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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Loading data\n",
      "=====================================================\n",
      "We load the dataset 'diabetes' using the sklearn load function: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import datasets\n",
      "# Load the diabetes dataset\n",
      "diabetes = datasets.load_diabetes()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The dataset consists of data and targets. Target tells us what is the desired output for specific example from data: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "X = diabetes.data\n",
      "y = diabetes.target\n",
      "print X.shape\n",
      "print y.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(442, 10)\n",
        "(442,)\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Splitting the data\n",
      "==================\n",
      "We want to split the data into train set and test set. We fit the linear model on the train set, and we show that it performs good on test set. \n",
      "\n",
      "Before splitting the data, we shuffle (mix) the examples, because for some datasets the examples are ordered. \n",
      "\n",
      "If we wouldn't shuffle, train set and test set could be totally different, thus linear model fitted on train set wouldn't be valid on test set.\n",
      "Now we shuffle:\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "from sklearn.utils import shuffle\n",
      "X, y = shuffle(X, y, random_state=1)\n",
      "print X.shape\n",
      "print y.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(442, 10)\n",
        "(442,)\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Each example of data has 10 columns in total.\n",
      "\n",
      "We want to work with 1-dim data because it is simple to visualize. Therefore select only one column, e.g column 2 and fit linear model on it:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Use only one column from data\n",
      "print(X.shape)\n",
      "X = X[:, 2:3]\n",
      "print(X.shape)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(442, 10)\n",
        "(442, 1)\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Split the data into training/testing sets"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_set_size = 250\n",
      "X_train = X[:train_set_size]  # selects first 250 rows (examples) for train set\n",
      "X_test = X[train_set_size:]   # selects from row 250 until the last one for test set\n",
      "print(X_train.shape)\n",
      "print(X_test.shape)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(250, 1)\n",
        "(192, 1)\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Split the targets into training/testing sets"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_train = y[:train_set_size]   # selects first 250 rows (targets) for train set\n",
      "y_test = y[train_set_size:]    # selects from row 250 until the last one for test set\n",
      "print(y_train.shape)\n",
      "print(y_test.shape)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(250,)\n",
        "(192,)\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can look at our train data. We can see that the examples have linear relation. \n",
      "\n",
      "Therefore, we can use linear model to make good classification of our examples.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "plt.scatter(X_train, y_train)\n",
      "plt.scatter(X_test, y_test)\n",
      "plt.xlabel('Data')\n",
      "plt.ylabel('Target');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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K1cCWLVuckhEbGwubzTFM1Iy4uPSrxXp5eeHw4QDExcVAq7XBZhsC+//RAoiObo+DB5/+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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107778190>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Linear regression\n",
      "=================\n",
      "Create linear regression object, which we use later to apply linear regression on data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import linear_model\n",
      "regr = linear_model.LinearRegression()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Fit the model using the training set"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "regr.fit(X_train, y_train);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We found the coefficients and the bias (the intercept)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "print(regr.coef_)\n",
      "print(regr.intercept_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 865.04619508]\n",
        "151.179169728\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we calculate the mean square error on the test set"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "# The mean square error\n",
      "print(\"Training error: \", np.mean((regr.predict(X_train) - y_train) ** 2))\n",
      "print(\"Test     error: \", np.mean((regr.predict(X_test) - y_test) ** 2))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('Training error: ', 3800.1408249628962)\n",
        "('Test     error: ', 4047.2429967010539)\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotting data and linear model\n",
      "==============================\n",
      "Now we want to plot the train data and teachers (marked as dots). \n",
      "\n",
      "With line we represents the data and predictions (linear model that we found):\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Visualises dots, where each dot represent a data exaple and corresponding teacher\n",
      "plt.scatter(X_train, y_train,  color='black')\n",
      "# Plots the linear model\n",
      "plt.plot(X_train, regr.predict(X_train), color='blue', linewidth=3);\n",
      "plt.xlabel('Data')\n",
      "plt.ylabel('Target')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.text.Text at 0x107766410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MRF5eHlQqFfbs2YOODnKaekpRMDwHS9HKeGDJzs7G2rVrkZKSgq5du6JTp04+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\n9auTjAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1077686d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We do similar with test data, and show that linear model is valid for a test set:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "# Visualises dots, where each dot represent a data exaple and corresponding teacher\n",
      "plt.scatter(X_test, y_test,  color='black')\n",
      "# Plots the linear model\n",
      "plt.plot(X_test, regr.predict(X_test), color='blue', linewidth=3);\n",
      "plt.xlabel('Data')\n",
      "plt.ylabel('Target');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qVCC1Wk0ymYxmzJhBRETLli3juaTcucJWr9fz0n5IJBKqUqUKdejQgU6fPl0s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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1080dfb50>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "Xdf = pd.DataFrame(diabetes.data)\n",
      "Xdf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0  </th>\n",
        "      <td> 0.038076</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.061696</td>\n",
        "      <td> 0.021872</td>\n",
        "      <td>-0.044223</td>\n",
        "      <td>-0.034821</td>\n",
        "      <td>-0.043401</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td> 0.019908</td>\n",
        "      <td>-0.017646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td>-0.001882</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.051474</td>\n",
        "      <td>-0.026328</td>\n",
        "      <td>-0.008449</td>\n",
        "      <td>-0.019163</td>\n",
        "      <td> 0.074412</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.068330</td>\n",
        "      <td>-0.092204</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2  </th>\n",
        "      <td> 0.085299</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.044451</td>\n",
        "      <td>-0.005671</td>\n",
        "      <td>-0.045599</td>\n",
        "      <td>-0.034194</td>\n",
        "      <td>-0.032356</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td> 0.002864</td>\n",
        "      <td>-0.025930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3  </th>\n",
        "      <td>-0.089063</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.011595</td>\n",
        "      <td>-0.036656</td>\n",
        "      <td> 0.012191</td>\n",
        "      <td> 0.024991</td>\n",
        "      <td>-0.036038</td>\n",
        "      <td> 0.034309</td>\n",
        "      <td> 0.022692</td>\n",
        "      <td>-0.009362</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4  </th>\n",
        "      <td> 0.005383</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.036385</td>\n",
        "      <td> 0.021872</td>\n",
        "      <td> 0.003935</td>\n",
        "      <td> 0.015596</td>\n",
        "      <td> 0.008142</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td>-0.031991</td>\n",
        "      <td>-0.046641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5  </th>\n",
        "      <td>-0.092695</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.040696</td>\n",
        "      <td>-0.019442</td>\n",
        "      <td>-0.068991</td>\n",
        "      <td>-0.079288</td>\n",
        "      <td> 0.041277</td>\n",
        "      <td>-0.076395</td>\n",
        "      <td>-0.041180</td>\n",
        "      <td>-0.096346</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6  </th>\n",
        "      <td>-0.045472</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td>-0.047163</td>\n",
        "      <td>-0.015999</td>\n",
        "      <td>-0.040096</td>\n",
        "      <td>-0.024800</td>\n",
        "      <td> 0.000779</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.062913</td>\n",
        "      <td>-0.038357</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7  </th>\n",
        "      <td> 0.063504</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td>-0.001895</td>\n",
        "      <td> 0.066630</td>\n",
        "      <td> 0.090620</td>\n",
        "      <td> 0.108914</td>\n",
        "      <td> 0.022869</td>\n",
        "      <td> 0.017703</td>\n",
        "      <td>-0.035817</td>\n",
        "      <td> 0.003064</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8  </th>\n",
        "      <td> 0.041708</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.061696</td>\n",
        "      <td>-0.040099</td>\n",
        "      <td>-0.013953</td>\n",
        "      <td> 0.006202</td>\n",
        "      <td>-0.028674</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td>-0.014956</td>\n",
        "      <td> 0.011349</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9  </th>\n",
        "      <td>-0.070900</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td> 0.039062</td>\n",
        "      <td>-0.033214</td>\n",
        "      <td>-0.012577</td>\n",
        "      <td>-0.034508</td>\n",
        "      <td>-0.024993</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td> 0.067736</td>\n",
        "      <td>-0.013504</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10 </th>\n",
        "      <td>-0.096328</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.083808</td>\n",
        "      <td> 0.008101</td>\n",
        "      <td>-0.103389</td>\n",
        "      <td>-0.090561</td>\n",
        "      <td>-0.013948</td>\n",
        "      <td>-0.076395</td>\n",
        "      <td>-0.062913</td>\n",
        "      <td>-0.034215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11 </th>\n",
        "      <td> 0.027178</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.017506</td>\n",
        "      <td>-0.033214</td>\n",
        "      <td>-0.007073</td>\n",
        "      <td> 0.045972</td>\n",
        "      <td>-0.065491</td>\n",
        "      <td> 0.071210</td>\n",
        "      <td>-0.096433</td>\n",
        "      <td>-0.059067</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12 </th>\n",
        "      <td> 0.016281</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.028840</td>\n",
        "      <td>-0.009113</td>\n",
        "      <td>-0.004321</td>\n",
        "      <td>-0.009769</td>\n",
        "      <td> 0.044958</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.030751</td>\n",
        "      <td>-0.042499</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13 </th>\n",
        "      <td> 0.005383</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td>-0.001895</td>\n",
        "      <td> 0.008101</td>\n",
        "      <td>-0.004321</td>\n",
        "      <td>-0.015719</td>\n",
        "      <td>-0.002903</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td> 0.038393</td>\n",
        "      <td>-0.013504</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14 </th>\n",
        "      <td> 0.045341</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.025607</td>\n",
        "      <td>-0.012556</td>\n",
        "      <td> 0.017694</td>\n",
        "      <td>-0.000061</td>\n",
        "      <td> 0.081775</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.031991</td>\n",
        "      <td>-0.075636</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15 </th>\n",
        "      <td>-0.052738</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td>-0.018062</td>\n",
        "      <td> 0.080401</td>\n",
        "      <td> 0.089244</td>\n",
        "      <td> 0.107662</td>\n",
        "      <td>-0.039719</td>\n",
        "      <td> 0.108111</td>\n",
        "      <td> 0.036056</td>\n",
        "      <td>-0.042499</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16 </th>\n",
        "      <td>-0.005515</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td> 0.042296</td>\n",
        "      <td> 0.049415</td>\n",
        "      <td> 0.024574</td>\n",
        "      <td>-0.023861</td>\n",
        "      <td> 0.074412</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td> 0.052280</td>\n",
        "      <td> 0.027917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17 </th>\n",
        "      <td> 0.070769</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.012117</td>\n",
        "      <td> 0.056301</td>\n",
        "      <td> 0.034206</td>\n",
        "      <td> 0.049416</td>\n",
        "      <td>-0.039719</td>\n",
        "      <td> 0.034309</td>\n",
        "      <td> 0.027368</td>\n",
        "      <td>-0.001078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18 </th>\n",
        "      <td>-0.038207</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.010517</td>\n",
        "      <td>-0.036656</td>\n",
        "      <td>-0.037344</td>\n",
        "      <td>-0.019476</td>\n",
        "      <td>-0.028674</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td>-0.018118</td>\n",
        "      <td>-0.017646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19 </th>\n",
        "      <td>-0.027310</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.018062</td>\n",
        "      <td>-0.040099</td>\n",
        "      <td>-0.002945</td>\n",
        "      <td>-0.011335</td>\n",
        "      <td> 0.037595</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.008944</td>\n",
        "      <td>-0.054925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20 </th>\n",
        "      <td>-0.049105</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.056863</td>\n",
        "      <td>-0.043542</td>\n",
        "      <td>-0.045599</td>\n",
        "      <td>-0.043276</td>\n",
        "      <td> 0.000779</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.011901</td>\n",
        "      <td> 0.015491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21 </th>\n",
        "      <td>-0.085430</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td>-0.022373</td>\n",
        "      <td> 0.001215</td>\n",
        "      <td>-0.037344</td>\n",
        "      <td>-0.026366</td>\n",
        "      <td> 0.015505</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.072128</td>\n",
        "      <td>-0.017646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22 </th>\n",
        "      <td>-0.085430</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.004050</td>\n",
        "      <td>-0.009113</td>\n",
        "      <td>-0.002945</td>\n",
        "      <td> 0.007767</td>\n",
        "      <td> 0.022869</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.061177</td>\n",
        "      <td>-0.013504</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23 </th>\n",
        "      <td> 0.045341</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.060618</td>\n",
        "      <td> 0.031053</td>\n",
        "      <td> 0.028702</td>\n",
        "      <td>-0.047347</td>\n",
        "      <td>-0.054446</td>\n",
        "      <td> 0.071210</td>\n",
        "      <td> 0.133599</td>\n",
        "      <td> 0.135612</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24 </th>\n",
        "      <td>-0.063635</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td> 0.035829</td>\n",
        "      <td>-0.022885</td>\n",
        "      <td>-0.030464</td>\n",
        "      <td>-0.018850</td>\n",
        "      <td>-0.006584</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td>-0.025952</td>\n",
        "      <td>-0.054925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25 </th>\n",
        "      <td>-0.067268</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td>-0.012673</td>\n",
        "      <td>-0.040099</td>\n",
        "      <td>-0.015328</td>\n",
        "      <td> 0.004636</td>\n",
        "      <td>-0.058127</td>\n",
        "      <td> 0.034309</td>\n",
        "      <td> 0.019199</td>\n",
        "      <td>-0.034215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26 </th>\n",
        "      <td>-0.107226</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.077342</td>\n",
        "      <td>-0.026328</td>\n",
        "      <td>-0.089630</td>\n",
        "      <td>-0.096198</td>\n",
        "      <td> 0.026550</td>\n",
        "      <td>-0.076395</td>\n",
        "      <td>-0.042572</td>\n",
        "      <td>-0.005220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27 </th>\n",
        "      <td>-0.023677</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td> 0.059541</td>\n",
        "      <td>-0.040099</td>\n",
        "      <td>-0.042848</td>\n",
        "      <td>-0.043589</td>\n",
        "      <td> 0.011824</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.015998</td>\n",
        "      <td> 0.040343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28 </th>\n",
        "      <td> 0.052606</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.021295</td>\n",
        "      <td>-0.074528</td>\n",
        "      <td>-0.040096</td>\n",
        "      <td>-0.037639</td>\n",
        "      <td>-0.006584</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.000609</td>\n",
        "      <td>-0.054925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29 </th>\n",
        "      <td> 0.067136</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td>-0.006206</td>\n",
        "      <td> 0.063187</td>\n",
        "      <td>-0.042848</td>\n",
        "      <td>-0.095885</td>\n",
        "      <td> 0.052322</td>\n",
        "      <td>-0.076395</td>\n",
        "      <td> 0.059424</td>\n",
        "      <td> 0.052770</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>412</th>\n",
        "      <td> 0.074401</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td> 0.085408</td>\n",
        "      <td> 0.063187</td>\n",
        "      <td> 0.014942</td>\n",
        "      <td> 0.013091</td>\n",
        "      <td> 0.015505</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td> 0.006209</td>\n",
        "      <td> 0.085907</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>413</th>\n",
        "      <td>-0.052738</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.000817</td>\n",
        "      <td>-0.026328</td>\n",
        "      <td> 0.010815</td>\n",
        "      <td> 0.007141</td>\n",
        "      <td> 0.048640</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.035817</td>\n",
        "      <td> 0.019633</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>414</th>\n",
        "      <td> 0.081666</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.006728</td>\n",
        "      <td>-0.004523</td>\n",
        "      <td> 0.109883</td>\n",
        "      <td> 0.117056</td>\n",
        "      <td>-0.032356</td>\n",
        "      <td> 0.091875</td>\n",
        "      <td> 0.054724</td>\n",
        "      <td> 0.007207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>415</th>\n",
        "      <td>-0.005515</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td> 0.008883</td>\n",
        "      <td>-0.050428</td>\n",
        "      <td> 0.025950</td>\n",
        "      <td> 0.047224</td>\n",
        "      <td>-0.043401</td>\n",
        "      <td> 0.071210</td>\n",
        "      <td> 0.014823</td>\n",
        "      <td> 0.003064</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>416</th>\n",
        "      <td>-0.027310</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td> 0.080019</td>\n",
        "      <td> 0.098763</td>\n",
        "      <td>-0.002945</td>\n",
        "      <td> 0.018101</td>\n",
        "      <td>-0.017629</td>\n",
        "      <td> 0.003312</td>\n",
        "      <td>-0.029528</td>\n",
        "      <td> 0.036201</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>417</th>\n",
        "      <td>-0.052738</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td> 0.071397</td>\n",
        "      <td>-0.074528</td>\n",
        "      <td>-0.015328</td>\n",
        "      <td>-0.001314</td>\n",
        "      <td> 0.004460</td>\n",
        "      <td>-0.021412</td>\n",
        "      <td>-0.046879</td>\n",
        "      <td> 0.003064</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>418</th>\n",
        "      <td> 0.009016</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.024529</td>\n",
        "      <td>-0.026328</td>\n",
        "      <td> 0.098876</td>\n",
        "      <td> 0.094196</td>\n",
        "      <td> 0.070730</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td>-0.021394</td>\n",
        "      <td> 0.007207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>419</th>\n",
        "      <td>-0.020045</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.054707</td>\n",
        "      <td>-0.053871</td>\n",
        "      <td>-0.066239</td>\n",
        "      <td>-0.057367</td>\n",
        "      <td> 0.011824</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.074089</td>\n",
        "      <td>-0.005220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>420</th>\n",
        "      <td> 0.023546</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.036385</td>\n",
        "      <td> 0.000068</td>\n",
        "      <td> 0.001183</td>\n",
        "      <td> 0.034698</td>\n",
        "      <td>-0.043401</td>\n",
        "      <td> 0.034309</td>\n",
        "      <td>-0.033249</td>\n",
        "      <td> 0.061054</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>421</th>\n",
        "      <td> 0.038076</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.016428</td>\n",
        "      <td> 0.021872</td>\n",
        "      <td> 0.039710</td>\n",
        "      <td> 0.045032</td>\n",
        "      <td>-0.043401</td>\n",
        "      <td> 0.071210</td>\n",
        "      <td> 0.049769</td>\n",
        "      <td> 0.015491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>422</th>\n",
        "      <td>-0.078165</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.077863</td>\n",
        "      <td> 0.052858</td>\n",
        "      <td> 0.078236</td>\n",
        "      <td> 0.064447</td>\n",
        "      <td> 0.026550</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td> 0.040672</td>\n",
        "      <td>-0.009362</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>423</th>\n",
        "      <td> 0.009016</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td>-0.039618</td>\n",
        "      <td> 0.028758</td>\n",
        "      <td> 0.038334</td>\n",
        "      <td> 0.073529</td>\n",
        "      <td>-0.072854</td>\n",
        "      <td> 0.108111</td>\n",
        "      <td> 0.015567</td>\n",
        "      <td>-0.046641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>424</th>\n",
        "      <td> 0.001751</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.011039</td>\n",
        "      <td>-0.019442</td>\n",
        "      <td>-0.016704</td>\n",
        "      <td>-0.003819</td>\n",
        "      <td>-0.047082</td>\n",
        "      <td> 0.034309</td>\n",
        "      <td> 0.024053</td>\n",
        "      <td> 0.023775</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>425</th>\n",
        "      <td>-0.078165</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.040696</td>\n",
        "      <td>-0.081414</td>\n",
        "      <td>-0.100638</td>\n",
        "      <td>-0.112795</td>\n",
        "      <td> 0.022869</td>\n",
        "      <td>-0.076395</td>\n",
        "      <td>-0.020289</td>\n",
        "      <td>-0.050783</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>426</th>\n",
        "      <td> 0.030811</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td>-0.034229</td>\n",
        "      <td> 0.043677</td>\n",
        "      <td> 0.057597</td>\n",
        "      <td> 0.068831</td>\n",
        "      <td>-0.032356</td>\n",
        "      <td> 0.057557</td>\n",
        "      <td> 0.035462</td>\n",
        "      <td> 0.085907</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>427</th>\n",
        "      <td>-0.034575</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.005650</td>\n",
        "      <td>-0.005671</td>\n",
        "      <td>-0.073119</td>\n",
        "      <td>-0.062691</td>\n",
        "      <td>-0.006584</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.045421</td>\n",
        "      <td> 0.032059</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>428</th>\n",
        "      <td> 0.048974</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.088642</td>\n",
        "      <td> 0.087287</td>\n",
        "      <td> 0.035582</td>\n",
        "      <td> 0.021546</td>\n",
        "      <td>-0.024993</td>\n",
        "      <td> 0.034309</td>\n",
        "      <td> 0.066048</td>\n",
        "      <td> 0.131470</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>429</th>\n",
        "      <td>-0.041840</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.033151</td>\n",
        "      <td>-0.022885</td>\n",
        "      <td> 0.046589</td>\n",
        "      <td> 0.041587</td>\n",
        "      <td> 0.056003</td>\n",
        "      <td>-0.024733</td>\n",
        "      <td>-0.025952</td>\n",
        "      <td>-0.038357</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>430</th>\n",
        "      <td>-0.009147</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.056863</td>\n",
        "      <td>-0.050428</td>\n",
        "      <td> 0.021822</td>\n",
        "      <td> 0.045345</td>\n",
        "      <td>-0.028674</td>\n",
        "      <td> 0.034309</td>\n",
        "      <td>-0.009919</td>\n",
        "      <td>-0.017646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>431</th>\n",
        "      <td> 0.070769</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td>-0.030996</td>\n",
        "      <td> 0.021872</td>\n",
        "      <td>-0.037344</td>\n",
        "      <td>-0.047034</td>\n",
        "      <td> 0.033914</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.014956</td>\n",
        "      <td>-0.001078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>432</th>\n",
        "      <td> 0.009016</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td> 0.055229</td>\n",
        "      <td>-0.005671</td>\n",
        "      <td> 0.057597</td>\n",
        "      <td> 0.044719</td>\n",
        "      <td>-0.002903</td>\n",
        "      <td> 0.023239</td>\n",
        "      <td> 0.055684</td>\n",
        "      <td> 0.106617</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>433</th>\n",
        "      <td>-0.027310</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.060097</td>\n",
        "      <td>-0.029771</td>\n",
        "      <td> 0.046589</td>\n",
        "      <td> 0.019980</td>\n",
        "      <td> 0.122273</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.051401</td>\n",
        "      <td>-0.009362</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>434</th>\n",
        "      <td> 0.016281</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td> 0.001339</td>\n",
        "      <td> 0.008101</td>\n",
        "      <td> 0.005311</td>\n",
        "      <td> 0.010899</td>\n",
        "      <td> 0.030232</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.045421</td>\n",
        "      <td> 0.032059</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>435</th>\n",
        "      <td>-0.012780</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.023451</td>\n",
        "      <td>-0.040099</td>\n",
        "      <td>-0.016704</td>\n",
        "      <td> 0.004636</td>\n",
        "      <td>-0.017629</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td>-0.038459</td>\n",
        "      <td>-0.038357</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>436</th>\n",
        "      <td>-0.056370</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.074108</td>\n",
        "      <td>-0.050428</td>\n",
        "      <td>-0.024960</td>\n",
        "      <td>-0.047034</td>\n",
        "      <td> 0.092820</td>\n",
        "      <td>-0.076395</td>\n",
        "      <td>-0.061177</td>\n",
        "      <td>-0.046641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>437</th>\n",
        "      <td> 0.041708</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td> 0.019662</td>\n",
        "      <td> 0.059744</td>\n",
        "      <td>-0.005697</td>\n",
        "      <td>-0.002566</td>\n",
        "      <td>-0.028674</td>\n",
        "      <td>-0.002592</td>\n",
        "      <td> 0.031193</td>\n",
        "      <td> 0.007207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>438</th>\n",
        "      <td>-0.005515</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td>-0.015906</td>\n",
        "      <td>-0.067642</td>\n",
        "      <td> 0.049341</td>\n",
        "      <td> 0.079165</td>\n",
        "      <td>-0.028674</td>\n",
        "      <td> 0.034309</td>\n",
        "      <td>-0.018118</td>\n",
        "      <td> 0.044485</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>439</th>\n",
        "      <td> 0.041708</td>\n",
        "      <td> 0.050680</td>\n",
        "      <td>-0.015906</td>\n",
        "      <td> 0.017282</td>\n",
        "      <td>-0.037344</td>\n",
        "      <td>-0.013840</td>\n",
        "      <td>-0.024993</td>\n",
        "      <td>-0.011080</td>\n",
        "      <td>-0.046879</td>\n",
        "      <td> 0.015491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>440</th>\n",
        "      <td>-0.045472</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td> 0.039062</td>\n",
        "      <td> 0.001215</td>\n",
        "      <td> 0.016318</td>\n",
        "      <td> 0.015283</td>\n",
        "      <td>-0.028674</td>\n",
        "      <td> 0.026560</td>\n",
        "      <td> 0.044528</td>\n",
        "      <td>-0.025930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>441</th>\n",
        "      <td>-0.045472</td>\n",
        "      <td>-0.044642</td>\n",
        "      <td>-0.073030</td>\n",
        "      <td>-0.081414</td>\n",
        "      <td> 0.083740</td>\n",
        "      <td> 0.027809</td>\n",
        "      <td> 0.173816</td>\n",
        "      <td>-0.039493</td>\n",
        "      <td>-0.004220</td>\n",
        "      <td> 0.003064</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>442 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "            0         1         2         3         4         5         6  \\\n",
        "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
        "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
        "2    0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
        "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
        "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
        "5   -0.092695 -0.044642 -0.040696 -0.019442 -0.068991 -0.079288  0.041277   \n",
        "6   -0.045472  0.050680 -0.047163 -0.015999 -0.040096 -0.024800  0.000779   \n",
        "7    0.063504  0.050680 -0.001895  0.066630  0.090620  0.108914  0.022869   \n",
        "8    0.041708  0.050680  0.061696 -0.040099 -0.013953  0.006202 -0.028674   \n",
        "9   -0.070900 -0.044642  0.039062 -0.033214 -0.012577 -0.034508 -0.024993   \n",
        "10  -0.096328 -0.044642 -0.083808  0.008101 -0.103389 -0.090561 -0.013948   \n",
        "11   0.027178  0.050680  0.017506 -0.033214 -0.007073  0.045972 -0.065491   \n",
        "12   0.016281 -0.044642 -0.028840 -0.009113 -0.004321 -0.009769  0.044958   \n",
        "13   0.005383  0.050680 -0.001895  0.008101 -0.004321 -0.015719 -0.002903   \n",
        "14   0.045341 -0.044642 -0.025607 -0.012556  0.017694 -0.000061  0.081775   \n",
        "15  -0.052738  0.050680 -0.018062  0.080401  0.089244  0.107662 -0.039719   \n",
        "16  -0.005515 -0.044642  0.042296  0.049415  0.024574 -0.023861  0.074412   \n",
        "17   0.070769  0.050680  0.012117  0.056301  0.034206  0.049416 -0.039719   \n",
        "18  -0.038207 -0.044642 -0.010517 -0.036656 -0.037344 -0.019476 -0.028674   \n",
        "19  -0.027310 -0.044642 -0.018062 -0.040099 -0.002945 -0.011335  0.037595   \n",
        "20  -0.049105 -0.044642 -0.056863 -0.043542 -0.045599 -0.043276  0.000779   \n",
        "21  -0.085430  0.050680 -0.022373  0.001215 -0.037344 -0.026366  0.015505   \n",
        "22  -0.085430 -0.044642 -0.004050 -0.009113 -0.002945  0.007767  0.022869   \n",
        "23   0.045341  0.050680  0.060618  0.031053  0.028702 -0.047347 -0.054446   \n",
        "24  -0.063635 -0.044642  0.035829 -0.022885 -0.030464 -0.018850 -0.006584   \n",
        "25  -0.067268  0.050680 -0.012673 -0.040099 -0.015328  0.004636 -0.058127   \n",
        "26  -0.107226 -0.044642 -0.077342 -0.026328 -0.089630 -0.096198  0.026550   \n",
        "27  -0.023677 -0.044642  0.059541 -0.040099 -0.042848 -0.043589  0.011824   \n",
        "28   0.052606 -0.044642 -0.021295 -0.074528 -0.040096 -0.037639 -0.006584   \n",
        "29   0.067136  0.050680 -0.006206  0.063187 -0.042848 -0.095885  0.052322   \n",
        "..        ...       ...       ...       ...       ...       ...       ...   \n",
        "412  0.074401 -0.044642  0.085408  0.063187  0.014942  0.013091  0.015505   \n",
        "413 -0.052738 -0.044642 -0.000817 -0.026328  0.010815  0.007141  0.048640   \n",
        "414  0.081666  0.050680  0.006728 -0.004523  0.109883  0.117056 -0.032356   \n",
        "415 -0.005515 -0.044642  0.008883 -0.050428  0.025950  0.047224 -0.043401   \n",
        "416 -0.027310 -0.044642  0.080019  0.098763 -0.002945  0.018101 -0.017629   \n",
        "417 -0.052738 -0.044642  0.071397 -0.074528 -0.015328 -0.001314  0.004460   \n",
        "418  0.009016 -0.044642 -0.024529 -0.026328  0.098876  0.094196  0.070730   \n",
        "419 -0.020045 -0.044642 -0.054707 -0.053871 -0.066239 -0.057367  0.011824   \n",
        "420  0.023546 -0.044642 -0.036385  0.000068  0.001183  0.034698 -0.043401   \n",
        "421  0.038076  0.050680  0.016428  0.021872  0.039710  0.045032 -0.043401   \n",
        "422 -0.078165  0.050680  0.077863  0.052858  0.078236  0.064447  0.026550   \n",
        "423  0.009016  0.050680 -0.039618  0.028758  0.038334  0.073529 -0.072854   \n",
        "424  0.001751  0.050680  0.011039 -0.019442 -0.016704 -0.003819 -0.047082   \n",
        "425 -0.078165 -0.044642 -0.040696 -0.081414 -0.100638 -0.112795  0.022869   \n",
        "426  0.030811  0.050680 -0.034229  0.043677  0.057597  0.068831 -0.032356   \n",
        "427 -0.034575  0.050680  0.005650 -0.005671 -0.073119 -0.062691 -0.006584   \n",
        "428  0.048974  0.050680  0.088642  0.087287  0.035582  0.021546 -0.024993   \n",
        "429 -0.041840 -0.044642 -0.033151 -0.022885  0.046589  0.041587  0.056003   \n",
        "430 -0.009147 -0.044642 -0.056863 -0.050428  0.021822  0.045345 -0.028674   \n",
        "431  0.070769  0.050680 -0.030996  0.021872 -0.037344 -0.047034  0.033914   \n",
        "432  0.009016 -0.044642  0.055229 -0.005671  0.057597  0.044719 -0.002903   \n",
        "433 -0.027310 -0.044642 -0.060097 -0.029771  0.046589  0.019980  0.122273   \n",
        "434  0.016281 -0.044642  0.001339  0.008101  0.005311  0.010899  0.030232   \n",
        "435 -0.012780 -0.044642 -0.023451 -0.040099 -0.016704  0.004636 -0.017629   \n",
        "436 -0.056370 -0.044642 -0.074108 -0.050428 -0.024960 -0.047034  0.092820   \n",
        "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
        "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
        "439  0.041708  0.050680 -0.015906  0.017282 -0.037344 -0.013840 -0.024993   \n",
        "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
        "441 -0.045472 -0.044642 -0.073030 -0.081414  0.083740  0.027809  0.173816   \n",
        "\n",
        "            7         8         9  \n",
        "0   -0.002592  0.019908 -0.017646  \n",
        "1   -0.039493 -0.068330 -0.092204  \n",
        "2   -0.002592  0.002864 -0.025930  \n",
        "3    0.034309  0.022692 -0.009362  \n",
        "4   -0.002592 -0.031991 -0.046641  \n",
        "5   -0.076395 -0.041180 -0.096346  \n",
        "6   -0.039493 -0.062913 -0.038357  \n",
        "7    0.017703 -0.035817  0.003064  \n",
        "8   -0.002592 -0.014956  0.011349  \n",
        "9   -0.002592  0.067736 -0.013504  \n",
        "10  -0.076395 -0.062913 -0.034215  \n",
        "11   0.071210 -0.096433 -0.059067  \n",
        "12  -0.039493 -0.030751 -0.042499  \n",
        "13  -0.002592  0.038393 -0.013504  \n",
        "14  -0.039493 -0.031991 -0.075636  \n",
        "15   0.108111  0.036056 -0.042499  \n",
        "16  -0.039493  0.052280  0.027917  \n",
        "17   0.034309  0.027368 -0.001078  \n",
        "18  -0.002592 -0.018118 -0.017646  \n",
        "19  -0.039493 -0.008944 -0.054925  \n",
        "20  -0.039493 -0.011901  0.015491  \n",
        "21  -0.039493 -0.072128 -0.017646  \n",
        "22  -0.039493 -0.061177 -0.013504  \n",
        "23   0.071210  0.133599  0.135612  \n",
        "24  -0.002592 -0.025952 -0.054925  \n",
        "25   0.034309  0.019199 -0.034215  \n",
        "26  -0.076395 -0.042572 -0.005220  \n",
        "27  -0.039493 -0.015998  0.040343  \n",
        "28  -0.039493 -0.000609 -0.054925  \n",
        "29  -0.076395  0.059424  0.052770  \n",
        "..        ...       ...       ...  \n",
        "412 -0.002592  0.006209  0.085907  \n",
        "413 -0.039493 -0.035817  0.019633  \n",
        "414  0.091875  0.054724  0.007207  \n",
        "415  0.071210  0.014823  0.003064  \n",
        "416  0.003312 -0.029528  0.036201  \n",
        "417 -0.021412 -0.046879  0.003064  \n",
        "418 -0.002592 -0.021394  0.007207  \n",
        "419 -0.039493 -0.074089 -0.005220  \n",
        "420  0.034309 -0.033249  0.061054  \n",
        "421  0.071210  0.049769  0.015491  \n",
        "422 -0.002592  0.040672 -0.009362  \n",
        "423  0.108111  0.015567 -0.046641  \n",
        "424  0.034309  0.024053  0.023775  \n",
        "425 -0.076395 -0.020289 -0.050783  \n",
        "426  0.057557  0.035462  0.085907  \n",
        "427 -0.039493 -0.045421  0.032059  \n",
        "428  0.034309  0.066048  0.131470  \n",
        "429 -0.024733 -0.025952 -0.038357  \n",
        "430  0.034309 -0.009919 -0.017646  \n",
        "431 -0.039493 -0.014956 -0.001078  \n",
        "432  0.023239  0.055684  0.106617  \n",
        "433 -0.039493 -0.051401 -0.009362  \n",
        "434 -0.039493 -0.045421  0.032059  \n",
        "435 -0.002592 -0.038459 -0.038357  \n",
        "436 -0.076395 -0.061177 -0.046641  \n",
        "437 -0.002592  0.031193  0.007207  \n",
        "438  0.034309 -0.018118  0.044485  \n",
        "439 -0.011080 -0.046879  0.015491  \n",
        "440  0.026560  0.044528 -0.025930  \n",
        "441 -0.039493 -0.004220  0.003064  \n",
        "\n",
        "[442 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ydf = pd.DataFrame(diabetes.target)\n",
      "ydf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0  </th>\n",
        "      <td> 151</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td>  75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2  </th>\n",
        "      <td> 141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3  </th>\n",
        "      <td> 206</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4  </th>\n",
        "      <td> 135</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5  </th>\n",
        "      <td>  97</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6  </th>\n",
        "      <td> 138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7  </th>\n",
        "      <td>  63</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8  </th>\n",
        "      <td> 110</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9  </th>\n",
        "      <td> 310</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10 </th>\n",
        "      <td> 101</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11 </th>\n",
        "      <td>  69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12 </th>\n",
        "      <td> 179</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13 </th>\n",
        "      <td> 185</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14 </th>\n",
        "      <td> 118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15 </th>\n",
        "      <td> 171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16 </th>\n",
        "      <td> 166</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17 </th>\n",
        "      <td> 144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18 </th>\n",
        "      <td>  97</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19 </th>\n",
        "      <td> 168</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20 </th>\n",
        "      <td>  68</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21 </th>\n",
        "      <td>  49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22 </th>\n",
        "      <td>  68</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23 </th>\n",
        "      <td> 245</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24 </th>\n",
        "      <td> 184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25 </th>\n",
        "      <td> 202</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26 </th>\n",
        "      <td> 137</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27 </th>\n",
        "      <td>  85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28 </th>\n",
        "      <td> 131</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29 </th>\n",
        "      <td> 283</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>412</th>\n",
        "      <td> 261</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>413</th>\n",
        "      <td> 113</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>414</th>\n",
        "      <td> 131</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>415</th>\n",
        "      <td> 174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>416</th>\n",
        "      <td> 257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>417</th>\n",
        "      <td>  55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>418</th>\n",
        "      <td>  84</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>419</th>\n",
        "      <td>  42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>420</th>\n",
        "      <td> 146</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>421</th>\n",
        "      <td> 212</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>422</th>\n",
        "      <td> 233</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>423</th>\n",
        "      <td>  91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>424</th>\n",
        "      <td> 111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>425</th>\n",
        "      <td> 152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>426</th>\n",
        "      <td> 120</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>427</th>\n",
        "      <td>  67</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>428</th>\n",
        "      <td> 310</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>429</th>\n",
        "      <td>  94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>430</th>\n",
        "      <td> 183</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>431</th>\n",
        "      <td>  66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>432</th>\n",
        "      <td> 173</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>433</th>\n",
        "      <td>  72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>434</th>\n",
        "      <td>  49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>435</th>\n",
        "      <td>  64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>436</th>\n",
        "      <td>  48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>437</th>\n",
        "      <td> 178</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>438</th>\n",
        "      <td> 104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>439</th>\n",
        "      <td> 132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>440</th>\n",
        "      <td> 220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>441</th>\n",
        "      <td>  57</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>442 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "       0\n",
        "0    151\n",
        "1     75\n",
        "2    141\n",
        "3    206\n",
        "4    135\n",
        "5     97\n",
        "6    138\n",
        "7     63\n",
        "8    110\n",
        "9    310\n",
        "10   101\n",
        "11    69\n",
        "12   179\n",
        "13   185\n",
        "14   118\n",
        "15   171\n",
        "16   166\n",
        "17   144\n",
        "18    97\n",
        "19   168\n",
        "20    68\n",
        "21    49\n",
        "22    68\n",
        "23   245\n",
        "24   184\n",
        "25   202\n",
        "26   137\n",
        "27    85\n",
        "28   131\n",
        "29   283\n",
        "..   ...\n",
        "412  261\n",
        "413  113\n",
        "414  131\n",
        "415  174\n",
        "416  257\n",
        "417   55\n",
        "418   84\n",
        "419   42\n",
        "420  146\n",
        "421  212\n",
        "422  233\n",
        "423   91\n",
        "424  111\n",
        "425  152\n",
        "426  120\n",
        "427   67\n",
        "428  310\n",
        "429   94\n",
        "430  183\n",
        "431   66\n",
        "432  173\n",
        "433   72\n",
        "434   49\n",
        "435   64\n",
        "436   48\n",
        "437  178\n",
        "438  104\n",
        "439  132\n",
        "440  220\n",
        "441   57\n",
        "\n",
        "[442 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "multi_regression = regr.fit(Xdf, ydf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(regr.coef_)\n",
      "coef = regr.coef_\n",
      "print(regr.intercept_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ -10.01219782 -239.81908937  519.83978679  324.39042769 -792.18416163\n",
        "   476.74583782  101.04457032  177.06417623  751.27932109   67.62538639]]\n",
        "[ 152.13348416]\n"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\"error: \", np.mean((regr.predict(Xdf) - ydf) ** 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('error: ', 0    2859.690399\n",
        "dtype: float64)\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Xdf_alt = Xdf.iloc[:, 1:]\n",
      "multi_regression_alt = regr.fit(Xdf_alt, ydf)\n",
      "print(regr.coef_)\n",
      "coef_alt = regr.coef_\n",
      "print(regr.intercept_)\n",
      "print(\"error: \", np.mean((regr.predict(Xdf_alt) - ydf) ** 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[-240.83456354  519.90454762  322.30578019 -790.89606275  474.37739627\n",
        "    99.71751786  177.4582476   749.50586804   66.16964998]]\n",
        "[ 152.13348416]\n",
        "('error: ', 0    2859.876709\n",
        "dtype: float64)\n"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Xdf_alt_2 = Xdf.iloc[:, (0,1,2,3,5,6,7,8,9)]\n",
      "multi_regression_alt = regr.fit(Xdf_alt_2, ydf)\n",
      "print(regr.coef_)\n",
      "coef_alt_2 = regr.coef_\n",
      "print(regr.intercept_)\n",
      "print(\"error: \", np.mean((regr.predict(Xdf_alt_2) - ydf) ** 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[  -7.91665554 -234.15865941  528.5262427   319.77035383 -143.2834799\n",
        "  -250.59872721   70.45074987  461.84022112   69.12602331]]\n",
        "[ 152.13348416]\n",
        "('error: ', 0    2883.672132\n",
        "dtype: float64)\n"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(range(1,10), coef_alt[0], label = \"alt\")\n",
      "plot([0,1,2,3,5,6,7,8,9], coef_alt_2[0], label = \"alt 2\")\n",
      "plot(range(10), coef[0], label = \"regression\")\n",
      "grid()\n",
      "legend(loc = 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "<matplotlib.legend.Legend at 0x10bed9890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ptncvN5sNVFtFcco2LMptG09ubD31wnJ2Nnb82f5P3qv5Hk3nNuVk5EkrGVoW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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bdfb190>"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(coef_alt[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "9"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(coef)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}