201014训练sklearn数据集.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 一、普通最小二乘法"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "reg = linear_model.LinearRegression()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "reg.fit([[1,1],[0,0],[1,1],[2,2]],[1,2,3,4])\n",
    "reg.coef_#模型参数"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.5, 0.5])"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 找方法，实例化对象，\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 糖尿病数据集"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "diabetes = datasets.load_diabetes()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "(442, 10)"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "(442,)"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.target.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "# diabetes.data[:,2].reshape\n",
    "diabetes_X = diabetes.data[:,2][:,np.newaxis]\n",
    "# diabetes.data[:,2][np.newaxis,:]#增加一个新的维度"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test=train_test_split(diabetes_X,diabetes.target,test_size=.1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(x_train,y_train)\n",
    "# reg.coef_#模型参数\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "y_pred = reg.predict(x_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "0.1860878409748078"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_test,y_test)#正确率"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x25280b96d48>]"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test,y_test,c='r')\n",
    "plt.plot(x_test,y_pred)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "array([1])"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "x = [[0,0],[1,1]]\n",
    "y =  [0,1]\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf.fit(x,y)\n",
    "clf.predict([[2,2]])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "array([1])"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "clf_i = tree.DecisionTreeClassifier()\n",
    "clf_i.fit(iris.data,iris.target)\n",
    "clf_i.predict([[1,2,3,4]])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## wine数据集"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "wine = datasets.load_wine()\n",
    "wine.data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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,\n       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, 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,\n       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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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,\n       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])"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n                       max_features=None, max_leaf_nodes=None,\n                       min_impurity_decrease=0.0, min_impurity_split=None,\n                       min_samples_leaf=1, min_samples_split=2,\n                       min_weight_fraction_leaf=0.0, presort=False,\n                       random_state=None, splitter='best')"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(wine.data,wine.target,test_size=.3)\n",
    "clf_w = tree.DecisionTreeClassifier()\n",
    "clf_w.fit(x_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "0.9074074074074074"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_w.score(x_test,y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "1.0"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(iris.data,iris.target,test_size=.1)\n",
    "knn.fit(x_train,y_train)\n",
    "knn.score(x_test,y_test)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 四，神经网络"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\19831\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([1, 0])"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import neural_network\n",
    "x\n",
    "y\n",
    "clf = neural_network.MLPClassifier()\n",
    "clf.fit(x,y)\n",
    "clf.predict([[2,2],[-1,-2]])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "data": {
      "text/plain": "[array([[-1.04145683e-01, -3.20049670e-01, -4.85820607e-02,\n         -4.05350759e-02, -1.33334372e-01, -6.74549528e-07,\n          3.10195784e-01, -2.71068109e-01, -8.26501220e-02,\n         -2.21742775e-05, -5.14438491e-02,  6.55581194e-07,\n         -7.82255619e-02, -1.84300211e-01,  1.00244288e-01,\n          1.09778965e-05,  1.05570975e-01, -4.05817210e-02,\n          2.66591331e-02,  2.04546846e-01,  6.87596036e-02,\n         -2.96571128e-02,  2.36581159e-01,  2.93669382e-03,\n          3.50035763e-01,  4.08185842e-01,  9.84881335e-05,\n          2.32999005e-02, -9.27515293e-02,  2.10251721e-01,\n          2.56914510e-01,  2.63484633e-01, -1.74616415e-02,\n          1.32045896e-01, -2.89694921e-02,  1.55364477e-01,\n         -3.65490518e-02, -3.07809812e-03,  3.00575174e-01,\n         -1.34312830e-02, -6.93574697e-04, -2.86862605e-01,\n         -2.72220524e-01, -7.86358607e-07, -3.49067642e-02,\n         -7.84881475e-02,  9.61189760e-02,  4.68649122e-04,\n         -2.54908880e-03,  3.92007856e-01, -1.78113786e-02,\n         -7.99892524e-02,  1.59679468e-01,  4.66179296e-02,\n         -1.31157631e-01,  3.21500378e-01,  8.06909848e-02,\n         -1.59438069e-01,  4.70104472e-05, -6.06249065e-02,\n         -7.13062102e-02,  3.65801319e-01,  9.97762946e-02,\n          2.71617497e-02, -2.65849509e-02,  6.99954002e-07,\n         -2.69006655e-01,  3.66378139e-02,  4.21030907e-01,\n         -8.57509215e-05, -6.59187665e-02,  3.03909271e-01,\n         -1.00335827e-01,  3.33043277e-01,  1.31719305e-01,\n          6.68967474e-02, -1.70641553e-02,  1.06420442e-02,\n         -2.18644119e-01, -7.94464757e-02,  3.87734347e-01,\n          1.98607747e-06,  3.41174590e-01, -2.09468127e-01,\n         -9.93883201e-02, -2.27360271e-02, -7.41371890e-02,\n         -1.29014211e-01, -1.34538702e-02,  3.30181114e-01,\n          1.03331639e-01, -1.28915259e-05, -2.36701525e-01,\n          1.25088499e-01, -3.23559347e-01,  2.33017268e-01,\n          2.93101690e-01, -5.54782285e-02,  1.27302458e-01,\n         -1.08953135e-02],\n        [-1.70492497e-02, -6.35474454e-02,  2.92949740e-02,\n         -7.02722358e-02, -1.58844762e-01, -3.73673948e-04,\n          1.54086549e-01,  1.09842840e-01, -1.56620934e-01,\n         -1.51587594e-03,  7.70162005e-03,  5.02168124e-07,\n          5.47959141e-07, -6.46184028e-02,  9.16687117e-02,\n         -1.06529127e-06, -9.09239243e-03,  7.44953213e-03,\n          8.74921920e-06,  3.15653349e-01,  3.78798412e-01,\n          3.03502783e-02,  1.98604238e-01, -3.39453094e-07,\n          2.15331748e-01,  3.33572438e-01, -4.35110137e-04,\n         -1.00513776e-06,  1.50497621e-01,  2.25406828e-01,\n          1.12291323e-01,  3.81919920e-01, -2.06064392e-01,\n         -1.13150857e-01, -1.46128222e-01,  5.63185408e-02,\n          4.18021806e-01,  3.40919395e-01,  7.21588441e-02,\n          5.09845021e-02,  6.57597223e-02, -8.42851306e-02,\n         -1.39617059e-01, -1.18977891e-02,  2.22225179e-05,\n          3.95894117e-01,  1.34210361e-01,  6.42768100e-04,\n         -2.73901195e-02,  1.99772508e-01, -2.34352425e-04,\n         -9.53084909e-02,  3.32953828e-01,  2.05898525e-01,\n         -1.04432705e-01,  3.48366233e-01,  1.38298756e-02,\n          1.07763565e-01, -2.60048993e-06, -7.25829512e-02,\n          4.59413526e-02,  2.71712970e-01,  1.79204405e-03,\n          9.76811614e-02,  2.30999200e-06, -1.73892212e-02,\n         -7.69394297e-02, -3.34112681e-02,  1.60950321e-01,\n         -3.10133427e-02,  7.86888437e-06,  2.97017100e-01,\n         -4.90317654e-02,  1.05694890e-01,  3.53614059e-01,\n          9.98808607e-02, -1.65175017e-03, -4.93242977e-03,\n         -1.33141287e-01, -4.70182959e-09,  2.64480652e-01,\n         -5.65472965e-06,  1.92290609e-01, -1.63274183e-01,\n          8.73566824e-02, -4.62494739e-09, -1.62043775e-01,\n         -1.44384711e-01,  5.40814041e-02,  2.45891053e-01,\n          5.55880878e-02, -1.13170499e-02, -1.15926599e-01,\n         -1.02557844e-01, -5.25075116e-02,  2.96306996e-01,\n          3.36070939e-01, -2.43586669e-02,  2.77084855e-01,\n         -3.28215670e-07]]), array([[-2.18790910e-02],\n        [-3.02057022e-01],\n        [-2.08149065e-01],\n        [-2.16408079e-02],\n        [-3.83345825e-01],\n        [ 3.74528672e-02],\n        [ 2.85838660e-01],\n        [-2.90353221e-01],\n        [-2.83092805e-01],\n        [-4.58293842e-06],\n        [-7.80433183e-02],\n        [-1.52048486e-03],\n        [-9.24446157e-04],\n        [-4.07492555e-01],\n        [-1.66066899e-01],\n        [ 2.65518121e-07],\n        [ 1.13850528e-01],\n        [-6.14491503e-07],\n        [ 7.83018891e-02],\n        [ 3.55803109e-01],\n        [ 2.69106114e-01],\n        [-3.40528525e-02],\n        [ 3.49777470e-01],\n        [-7.20304711e-02],\n        [ 1.80826733e-01],\n        [ 2.28068943e-01],\n        [ 6.96906553e-04],\n        [ 1.73448684e-06],\n        [-5.85616838e-02],\n        [ 2.83325427e-01],\n        [ 3.82263731e-01],\n        [ 2.98809162e-01],\n        [-3.94589434e-01],\n        [-5.81454123e-02],\n        [-4.77181785e-02],\n        [-2.05240643e-01],\n        [ 2.58218527e-01],\n        [ 2.97490126e-01],\n        [ 3.06356370e-01],\n        [-1.36444806e-03],\n        [-4.45431627e-07],\n        [-3.58390203e-01],\n        [-3.08122825e-01],\n        [ 7.42106277e-02],\n        [-7.70292314e-02],\n        [ 3.44686272e-01],\n        [-1.98769146e-01],\n        [-3.39597104e-02],\n        [-4.81247646e-02],\n        [ 2.28758399e-01],\n        [-4.70083800e-02],\n        [-3.66577882e-01],\n        [ 2.19843518e-01],\n        [ 2.87998713e-01],\n        [-2.47049937e-01],\n        [ 2.92995565e-01],\n        [-1.21662750e-01],\n        [-5.95275495e-02],\n        [-5.76467231e-02],\n        [-1.98275827e-01],\n        [ 7.89291096e-03],\n        [ 2.16771001e-01],\n        [-9.72751284e-02],\n        [-1.52956883e-01],\n        [-5.38544503e-02],\n        [ 1.59457421e-01],\n        [-1.95581777e-01],\n        [ 6.89184316e-02],\n        [ 1.37426334e-01],\n        [ 7.87748839e-02],\n        [-2.57462992e-02],\n        [ 2.52783009e-01],\n        [-1.31777295e-01],\n        [ 2.42321946e-01],\n        [ 1.87170419e-01],\n        [-5.34258076e-03],\n        [ 6.71570253e-02],\n        [-5.91604221e-02],\n        [-3.52036988e-01],\n        [-2.96590208e-03],\n        [ 1.22455920e-01],\n        [ 5.39281453e-02],\n        [ 1.85969865e-01],\n        [-1.43488357e-01],\n        [-1.81635829e-01],\n        [ 2.00221262e-05],\n        [-2.99021430e-01],\n        [-1.65879354e-01],\n        [-5.04548441e-02],\n        [ 3.44055925e-01],\n        [ 7.13726120e-02],\n        [ 4.88433540e-02],\n        [-4.08951950e-01],\n        [-7.17677094e-02],\n        [-2.96135810e-01],\n        [ 2.20748194e-01],\n        [ 1.75528431e-01],\n        [ 4.90745519e-06],\n        [ 3.85574544e-01],\n        [-7.96447959e-02]])]"
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coefs_\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "[(2, 100), (100, 1)]"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[coef.shape for coef in clf.coefs_]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 五、k均值聚类"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [],
   "source": [
    "from sklearn import cluster\n",
    "x,y = datasets.make_blobs(n_samples=300,n_features=2,centers=4)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n       n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n       random_state=None, tol=0.0001, verbose=0)"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = cluster.KMeans(n_clusters = 4)\n",
    "kmeans.fit(x)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "array([1, 2, 2, 0, 2, 1, 0, 3, 0, 2, 0, 2, 3, 2, 0, 1, 2, 1, 1, 0, 2, 3,\n       1, 2, 2, 3, 0, 0, 0, 2, 1, 2, 3, 1, 0, 2, 0, 0, 0, 2, 3, 3, 3, 2,\n       3, 1, 2, 2, 3, 2, 0, 2, 3, 1, 2, 0, 2, 1, 3, 0, 3, 1, 2, 0, 0, 1,\n       0, 1, 0, 3, 2, 1, 3, 1, 1, 0, 0, 3, 3, 3, 0, 0, 0, 3, 3, 1, 3, 3,\n       3, 2, 0, 1, 1, 0, 1, 2, 0, 1, 1, 3, 3, 0, 0, 2, 3, 3, 2, 3, 0, 2,\n       0, 3, 2, 3, 2, 0, 2, 2, 2, 2, 3, 2, 0, 2, 3, 3, 3, 2, 0, 1, 0, 1,\n       0, 1, 3, 0, 1, 1, 1, 1, 1, 0, 2, 2, 3, 2, 2, 2, 1, 2, 0, 3, 2, 3,\n       1, 2, 1, 3, 3, 2, 2, 1, 0, 1, 3, 0, 2, 0, 2, 1, 2, 0, 0, 2, 2, 2,\n       0, 1, 1, 0, 0, 3, 1, 1, 1, 1, 1, 1, 2, 0, 3, 2, 3, 2, 1, 1, 3, 0,\n       2, 0, 0, 1, 3, 0, 1, 1, 3, 0, 0, 1, 1, 2, 3, 1, 0, 0, 0, 3, 1, 1,\n       0, 3, 3, 3, 3, 2, 1, 3, 1, 1, 3, 0, 3, 0, 1, 0, 1, 3, 0, 1, 1, 0,\n       2, 1, 1, 3, 3, 1, 1, 2, 3, 2, 3, 2, 3, 2, 2, 0, 0, 2, 3, 1, 0, 3,\n       2, 2, 2, 0, 0, 3, 1, 3, 0, 1, 1, 3, 3, 3, 0, 1, 3, 2, 3, 0, 2, 1,\n       2, 3, 0, 2, 0, 1, 0, 2, 2, 0, 3, 1, 3, 1])"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x252867f14c8>"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[kmeans.labels_==0][:,0],x[kmeans.labels_==0][:,1],c='r')\n",
    "plt.scatter(x[kmeans.labels_==1][:,0],x[kmeans.labels_==1][:,1],c='g')\n",
    "plt.scatter(x[kmeans.labels_==2][:,0],x[kmeans.labels_==2][:,1],c='b')\n",
    "plt.scatter(x[kmeans.labels_==3][:,0],x[kmeans.labels_==3][:,1],c='k')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 随机找到样本点，然后指定半径画圈，是否是核心点，边界点，噪声"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 六、密度聚类"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "dbs = DBSCAN(eps=1.0,min_samples=4)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x2528613bcc8>"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dbs.fit(x)\n",
    "plt.scatter(x[dbs.labels_==0][:,0],x[dbs.labels_==0][:,1],c='k')\n",
    "plt.scatter(x[dbs.labels_==1][:,0],x[dbs.labels_==1][:,1],c='g')\n",
    "plt.scatter(x[dbs.labels_==2][:,0],x[dbs.labels_==2][:,1],c='b')\n",
    "plt.scatter(x[dbs.labels_==-1][:,0],x[dbs.labels_==-1][:,1],c='r')#噪声点"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}