Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use(\"ggplot\")\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = [1, 5, 1.5, 8, 1, 9]\n",
    "y = [2, 8, 1.8, 8, 0.6, 11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UbW2tVqxY4fSUqHfnzh1duXJFy5cvlyS53W7Fx8c7vGqk+vp6ZWRkKC0tzekpkob+9Tcw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\nEAQdAAxB0AHAEAQdAAzxPxrMrmWMIIZzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ac08358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.array([[1,2],\n",
    "             [5,8],\n",
    "             [1.5,1.8],\n",
    "             [8,8],\n",
    "             [1,0.6],\n",
    "             [9,11]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = [0,1,0,1,0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = svm.SVC(kernel='linear', C = 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "[0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Volumes/Data/M1D0Z82/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n",
      "/Volumes/Data/M1D0Z82/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict([10.58,10.76]))\n",
    "print(clf.predict([0.58,0.76]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.1380943   0.24462418]\n",
      "[  0.           0.24489796   0.48979592   0.73469388   0.97959184\n",
      "   1.2244898    1.46938776   1.71428571   1.95918367   2.20408163\n",
      "   2.44897959   2.69387755   2.93877551   3.18367347   3.42857143\n",
      "   3.67346939   3.91836735   4.16326531   4.40816327   4.65306122\n",
      "   4.89795918   5.14285714   5.3877551    5.63265306   5.87755102\n",
      "   6.12244898   6.36734694   6.6122449    6.85714286   7.10204082\n",
      "   7.34693878   7.59183673   7.83673469   8.08163265   8.32653061\n",
      "   8.57142857   8.81632653   9.06122449   9.30612245   9.55102041\n",
      "   9.79591837  10.04081633  10.28571429  10.53061224  10.7755102\n",
      "  11.02040816  11.26530612  11.51020408  11.75510204  12.        ]\n",
      "[ 6.73467744  6.59642859  6.45817974  6.31993089  6.18168205  6.0434332\n",
      "  5.90518435  5.7669355   5.62868665  5.49043781  5.35218896  5.21394011\n",
      "  5.07569126  4.93744241  4.79919357  4.66094472  4.52269587  4.38444702\n",
      "  4.24619818  4.10794933  3.96970048  3.83145163  3.69320278  3.55495394\n",
      "  3.41670509  3.27845624  3.14020739  3.00195854  2.8637097   2.72546085\n",
      "  2.587212    2.44896315  2.3107143   2.17246546  2.03421661  1.89596776\n",
      "  1.75771891  1.61947006  1.48122122  1.34297237  1.20472352  1.06647467\n",
      "  0.92822582  0.78997698  0.65172813  0.51347928  0.37523043  0.23698159\n",
      "  0.09873274 -0.03951611]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ycpkTtJBfDv1K/u18zMzNqP90HSbFhlDZzrrErwfyP6kp0ksfAI6Ojnz55ZfE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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1132b9898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = clf.coef_[0]\n",
    "print(w)\n",
    "\n",
    "a = -w[0] / w[1]\n",
    "\n",
    "xx = np.linspace(0,12)\n",
    "print(xx)\n",
    "yy = a * xx - clf.intercept_[0] / w[1]\n",
    "print (yy)\n",
    "h0 = plt.plot(xx, yy, 'k-', label=\"non weighted div\")\n",
    "\n",
    "plt.scatter(X[:, 0], X[:, 1], c = y)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}