ch01-导论/d2-5鸢尾花数据.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据和简单数据探索\n",
    "- scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 内置数据集\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "====================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML iris datasets.\n",
      "http://archive.ics.uci.edu/ml/datasets/Iris\n",
      "\n",
      "The famous Iris database, first used by Sir R.A Fisher\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "References\n",
      "----------\n",
      "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'],\n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 只取前2个维度\n",
    "A = iris.data[:,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x106d18ba8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1068838d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 矩阵中的第0列 第1列\n",
    "plt.scatter(A[:,0], A[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x106dc5198>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD6CAYAAAC1W2xyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAIABJREFUeJzt3X2QFPW97/H3d2dUXEjQKCYpDbtGPRrRMhUeIg8ngosEL0J8IFXoamI4uJ7dxDrKERODiUCyasSQB3LZc8fEVJRNTJ1KxSsYogHlJqioWBqMohUluwo+FILi4SnC8rt/zC4sS892705PT3fP51U1tTs9v+n+dk/vj+H3+/a3zTmHiIikR1W5AxARkXCpYxcRSRl17CIiKaOOXUQkZdSxi4ikjDp2EZGUUccuIpIy6thFRFJGHbuISMpkgzY0sxuAKc65iR6v/TtwI/B256IpzrnthdZ1/PHHu9ra2j6GKiJS2Z599tl3nXND/NoF6tjNrAa4GtjSS7NbnXOtQdZXW1vLunXrgjQVEZFOZtYepF3QoZifADf7tPmGmT1nZj8JuE4RESkB347dzK4A/gq81EuzZ8kPxYwALjGzWo/1NJjZOjNbt2VLb1/8RUSkGEG+sV8E1AH3A8PN7BsebV4H1jrnOoBNwAk9Gzjncs65Ec65EUOG+A4RiYhIP/mOsTvnrgDo/Bb+c+fczzyaLQJyZvY0MBT4e4gxikiM7d27l02bNrFnz55yh5IaAwYM4KSTTuKII47o1/sDZ8V0MbMxwBjn3F3dFt8G/Bw4EljgnHuvX9GISOJs2rSJj3zkI9TW1mJm5Q4n8ZxzbN26lU2bNnHyySf3ax2BO3bnXBvQler4RI/XXgRG9ysCEUm0PXv2qFMPkZlx3HHHUcxcpC5QkvJqbYXaWqiqyv9sDZQxKzGjTj1cxR7PPg/FiISmtRUaGmDXrvzz9vb8c4D6+vLFJZJw+sYu5TN37sFOvcuuXfnlIiWwevVq2trayh1Gyaljl/J5/fW+LZd0KOPwmzp2kVIbOrRvyyX5uobf2tvBuYPDb0V27nv27GHq1KmMGTOG6dOn88EHHzB9+nTGjh3L17/+dQC+8pWvcM8993DdddcxY8YMALZu3cqUKVMYPXo0119/PQBbtmxhwoQJnHvuuTQ1NQHw1ltvMWHCBMaNG8fcBPyPUh27lE9zM1RXH7qsujq/XNKpRMNvL774ImbGE088waxZs7jnnns466yzePzxx3nrrbdYv3499957LzNnzmTx4sXcf//9ANx+++3MmDGDJ598kvfee4+HH36YP//5z5x11lmsXbuW8ePHs3//ft544w3mz5/PihUrePDBB4uKNQrq2KV86ushl4OaGjDL/8zlNHGaZiUafvvc5z7H2WefzdSpU1mxYgWvvPIKv//97xk/fjwbN25k8+bNnu976aWXGD06n6k9evRoXnrpJS688EIALrroIjZs2EBVVRVHHXUUP/zhD7n22mvZsWNHUbFGQR27lFd9PbS1wf79+Z/q1NOtRMNvzz//POeeey7Lli3j3Xff5dRTT+X6669n9erVzJ8/n0996lMAHH300ezcuRPIXwg0bNgw1q5dC8DatWsZNmwYjz/+OJdffjnLly/nkUce4bXXXuOuu+7ipptuIpfLJSK1Ux27iESnRMNvJ598MosXL2bUqFG8+eabzJo1iz/84Q+MGTOGXC7H0M5/OC677DJuv/12Pv/5z7Nx40Zuvvlm7r//fkaPHs0xxxzDpEmTOO2007jpppsYOXIkJ5xwAjU1NUydOpVrrrmGSy65hIEDB/Lmm28WFW+pmXMu8o2OGDHCqR67SDps2LCBz3zmM8Hf0NqaH1N//fX8N/XmZv1PzYPXcTWzZ51zI/zeqwuURCRa9fXqyEtMQzEiIimjjl1EJGXUsYuIpIw6dhGRlFHHLiLSw9tvv81tt93Wr/e2tbVx9dVXhxtQH6ljF5FIJaEE/yc+8Qm+/e1vlzuMflPHLsVJwl+pxEaJaoCxaNEili5dCsDixYu59957DysC1tbWRn19PbNmzWLmzJkAvPLKK4wdO5aRI0fS3O0iqZ7ful977TUmTJjAiBEjDnT4//jHP5gwYQKjRo1i4cKFBWPzard69Wpmz57N9OnTueWWW4rbeQ/q2KX/SvVXKqlVqhL806dP5+GHHwZg1apVbNu27bAiYADLli07UCQMYPny5Vx66aU888wznHLKKQXXP2fOHG677TbWrVvH7t272bFjB3PmzGHBggWsXbuWFStWsGHDhoLv9Wr329/+lttvv53vf//7xe28B3Xs0n+6UYb0UalK8A8dOpRt27axc+dOMplMwSJgkyZN4txzzz3wvquuuooXX3yRiy66iPfee6/g+l9++WVGjhwJwMKFCxk4cCAbNmxg9OjRVFVVMWrUKF5++WXP9xZqd/nll3PaaacVt+MFqGOX/tONMqSPSlmC/7zzzmPhwoVMmTKF008/3bMI2KBBgw55z6OPPsq3vvUtHnzwQX7wgx+wd+9ez3WfccYZPP300wBMnjyZV199lTPPPJO1a9finOOZZ54pWFahULuesYRJJQWk/4YOzQ+/eC0X8dDcfOhtbiG8EvzTp0/ns5/9LG1tbRx11FFcffXV3H333Rx77LH85je/Ydu2bYe959RTT+Wqq65i7969TJ48mSOOOMJz3XfeeSezZs1iz549fPGLX+S0007jzjvvZObMmezcuZMvf/nLnHHGGQXf27Pd22+/XfwO90JFwKT/et6MGvJ/paqpXlH6WgRMNcCCUREwKY+uv0b9lUofqAZY6aljl+Lor1QkdjR5mmbKMRepSPrGnlY9x7+7csxB37BFUk7f2NNKOeYiFUsde1opx1yE6667ruTvLWYbpaKOPa1KeSWISBFaX2il9se1VM2vovbHtbS+ULq5n8WLF5f8vcVso1TUsadVie4GL1KM1hdaaVjWQPv2dhyO9u3tNCxrKLpz9yoCBjB+/PhD2o0fP56bb76ZyZMnA/DOO+8wbtw4Ro4ceeCCpu5tu8ybN49bbrmFsWPHcs455xxygVH3dnv27GHGjBmMHj2aadOmsXv3bnbu3MmUKVP4whe+wNe+9rWi9jModexpVV+fv1CopgbM8j914ZCU2dxVc9m199C5n117dzF3VXFzPz2LgE2bNs2z3VNPPcXIkSP54x//CMATTzzBhRdeyAMPPMDWrVu55pprCm7jlVdeYc2aNVxxxRU8+uijnm1yuRznnHMOTz75JNOmTWP9+vVs3ryZa6+9lkceeYSNGzfyzjvvFLWvQSgrJs2UYy4x8/p27zmeQsuD6lkE7JhjjvFsN2zYMC699NIDz0855RS+973vsWLFCubNm9frNr761a9iZnz84x/nww8/9Gzz8ssvc9lllwEwc+ZMnHNs3ryZ++67j/vuu4/333+f3bt3928n+yDwN3Yzu8HMVhZ47Xgz+4uZvWBmd4QXngjKx0+RoYO953gKLe+L7kXACulZeOuBBx7gF7/4BWvWrGHixIm9rj9I0a4zzjiDp556CoDm5mZ++ctfcvfdd3PxxRfz61//moEDBwbYk+IF6tjNrAa4upcm1wMPAecAF5rZvxQfmgiq+Z4yzXXNVB9x6NxP9RHVNNcVP/czffp0Fi1axMUXXxz4PcOHD+dLX/oS48eP58orrzxQ3re/GhoaeP755xk3bhzPPfccV155JRdccAHNzc3U1dVhZrz55ptFbSOIQEXAzOwBIAfMds4d9s+amT0BXOece9bMfgq86Jz7P4XWpyJgElhtrXcFyZoaaGuLOhrx0OciYC+0MnfVXF7f/jpDBw+lua6Z+rPLM2Q4b948HnvsMY488kgGDBjAHXfcwbBhw8oSS0/FFAHz7djN7ArgdOCXwM8LdOyvAFOcc6+a2feBnc6523u0aQAaAIYOHTq83euPVaSnqqr8N/WezGD//ujjkcP0tWOXYIrp2IMMxVwE1AH3A8PN7Bsebd4FBnf+Prjz+SGccznn3Ajn3IghQ4YE2KwIysdPiHKU/06zYo+nb8funLvCOTcOmAE865z7mUezVcAkM6sCzgMeKyoqkS7Kx4+9AQMGsHXrVnXuIXHOsXXrVgYMGNDvdfQ53dHMxgBjnHN3dVv8U+D3QD2wzDn3ar8jEulONd9j76STTmLTpk1s2bKl3KGkxoABAzjppJP6/X7dQUlEJCHCHGOXStXUBNlsfqIym80/F5HY05Wn4q2pCVpaDj7v6Dj4fMmS8sQkIoHoG7t4y+X6tlxEYkMdu3jr6OjbchGJDXXs4i2T6dtyEYkNdezirev+qEGXi0hsaPJUvHVNkOZy+eGXTCbfqWviVCT21LFLYUuWqCMXSSANxSTVxIn5/PKuh08t6dhSrXWJsTDuzxrlPV676Bt7Ek2cCKtWHbps1ar88pWe90KJp65a67s6b5XWVWsdVDJAyq7r/qxdt/Lruj8rELjMcBjr6A+VFEgis8KvJakQk2qtS4zV/riW9u2Hn581g2tou74tsnV0p5ICEn+vF7jPZaHlIhEK4/6spbrHqx917FI+qrUuMRbG/VlLeY/X3qhjT6K6ur4tjyvVWpcYC+P+rKW8x2tv1LEn0cqVh3fidXXJmjiF/ARpLpcfUzfL/8zlNHEqsVB/dj25qTlqBtdgGDWDa8hNzfVp0jOMdfSHJk9FRBJCk6dpF0b+t986lGMukkjKY0+iMPK//dahHHORxNJQTBKFkf/ttw7lmIvEjoZi0iyM/G+/dSjHXCSx1LEnURj5337rUI65SGKpY0+iMPK//dahHHORxFLHnkRh5H/7rUM55iKJpclTEZGE0ORpf8UldzsucYiUQDlqlFcS5bF3F5fc7bjEIVIC5apRXkk0FNNdXHK34xKHSAmEXaO8kmgopj/ikrsdlzhESqBcNcoriTr27uKSux2XOERKoFw1yiuJOvbu4pK7HZc4REqgXDXKK4k69u7ikrsdlzhESqBcNcoriSZPRUQSIrTJUzPLmtl/m9njZnZPgTaTzWyTma3pfJzen6Clm6YmyGbz39iz2fzzvrwO0eTCK99eJH6cc70+gOnALZ2/rwA+69FmMjDXb11dj+HDhzvpRWOjc3D4o7Ex2OvOObd0qXPV1Ye+Xl2dXx6WKLYhIgcA61yAPtZ3KMbMBgH7gQ+BvwD1zrmNPdpMBm4D9gFvANNdLyvWUIyPbBY6Og5fnsnAvn3+r0M0ufDKtxeJVGhDMc65Hc65XcDjwDs9O/VOrwHfcc6NAj4JnOcRUIOZrTOzdVu2bPHfg0rm1Wl3X+73OkSTC698e5FYCjLGfpyZHQWMAY41swkezbYBKzt/bwNO6NnAOZdzzo1wzo0YMmRIESFXgEym9+V+r0M0ufDKtxeJpSDpjv8JfNk51wHsAo72aDMbmGFmVcBZwN/CC7ECddWFKbTc73WIJhde+fYi8eQ3CA+cCDwKPAksBU4B7urR5pPAauAZYL7fOjV5GkBjo3OZTH5CMpM5dGI0yOvO5Scxa2qcM8v/LMWkZhTbEBHnXIiTp6WgyVMRkb5TEbD+CiMvO0iOebHrCBJnsfsSxn7EQBgfaZD64aoxLrER5Gt92I/YDsWEkZcdJMe82HUEibPYfQljP2IgjI906fqlrrq52jGPA4/q5mq3dP3SPrURKRYaiumHMPKyg+SYF7uOIHEWuy9h7EcMhPGRBqkfrhrjEgUNxfRHGHnZQXLMi11HkDiL3Zcw9iMGwvhIg9QPV41xiRN17N2FkZcdJMe82HUEibPYfQljP2IgjI80SP1w1RiXOFHH3l0YedlBcsyLXUeQOIvdlzD2IwbC+EiD1A9XjXGJlSAD8WE/Yjt56lw4edlBcsyLXUeQOIvdlzD2IwbC+EiXrl/qan5U42yeuZof1XhOigZpI1IMNHkqIpIumjxNOr/ka9VBj52mllayc2qxeVVk59TS1BL9Z9L0UBPZBVlsvpFdkKXpoWReeyDFyZY7APHQ2pofy961K/+8vf3g2HZ9vf/rErmmllZaNjfAoPxn0jGoPf+8BZY0RvOZND3URMu6lgPPO1zHgedLpiyJJAaJBw3FxJFf8rXqoMdOdk4tHYMO/0wyO2rYt7AtmhgWZOlwh6ejZizDvu8m59oDKUxDMUnml3ytOuix0zHQ+9gXWl6SGDw69d6WS3qpY48jv+Rr1UGPncxO72NfaHlJYjDvawwKLZf0UsceR37J16qDHjsNn26GvT0+k73V+eVRxTDc+xqDQsslvdSxx1F9PeRy+TFzs/zPXO7gxKjf6xK5JY31NJ6YI7OjBpyR2VFD44m5yCZOIT9B2jii8cA39IxlaBzRqInTCqTJUxGRhKjcydNi87uDvD+KOuXKUw8sKYfKL889inruYdSVj6q+vRQhyOWpYT9KVlKg2OLbQd4fRZ3yMIqIV4ikHKrGJUsdcw+t187cate4JB9oFPXcw6grH1V9e/FGRZYUKDa/O8j7o6hTrjz1wJJyqPzy3KOo5x5GXfmo6tuLt8ociik2vzvI+6OoU6489cCScqj88tyjqOceRl35qOrbS3HS1bEXm98d5P1R1ClXnnpgSTlUfnnuUdRzD6OufFT17aU46erYi83vDvL+KOqUK089sKQcKr889yjquYdRVz6q+vZSpCAD8WE/SlqPvdji20HeH0Wd8jCKiFeIpByqxiVLXebGGset5jI31hyYOO0SRT33MOrKR1XfXg5HRU6eioikWGVOnoYhlCRdn3VMnJjPge96TJxYfNwSe1Hkbk+c3YrdkM+VtxtqmTi779uYuKgJuzWLzTPs1iwTF6mme9KoY++uq855e3s+RberznlfOne/dUycCKtWHfqeVavUuadc6wutNCxroH17Ow5H+/Z2GpY1hNq5T5zdyqqjG+CYdjAHx7Sz6uiGPnXuExc1seqDFqjqAAOqOlj1QYs694TRUEx3oSTp+qzDrPB7y/BZSDSiyN22G2rznXpP79fgfhRsG3ZrNt+p97Q/g5uvmu7lpqGY/gglSTchidUSqUhytwcXWFeh5V6swPUYhZZLLKlj7y6UJN2EJFZLpCLJ3d5eYF2FlntxBa7HKLRcYkkde3ehJOn6rKOuzvt9hZZLKkSRu11nzfBhj3Pvw+r88qDrGNwAPUcEXedySQx17N2FUefcbx0rVx7eidfV5ZdLatWfXU9uao6awTUYRs3gGnJTc9SfHV699pWL6qnbnYP38zXheb+Gut05Vi4Kvo2Vs5dQ99FG2J/Jd/D7M9R9tJGVs1XTPUk0eSoikhChTZ6aWdbM/tvMHjezewq0GWBmy83sr2Z2n1lvqR9FCJJjHofi3H712hOyH2GEEMah8N1GCHXO/dYRhYn3TsTm24HHxHsPT4H1O15xqPkeZDtxqMceRm362PK7NBWYDtzS+fsK4LMebWYB/9X5+3JgUm/r7FdJgSCFoONQnNuvXntC9iOMEMI4FL7bCKHOud86olD3q7pDt9/5qPtV3YE2fscrDjXfg2wnDvXYw6hNXw6EVVLAzAYB+4EPgb8A9c65jT3a/Br4nXPud2Y2GxjinLu50Dr7NRQTJMc8DsW5/eq1J2Q/wgghjEPhu40Q6pz7rSMKNr/wf3Ldrfm/Ub/jFYea7xCgpnsM6rGHUZu+HEIbinHO7XDO7QIeB97p2al3Og7Y3vn7B8DHPAJqMLN1ZrZuy5Ytfps9XJD88DjkkPvVa0/IfoQRQhiHwncbIdQ591tHXPgdrzjUfA+ynTjUYw+jNn2cBRljP87MjgLGAMea2QSPZu8Cgzt/H9z5/BDOuZxzboRzbsSQIUP6HmmQ/PA45JD71WtPyH6EEUIYh8J3GyHUOfdbR1z4Ha841HwPsp041GMPozZ9nAVJd/xP4MvOuQ5gF3C0R5tVwKTO388HHgsnvG6C5JjHoTi3X732hOxHGCGEcSh8txFCnXO/dUSh7mTv6xi6L/c7XnGo+R5kO3Goxx5GbfpY8xuEB04EHgWeBJYCpwB39WhzFPlJ0/XAfXSmURZ69Lsee5BC0HEozu1Xrz0h+xFGCGEcCt9thFDn3G8dUeg5gdp94rSL3/GKQ833INuJQz32MGrTRw3VYxcRSRcVARMRqVDp69hjcGGPHOT3cUTxcYVxPVigdfhdlBPBPVzSIrEXBsVFkPGasB8lu+dpDC7skYP8Po4oPq4wrgcLtA6/i3JC2NdKOb3jeGFQXFCRY+wxuLBHDvL7OKL4uMK4HizQOvwuygmwDj+VcnrH8cKguAg6xp6ujr2qyvsuRGawf3/425Ne+X0cUXxcQbYRRpxV86twh9W7BcPYf+v+UPa1Uk5vv2NZySpz8jQGF/bIQX4fRxQfVxjXgwVah99FObqHS2BJvjAoLtLVscfgwh45yO/jiOLjCuN6sEDr8LsoJ4J7uKRFoi8MiosgA/FhP0o2eepcLC7skYP8Po4oPq4wrgcLtA6/i3JC2NdKOb3jdmFQXFCRk6ciIilWmWPsEjt++ch+N+IIso4w+N4QJISbMjQ91ER2QRabb2QXZGl66NCNxCWnPymiOC8Sm08f5Gt92I+SDsVIbPjlI/vdiCPIOsLge0OQEG7K0Li80fNGGo3L8xuJS05/UkRxXsQxnx4NxUi5+eUj+92II8g6wuB7Q5AQbsqQXZClwx2+kYxl2PfdfbHJ6U+KKM6LOObTayhGys7vRgV+N+IIso4w+N4QJISbMnh16t2XR3FvlRjcvyU0UZwXqb7Rhkh/+eUj+92II8g6wuB7Q5AQbsqQMe+NdC2PS05/UkRxXiQ5n14du5SMXz6y3404gqwjDL43BAnhpgwNw7030rU8Ljn9SRHFeZHofPogA/FhPzR5Wjn88pH9bsQRZB1h8L0hSAg3ZWhc3ugy8zOOebjM/MyBidMD749JTn9SRHFexC2fHk2eioikiyZPJRY5y2HEMOybTdh3s9g8w76bZdg3PZLdSxxDoO341WNPak60JE623AFIabS25seId+3KP29vPzhmXF+fnBiGfbOJl45uAetckOngpaNbGPZNePEHSyKJIYjWF1ppWNbArr35DbVvb6dhWX5D9WfX+74uEiYNxaRUHHKWw4jBvpuFjEeqYEcGt2BfJDEE4VuPPYY50ZI8GoqpcHHIWQ4lhqoCSeaFlpcihiDb8cl5TnJOtCSPOvaUikPOcigx7C+QZF5oeSliCLIdv3rsCc6JluRRx55ScchZDiOGM//ZwGE303GdyyOKIdB2/OqxJzknWpInSE5k2A/lsUcjDjnLYcRw5k2Nju9kHLfi+E7GnXmTR7J7iWMItB2/euwxy4mW5EF57CIi6aLJU4lEsTniQd4fSZ1y5ZiLh6SeF8pjl34rNkc8yPujyENXjrl4SfJ5oaEY6bdic8SDvD+SOuXKMRcPcTwvNBQjJVdsjniQ90dSp1w55uIhyeeFOnbpt2JzxIO8P5I65coxFw9JPi/UsUu/FZsjHuT9kdQpV465eEj0eREkJzLsh/LY06PYHPEg74+kTrlyzMVD3M4LlMcuIpIuoU2eWt6vzGytmT1oZoelSJrZZDPbZGZrOh+n9zfwShBGbmwcaq0HicP39YTmCXtpamklO6cWm1dFdk4tTS3R70uajqf0X5A89rFA1jl3rpmtBiYBf/Bo1+KcS8DgU3mFkRsbh1rrQeLwfT3BecI9NbW00rK5AQbl96VjUHv+eQssaYxmX9J0PKU4vkMxZnYacKxz7mkzewT4sXPuDz3aTAZuA/YBbwDTXS8rruShmDByY+NQaz1IHL6vxzBPuL+yc2rpGHT4vmR21LBvYVskMaTpeIq30IZinHN/7+zULwGOBB72aPYa8B3n3Cjgk8B5HgE1mNk6M1u3ZcsW/z1IqTByY+NQaz1IHL6vJzhPuKeOgd4xF1peCmk6nlKcQOmOZjYN+A9gqnPO6w4H24CVnb+3ASf0bOCcyznnRjjnRgwZMqSf4SZfGLmxcai1HiQO39cTnCfcU2and8yFlpdCmo6nFCfI5OkngDnAFOfc/xRoNhuYYWZVwFnA38ILMV3CyI2NQ631IHH4vp7kPOEeGj7dDHt77Oze6vzyiKTpeEqR/PIhgW8CrwJrOh//BtzVo80ngdXAM8B8v3VWeh57GLmxcai1HiQO39djlidcjMYlS13mxhrHreYyN9a4xiXR70uajqccDuWxi4iki4qAxVhcctDD0NQE2SyY5X82NZU7IhFRPfaIxSUHPQxNTdDScvB5R8fB50uWlCcmEVE99sjFJQc9DNlsvjPvKZOBffuij0ck7TQUE1NxyUEPg1en3ttyEYmGOvaIxSUHPQyZTN+Wi0g01LFHLC456GHomhsIulxEoqGOPWL19ZDL5cfUzfI/c7nkTZxCfoK0sfHgN/RMJv9cE6ci5aXJUxGRhNDkaSEJSSJPSJiJiTMKOhYSG0EuTw37UbaSAkuXOldd7RwcfFRXl+96/AISEmZi4oyCjoVEAZUU8JCQJPKEhJmYOKOgYyFRCDoUU1kde1VV/stUT2awf3/08RSQkDATE2cUdCwkChpj95KQJPKEhJmYOKOgYyFxUlkde0KSyBMSZmLijIKOhcRKkIH4sB9lrccel0LmPhISZmLijIKOhZQamjwVEUkXjbGLdGpqaSU7pxabV0V2Ti1NLX1PMFeOuiSJOnZJtaaWVlo2N9AxqB3M0TGonZbNDX3q3Ltq6Le35zNfumroq3OXuNJQjKRadk5tvlPvIbOjhn0L2wKtQznqEhcaihEBOgZ6F7ovtNxLmmroS2VQxy6pltnpnUheaLkX5ahL0qhjl1Rr+HQz7O2RYL63Or88IOWoS9KoY5dUW9JYT+OJOTI7asAZmR01NJ6YY0lj8AL4aaqhL5VBk6ciIgmhyVMRkQqljl1EJGXUsYuIpIw6dhGRlFHHLiKSMurYRURSRh27iEjKqGMXEUkZ347d8n5lZmvN7EEzy3q0GWBmy83sr2Z2n5lZacKtHKr/LSL9FeQb+1gg65w7F/goMMmjzZXAJufcOcCxwAXhhVh5VP9bRIoRpGN/B/hJ5+8fFmhzPvCnzt8fBSYUGVdFmzsXdu06dNmuXfnlIiJ+DhtW6ck593cAM7uDJ2rDAAAEj0lEQVQEOBJ42KPZccD2zt8/AE7v2cDMGoAGgKGqd9or1f8WkWIEmjw1s2nAfwBTnXMdHk3eBQZ3/j648/khnHM559wI59yIIUOG9DfeiqD63yJSjCCTp58A5gBTnHP/U6DZKg6OvZ8PPBZOeJVJ9b9FpBhBvrF/Ffgk8LCZrTGzfzOzu3q0aQVONLP1wDbyHb30k+p/i0gxVI9dRCQhVI9dRKRCqWMXEUkZdewiIimjjl1EJGXUsYuIpIw6dhGRlFHHLiKSMurYRURSpiwXKJnZFqA98g0f6ng8atrEkOIMTxJiBMUZtjTFWeOc8y22VZaOPQ7MbF2QK7jKTXGGJwkxguIMWyXGqaEYEZGUUccuIpIyldyx58odQECKMzxJiBEUZ9gqLs6KHWMXEUmrSv7GLiKSShXRsZvZDWa2ssBr/25mr3beRGSNmQ32alfi+Cab2aZuMXjdM3aAmS03s7+a2X1mZjGM0bdNVMzsJjP7i5mtMLMjPV4/vvP1F8zsjpjGGIdzc3y37b9hZl/1aFPWc7MPcZb9/DSzgWb2f83scTO7s0Cbos/N1HfsZlYDXO3T7Fbn3LjOx3aftqXS0i2GVzxevxLY5Jw7BzgWuCDa8AD/GIO2KSkz+zQwzDn3r8AK4CSPZtcDDwHnABea2b9EGGLQGKHM56ZzbnXX9oH1wHMezcp+bgaME8p/ftYDa51zY4FhZvYZjzZFn5up79iBnwA3+7T5hpk9Z2Y/iSKgAi4zs6fN7HcFvvGcD/yp8/dHgQnRhXaAX4xB25RaHXCsmf0Z+FfgHx5tzgf+5JzbD/w/oj+eQWKEeJybmFk1cKpzbr3Hy3E4NwHfOKH85+c/gerObQ8APvRoU/S5meqO3cyuAP4KvNRLs2eBG4ERwCVmVlv6yA7zGvAd59wo8veXPc+jzXFA1ze2D4CPRRRblyAxBmkThSHAFufcF8h/Ex7n0abcxzNIjHE4N7tcQOF7GZf7WHbXW5xxOD9/DVwIbABeds695tGm6OOZ6o4duIj8N6P7geFm9g2PNq+T/69RB7AJOCHC+LpsA7rmANoKxPAu0DXGOpjoL5EOEmOQNlH4AOj6b/ZG4ESPNuU+nkFijMO52WUqsLzAa+U+lt31Fmcczs+bgf9yzp0BfMzMxni0Kfp4prpjd85d0TnmNgN41jn3M49mi4BxZnY0MBT4e5QxdpoNzDCzKuAs4G8ebVYBkzp/Px94LKLYugSJMUibKDwLjOz8/VTyHWdPq4BJnbGeR/THM0iMcTg36Rw2mEB+mMVLuc9NIFCccTg/PwLs6fz9n8AgjzZFn5up7th7MrMxZnZjj8W3AXcAa4AFzrn3oo+MnwFfA54Cfg/sNrO7erRpBU40s/Xkv3kU+u9mqQSJ8ZA2zrnehsBKxjn3JPCumT1D/ltx1uNz/ynwv8hPtD3knHs1hjHG4dyE/D9ALzrn9pjZyTE8N7v4xRmH8/N/A41m9iRwNLCzFOemLlASEUmZivrGLiJSCdSxi4ikjDp2EZGUUccuIpIy6thFRFJGHbuISMqoYxcRSZn/D2JaoeSMZWYFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106d18e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(A[y==0,0], A[y==0,1], color='red', label='setosa')\n",
    "plt.scatter(A[y==1,0], A[y==1,1], color='blue', label='versicolor')\n",
    "plt.scatter(A[y==2,0], A[y==2,1], color='green', label='virginica')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x106e43588>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106d70eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(A[y==0,0], A[y==0,1], color='red', marker='o', label='setosa')\n",
    "plt.scatter(A[y==1,0], A[y==1,1], color='blue', marker='+', label='versicolor')\n",
    "plt.scatter(A[y==2,0], A[y==2,1], color='green', marker='*', label='virginica')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 后面2个维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 只取前2个维度\n",
    "B = iris.data[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x106eaed30>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD6CAYAAAC1W2xyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAHvlJREFUeJzt3X1wVPW5B/DvExISgZog0ohQCBWGt1SsJLlAuJIQoFhepJJ2eBOUS3GqUL1c6a1Vr4AiVdRph97rDB1oFZCqZaToLcWrlHpBEUILDAQYUAMGhRsCBHkJr8/9Y3ch2ZzNnt1zds/Lfj8zZ3b35HfOeXYdHn/72+f8fqKqICIi/0hzOgAiIrIXEzsRkc8wsRMR+QwTOxGRzzCxExH5DBM7EZHPMLETEfkMEzsRkc8wsRMR+Uy6Exe9+eabNS8vz4lLExF51vbt24+ravto7RxJ7Hl5eaioqHDi0kREniUih8y041AMEZHPMLETEfkMEzsRkc84MsZu5NKlS6iurkZ9fb3TofhGVlYWOnXqhIyMDKdDIaIkck1ir66uxje+8Q3k5eVBRJwOx/NUFbW1taiurkbXrl2dDoeIksg1QzH19fVo164dk7pNRATt2rXjNyCiFOSaxA6ASd1m/DyJUlPUxC4Br4rIFhFZKyJNhm9EZISIVIvIpuDWIzHhEpGX1dXXoc9/9kFdfZ1tx1s9px+Z6bEXA0hX1f4AbgQwPEK7V1R1UHDbb1uELrRx40ZUVVU5HQaR5/z3gf9G5fFK/PnAn2073uo5/chMYj8G4NfB5xebaTdORLaKyGpJxhjAypVAXh6QlhZ4XLky4ZcMYWInis3E1RPR5rk2mLpmKgBgypopaPNcG0xcPTHu49PnpyN9fnrc5/SzqIldVQ+o6lYR+QGAlgDWGzT7FMBTqloEoAOAweENRGSGiFSISEVNTY21qFeuBGbMAA4dAlQDjzNmWE7u9fX1GD16NAYOHIjy8nKcPn0a5eXlKC4uxsMPPwwAmDJlCpYtW4ZZs2Zh/PjxAIDa2lqMHDkSAwYMwKOPPgoAqKmpQWlpKfr374+HHnoIAPDVV1+htLQUgwYNwhNPPGEpViIvmV86H52zOyMjLVB6m5GWgS45XfBM6TNxH9+1bVfk5eTFfU4/M/XjqYiMAfAIgNGqesWgyQkA7wefVwH4ZngDVV2iqgWqWtC+fdQ5bJr3xBPAuXON9507F9hvwZ49eyAi+OijjzB9+nQsW7YM+fn52Lx5M7766ivs2rULr732GqZNm4bFixfjD3/4AwBg4cKFGD9+PD7++GOcPHkS69evx4cffoj8/Hxs2bIFJSUluHr1Kr744gvMmzcP69atw9q1ay3FSuQl3W7qhvml83Hp6iW0zmiNS1cvYV7JPNx2021xH7+wbCF+OfSXcZ/Tz8z8eHoLgDkARqrq1xGazQYwXkTSAOQD2G1fiAYOH45tv0l33nknvvOd72D06NFYt24d9u/fj7fffhslJSX47LPPcOTIEcPjKisrMWDAAADAgAEDUFlZibvvvhsAMGrUKOzduxdpaWnIzMzESy+9hAcffBBnzpyxFCuR17y55020zmiNeSXz0DqjNd7a85bl462e06/M3KA0FYHhlfXBofPfAeilqo81aPMbAKsAzATwtqpW2h1oI507B4ZfjPZbsGPHDvTv3x8LFizApEmTcOedd6KoqAgPPPAA/vSnP+Fb3/oWAOCGG27A2bNnAQRuBOrTpw+2bNmCbt26YcuWLZg4cSI2b96MCRMmYODAgSguLsbkyZPx4osv4mc/+xn69u2L22+/3VKsRF4zZ+AcLL57MXLb5GLy7ZPxxekvLB+vqpbO6VuqmvStX79+Gq6ysrLJvohWrFBt1Uo1MMIe2Fq1Cuy34OTJkzps2DAtLCzUkpISPXXqlJaXl+uAAQP0+9//vtbV1amq6oEDB7S4uFiLior04MGDWltbqyNHjtT+/fvrI488oqqqn3/+uRYXF2tBQYGOHTtWL126pG+88Yb26tVLhw4dqvn5+XrkyBFL8ZoR0+dKRK4GoEJN5FgJtE2ugoICDZ+Pfe/evejVq5f5k6xcGRhTP3w40FNfsACYNMnmSL0v5s+VUkJdfR0GLh2Ij/7lI2RnZSfkGofrDqP74u44MOsAOmdb+zZNASKyXVULorVz1Z2nMZk0CaiqAq5eDTwyqROZloza7+c3P4+LVy5i0eZFCbsGGfNuj51M4edKDU1cPRFr96/FhSsXcPnqZaSnpSOzRSbG9BiD18e9bss18n6Vh0N1TX8D65LdBVWPVtlyjVTl/x47EcXMaj25GUvHLEXLFi0b7WvZoiWW3bPMtmtQ85jYiVKI1XpyM8q+XYaZRTMb7ZtZNBNDug6x7RrUPCZ2ohSTjNrvN3e/CQAY1X1Uo9eUHK5ZaMOPjh49imXLluEXv/hFzMdWVVVh7ty5+P3vf29/YJTSrNaTm/HskGfRr0M/5OfmY/ex3fj70b/bfg2KzNOJvaQk8Lhxo5NRRHbLLbfEldSJEqmwY+G157ltcpHbJtf2a0y9Y+q15/m5+cjPzbf9GhQZh2IaePnll7FixQoAwOLFi/Haa681mQSsqqoKkyZNwvTp0zFt2jQAwP79+1FcXIzCwkIsWLDg2vmqqqpw//33X3v96aeforS0FAUFBdcS/ueff47S0lIUFRVh0aLIZWFG7TZu3IjZs2ejvLwcTz75pK2fBRF5lycTe0lJYPvb3wJb6LVV5eXlWL8+MHnlBx98gBMnTjSZBAwA3nnnnWuThAHAu+++i3vvvRfbtm3DbbdF/hFqzpw5eO6551BRUYHz58/jzJkzmDNnDubPn48tW7Zg3bp12Lt3b8Rjjdq98cYbWLhwIZ599lnrHwClDLMLVti9sEUsx5pt64WFNpIdoycTe6J07twZJ06cwNmzZ9GiRYuIk4ANHz4c/fv3v3bcfffdhz179mDUqFE4efJkxPPv27cPhYWBr8GLFi1C69atsXfvXgwYMABpaWkoKirCvn37DI+N1G7ChAno3r27XR8BpQizC1bYvbBFLMeabeuFhTaSHaMnE/vGjYFt8ODAFnpth8GDB2PRokUYOXIkevTogUcffRQbN27EvHnzrk0C1qZNm0bHbNiwAT//+c+xdu1aPP/887h06ZLhuXv27ImtW7cCAEaMGIGDBw+id+/e2LJlC1QV27Zti3gzUaR24bEQNcfsghVm95ld2CKWhTbMtrW6eEcyOBWjp388TYTy8nLccccdqKqqQmZmJu6//3789re/Rdu2bbFq1SqcOHGiyTHdunXDfffdh0uXLmHEiBHIyMgwPPcLL7yA6dOno76+Ht/73vfQvXt3vPDCC5g2bRrOnj2LH/7wh+jZs2fEY8PbHT161Nb3Tv43v3Q+dhzdgapTVbh89TIy0jLQ8caOUFV8+fWX1/d9oyMUYfsM2pm9ucnoupGONds2lnM6xakYOaWAz/FzpXB/rPwjJqyegMwWmbhw5QJWjVsFAHHvK+9dHvd1Ix1rtm0s53SKnTFySgEiMmR2wQq7F7aI5Vizbb2w0IYTMbLH7nP8XCnctiPb0Dm7M3Lb5OLYmWPXFqyId1/BrVE7kBGvG+lYs21jOadT7IzRbI+did3n+LkS+QeHYojIUYmoWXcbt8bNxE5ECZGImnW3cWvcTOwmzJo1K+HHWrkGkZskombdbdwet6cTe7K+Bi1evDjhx1q5BpGbxLKYRzIW/kgEt8ft6cRu99cgo0nAAKAkbCKakpISPP744xgxYgQA4NixYxg0aBAKCwuv3dDUsG3I3Llz8eSTT6K4uBh9+/ZtdINRw3b19fUYP348BgwYgDFjxuD8+fM4e/YsRo4cibvuugsPPPCALe+XKBFiWcwjGQt/JILb4/ZkYk/U16DwScDGjBlj2O6TTz5BYWEh/vKXvwAAPvroI9x9991Ys2YNamtr8eMf/zjiNfbv349NmzZh4sSJ2LBhg2GbJUuWoG/fvvj4448xZswY7Nq1C0eOHMGDDz6I9957D5999hmOHTtm6b0SJVIiatbdxs1xe3JKgUTdphs+CVhOTo5huz59+uDee++99vq2227DM888g3Xr1mHu3LnNXmPq1KkQEeTm5uLixYuGbfbt24dx48YBAKZNmwZVxZEjR7B8+XIsX74cp06dwvnz5+N7k0RJEMtiHslY+CMR3By3JxN76GvQhNUT0DqjNS5cuWDb16CGk4BFEj7x1po1a7B06VJ897vfjXp+M5N29ezZE5988gnKysqwYMECdOjQAYcOHcLYsWPxox/9CIMHD47+RogcFMtiHslY+CMR3By3J4digMR9DSovL8fLL7+MsWPHmj6mX79+uOeee1BSUoLJkydfm943XjNmzMCOHTswaNAg/OMf/8DkyZMxbNgwLFiwAGVlZRARfPnll5auQRSvZM3l7qYacTfFYoqqJn3r16+fhqusrGyyrzlbq7fq0a+Pqqrq0a+P6rYj22I63k5PP/203nXXXTp06FAdNWqU7t6927FYwsX6uRJFs3LXSsVc6Ou7XrdlXyzXcYpbYgFQoSZyLKcU8Dl+rmSXiasnYu3+tbhw5QIuX72M9LR0hPKHiMS8L7NFJsb0GIPXx70e9TqR2jrxnp2KBfDolAJO/E/Gz/h5kp2Mare75nRFXk5e431tDfYZtPNCbbubYomFaxJ7VlYWamtrmYxsoqqora1FVlaW06GQTxjVbi8cuhC/HPrLxvvKDPYZtPNCbbubYomFa6piOnXqhOrqatTU1Dgdim9kZWWhU6dOTodBPhIqWnjqrqfwzIfP4K09b0Ghce+LtOCE0XWcWkDDTbGY5ZoxdiJyv2TN5e6medbdFIvn5mMnIqLm2fbjqQS8KiJbRGStiDQZvhGRLBF5V0R2ishyEZF4Ayei+ESqtU5EjblfWH3Pbv3MzPx4WgwgXVX7A7gRwHCDNpMBVKtqXwBtAQyzL0QiMiPSpHhG+83u8zur79mtn1nUoRgR6Q6grapuFZH3APxKVf8c1uZ1AKtVdbWIzAbQXlUfj3RODsUQ2SdSrfXNrW7G8XPH46o7d7JWOxms1qc7Vd9u21CMqh4IJvUfAGgJYL1Bs3YAQt9FTgO4ySCgGSJSISIVrHwhsk+kWuul9yyNu+7cC7XaVlitT3d7fbupOnYRGQPgEQCjVfWKQZPjALKDz7ODrxtR1SWqWqCqBe3bt483XiIKE6nWuqxrWdx1516o1bbCan262+vbzfx4eguAOQBGqurXEZp9gOtj70MA/NWe8IjIjEiT4hntN7vP76y+Zzd/ZmbG2P8dwI8BhJb7+R2AXqr6WIM2mQBWA+gMYCeAKdrMiTnGTmSvSLXWVurOnarVThar9elO1Lezjp2IyGc8OQkYEdnPqNb6cN1hZD6bicN1h2M+1m28EGOyMbET+ZxRrfXzm5/HxSsXsWjzopiPdRsvxJhsHIoh8imjWusrV69A0fTffJfsLqh6tKrZY91W2+6FGO3GoRiiFGdUa93wdUjLFi2x7J5lUY91U5024I0YncLETuRTRrXWLw5/EbP+aVajdjOLZmJI1yFRj3VTnTbgjRidwsRO5GOGNeu73wQAjOo+KtAm+NrMsW7jhRidwDF2Ih8zqrXe83970K9DP+Tn5mP3sd34+9G/Y0rfKaaOdVttuxditBPr2ImIfIY/nhL5WP/hh5H2dOM6dLO16YD9td9G147lGmbbWok7lerdmdiJPOhwl+ehaY3r0M3WpgP2134bXTuWa5htayXuVKp351AMkYdk/TwPF7IOBV4IcK0k3WDNsvDadMD+2u+8X+XhUN0hw7+lp6VHvYbZeKzE7ad6dw7FEPlQj31LIdqy8U5NR3rYipVGtemA/bXfS8csRcsWjePJSMtA5xvNXcNsPFbiTsV6dyZ2Ig/ZuaYM/1o8M/BCAQgwu/in+Gn/nzZqZ1SbDthf+1327TLMLJrZaN+sf5qFl773kqlrmI3HStypWO/OxE7kMaG685tqr9ehm61NB+yv/Ta6dizXMNvWStypVu/OMXYij3l1x6tN6tBV1VRtOmB/7bdRPL1u7mX6GmbjsRK3X+rdWcdOROQz/PGUyOWSVVddUhLYKHUwsRM5JJXqqim5OBRDlGTJqqsO9dL/9rfA4+DBgceNG227BCUZh2KIXCoV66opudKjNyEiO4XqqiesnoDWGa1x4cqFhNRVh3rmoZ47e+qpgz12IgekWl01JRfH2Ikc4Je6akous2PsHIohckBhx8Jrz3Pb5CK3Ta6D0ZDfcCiGyCFm68sTUYfuttr2VJorPRmY2InIcazptxfH2ImSzGx9eSLq0N1W2+6nudKTgXXsROR6rOlPDPbYiRxitr48EXXobqpt/2PlHzFh9QRktsjEhSsXsGrcKpT3Lnc6LFdij52IPIE1/fZjj52IHMWafvNYx05EnsCafvuZGooRkQwReaeZv48QkWoR2RTcetgXIpF7WakHFwls0c5n9ho5OYEtnFGNOOvG/S1qYheRGwBsBzAsStNXVHVQcNtvS3REZJlRjTjrxv3N9Bi7iBxU1W4R/jYCwHMALgP4AkC5NnNijrGT11mpBw/vpYcMHtz4fDt2AHfcEf0aoV56XbDznZ0dePz+0qY14qF/liLCunEPSnZVzKcAnlLVIgAdAAw2CGiGiFSISEVNTY1NlyWiSIxqxLu27Yq8nDzWjfucXT32dgDOqOoFEXkdwBpVfTPSudhjJ7+wUg8e6rk3/CdodD6z1wj13E+dur7PqEYcAOvGPSrZPfbZAMaLSBqAfAC7bTovEVlgVCPOunH/i7nHLiJdATysqo81+FsHAKsAtAbwZ1V9urlzscdOlBxGNeKqyrpxjzLbY+cNSkREHsEpBYhMSNa85JFqzMMZxWN0rNvmUzfCWnnnMLETUUKwVt45HIqhlJSseckj1Zg3rFyJFM+mTUCbNo2PPXMGGDTIPfOpG+Ec64nDoRgicgTnWHcee+yU0pI1L7lRjbkRo3iMjnXTfOpGOMd6YrDHTkSOYa28s9hjJyLbcY71xOB87ETkGM6x7iwOxRAR+QwTO3me3TfrpKcHtnBGNwqZ3Wd2AQ0v3HhE7sfETkTkMxxjJ88Kv6nHaglgqJd+5Urj123aBB5DNwrl5ASeZ2dH3xd+Q1FJSdMFNCLts/JeKLWxx05E5DPssZNnhXqzdvVuL18OPIZ66qHXIUY3CpndZ3YBDfbUyQ7ssRMR+QxvUCIi8ghOKUBElKKY2CmlWa0lN9vWSn06a9spVkzsREQ+w6oYSklGNfCx1JKbraG3Umtvd50+pQ722ImIfIZVMZTSrNaSm21rpbfNnjqFsCqGiChFscdOROQR7LETEaUoJnayhRdqrTn/OaUKJnYiIp9hHTtZ4oVaa6s160Rewx47EZHPsMdOltg9J3oiNBejm+Mmihd77EREPsMeO9nCCz1eoxi9EDdRrNhjJyLyGVOJXUQyROSdZv6eJSLvishOEVkuImJfiJQKcnKurxVqR7tEzH/OmnfyiqiJXURuALAdwLBmmk0GUK2qfQG0jdKWiIgSKOoYu6qeB3C7iBxsptkQAKuDzzcAKAXwnvXwyO9Cve+6usavT52Kr10i5j8PYc07eYVdY+ztAAT/yeE0gJvCG4jIDBGpEJGKmpoamy5LRETh7KqKOQ4gO/g8O/i6EVVdAmAJEJjd0abrkseFetyReuCxtrNSVx/tWPbUySvs6rF/AGB48PkQAH+16bxERBQj0/Oxi8hBVe0mIl0BPKyqjzX4WyYCY+ydAewEMEWbOTHnYyciip3Z+dhND8Woarfg4+cAHgv72wUAo2INkoiI7McblIiIfIaJnYjIZ5jYiYh8homdiMhnmNiJiHyGiZ2IyGeY2ImIfIaJnYjIZ5jYiYh8homdiMhnmNiJiHyGiZ2IyGeY2ImIfIaJnYjIZ5jYiYh8homdiMhnmNiJiHyGiZ2IyGeY2ImIfIaJnYjIZ5jYiYh8homdiMhnmNiJiHyGiZ2IyGeY2M1auRLIywPS0gKPK1c6HRERkaF0pwPwhJUrgRkzgHPnAq8PHQq8BoBJk5yLi4jIAHvsZjzxxPWkHnLuXGA/EZHLMLGbcfhwbPuJiBzExG5G586x7ScichATuxkLFgCtWjXe16pVYD8RkcswsZsxaRKwZAnQpQsgEnhcsoQ/nBKRK7EqxqxJk5jIicgTmu2xi0iWiLwrIjtFZLmIiEGbESJSLSKbgluPxIXrMqxtJyIXijYUMxlAtar2BdAWwLAI7V5R1UHBbb+tEbpVqLb90CFA9XptO5M7ETksWmIfAuB/gs83ACiN0G6ciGwVkdVGvXpfYm07EblUtMTeDkBd8PlpADcZtPkUwFOqWgSgA4DBRicSkRkiUiEiFTU1NfHG6x6sbScil4qW2I8DyA4+zw6+DncCwPvB51UAvml0IlVdoqoFqlrQvn37OEJ1Gda2E5FLRUvsHwAYHnw+BMBfDdrMBjBeRNIA5APYbV94LsbadiJyqWiJfSWAjiKyC4Ge+aci8mJYm98AeADAJwDeVtVK+8N0Ida2E5FLiaom/aIFBQVaUVGR9OsSEXmZiGxX1YJo7VLnzlOzNedDhwZ64KFt6NDIx5s9J+vdiSiZVDXpW79+/TSpVqxQbdVKNVBxHthatQrsb6isrHGb0Na7d9PjW7ZUzciIfk6z1yYiigJAhZrIsakxFJOXF7iBKFyXLkBV1fXXdpTgh5/T7LWJiKLgUExDyaw5Dz8n692JKMlSI7Ens+Y8/JysdyeiJEuNxG625ryszPj43r2bHt+yJZCREf2crHcnoiRLjcRutub8/febJveyMmDPnqbHL1sG/O530c/JenciSrLU+PGUiMgH+OMpEVGKSp3E/tBDQHp6YDgkPT3w2uhmpFhuJuKNR0TkQqkxFPPQQ8Arr5hrKxK4jSikVSvjMfHQQhsN52SP1JaIyAZmh2JSI7GnpwNXrsR/vNHNRLzxiIiSjGPsDVlJ6oDxzUS88YiIXCo1EnuLFtaON7qZiDceEZFLpUZinzHDfNvw+WIi3UzEG4+IyKVSI7H/138BP/nJ9Z57ixaB10Y3Iy1fbu5mIt54REQulRo/nhIR+YA/fzw1WzduVLPep0/jmvU+fQLzvTTc17Il0LZt431t2wbO2bFj4/0dO3KhDSJyJzOTttu9xbXQhtkFK37yE+PFMpKxcaENIkog+G6hDbN141Zr1q3iQhtElCD+G4oxWzfuZFIHuNAGETnOO4ndbN241Zp1q7jQBhE5zDuJ3WzdeCw163bjQhtE5ALeSexm68Yj1az37t24Xe/eTVdAysgAcnIa78vJCfzkeeutjfffeiuwYgUX2iAi1/HOj6dERCnOfz+eRmKlRtzoWKN6dyIiD0l3OgBLwudEP3To+hh7tKEOo2MnT27arrIykNz37LEvbiKiBPL2UIyVGvFIx0biwOdERNRQagzFWKkRZx05EfmUtxO7lRpx1pETkU95O7FbqRE3OjaS8FJJIiIX83Zit1IjbnTsihXG9e784ZSIPMTbP54SEaUQW348FZEsEXlXRHaKyHKR8HXjzLUhIqLkiTYUMxlAtar2BdAWwLA42xARUZJES+xDAPxP8PkGAKVxtiEioiSJltjbAagLPj8N4KY420BEZohIhYhU1NTUxBMrERGZEC2xHweQHXyeHXwdTxuo6hJVLVDVgvbt28cTKxERmRAtsX8AYHjw+RAAf42zDRERJUmz5Y4ikglgNYDOAHYC+A8AD6vqY820maJRaihFpAZADBO1NHEzInwz8CC+F3fy03sB/PV+Uvm9dFHVqEMejtSxWyUiFWZqOb2A78Wd/PReAH+9H76X6Lx95ykRETXBxE5E5DNeTexLnA7ARnwv7uSn9wL46/3wvUThyTF2IiKKzKs9diIiisBziV1EMkTkHafjsEoCXhWRLSKyVkQ8u/6siKSLyFsisllEljkdjx1E5F9F5H2n47BCREaISLWIbApuPZyOySoR+ZmI/K+IrBORlk7HEy8RKWnw3+ULEZlq5/k9ldhF5AYA2+GPicaKAaSran8AN+L6TV5eNBbATlUtBtBBRO5wOiArRKQLgPudjsMmr6jqoOC23+lgrBCRbwPoo6r/DGAdgE4OhxQ3Vd0Y+u8CYBeAf9h5fk8ldlU9r6q3A6h2OhYbHAPw6+Dzi04GYoO/AHg5+K0jB4E5g7zs1wAedzoIm4wTka0istoHU2qXAWgrIh8C+GcAnzscj2Ui0gpAN1XdZed5PZXY/URVD6jqVhH5AYCWANY7HVO8VPWMqp4DsBnAMVX9zOmY4iUiExG4g7rS6Vhs8CmAp1S1CEAHAIMdjseq9gBqVPUuBHrrgxyOxw7DEJiWxVZM7A4SkTEAHgEwWlWvOB1PvESkXXBqiYEI9Ki8PHXzKAR6hn8A0E9EZjocjxUnAIR+J6gC8E3nQrHFaQCh4aTPAHR0MBa7jAbwrt0nZWJ3iIjcAmAOgJGq+rXT8Vj0bwB+GPyf0zkANzgcT9xUdWJw3HM8gO2q+hunY7JgNoDxIpIGIB/AbofjsWo7gMLg824IJHfPCg6NlSKwjoWtmNidMxWBr8frg7+MT3M6IAv+E8A0EfkYQC08PKzkM78B8ACATwC8raqeHl5S1Y8BHBeRbQD2q+pWp2OyqBDAHlWtt/vEvEGJiMhn2GMnIvIZJnYiIp9hYici8hkmdiIin2FiJyLyGSZ2IiKfYWInIvKZ/wftj5Ea84GL6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106d98908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(B[y==0,0], B[y==0,1], color='red', marker='o', label='setosa')\n",
    "plt.scatter(B[y==1,0], B[y==1,1], color='blue', marker='+', label='versicolor')\n",
    "plt.scatter(B[y==2,0], B[y==2,1], color='green', marker='*', label='virginica')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}