sample..ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use the Azure Machine Learning data collector to log various metrics\n",
    "from azureml.logging import get_azureml_logger\n",
    "logger = get_azureml_logger()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\azureuser\\AppData\\local\\AmlWorkbench\\Python\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "height weight\n",
      "4.0 -> 42\n",
      "4.5 -> 44\n",
      "5.0 -> 49\n",
      "5.2 -> 55\n",
      "5.4 -> 53\n",
      "5.8 -> 58\n",
      "6.1 -> 60\n",
      "6.2 -> 64\n",
      "6.4 -> 66\n",
      "6.8 -> 69\n"
     ]
    }
   ],
   "source": [
    "# Use Azure Machine Learning history magic to control history collection\n",
    "# History is off by default, options are \"on\", \"off\", or \"show\"\n",
    "# %azureml history on\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import linear_model\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import pickle\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "height=[[4.0],[4.5],[5.0],[5.2],[5.4],[5.8],[6.1],[6.2],[6.4],[6.8]]\n",
    "weight=[  42 ,  44 , 49, 55  , 53  , 58   , 60  , 64  ,  66 ,  69]\n",
    "\n",
    "print(\"height weight\")\n",
    "for row in zip(height, weight):\n",
    "    print(row[0][0],\"->\",row[1])\n",
    "\n",
    "plt.scatter(height,weight,color='black')\n",
    "plt.xlabel(\"height\")\n",
    "plt.ylabel(\"weight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope= 10.1936218679 intercept= -0.472665148064\n"
     ]
    }
   ],
   "source": [
    "reg=linear_model.LinearRegression()\n",
    "reg.fit(height,weight)\n",
    "\n",
    "#emit slope and intercept\n",
    "m=reg.coef_[0]\n",
    "b=reg.intercept_\n",
    "print(\"slope=\",m, \"intercept=\",b)\n",
    "\n",
    "#use slope and intercept to fit our data points\n",
    "plt.scatter(height,weight,color='black')\n",
    "predicted_values = [reg.coef_ * i + reg.intercept_ for i in height]\n",
    "plt.plot(height, predicted_values, 'b')\n",
    "plt.xlabel(\"height\")\n",
    "plt.ylabel(\"weight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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