examprep_iris.ipynb

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       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
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       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2],\n",
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       "       [ 6.1,  2.8,  4. ,  1.3],\n",
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       "       [ 6. ,  2.7,  5.1,  1.6],\n",
       "       [ 5.4,  3. ,  4.5,  1.5],\n",
       "       [ 6. ,  3.4,  4.5,  1.6],\n",
       "       [ 6.7,  3.1,  4.7,  1.5],\n",
       "       [ 6.3,  2.3,  4.4,  1.3],\n",
       "       [ 5.6,  3. ,  4.1,  1.3],\n",
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       "       [ 5.8,  2.6,  4. ,  1.2],\n",
       "       [ 5. ,  2.3,  3.3,  1. ],\n",
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       "       [ 5.7,  3. ,  4.2,  1.2],\n",
       "       [ 5.7,  2.9,  4.2,  1.3],\n",
       "       [ 6.2,  2.9,  4.3,  1.3],\n",
       "       [ 5.1,  2.5,  3. ,  1.1],\n",
       "       [ 5.7,  2.8,  4.1,  1.3],\n",
       "       [ 6.3,  3.3,  6. ,  2.5],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 7.1,  3. ,  5.9,  2.1],\n",
       "       [ 6.3,  2.9,  5.6,  1.8],\n",
       "       [ 6.5,  3. ,  5.8,  2.2],\n",
       "       [ 7.6,  3. ,  6.6,  2.1],\n",
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       "       [ 7.3,  2.9,  6.3,  1.8],\n",
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       "       [ 6.2,  2.8,  4.8,  1.8],\n",
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       "       [ 6.7,  3.3,  5.7,  2.5],\n",
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       "       [ 6.3,  2.5,  5. ,  1.9],\n",
       "       [ 6.5,  3. ,  5.2,  2. ],\n",
       "       [ 6.2,  3.4,  5.4,  2.3],\n",
       "       [ 5.9,  3. ,  5.1,  1.8]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model, datasets\n",
    "from sklearn.preprocessing import Imputer\n",
    "import pandas as pd\n",
    "\n",
    "fill_NaN = Imputer(missing_values=np.nan, strategy='mean', axis=1)\n",
    "r_cols = ['sepallength','sepalwidth', 'petallength', 'petalwidth', 'type']\n",
    "iris = pd.read_csv('iris.data', sep=',', names=r_cols,encoding='latin-1')\n",
    "\n",
    "iris['type'] = iris['type'].apply(lambda x: 1 if x.strip() == \"Iris-setosa\" else 2 if x.strip() == \"Iris-versicolor\" else 3)\n",
    "#iris = pd.DataFrame(fill_NaN.fit_transform(iris),columns=r_cols)\n",
    "#iris.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Code source: Gaël Varoquaux\n",
    "# Modified for documentation by Jaques Grobler\n",
    "# License: BSD 3 clause\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model, datasets\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "iris = datasets.load_iris()\n",
    "iris.data\n",
    "X = iris.data[:, :2]  # we only take the first two features.\n",
    "#print(X)\n",
    "Y = iris.target\n",
    "#print(Y)\n",
    "h = .02  # step size in the mesh\n",
    "#print(X[:0])\n",
    "#x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "#y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
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   "outputs": [
    {
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DUqlkztsvcsMTL9W4RwjB2Eum8c7Dt3LRDXeRcTiZ1AN7USqVvPfYHYy/bAbb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cad62d8ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logreg = linear_model.LogisticRegression(C=1e5)\n",
    "\n",
    "# we create an instance of Neighbours Classifier and fit the data.\n",
    "logreg.fit(X, Y)\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1, figsize=(4, 3))\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "\n",
    "# Plot also the training points\n",
    "plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}