\n",
    "B = []\n",
    "for i in range(0,number_training):\n",
    "    B.append(B1[i])\n",
    "    B.append(B2[i])\n",
    "\n",
    "#X are normalizated over [-1,1]\n",
    "if Bmax-Bmin!= 0:\n",
    "    B = (np.array(B)- Bmin)*2/(Bmax-Bmin)\n",
    "B = np.array(B) -1\n",
    "#X = Sqrt(B)\n",
    "B = np.clip(B,-1,1)\n",
    "\n",
    "############################################################\n",
    "\n",
    "X1 = []\n",
    "X2 = []\n",
    "Y1 = []\n",
    "Y2 = []\n",
    "Z1 = []\n",
    "Z2 = []\n",
    "A1 = []\n",
    "A2 = []\n",
    "B1 = []\n",
    "B2 = []\n",
    "\n",
    "\n",
    "for i in range(0,len(X)):\n",
    "    if i % 2 == 0:\n",
    "        X1.append(X[i])\n",
    "        Y1.append(Y[i])\n",
    "        Z1.append(Z[i])\n",
    "        A1.append(A[i])\n",
    "        B1.append(B[i])\n",
    "    else:\n",
    "        X2.append(X[i])\n",
    "        Y2.append(Y[i])\n",
    "        Z2.append(Z[i])\n",
    "        A2.append(A[i])\n",
    "        B2.append(B[i])\n",
    "\n",
    "#TRAINING SET VISUALIZATION\n",
    "if plot_data == 1:\n",
    "    %matplotlib inline\n",
    "    fig = plt.figure(figsize = (10,10)) \n",
    "    ax = plt.axes(projection='3d')\n",
    "    ax.plot3D(X1,Y1,Z1,'ro',markersize = 1)\n",
    "    ax.plot3D(X2,Y2,Z2,'bo',markersize = 1)\n",
    "    ax.set_xlabel('x')   \n",
    "    ax.set_ylabel('y')   \n",
    "    ax.set_zlabel('z') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing for the training\n",
    "division of the initial vector in smaller vectors in order to fit \"max_circuits_supported\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this part of the program is needed in the case where there exists a maximal number of trainable circuits per-time\n",
    "N = int(len(X)/max_circuits_supported)\n",
    "\n",
    "v = np.zeros((N, max_circuits_supported))\n",
    "for i in range(0,N):\n",
    "    for j in range(0,max_circuits_supported):\n",
    "        v[i][j] = X[j+i*max_circuits_supported]\n",
    "        \n",
    "remainder = len(X)%max_circuits_supported     \n",
    "v_last = np.zeros(remainder)\n",
    "for j in range(0,remainder):\n",
    "    v_last[j] = X[j+N*max_circuits_supported]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "training set mapping function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_qubits = 1 #for EACH variable\n",
    "number_bits = number_classes*2\n",
    "\n",
    "#evaluation of phi\n",
    "Phi_f = np.zeros((len(X), 2*number_var))\n",
    "for i in range(0,len(X)):\n",
    "    for j in range(0, 2*number_var):\n",
    "        Phi_f[i][j] = Phi_function(X[i],Y[i],Z[i],A[i],B[i])[j]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "training set mapping function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining the label function for the trainingset, remember that odd and even events correspond to different classes\n",
    "\n",
    "f_l = []\n",
    "for i in range(0,len(X)):\n",
    "    f_l.append(f_label(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build the variational circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_parameters = 4*number_qubits*number_var\n",
    "\n",
    "Phi = ParameterVector('Φ', 2*number_var) #int(number_qubits*(number_qubits+1)/2)\n",
    "theta = ParameterVector('θ',number_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Circuit construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1591.6x806.68 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit = QuantumCircuit(number_var*number_qubits, number_bits)\n",
    "\n",
    "for i in range(0,L1):\n",
    "\n",
    "    for j in range(0,number_var):\n",
    "        for k in range(0,number_qubits):\n",
    "            circuit.rx(2*Phi[j*2],j+(number_var)*k)  \n",
    "            circuit.rz(2*Phi[j*2+1],j+(number_var)*k) \n",
    "circuit.barrier()\n",
    "\n",
    "#rotations\n",
    "s = 0\n",
    "for i in range(0,number_qubits*number_var):\n",
    "    variational_circuit = QuantumCircuit(1)\n",
    "    variational_circuit.rx(theta[s],0)\n",
    "    s += 1\n",
    "    variational_circuit.rz(theta[s],0)\n",
    "    s += 1\n",
    "    var_inst = variational_circuit.to_instruction()\n",
    "\n",
    "    circuit.append(var_inst,[i])\n",
    "circuit.barrier()\n",
    "S = s\n",
    "\n",
    "for k in range(0,L2):    \n",
    "    #entanglement  \n",
    "\n",
    "    circuit.cx(0,1)\n",
    "    circuit.cx(2,3)\n",
    "    circuit.h(3)\n",
    "    circuit.cx(2,3)\n",
    "    circuit.cx(3,4)\n",
    "    circuit.cx(2,3)\n",
    "\n",
    "    \n",
    "    circuit.barrier()\n",
    "    \n",
    "    #rotations\n",
    "    s = S\n",
    "    for i in range(0,number_qubits*number_var):\n",
    "        variational_circuit = QuantumCircuit(1)\n",
    "        variational_circuit.rx(theta[s],0)\n",
    "        s += 1\n",
    "        variational_circuit.rz(theta[s],0)\n",
    "        s += 1\n",
    "        var_inst = variational_circuit.to_instruction()\n",
    "\n",
    "        circuit.append(var_inst,[i])\n",
    "    circuit.barrier()\n",
    "\n",
    "    \n",
    "# circuit.measure(range(0,2),range(0,2))\n",
    "circuit.measure(range(0,4),range(0,4))\n",
    "\n",
    "\n",
    "%matplotlib inline  \n",
    "decomposed_circuit = circuit.decompose()\n",
    "decomposed_circuit.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cost function production and plot\n",
    "The cost function is chosen to be the kullback-Leibler divergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"468.88890131020287\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "X_plot = []\n",
    "Y_plot = []\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "plt.ion()\n",
    "\n",
    "fig.show()\n",
    "fig.canvas.draw()\n",
    "iter_count = 0\n",
    "\n",
    "def plot_cost_fun(y):\n",
    "    global iter_count\n",
    "    global X_plot\n",
    "    global Y_plot\n",
    "    X_plot.append(iter_count)\n",
    "    Y_plot.append(y)\n",
    "    ax.clear()\n",
    "    ax.plot(X_plot,Y_plot)\n",
    "    fig.canvas.draw()\n",
    "    iter_count += 1   \n",
    "    \n",
    "\n",
    "\n",
    "def cost_function(par,*args): #par will contain the parameters at each step of the optimization\n",
    "\n",
    "    circuit = args[0]\n",
    "    theta = args[1]\n",
    "    Phi_f = args[2]\n",
    "    \n",
    "    counts = training_set(par,circuit,theta,Phi_f)\n",
    "\n",
    "    L = 0\n",
    "    print(\"\\n current value of parameters: \" , par )\n",
    "        \n",
    "    #estimated label \n",
    "    for i in range(0,len(X)):\n",
    "        for k in range(0,2):\n",
    "            yi = f_measuration(counts[i])\n",
    "            Yi = ((c_e_ii+c_e_jj)*yi[k] + (c_e_kk+c_e_ll)*yi[2+k])/(c_e_ii+c_e_jj+c_e_kk+c_e_ll)           \n",
    "            L += (-f_l[i][k]*math.log(Yi))*(((i+1)%2)*ratio + i%2)\n",
    "            \n",
    "    if print_loss_func == 1:\n",
    "        plot_cost_fun(L)\n",
    "    \n",
    "    print(\"current value for loss function: \" , L , \"\\n\\n\" )\n",
    "    return L"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "factor = 100\n",
    "\n",
    "#creation of the eigenstates list, i.e: ['000', '001',... ]\n",
    "eig = []\n",
    "for i in range(0,2**(number_bits)):\n",
    "    eig.append('')\n",
    "\n",
    "for j in range(0,number_bits):\n",
    "    for i in range(0,2**j): \n",
    "        for k in range(0,(2**(number_bits-j))):            \n",
    "            if (k % 2) == 0:\n",
    "                eig[i+k*2**j] = '0' + eig[i+k*2**j] \n",
    "            else:\n",
    "                eig[i+k*2**j] = '1' + eig[i+k*2**j]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dado\\anaconda3\\lib\\site-packages\\qiskit\\aqua\\components\\optimizers\\spsa.py:93: DeprecationWarning: The max_trials parameter is deprecated as of 0.8.0 and will be removed no sooner than 3 months after the release. You should use maxiter instead.\n",
      "  warnings.warn('The max_trials parameter is deprecated as of '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [3.52352685 4.06177634 5.35458037 0.19650071 1.57024846 3.45726756\n",
      " 1.08973322 0.99861795 5.32663314 3.45985861 5.49811653 4.83037439\n",
      " 4.87796859 3.50234151 5.71185625 0.41592507 2.36964031 5.94868755\n",
      " 2.14953282 1.07823657]\n",
      "current value for loss function:  289.18785049193644 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 2.92352685  3.46177634  5.95458037  0.79650071  0.97024846  2.85726756\n",
      "  0.48973322  0.39861795  5.92663314  2.85985861  6.09811653  5.43037439\n",
      "  5.47796859  4.10234151  5.11185625 -0.18407493  1.76964031  5.34868755\n",
      "  2.74953282  1.67823657]\n",
      "current value for loss function:  291.46355522541586 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [3.59784057 4.13609007 5.28026664 0.12218698 1.64456218 3.16223796\n",
      " 0.79470362 0.70358835 5.25231942 3.53417234 5.4238028  4.75606066\n",
      " 4.80365487 3.79737111 5.78616997 0.49023879 2.07461071 5.65365795\n",
      " 2.44456242 1.00392285]\n",
      "current value for loss function:  286.4706882204465 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [3.22849725 3.76674674 5.64960997 0.49153031 1.27521886 3.53158128\n",
      " 1.16404694 1.07293167 5.62166274 3.16482901 5.79314613 5.12540399\n",
      " 5.17299819 3.42802779 5.41682665 0.12089547 2.44395404 6.02300127\n",
      " 2.07521909 1.37326617]\n",
      "current value for loss function:  293.3945847761537 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 3.74279262  4.55911995  4.85723676 -0.02276507  2.06759207  3.0172859\n",
      "  0.64975157  0.5586363   5.10736737  3.95720222  5.27885075  4.61110861\n",
      "  4.38062498  4.220401    5.93112203  0.91326868  1.65158083  5.5087059\n",
      "  2.8675923   0.8589708 ]\n",
      "current value for loss function:  295.9690085622488 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.02087046  4.28104212  5.13531459 -0.3008429   1.78951423  2.73920807\n",
      "  0.37167373  0.28055846  4.82928954  3.67912439  5.00077292  4.33303078\n",
      "  4.65870282  3.94232316  6.20919986  0.63519085  1.92965866  5.23062806\n",
      "  2.58951447  0.58089296]\n",
      "current value for loss function:  286.67051282011346 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.55281802  3.97645204  5.43990467 -0.83279047  1.25756667  2.43461799\n",
      " -0.16027383 -0.02403162  4.29734197  3.14717682  4.69618284  4.0284407\n",
      "  4.9632929   3.63773308  6.51378994  0.10324328  2.46160623  4.6986805\n",
      "  2.28492439  0.0489454 ]\n",
      "current value for loss function:  279.2331728845239 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.32546054  3.74909455  5.66726216 -0.60543298  1.48492416  2.20726051\n",
      "  0.06708365 -0.2513891   4.52469946  3.37453431  4.46882535  3.80108321\n",
      "  5.19065038  3.4103756   6.74114742  0.33060077  2.23424874  4.92603798\n",
      "  2.0575669   0.27630288]\n",
      "current value for loss function:  284.4300259926359 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.82209904  4.24573306  5.36510244 -0.90759269  0.98828565  2.50942022\n",
      " -0.42955485  0.05077061  4.02806095  2.8778958   4.77098507  4.29772172\n",
      "  4.88849067  3.9070141   6.24450892 -0.16603774  2.73088725  4.42939948\n",
      "  2.35972662 -0.02585683]\n",
      "current value for loss function:  275.61420595145813 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.62762025  4.05125426  5.17062365 -1.10207149  1.18276444  2.70389901\n",
      " -0.23507606  0.2452494   4.22253974  3.0723746   4.96546386  4.10324293\n",
      "  4.69401188  3.71253531  6.43898771  0.02844105  2.53640845  4.62387827\n",
      "  2.55420541 -0.22033562]\n",
      "current value for loss function:  281.8262870219566 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.95869318  4.3823272   5.50169659 -0.59982157  0.85169151  2.20164909\n",
      " -0.566149   -0.08582353  3.72028982  2.74130166  4.63439092  4.60549285\n",
      "  5.02508481  4.21478523  6.10791478 -0.30263188  3.03865838  4.12162835\n",
      "  2.22313247  0.11073731]\n",
      "current value for loss function:  269.4682196428655 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 5.12987017  4.55350419  5.67287357 -0.77099855  0.68051452  2.37282608\n",
      " -0.73732598 -0.25700052  3.89146681  2.57012467  4.46321394  4.43431586\n",
      "  5.1962618   4.04360825  5.93673779 -0.47380887  2.86748139  4.29280534\n",
      "  2.05195549  0.2819143 ]\n",
      "current value for loss function:  270.7706644852415 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.90404148  4.32767549  5.44704488 -0.54516986  0.90634322  2.14699738\n",
      " -0.665165   -0.18483953  3.66563811  2.79595337  4.53537492  4.50647684\n",
      "  4.97043311  4.11576923  6.00889877 -0.24798017  3.09331008  4.22064435\n",
      "  2.27778418  0.05608561]\n",
      "current value for loss function:  266.66822832818855 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 5.05770919  4.4813432   5.60071259 -0.69883757  0.7526755   2.30066509\n",
      " -0.51149729 -0.03117182  3.81930583  2.64228566  4.68904263  4.66014455\n",
      "  5.12410082  4.26943694  6.16256648 -0.40164789  2.93964237  4.06697664\n",
      "  2.12411647  0.20975332]\n",
      "current value for loss function:  272.46428592948354 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.78143764  4.06511671  5.1844861  -0.42256603  1.168902    1.8844386\n",
      " -0.92772378 -0.30744337  3.54303428  2.9185572   4.41277109  4.24391806\n",
      "  4.70787432  3.99316539  5.74633999  0.01457861  3.35586886  4.34324819\n",
      "  2.54034296 -0.06651823]\n",
      "current value for loss function:  268.6697063261562 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.64148269  4.20507166  5.32444105 -0.28261108  1.02894705  2.02439355\n",
      " -0.78776883 -0.44739832  3.40307933  3.05851215  4.27281614  4.38387301\n",
      "  4.84782927  3.85321045  5.88629494 -0.12537634  3.21591392  4.48320314\n",
      "  2.40038801 -0.20647318]\n",
      "current value for loss function:  264.0628537476958 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.4412915   4.27638405  5.39575343 -0.21129869  0.82875586  2.09570594\n",
      " -0.58757764 -0.64758951  3.33176694  3.12982454  4.07262495  4.5840642\n",
      "  5.04802046  3.65301925  5.95760733 -0.19668873  3.01572272  4.55451552\n",
      "  2.32907563 -0.40666437]\n",
      "current value for loss function:  260.12858867958164 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.57017031  4.40526285  5.52463224 -0.08241989  0.95763466  2.22458474\n",
      " -0.71645644 -0.5187107   3.20288814  3.25870334  4.20150375  4.4551854\n",
      "  4.91914166  3.78189806  6.08648613 -0.32556753  3.14460153  4.68339433\n",
      "  2.20019682 -0.27778556]\n",
      "current value for loss function:  262.98483452143887 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.44246509  4.27755764  5.27721129 -0.2101251   0.71021371  1.97716379\n",
      " -0.58875123 -0.64641592  3.45030909  3.13099813  3.9540828   4.70260635\n",
      "  5.04684687  3.53447711  5.95878092 -0.07814658  2.89718058  4.43597338\n",
      "  2.32790203 -0.52520651]\n",
      "current value for loss function:  260.47256869144053 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.32274936  4.1578419   5.39692703 -0.32984084  0.82992945  2.09687953\n",
      " -0.46903549 -0.76613166  3.33059335  3.01128239  4.07379854  4.58289061\n",
      "  5.16656261  3.65419285  5.83906518 -0.19786232  3.01689632  4.55568912\n",
      "  2.44761777 -0.40549078]\n",
      "current value for loss function:  259.1901402855153 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.2730512   4.10814374  5.33463592 -0.26754973  0.8796276   2.03458843\n",
      " -0.41933734 -0.70384055  3.2808952   3.07357349  4.12349669  4.53319245\n",
      "  5.21626077  3.703891    5.78936703 -0.13557122  2.95460521  4.49339801\n",
      "  2.49731593 -0.46778188]\n",
      "current value for loss function:  259.1756134135477 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.38504046  4.220133    5.44662518 -0.37953899  0.76763835  2.14657768\n",
      " -0.5313266  -0.81582981  3.39288446  2.96158424  4.01150743  4.64518171\n",
      "  5.10427151  3.59190174  5.90135628 -0.24756048  3.06659447  4.60538727\n",
      "  2.38532667 -0.35579262]\n",
      "current value for loss function:  259.04500315103735 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.28166118  4.22212552  5.3432459  -0.38153151  0.87101762  2.1485702\n",
      " -0.53331911 -0.71245053  3.39487697  2.95959172  4.11488671  4.54180243\n",
      "  5.20765079  3.58990923  5.79797701 -0.1441812   2.96321519  4.50200799\n",
      "  2.38333415 -0.3538001 ]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current value for loss function:  259.89577188447976 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.38703298  4.11675373  5.4486177  -0.27615971  0.76564583  2.04319841\n",
      " -0.42794732 -0.81782233  3.28950518  3.06496351  4.00951492  4.64717423\n",
      "  5.10227899  3.69528102  5.9033488  -0.24955299  3.06858699  4.60737979\n",
      "  2.48870595 -0.4591719 ]\n",
      "current value for loss function:  258.296979944053 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.34775226  4.05640427  5.40933698 -0.31544043  0.70529637  1.98284895\n",
      " -0.46722804 -0.87817179  3.22915572  3.12531297  3.94916546  4.70752369\n",
      "  5.04192953  3.75563048  5.96369826 -0.30990245  3.02930627  4.66772925\n",
      "  2.54905541 -0.51952136]\n",
      "current value for loss function:  258.94268678208397 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.44738244  4.15603444  5.50896716 -0.21581026  0.80492655  2.08247913\n",
      " -0.36759786 -0.77854161  3.3287859   3.02568279  4.04879563  4.60789351\n",
      "  5.14155971  3.6560003   5.86406808 -0.21027227  3.12893645  4.56809907\n",
      "  2.44942523 -0.41989118]\n",
      "current value for loss function:  258.67084611352 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.36076478  4.06941679  5.51694307 -0.20783434  0.71830889  1.99586147\n",
      " -0.35962194 -0.86515926  3.24216824  3.01770688  3.96217798  4.59991759\n",
      "  5.05494205  3.64802438  5.95068574 -0.29688993  3.13691237  4.56012315\n",
      "  2.44144931 -0.50650883]\n",
      "current value for loss function:  257.7889646703344 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.45535835  4.16401036  5.4223495  -0.30242791  0.81290246  2.09045504\n",
      " -0.45421552 -0.77056569  3.33676182  3.11230045  4.05677155  4.69451117\n",
      "  5.14953563  3.74261796  5.85609217 -0.20229636  3.04231879  4.65471673\n",
      "  2.53604288 -0.41191526]\n",
      "current value for loss function:  259.4036858682958 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.39216381  4.10081582  5.48554404 -0.23923337  0.74970792  2.0272605\n",
      " -0.39102097 -0.92389397  3.18343354  3.04910591  3.90344328  4.63131662\n",
      "  4.99620735  3.58928968  6.00942044 -0.2654909   3.19564707  4.50138845\n",
      "  2.47284834 -0.56524354]\n",
      "current value for loss function:  256.82548436659516 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.30203008  4.01068209  5.57567778 -0.14909963  0.65957419  1.93712677\n",
      " -0.30088724 -0.83376023  3.27356727  2.95897217  3.99357701  4.54118289\n",
      "  5.08634109  3.67942341  5.91928671 -0.35562463  3.10551333  4.59152218\n",
      "  2.38271461 -0.4751098 ]\n",
      "current value for loss function:  258.1338907168547 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.35240835  4.06106036  5.52529951 -0.28563028  0.79610484  2.07365742\n",
      " -0.35126551 -0.88413851  3.13703663  3.09550282  3.94319874  4.59156116\n",
      "  4.94981044  3.62904514  5.96966498 -0.30524636  3.24204398  4.45499154\n",
      "  2.43309288 -0.52548807]\n",
      "current value for loss function:  257.1123945535807 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.43856073  4.14721273  5.43914713 -0.19947791  0.70995246  1.98750504\n",
      " -0.43741789 -0.97029088  3.223189    3.00935044  3.85704636  4.67771354\n",
      "  5.03596281  3.54289277  6.05581736 -0.21909398  3.15589161  4.54114391\n",
      "  2.51924526 -0.61164045]\n",
      "current value for loss function:  256.72197213136855 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.36835991  4.15958471  5.42677515 -0.26967872  0.69758048  1.97513306\n",
      " -0.44978987 -0.90009006  3.23556098  3.07955126  3.92724718  4.69008552\n",
      "  4.96576199  3.61309358  6.06818934 -0.2892948   3.14351963  4.55351589\n",
      "  2.44904444 -0.62401243]\n",
      "current value for loss function:  257.67627864317524 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.45093271  4.07701192  5.50934795 -0.18710593  0.78015328  2.05770586\n",
      " -0.36721707 -0.98266286  3.15298818  2.99697846  3.84467438  4.60751272\n",
      "  5.04833479  3.53052079  5.98561654 -0.206722    3.22609243  4.47094309\n",
      "  2.53161724 -0.54143963]\n",
      "current value for loss function:  256.5212304149204 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.4904553   4.03748932  5.46953633 -0.14758333  0.74034166  2.09722845\n",
      " -0.32769448 -1.02218545  3.1927998   3.03679008  3.884486    4.64732434\n",
      "  5.08785738  3.5703324   5.94609395 -0.24653362  3.26561502  4.4314205\n",
      "  2.57113983 -0.50191704]\n",
      "current value for loss function:  256.23636750240445 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.41112109  4.11682353  5.54887054 -0.22691754  0.81967587  2.01789424\n",
      " -0.40702869 -0.94285124  3.11346559  2.95745587  3.80515179  4.56799013\n",
      "  5.00852318  3.49099819  6.02542816 -0.16719941  3.18628081  4.51075471\n",
      "  2.49180562 -0.58125125]\n",
      "current value for loss function:  256.4426279725282 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.41981624  4.10812838  5.46378763 -0.14183464  0.73459296  2.10297715\n",
      " -0.32194578 -0.9515464   3.1985485   2.96615103  3.8902347   4.57668528\n",
      "  5.01721833  3.5760811   6.016733   -0.25228232  3.27136371  4.50205956\n",
      "  2.57688852 -0.5725561 ]\n",
      "current value for loss function:  256.75209377842754 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.49620399  4.03174063  5.54017539 -0.21822239  0.81098072  2.02658939\n",
      " -0.39833353 -1.02793415  3.12216075  3.04253878  3.81384695  4.65307303\n",
      "  5.09360608  3.49969335  5.94034525 -0.17589457  3.19497596  4.42567181\n",
      "  2.50050077 -0.49616834]\n",
      "current value for loss function:  255.76797239588606 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.52876021  3.99918441  5.49903792 -0.17708492  0.84353693  1.99403318\n",
      " -0.43088975 -1.06049037  3.08960453  3.00140131  3.85498441  4.61193557\n",
      "  5.1261623   3.46713713  5.90778903 -0.14333835  3.16241975  4.46680927\n",
      "  2.54163824 -0.46361213]\n",
      "current value for loss function:  255.98473542274084 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.45506653  4.07287809  5.5727316  -0.25077861  0.76984325  2.06772686\n",
      " -0.35719607 -0.98679668  3.16329821  3.075095    3.78129073  4.68562925\n",
      "  5.05246862  3.54083081  5.98148272 -0.21703203  3.23611343  4.39311559\n",
      "  2.46794456 -0.53730581]\n",
      "current value for loss function:  255.79451196094055 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.52106984  4.00687478  5.57794759 -0.18477529  0.83584656  2.00172355\n",
      " -0.42319938 -0.98158069  3.16851421  3.08031099  3.84729404  4.61962594\n",
      "  5.04725262  3.54604681  5.9154794  -0.15102872  3.24132942  4.3878996\n",
      "  2.46272856 -0.4713025 ]\n",
      "current value for loss function:  256.1522126292933 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.44985053  4.07809409  5.50672829 -0.2559946   0.76462726  2.07294285\n",
      " -0.35198007 -1.0528      3.0972949   3.00909168  3.77607474  4.69084524\n",
      "  5.11847193  3.4748275   5.98669871 -0.22224803  3.17011011  4.4591189\n",
      "  2.53394787 -0.5425218 ]\n",
      "current value for loss function:  255.83833595362108 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.50943563  4.01850899  5.49737591 -0.26534698  0.75527487  2.01335776\n",
      " -0.34262769 -1.06215238  3.15687999  2.9997393   3.83565983  4.63126015\n",
      "  5.12782431  3.46547512  5.92711362 -0.16266293  3.16075773  4.39953381\n",
      "  2.54330025 -0.48293671]\n",
      "current value for loss function:  255.9432671740986 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.44049815  4.08744647  5.56631338 -0.19640951  0.82421235  2.08229524\n",
      " -0.41156517 -0.9932149   3.08794252  3.06867677  3.76672235  4.70019763\n",
      "  5.05888684  3.53441259  5.99605109 -0.23160041  3.22969521  4.46847129\n",
      "  2.47436278 -0.55187419]\n",
      "current value for loss function:  255.8446082242439 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.43830159  4.08964303  5.50168451 -0.19421294  0.75958348  2.0844918\n",
      " -0.41376173 -0.99101834  3.15257139  3.00404791  3.76452579  4.63556876\n",
      "  5.12351571  3.46978372  5.99824765 -0.23379697  3.23189177  4.47066785\n",
      "  2.47216622 -0.48724532]\n",
      "current value for loss function:  255.64513974618615 \n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.50512702  4.0228176   5.56850994 -0.26103838  0.82640891  2.01766637\n",
      " -0.3469363  -1.05784377  3.08574596  3.07087334  3.83135122  4.70239419\n",
      "  5.05669028  3.53660916  5.93142222 -0.16697154  3.16506634  4.40384242\n",
      "  2.53899165 -0.55407075]\n",
      "current value for loss function:  255.8068493306099 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.49888567  4.02905895  5.49740464 -0.18993307  0.82016756  2.02390772\n",
      " -0.35317765 -0.98673847  3.15685126  3.06463198  3.76024592  4.69615284\n",
      "  5.06293163  3.46550386  5.93766357 -0.23807684  3.23617164  4.47494772\n",
      "  2.5327503  -0.5478294 ]\n",
      "current value for loss function:  255.33962115290595 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.43402172  4.0939229   5.56226859 -0.25479702  0.75530361  2.08877167\n",
      " -0.4180416  -1.05160242  3.09198731  2.99976804  3.82510987  4.63128889\n",
      "  5.12779558  3.5303678   6.00252752 -0.17321289  3.17130769  4.41008377\n",
      "  2.46788635 -0.48296545]\n",
      "current value for loss function:  256.286712851022 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.5283912   3.99955342  5.53093578 -0.16042754  0.84967309  2.05743885\n",
      " -0.38670878 -1.02026961  3.18635679  3.03110085  3.79377706  4.72565837\n",
      "  5.09646276  3.49903499  5.90815804 -0.20454571  3.26567717  4.44141658\n",
      "  2.49921916 -0.51429826]\n",
      "current value for loss function:  255.76607441721598 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.46535454  4.06259009  5.46789911 -0.22346421  0.78663642  1.99440218\n",
      " -0.32367212 -0.95723294  3.12332012  3.09413752  3.73074039  4.6626217\n",
      "  5.03342609  3.43599832  5.97119471 -0.26758237  3.2026405   4.50445325\n",
      "  2.56225583 -0.57733493]\n",
      "current value for loss function:  255.16676842759864 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.44719356  4.01942149  5.44973814 -0.18029561  0.76847545  1.97624121\n",
      " -0.36684071 -0.93907197  3.16648872  3.05096892  3.71257942  4.7057903\n",
      "  5.07659469  3.41783735  5.98935568 -0.28574334  3.2458091   4.46128465\n",
      "  2.51908723 -0.5954959 ]\n",
      "current value for loss function:  254.8981475759264 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.50852313  4.08075106  5.51106771 -0.24162518  0.82980502  2.03757078\n",
      " -0.30551115 -1.00040154  3.10515915  3.11229849  3.77390899  4.64446073\n",
      "  5.01526512  3.47916692  5.92802611 -0.22441378  3.18447953  4.52261422\n",
      "  2.5804168  -0.53416633]\n",
      "current value for loss function:  255.70775363683205 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.4226067   4.05456519  5.42515128 -0.15570875  0.74388859  1.95165435\n",
      " -0.39142758 -0.91448511  3.19107558  3.08611262  3.68799256  4.6706466\n",
      "  5.10118155  3.39325049  6.01394254 -0.25059964  3.27039596  4.43669779\n",
      "  2.55423093 -0.62008276]\n",
      "current value for loss function:  254.92353742922526 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.48233727  3.99483463  5.48488184 -0.21543931  0.80361916  2.01138492\n",
      " -0.33169701 -0.97421567  3.13134502  3.02638206  3.74772312  4.73037716\n",
      "  5.04145099  3.45298105  5.95421198 -0.31033021  3.2106654   4.49642835\n",
      "  2.49450037 -0.5603522 ]\n",
      "current value for loss function:  255.15916229952254 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.41605222  4.06111967  5.41859679 -0.14915427  0.79556328  2.00332904\n",
      " -0.33975288 -0.9661598   3.19763006  3.03443793  3.73966725  4.72232129\n",
      "  5.10773604  3.38669601  6.02049702 -0.24404516  3.27695044  4.48837248\n",
      "  2.56078541 -0.56840807]\n",
      "current value for loss function:  254.8842496949187 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.47428139  4.0028905   5.47682597 -0.20738344  0.73733411  1.94509987\n",
      " -0.39798206 -0.90793063  3.13940089  3.0926671   3.68143808  4.66409211\n",
      "  5.04950686  3.44492518  5.96226785 -0.30227433  3.21872127  4.43014331\n",
      "  2.50255624 -0.62663724]\n",
      "current value for loss function:  255.13611517791114 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.40903472  4.06813718  5.46839555 -0.19895303  0.74576453  2.01034655\n",
      " -0.33273538 -0.9731773   3.14783131  3.08423669  3.74668475  4.72933879\n",
      "  5.05793728  3.3796785   5.97069826 -0.23702766  3.22715169  4.49538998\n",
      "  2.56780292 -0.61820683]\n",
      "current value for loss function:  255.03593794525312 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.46585098  4.01132091  5.41157929 -0.14213676  0.80258079  1.95353029\n",
      " -0.38955164 -0.91636104  3.20464757  3.02742043  3.68986849  4.67252253\n",
      "  5.11475354  3.43649477  6.02751453 -0.29384392  3.28396795  4.43857372\n",
      "  2.51098666 -0.56139057]\n",
      "current value for loss function:  255.30848186709548 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.40143036  4.07574153  5.47599991 -0.20655738  0.79364399  1.96246708\n",
      " -0.38061484 -0.92529784  3.19571077  3.09184105  3.69880529  4.68145933\n",
      "  5.10581674  3.37207415  5.96309391 -0.28490712  3.21954733  4.50299434\n",
      "  2.57540728 -0.57032736]\n",
      "current value for loss function:  255.08839227529634 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.45691418  4.02025771  5.42051609 -0.15107356  0.73816017  2.01795091\n",
      " -0.32513102 -0.98078166  3.14022695  3.03635722  3.75428911  4.73694315\n",
      "  5.05033292  3.42755797  6.01857773 -0.2294233   3.27503115  4.44751052\n",
      "  2.51992345 -0.62581119]\n",
      "current value for loss function:  254.91083643671934 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.40739343  4.06977847  5.47003684 -0.1463695   0.73345611  2.02265496\n",
      " -0.37465178 -0.98548572  3.13552289  3.03165317  3.75899317  4.74164721\n",
      "  5.04562886  3.43226203  5.96905697 -0.27894405  3.27973521  4.44280646\n",
      "  2.5152194  -0.57629043]\n",
      "current value for loss function:  255.08027007162545 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.46161824  4.01555365  5.41581203 -0.20059432  0.78768092  1.96843015\n",
      " -0.32042696 -0.93126091  3.1897477   3.08587798  3.70476836  4.68742239\n",
      "  5.09985367  3.37803721  6.02328179 -0.22471924  3.22551039  4.49703128\n",
      "  2.56944421 -0.63051524]\n",
      "current value for loss function:  255.04724865660694 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.40897154  4.01516734  5.41542572 -0.20098063  0.73503423  2.02107685\n",
      " -0.37307366 -0.9839076   3.13710101  3.03323128  3.75741505  4.74006909\n",
      "  5.04720698  3.3776509   6.0236681  -0.27736594  3.27815709  4.49741759\n",
      "  2.56983052 -0.57786855]\n",
      "current value for loss function:  254.9690705659593 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.46200455  4.06820035  5.46845873 -0.14794762  0.78806723  1.96804384\n",
      " -0.32004065 -0.9308746   3.19013401  3.08626429  3.70438204  4.68703608\n",
      "  5.10023999  3.43068391  5.97063509 -0.22433293  3.22512408  4.44438458\n",
      "  2.51679751 -0.63090155]\n",
      "current value for loss function:  255.35022133947896 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.39830684  4.05640553  5.4566639  -0.21164533  0.77627241  2.03174155\n",
      " -0.38373836 -0.94266942  3.17833919  3.02256658  3.76807975  4.75073379\n",
      "  5.08844516  3.41888909  6.0343328  -0.23612775  3.23691891  4.50808229\n",
      "  2.52859234 -0.61910673]\n",
      "current value for loss function:  255.31056723566863 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.45020973  4.00450264  5.40476102 -0.15974244  0.72436953  1.97983866\n",
      " -0.33183548 -0.9945723   3.12643631  3.07446947  3.71617687  4.69883091\n",
      "  5.03654228  3.3669862   5.98242991 -0.28803064  3.28882179  4.4561794\n",
      "  2.58049522 -0.56720385]\n",
      "current value for loss function:  254.6889958036401 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.41698847  4.0377239   5.38715276 -0.1929637   0.75759078  1.9622304\n",
      " -0.31422722 -1.01218057  3.10882804  3.09207773  3.74939813  4.73205217\n",
      "  5.01893402  3.40020746  6.01565117 -0.3056389   3.30643005  4.43857114\n",
      "  2.54727396 -0.54959558]\n",
      "current value for loss function:  254.60429841063356 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.46781799  3.98689438  5.43798228 -0.14213418  0.70676126  2.01305992\n",
      " -0.36505674 -0.96135105  3.15965756  3.04124821  3.69856861  4.68122265\n",
      "  5.06976354  3.34937794  5.96482165 -0.25480938  3.25560053  4.48940066\n",
      "  2.59810348 -0.6004251 ]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current value for loss function:  255.06387120533162 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.45401123  4.00070114  5.374367   -0.20574946  0.77037654  1.94944465\n",
      " -0.30144146 -1.02496632  3.09604229  3.05505497  3.71237537  4.74483792\n",
      "  5.00614826  3.3631847   6.02843693 -0.31842465  3.26940729  4.4755939\n",
      "  2.53448821 -0.53680983]\n",
      "current value for loss function:  254.672516485998 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.40420271  4.05050966  5.42417552 -0.15594094  0.72056802  1.99925316\n",
      " -0.35124998 -0.9751578   3.14585081  3.10486348  3.76218388  4.6950294\n",
      "  5.05595678  3.41299322  5.97862841 -0.26861614  3.31921581  4.42578539\n",
      "  2.58429673 -0.58661835]\n",
      "current value for loss function:  255.00086115179255 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.41410634  4.04060602  5.41427189 -0.16584457  0.77930759  1.98934953\n",
      " -0.29251041 -1.03389737  3.13594717  3.09495985  3.75228025  4.70493304\n",
      "  4.99721721  3.40308958  5.98853204 -0.3273557   3.30931218  4.43568902\n",
      "  2.57439309 -0.52787878]\n",
      "current value for loss function:  254.87823818457179 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.46294228  3.99177009  5.36543595 -0.21468051  0.73047165  1.9405136\n",
      " -0.34134635 -0.98506144  3.08711124  3.04612392  3.70344432  4.75376897\n",
      "  5.04605315  3.35425365  6.03736798 -0.27851977  3.26047624  4.48452495\n",
      "  2.52555716 -0.57671471]\n",
      "current value for loss function:  254.69407635024913 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.41980774  3.9869964   5.36066227 -0.17154597  0.72569797  1.93573991\n",
      " -0.29821181 -1.02819597  3.13024577  3.08925845  3.69867063  4.71063444\n",
      "  5.05082683  3.39738818  5.99423344 -0.3216543   3.30361078  4.44139042\n",
      "  2.56869169 -0.53358018]\n",
      "current value for loss function:  254.69269989621947 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.46771597  4.03490462  5.40857049 -0.2194542   0.77360619  1.98364813\n",
      " -0.34612003 -0.98028775  3.08233755  3.04135023  3.74657885  4.75854266\n",
      "  5.00291861  3.34947996  6.04214166 -0.27374608  3.25570256  4.48929864\n",
      "  2.52078347 -0.5814884 ]\n",
      "current value for loss function:  254.58527784436868 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.42328083  3.99046949  5.41115753 -0.17501906  0.72917105  1.98623517\n",
      " -0.3016849  -1.02472289  3.07975051  3.08578537  3.74916589  4.71410752\n",
      "  5.04735375  3.3939151   5.99770653 -0.27115904  3.30013769  4.49188568\n",
      "  2.51819643 -0.53705326]\n",
      "current value for loss function:  254.5596094642348 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.47030301  4.03749167  5.36413535 -0.22204124  0.77619323  1.939213\n",
      " -0.34870707 -0.97770071  3.12677269  3.03876319  3.70214371  4.7611297\n",
      "  5.00033157  3.34689292  6.0447287  -0.31818122  3.25311551  4.44486351\n",
      "  2.56521861 -0.58407544]\n",
      "current value for loss function:  254.818686263833 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.4164549   4.02981846  5.41798346 -0.16819313  0.72234512  1.99306111\n",
      " -0.29485896 -0.98537391  3.07292458  3.04643639  3.70981692  4.75345649\n",
      "  5.00800477  3.40074103  6.0370555  -0.31050801  3.26078872  4.45253671\n",
      "  2.5575454  -0.57640224]\n",
      "current value for loss function:  254.9304484636263 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.4626298   3.98364355  5.37180856 -0.21436803  0.76852003  1.9468862\n",
      " -0.34103387 -1.03154882  3.11909948  3.0926113   3.75599183  4.70728159\n",
      "  5.05417968  3.35456612  5.99088059 -0.26433311  3.30696363  4.49871162\n",
      "  2.5113705  -0.53022733]\n",
      "current value for loss function:  254.42318342534094 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.43094473  4.01532863  5.35812985 -0.22804674  0.78219873  1.93320749\n",
      " -0.30934879 -0.99986374  3.08741441  3.06092622  3.76967053  4.73896666\n",
      "  5.06785839  3.34088742  5.97720189 -0.29601818  3.32064233  4.51239032\n",
      "  2.54305557 -0.56191241]\n",
      "current value for loss function:  254.7175184045097 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.47630851  3.96996485  5.40349363 -0.18268296  0.73683495  1.97857128\n",
      " -0.35471258 -1.04522753  3.13277819  3.10629001  3.72430675  4.69360288\n",
      "  5.02249461  3.3862512   6.02256567 -0.2506544   3.27527855  4.46702654\n",
      "  2.49769179 -0.51654862]\n",
      "current value for loss function:  254.64068520810608 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.47803697  3.96823639  5.40522209 -0.1809545   0.73510649  1.93571332\n",
      " -0.35644103 -1.04695598  3.13450665  3.10801846  3.72257829  4.69187442\n",
      "  5.02076615  3.38797966  5.97970771 -0.24892594  3.31813651  4.46529808\n",
      "  2.54054975 -0.51482017]\n",
      "current value for loss function:  254.41421232311907 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.43345055  4.01282281  5.36063567 -0.22554091  0.77969291  1.98029973\n",
      " -0.31185462 -1.00236957  3.08992023  3.06343205  3.76716471  4.73646084\n",
      "  5.06535256  3.34339324  6.02429413 -0.29351236  3.27355009  4.5098845\n",
      "  2.49596333 -0.55940658]\n",
      "current value for loss function:  254.66832097421772 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.44077374  4.00549962  5.36795886 -0.1743771   0.77236972  1.97297655\n",
      " -0.3191778  -1.05353338  3.14108404  3.11459586  3.75984152  4.68529703\n",
      "  5.01418875  3.35071643  5.97313031 -0.24234855  3.28087328  4.50256131\n",
      "  2.54712715 -0.5520834 ]\n",
      "current value for loss function:  254.73449893925593 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.48461436  3.96165899  5.41179949 -0.21821773  0.7285291   1.92913592\n",
      " -0.36301843 -1.00969275  3.09724342  3.07075523  3.7160009   4.72913765\n",
      "  5.05802938  3.39455705  6.01697094 -0.28618917  3.32471391  4.45872069\n",
      "  2.50328652 -0.50824277]\n",
      "current value for loss function:  254.42301368627753 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.4495901   3.99668325  5.37677523 -0.18319347  0.72042893  1.96416018\n",
      " -0.3711186  -1.04471701  3.13226768  3.06265507  3.75102515  4.6941134\n",
      "  5.06612955  3.40265722  5.98194668 -0.29428934  3.33281407  4.49374494\n",
      "  2.53831078 -0.5001426 ]\n",
      "current value for loss function:  254.82785261679626 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.49271453  3.95355883  5.41989965 -0.2263179   0.76355335  1.92103575\n",
      " -0.32799417 -1.00159259  3.08914325  3.10577949  3.70790073  4.73723782\n",
      "  5.02300512  3.3595328   6.02507111 -0.25116492  3.28968965  4.45062052\n",
      "  2.49518635 -0.54326703]\n",
      "current value for loss function:  254.17329045144785 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.51001968  3.93625368  5.4372048  -0.20118705  0.7808585   1.9461666\n",
      " -0.35312502 -0.98428744  3.0718381   3.08064865  3.69059558  4.75454297\n",
      "  5.04813596  3.38466364  5.99994026 -0.23385977  3.2723845   4.43331537\n",
      "  2.52031719 -0.56057218]\n",
      "current value for loss function:  254.37795840716967 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.46758369  3.97868967  5.39476881 -0.24362305  0.73842251  1.9037306\n",
      " -0.31068902 -1.02672343  3.11427409  3.12308464  3.73303157  4.71210698\n",
      "  5.00569997  3.34222765  6.04237626 -0.27629576  3.31482049  4.47575136\n",
      "  2.4778812  -0.51813618]\n",
      "current value for loss function:  254.52353184199583 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.47181306  3.97446029  5.44077184 -0.19762001  0.74265189  1.94973364\n",
      " -0.3149184  -1.02249405  3.11004472  3.07708161  3.72880219  4.75811001\n",
      "  5.051703    3.38823068  5.99637322 -0.23029273  3.26881746  4.42974833\n",
      "  2.52388423 -0.56413922]\n",
      "current value for loss function:  254.25592284170142 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.51358672  3.93268664  5.39899819 -0.23939367  0.78442554  1.90795998\n",
      " -0.35669206 -0.9807204   3.06827106  3.11885526  3.68702854  4.71633636\n",
      "  5.00992935  3.34645702  6.03814688 -0.27206638  3.31059111  4.47152198\n",
      "  2.48211058 -0.52236556]\n",
      "current value for loss function:  254.1712343567413 \n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.47438453  3.97188883  5.3970645  -0.20019148  0.78635923  1.94716217\n",
      " -0.35862574 -0.97878671  3.10747325  3.07965307  3.68509485  4.75553855\n",
      "  5.00799566  3.34452334  6.04008057 -0.23286419  3.3125248   4.47345567\n",
      "  2.48017689 -0.52043188]\n",
      "current value for loss function:  254.3262164059188 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.51552041  3.93075295  5.43820038 -0.24132736  0.74522335  1.90602629\n",
      " -0.31748986 -1.01992259  3.06633737  3.12078895  3.72623073  4.71440267\n",
      "  5.04913154  3.38565922  5.99894469 -0.27400007  3.27138892  4.43231979\n",
      "  2.52131277 -0.56156775]\n",
      "current value for loss function:  254.38043031937022 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.47325932  3.97301403  5.39593929 -0.23958751  0.74696319  1.90776614\n",
      " -0.31922971 -1.01818274  3.10859846  3.11904911  3.68396964  4.71614251\n",
      "  5.0473917   3.38391937  6.04120577 -0.23173898  3.31365001  4.43405964\n",
      "  2.51957293 -0.55982791]\n",
      "current value for loss function:  254.28724481763285 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.51378056  3.93249279  5.43646054 -0.19906627  0.78748444  1.94828738\n",
      " -0.35975095 -0.9776615   3.06807722  3.07852787  3.72449089  4.75666375\n",
      "  5.00687045  3.34339813  6.00068453 -0.27226023  3.27312876  4.47458088\n",
      "  2.47905168 -0.51930667]\n",
      "current value for loss function:  254.23081705550484 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.5149656   3.93130776  5.39771713 -0.23780968  0.78866947  1.94947241\n",
      " -0.36093598 -0.97647647  3.10682063  3.07734283  3.72567592  4.71792034\n",
      "  5.04561386  3.3422131   5.9994995  -0.23351682  3.27194373  4.43583747\n",
      "  2.47786665 -0.55805008]\n",
      "current value for loss function:  254.25149418897564 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.47503716  3.9712362   5.43764557 -0.19788124  0.74874103  1.90954397\n",
      " -0.32100754 -1.01640491  3.06689218  3.11727128  3.68574748  4.75784879\n",
      "  5.00568542  3.38214154  6.03942794 -0.27344526  3.31187217  4.47576591\n",
      "  2.51779509 -0.51812163]\n",
      "current value for loss function:  254.19359770948014 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.47381282  3.97246054  5.39951363 -0.1966569   0.78687296  1.90831963\n",
      " -0.3197832  -0.97827297  3.10502412  3.07913934  3.68452314  4.75907313\n",
      "  5.00446108  3.38336588  6.001296   -0.2746696   3.27374024  4.47699025\n",
      "  2.51901943 -0.51689729]\n",
      "current value for loss function:  254.519759630294 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.51316909  3.93310426  5.43886991 -0.23601317  0.74751669  1.9476759\n",
      " -0.35913948 -1.01762925  3.06566784  3.11849561  3.72387941  4.71971685\n",
      "  5.04381736  3.3440096   6.04065228 -0.23531332  3.31309651  4.43763398\n",
      "  2.47966316 -0.55625357]\n",
      "current value for loss function:  254.0289677316993 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.48681413  3.92065561  5.41251495 -0.20965821  0.73506803  1.92132094\n",
      " -0.33278451 -1.03007791  3.09202281  3.09214065  3.69752445  4.70726819\n",
      "  5.01746239  3.37036456  6.01429732 -0.26166828  3.32554517  4.46398894\n",
      "  2.4672145  -0.52989861]\n",
      "current value for loss function:  254.42936629265392 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.52561775  3.95945923  5.45131857 -0.24846183  0.77387165  1.96012456\n",
      " -0.37158814 -0.99127429  3.05321918  3.13094427  3.73632807  4.74607181\n",
      "  5.05626601  3.33156094  6.05310094 -0.22286466  3.28674155  4.42518532\n",
      "  2.50601812 -0.56870223]\n",
      "current value for loss function:  253.99401512684716 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.53657001  3.97041148  5.46227082 -0.22114464  0.78482391  1.93280738\n",
      " -0.38254039 -0.98032203  3.04226693  3.10362709  3.74728033  4.71875463\n",
      "  5.06721827  3.32060869  6.06405319 -0.21191241  3.31405874  4.4525025\n",
      "  2.47870093 -0.54138504]\n",
      "current value for loss function:  253.98202800670336 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.49830056  3.93214204  5.42400138 -0.25941409  0.74655446  1.97107682\n",
      " -0.34427095 -1.01859148  3.08053637  3.14189653  3.70901088  4.75702407\n",
      "  5.02894883  3.35887813  6.02578375 -0.25018185  3.2757893   4.41423306\n",
      "  2.51697037 -0.57965448]\n",
      "current value for loss function:  254.31155470683805 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.54475354  3.97859501  5.43270158 -0.25071389  0.79300744  1.96237662\n",
      " -0.39072392 -0.9721385   3.0340834   3.13319633  3.75546386  4.74832387\n",
      "  5.03764902  3.35017794  6.07223672 -0.24148166  3.28448949  4.46068603\n",
      "  2.50827018 -0.53320151]\n",
      "current value for loss function:  253.92040633450958 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.50700076  3.94084223  5.47045435 -0.21296111  0.75525466  1.92462385\n",
      " -0.35297114 -1.00989128  3.07183618  3.09544355  3.71771108  4.7105711\n",
      "  5.0754018   3.31242516  6.03448395 -0.20372888  3.32224227  4.42293326\n",
      "  2.4705174  -0.57095429]\n",
      "current value for loss function:  254.2369451893549 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.51531282  3.94915429  5.42488956 -0.22127317  0.80081945  1.97018864\n",
      " -0.3612832  -0.96432649  3.02627138  3.10375561  3.72602314  4.71888315\n",
      "  5.02983701  3.35798995  6.08004874 -0.24929367  3.31393021  4.46849805\n",
      "  2.51608219 -0.56264223]\n",
      "current value for loss function:  254.08762380414416 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.55256555  3.98640703  5.4621423  -0.25852591  0.76356672  1.9329359\n",
      " -0.39853594 -1.00157922  3.06352412  3.14100835  3.76327587  4.75613589\n",
      "  5.06708974  3.32073722  6.04279601 -0.21204094  3.27667748  4.43124532\n",
      "  2.47882946 -0.5253895 ]\n",
      "current value for loss function:  254.06180947749732 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.51620867  3.98681863  5.46255389 -0.22216903  0.79992359  1.93252431\n",
      " -0.36217906 -0.96522234  3.06393572  3.10465147  3.726919    4.71977901\n",
      "  5.03073286  3.35709409  6.07915288 -0.24839781  3.31303435  4.46760219\n",
      "  2.47841786 -0.5249779 ]\n",
      "current value for loss function:  254.13547031972715 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.55297715  3.95005015  5.42578542 -0.2589375   0.76315512  1.96929278\n",
      " -0.39894753 -1.00199082  3.02716724  3.14141994  3.76368747  4.75654749\n",
      "  5.06750134  3.32032562  6.04238441 -0.21162934  3.27626588  4.43083372\n",
      "  2.51518634 -0.56174637]\n",
      "current value for loss function:  253.96199635183063 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.55711105  3.98221547  5.42165152 -0.22677218  0.79532044  1.97342668\n",
      " -0.40308144 -0.96982549  3.05933257  3.14555385  3.73152215  4.72438216\n",
      "  5.07163524  3.35249094  6.07454973 -0.24379466  3.27213198  4.46299904\n",
      "  2.48302101 -0.56588027]\n",
      "current value for loss function:  254.08568103784933 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.52081183  3.94591625  5.45795074 -0.26307141  0.75902122  1.93712746\n",
      " -0.36678221 -1.00612472  3.02303334  3.10925462  3.76782137  4.76068139\n",
      "  5.03533601  3.31619172  6.03825051 -0.20749544  3.3084312   4.42669982\n",
      "  2.51932024 -0.52958105]\n",
      "current value for loss function:  253.82146016205135 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.51442207  3.93952649  5.42849624 -0.23361691  0.78847572  1.96658196\n",
      " -0.36039245 -1.01251448  3.01664358  3.13870912  3.77421113  4.76707115\n",
      "  5.02894625  3.34564622  6.03186074 -0.23694994  3.31482096  4.45615432\n",
      "  2.52571    -0.55903555]\n",
      "current value for loss function:  253.73761926996943 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.55026633  3.97537075  5.4643405  -0.26946117  0.75263146  1.9307377\n",
      " -0.39623671 -0.97667022  3.05248784  3.10286486  3.73836687  4.73122689\n",
      "  5.06479052  3.30980196  6.06770501 -0.20110568  3.2789767   4.42031006\n",
      "  2.48986574 -0.52319129]\n",
      "current value for loss function:  254.2167516759308 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.53811077  3.92781229  5.41678205 -0.22190271  0.80018991  1.97829615\n",
      " -0.34867825 -0.98882578  3.04033228  3.15042332  3.75052243  4.77878534\n",
      "  5.05263496  3.35736041  6.05554945 -0.21326123  3.32653516  4.46786851\n",
      "  2.53742419 -0.57074974]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current value for loss function:  253.86544294788928 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.50270787  3.96321519  5.45218495 -0.25730561  0.76478701  1.94289325\n",
      " -0.38408115 -1.02422868  3.00492938  3.11502042  3.78592533  4.74338244\n",
      "  5.01723206  3.32195751  6.02014655 -0.24866413  3.29113226  4.43246561\n",
      "  2.50202129 -0.53534684]\n",
      "current value for loss function:  254.19239135350367 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.51102301  3.95490006  5.44386981 -0.24899047  0.80807666  1.9861829\n",
      " -0.34079151 -0.98093903  3.04821903  3.12333555  3.77761019  4.78667209\n",
      "  5.06052171  3.36524716  6.02846169 -0.20537449  3.33442191  4.44078075\n",
      "  2.54531094 -0.57863649]\n",
      "current value for loss function:  254.14947428878938 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.54599752  3.91992555  5.4088953  -0.21401596  0.77310215  1.95120839\n",
      " -0.37576602 -1.01591354  3.01324452  3.15831006  3.74263568  4.75169758\n",
      "  5.0255472   3.33027265  6.0634362  -0.240349    3.29944739  4.47575526\n",
      "  2.51033643 -0.54366198]\n",
      "current value for loss function:  253.89038644592495 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.55217564  3.94830593  5.40271718 -0.24239634  0.76692403  1.97958877\n",
      " -0.38194414 -0.98753316  3.0070664   3.12992968  3.77101606  4.78007796\n",
      "  5.05392758  3.32409453  6.06961431 -0.21196862  3.29326928  4.44737488\n",
      "  2.53871681 -0.57204236]\n",
      "current value for loss function:  253.93776791915235 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.51761714  3.91374743  5.43727568 -0.20783785  0.80148253  1.94503027\n",
      " -0.34738564 -1.02209166  3.0416249   3.16448818  3.73645756  4.74551946\n",
      "  5.01936908  3.35865303  6.03505582 -0.24652711  3.32782777  4.48193338\n",
      "  2.50415831 -0.53748386]\n",
      "current value for loss function:  253.75238312394015 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.51327316  3.94355774  5.40746536 -0.20349386  0.80582651  1.94068629\n",
      " -0.37719595 -0.99228134  3.01181458  3.13467787  3.76626788  4.74117548\n",
      "  5.04917939  3.36299701  6.06486613 -0.2167168   3.29801746  4.45212306\n",
      "  2.49981433 -0.56729418]\n",
      "current value for loss function:  254.1889129475528 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.54742745  3.90940345  5.44161966 -0.23764816  0.77167221  1.97484059\n",
      " -0.34304166 -1.02643564  3.04596888  3.16883216  3.73211358  4.77532978\n",
      "  5.0150251   3.32884271  6.03071184 -0.2508711   3.33217176  4.48627736\n",
      "  2.53396863 -0.53313988]\n",
      "current value for loss function:  253.61024134208643 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.56135005  3.89548085  5.42178087 -0.21780937  0.79151101  1.95500179\n",
      " -0.32911906 -1.04035824  3.02613009  3.18275476  3.75195237  4.75549098\n",
      "  5.03486389  3.31492011  6.01678924 -0.2647937   3.34609436  4.46643857\n",
      "  2.51412983 -0.55297867]\n",
      "current value for loss function:  253.66656470384052 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.52758866  3.92924224  5.45554226 -0.25157076  0.75774961  1.98876319\n",
      " -0.36288045 -1.00659685  3.05989148  3.14899337  3.71819098  4.78925238\n",
      "  5.0011025   3.34868151  6.05055063 -0.2310323   3.31233296  4.50019996\n",
      "  2.54789123 -0.51921728]\n",
      "current value for loss function:  253.97291586013233 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.53521742  3.92161347  5.41453421 -0.243942    0.79875767  1.98113442\n",
      " -0.3218724  -1.01422561  3.05226272  3.19000142  3.72581974  4.74824433\n",
      "  5.00873126  3.30767345  6.00954258 -0.23866107  3.31996173  4.45919191\n",
      "  2.50688318 -0.52684604]\n",
      "current value for loss function:  253.66867715400898 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.56859671  3.88823419  5.4479135  -0.21056271  0.76537838  1.94775513\n",
      " -0.35525169 -1.0476049   3.01888343  3.15662213  3.75919903  4.78162361\n",
      "  5.04211055  3.34105274  6.04292186 -0.27204036  3.35334102  4.4925712\n",
      "  2.54026246 -0.56022533]\n",
      "current value for loss function:  253.47610093111228 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.57306352  3.88376738  5.4523803  -0.20609591  0.79391909  1.97629585\n",
      " -0.35971849 -1.01906418  3.04742415  3.18516285  3.73065832  4.7530829\n",
      "  5.04657735  3.31251203  6.04738867 -0.24349964  3.3248003   4.46403048\n",
      "  2.51172175 -0.56469213]\n",
      "current value for loss function:  253.32642010063879 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.540056    3.9167749   5.41937278 -0.23910342  0.76091158  1.94328833\n",
      " -0.32671097 -1.0520717   3.01441663  3.15215533  3.76366583  4.78609042\n",
      "  5.01356983  3.34551955  6.01438115 -0.27650716  3.35780782  4.497038\n",
      "  2.54472927 -0.53168462]\n",
      "current value for loss function:  253.70044341301914 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.54923015  3.8749551   5.42854694 -0.19728362  0.80273138  1.98510813\n",
      " -0.33588513 -1.0102519   3.05623643  3.19397513  3.75449168  4.74427061\n",
      "  5.05538964  3.33634539  6.02355531 -0.267333    3.31598802  4.48786384\n",
      "  2.50290946 -0.54085877]\n",
      "current value for loss function:  253.57329185016474 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.5818758   3.90760075  5.46119259 -0.22992927  0.77008573  1.95246249\n",
      " -0.36853077 -1.04289754  3.02359078  3.16132949  3.72184603  4.77691626\n",
      "  5.02274399  3.30369974  6.05620095 -0.23468736  3.34863366  4.4552182\n",
      "  2.53555511 -0.57350442]\n",
      "current value for loss function:  253.4490300249577 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58467334  3.91039829  5.43169687 -0.20043355  0.76728819  1.98195821\n",
      " -0.37132832 -1.04569509  3.02079324  3.15853194  3.71904849  4.74742054\n",
      "  5.05223971  3.3009022   6.0589985  -0.26418307  3.31913794  4.48471391\n",
      "  2.50605939 -0.5440087 ]\n",
      "current value for loss function:  253.75910880785864 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.55238008  3.87810503  5.46399013 -0.23272681  0.79958145  1.94966494\n",
      " -0.33903506 -1.01340183  3.0530865   3.19082521  3.75134175  4.7797138\n",
      "  5.01994645  3.33319546  6.02670524 -0.23188981  3.35143121  4.45242065\n",
      "  2.53835265 -0.57630196]\n",
      "current value for loss function:  253.51276563688293 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.57863377  3.90435871  5.43773645 -0.2384231   0.77332777  1.97591863\n",
      " -0.33333877 -1.00770554  3.02683282  3.16457152  3.72508807  4.78541009\n",
      "  5.04620013  3.30694178  6.05295892 -0.25814349  3.32517752  4.44672436\n",
      "  2.54404894 -0.55004828]\n",
      "current value for loss function:  253.71600184968642 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.54668379  3.87240874  5.46968642 -0.20647313  0.80527774  1.94396865\n",
      " -0.36528874 -1.03965551  3.05878279  3.1965215   3.75703804  4.75346012\n",
      "  5.01425015  3.33889175  6.02100894 -0.22619352  3.3571275   4.47867433\n",
      "  2.51209897 -0.58199825]\n",
      "current value for loss function:  253.3308518517385 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.53771866  3.89505902  5.47865155 -0.197508    0.81424287  1.96661894\n",
      " -0.37425387 -1.04862064  3.06774792  3.17387121  3.76600317  4.74449499\n",
      "  5.00528502  3.34785688  6.01204381 -0.21722839  3.36609263  4.45602405\n",
      "  2.53474925 -0.59096339]\n",
      "current value for loss function:  253.87437271715058 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.56933408  3.8634436   5.44703614 -0.22912341  0.78262746  1.93500352\n",
      " -0.34263846 -1.01700523  3.03613251  3.20548663  3.73438776  4.7761104\n",
      "  5.03690044  3.31624147  6.04365923 -0.2488438   3.33447721  4.48763947\n",
      "  2.50313384 -0.55934797]\n",
      "current value for loss function:  253.3198494678509 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58226028  3.8505174   5.46539918 -0.24204961  0.76970126  1.92207732\n",
      " -0.36100149 -1.00407903  3.05449555  3.18712359  3.7527508   4.78903661\n",
      "  5.0185374   3.33460451  6.05658543 -0.26177     3.35284025  4.46927643\n",
      "  2.52149688 -0.54642177]\n",
      "current value for loss function:  253.22157905101903 \n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.55097104  3.88180664  5.43410994 -0.21076037  0.8009905   1.95336656\n",
      " -0.32971225 -1.03536827  3.02320631  3.21841283  3.72146156  4.75774737\n",
      "  5.04982664  3.30331527  6.02529619 -0.23048077  3.32155101  4.50056567\n",
      "  2.49020764 -0.57771101]\n",
      "current value for loss function:  253.6635930419496 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.56151738  3.84028918  5.4756274  -0.22130672  0.75947303  1.94282021\n",
      " -0.3402586  -0.9938508   3.06472377  3.20786648  3.7320079   4.76829371\n",
      "  5.00830917  3.34483273  6.03584254 -0.27199823  3.33209736  4.4590482\n",
      "  2.5317251  -0.56716466]\n",
      "current value for loss function:  253.69113092651367 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.5924885   3.8712603   5.44465628 -0.25227784  0.79044415  1.91184909\n",
      " -0.37122972 -1.02482192  3.03375265  3.17689537  3.76297902  4.79926483\n",
      "  5.03928029  3.31386161  6.06681365 -0.24102711  3.36306848  4.49001932\n",
      "  2.50075398 -0.53619354]\n",
      "current value for loss function:  252.9941231278674 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.57798061  3.88741314  5.42850344 -0.23776995  0.80659699  1.92635699\n",
      " -0.35672183 -1.04097477  3.04826055  3.16074252  3.77913187  4.78475694\n",
      "  5.0247724   3.32836951  6.0829665  -0.22487427  3.37922132  4.47551143\n",
      "  2.51526188 -0.5200407 ]\n",
      "current value for loss function:  253.2969373063521 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.60864135  3.8567524   5.45916418 -0.26843068  0.77593626  1.89569625\n",
      " -0.38738257 -1.01031403  3.01759981  3.19140326  3.74847113  4.81541768\n",
      "  5.05543314  3.29770877  6.05230576 -0.255535    3.34856058  4.50617217\n",
      "  2.48460114 -0.55070144]\n",
      "current value for loss function:  253.050052431509 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.61424142  3.85115233  5.43440644 -0.24367295  0.80069399  1.89009618\n",
      " -0.36262483 -1.03507176  3.04235755  3.19700333  3.77322887  4.82101775\n",
      "  5.06103321  3.2921087   6.04670569 -0.26113507  3.37331832  4.48141443\n",
      "  2.50935888 -0.55630151]\n",
      "current value for loss function:  253.03673142816365 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58388361  3.88151014  5.46476425 -0.27403075  0.77033619  1.92045398\n",
      " -0.39298264 -1.00471396  3.01199974  3.16664552  3.74287106  4.79065994\n",
      "  5.0306754   3.3224665   6.0770635  -0.23077727  3.34296051  4.51177224\n",
      "  2.47900107 -0.5259437 ]\n",
      "current value for loss function:  253.47816948833506 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.59427175  3.84105995  5.42431406 -0.26364261  0.81078637  1.8800038\n",
      " -0.38259449 -1.0151021   3.05244992  3.20709571  3.7532592   4.83111013\n",
      "  5.04106354  3.31207836  6.06667536 -0.24116541  3.35334866  4.47132205\n",
      "  2.48938921 -0.53633184]\n",
      "current value for loss function:  252.882517075837 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.6243338   3.871122    5.4543761  -0.23358057  0.78072433  1.91006584\n",
      " -0.35253245 -1.04516414  3.02238788  3.17703366  3.78332124  4.80104808\n",
      "  5.07112559  3.28201632  6.03661331 -0.27122745  3.3834107   4.50138409\n",
      "  2.51945125 -0.56639389]\n",
      "current value for loss function:  253.23466704910763 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58628139  3.83306959  5.44609688 -0.2418598   0.78900356  1.87201343\n",
      " -0.36081168 -1.00711173  3.06044029  3.21508607  3.77504202  4.80932731\n",
      "  5.06284636  3.32006873  6.07466572 -0.23317505  3.34535829  4.49310487\n",
      "  2.48139885 -0.52834148]\n",
      "current value for loss function:  253.04064467152375 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.61605457  3.86284277  5.41632369 -0.27163298  0.81877674  1.90178661\n",
      " -0.39058486 -1.03688492  3.03066711  3.18531289  3.74526884  4.83910049\n",
      "  5.03307318  3.29029554  6.04489254 -0.26294823  3.37513147  4.46333169\n",
      "  2.51117203 -0.55811466]\n",
      "current value for loss function:  252.79001690909809 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.59218818  3.83897638  5.41069911 -0.24776659  0.79491035  1.87792023\n",
      " -0.39620944 -1.01301853  3.0545335   3.20917928  3.73964425  4.8152341\n",
      "  5.05693957  3.28467096  6.03926796 -0.23908184  3.38075605  4.4577071\n",
      "  2.48730564 -0.56373924]\n",
      "current value for loss function:  252.8047970858684 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.62167915  3.86846735  5.44019008 -0.27725756  0.82440132  1.9074112\n",
      " -0.36671847 -1.0425095   3.02504253  3.17968831  3.76913522  4.84472507\n",
      "  5.0274486   3.31416193  6.06875893 -0.26857281  3.35126509  4.48719807\n",
      "  2.51679661 -0.53424827]\n",
      "current value for loss function:  253.12990756754834 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58487739  3.86088076  5.43260349 -0.24045579  0.78759955  1.87060943\n",
      " -0.37430507 -1.00570773  3.03262912  3.21649007  3.73233346  4.83713848\n",
      "  5.03503519  3.30657534  6.03195716 -0.26098621  3.35885168  4.45039631\n",
      "  2.50921001 -0.57105003]\n",
      "current value for loss function:  253.13431922536128 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.61409256  3.83166559  5.40338832 -0.26967096  0.81681472  1.8998246\n",
      " -0.40352023 -1.0349229   3.06184429  3.18727491  3.76154863  4.80792331\n",
      "  5.06425036  3.27736017  6.06117233 -0.23177104  3.38806685  4.47961148\n",
      "  2.47999484 -0.54183487]\n",
      "current value for loss function:  252.8491171522525 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.62046583  3.85423786  5.42596059 -0.24709869  0.79424245  1.90619787\n",
      " -0.4098935  -1.01235063  3.03927202  3.20984718  3.7679219   4.80155004\n",
      "  5.07062363  3.29993244  6.03860006 -0.25434332  3.36549458  4.4570392\n",
      "  2.47362157 -0.5354616 ]\n",
      "current value for loss function:  252.84332470899412 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.59152028  3.82529232  5.39701505 -0.27604423  0.82318799  1.87725233\n",
      " -0.38094796 -1.04129617  3.06821756  3.18090163  3.73897635  4.83049558\n",
      "  5.04167809  3.2709869   6.0675456  -0.22539777  3.39444012  4.48598475\n",
      "  2.50256712 -0.56440714]\n",
      "current value for loss function:  253.0119481443943 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.62416659  3.85793863  5.42966136 -0.24339792  0.81922357  1.88121675\n",
      " -0.41359427 -1.00864986  3.06425314  3.18486605  3.74294077  4.82653116\n",
      "  5.04564251  3.30363321  6.06358118 -0.22936219  3.36179381  4.45333844\n",
      "  2.46992081 -0.56044272]\n",
      "current value for loss function:  253.06641218018757 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.5954847   3.82925674  5.40097947 -0.27207981  0.79054168  1.90989863\n",
      " -0.38491238 -1.03733175  3.03557125  3.21354794  3.77162266  4.79784927\n",
      "  5.0743244   3.27495132  6.03489929 -0.25804408  3.3904757   4.48202033\n",
      "  2.4986027  -0.53176083]\n",
      "current value for loss function:  252.96773738427132 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.62180367  3.82715171  5.42729843 -0.24576085  0.78843666  1.88357967\n",
      " -0.38280736 -1.03943677  3.03346623  3.18722898  3.7453037   4.79574425\n",
      "  5.07642942  3.2728463   6.03279427 -0.2601491   3.39258072  4.48412535\n",
      "  2.50070772 -0.5580798 ]\n",
      "current value for loss function:  252.87845156421844 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.59337968  3.8555757   5.39887445 -0.27418484  0.81686065  1.91200366\n",
      " -0.41123135 -1.01101278  3.06189022  3.21565297  3.77372769  4.82416824\n",
      "  5.04800543  3.30127028  6.06121826 -0.23172512  3.36415673  4.45570136\n",
      "  2.47228373 -0.52965581]\n",
      "current value for loss function:  252.85913337866126 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.59307018  3.8558852   5.4267366  -0.24632268  0.7889985   1.88414151\n",
      " -0.41154085 -1.01070329  3.03402807  3.18779082  3.74586554  4.79630609\n",
      "  5.07586758  3.27340813  6.03335611 -0.25958727  3.39201888  4.45539186\n",
      "  2.47197423 -0.52934631]\n",
      "current value for loss function:  252.8688844324789 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.62124183  3.82771355  5.39856495 -0.27449433  0.81717015  1.91231316\n",
      " -0.3833692  -1.03887494  3.06219972  3.21596247  3.77403719  4.82447774\n",
      "  5.04769593  3.30157978  6.06152776 -0.23141562  3.36384723  4.48356351\n",
      "  2.50014588 -0.55751796]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current value for loss function:  252.8192966767007 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.6222324   3.85464767  5.42549906 -0.24756022  0.81816072  1.91330372\n",
      " -0.41030331 -1.01194082  3.06319029  3.21695303  3.74710307  4.79754362\n",
      "  5.07463005  3.30257035  6.06251833 -0.23042505  3.36285667  4.48455408\n",
      "  2.50113645 -0.55850853]\n",
      "current value for loss function:  253.04677663336204 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.59430771  3.82672299  5.39757438 -0.2754849   0.79023603  1.88537904\n",
      " -0.38237863 -1.0398655   3.0352656   3.18902835  3.77502775  4.82546831\n",
      "  5.04670536  3.27464567  6.03459364 -0.25834973  3.39078135  4.4566294\n",
      "  2.47321177 -0.53058384]\n",
      "current value for loss function:  252.77214582793158 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58828192  3.8483801   5.41923149 -0.25382779  0.78421024  1.87935324\n",
      " -0.40403574 -1.01820839  3.05692271  3.18300255  3.75337064  4.83149411\n",
      "  5.06836247  3.26861987  6.05625076 -0.26437553  3.39680715  4.4506036\n",
      "  2.49486888 -0.55224096]\n",
      "current value for loss function:  252.5375827052091 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.61596483  3.82069719  5.39154858 -0.2815107   0.81189315  1.90703615\n",
      " -0.37635283 -1.0458913   3.0292398   3.21068546  3.78105355  4.8038112\n",
      "  5.04067956  3.29630278  6.02856785 -0.23669262  3.36912424  4.47828651\n",
      "  2.46718597 -0.52455805]\n",
      "current value for loss function:  252.9605844000355 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.6064142   3.85769397  5.42854536 -0.24451392  0.77489636  1.87003937\n",
      " -0.41334961 -1.00889452  3.03879043  3.17368868  3.77150292  4.81336182\n",
      "  5.05023019  3.25930599  6.03811847 -0.27368941  3.37867487  4.44128972\n",
      "  2.50418275 -0.56155483]\n",
      "current value for loss function:  252.80467795158685 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.57896804  3.83024781  5.40109921 -0.27196007  0.80234252  1.89748552\n",
      " -0.38590346 -1.03634067  3.06623659  3.20113483  3.74405677  4.84080798\n",
      "  5.07767635  3.28675215  6.06556463 -0.24624325  3.40612102  4.46873588\n",
      "  2.47673659 -0.53410867]\n",
      "current value for loss function:  252.8185221278252 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.60660582  3.85788559  5.40152272 -0.24432229  0.801919    1.86984775\n",
      " -0.38632697 -1.03591716  3.06581307  3.17349705  3.74448028  4.8131702\n",
      "  5.05003857  3.25911437  6.03792685 -0.24666676  3.37848324  4.4410981\n",
      "  2.50437437 -0.56174645]\n",
      "current value for loss function:  252.81601873845477 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.57939156  3.83067133  5.42873699 -0.27153656  0.77470474  1.89706201\n",
      " -0.41354124 -1.0087029   3.03859881  3.20071132  3.77169455  4.84038447\n",
      "  5.07725283  3.28632864  6.06514112 -0.27388103  3.40569751  4.46831237\n",
      "  2.47716011 -0.53453219]\n",
      "current value for loss function:  252.81738690865393 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.60652251  3.83081521  5.4285931  -0.27139268  0.77484862  1.86993106\n",
      " -0.38641028 -1.03583385  3.06572976  3.20056744  3.77155066  4.84024058\n",
      "  5.05012188  3.28618475  6.06499723 -0.24675007  3.37856656  4.44118141\n",
      "  2.50429106 -0.56166314]\n",
      "current value for loss function:  252.7268623782544 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.57953544  3.85780228  5.40160603 -0.24440561  0.80183569  1.89691813\n",
      " -0.41339735 -1.00884678  3.03874269  3.17358037  3.74456359  4.81325351\n",
      "  5.07710895  3.25919768  6.03801016 -0.27373714  3.40555363  4.46816848\n",
      "  2.47730399 -0.53467607]\n",
      "current value for loss function:  252.94144502437732 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58437968  3.82619361  5.40645028 -0.27601428  0.79699145  1.86530945\n",
      " -0.38178868 -1.04045545  3.04358694  3.20518904  3.77617227  4.84486219\n",
      "  5.04550028  3.26404193  6.04285441 -0.2688929   3.37394495  4.46332424\n",
      "  2.50891267 -0.56628474]\n",
      "current value for loss function:  252.6362059823085 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.61114411  3.85295804  5.43321471 -0.24924985  0.77022702  1.89207388\n",
      " -0.40855311 -1.01369102  3.07035137  3.17842461  3.74940784  4.81809776\n",
      "  5.07226471  3.29080636  6.06961884 -0.24212847  3.40070938  4.43655981\n",
      "  2.48214824 -0.53952031]\n",
      "current value for loss function:  252.9842725372236 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.6033851   3.84519903  5.4254557  -0.25700886  0.80453222  1.85776868\n",
      " -0.4007941  -1.04799623  3.06259236  3.18618362  3.78371305  4.85240296\n",
      "  5.0645057   3.25650115  6.03531363 -0.27643368  3.36640417  4.47086502\n",
      "  2.51645344 -0.54727932]\n",
      "current value for loss function:  252.6686637225019 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.57683891  3.81865283  5.3989095  -0.28355506  0.77798603  1.88431487\n",
      " -0.3742479  -1.02145003  3.03604616  3.21272982  3.75716685  4.82585677\n",
      "  5.0379595   3.28304735  6.06185983 -0.24988748  3.39295037  4.44431882\n",
      "  2.48990725 -0.57382552]\n",
      "current value for loss function:  253.01859307986194 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58460978  3.85275594  5.43301261 -0.27578418  0.7857569   1.876544\n",
      " -0.40835102 -1.05555315  3.07014927  3.1786267   3.79126996  4.83362764\n",
      "  5.04573038  3.24894424  6.05408895 -0.28399059  3.35884726  4.47842193\n",
      "  2.52401036 -0.56605465]\n",
      "current value for loss function:  252.93046643732404 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.61094202  3.82642371  5.40668038 -0.24945194  0.81208914  1.85021176\n",
      " -0.38201878 -1.02922091  3.04381704  3.20495894  3.76493772  4.85995988\n",
      "  5.07206261  3.27527647  6.02775671 -0.25765835  3.3851795   4.45208969\n",
      "  2.49767812 -0.53972241]\n",
      "current value for loss function:  252.7554516719111 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58853447  3.84883126  5.40296551 -0.24573708  0.78968159  1.8464969\n",
      " -0.37830391 -1.05162846  3.04010217  3.18255139  3.78734528  4.86367474\n",
      "  5.07577748  3.25286892  6.02404185 -0.25394349  3.38889436  4.47449725\n",
      "  2.49396326 -0.53600754]\n",
      "current value for loss function:  252.53251330104555 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.61465688  3.82270884  5.42908793 -0.2718595   0.815804    1.87261931\n",
      " -0.40442633 -1.02550604  3.06622459  3.20867381  3.76122286  4.83755233\n",
      "  5.04965506  3.27899134  6.05016427 -0.28006591  3.36277194  4.44837483\n",
      "  2.52008568 -0.56212996]\n",
      "current value for loss function:  252.68307531817825 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.61127915  3.82608658  5.42571019 -0.26848176  0.78650965  1.84332496\n",
      " -0.37513198 -1.02888378  3.06284685  3.17937946  3.79051721  4.84093006\n",
      "  5.0530328   3.24969699  6.04678653 -0.27668817  3.39206629  4.45175257\n",
      "  2.49079133 -0.53283561]\n",
      "current value for loss function:  252.7034858494694 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58536253  3.85200319  5.39979358 -0.24256515  0.81242627  1.86924158\n",
      " -0.40104859 -1.05480039  3.03693024  3.20529607  3.7646006   4.86684668\n",
      "  5.07894941  3.2756136   6.02086992 -0.25077156  3.36614968  4.47766918\n",
      "  2.51670794 -0.55875222]\n",
      "current value for loss function:  252.7724286129993 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.61267267  3.82469305  5.40138902 -0.24416058  0.81083083  1.84193144\n",
      " -0.37373846 -1.05320496  3.03852568  3.20370063  3.79191073  4.86525124\n",
      "  5.05163927  3.27401816  6.04818006 -0.2780817   3.39345982  4.47607374\n",
      "  2.5151125  -0.53144209]\n",
      "current value for loss function:  252.71907835730718 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58695797  3.85040776  5.42710372 -0.26987529  0.78511613  1.86764614\n",
      " -0.39945316 -1.02749025  3.06424038  3.17798593  3.76619603  4.83953654\n",
      "  5.07735397  3.24830346  6.02246535 -0.252367    3.36774512  4.45035904\n",
      "  2.4893978  -0.55715679]\n",
      "current value for loss function:  252.9197493987159 \n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.61690902  3.84597327  5.42266924 -0.2654408   0.78955061  1.83769509\n",
      " -0.36950211 -1.05744131  3.03428933  3.20793698  3.79614708  4.84397102\n",
      "  5.04740292  3.25273794  6.02689984 -0.28231805  3.3721796   4.48031009\n",
      "  2.49383228 -0.55272231]\n",
      "current value for loss function:  252.86824125565218 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.59139245  3.8204567   5.39715267 -0.23992424  0.81506718  1.86321166\n",
      " -0.39501868 -1.03192474  3.0598059   3.18242041  3.77063051  4.86948759\n",
      "  5.07291949  3.27825451  6.05241641 -0.25680148  3.39769617  4.45479352\n",
      "  2.51934885 -0.52720574]\n",
      "current value for loss function:  252.52065447562714 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.60932717  3.81306932  5.38976528 -0.23253685  0.79713246  1.84527694\n",
      " -0.37708396 -1.02453735  3.06719328  3.20035513  3.78856523  4.85155287\n",
      "  5.08030688  3.26031979  6.03448169 -0.2747362   3.40508355  4.44740614\n",
      "  2.52673623 -0.54514046]\n",
      "current value for loss function:  252.52335452410844 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58400507  3.83839142  5.41508739 -0.25785895  0.82245456  1.87059904\n",
      " -0.40240606 -1.04985946  3.04187118  3.17503303  3.76324313  4.87687497\n",
      "  5.05498477  3.2856419   6.05980379 -0.24941409  3.37976145  4.47272824\n",
      "  2.50141413 -0.51981835]\n",
      "current value for loss function:  252.52754259373657 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.60932161  3.81307489  5.38977085 -0.25767361  0.79713803  1.8452825\n",
      " -0.37708952 -1.02454292  3.04205652  3.20034956  3.78855967  4.85155844\n",
      "  5.08030131  3.28545656  6.05961845 -0.24959943  3.37994679  4.4725429\n",
      "  2.50159947 -0.52000369]\n",
      "current value for loss function:  252.6554256298681 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58419041  3.83820608  5.41490205 -0.23254242  0.82226922  1.8704137\n",
      " -0.40222072 -1.04967412  3.06718771  3.17521837  3.76342847  4.87668963\n",
      "  5.05517011  3.26032536  6.03448725 -0.27473063  3.40507799  4.4474117\n",
      "  2.52673067 -0.54513489]\n",
      "current value for loss function:  252.79386044963857 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58724568  3.81020706  5.41184677 -0.23559769  0.81921395  1.86735843\n",
      " -0.39916545 -1.02167509  3.06413244  3.20321739  3.76648374  4.87363436\n",
      "  5.08316914  3.26338063  6.06248628 -0.24673161  3.40202271  4.47541073\n",
      "  2.49873164 -0.54207962]\n",
      "current value for loss function:  252.53718941354492 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.61218943  3.83515081  5.38690302 -0.26054144  0.7942702   1.84241468\n",
      " -0.3742217  -1.04661884  3.03918869  3.17827364  3.7914275   4.84869061\n",
      "  5.05822539  3.28832438  6.03754253 -0.27167536  3.37707896  4.45046698\n",
      "  2.52367539 -0.51713587]\n",
      "current value for loss function:  252.88673241860678 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.57988387  3.82760491  5.39444892 -0.25299554  0.8018161   1.84996058\n",
      " -0.3817676  -1.01431328  3.04673459  3.18581954  3.7838816   4.88099617\n",
      "  5.09053095  3.28077848  6.04508843 -0.23936979  3.38462486  4.48277254\n",
      "  2.49136983 -0.52468177]\n",
      "current value for loss function:  252.85712756731803 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.60464353  3.80284525  5.41920858 -0.22823588  0.82657576  1.87472024\n",
      " -0.40652726 -1.03907294  3.07149425  3.2105792   3.75912193  4.85623651\n",
      "  5.06577129  3.25601882  6.06984809 -0.26412946  3.40938452  4.45801288\n",
      "  2.51612949 -0.54944143]\n",
      "current value for loss function:  252.55497438064003 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58639715  3.79651279  5.42554104 -0.24648227  0.83290822  1.85647385\n",
      " -0.38828087 -1.02082656  3.07782671  3.21691166  3.77736832  4.87448289\n",
      "  5.08401767  3.27426521  6.07618054 -0.24588307  3.39113814  4.47625927\n",
      "  2.49788311 -0.53119504]\n",
      "current value for loss function:  252.33658077000828 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.61097599  3.82109163  5.4009622  -0.22190342  0.80832938  1.8810527\n",
      " -0.41285971 -1.0454054   3.05324787  3.19233282  3.75278948  4.84990405\n",
      "  5.05943883  3.24968637  6.0516017  -0.27046191  3.41571698  4.45168042\n",
      "  2.52246195 -0.55577388]\n",
      "current value for loss function:  252.55501272946728 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58185733  3.81637417  5.40567967 -0.25102208  0.83744803  1.85193404\n",
      " -0.40814225 -1.01628674  3.05796533  3.19705028  3.78190814  4.87902271\n",
      "  5.08855749  3.27880502  6.05631917 -0.26574444  3.38659832  4.45639789\n",
      "  2.49334329 -0.55105642]\n",
      "current value for loss function:  252.53495444899903 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.60625852  3.79197297  5.43008086 -0.22662089  0.81304684  1.87633523\n",
      " -0.38374106 -1.04068793  3.08236652  3.22145148  3.75750694  4.85462152\n",
      "  5.0641563   3.25440383  6.08072036 -0.24134325  3.41099951  4.48079908\n",
      "  2.51774448 -0.52665523]\n",
      "current value for loss function:  252.50950718733046 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.6067088   3.81574932  5.43053114 -0.22617061  0.81259656  1.85255888\n",
      " -0.38329077 -1.01691158  3.05859018  3.19767513  3.75705666  4.85417124\n",
      "  5.08793265  3.25395355  6.08117064 -0.2651196   3.41144979  4.48124936\n",
      "  2.51819476 -0.55043157]\n",
      "current value for loss function:  252.47772037630415 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58248217  3.79152269  5.40630451 -0.25039724  0.83682319  1.87678551\n",
      " -0.4075174  -1.04113821  3.08281681  3.22190176  3.78128329  4.87839787\n",
      "  5.06370602  3.27818018  6.05694401 -0.24089297  3.38722316  4.45702273\n",
      "  2.49396813 -0.52620494]\n",
      "current value for loss function:  252.5854782465454 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58483729  3.79387782  5.40865963 -0.24804212  0.83446807  1.85037532\n",
      " -0.40516228 -1.03878309  3.08046168  3.19549157  3.7548731   4.85198768\n",
      "  5.09011621  3.27582506  6.0833542  -0.26730316  3.41363335  4.48343292\n",
      "  2.49632326 -0.52856007]\n",
      "current value for loss function:  252.512750099445 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.60889236  3.81793289  5.4327147  -0.22398705  0.810413    1.87443039\n",
      " -0.38110721 -1.01472802  3.05640661  3.21954663  3.77892817  4.87604275\n",
      "  5.06606114  3.25176999  6.05929913 -0.24324809  3.38957828  4.45937786\n",
      "  2.52037832 -0.55261514]\n",
      "current value for loss function:  252.28842429202928 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.58963147  3.82255842  5.4134538  -0.24324794  0.80578747  1.85516949\n",
      " -0.37648168 -1.03398892  3.05178108  3.20028574  3.75966727  4.85678185\n",
      "  5.06143561  3.27103089  6.0546736  -0.26250899  3.38495275  4.45475232\n",
      "  2.52500386 -0.55724067]\n",
      "current value for loss function:  252.71528302423124 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.6135179   3.79867199  5.43734023 -0.21936151  0.8296739   1.87905592\n",
      " -0.40036811 -1.01010248  3.07566751  3.22417217  3.78355371  4.88066828\n",
      "  5.08532204  3.24714446  6.07856003 -0.23862256  3.40883918  4.47863875\n",
      "  2.50111743 -0.53335424]\n",
      "current value for loss function:  252.07374962990764 \n",
      "\n",
      "\n",
      "Job Status: job has successfully run\n",
      "\n",
      " current value of parameters:  [ 4.60448101  3.80456898  5.42830334 -0.22839841  0.82377691  1.87001903\n",
      " -0.39447112 -1.01913938  3.06977052  3.21513528  3.77451681  4.87163139\n",
      "  5.07942505  3.25618135  6.07266304 -0.24765945  3.40294219  4.47274176\n",
      "  2.50701442 -0.53925123]\n",
      "current value for loss function:  252.44733681632275 \n",
      "\n",
      "\n",
      "252.44733681632275 [ 4.60448101  3.80456898  5.42830334 -0.22839841  0.82377691  1.87001903\n",
      " -0.39447112 -1.01913938  3.06977052  3.21513528  3.77451681  4.87163139\n",
      "  5.07942505  3.25618135  6.07266304 -0.24765945  3.40294219  4.47274176\n",
      "  2.50701442 -0.53925123]\n",
      "27771.01150369644\n"
     ]
    }
   ],
   "source": [
    "#initial values for parameters are chosen randomly\n",
    "theta_0 = []\n",
    "\n",
    "\n",
    "for i in range(0, number_parameters):\n",
    "    theta_0.append(random.uniform(0, 2*np.pi))\n",
    "theta_0 = np.array(theta_0)\n",
    "\n",
    "#In the following c0 is the firt step \"a\", c1 is the initial \"c\", c2 is the \"alpha\" parameter, c3 the \"gamma\" parameter\n",
    "#and finally c4 another parameter to control \"a\" (as like as \"alpha\" does)\n",
    "t = time.time()\n",
    "\n",
    "Optimizer = SPSA(max_trials = 1e3, save_steps = 1, last_avg = 1, c0 = 10/(number_training), c1 = 0.3, c2 = 1, c3 = 0.7, c4 = 0, \n",
    "                 skip_calibration = False)\n",
    "\n",
    "# cost_final, theta_best, cost_plus_save, cost_minus_save, theta_plus_save, theta_minus_save =  (\n",
    "#     Optimizer._optimization(cost_function,theta_0, SPSA_steps, save_steps = 1, last_avg = 3))\n",
    "\n",
    "fw = Optimizer.wrap_function(cost_function,(circuit,theta,Phi_f))\n",
    "\n",
    "cost_final, theta_best, cost_plus_save, cost_minus_save, theta_plus_save, theta_minus_save =  (\n",
    "    Optimizer._optimization(fw,theta_0, SPSA_steps, save_steps = 1, last_avg = 3) )\n",
    "\n",
    "print(cost_final,theta_best)\n",
    "\n",
    "elapsed = time.time() - t\n",
    "print(elapsed)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data for testing\n",
    "data loading variable-per-variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data capture\n",
    "# data2 = pickle.load(open(\"Wjets_part1.pkl\",\"rb\"))\n",
    "# data1 = pickle.load(open(\"VBS_part1.pkl\",\"rb\"))\n",
    "# Ind = data1.columns\n",
    "\n",
    "\n",
    "X1 = np.array(data1[Ind[ii]])\n",
    "X2 = np.array(data2[Ind[ii]])\n",
    "\n",
    "X = []\n",
    "for i in range(start + number_training, start + number_training + number_testing):\n",
    "    X.append(X1[i])\n",
    "    X.append(X2[i])\n",
    "\n",
    "#X are normalizated over [-1,1]\n",
    "if Xmax-Xmin!= 0:\n",
    "    X = (np.array(X)- Xmin)*2/(Xmax-Xmin)\n",
    "X = np.array(X) -1\n",
    "#X = Sqrt(X)\n",
    "X = np.clip(X,-1,1)\n",
    "\n",
    "############################################################\n",
    "\n",
    "Y1 = np.array(data1[Ind[jj]])\n",
    "Y2 = np.array(data2[Ind[jj]])\n",
    "\n",
    "\n",
    "Y = []\n",
    "for i in range(start + number_training, start + number_training + number_testing):\n",
    "    Y.append(Y1[i])\n",
    "    Y.append(Y2[i])\n",
    "\n",
    "#Y are normalizated over [-1,1]\n",
    "if Ymax-Ymin!= 0:\n",
    "    Y = (np.array(Y)- Ymin)*2/(Ymax-Ymin)\n",
    "Y = np.array(Y)-1\n",
    "Y = Sqrt(Y)\n",
    "Y = np.clip(Y,-1,1)\n",
    "\n",
    "############################################################\n",
    "\n",
    "Z1 = np.array(data1[Ind[kk]])\n",
    "Z2 = np.array(data2[Ind[kk]])\n",
    "\n",
    "Z = []\n",
    "for i in range(start + number_training, start + number_training + number_testing):\n",
    "    Z.append(Z1[i])\n",
    "    Z.append(Z2[i])\n",
    "\n",
    "#Z are normalizated over [-1,1]\n",
    "if Zmax-Zmin!= 0:\n",
    "    Z = (np.array(Z)- Zmin)*2/(Zmax-Zmin)\n",
    "Z = np.array(Z)-1\n",
    "#Z = Cheb(Z)\n",
    "Z = Sqrt(Z)\n",
    "Z = np.clip(Z,-1,1)\n",
    "\n",
    "############################################################\n",
    "\n",
    "A1 = np.array(data1[Ind[ll]])\n",
    "A2 = np.array(data2[Ind[ll]])\n",
    "\n",
    "A = []\n",
    "for i in range(start + number_training, start + number_training + number_testing):\n",
    "    A.append(A1[i])\n",
    "    A.append(A2[i])\n",
    "\n",
    "#Z are normalizated over [-1,1]\n",
    "if Amax-Amin!= 0:\n",
    "    A = (np.array(A)- Amin)*2/(Amax-Amin)\n",
    "A = np.array(A)  -1\n",
    "#A = Sqrt(A)\n",
    "A = np.clip(A,-1,1)\n",
    "\n",
    "############################################################\n",
    "\n",
    "B1 = np.array(data1[Ind[mm]])\n",
    "B2 = np.array(data2[Ind[mm]])\n",
    "\n",
    "B = []\n",
    "for i in range(start + number_training, start + number_training + number_testing):\n",
    "    B.append(B1[i])\n",
    "    B.append(B2[i])\n",
    "\n",
    "#Z are normalizated over [-1,1]\n",
    "if Bmax-Bmin!= 0:\n",
    "    B = (np.array(B)- Bmin)*2/(Bmax-Bmin)\n",
    "B = np.array(B)  -1\n",
    "#B = Sqrt(B)\n",
    "B = np.clip(B,-1,1)\n",
    "\n",
    "############################################################ \n",
    "\n",
    "X1 = []\n",
    "X2 = []\n",
    "Y1 = []\n",
    "Y2 = []\n",
    "Z1 = []\n",
    "Z2 = []\n",
    "A1 = []\n",
    "A2 = []\n",
    "B1 = []\n",
    "B2 = []\n",
    "\n",
    "for i in range(0,len(X)):\n",
    "    if i % 2 == 0:\n",
    "        X1.append(X[i])\n",
    "        Y1.append(Y[i])\n",
    "        Z1.append(Z[i])\n",
    "        A1.append(A[i])\n",
    "        B1.append(B[i])\n",
    "    else:\n",
    "        X2.append(X[i])\n",
    "        Y2.append(Y[i])\n",
    "        Z2.append(Z[i])\n",
    "        A2.append(A[i])\n",
    "        B2.append(B[i])\n",
    "\n",
    "#TESTING SET VISUALIZATION\n",
    "if plot_data == 1:\n",
    "    %matplotlib inline\n",
    "    fig = plt.figure(figsize = (10,10)) \n",
    "    ax = plt.axes(projection='3d')\n",
    "    ax.plot3D(X1,Y1,Z1,'ro',markersize = 0.5)\n",
    "    ax.plot3D(X2,Y2,Z2,'bo',markersize = 0.5)\n",
    "    ax.set_xlabel('x')   \n",
    "    ax.set_ylabel('y')   \n",
    "    ax.set_zlabel('z')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing for testing\n",
    "division of the initial vector in smaller vectors in order to fit \"max_circuits_supported\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = int(len(X)/max_circuits_supported)\n",
    "\n",
    "v = np.zeros((N, max_circuits_supported))\n",
    "for i in range(0,N):\n",
    "    for j in range(0,max_circuits_supported):\n",
    "        v[i][j] = X[j+i*max_circuits_supported]\n",
    "        \n",
    "remainder = len(X)%max_circuits_supported     \n",
    "v_last = np.zeros(remainder)\n",
    "for j in range(0,remainder):\n",
    "    v_last[j] = X[j+N*max_circuits_supported]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "testing set mapping function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "Phi_f = np.zeros((len(X), 2*number_var))\n",
    "for i in range(0,len(X)):\n",
    "    for j in range(0, 2*number_var):\n",
    "        Phi_f[i][j] = Phi_function(X[i],Y[i],Z[i],A[i],B[i])[j]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "traing set labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_l = []\n",
    "for i in range(0,len(X)):\n",
    "    f_l.append(f_label(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing & AUC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ROC production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "if plot_AUC_image == 1:\n",
    "    fig2 = plt.figure(figsize = (10,10)) \n",
    "    plt.ylim((0,1))\n",
    "    plt.xlim((0,1))\n",
    "\n",
    "counts = training_set(theta_best,circuit,theta,Phi_f)\n",
    "\n",
    "index_sorted = []\n",
    "classification = []\n",
    "for i in range(0,len(X)):\n",
    "    classification.append(((c_e_ii+c_e_jj)*f_measuration(counts[i])[0]+(c_e_kk+c_e_ll)*f_measuration(counts[i])[2])/(c_e_ii+c_e_jj+c_e_kk+c_e_ll)) \n",
    "\n",
    "sort_index = np.argsort(classification)\n",
    "\n",
    "Classification = []\n",
    "Labelling = []\n",
    "for i in range(0,len(X)):\n",
    "    Classification.append(((c_e_ii+c_e_jj)*f_measuration(counts[sort_index[i]])[0]+(c_e_kk+c_e_ll)*f_measuration(counts[sort_index[i]])[2])/(c_e_ii+c_e_jj+c_e_kk+c_e_ll))  \n",
    "    Labelling.append((sort_index[i]+1)%2)\n",
    "\n",
    "treshold = np.linspace(0,1,2000)\n",
    "False_positive_rate = []\n",
    "True_positive_rate = []\n",
    "\n",
    "for k in range(0,len(treshold)):\n",
    "    C = Classification > treshold[k]\n",
    "    false_negatives = sum((Labelling - C) == np.int_(np.ones(len(X))))\n",
    "    false_positives = sum(np.array(Labelling - C) ==np.int_(-np.ones(len(X)))) \n",
    "    true_positives = sum(Labelling) - false_negatives \n",
    "    true_negatives = sum((Labelling) == np.int_(np.zeros(len(X)))) - false_positives\n",
    "\n",
    "    False_positive_rate.append(false_positives/(false_positives+true_negatives+0.000001))\n",
    "    True_positive_rate.append(true_positives/(true_positives+false_negatives+0.000001))\n",
    "    if plot_AUC_image == 1:\n",
    "        plt.plot(False_positive_rate[k],True_positive_rate[k],'ro',label = \"ROC\", markersize=3)\n",
    "        \n",
    "if plot_AUC_image == 1:    \n",
    "    plt.plot(treshold,treshold,'b', markersize=3)                  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AUC evaluation\n",
    "(should be improved with Simpson rule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7320403742679595\n"
     ]
    }
   ],
   "source": [
    "def sorter(x,y):\n",
    "    index = np.zeros(len(x))\n",
    "    for k in range(0,len(x)):\n",
    "        min = x[k]\n",
    "        index[k] = k\n",
    "        for i in range(k,len(x)):\n",
    "            if x[i] < min:\n",
    "                index[k] = i\n",
    "        buffer = x[k] \n",
    "        x[k] = x[int(index[k])]\n",
    "        x[int(index[k])] = buffer\n",
    "        buffer = y[k] \n",
    "        y[k] = y[int(index[k])]\n",
    "        y[int(index[k])] = buffer\n",
    "    return x,y\n",
    "    \n",
    "False_positive_rate, True_positive_rate = sorter(False_positive_rate, True_positive_rate)\n",
    "\n",
    "AUC = 0\n",
    "for k in range(0,len(False_positive_rate)-1):\n",
    "    AUC += (True_positive_rate[k]+True_positive_rate[k+1])*(False_positive_rate[k+1]-False_positive_rate[k])/2\n",
    "\n",
    "print(AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Resulting visualzation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "fig = plt.figure(figsize = (10,10)) \n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "classificator = []\n",
    "f_m = []\n",
    "for i in range(0,len(X)):\n",
    "    f_m.append(((c_e_ii+c_e_jj)*f_measuration(counts[i])[0]+(c_e_kk+c_e_ll)*f_measuration(counts[i])[2])/(c_e_ii+c_e_jj+c_e_kk+c_e_ll))\n",
    "\n",
    "Min = min(f_m)\n",
    "Max = max(f_m)\n",
    "delta = Max - Min\n",
    "    \n",
    "for i in range(0,len(X)):\n",
    "    x = [X[i]]\n",
    "    y = [Y[i]]\n",
    "    z = [Z[i]]\n",
    "    ax.plot3D(x,y,z,'o',color = [np.clip((((c_e_ii+c_e_jj)*f_measuration(counts[i])[0]+(c_e_kk+c_e_ll)*f_measuration(counts[sort_index[i]])[2])/(c_e_ii+c_e_jj+c_e_kk+c_e_ll)-Min)/delta,0,1), 0, np.clip(1-(((c_e_ii+c_e_jj)*f_measuration(counts[i])[0]+(c_e_kk+c_e_ll)*f_measuration(counts[sort_index[i]])[2])/(c_e_ii+c_e_jj+c_e_kk+c_e_ll)-Min)/delta,0,1)] ,markersize = 0.5)\n",
    "\n",
    "if sound == 1:\n",
    "    import winsound\n",
    "    frequency = 1000  # Set Frequency To 2500 Hertz\n",
    "    duration = 1000 # Set Duration To 1000 ms == 1 second\n",
    "    winsound.Beep(frequency, duration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving results on file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "if save_results == 1:     \n",
    "    file1 = open(\"real_device_record.txt\",\"a\")\n",
    "    FP = str(False_positive_rate).strip('[]')\n",
    "    TP = str(True_positive_rate).strip('[]')\n",
    "    FM = str(f_m).strip('[]')\n",
    "\n",
    "    file1.write(\"BEGIN ATTEMPT \\n\\n\")\n",
    "    file1.write(\"number_training = %d \\n\\n\" %number_training)\n",
    "    file1.write(\"number_testing = %d \\n\\n\" %number_testing)\n",
    "    file1.write(\"SPSA_steps = %d \\n\\n\" %SPSA_steps)\n",
    "    file1.write(\"number_var = %d \\n\\n\" %number_var)\n",
    "    file1.write(\"variables: %d \" %ii + \"% d \" %jj + \"%d \\n\\n\" %kk)\n",
    "    file1.write(\"AUC = %d \\n\\n\" %AUC)\n",
    "\n",
    "    file1.write(\"False Positives: \\n\\n\"+FP+ \"\\n\\n\"+\"True Positives: \\n\\n\"+TP+ \"\\n\\n\"+\"f_measuration: \\n\\n\"+FM + \"\\n\\n\\n\\n\\n\")\n",
    "    file1.close() "
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}