Logistic Regression 实例及决策边界.ipynb

{
 "cells": [
  {
   "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": [
    "from LogisticRegression import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_iris()\n",
    "X = data.data\n",
    "y = data.target\n",
    "X = X[y<2,:2]\n",
    "y =y[y<2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y==0,0],X[y==0,1],color=\"r\")\n",
    "plt.scatter(X[y==1,0],X[y==1,1],color=\"b\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分割数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "logisticRegression = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegression.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegression.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.82123123,  0.03228488,  0.09153798,  0.02669429,  0.13132865,\n",
       "        0.97159908,  0.14291257,  0.97669236,  0.92636147,  0.93905857,\n",
       "        0.95826336,  0.0526657 ,  0.87252712,  0.06292991,  0.14206232,\n",
       "        0.9786764 ,  0.98936537,  0.71424794,  0.03548951,  0.9870046 ,\n",
       "        0.00323706,  0.03548951,  0.01608174,  0.16960374,  0.05739493])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "logisticRegression.predict_probality(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 决策边界"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def x2(x1):\n",
    "    return (-logisticRegression.coef_[0]*x1 - logisticRegression.intercept_)/logisticRegression.coef_[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_x = np.linspace(4,10,1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_y = x2(plot_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7mn73PL1p1krdtv9oSPZb3dGokTjG+PHOa9yVFU8jWNM2plqwgmoIhaPwCoH1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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y==0,0],X[y==0,1],color=\"r\")\n",
    "plt.scatter(X[y==1,0],X[y==1,1],color=\"b\")\n",
    "plt.plot(plot_x,plot_y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试集上的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7tebd3x3P6CM6KNglZOXe525m9wDz3X02cJOZjSJ/dr8TuDw85UmVGD8ennzyx/u5uT/e\nnzIlmJqqqczsXB57bylPfbKSFg1qM+2SIzm1X5ugy5IoVOppmcqi0zIRJD4+P9CLiouDnJyqr6ea\n+nrlDibNTGHV9v2MPaojk8/sQ+O6avQlPxXqaRldoSrFB/vhjktY7c3M5uG5i3nhq7V0bFaXF68e\nxojuLYIuS6Kcwl3yZ+glzdylUn24eCu3zkph855Mrjq2C/97ak/q1dJfS6k4/SkSGDfup+fcCx+X\nSrFzfxb3vJHGaz9spEerBiRdfwxDEpoGXZbEEIW7/LhoOm1a/gw+Li4/2LWYGnbuzpzkTdw9O42M\ng9ncfHIPxp/Yjdrx+l+ShJfCXfJNmaIwr2Rb9mRy26xU3lu0hYEdGvPiNcPo3aakVk0iFaNwF6lk\n7s4r367j/rcWkZWTx21n9uGKEZ3V6EsqlcJdpBKt2bGfyTNT+GLFDoZ1acbDYwbSuUX9oMuSakDh\nLlIJcvOcZz9fxZ/mLSG+Rg0eGD2AsUd1VKMvqTIKd5EwW7J5LxOSklm4bjcn927FfaP707axGn1J\n1VK4i4RJVk4eUz5azj8+XE7DOjV5fOxgRg1qp34wEgiFu0gYLFy3mwkzklmyZS/nDm7HnWf3pXmD\n2kGXJdWYwl2kAg5m5fKXd5fwzGeraNWwDk9fmsgpfVsHXZaIwl2kvL5YsZ3JM1NYs+MAvxqWwKQz\netOojhp9SWRQuIuU0Z7MbB58azEvf7OWTs3r8dI1wzimmxp9SWRRuIuUwXvpW7jttRS27T3EuJFd\nueWUntStpdYBEnkU7iIh2LHvEH98I53ZCzfSu01Dpl2SyKCOTYIuS6RECneRw3B3Zi/cyN2z09h3\nKIdbTunJ9Sd0o1a8WgdIZFO4i5RgU8ZBbp+VyvuLtzK4YxMeuWAgPVs3DLoskZAo3EWKyMtzXv52\nLQ++tZicvDxuP6sPV4zoQpxaB0gUCTnczSwOmA9scPezizxWG3gOOBLYAVzk7qvDWKdIlVi1fT+T\nkpL5etVOjunWnIfOH0hC83pBlyVSZmWZud8MLAKKa0B9FbDL3bub2VjgYeCiMNQnUiVycvP41+er\n+PO8pdSKr8HDYwbwP4kd1TpAolZI4W5mHYCzgPuB3xUz5Fzg7oLbM4AnzMzc3cNRpEhlWrRpDxOT\nkklen8Ev+rbmvvP607pRnaDLEqmQUGfufwUmACWtJrUH1gG4e46ZZQDNge2FB5nZOGAcQEJCQnnq\nFQmbQzm5/OPDFUz5cDmN69bkiV8dwVkD2mq2LjGh1HA3s7OBre6+wMxOKGlYMcd+Nmt392nANIDE\nxETN6iUw363dxcQZySzbuo/RR7TnzrP70rR+raDLEgmbUGbuI4BRZnYmUAdoZGYvuPvFhcasBzoC\n680sHmgM7Ax7tSIVdCArhz+9s5Rnv1hFm0Z1ePbyozixd6ugyxIJu1KvxHD3ye7ewd07A2OBD4oE\nO8Bs4LKC2xcUjNHMvCqNHw/x8WCW/3n8+KArijifL9/OaX/9hH99voqLh3Vi3i0jFewSs8q9z93M\n7gHmu/ts4BngeTNbTv6MfWyY6pNQjB8PTz754/3c3B/vT5kSTE0RJONgNg+8uYhX5q+jS4v6vDLu\naIZ1bR50WSKVyoKaYCcmJvr8+fMDee2YEx+fH+hFxcVBTk7V1xNB5qVt5vbXUtmxP4trjuvKb0/p\nQZ2aavQl0cvMFrh7YmnjdIVqLCgu2A93vBrYtvcQd7+RxpvJm+jTthHPXHYUAzo0DroskSqjcI8F\ncXElz9yrGXdn1vcbuGdOOgcO5fL7U3ty7fHdqBmnRl9SvSjcY8G4cT895174eDWyYfdBbpuVwkdL\ntjEkIb/RV/dWavQl1ZPCPRb8d9F02rT8GXxcXH6wV5PF1Lw858Wv1/DQ24vJc7jrnL5cOryzGn1J\ntaZwjxVTplSbMC9s5bZ9TEpK4ZvVOzmuRwseGD2Ajs3U6EtE4S5RKSc3j6c+XcVj7y2lTnwNHr1g\nIBcc2UGtA0QKKNwl6qRtzGBiUjKpG/ZwWr/W3Htuf1qp0ZfITyjcJWpkZufy9w+WMfXjlTStV4sn\nfz2EMwa0DboskYikcJeosGDNTibMSGbFtv2MGdKBO87uQ5N6avQlUhKFu0S0/YdyePSdJUz/cjXt\nGtdl+pVDOb5ny6DLEol4CneJWJ8s3cbkmSlszDjIpUd34g+n96ZBbf2RFQmF/qZIxMk4kM29b6Yz\nY8F6urasz3+uHc5RnZsFXZZIVFG4S0SZm7qJO15PY+f+LMaf0I2bTlajL5HyULhLRNi6N5O7Xk/j\n7dTN9G3biGcvP4r+7dXoS6S8FO4SKHdnxoL13PfmIg5m5zLh9F5cc1xXNfoSqSCFuwRm3c4D3Dor\nhU+Xbeeozk15aMxAurVsEHRZIjFB4S5VLi/Pee7L1TzyzhIMuOfcflw8rBM11OhLJGwU7lKllm/d\nx6SkZOav2cXIni15YHR/OjRVoy+RcCs13M2sDvAJULtg/Ax3v6vImMuBR4ENBYeecPenw1uqRLPs\n3DymfbKSx99bRt1acfz5wkGcP6S9Gn2JVJJQVq0OASe5+yBgMHC6mR1dzLhX3H1wwYeCvTT9+oHZ\njx/9+hU/bvz4/N+Rapb/efz4sr1ORb8+DFI3ZHDuE5/z6DtLOKVvK9773fGMUQdHkUpV6szd83+D\n9r6CuzULPoL5rdqxol8/SE//6bH09PzjaWk/Hhs//qe/YSk398f7ofRur+jXV1Bmdi6Pv7+MaZ+s\npFn9Wky9+EhO79+m0l9XRMDys7uUQWZxwAKgO/APd59Y5PHLgQeBbcBS4BZ3X3e450xMTPT58+eX\ns+wod7gZa+GfR3x8yb8bNSen9Nep6NdXwLerdzJxRjIrt+/nfxI7cNuZfWlcr2alvqZIdWBmC9w9\nsbRxIW0mdvdcdx8MdACGmln/IkPeADq7+0DgPWB6CUWNM7P5ZjZ/27Ztobx09VZcMB/ueLi/vhz2\nHcrhztdTuXDql2Tl5vHCVcN45IJBCnaRKhbSzP0nX2B2F7Df3f9UwuNxwE53P+zlhZq5lyCKZ+4f\nLtnKbTNT2LQnk8uP6czvT+1FfTX6EgmrsM3czaylmTUpuF0XOAVYXGRM4d+YMApYVLZyY0goC5h9\n+xb/tUWPjxtX/LiSjoc6LtSvD9Gu/Vn87pUfuOLZb6lXO54Z1x3DXef0U7CLBCiUv31tgekFM/Ia\nwH/cfY6Z3QPMd/fZwE1mNgrIAXYCl1dWwREt1AXM44//+YLqf48X9t+vmTYt/7ni4vKDOdTF0Ip+\nfSncnbdSNnPX7FR2H8jmNyd158aTulM7Xo2+RIJW5tMy4RKTp2VCPQ0S4EJnuGzdk8ntr6UyL30L\nA9o35uExA+nbrlHQZYnEvFBPy+j/zeEU6gJmAAud4eLuvDp/Pfe+mU5WTh6Tz+jNVcd2IV6NvkQi\nisI9nOLiSp6Rl2dchFm38wCTZ6bw2fLtDO3SjIfOH0BXNfoSiUiaboVTqAuYVbTQGS65ec6/PlvF\nqY99wg/rdnPfef359zVHK9hFIphm7uEU6gLmlCnw8cc/XVTt27dKrhotq2Vb9jIhKZnv1+7mhF4t\neWD0ANo1qRt0WSJSCoV7uE2ZUnpIjx9ffPuB8eMjJuCzcvKY+vEKnvhgOfVrx/HXiwZz7uB26gcj\nEiW0WyYIEb5bJnn9bibMSGbx5r2cM6gdd53TlxYNagddloig3TKRLUJ3y2Rm5/LYu0t56tOVtGxY\nm6cuTeQXfVsHWpOIlI/CPQgRuFvmq5U7mJSUzOodB/jl0I5MOqMPjeuqH4xItNJumSD6nUfQbpm9\nmdncNiuFsdO+Is/hpauH8eD5A0sM9ghoDy8iIajeM/eA+50H7YPFW7htVipb9mRy9bFd+N2pPalX\nq+Q/EtX82yUSVar3gmpQC5sBL6ju3J/FPW+k8doPG+nZugEPjxnIEQlNS/26CF8HFqkWtKAaiqAW\nNgN6XXfnjeRN3D07jb2Z2dx8cg9uOLE7teJDOzsXoevAIlKM6h3uQS1sBvC6mzPyG329t2gLgzo0\n5uELhtG7TdkafUXgOrCIlKB6L6gGtbBZha/r7rz8zVp+8ZeP+Wz5Nm47sw8zx48oc7AfrrwI7Zog\nUq1V73CfMgWuv/7HqWdcXP79iqwOFredpOgxCP/rFmPNjv386qmvmTwzhX7tGzH35pFcM7IrcTXK\nd5VpZXy7wk27eUTyVe8F1XArup3kcCoxFXPznGc/X8Wf5i2hZo0aTD6zD2OP6kiNcoZ6tCjp2x9p\n/wCJVESoC6oK93AqaTtJcSppi8mSzfmNvhau283JvVtx3+j+tG1cPRp9aTePVAfaLROEsmwbCfMW\nk6ycPKZ8tJx/fLichnVq8rdfHsE5A9tWq0Zf2s0j8iOFeziVtJ2kpLFh8sO63UyckcySLXs5d3A7\n7jqnH83q1wrb80cL7eYR+VGpC6pmVsfMvjGzhWaWZmZ/LGZMbTN7xcyWm9nXZta5MoqtsMpebSvL\ntpEwbDE5mJXLfXPSOX/K52QczOaZyxJ5fOwRlRrskbxgqd08Ij8KZeZ+CDjJ3feZWU3gMzN7292/\nKjTmKmCXu3c3s7HAw8BFlVBv+VXFtfMl/bKO4o5V8DW/WLGdSUkprN15gF8NS2DSGb1pVKdyG32p\n/YBI9CjTgqqZ1QM+A653968LHX8HuNvdvzSzeGAz0NIP8+RVvqAaI6ttezKzefCtRbz8zTo6Na/H\nQ+cPZHi35lXy2pH+LYz0+kTCIawLqmYWBywAugP/KBzsBdoD6wDcPcfMMoDmwPYizzMOGAeQkJAQ\nykuHTwystr2XvoXbXkth295DXDuyK789pSd1a1XdCeVI/xZGen0iVSmkcHf3XGCwmTUBZplZf3dP\nLTSkuC0ZP5u1u/s0YBrkz9zLUW/5RfFq2459h7j7jXTeWLiR3m0a8tSliQzs0KTK64j0b2Gk1ydS\nlcp0haq77wY+Ak4v8tB6oCNAwWmZxsDOMNQXPlG42ubuvPb9Bk75y8fMTd3E737Rk9k3HhtIsEPk\nfwsjvT6RqhTKbpmWBTN2zKwucAqwuMiw2cBlBbcvAD443Pn2QETDtfOFbNx9kKumz+e3r/xAp+b1\nefOm47jp5B4hd3A8nPLueIm0b2FAXR1EokKpC6pmNhCYDsSR/4/Bf9z9HjO7B5jv7rPNrA7wPHAE\n+TP2se6+8nDPG5NXqIZBXp7z0jdreejtxeTmOb8/rReXH9O53P1gioqVS/Rj5X2IlJXaD0ShVdv3\nMykpma9X7WRE9+Y8OHogCc3rhfU1YmVHSay8D5GyUvuBKJKTm8czn63iL+8upVZ8DR4ZM5ALEztU\nSuuAWNlREivvQ6SyKNwDlr5xDxOTkknZkMEv+rbmvvP607pRnUp7vVjZURIr70OkslTvfu4BOpST\ny5/nLWHUE5+xKeMg//jVEKZdcmSlBjtU3Y6SoDo9aGeMSD7N3AOwYM0uJiYls3zrPs4/oj13nN2X\nplXU6KukDgnhXIQMstODFlNF8mlBtQodyMrh0XeW8H9frKZtozrcf/4ATuzVKuiywk6LnSKVRwuq\nEeazZduZNDOZ9bsOcsnRnZhwei8aVnKjr6BosVMkeAr3SpZxMJv730znP/PX06VFff5z7XCGdmkW\ndFmVSoudIsFTuFeid9I2c8drqezYn8X1J3Tj5pN7UKdm7CfcuHHFX2CkxU6RqqPdMpVg295D3PDi\nd1z7/AKaN6jNa+NHMPH03hET7KHuZCnvOFAbAJGgaeYeRu7OzO82cM+cdA5m5fKH03oxbmRXasZF\nzr+hoe5kqei466/X4qlIkLRbJkw27D7IrTNT+HjpNoYkNOGRCwbSvVXDoMv6mVB3soR7nIiEh3bL\nVJG8POeFr9fw8NuLceDuc/pyyfDwNfoKt1B3soR7nIhULYV7BazYto9JScl8u3oXx/VowQOjB9Cx\nWXgbfYVbqDtZwj1ORKpW5JwMjiLZuXlM+Wg5Zzz+KUs27+XRCwby3JVDIzLYiy529upV/LiiO1lC\nvbxfbQBEIpNm7mWUuiGDiUnJpG3cw+n92nDPef1o1bBy+8GUV3GLnenp0LcvLFly+Mv2Q728X20A\nRCKTFlRDlJmdy98/WMbUj1fStF4t7j23H2cMaBt0WYelxU6R2KMF1TCav3onE5KSWbltP2OGdOCO\ns/vQpF7VNPqqCC12ilRfCvdTlD9fAAAHOUlEQVTD2H8ov9HX9C9X065xXaZfOZTje7YMuqyQabFT\npPoK5RdkdzSzD81skZmlmdnNxYw5wcwyzOyHgo87K6fcqvPx0m2c+tgnTP9yNZcN78y8W0ZGVbCD\nFjtFqrNQZu45wP+6+3dm1hBYYGbvunt6kXGfuvvZ4S+xau0+kMW9cxaR9N16urasz6vXDiexc3Q2\n+tJip0j1VWq4u/smYFPB7b1mtghoDxQN96j3dsom7ng9jV0HsrjhxG785qTob/Q1ZYrCXKQ6KtM5\ndzPrDBwBfF3Mw8PNbCGwEfi9u6dVuLoqsnVPJne+nsbctM30a9eI6VceRb92jYMuS0Sk3EIOdzNr\nACQBv3X3PUUe/g7o5O77zOxM4DWgRzHPMQ4YB5CQkFDuosPF3ZmxYD33zkknMyePiaf35urjukRU\noy8RkfIIaZ+7mdUE5gDvuPtfQhi/Gkh09+0ljQl6n/u6nQe4dVYKny7bzlGdm/LQmIF0a9kgsHpE\nREIRtn3uZmbAM8CikoLdzNoAW9zdzWwo+btwdpSx5iqRm+c8/+VqHnlnCQbce24/fj2sEzUitNGX\niEh5hHJaZgRwCZBiZj8UHLsVSABw96nABcD1ZpYDHATGelCXvh7G8q17mZiUwoI1uzi+Z0vuH92f\nDk0jrx+MiEhFhbJb5jPgsNNad38CeCJcRYVbdm4e//x4BX97fzn1asfxl/8ZxOgj2pP/nxIRkdgT\n81eopm7I4A8zklm0aQ9nDWjL3aP60bJh7aDLEhGpVDEb7pnZufz1vWU89elKmtWvxdSLj+T0/m2C\nLktEpErEZLh/s2onk5KSWbl9PxclduTWM/vQuF7NoMsSEakyMRXuezOzeWTuEp7/ag0dmtblhauG\ncWyPFkGXJSJS5WIm3D9cspXbZqawaU8mV47owu9P60m9WjHz9kREyiTq02/X/izunZPOzO830L1V\nA2ZcdwxHdmoadFkiIoGK2nB3d95M2cRdr6eRcTCbm07qzg0ndad2fHQ3+hIRCYeoDPctezK547VU\n5qVvYUD7xrxw9TD6tG0UdFkiIhEj6sL9w8Vbuenf35OVk8fkM3pz1bFdiFejLxGRn4i6cO/Soj5D\nEppy96h+dGlRP+hyREQiUtSFe+cW9Zl+5dCgyxARiWg6nyEiEoMU7iIiMUjhLiISgxTuIiIxSOEu\nIhKDFO4iIjFI4S4iEoMU7iIiMciC+j3WZrYNWFPOL28BbA9jOUHSe4lMsfJeYuV9gN7Lf3Vy95al\nDQos3CvCzOa7e2LQdYSD3ktkipX3EivvA/ReykqnZUREYpDCXUQkBkVruE8LuoAw0nuJTLHyXmLl\nfYDeS5lE5Tl3ERE5vGiduYuIyGFEZbibWZyZfW9mc4KupSLMbLWZpZjZD2Y2P+h6ysvMmpjZDDNb\nbGaLzGx40DWVh5n1KvhZ/Pdjj5n9Nui6ysvMbjGzNDNLNbOXzaxO0DWVh5ndXPAe0qLx52Fm/zKz\nrWaWWuhYMzN718yWFXxuGu7XjcpwB24GFgVdRJic6O6Do3yL1+PAXHfvDQwiSn827r6k4GcxGDgS\nOADMCriscjGz9sBNQKK79wfigLHBVlV2ZtYfuAYYSv6frbPNrEewVZXZ/wGnFzk2CXjf3XsA7xfc\nD6uoC3cz6wCcBTwddC0CZtYIGAk8A+DuWe6+O9iqwuJkYIW7l/dCu0gQD9Q1s3igHrAx4HrKow/w\nlbsfcPcc4GNgdMA1lYm7fwLsLHL4XGB6we3pwHnhft2oC3fgr8AEIC/oQsLAgXlmtsDMxgVdTDl1\nBbYBzxacKnvazGLhl9uOBV4OuojycvcNwJ+AtcAmIMPd5wVbVbmkAiPNrLmZ1QPOBDoGXFM4tHb3\nTQAFn1uF+wWiKtzN7Gxgq7svCLqWMBnh7kOAM4AbzGxk0AWVQzwwBHjS3Y8A9lMJ/8WsSmZWCxgF\nvBp0LeVVcA73XKAL0A6ob2YXB1tV2bn7IuBh4F1gLrAQyAm0qCgRVeEOjABGmdlq4N/ASWb2QrAl\nlZ+7byz4vJX8c7vR+Ju/1wPr3f3rgvszyA/7aHYG8J27bwm6kAo4BVjl7tvcPRuYCRwTcE3l4u7P\nuPsQdx9J/umNZUHXFAZbzKwtQMHnreF+gagKd3ef7O4d3L0z+f9t/sDdo242AmBm9c2s4X9vA6eS\n/1/QqOLum4F1Ztar4NDJQHqAJYXDL4niUzIF1gJHm1k9MzPyfy5RudBtZq0KPicA5xP9PxuA2cBl\nBbcvA14P9wvEh/sJJWStgVn5f++IB15y97nBllRuvwFeLDidsRK4IuB6yq3gvO4vgGuDrqUi3P1r\nM5sBfEf+aYzvid4rPJPMrDmQDdzg7ruCLqgszOxl4ASghZmtB+4CHgL+Y2ZXkf8P8YVhf11doSoi\nEnui6rSMiIiERuEuIhKDFO4iIjFI4S4iEoMU7iIiMUjhLiISgxTuIiIxSOEuIhKD/h8rXG5998Yc\n4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test[y_test==0,0], X_test[y_test==0,1], color=\"red\")\n",
    "plt.scatter(X_test[y_test==1,0], X_test[y_test==1,1], color=\"blue\")\n",
    "plt.plot(plot_x,plot_y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_decision_boundary(model, axis):\n",
    "    \n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
    "    \n",
    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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PLmpaZruJlTFjdaGRv5g/M9VjFSGk/mMjY7zrtScYnjzK6SUrOTYytqCf+dKqTaU6R0UJ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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    plot_decision_boundary(logisticRegression, axis=[4, 7.5, 1.5, 4.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 看看KNN的决策边界"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_cls = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cls.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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FfuXul5nZzcBngA/1od6uhcQaU15FMmRVzdD6U32fMWN1oZG/mN8z1WMVIaT+sr/HMus4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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(knn_cls, axis=[4, 7.5, 1.5, 4.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN多分类问题的决策边界"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf_all = KNeighborsClassifier()\n",
    "knn_clf_all.fit(data.data[:,:2], data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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9pNorm45jWh+21bTNMah+uUz4YevTbd6fRNvgtOvsk7ohKhIfWQwUJoxeVphQXu4QU52+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\n5QQTPhFRTjDhExHlBBM+EVFO/H/6ekHK0u8anQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(knn_clf_all, axis=[4, 8, 1.5, 4.5])\n",
    "plt.scatter(data.data[data.target==0,0], data.data[data.target==0,1])\n",
    "plt.scatter(data.data[data.target==1,0], data.data[data.target==1,1])\n",
    "plt.scatter(data.data[data.target==2,0], data.data[data.target==2,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "过拟合了！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 调整超参数！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=100, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf_all = KNeighborsClassifier(n_neighbors=100)\n",
    "knn_clf_all.fit(data.data[:,:2], data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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X/0nrjVGOe7xy0PcJWt8//mrN9fWWN1ODNC6XDT9qPj3M45MYG9zpOftYRRx9nJR6efwl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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(knn_clf_all, axis=[4, 8, 1.5, 4.5])\n",
    "plt.scatter(data.data[data.target==0,0], data.data[data.target==0,1])\n",
    "plt.scatter(data.data[data.target==1,0], data.data[data.target==1,1])\n",
    "plt.scatter(data.data[data.target==2,0], data.data[data.target==2,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}