Sigmoid_Neuron.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sigmoid_Neuron.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ak-ghoul/Deep-Learning/blob/master/Sigmoid_Neuron.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "8cHkgczgUO2v",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Plotting Sigmoid Function"
      ]
    },
    {
      "metadata": {
        "id": "Hs_aB2HjROA5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tslZOZ8lRl0C",
        "colab_type": "code",
        "outputId": "bb80545b-ba57-4909-b83b-60b8e9306ff8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "a = np.ones(15)  # just random thing\n",
        "print(a)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YwtqyANqRs4W",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def sigmoid(x, w, b):\n",
        "  return 1/(1 + np.exp(-(w*x + b)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SJpvJ0s0S-EK",
        "colab_type": "code",
        "outputId": "d77774b0-6ef2-4e97-aa90-fc5cc39baa62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "sigmoid(3,.5,2)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9706877692486436"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "Mrf4pI2YTFs4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x = np.linspace(-10,10,100)\n",
        "w = 2.7   #@param {type: \"slider\", min: -3, max: 3, step: 0.1}\n",
        "b = 0.8     #@param {type: \"slider\", min: -3, max: 3, step: 0.1}\n",
        "y = sigmoid(x, w, b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "h9qvb_x2TZ3T",
        "colab_type": "code",
        "outputId": "b3a9a107-00fb-409b-bdbf-bd1c5e8ee0f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "type(y)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "B_8UZBH3TbQn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CSYWPshmTdsf",
        "colab_type": "code",
        "outputId": "0ba7a5aa-3a67-4895-c2a1-32ebe6e66167",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(x,y)\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aBQIBSVJBQYEmTJigKVOmyO1267rrrtPBgweTVDKAoei5UEeBj+FowKoGDOGioiKVl5dL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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "dd_mTj-WTxEW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def sigmoid_2d(x1,x2,w1,w2,b):\n",
        "  return 1/(1 + np.exp(-(w1*x1 + w2*x2 + b)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CbKkelQ4gIwQ",
        "colab_type": "code",
        "outputId": "80442049-0be6-43f1-eeab-a49389364f2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "sigmoid_2d(3, 0, .5, 0, 2)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9706877692486436"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "f9tdSWaKgTf4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x1 = np.linspace(-10, 10, 100)\n",
        "x2 = np.linspace(-10, 10, 100)\n",
        "\n",
        "Y = sigmoid_2d(x1, x2, 0.5, .5 ,2)  #this takes respective values of x1 & x2 like at index 0,0 then 1,1 & returns a 100 element list in Y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yj48GR4miVMC",
        "colab_type": "code",
        "outputId": "0e549378-68ef-40be-e948-63ebf1153633",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print(len(Y))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "U2nLz11giaCs",
        "colab_type": "code",
        "outputId": "039809d7-a118-44fd-951b-37c111f1da99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(x1, Y)\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vBgCUlpbCZDL5uqABYOPGjXjqqacwf/58P5VKdOc+PHwNHq+IZXPGcHAOIpK9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\nREREEmEIExERSeT/A/1wHhrkZPv1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "yxlmMJgWjWEI",
        "colab_type": "code",
        "outputId": "5b2bec5b-3f52-47b8-dc68-3d5dee0283ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(x2, Y)\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vBgCUlpbCZDL5uqABYOPGjXjqqacwf/58P5VKdOc+PHwNHq+IZXPGcHAOIpK9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\nREREEmEIExERSeT/A/1wHhrkZPv1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "0j9Owdwyjaa9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from mpl_toolkits import mplot3d"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ChLWGrf3j_g5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "w1 = 2\n",
        "w2 = .8\n",
        "b = 2\n",
        "XX1, XX2 = np.meshgrid(x1, x2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CUa4Wc8zkXxO",
        "colab_type": "code",
        "outputId": "485632dd-7091-4d6b-ca71-c7346f7c754f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print(XX1.shape, XX2.shape)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 100) (100, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "j9U21U15kb5T",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "YY = sigmoid_2d(XX1, XX2, w1, w2, b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LCdg7XI4ioBt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "my_cmap = matplotlib.colors.LinearSegmentedColormap.from_list(\"\",[\"red\", \"orange\", \"black\"])\n",
        "plt.contourf(XX1, XX2, YY, cmap = my_cmap, alpha = 0.7)# it represents the output using cololrs i.e. y is displayed from 0 to 1 as white to black\n",
        "plt.plot(X,'*')  # just fun\n",
        "plt.show()                                  # alpha here represents the opacity"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DW6mvcUkkmrW",
        "colab_type": "code",
        "outputId": "357b3bd3-493f-4499-834c-57842ae6c9aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        }
      },
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = plt.axes(projection='3d')\n",
        "ax.plot_surface(XX1, XX2, YY, cmap='viridis')\n",
        "ax.set_xlabel('x1')\n",
        "ax.set_ylabel('x2')\n",
        "ax.set_zlabel('y');"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AzaqF1l0BnAXaWBbI+ErPZy9xebVolBzNGjzDOI6S8zGe3Xo+A0BBEWYouI3Q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\nU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R+CQOOFoF5CqPvlHkSi9f5VQRAesG3hTjvQ/WqVzS86vlfz6ZCoL2QyMM2dlPNe/1w4\nOJ1b3+ZhwUMfYYqi/w/sFvRUKmZjkYjFQmQy+0drh8EwNEzTbqnmc5Jtm4+5FaIs58v8wS/+f3zm\nI19oEE/HQJK+ie6a3Z0f3eF5dI91+YIeoTa0eTCJoshsLG01+i7b+xOEEyHuzmzQNdyOEdX9lh1B\n4OpXbzb22z/Vg6IpyIqMKIsUt0soAZlKscrq9BrgTxOZfH4M13GpFKukV7dJrW4z/uwIm0upRptK\nW3cMPRKknK+weH2ZSrGKKAlMPDfO0o0Vou1hwomQb2MYVLn92gzlvB9VxLuitHXH2VpO09YVQ9VV\ntJCGVbWxTZtsSGR5JIxQBdH1ezAL60WMYAAxZxNSFH7yp3+AyYv9WNbx64d1FeqDNAGAOmEFa3U4\ntxG9ndaYrfthdxq0dKqtIs1q4zqZNrfs1DUKZ92/uhf1qNJxXAqF0rHukXuzBUdPufuWiTqqur+Q\n6SjYTaLiQxNhPiJMETTNF/TU65R7i+wnIcx6Q7dpHv+mPMm29WN2XQ/XdY9MlJ7n8Wef+Aaf+Nnf\np5Qr0zncTjQZRtMDbC6nGxNGJEVi4tIoW0spou0RFE2hnC9jxHTuvDaLWfGjGCOmM/LkIFbVxnM8\nMptZCttF+qd6uf2aX8+MJMP0T/WiGQHyqTyrMxsUtgvEu2J0DbVz+8osnUPthJMhRElEURTe/ep1\n3NpkhY6hJOG4gev6ZGpWTOyqjWO7LN/y65mCIHDuxQn/Ifc8itkyGwub9E32kFpNN5yG2gcS9Ix1\n4VgOdtUmu5XHth2C4SAL7/rnHmozEJ7rY0H1EE2PSDyMV3FRECBrERRleuIh/u2v/vix64fNlnbF\nYvmBqRYPE/U0u/vskOjpGBO8FzVSnyRldD1YE+VR6z+uC6XshknBWWAnqiydWlvaUUi0noK1LJti\nsdRy5sDfri4uam3Q/fsZjwjzPgiFfBePgwQ90ahBLne8N8A6gsFAzYj8+A/FSbaNRg1M08I0rSM/\n+LdfneFTP/+HWKYNAuS3CgRDAUr5Cndn1gHQozoTz47g2A6lXJmt5TTZzRxjz46QWk6TWc/SPpAg\n1hHx+y5zZeavLvptJwJMPT/OxsIm4bYQesTvawtoCrNvLzSiO1mVuPid56kWq36UmSmRurvNwLk+\nbr16B6tqo2oKfVM9hBNhKoUK23e32VhMYcR0+qd6uPXqDJFEmGR/gmAogKLK3H59jmLNuCDR20a0\nPVN5bwkAACAASURBVEw+XSDWGfWdXDwPx3SYfmPO/0IEmLo8zuxbC8iqTKQtROdIB8VsmflBnZwm\no7gi5XSRoKSiSwpi1UO2XH7khy/xF3/kUuO7rS9miiLfU5uyLBvXddE0v2buG4c/uJ7KVglrb9R2\n3J7K5pRzoVB8YDVS2GnZaI7gdyLRHcJp7nutn9NJSFSWJUIhA8dxjh1VtoJmEg0ElJpbkNd4IWgl\nnev/qcgOYX774RFh3ge+cODgv4lEDAqFcksPiqapAC2l1VrZtp5+FUWx0U8pyzKCQOMBqT8s9Uuf\nWt3mMx/5E9796k3WZtcxyxbJvgSJ7jizVxfoGmrHiPuzJEVJ5FpTCrVrpAMjqoMgoGoK5VwZx/FF\nRXW1qqRInLs8DoBZtchu5lif32ToQj/lQoX1eZ9A2wcSxNojuI7HzFtz5NM+uY09O8L26jaqphDr\njCGKICsyy7fvklr1jdQFAc5/YJJyrkLACGBVLVIraTqHO5m/utAwKOgYSNI93oVVtSjnKmwub+E5\nHv3nen2HH88jFDcYuNDntzLYDrblUClWiXfHWHh3Gdd12fpAH2LUQKh4yJ6Ina6gShKy6dEWUPn3\nv/7jh9Z26ipJvxXEvwnPOvW5e/+nL+rZKyq6nwinHmE96JRznbBc1yesw1o27iXR5mfpeCRaT7MX\nCg9ucDf45xwOG7Wosrzvi8HuSHT/NqRv96iyGY8I8z44CmGeROlaf6trpTZyHMI8rE65X5tBtVzl\n937xM3z8X/1eo+E/GAly/vlxysUKruOS3cqT3coxeL6f21dmcCwHParTN9FNMKJRzJTYmN8klyoQ\najPoHe/mzpVZEr1x4p0x5ICMEpC59rVb2LUeynh3jGRvG9WyiREJYlkO1WKFoBH01ao1jD41RCAU\nwLNdSvky63ObxLtjyIrM0o0VlIBM13AH7QNJREFgZXqNtdl1PM+fZVkpVClmS7QPJHyzdUmgkC6w\neH2lsY/J58bIbuUIx0OoQQXTtAkaAW69OkO16F+zvqkeqiWLzSVf0Tv4WD/XRwxMBwxZQUFCdgTE\nqoNmC7xwaYi/+RN/+dBr5vcY6rXF238hkySpIcJpJWo7Kgxj/7FfZ4HmhVlRlJq1m4dpWrWXt9Np\nBzkMp0VYxyXRBx1VNuOo57wTie6cl+t6/NRPfZhkMsnIyDiTk+dIJjv4do0qm/GIMO+DoxBmq+0d\nQM26TKRUOj5hHpVsW2kT+eYfXuHj//J32VxK0T6QJNkbx4jolHJl7rw5h1WxQPAJZXNxCyNmEIr5\nfqVKQGXx3SUKGT8CFGWRCy9NUSlWkFWZUq5MaiVN/7leZt+ap1oykRSJnvEu2rrjmCWT9No26/Ob\nBIIq48+McvOVOwRDGu0DCYJhDSWgMPf2AtnaPWhEdfrP97K9liHWEUUQBIq5EkZEb0SGAB2DSToG\n26GWXt1aTeO5vojnzuu+V20kGWbgQp+fis9X2FpJk1pJM/z4ALlUgdRK2q9n9iXoGu2kUvLnGwIE\ngipV0+adYR3Rk7C2y7glh5iuI5ZsVFfgX3/kx4gfoI5tlvIXi+VDF++jRm1HwXtVI93bKuJ53j0i\nnL2R9WnVD5tt7c7qnO9HonUv6nK5QqVinllNdC9Og6RFUeTVV1/m7bff5t13r3Hr1g1kWebChcf5\nqZ/6GTRNO4Mjf3/gEWHeB0chzJOoVRVFRlVlisXjixlU1U+n3q+u5EeV7rEehvmrS3z8Z36XYq7s\np1DzFQTPbzlYqAt6ZIkLH5hCFAQs0yK9lmFtdp3BC/1UyyZ3Z9YxYgYdgwkiyTCO7TJ/dZFCLYU6\n8tQQ+a08CALxriiiJCIAWytpX1lbw/mXJrEqFmpQpVoxSa+mSfYlWJvZaMzFbOuJMXC+H9u0yKcL\nrM9tUi2bTD4/xuK1ZcyKRedQO5FECDWoklrd9oU+HsgBmfFnRrg7s05bl6+YtU0HPRLknS9fw657\n1SZD9Ix2UcqVMaLBmr0SWJbDwrtL2KZDrCtKtD3C4rVlnKCM8wOP4ZVcAkh4FQel6qLYMNrXxj/9\nv/7H+37/O/VCv6eyFTS3ThyVcJpVqA+6RtrcKuL3ke6n3DwoattJFR6HcAQBDEM/stnDaaKeBnVd\ntzbIWqw5FO09p9O3Maynu08aSe+tVXqex/r6GktLCzzzzHP3HUrx7YBHhHkA5EMU0b7Tj9vSzedb\nyvmpr+Nvuz/ZttImktvK8+n/8nmuff0Wa3ObFLeLRNsj9I13c+OVOyT72nzxi1JPod5s+MPGu2J0\nDiaplkxCcQOralHMlwjqGrdrE0YAhh8fQI/4ziylXJn1uQ2iHRH0cJD5q0vIqkznUDsdA0lkRWLp\n5gpr85vgQc94F6Ikkt3I1VKoKq4LZtVk7q0de7zBC32Isj/oWRRF8ukCckCinK+yMb8J1NpOLo/h\nWC7VcpXtuxm2ltOMPzvC1lKK7fUsobhBx0DSj3jLJqnVNGu1iHfwQj83X77TWNjPvzRJajWDIPre\nneK5Dm44NpIDIUNHKrkoDkiWw//6j/4yT1wauef7l2UJwzg7E4C9hKMoO4tzff8Puhn/pK0izedU\nT1PD0QhnN0m3rgZtBYYRrD3z9xLWfqUROB0SbTY/yOdPkvr18Lw6UX77kuJBeESYB+AwwjyJWtU3\nylYpFI5PmHu3bYUobcvmS7/1dT7xs79HKed/jqzKPPFd5ynmyyAIFLNFUitpBi/0N1KoSkCmZ7yL\neGeMaqlKaiXNxmKq0eA//foswVCQruF2guEgiqpw542ZRgo1GNYYeqyf7fUs0fYIAgKFbIF4e4xr\n37jVsMdr64n7bRy2g1W12FpOY1s2vePd3H51Bs/zaOuJ0z3SgaIpbN/NcHd2A7NsEuuM0t6fYP7q\nIh2D7YTbQggiyKrC1T+73shQt/cniHVEcR2XYFijWjaxLd8Gb+XW3cZ3NXlplFKhQtDQCOiq35+p\na2wsbWFWLQQg2Z9kTnPYjupIVY/KVpG2cAi3ZNEd0/m5j/7NXd+/n4rUHli9sBmyLBMO+6Yb9fpo\nq/Z4x8VZtYocZvnnOE5jCPSDnuBS/76Pm/q9P4nuFuEcRKKHGbUfFTvCnjphPpx4RJgH4DDCPInS\nVZJEdF0jny+1sK0/mDqfL7VUp3zzC1f5zQ9/krsz60Q7InQMJIkmwlTLJnfemKNS8BeysaeHyWzk\nkFWJaEcEai4vG4tbpFa2G5937sUJzLLpi2PKvgima6SDtdmNhhNO+0CCoQv92KZDem2b1ek1bNPm\n3AuTLN1coVKq+j2VbQaKppJe3W6oaX1TgTG2ltNEEmECukqlWCEYDjL71nxD6apoMudfnKRaNEGA\nXCpPaiXNyJNDzLwxh1mx0IwA3WOdxNoj/rEup9lY2CQY1hh8rJ9br0wTCKq0DySJdUSQVZnU6jbp\nu9sU0kXGnhlhfWGr4Tc78vgA2xs5MhtZ7Of68XrbUGxQXBGv4hAwPT74oef5C9//TOP72rG0u38q\n8izgN6bvL+ppbjNQlL3Wa3ZtpJvdckT2XrSK1M8pEFBRVQWgMez5rF8M6jgoqmwFR/UCFkWhMf6r\nUDj6UIh74eF5daJ8OKPKZjwizANwGGGeROnq39B6S160/uITpFAo7/J4PAyr02v81r/4HTLrWQJ6\ngGqximXaBIJqo8dQEATGnx1BDSrYpkMulWdtboOOgSSBoMrCtWXf/HzYNz8XBIHF68ts1yZ79E50\nIYgihXSBZH8CRVV8NWfFYu6dxcaxDF8cQA2qKKqM58H2+jaKJmNVHVbv+M49eiTI2LMjeK5LKVdm\nc2GLXKrA4MV+yvkKG/ObJPsSxLtjaKEAdtVm+o1ZrIofPYw8OUgx4zsCheKhmlEDbC2n7yH8SrGK\npqs4jkt2M0tbd5y5txep1BSxoTaDnrEu8qkCeiSIHFAIhjXMsr8IKgGFa0HIVm2iIQPB9tBMCKkK\n//E3/pdGFsAwdERRqKUiH5zNWiuint0m4DKtOvu8V60iddJo/r4PezHYaZ042b4fhKCojr1K1nra\n3XEcqlWr5ReDR1HlvXhEmAfg8IklB4tvDoJvhH745JC9qC+8mqY2mt2bvTPrbQbNKOVKfOYjX+Cd\nL1/n7uw6hXQRPRJk9Ikhrr98m3inb/cmqzKqpnD9G7cxy/7CZsR0Bs71UdguEIob2LbrN/W3R7j1\n6jRe7c21c7iDjsEktmlTKVTZWNhElEV69trjDSQIGhrr85ss37mLa7tE2yN0DXewcG2JrpEOwvEQ\nju2gagpv/em1xnnEuqJ0DbZjWw6KplDKlSjlK0STYaZf9wlfkkWGnxgkGNIwqxa5zTzr8xvEu+L+\nSLE359GMAJ1D7UQ7IoiiyMrtu43WkN7JblzHZWspRbI/QTge8omxYpHZyJJLFdDDQbSQxnItbTv8\nxCD5dJGtJ7oQVZViqkgsoCFVXV56bpS/91M/iKoqu0ZRnbVjTB2nLeqpO/vsGC3cv71l71SRs5rF\nuR80TUXXg5TLvuftQTitFwPYieJVVaVQKD7Q1K8oioTDBuBRLJZrRHp8i7xHUeX98YgwD8BhhHkS\npSscz1rvoDrlbreY+gPvYJoWX/jNr/DrH/7tRmpUEAWe+p6LlPJlBBGKmTLrCxsMXxxk6eYqxUwR\nSZHoGumgvT+BWbHYqpmii6KfGq33K3YOJgmGNWRV5u7MekPpKkoCk8+Ns72eIdwWQlIkCpkisWSU\n2bfmKeX9mmnACHDu8hhmxfdhTd/dZns9w8SlMebeXqBSrBJJhOke6SSSCFHIFFm6tUphu9gwWJ97\ne4FIMuyP+pJFlIDC9JXZHXeggMzkpTGKmSKheAizapLdzNHWFeP2lVmcmiI21hmlb6oHuza8Or9d\nwKpaGFGD+XpkLAhMXR5n8fqKb8UXkOmb6uXOG/NUyibuX7mIV7YxBAUqNjoC/+Yjf4u+wXZs26FU\nKrPbpuxexWcr/rL3Q71eeBTSOAn2a2/x71OBarVKpXL88XetwicNHRBOlPpthUQfZFS5F/VrXSpV\nqFT2z3jtb5Hnu/t88pOfpLOzi9HRcaLRGA+zsOcgPCLMA3AYYZ5EuANHJ8xW6pS3Xpnm1//PTzD3\nzqKvZh1IYsQMXNvhxqt3GnW/wcf6sCq+cCDaEQYPXMelsF1s2N4BjD0z7KsTVRmzYrK1lCbeGW04\n8oAv1Bk434vreGQ2sqzVRDgjTwxSzlfYXNqiY6idSCKMrEp+zfTKbOO0hh8fwKxYKJriR4iVWi1y\nI9+IAAEuftd5PMdFEAXy6QIr02sMnu8js5FlazndpLpN4LmwsbjJ3RnfuGDs6WFSq2nKhQqdg+3o\nUR1Jlchv5v32Ga9G+M+PM/vWPEbMINoeIRQ3kGSJYraMbdo4tkO8J05uM+8rd6NB3pU8VAsCooRs\ne5wfbOdnPvK3DhSZCILQiNZOOp+yDr/Xzh807LvWPDhruXrqt37cO1ZyZ9dPWUcwGCAYPJg0ToL7\nRde27SBJIqIovSdRZX2aij/A/rgzckVEUeCXf/m/8vbbb3H9+nUikSiTk+d47LGL/PAP/7WGCvkR\nHhHmgTiMME8i3IHDvWhb6afcXErx8Z/9PTYWtlA1hUq+QrlYJtYe4+Yr/sgqURQYe2aEUFSnUjYb\n5BbtiJDsaePO67MEdJWu4Q4i7RFkRWL+6iLba1nAd+RJ9MT9tGVfAiWgUK1UUVSVW7V9ACT72uga\n6sDzPBzHJbORxaraxDujjZqpFgo06pnFbImtmu9sW0+MeFecmTfmiHVGSfa1ETQ0REXk1svTjdpi\noidOW0+cUrZErDOG4HmUCmVkVeH2azvuQF2jncQ7IoCAbdts380gSAJ6WGfh3SUAArrK+LOj/vHa\nDuV8he21DD3j3cxfXWrsc/CxPnKpApn1LKIsMX5plNmtAtZUB06uiq6oqFX4iX/2fUw83nvka1fH\n/fxl9/p87sVBop6zxmGtIkfrpzxp28SDf0Gop37rx71jHnG6Dkz7oS4eO+k0lWZrO9eF5eVFbt68\nydLSAn/jb/xPBAKB0zrkb3k8IswD0Dzia//fty7cgboX7b21nVbaRKqlKp/7lS/x2mfeZOX2Xcr5\nCmpQYerSOLeuzBDtiBDvjCKIAmpA4faVmUaUqQYVpp4fJ58uEorqmFWL9No27f0Jpl+fa0wYaeuJ\n019LWxazZdbnN7Atm7GnR5h5Yw7LtOkYTNLWFceIBEmtbrN4YxnHdlE0mbGnR1i8vkKiJ4YR1bEs\nx7ebe2W6sQ85IDP1/DiVQqUx6SS9uk3vZA/Tr89hVS0EUfBnb9Z6QLObOdbmNhAlkYlnR5h+fc7/\nm5FOIokwalBldfouq9PrjfMdfWqY5ZurtPXE0cMajuOiGRrvfvUGbn0o9UACLRRkY2GTWGcUI6YT\nioeolv1WEkkWcT1IrWyTaQ9TSoTQBQm55JCMaPzrX/6b916oFrGTIrx3Kohl2bV5rdoDd+qB1ltF\njqr4POhcDpsZeVZodmXam0E4TQem/VAXMwnCydLO8HAYpp8mHhHmATiMMH3hjkE229qIr71etK0Q\nJcA3/+AKH/vpT5GuGY6LosCT33ORatnEsXeUrqNPDrG1nGJ7LYuqKXQOd5DoiWNbDnen1xqG5ROX\nRtlaSWObNl3DHQSCKgiQXt1mpaZgBT9NWy2ZBMNazSwgjxE1SN/dbgyVVjWFcy9O4FgO1Yrlz6hc\nSTP69BCZtSyp1W3CiRAdA0lCbQZWxWbh3SWKWT9qH3lqiNxmDkEUiHfFGi4i6bVtNua3Gscy/uww\nrgN6SMMybVKraSLJMJmNHKkV/1j0aJDzlydwbJdyoczWUprU3W0mLo1yd2adXCqPElDoHEqS7Etg\nVS2qJZNiroQo+R6ad2uk2zPRRbVk4bkuyYEEK3GDbMUiLKlIlsv/8Fee4Pv/xkut3BZHRt2DVdPU\nxveytx56ltHWWbSKHLW9xTeIN94TQVHzgGW/Lejgv/ddw+RdXsDNczePIwDbiSpPVpd+mAzTTxOP\nCPMAHEaYcLKZmM1etK3UKWffXuDX/+knuHNllmR/grbuGJoRQEDgna9cb0RK3aOdBIIqju1gxA0c\n2/VVsJ7HwrXlxucNXewnGA6CB5VilfX5DcKJEMFQkPmriwiCQMdgkr7JHgRBYH1+g9WZdRzLoXMo\nSTgeZuH6El3DHf4UEwQESeD612419pHsbyPWEcWxHTRDo1qqUilU0EIas03OPSNPDqJHdGzLppAu\nsja/QTThi3tm3pxv1CkTPTFkVWH1zl3W5uq11BixzhhrM+t0DCYJhoM4to2sKruOpWMgSbQ9guu4\nGJEgtuWrPcv5UkMBK4giU5fHmX5jrmZwEGT48QEWb6xQzJboHukkl61QeLIPqh4BBCKyzL/7tb99\n5hZh9ZpdffHc3xpPOJW05148yFaR/QQ4AJZl18bUne30ljoOiipb+ax7h1fff2SYIPjZLEkSyedL\nJ0rzPooqW8f9CPNRlfcBwnWPJ+rIrGf55M//Iat31hAlke7RTsr5MrIs8c6Xr+O5HkpAYfjxQcJx\nnVK+Qmo5TWp1uzYXspfF68uEYgYjTw42TM2nr8w1jNP1SJCBC32kVvzxWVOXxylki4Tbwrz1xas4\nNTKOtIUYe2YEq+orXeOdMTYWtxiJhxpTTIyYTs9oF0Zcp7BdZG1ug0K62EiNLt1YIZIIM35pxLfg\n02Smr8ztOBAFZCaeHSG76UeAk8+Nsr2ZI9oe5uYrM40WmHCbwciTQ1hVG7NsogRVlm+tMv7sKPPv\nLGFWLMKJEJ1D7YTiBsVsic3FFJn1bKOWOvPGLIIo0jXcQc9Ylz89o2wx/PgAVtUi2h4hdTdDsreN\nkccHcByoWFsIoogsgWB7PP704JmSZbOoJ5PJNxZVz/NJxF/I/XS7vzD70Y0/Lkw/kaiouVUkk8k9\nkMjOcVwcx8R1ZVRVoVo1qVR843tFkdE0dZcApx6xnWbtcMdSz2R7O3fiz6urU++9VvUZlSqG4dd5\n625MpmmSyxVOaELwcFvbnRUeRZgczYC91SHSruuhaQrBYGBPH+X935Zt0+ZPP/ZVvvT/fo3Fa8u4\njocki5x/cZLF68tE2iONySFqQGHuncUG6QiiwIUPTFIuVH0xUKHC+sImA+f6Gu0kgiCQ7G+jd7IH\nu2qzvZ5hbXYDx3KYfH6M1ek1yvkKXcPthOIhND1AbivP7Ds7keHU8+NkNrKE4yG0UIBKsYKkyKzN\nbpDZyDb+7rHvnMIy/dpbMVtibXadgXN9bK9nSa9uI8kinUMddAwlcWyHjYUtX43r+dFnPlWgkCnS\nNdyBZmgIkkgpV9ppAaGmuq1aaHrAN3IvVhElke31bCNNC3D+pQmqJRNVU32FcKZArCPKrVdnsE0/\nihi6OEAhW2oYHlz8rvNc/+Ydwm0GbY8PcqtcJRQIoFkeP/sfP0SiPXKs++EoOC1Rz/3Snju9lPfe\ng3unijxIw/KjRnZnUTts3nc+f3zf25OgHlXKsoRl2Y3rBjsvPPVrdtj6815FlbOz0/yTf/ITfPCD\nf50f+qEPsr6+xs/8zE/hui6JRJIPf/hfoqrqrm1+6Zf+HdeuvYsgCPz9v/8TnDt34YEd72F4lJI9\nAEcbIq1TKFSOXCvar04piuI9rQW7m8FtXv30m3zsn3+KtbkNwB8n9dh3nsOxHAq5EltLfqQ0/PgA\nxVyZjfnNRm3QiOu4tsvMm/ON/sSBC33YVRvLtPzaoCTiWA7lQqVhSQcwcL6XgK4iyTKu45Je20bT\nA8iyzPw1X12qR3VGnxz0HX62C2wsblFIF+kc7kAPacxdXSTZ10Z7XxJVVxFFgXe/dtMfFYafpo0k\nwpQLvgmB50G5UEFRZWbenG8cS9dIB4meNkRRwCybrC/6NcxkX1vDuCCSDNM72UMgqJDd8E0LSrly\nQ2k7/foc8a4Yid44wZCGpEjMvb1IdtOPGnonu3Esh7VZPx0d74wS745TLZl+76UiIwdU8ttFCpkC\n8e42FioWZjJEoOIyPpDgJ3/+R07d1Pusx2/t33foNKJPVVUPnCpyVmiO7I5SL2zGQbXDZqOF+z27\nzftuZUjCSVDfd7V67/Saw8RSlmWRy+XRdd/U/70yISiXy/zkT/7v9PUNMDY2xg/90Af5V//qX3D5\n8kv8+T//F/joR/9vOjo6+cEf/OHGNm+++Tof//hv8m/+zX9gfn6On/u5f8lHP/r/PNDjPgiPUrIn\nhOcd3HrSjOY6ZfM2voG7SbVJGV5/GO5Or/Mr//hjXH/5Dj0jnTz2gSk810WQJF7/3NuNv0/0tjFx\naRTHcUn0xAmGNUq5MoIo8M6fXgf8N9bhJwYbk0Vym77Xqln2R2HduTJba8bv8X1UAwrLN1caRgVG\n3GDofB+LN1Zo709w7sUJquUqeijIta/tGKcHwxqPfccU1YqJLEv0n+vxW1JEePerN3AsB0mWGL44\nQKK3jXKh4k8FmdtgaznF2NMj3J1eQ5IlBh/rR48GCWgqd6fXufa1mwBIisTEpVE2FjbxXI9zL05Q\nzpfRI7rvL1trARFlkce/+zyVYhVJFhm40MfWSgoloOxS5yZ64vRMdGNXLTwPNEPzxSaWx9u176+9\n3yf81el1Iu0hRp8aobBdREroSBUXwfX4a3/7u2hrizU1udeFKq2lB/0+uyCiKJHPF88swqmnPZvv\nQUWR0fUgkiTieb67lCxLp6b2PAg7NTuJfL7QUo2yOXNzWNpz77gwwwgiy63vu1Xsjmj337fr+kO2\nm6P85qzB+voaH/rQXycSiTA+PsnU1PnGP6HQ/eexnjYUReHf/ttf5GMf+/XGz95883X+4T/8PwB4\n6aXv4OMf/81dhPn666/xHd/x3QAMDQ2Tz+coFgsYxoM77lbwiDCPCM/zHU0OQj2q9MnyaMhs5fj9\nf/9pFq4uUS5VUQISa/MbjD05zPVX7mBbDm3dMbpGOtH0AJn1LPPvLmKWrUZtcG1mHVEUmHx+DEEA\nRVO509ROIikS516YIL9dRJJFf8zVSopw3NgVjYYTISaeHcWxHPLpAo7tMPvOApPPj7Exv0Vhu0ii\nt422bn+2ZDFb4uYrdxpjwCYujVAIlMhu5Bh/ZgTP9RBEWJ/fZO7qTgr1/IuTuI6DLEkMPz7IxuIW\nkiyxtZRmsxZNRtvDDD8+iOt65DZztaHU2ww+1kelUGXh3WWS/W0MnO8joPtCpxsv3274yw5c6CMc\nD7G1lGLwYr8fdUgCua0CV7/sE6Moi0w+N95wAjKiOmNPDbE2v0k5XybWESbaEeOtL15j8vIYhWwF\nVVVpC2n0j3eQSmWQJKkR2Wiadk968CgK1t2intZal1pFc6tINuvfB82iIlVV0HXtTERFdSVopVIl\nnz/d895dO/TRHLHputaY3mJZFoqiNM7xrCPrZvXtceukvuWib7uYSCT59Kc/x8rKCjdu3OTmzev8\n6q/+VxRF4Rd/8b+c0dHfi3obVDPK5XIjBRuPt5FKpXb9PpVKMTk51fj/WCxOKpV6RJjfCjjK83EQ\nYbbSJuI6Ll/91Df5vX/3aTYWfJIQBDj/0hTbaxnKpQpjz46QTxcIxXRW76w1aoOSLPH0f/c4VsXC\nNm1iHVHW5zcJxY1GC0dAVxm40Ee8K4brOCxdXyGz4T+cQxcHUAIqK7fX6BnrQg36N3a1WN0VzfZN\n9dAV6MB1PPqmesinC4BHuVDxnXvwyXjqxQlkRcIsWyiqxOr0GpZlY0T8OZjBkMbQxX7CiTCyIjLz\n5kLDwq+tJ/7/t3fm8VHV995/n9nXZJJM9p0ASdhXAUEQ17bWW69atVbrVooWlOtWEStyK16LhauI\nSrFFW/v0udX2Wvu4VutWt8q+Q0hIQsi+ZzLJ7DPPHydzZib7niDn/Xr5Mpkzk/kdMjnf8/sunw8J\n6VbqyxtIyIgnKSuedrsTjVbFgQ+PSGuJsprJmpGBz+NDb9Kj0qjExiUBDn96vOPfRUH2jAzMwMwF\ntAAAIABJREFUcWbcDjc+j4+ayjo8Lg/WjllTAKPFSPaMdPH3FoDceRNwOdyY40xUFdeiVCnQW6KI\njovC7fKQv2giVWWNaPNSULZ7WXBBrrQuny+4qwzVGYM1w56CTVAWr6emntEgfFSkpaU1YgfZc1NR\n8MZAbCoCuogs9CfYBFVrFAqBlhb7iA38d8bvF4OoVqsBBFpaWvH7A9J56fU6VKqREWmHcENr1ZCV\ngoK1SoVCQXp6Nunp2Vx22beHvsgRoD+fidFM/w8FOWD2k+4CZudA2d+U7ZHPTvDKz1/lzHExBZqQ\nYSUjPw0IUFVcQ3VpLX6vn9TcZAQEivaVkpSdQNKEBBRKgUAA9r1/SPp5yRMSmDwvB6/bS/KERCxJ\nFpytDlRqJQfDgk56firRVjM+n59AwE9LbQsNFU0kT0jg+L8KpXGS+HQrap2a2tN1lB4pw+8LdHTd\nplB84DQJmVbyFk3C7/OjM2o5+tkJqZtWa9AwdUke7bZ2dCYdE2Zn0VjVhN6s58RXJ/G4xIuENS2W\n9LxUfF5xN2u3tdO87xR5CyZRUVCJ2+EmJSdRdCgx6Giqaebk10Xi+whi01Frg5225nbyFk7C7/ej\n1qgoPXIm5JYiiA4ltrpWFEoF+Ysm42x3YrIYOfr5Cfy+gPTv4rC7ONUx7jJhViY1pxs4fbhc7C6O\nNmJOi6Xd5UWvUnLlTYt6/f12nx4MdbCaTKIGqiCA2y12gg4kKzFUBjMqEr5jc3SU2sLTg52DTXgg\nDWe0dG+7Q6NRYzIZcDrdETva4I4tSHid12DQDEmkPUi4/mxzs20IAfjsEEzX6w24XE60Wh11dbVY\nrdaI41arNWLXWV9f3+U54xE5YPYTsYYZiog91Sl7o6a0jj9u+AtnjlcQnRAlKt20OTFE6dnz9wPS\n8+JSYkjKScTv8+N1ezsG81swx5o4/lUhAb8YwFInJ2Mw67E12qk4WUVbSztao5ZJcyZw+sgZLPFR\n5C6YiEqtQqVVUrinWArSSrWSKYsm01zTAgLkLpxIS60NS1I0xQdDaVqtQUP+osl4XB7cTg+mWCPl\nBVXkLhBFAFob7BgtRnE+M9aE2+mm9EgZbc2iIEHOnCwUgkBNSS1Z0zNQqlUQCGCrb2X/Pw5L55w1\nIx0BAZ/XR+b0dGx1NhRqBY1VzZLWrUanZvrSKQiCgMPuRGvQUHGyiuSJiSiVSsoLKsWd9ZRUYpJi\ngADVJbXUnq4n4A+QNS2d1qY2Sg6WoTNqsabHEZcai6vdjSnWRHx6HFqDBrfLQ1puEjqTjuO7SohJ\njMZpNaNUqJg+Nw2lcmAXKjHYePB4PGEarG7cbm/HRVnf6aI8tHpoTwz3qEh4ejBIeLAJGjmLdVNR\nb9bvD3TZ0Y404XVSm63vWmV3dd6gvqy4wx7YeEvQK3O4dpVnw7jIvHnn8cknH3H55d/h008/YsGC\n8yOOn3feQnbu3MFVV11DQcEJrFYrBoNxjFbbf+Qu2Q76ayLd3u5ioHVKp93J27/+B0c/P0HdmXrq\nzzSi1qnJP28SBbuLEBSC2GkabUCtUVFZVE39mZAjyOTzJtJQLiraaPQa2m3t6KMMVJyo7EiTiheF\nacvypB2craGV6uJacmZl01gpzmYqVUqScxJJzk7A5w9QcbJSElTPnJaOs81JS62NxKx4DFEGEAK4\nnV5OdejBAqRMTEJrFBVnNHoNjlYnbpcbtUYtabUC5MzKQh+lFwUJmuwd3ahmrKmxFO4pRqlSkpgV\nT2xKDCqtiqqiammcxBRrJHVSMqePnCEh04ox2oDP60dr0HDksxOS1ZjerGPS3Am02xzoTaI4QlNN\nC9a0OAp2FUnNSZbEKFIniV2xaq0aZ5sLr8eL2+Wj4qQoXGCKNZKQYZV2p/mLc/F4fKg1KlpbHDiT\nY/F6Azzx7I3ExHXfQdcboTSkAru9rduLdnei38Oh6DOWoyIgBgytVvQgFQRh2CXkeiO8TjoYe77e\n6Gu8JRAIYDTqh8HVZHzvKk+cOM5zzz1NdXUVKpUKqzWexx7byBNPbMDtdpOUlMy6dY+hUql47LGH\nWbfuMbRaHdu3b+Pgwf0IgsB99z3EpEmTx/pUJOSxkj7oj8WXTqfB6XR3SF31r65x6JNjbL/7ZXEn\n18GMZVNwOdwoVAqcdlFpJ2lCIq0NIbeO2GQLGVPTCfj9NFY2U1VSg9flFdOYPh9VHeo2loRoVGol\nAeDYFwWSgFBClhhovC5vh+qPD6fdid6o5+SekFh5yqQk4tNi8Xr8ONucVJfWoejQcC3cXUwgEMCS\nEEVSThJ6o5bGanHH53a4JRGAwr0lxCZbROstpQKNXs2Jr4okoQGlWknegom0NrYRFWeSJO2sqXGU\nHimTmpP0Zh25CyfhcXpwtjmpK2vAVt9K7nnibGhro10aoTHHGnG2uzlzvELazU4+L0fcTfr8JGbG\nozVqUaoUNFQ1SzvroF3YyV1F+H0BTDFGMjt2nn5/AL/PR2xKLMe+KERr1DB5wWTabA6q9Fpy0mJZ\n+9T1/f1ISXRW6hkIwVRuT4o+fdmEhUYmRn9UpDex9K7n1dnlZGhNRZHdt22jUicNH2/RarVSJiLc\nv3ag7i2ytN3YIAfMPugtYPr9fvz+AFqtqosXZSgl0/NdcmNVEyUHTlN6+AxN1c18/vrXuNrFYJKQ\nEYcpxkRrk13SUHXY29HotRTuLpZ+RkyyheScRPxeP36/n8bKJpztLtImJ1Ow6xQBfwCdUUtaXgpG\ni4G2Fge1JbXYGuxoDBpy5+ZwYlcRepOW+AxxbEKtUXH68BlaOhpwFEoF0xbnUV/RQFR8FAqlAlu9\nDVOMkTMnqmjrUAdSqhRMW5qPx+XB5xXdSWpK65g4O5uGyiaaqpsjNGwJQHlBJfUdIgIZU9Nwt7ux\nNdpJzIpHZ9CKA/VOL6cOlErnnJidgDFabA4RjaQduNrd6IxaSsO6brNnZmKI0uP3+mhvdVJ7ug6j\nxYA5xiTtGLUGDfkLJ+Pz+vD7REcQp92FJSma0iPl+Dw+VBolmVPF+Va9SUeb3UVdRSNxk1JoVClZ\nde/lzFyQ0+/P1EjYbwWbb8I/h+FjFcGAI44t9OwqMtIYDHp0uv4LL4Q3FQUDKXQNNv0J+CO5q+wL\n8Xcuat/a7e3SY4NxbxkP0nZvvfUG7733jvR9QcFxPvjgM+n7ZcsWMH36TOn7rVu3o+xrqP0sQA6Y\nfdBdwOxL9zX8D0GtVkXc/QfrGkHVFrVaTXt7Oy6XB5/Xx5njlZQePk3B7lOc3HWKqqIa1DoVObOz\nRRcOQRzgN8YY0ejUVBfVUBPspu0YIakrqyfKakZn1Ek1vcrCalobQpq3My6cgtftQ1CArdEuKe20\n1NmoL29EEATiM62kTkzC5/NTV1ZPdUktAX+AjPxUAgGoKq4hJScJc6wRhAB+Pxz/8qT0HvHpcURZ\nzXg9PgxRetxON+0tDkwxRqmbFsTziU+Lw+v14Wh1UFNaJ3b55adIQT8uNZb4jDi0Bi1N1c1UFlXj\ndXklF5RT+0RBAkuHI4tap6Zwd+QIzeT5OTRUNBKbZEGtVeOwO9FH6Tjyz1A6Ny03BZ8vINVHE7Pj\nAYHasgYM0XqSJiah02sIKFU4dGrQqPmvHbf267MkpkB1aDSaUUmBdmcsDmIHr6hhPDoarCDuHM1m\nUYXKbh+4MlY4oTGQULDpLNAefiMQdPdQKIQh67AOhmAzld3e+++8J2/Uv/71r5SXlzNp0mQmT87H\nYolhPO0q9+/fy0cf/YP7739IeuyKKy7m7bc/HMNVjQxywOyD8IA5WDeRznf/arV4l9yfC1dbSzvF\nB09zctcpivYWU7SvhITMeGz1rdSXi2na6IQosqdnEAgEaKppobq4BrfD09FNCxUnq7GmxxKTZEGr\n06DUKDn8yTGpg9WaFkt0fBSOVgdR1ij8fj/tLQ50Zi1Fe0J1ythkCymTkvF5fXhcHurLG3E73GRN\nz+DEvwrx+/yYYsQ6o9FioLWhlcqiGtpa2lGqleSel8Op/aUYo41YU2NRapQo1QrKjlVgC/tM5S6Y\niL1JbBpSKBTYGloxWgzUnq6npWMERqlWMnVJLl6PD5/bR1NtM7Wn68malkFbSxt1ZQ2SQHt8Rhx+\nn5+G8iaqS2rwef3kzM6iobKJ5poWtAYN1rQ4rOlxopBBQLyL1+o1Ur1To9NgtzkoPlTG9Aun0Nbi\noEGp5rLvzeLKmyIbF7pjbFOgoVERh8OBICh6rIcOd90weGM40jcJ3SsViV3fSqUSl2v01XqCqWef\nzz/omwSFQkFBwXE++eRjjhw5yvHjosFzXt4Urr/+h0ybNn0EVj4w1qy5i/XrHycuLtTNKgfMMM6l\ngClqaA8uUHZGvGjqxZ2UwyX9kYt6nspOu9CeazVVxTUU7RGDZ9mxcgIBKPi6SDoeHW8Wd0pePwF/\ngKaaZuxNbUyYkcmxr07i9/nR6NSkTk4WA6XdQe3pepqqW6SdWPGBUqkBR2/WodKoqCyslmy7AHLP\nm0hzXQumGBManZp2WzsanZr6cjH9GmTmhVPFtGMgQHOdjYqialInJeF1izVXpUpBQlY8CRlWEASq\nTlVL1l0JGXEYog1UnKwWd9YWgygur1Fx5LMT0ntExZlIzknC2e7CYNbjcXuwN7URbTVTsCtUm41N\ntpCYnYDP60OpVOBqd+P1+fC6fVQWBv0yNWTPzOBkx+uSchJxOb1ExZqwpsVxcn8pOXMmUO8N8Pxf\n7sbv93fZ1QTpT1PPSNKfUZGh1kN7InxkYrQ9OhUKAbNZvOHydvyuR/rmIBy9XodePxw+nZGC6X6/\nn/LyMo4fP8akSblMmND/UsBIcPz4UV5//c888siGiMcvvfQCFi9eSk1NFcuWXcQNN9w0NgscZuSA\n2QeCIN6lioFycPUCpVKJ0ahHoRBoa3N020Le2ZpJrVZ1EmXv/oIM4HZ6KDl4mqJ9JdSU1nL8y5OS\nPRUC5C+aTEN5I6ZYI1qDFle7C7VGRc3p+oimo/xFk/D5/CiVCtpbnVQX15CYlYDH5ZFSlNa0WFIm\nJ6NQCNSdaZDE2RMy4jBYjJQdLSch00p0QhSCQkCr03Do46NSI1SU1UxGXiqtTXZMFhNej4fmWhux\nyRYKdp2SdnRR8Wayp2dInpR1ZfW02RxMni/uUt0ON+ZYEwmZVsxxJtpa2qksrJYafXIXTuTM8UqU\nSgVJWfGodWqUGhUNFU1UFor/NuGNPggCMYnRJOUk4nZ6pflaY7SBuvJG6iuayJ6RgdPhITrJgl8Q\niM5K5D8ev7rH+rVKpUCr1eJwOHE4wmYRRoHwUZGB+kX2tx7a0xViOG2wBkNvnpHhf18D1ZbtD0ql\nArPZ2FGfbhtSg9J4qFX2xVNPPcEll1zOnDnzIh5/442/cNll30EQBFatWsHPfraOvLwpY7TK4UMO\nmH0SIFSrDP2/PwE0WLPSajWD8g3srgbVWZS9pzvkpppmSg+eoe50Pbvf20/h3hJc7S4Ss+PRGXWc\nPnIGa1ossckxqLVqlGoFRz8vwOcRd0BRVjPJExJoqW8lOiEKAkjdrAW7iqSanyHawMQ52XhcHrwe\nH40VjTTX2cidn8OpA6ViM45JR9KEBCzxUTjtTiqLRLNmEIN5ZWEVgQAkZsV3qAsFIuYsATKnpxPw\n+dHoxaakNpsDAgFxZ1gUaWqt0WsQAgKOVgeVxdVEx0eh0WmkjlidScvEuRPw+/xio4/TQ2tTGwlp\ncRzfJVqSCYJA/vmTKdxXQnx6HNZ0K001LagNWlRaNV6Djnt/dR3WREvEv7tSqUSrVaPTaaX53KF0\nQw6UkRoVifwsikGnu6H9oAjAWKRAg7t5QRD6bWgd3lQUPD+Azj0H/dkdB7ueh2dXOX7HRcL5wQ+u\n5pVXXkWtVvf4nBde2EpmZjZXXPFvo7iykUEWX++T7u7uAmF/QKGAGgyiXq+Xzz77J9/61mUANDXZ\nBpWO6m4APHSH3FVeLfjHrVAoyJqcRnZuOnZ7O5etWI7f5+fMiUpO7Svh5B7RtqruTAPxGVZO7j6F\nx+VBrVWROiWV2JQYnG1uak/X0lgpWnzlLpiIrd5GY1UjmVPT0Jv1HVZZzRz6+Ki0vqzp6Rii9Hjd\nXibOmUBbSxsBfwB3uztC0i5ndhZao5aAz09UQhTVxbU0VjUTm2yhcG8xgiCQlBVPQlYCKo2SmpJa\nqkpEpSNDlJ6MKakU7hWDWe6CieJuVq/h6BcFeDtmToOaug2VTeiMWvIXTcZhd2CKMXE0bG4zPtOK\nVq/lyBcnUWtVxKbHYUmMovRwGT63F0uShRO7i5k0J5O6ihZS81Mxpcd1CZaCIKDTadFo1FKDRyhz\nIIp9m0zin9ZgpOP6IrxOKqrGDF8KtHsxgtB8qE4XGpcQTZ3FNOhoCRHodBoMBv2Ad/N9acuKSkVK\nAoFAj7J4wRpxIDB0KcPQrjK4sxy/1NfXodcbugTLsrJSXnrpNzz22EZ8Ph+HDx/kwgsvHqNVjg5y\nwOyV7oKon0AAdu/+F9u2PUN8fAILFizssNgZPoJuCuHyasG7fr1eF3aH7O2QVhNfp1AqyJyaRubU\nNC66+QIA2m3tFO0rpWhfCUV7i7E3t2Gra+XAP8IC25wsNFo1IBCXFktNca24u0OQRjhiU2JIyUlE\no9fQWNlEdUktno45z7TJyRTuKcaaFttjYFNplEyeP5GWOhtKtYLc8ybSWNOMJdlC4d5TkrqQWqdm\nziUzcDlcuBweLAnRVBfXYrIYqa9olBp4UqakEZtswef1UV1aR11ZPbWldeTMyaK51kbp4TOY40zE\nJluITY7B5fDgdXnImpaGSiv++7W1OEjMiseSbMHR6iQhNQZ7q4uU3GT8Crj4qtkRv5dwK6jwYBWp\nv9rxuwjr8hTFvlX4/YOXWFMoQqMiI+lo0pmQXq54/g6HU1IpGklx9nDCd5XDpRTUkxtI8O8sXBYv\nEAigVCpwOrvacA2Ms2dXGaS+vp6YmFjp+z/84XfMnj2HadNmkJCQyIoVtyAIAkuWLGXKlGljuNKR\nR07JDpBAIMD69Q9TUHCc1avv5YILloaNo3TdhQ43wTts0SbM3eGWoQpTGvFGpHJ7umjVlNRSuLeE\non0ltLW08fWb+yTfSo1eQ87sLJprWoiyiqmJ5toW4lJiOLW/VJohVWlVTFuSh9vpxuv20VjdREN5\nExPnZUudrhq9hqTsBGKTLXi9PspPVEr1VHG0w0dLrY2ErHj0Jh0+r4+APxAxjmJNi8OaGoPP40dv\n0tJud2JrbCU6LorCveHPiyUh04rfFyBAgLZmcfZRISgoLxC9PwWlgvyFkyjYXYzfJ6oHpeencerA\naWKSLKTmpVBb3oQlyUJyXio/2XAVECkYLs41Dq6pZ7BqPuGuIqM9WxiZAu1+XGOo9dDeCOnPjn6N\nWKFQYDYbOm4I/KhU4U1FfZdMwgmJEIzdrnLfvj2sX7+WrKwJAOTkTOTee38mHd+9+2tefPF5FAol\nixYt5tZbfzwm6xxr5BrmMPLll58zd+58tFptD88I0F1NdChBNDgEHwgEsNsdPVy0QmnB4MWrvw1F\nHpeHkkNlFO8vpfxkJYc+OUZdmTjOkjU9nXabg9ZGO0nZCehMOgL+AB6XJ8L4OT7TSlSsiUAggNag\nxWl3YbfZiY6LkpxCQOxGjU+Pw+v20m5rp7q4lkBAlNM7uVsUWI9NiSE+PQ69SUdLnY2Kk1W4nR4E\nhcDUxbkUHzyN3qzHmhqLSi2OrVQUVtNQ0QQErbsmUrS3GJ1RhyUxuqNBSYHPLV64VWolgkqJy+HB\n7XDhdPpQqhRozHpiUmO55AcLmbUkV+qEHKkLdmT3auRgu8/nR6cTP2f9rdcNJ0MJVv2th/b2erNZ\nFKofm3MXb07b2504nZHn3nUGu7emovGzq9y3bw+vv/4aGzc+1e3xm276Plu2bCM+PoHVq3/Cgw+u\nIzt7wiivcuyRa5jDyPnnL+njGZ1TuUGfzNDX0L8AGupEVNPe3nuTQfdpwdBFy2gMppgi3SR8Pj9q\nrZrJ83OYPD/Uvt5ca+PM8XJO7Snh+L8KsdW3UltWT1peCie/PkUgECA22UJCphWtUUdzTTOnj5Xj\ndXmlgFV5qhr8omydIAio1ErKTlRQHdboM2leNi6HB3/Az8S5E2iuFVOuTVXNFHT4YypVSvIXT0ah\nVOBxeYmON1NTWo9WrxZ1bI+VAxCbZCFzWho+XwBnm5OETCtN1S0Yog0U7imRdsc5s7MpO1FFu82B\nJTGatPxU1O0uVHotWoMWk8XI3GX5mM0m/H7/iNpvdeduolarOppLxD9Rv9+PwaCXRpFGemwlNNM5\n+Hpd3/VQTY8jIMFA3V2wGmmCAgi9pX9DJRORzobVRUVHWL16Nfn5+ZK5c37+NGJj40bzVAZERUU5\nZnMUiYlJACxatJi9e3edkwGzJ+SAOSr01lDU/S7U6/Xy5Zef8Z3vfKfDL7FlUOms3hqKOtefwi/G\ngiCQMTGZrMlpzLlkhpQqLC+oomhvMSk5SRTuLUZv1FJTWkdTdYdXp1rJtGX5EEAcCYkxiuICahUq\ntZIzJyrR6NSk56diSRR1cEsPl0mvN8eZSJ6QSNnxClJyEknItOJ0uNAbdRHC6xq9mryFk2htbEUf\nrSd3QQ6tDW1Ykiwc/OiolIqOTY4heWIyZ45XEpMYjc6kIzo+CntTGwnpsai0KhqqbBz9rICZF0+j\n9kw9MWlW5iybQlSUeUzEypVK0cHE5/PR1NSC3x/o5AJi6PHGZzgYvtnCrgTroeE/t/PnUaFQEAgE\ncLncYkpdIQxrPbQ3ehtV6Y3OTUUZGVm89NLvKCgo4PDho/zlL69x4sR/MnlyHlu3vjBSy+8XpaUl\nPPTQvdhsNm6/fQXz5y8EoLGxoUNdSCQmJoaKioqxWua4RA6YY0bPu9Ddu//F1q1Pk5aWxqJF56NW\n95T6HRzBu+PgnXvPDUU+nE6ndLFSKBRk5KeSkZ/KRTd1NBS1OijeX0rh3mJO7S/F6/Zy8KNQN63O\npGXKklycdidag5bsGRnUlzdiijFy4qtCPC4xGEXHR5E9KxOfy0tbizhjWXzwNJPnT6C2tB5bx9iL\nNS0OU4wBt9PD6aNnpHnMCTMzcbY5Kfi6kISseMxxJgxRBtwOUdhAo1eL/pcBURAfYMKsLAIBBUnZ\nVjKnpVNy5AyZ0zKwZsRz/rdnDPomZbD0NirSneVU105qBeE1w4EKEQR1UP3+4bH/6i/Bz6MgiCII\nbW2ODjuwkFl1qLQQqhsO5+8mXFZvqKbWwQ7YuLhEzj8/mfPPv7Dj8QBO5+jWnzuTnp7Bbbet4KKL\nLqWysoK7717Jq6++0e24yFni6TyqyAFz3CDg8/nZsOERCgqOc88997N48RIEgUGlcgdCICB2CwYC\nAbRaDW63B6fTiUIRTJ2Jd/2RNRqxochg1jNtaT7TluZLP6+mtI6ifSWcPnKGsmPlHP28AK9bvPPO\nmp6OzqSjvryR7JkZCAoBr9uHy+HiwAchf8yETCsZ+akICoHkiYkYLQacbU4EAQ5+GArI6XkpRCdG\n4/P4sCRG4/MH8Dg9eJxeDu4OdQFPPk8UOGiqaUZr0JIzO4uCXcX4vD5mLp+K1+VlwqxM1Bo1+fMz\n8fpGdwh/MKMi3XVShzwbtR017/7VsIMWXH3poI4EPaV/ezKrHo6O43CGU6y9NxECQRDQ6/VD+vlD\nJT4+gYsvFsfgUlPTiIuLo66ulpSUVKzWeBobQ6bO3Rk/n+vIAXMcIQgCF154MT//+X/20FDUuRYa\n+n9/Tay7Q6FQYDSKJsaRii2h1Fl4Q5FOp+l01x+aDQVRmCAxK57FV58HiA1Fp4+coex4Jcc+P0Hh\n3mJqT9djb7KTOTWd4gOlqLVqsqalY7DoUWs1VJ6s4uRuUbIuKDZvb2rD1eYSlYq8ordl6ZEznDlR\n2fEPCHkLJ1F1qgavx8vk83JQ69RotBpa6mzEpcXi9/pJyLJia7AzaW4WOrOewn2lJGUn0mZ3MnHe\nRK64eRmC0HWGciQYzlGR8LRg50DTuYYd3IGCKKvn8XgHPUc8FIIiAH3VKodSD+2JcAuw4dlVjn8b\nrvfff5f6+npuvPFmGhrqaWxsJD4+AYDk5BTa2tqoqqokPj6BL7/8nPXrHx/jFY8v5C7Zs57OddCB\n7UJDHaAD92oMn1kL1tf6a3hsq2+l5PBpSvaXcXL3KQp2F5GYFU9rY5skNh8VZyZrRgYQEBuOTtfT\nbnOQkGVFZ9BSdqwCnVG0K4tNtiAIAvXljdRXNOC0u5gwO4v68iZsHfZl1vRY1Fo11cW1AEy5IJ/q\n0jqyp6RRerKG7NlZ3PLzfyMu2RKm/9t1/CMYSIfaBDRWoyLBUSS9XotCMXTPxsGtISQCMFzWZ9C9\nJF7n0Ra/PyApFQ19rvLskLYL0t7exoYNP8dub8Xj8XD77StoamrCaDSxbNlyDhzYx/bt2wBYtuwi\nbrzx5jFe8dggj5WcU4Ta2cP/Hx5Ejx49wsyZ09HrDbS1OUbwgtW1oSgQCEg7i2CwCAQCVBZWU7i3\nmKK9JZQcPo1Or+X4vwqlRh+1TkXegkm42t0oVErabe00VjaRnp/Kyd2nJLk/Q5SezGnp2Jvb0Bt1\nKFRKtAYtPq8Pn1dsItEatNScrsfZ5iI2w4o+ysBlt1zA3It61sHsfvxj4H6N4a4iYzEuEemoEvRs\njBxHgpFRKYLQTdpgZCQHSnc+m4IgEAiAy+WS1IoGd26Rguky3xzkgHlOE9p5VlVVsG3bM5SWlrBt\n2/NYrfEj+s7hajfhxsB+fwCnU7xg9ZQKczvclBwqo2hfCeUFlVQVVUc4kmRMTcNpd+L3+bEkRqPW\nqDoCqYMzJyrwurzojFoyp2dwssOM2xRjJCEznvKTVaRMTkJr0lNTWs+qbbcwdeHEIZ1bZF1NDKSd\nz60/riIjRTAF2TX13pXez21waeqR2lX2F7Va3eGqIqr7hH8mBzIfCuNrV/nCC1s5ePAbpEcJAAAe\nVUlEQVQAPp+Pm2++lWXLLpKOXXvtlSQkJEqZhMce2yilYGV6Rg6YMrz22v/wu9/9luuvv5Ebbvgh\nWq0ogC4ysgpF4fOkYuo3IF2QOzcU9dbdaW9qo/hAKZVFNZz4upCSg6epK2vAGGMgOSeRwo7AKAgC\nE+dNwOPyQkf3pTnWTLvNQUtDK26XB7VRR0KGlZsevYr0ycnDcp6RSj5B9SWfOOuqVuH1egfsKjIc\nhBpbBp+CHKxKEYzsqEpfCAIYjQbUahWtre3d1olDYztdbfjcbg/l5eXExyegUAjjRoQARCGC//t/\nX2Hz5mdpaWnmttt+yOuvvy0dv/baK3nllVeHXbrzm44cMGV45503mT17LsnJKd0cHbxbS18EL9Yu\nl7vDVDnyeGTaLCirFuiSyu2J9lYHFQWVlB2roKKwiqpTNWgNWg5/eoJ2mxgcpi7Oo+hAKQEgZWIS\nc781g2lLcsk7b2R9BoNNPWq1SrJUg87pzuEdkej8/sFxiaFI+vVEbypF4o0PmEz6YbHBGgyiV6cR\nt9tDW1v7gF4b/Ew2NjZyxx230djYyOTJueTnT2XKlGlMmTJtzHdrPp8Pt9uNXi/O7V555WW8+eb7\nKJVKQA6Yg0UOmDKDYGgNRUqlKOcH9KhB2hNDaSgKolKp0KjVtDa2Yrc7UCgFTDFGyY5rpImsFYaM\nlbu3c/NHNBQNpWMzSMjZY+ANXYMlfKZXq9VIIgTB7t3RUCkS1wEGg6HDUaZtiF6dorSdzdbKsWPH\nOX78KMePH6WyspKXX/5jr5ZXo8nf/vY6hw7t59FHQ52t1157JTNmzKKqqpIZM2Zx552rR+3zfzYj\nB0yZYaB/u1CHox2fz0tGRvqw1uoig0x3TTfijiY8/TsWSj3hoyLirq7vi3VIRF/ZbZp6IO4fY63B\nqlQqMZsN+P1+7Pb2jgxC5M3PSKkUQXBXacDjEdPfQ9m9jwfB9P7w2Wef8MorL/P0089jMpmkx999\n9y0WLjwfszmKdese4Nvf/i7Ll18yhis9O5ADpswIEQqigYCff/7zY7Zt28oNN9zAddf9YETn+rpr\nKPL7AygUAh6Pl/b2drzesRErH+qoSHfdnf0RIejvXONIEWxq6qtWGQqe4vkNVaUoiNGoR6PRdDQ1\nDeVGafwIpvfF119/xW9/u50tW7YRFRXd4/Nef/3PNDU1cscdK0dxdWcnsvi6zAghdgnW19exadNG\nqqoqefTRDcycOadjRyQwHOIK3RHuZxhuv+VwBL83oVAMz4W4L8JHRYbDr7EnEYJg8OwsQuDz+dFq\n1R26wyMnFN8TQVk9Uf+2bwGEnlSKgibVA1EpCr6/2WzE4/EO2VQ7sgN2fAdLu93OCy9s5ZlnXugS\nLO12O+vXr2XTpqdRq9UcOLDvG2/wPNLIAVNmWDhzpow5c+Zz7bXXd1PTGbxbS38IzfV13VV1fyHu\nf0NRfxitURG/34/L5cflihTSNxh0aLU6AoFAR5OPYcRvEMIJ7SrbI9Y2EDqLl0PvNwjhtV6DQY9O\nNxyyfmfPrjLIhx++T3NzM48+ulZ6bO7c+UyYMJFly5azcOFiVq68Fa1Wy6RJuSxfLgfMoXBOp2T3\n79/Lo4+u5eGH17N4sSgmXlh4ki1bfokgQE7OJB544OGI13i9Xp54YgPV1VUolUoefng9qalpY7H8\ns5yhe4aqVGKtyufzDWhUo7cRgoGo+Az2/YeL8F2d3d5OIBDowcg50OX8Rur9R5rwWq9arYpwNhmK\nAtPZIm0nMzrIKdlOVFSU8+qrf2T69JkRjz/77BbWrLmf/PypbNjwCF999QWLFi2Wjn/wwXuYTGa2\nb9/Irl3/YseO5/nFL54c7eV/Axi8Z6jP5yMqyoRGoxlUU0/I+SPcYkoMnuIIjKobLdnQ6EdvriKj\nRbBW1/n9e96piUHGaBwea7Dh29UNjKA9WNCWrrW1jUAgIPlQmkwDVykaTyIEzz67haNHjyAIgnQd\nCrJ799e8+OLzKBRKFi1azK23/ngMV3pucs7eSsXFWXniiV9FdJR5PB6qqiqlD+nixRewZ8+uiNft\n2bOLpUsvBGDevPM4fPjgqK35m014KkyJeC+nJBBQEgiIw+J+f4B3332Hf//3f6OoqJDm5pZhu1h7\nvV4cDhetrW00NbXQ3GzD5XJLwTE21oLFYiYqykRMTBSCINDcbBv1YKlWqwb8/mIq101bm4OWllYa\nGpo7xnz8aDRqoqJMxMZGExVlwmDQoVarexw9UKmUWCxRKJUKmppG//yVSiUWixmVSimdv1jnddLa\n2kZjY+ffnY6YmGgsliipxl1cfAqPx0MgQKcU7NgGy/3791JefoYdO15m7dpHeeaZzRHHt27dzMaN\nT7F9+0527foXJSXFY7TSc5dzdoep0+m6PNbS0ozZHNqKx8TE0tBQH/GccJNVhUKBIAh4PJ5xM4v1\nzSJ0x19WVspTT/0XDkc7mzc/TVJSOmLmbXjEFToT3lAEIQGAYPpWrVYSExPdpSllpNKSoVEZVZ+y\ndv2hd0/UoAdlZK1Xq9WMmQUY9F8tqPPvDkIqRXV1tTz++H9y5swZJkzI6RAhmM706TN6EPQYPfbu\n3c0FF1wIQFZWNq2tNtra7BiNJioqyjGbo0hMTAJg0aLF7N27i+zsCWO44nOPcyJgvvnmG7z55hsR\nj91xx0oWLFjU6+v6c/EbbTukc5U//emPLF26nKuv/r6kRysSCPsdjIxnaPioiM1mlx4PrxeGB5nh\ntgULCSC4OzpAh/wjuxD0RI0MMmKtV3x/0cfR5/OhVqs6bhT7Fo8YDsI1aAdrbB1M5cbFWXnppT/Q\n3u6goKCA48eP8s9/fsz//u+rbN++U9JcHQsaGhrIzc2TvrdYYmhoaMBoNEXcqAPExMRQUVExFss8\npzknAuaVV17FlVde1efzLJYYWlpapO/r6+u6iJOHm6wGdxS97S5///ud7N79NSBelBoaGvjTn16X\njldVVfKjH90g/aFYLDFs3Lip/yd3jvCznz3Sw5G+aqGh/w90rKWvUZHu6oXhDUWdPRoH2pQS7tfY\n2mofFYWccHw+Pzqd2KXa2toWIViu0agxGPRdpPCG09UEwudKh96BLC5LCQgYDCZmz57L7Nlzh2OZ\nI0LfddeunDpVxNNPPwWA2+3m/vvXRgRhmaFxTgTM/qJSqcjMzOLgwQPMnDmLTz/9iGuuuT7iOfPn\nL+Tjj//BggWL+OKLfzJnzrxef+Ytt9zBLbfcAYiqG01NjV2ek5GRyXPPvTh8J3JO013jxsDHWgY7\nKtJdQ1G4VFxkU0po9KMzIbF0sa462qhUKsxmQ5e5xmAKOnx+MtiRq9frOgQW/EPeZYtqRUZgOOZK\nz45xEavVSkNDg/R9fX09Vqu141joRh2grq5WOhbOk0/+gjvvXM28eefx+eef8txzT7Nt246RX/w5\nwvj99IwwX375OatX/4Svv/6KHTue4957VwFwzz33s2PHc9x11+2kpqYxf/4CANauvQ+Aiy++FL/f\nz1133cHrr/+ZlStX9ev9vF4vf/3rX7jmmutG5oRkeqG7hiJFREOR2AAChw8fZM2a1TgcDpqbbcMy\nVyk2pbjCmlJacbncKBQKDAY9cXFiQ5HRaECn0xIVZUKv19LSYh9VY+kgRqMes9lIW5ujz3GRYCq3\nvd2BzWansbEZm82Ox+PtGDsxEBdnITra3NHZq+4z7anTabFYzLhcblpa7EMKluLvNfh7H9+Xu/PO\nW8gnn3wIQEHBCaxWKwaDmOFITk6hra2NqqpKvF4vX375ObW1tWzatBEQa/w33ngNzzzzAnPnzgfA\nYomlpaV5bE7mG8o5PYc5mnz44QeUlhZ3kaWqqqrkpz/9MVOnTqO+vp6rr/4+l1327TFa5blLa6uN\n7du38dVXn/PAAz+T5nJHyu6sM2KzjQ6NRh22mxu5VGd3RGqwOobt/QQBIrVkuzffDqagg+Mi58Ku\nsjPbt2/j4MH9CILAffc9RGFhAUajiWXLlnPgwD62b98GwLJlF3HDDT/k7rtXsnLlKnbu3MGtt/5Y\nSjEHAgHWrXuQWbNmc/31PxzLUzorkbVkR4Hemovuu281Dz64rksnXnt7Gx9//CGXX/4d7HY7K1bc\nwvbtO7tNt8iMHLfffhP5+VO48867wzqlhy6u0B9CtdKANO4RFCwPt84ajlRndwyvs0f/6MlY3Ov1\n4XK5ujXf7i/nkghBefkZVq/+CcuXX8KaNfcD4o3Ixo2PodFoePjh9bI7ySCQhQtGgZ6aixwOB7W1\ntd22rRsMRq644t8AsFgs5OXlU1ZW2q+A+c47b/Lb3/6alJRUAObPXyDVS4O8//67vPba/yAIAt/7\n3r/z3e/23fx0LrJ9+060Wm2nRwcvrtBfepL1ExuKPBEC4sGGIlHmr7uGov47mgQJ31U2N7eMmC9n\nZ4KjH16vF5VKVAtqb3d0WJ+p0Ol0kvl2/89v/O0qvV4vv/zl41RUlOPz+Vi16j+YOXNWxHOWLVsQ\nIaCydet2yc+yL2y2FvR6PTU11YDYDbxu3YNkZ0+QrbxGADlgjgJFRSfJzMzs9ti+fXv44ot/cvfd\n9+FwOCgsPEl6eka/f/ZFF13K6tX/0e0xh8PByy//ht/85hXUahU//vGPWLp0ea+OBucqXYNld3Tf\nUDSYsZaQBVb/m1o6NxSFUp1BlZv+C5YLApKx9XDMdQ6GYGNTV7/Ons8PQio+ra2tuN1uDAbjuBVM\n//vf30Gn07N9+06Ki0/x5JP/yW9+80rEc0wm06Ca/lwuF5s3P8mmTU+zc+evee+9t6moKCcjI5O7\n7rp7uE5BJgw5YI4CDQ31xMTERjy2desWvv/9G5gxYxbvvvsWK1feht/v4+abbx02F/djx46Qnz9V\nUjOaPn0mhw4dZMmSpcPy82Wgv2MtwQDqcjl5883/xzXXXI3Doex1AL8vAgG6lcELzoaGC5aH+2qK\nTi5G3G7PiM119kZoXEZBS4u9x9RrX+f33nvv8swzT5OSkkJe3hSmTJnOlCnTyM6e0GlWd+y4/PLv\ncMkllwPi7GT42NpQ2bnz1yxdupyMjEzWrHmAlStvp6amiry8Kaxe/RPpeQPZscr0jlzDPIt55503\nef31PxMVFY3P52XVqjVMnhyauXr//fc4ceIo99wj1jZ+85vtJCQk8r3vXT1WSz5HEYPowYP72bTp\nv8jLy+Ohh9ah02kYrYaiYCpXlL2jY5zFM2oNRUE0GjUmk2HIfqFAx27ax6lTpzh69CjHjh3h2LEj\nzJgxi7VrHx2mFQ8fO3Y8j0KhYMWKuyIev/TSC1i8eCk1NVUdzTw3jdEKZYLINcyznO4aii655HJu\nv/0nnH/+Eo4cOcTGjY/xyiuv9vgzZFWisUJg584XefPNN7jvvoc6tIj9wyKu0B+CHah6vQ6XS0x/\nBrtWg7OTfr8/Yi50sA03PREuwmCzDV2EIZiCVamU5OZOITd3Cldf/f1hWetg6a3p73//9zUKCk7w\n1FNPd3ndqlVruOyy7yAIAqtWrWDWrDnk5U0ZrWXLDAA5YJ4l9KVWNG3aDJqbm/H5fFL6pesgdB1T\np04f8bXKdGXOnHl8//s/ICoqquORznW2kfEMjdSgDXXA9iSDF/QNFRtuvBGp3MHalwWl/Vwu95BF\nGMZzB2xPf6NvvfUGX3zxGU8+ubnbVPFVV10rfT1v3nxOnSqSA+Y4RQ6YZzF//OPvSUhI5NJLv0Vx\ncREWiyWiVjF16jQ2bdpIa2srSqWSQ4cOSunZ/jDSHX7nEn1LsPXWUDS4sZZwDdqmJluvz+2toUin\n03To5IY3FPU91hIerIdD2m882XD1l4qKct5443Wee+7FbhvLyspKeeml3/DYYxvx+XwcPnyQCy+U\nTZ7HK3LAPIu59NJv8fjj6/nb317H5/NKdZs//OF3zJ49h2nTZnDnnau57z6xvfz221dE2Jn1xUh2\n+Mn0h8GNtbS2tqLRiDZgra1tPXbK9kZ4w43DIT4m+mqK9dDufDXDxdjFcRVjv4J1P1bTodYz/naV\nffHWW3+jpaWFBx64R3rs6aef509/+qP0N5qQkMiKFbcgCAJLlixlypRpY7himd6Qm35kekQ0Fvah\n1Wppampk5crbeO21v0U854orLubttz8coxXKdBZX+PTTj3jmmf/m7rvv4eKLLx3xdw9vKFKplAiC\nQCAQQBAEHA4nTqd7SLXzs3FXKXP2Izf9yAyYYEoO4LXX/odLL/1Wl+e43W42bHhE7vAbM8RAYrO1\nsGXLLykoKODxx59k2rQZEfOhIyXxF+6rGRRBENO7PtRqNXq9PqyhyDsABZ/xJ0IAsljIuY4cMGUA\nucPvbKeg4AQpKWmsW/cYWm1nc/SR9wwVhdU13UrrKZVKKZWr0+lQKBT4fD03FIV2lcGd5fhCFgs5\nd5EDpgwwuh1+zz67haNHjyAIAmvW3E9+/lTp2O7dX/Pii8+jUChZtGgxt9764yGc1bnD/PkLJGed\nroycZ6hKpcRsNnaxAQsnaN4cUvARpFSuaKHm5eabbyI7O5vc3HymTJlObm5+N4F//COLhXyzGT+5\nDplxR7DD77/+61c9dvht2PAIgUAAr9fL4cMHyc6e0OvP3L9/L+XlZ9ix42XWrn2UZ57ZHHF869bN\nbNz4FNu372TXrn9RUlI8rOckA13tzoL/KQgEFBF2Z6GA2hXRBszULxuwcILG2w6Hs2MmM8Avf/kr\nlixZSlVVFc8++zRXXHEJK1b8iNramqGe7LBz4MA+7rvvbtasuYuTJ09EHGtoaMBisUjfx8TE0tBQ\nP9pLlBkh5B2mTI+MRIff3r27ueCCCwHIysqmtdVGW5sdo9FERUU5ZnMUiYlJACxatJi9e3f1GYRl\nhoPummq6F1c4ceIY27c/z9atz6JQqIYoiCHWKpOSUklKSufii0VrO5fLSUlJcRdJydFEFguR6Ywc\nMGV6ZOXKVd0aZN98863S1z/96T1djvdGQ0MDubkh+T6LJYaGhgaMRhONjQ1YLDHSsZiYGCoqKga+\ncJlhIjIB5Xa7eOmlF3n77Td58MGfAUr8/sE3FPUmmK7V6sa8Fi6Lhch0Rk7Jyowpvd2Byzfn44v1\n6x/m9OlSfv/7/2Hp0ksQ77eVBALKjjRu/1K5EB4slZxNl6E//vH3fPDBewA9ioWcOHGM1tZW2tvb\nOXToIDNnzh6r5coMM/IOU2ZU6XoHXi95f1qt8TQ2ho7V1dUOykj7hRe2cvDgAXw+0f1l2bKLpGPX\nXnslCQmJKBTiRfqxxzYOmzvMN5116zZgNps7eSwOTFxhPEvb9YeRFguRGd/IAVNmVPH5fPz3f28i\nNTWNqKhorFYrBoMRgOTkFNra2qiqqiQ+PoEvv/yc9esfH9DP37dvD8XFp9ix42VaWpq57bYfRgRM\ngM2bn8VgMAzbOZ0rhHRwe6Mvz9CennN2kJCQyLZtO7o8Hl6mWL78EpYvv2QUVyUzWsgBU2bUeO+9\nt7HbW4mNjWPr1s2YzVHcd99DvPPOmxiNJpYtW84DD6xlw4ZHAHHeLSOje+Ptnpg5c7Y0pmIymXE6\nnRE1JpmxYPwFyN//fie7d38NiGWBhoYG/vSn16XjVVWV/OhHN0j1doslho0bN43JWmXGD7I0nsyI\n8Kc//R9Ony7loYd+TllZKWvX3s+LL/4ek8nE6tU/4ZZb7uhlbnB4+NvfXufQof08+mhol3rttVcy\nY8YsqqoqmTFjFnfeubpTilHmXOPdd9+iqamRG2/8kfRYVVUlP//5Q+zc+YcxXJnMWNGTNN7ZV0SQ\nOSu47robKSs7zaFDB9iyZRMPPrhuVGs5n332CW+99TfuvfehiMfvuGMld999L9u27aCk5BSffCLr\n4J7LeL1e/vrXv3DNNdeN9VJkzgLkgCkzIigUCh5+eD3r1z/MhAkT+2FvNXx8/fVXvPLKS2zZ8myX\nIP3tb3+XmJhYVCoVCxcuprj41KitS2b88emnH7NgwaJuVYUaGxv4+c9/xp133s777787BquTGW/I\nNUyZEcNma0Gv11NTUz1q72m323nhha0888wLXfQ77XY769evZdOmp1Gr1Rw4sG/A3oP79u1h/fq1\nZGWJYgo5ORO5996fScdlab/xR286yW+//TcefHBdl9dER0fz4x/fyeWXfwe73c6KFbcwZ878QXVt\ny3xzkAOmzIjgcrnYvPlJNm16mp07f817773Nt751xYi/74cfvk9zczOPPrpWemzu3PlMmDCRZcuW\ns3DhYlauvBWtVsukSbksXz5ws95Zs+awceNT3R7bunUzW7ZsIz4+gdWrf8KyZRfJSkVjTE8CBA6H\ng9raWpKTU7ocMxiMXHHFvwFgsVjIy8unrKxUDpjnOHLAlBkRdu78NUuXLicjI5M1ax5g5crbqaqq\nZO/e3RQVneS5557BbDbz+OObiImJ6fsH9pPvfe9qvve9q3s8ft11P+C6634wbO8Xjiztd3ZRVHSS\nzMzuu7D37dvDF1/8k7vvvg+Hw0Fh4UnS0zNGeYUy4w05YMqMCD/96Rrp69jYOP78Z9F4+rbbVozV\nkoaN0tISHnroXmw2G7ffvoL58xcCyNJ+ZxkNDfVdtGq3bt3C979/AzNmzOLdd99i5crb8PtFAQxZ\n4EJGDpgyMgMgPT2D225bwUUXXUplZQV3372SV199A7Va3eW5srTf+ObCCy/uUsNes+Z+6etHHtkw\nyiuSGe/IAVNGZgDExydw8cWXAZCamkZcXBx1dbWkpKQOm7TfW2+9wXvvvSN9X1BwnA8++Ez6ftmy\nBUyfPlP6fuvW7eekMMP+/Xt59NG1PPzwehYvvgCAwsKTbNnySwQBcnIm8cADD0e8xuv18sQTG6iu\nrkKpVPLww+tJTU0bi+XLnIXIAVNGZgC8//671NfXc+ONN9PQUE9jY6OUqhsOaT+A7373Kr77XbFJ\nZf/+vXz00T8ijptMJp577sWhn8xZTEVFOa+++seIGwcQzcmDpuQbNjzCV199waJFi6XjH3zwHiaT\nme3bN7Jr17/YseN5fvGLJ0d7+TJnKfIcpozMAFiyZCkHDuzjpz/9MWvX3s8DD6zlgw/e49NPPwaQ\npP1WrVoxKGm/zvzud7/l1lvvGI6lf6OIi7PyxBO/ipiz9Xg8VFVVStKIixdfwJ49uyJet2fPLpYu\nvRCAefPO4/Dhg6O2ZpmzH3mHKSMzAAwGI0899XSPx2fNmsOOHS8Py3sdP36UhIRE4uIi07put5sN\nGx6hpqaKZcsu4oYbbhqW9zub0Om6Cg20tDRjNockzWJiYmloqI94TnhjlkKhQBAEPB5PtzVoGZnO\nyAFTRmac8uabb/Dtb3+3y+OrVq3hssu+gyAIrFq1glmz5oy52fJI0pvwQG/05rU6kOfIyASRA6aM\nzDhl//69ESpCQa666lrp63nz5nPqVFG/A2ZxcRFr197P9dffyDXXXE9NTTWPP74ev99PXJyVRx/9\nBRqNJuI1zz67haNHjyAIglQfHE16Eh7ojMUSQ0tLi/R9fX0dVmt8xHPCG7O8Xi+BQEDeXcr0G7mG\nKSMzDqmvr0OvN3S5mJeVlbJhwyMEAgG8Xi+HDx/stzCCw+Hg6ad/xdy550mP7dy5g6uvvo4XXvgt\naWnpvP32/4t4zf79eykvP8OOHS+zdu2jPPPM5qGf3AihUqnIzMzi4MEDAHz66UdddqHz5y/k44/F\nJqovvvgnc+bMG/V1ypy9yAFTRmYcUl8fOVT/hz/8jiNHDpGRkUVCQiIrVtzCXXfdwcKFi5kyZVq/\nfqZarWbz5q0Roy779+9lyZKlQLBJ5uuI1+zdu5sLLrgQgKysbFpbbbS12Yd4dkPnyy8/Z/Xqn/D1\n11+xY8dz3HvvKgDuued+dux4jrvuup3U1DTJQm7t2vsAuPjiS/H7/dx11x28/vqfWbly1Zidg8zZ\nh5ySlZEZh+Tl5bNly7PS9zfffKv09U9/es+gfqZKpUKlivyTdzgcUgpWbJJpiDje0NAgmSiDmPZs\naGjAaBw9q7buOP/8JZx//pIuj2dnT+CFF37b5fFf/vK/AVAqlaxb99iIr0/mm0mvAbMnE00ZGZmz\nF6NRi8mkIz7ejEIhSH/n7e0G1GplxN+9Xq8mKkovPaZSKYiNNcrXBplzEjklKyNzDmMwGHA6nQDU\n1NSQkBCpl5qQkEB9fWg0o7a2lvj4yEYaGZlzBTlgysicw5x//vn8/e9/B+D999/nggsuiDi+ePFi\n6fjRo0dJSEjoYsotI3OuIATkQSQZmXOCI0eOsGnTJioqKlCpVCQmJrJ582bWrl2Ly+UiJSWFJ598\nErVazb333suTTz6JTqdj8+bN7NmzB0EQeOyxx8jLy+v7zWRkvoHIAVNGRkZGRqYfyClZGRkZGRmZ\nfiAHTBkZGRkZmX7w/wEewha8eQuxOgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "9gUzh-xpkzYZ",
        "colab_type": "code",
        "outputId": "858528e2-ae40-4f91-9913-c7ac2fe82bda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        }
      },
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = plt.axes(projection='3d')\n",
        "ax.plot_surface(XX1, XX2, YY, cmap='viridis')\n",
        "ax.set_xlabel('x1')\n",
        "ax.set_ylabel('x2')\n",
        "ax.set_zlabel('y');\n",
        "\n",
        "ax.view_init(30, 180)  # for better viewing in detail of x1 & x2"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+PyPLsr0JgenhDUQfFxlfHo5yBzg3i4RFlklrrvmtFTyKfsY08zQqjBT9JIng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\nT3zw+60WGkShh1PbiQKQvnvYQ96YRJJOLMVhKw4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PE4j3KQFIe5Y+TNPUikiP8OwxIsxj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\nMCkUCoVCaQHUYFIoFAqF0gL+H5SpFcfNa21UAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5KXrsA7VnSTC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X = np.random.random(25) * 20 - 10  #because we want x to be in between -10 to 10\n",
        "print(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "87DgUvNHFWEB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "wt = .5\n",
        "b_unknown = 0.25\n",
        "\n",
        "Y = sigmoid(X, wt, b_unknown)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Qw942-q6Ge7L",
        "colab_type": "code",
        "outputId": "a3688169-0dc8-46d1-8ad4-8847a605ac2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(X,Y,'*')\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S83vsbX+dBXa3IZybm6snnnhCBQUFqqurk8/nU2pqqiTpsssu02WXXSZJOnz4sO65554u\nAxhwEk5JAtBf3YZwTk6OsrOzVVBQIJfLpZKSElVUVCgtLU0LFixIRI3AoMQpSQD6q0f7hFetWtVm\nOSsrq91rJk6cqOeff35gqgIAYAjg2tEAABhCCAMAYAghDACAIYQwAACGEMJAJ7guNIB441aGQCda\nrgmdNdndzSsBoG8YCQOn4brQABKFEAZOkzXZrcKFU2PLhZdOYzQMIC6YjgY6sMcfil0Xeq8/pPQE\nXhcawNBBCAMd4LrQABKB6WigA1wXGkAiEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAgh\nDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMIAABhCCAMAYAghDACAIYQwAACGEMJwpLcO\nRuQPRE2XAQBdSjFdABAPf97m1+efNStrstt0KQDQKUIYjuIPRLVl+yHtDzZIksrKa3R1XgZhDGBQ\nYjoajpI12a3ChVNjy4WXTiOAAQxajIThOHv8IS1dOE3Hj/9Xe/0hpedlmC4JADpECMNx0r2pWjQn\nU+HwMe3xh0yXAwCdYjoajjM7y9fhYwAYbAhhAAAMIYQBADCEEAYAwBBCGLbjD0S5GhYAR+DoaNjO\nlu2HJInzfwHYHiNh2IY/EFVZeY32Bxu0P9igsvIaRsQAbI0Qhm1wNSwATsN0NGxljz+kxblTJImr\nYQGwPUIYtpLuTY1dgIOrYQGwO6ajYStcDQuAkxDCAAAYQggDAGAIIQwAgCE9OjBrzZo1qq2tlcvl\nUnFxsWbMmBFbt2vXLj366KNKSkpSRkaGSktLlZREtgMA0J1u07K6ulqBQEAbN25UaWmpSktL26z/\n+c9/rscff1wvvPCCjh8/rtdeey1uxQIA4CTdhnBVVZXy8/MlSZmZmTp69KgaGxtj6ysqKjRu3DhJ\nksfjUTTKFYwAAOiJbkM4EonI7f7yqkQej0fhcDi2nJqaKkkKhULasWOH5s2bF4cyAQBwnl5frMOy\nrHbPffzxx7rppptUUlLSJrA74naPUkpKcm+/NuG83jTTJcSd03ukP3ujP/tzeo8D0V+3Iezz+RSJ\nRGLLoVBIXq83ttzY2Kgf/vCHuuOOO5SXl9ftF0ajJ/pYauJ4vWkKh4+ZLiOunN4j/dkb/dmf03vs\nbX+dBXa309G5ubmqrKyUJNXV1cnn88WmoCVp7dq1uvHGGzV37tweFwMAAHowEs7JyVF2drYKCgrk\ncrlUUlKiiooKpaWlKS8vTy+99JICgYA2b94sSbryyiu1ZMmSuBcOAIDd9Wif8KpVq9osZ2VlxR7v\n27dvYCsCAGCI4KoaAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAY\nQggDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGEMAAAhhDCAAAYQggjxh+Iyh+Imi4DAIaM\nFNMFYHDwB6La8LcDGjVymLImu02XAwBDAiNhyB+I6omKNxUMH9f+YIPKymsYEQNAAhDCQ5w/ENWW\n7Yd08r/Nsedyzx3PaBgAEoAQHuKyJrtVuHBqbPmSmRP08SefGqwIAIYO9gkPAS1Ty52Nbvf4Q5o1\nzasJY0fL5XJpwtjRiSwPAIYsQngI2LL9kKTOQzjdm6pvz/mqpFOBPDvLl7DaAGAoYzrawfyBqMrK\na7Q/2NDlAVetQ5cABoDEIYQd7PT9vYWXTuOAKwAYRJiOdrg9/pAW506RJO31h5Sel2G2IABADCHs\ncOne1NgU8x5/yHA1AIDWmI52OPb3AsDgRQgDAGAIIQwAgCGEMAAAhhDCAAAYQggDAGAIIQwAgCGE\nMAAAhhDCAAAYQggDAGAIIQwAgCGEcBf8gWiHt/4DAGAgcAOHVloC1+tNkyRt2X5Ikrj9HwAgLhwf\nwi3B2pMgbQndM88cpedertP+YIMkqay8RlfnZRDGAIABZesQ7knA9mQ06w9EtWX7oVjo/nmbX7nn\njostF146TeljRw9U2QAASLL5PuEt2w/FQvZ0/kBUZeU12h9s0P5gg8rKazrdv5s12a3ChVNjyzf/\n7wxFjn6qxblTtDh3ivZyH14AQBzYciR8+si1o+nirMlupY0apvvXV0vqfjS7xx/S4twpkqQdtR8q\n3Zsau//uHkIYABAHtgzhngZs62Dd6w8pPS+j089sHbr7P/xE0yacEVvX8jwAAAPJliEs9SxgezOa\nbR20eeelKxw+NnDFAgDQgR6F8Jo1a1RbWyuXy6Xi4mLNmDEjtm7nzp169NFHlZycrLlz52rlypVx\nK7a1ngRs62BlNAsAGGy6PTCrurpagUBAGzduVGlpqUpLS9usf+CBB/TEE09ow4YN2rFjhw4ePBi3\nYlsjYAEAdtdtCFdVVSk/P1+SlJmZqaNHj6qxsVGSFAwGNWbMGI0fP15JSUmaN2+eqqqq4lsxAAAO\n0e10dCQSUXZ2dmzZ4/EoHA4rNTVV4XBYHo+nzbpgMNjl57ndo5SSktyPkhOj5apZTub0HunP3ujP\n/pze40D01+sDsyzL6tcXRqMn+vX+RPB60xx/YJbTe6Q/e6M/+3N6j73tr7PA7nY62ufzKRKJxJZD\noZC8Xm+H6+rr6+XzsX8WAICe6DaEc3NzVVlZKUmqq6uTz+dTamqqJGnixIlqbGzU4cOH1dTUpFdf\nfVW5ubnxrRgAAIfodjo6JydH2dnZKigokMvlUklJiSoqKpSWlqYFCxZo9erVKioqkiQtWrRIGRmd\nXxADAAB8qUf7hFetWtVmOSsrK/Z49uzZ2rhx48BWBQDAEGDrGzgAAGBnhDAAAIYQwgAAGOKy+nvi\nLwAA6BNGwgAAGEIIAwBgCCEMAIAhhDAAAIYQwgAAGEIIAwBgSK9vZehEv/vd77Rz505J0hdffKFI\nJBK7aYUkHT58WFdddZWmT58uSXK73Xr88ceN1NoXFRUVeuyxxzRp0iRJ0kUXXaSbb765zWu2bt2q\n5557TklJSbrhhht0/fXXmyi1T5qamnTvvffq/fffV3Nzs+666y7NmjWrzWuys7OVk5MTW3722WeV\nnDz472u9Zs0a1dbWyuVyqbi4WDNmzIit27lzpx599FElJydr7ty5WrlypcFK++6hhx7S66+/rqam\nJv3oRz/SwoULY+vmz5+vcePGxbbVww8/rLPOOstUqb22e/du3X777fr6178uSZo6daruv//+2Hq7\nb8NNmzZp69atseV9+/bpjTfeiC3b9XcnSQcOHNAtt9yi733veyosLNRHH32ku+66S83NzfJ6vfr1\nr3+t4cOHt3lPV7/XTlloo6Kiwlq3bl2b54LBoHXNNdcYqqj/XnzxRWvt2rWdrj9+/Li1cOFC65NP\nPrFOnjxpXXHFFVY0Gk1ghf2zefNmq6SkxLIsyzpw4IB17bXXtnvN+eefn+Cq+m/37t3WihUrLMuy\nrIMHD1o33HBDm/WXX3659eGHH1rNzc3W0qVLrbfffttEmf1SVVVl/eAHP7Asy7KOHDlizZs3r836\nSy65xGpsbDRQ2cDYtWuXdeutt3a63gnbsMXu3but1atXt3nOjr87yzr1N7GwsNC67777rOeff96y\nLMu6++67rVdeecWyLMt65JFHrPLy8jbv6e732hmmo1tpamrShg0bVFhYaLqUhKqtrdW5556rtLQ0\njRw5Ujk5OaqpqTFdVo8tXrxY99xzjyTJ4/GooaHBcEUDo6qqSvn5+ZKkzMxMHT16VI2NjZKkYDCo\nMWPGaPz48UpKStK8efNUVVVlstw+mT17th577DFJ0hlnnKGTJ0+qubnZcFWJ4ZRt2OLJJ5/ULbfc\nYrqMATF8+HCtW7dOPp8v9tzu3bv1rW99S5J0ySWXtNtWXf1eu0IIt7Jt2zbl5eVp5MiR7dZFIhHd\ndtttKigoaDP9YhfV1dVavny5brzxRv373/9usy4Sicjj8cSWPR6PwuFwokvss2HDhmnEiBGSpOee\ne05XXnllu9d89tlnKioqUkFBgZ555plEl9gnkUhEbrc7ttx6u4TDYVtvsxbJyckaNWqUJGnz5s2a\nO3duu+nKkpISLV26VA8//LAsG17g7+DBg7rpppu0dOlS7dixI/a8U7ahJL355psaP368vF5vm+ft\n+LuTpJSUlHY5cPLkydj081e+8pV226qr32uX3zUA9drKpk2btGnTpjbP3XrrrZozZ45efPFF/eIX\nv2j3njPPPFO33367Fi9erGPHjun666/XBRdc0OZ/SYNFR/1dccUVuvXWW3XxxRfrjTfe0M9+9jP9\n5S9/6fQzBvMfuq62X3l5uerq6vTUU0+1e99dd92lxYsXy+VyqbCwULNmzdK5556bqLIHxGDeLv31\nt7/9TZs3b9Yf//jHNs/fdtttmjNnjsaMGaOVK1eqsrJSl112maEqe2/KlCn68Y9/rMsvv1zBYFDL\nli3Ttm3b2u1LtLvNmzfrmmuuafe8E353HenJb7Gnv9chF8LXX399hwcdnThxQv/5z380ceLEdutS\nU1N17bXXSjr1v5vp06fr3XffHZQh3Fl/LWbOnKkjR46oubk5NuLw+XyKRCKx14RCIX3jG9+Ie619\n0Vl/mzZt0j/+8Q/99re/1bBhw9qtX7p0aezxBRdcoAMHDgz6PwYdbZeWkcbp6+rr6wflv8eeeO21\n1/TUU0/pD3/4g9LS0tqs+/a3vx17PHfuXB04cMBWIXzWWWdp0aJFkqRJkyZp7Nixqq+v19lnn+2o\nbbh7927dd9997Z634++uM6NGjdKnn36qkSNHdrituvq9doXp6P/j9/v11a9+tcN1u3bt0oMPPijp\nVFj7/X5lZGQksrx+WbdunV5++WVJp47483g8bab8zjvvPL311lv65JNPdPz4cdXU1LQ7ungwCwaD\neuGFF/Sb3/wmNi3d2rvvvquioiJZlqWmpibV1NTEjlYdzHJzc2NH6dfV1cnn8yk1NVWSNHHiRDU2\nNurw4cNqamrSq6++qtzcXJPl9smxY8f00EMP6emnn9aZZ57Zbt3y5cv12WefSZL27Nlji+3W2tat\nW7V+/XpJp6afP/7449jR3U7ZhvX19Ro9enS70b1df3edueiii2K/x23btmnOnDlt1nf1e+3KkBsJ\nd+b0/TOSVFpaqmXLlmnWrFl66aWXtGTJEjU3N2vFihW2Ok3iqquu0k9/+lO98MILampqUmlpqSTp\n97//vWbPnq2ZM2eqqKhIy5cvl8vl0sqVK9uNSAazTZs2qaGhQStWrIg9t379ej377LOx/saNG6fr\nrrtOSUlJmj9/fs9OHTAsJydH2dnZKigokMvlUklJiSoqKpSWlqYFCxZo9erVKioqkiQtWrTIVv8x\nbPHKK68oGo3qjjvuiD33zW9+U9OmTdOCBQs0d+5cLVmyRCNGjNA555xjq1GwdOoUq1WrVunvf/+7\nPv/8c61evVovv/yyo7bh6X87W/9dsePvTjp1qlVZWZk++OADpaSkqLKyUg8//LDuvvtubdy4URMm\nTIjN0tx555168MEHO/y99gS3MgQAwBCmowEAMIQQBgDAEEIYAABDCGEAAAwhhAEAMIQQBgDAEEIY\nAABDCGEAAAz5/zAohqQSzdAGAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3LaP_r2AGjyC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def loss(X, Y, w_new, b_new):\n",
        "  loss = 0\n",
        "  for x,y in zip(X, Y):\n",
        "    loss+= (y - sigmoid(x, w_new, b_new))**2\n",
        "  return loss  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-NYErNBhkeV3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "w_new = np.linspace(0, 2, 101)\n",
        "b_new = np.linspace(0, 2, 101)\n",
        "\n",
        "WW, BB = np.meshgrid(w_new, b_new)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HwpzWiprldjn",
        "colab_type": "code",
        "outputId": "b430100a-65fa-4fde-97f1-334bc898e226",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "cell_type": "code",
      "source": [
        "print(WW)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.   0.02 0.04 ... 1.96 1.98 2.  ]\n",
            " [0.   0.02 0.04 ... 1.96 1.98 2.  ]\n",
            " [0.   0.02 0.04 ... 1.96 1.98 2.  ]\n",
            " ...\n",
            " [0.   0.02 0.04 ... 1.96 1.98 2.  ]\n",
            " [0.   0.02 0.04 ... 1.96 1.98 2.  ]\n",
            " [0.   0.02 0.04 ... 1.96 1.98 2.  ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_bXfSGSelfJy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "W_nee = np.meshgrid(w_new)  # to check if single argument returns a matrix or not"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SNS4OwCcnlzk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(W_nee)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YvXEY21bnntG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Loss = np.zeros(WW.shape)\n",
        "for i in range(WW.shape[0]):\n",
        "  for j in range(WW.shape[1]):\n",
        "    Loss[i,j] = loss(X, Y, WW[i,j],BB[i,j])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DrP3BPOfxj8H",
        "colab_type": "code",
        "outputId": "281d77f1-003a-40e1-bbed-9910775b3f95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        }
      },
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = plt.axes(projection='3d')\n",
        "ax.plot_surface(WW, BB, Loss, cmap='viridis')\n",
        "ax.set_xlabel('w')\n",
        "ax.set_ylabel('b')\n",
        "ax.set_zlabel('loss');\n",
        "\n",
        "ax.view_init(30,270)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HP+CIcSJaiay7TlKP0vCLZCIXaYob+IwBUPZafLb+d3sWzDBTMMJ1kw6XTzbMFIww1/oG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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "pNoTWRZkyCBD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "ij = np.argmin(Loss)  # gives u location number of min value in a matrix\n",
        "\n",
        "i = int(np.floor(ij/Loss.shape[1]))  # floor will return float but i,j are indices which cannot be float so int()\n",
        "j = int(ij - i* Loss.shape[1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jvmN92YpzWE3",
        "colab_type": "code",
        "outputId": "25986430-c16a-4f7e-9d24-281d5877b78c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print(i, j)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12 25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8MngZ07_04zn",
        "colab_type": "code",
        "outputId": "5a72e361-ae99-4b7c-9c66-4d11424b4caa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print(WW[i, j], BB[i, j])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5 0.24\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "918D345XvqcY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Loading Data Set"
      ]
    },
    {
      "metadata": {
        "id": "IPZqdSKIlfvU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Sigmoid Neuron Class\n",
        "\n",
        "Dealing with differentiation means using Gradient Descent\n"
      ]
    },
    {
      "metadata": {
        "id": "ts35Q3ttljCS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class sigmoid:\n",
        "  \n",
        "  def __init__(self):\n",
        "    self.w = None\n",
        "    self.b = None\n",
        "   \n",
        "  def perceptron(self, x):            # basically perceptron does that\n",
        "    return np.dot(self.w, x) + self.b\n",
        "  \n",
        "  def model(self, x):\n",
        "    return 1.0/(1.0 + np.exp(-x))\n",
        "  \n",
        "  def grad_w(self, x, y):\n",
        "    y_pred = self.model(self.perceptron(x)) \n",
        "    return (y_pred -y) * y_pred * (1 - y_pred) * x\n",
        "  \n",
        "  def grad_b(self, x, y):\n",
        "    y_pred = self.model(self.perceptron(x))\n",
        "    return (y_pred - y) * y_pred * (1 - y_pred)\n",
        "  \n",
        "  def fit(self, X, Y, epochs = 1, lr = 1, initialize = True): # if w & b r already initialized then pass initialize = False\n",
        "    \n",
        "    wt_matrix = list()\n",
        "    \n",
        "    if initialize:      \n",
        "      self.w = np.random.randn(1, X.shape[1])  # 1 tells us that we want one set of values\n",
        "      self.b = 0\n",
        "      \n",
        "    for i in range(epochs):\n",
        "      dw = 0\n",
        "      db = 0  #value of w & b should not be set to 0 each time epoch is run because that will make no sense to epochs\n",
        "      for x,y in zip(X,Y):\n",
        "        dw += self.grad_w(x,y)\n",
        "        db += self.grad_b(x,y)\n",
        "      self.w -= lr * dw    # we can keep changing w&b inside the loop just like perceptron but their we were changing w&b on basis of x value & here we r changing\n",
        "      self.b -= lr * db    # em on basis of dw & db value so it doesn't matters if we do small changes inside the loop or directly subtract em outside loop\n",
        "      wt_matrix.append(self.w.copy())  #.copy() otherwise only last value of w will be appended epoch number of times\n",
        "      \n",
        "    return np.array(wt_matrix)  \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hEYj-QnLFxTa",
        "colab_type": "code",
        "outputId": "70925b5b-3d86-4cb3-a2f3-c6172fa63161",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "cell_type": "code",
      "source": [
        "X = np.asarray([[2.5,2.5],[4,-1],[1,-4],[3,1.25],[2,4],[1,5]])\n",
        "Y = [1 ,1 ,1 ,0 ,0 ,0]\n",
        "for x,y in zip(X,Y):\n",
        "  print(x, y)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2.5 2.5] 1\n",
            "[ 4. -1.] 1\n",
            "[ 1. -4.] 1\n",
            "[3.   1.25] 0\n",
            "[2. 4.] 0\n",
            "[1. 5.] 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Hu3WglZehnQE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sm = sigmoid()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PTKwNVqlhqbb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "wt_matrix = sm.fit(X, Y, 10, .5, True)\n",
        "print(wt_matrix, sm.b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "70TX6ycehzSo",
        "colab_type": "code",
        "outputId": "b7bfca25-68bc-4776-ea27-1d761d351b5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(wt_matrix.ravel())  # .ravel() because there's double list inside the wt_matrix. So, gotta ravel it\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PtuPvHaMCNjPQMgiRnN/lTMTT2jIxnlloN/WoCaizdnqjmY17dzTgiYIi517LiJzzaGrh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\nqhZFL+GIkkgBEiJSNDtQyxXdZBU+dw0Bwut23N2dkTqdhL0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SS5KMNwOZmeXU47ieirSs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\nyplhGIZhiMHKmWEYhmGI8f9ig8LU1Ko+mgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "1LWWJFw9itof",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_the_shit(X, Y, sm):\n",
        "  X1 = np.linspace(-10, 10, 100)\n",
        "  X2 = np.linspace(-10, 10, 100)\n",
        "  \n",
        "  XXX1, XXX2 = np.meshgrid(X1, X2)\n",
        "  YY = np.zeros(XXX1.shape)\n",
        "  \n",
        "  for i in range(X2.size):\n",
        "    for j in range(X1.size):\n",
        "      val = np.asarray(X1[j], X2[i])\n",
        "      YY[i,j] = sm.model(sm.perceptron(val))\n",
        "  plt.contourf(XXX1, XX2, YY, cmap = my_cmap, alpha = 0.6)\n",
        "  plt.scatter(X[:,0], X[:,1], c=Y, cmap = my_cmap)\n",
        "  plt.plot()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "epBWyDUe_LPU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "plot_the_shit(X, Y, sm)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mVPKqw93vu31",
        "colab_type": "code",
        "outputId": "ed957c3a-f7e2-4fa3-ad1d-0bd7c77730f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "e4gTLEj4v4HK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TZkcnDRCw5_Y",
        "colab_type": "code",
        "outputId": "5d68639d-dc3b-4686-cd94-028326b3f509",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "# Install the PyDrive wrapper & import libraries.\n",
        "# This only needs to be done once in a notebook.\n",
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# Authenticate and create the PyDrive client.\n",
        "# This only needs to be done once in a notebook.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "# Create & upload a text file.\n",
        "uploaded = drive.CreateFile({'title': 'Sample file.txt'})\n",
        "uploaded.SetContentString('Sample upload file content')\n",
        "uploaded.Upload()\n",
        "print('Uploaded file with ID {}'.format(uploaded.get('id')))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[?25l\r\u001b[K    1% |▎                               | 10kB 19.2MB/s eta 0:00:01\r\u001b[K    2% |▋                               | 20kB 1.8MB/s eta 0:00:01\r\u001b[K    3% |█                               | 30kB 2.7MB/s eta 0:00:01\r\u001b[K    4% |█▎                              | 40kB 1.7MB/s eta 0:00:01\r\u001b[K    5% |█▋                              | 51kB 2.1MB/s eta 0:00:01\r\u001b[K    6% |██                              | 61kB 2.5MB/s eta 0:00:01\r\u001b[K    7% |██▎                             | 71kB 2.9MB/s eta 0:00:01\r\u001b[K    8% |██▋                             | 81kB 3.3MB/s eta 0:00:01\r\u001b[K    9% |███                             | 92kB 3.7MB/s eta 0:00:01\r\u001b[K    10% |███▎                            | 102kB 2.8MB/s eta 0:00:01\r\u001b[K    11% |███▋                            | 112kB 2.8MB/s eta 0:00:01\r\u001b[K    12% |████                            | 122kB 4.0MB/s eta 0:00:01\r\u001b[K    13% |████▎                           | 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            "\u001b[?25h  Building wheel for PyDrive (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25hUploaded file with ID 1OaUmi04JKPcg4cW7O6C9uhIg5LKOQoF4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ARYMiyRqw_0g",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Download a file based on its file ID.\n",
        "#\n",
        "# A file ID looks like: laggVyWshwcyP6kEI-y_W3P8D26sz\n",
        "file_id = '1HwkFnPQPUAjzvLp2Lp0015l8aU23phmE'     #https://drive.google.com/open?id=1HwkFnPQPUAjzvLp2Lp0015l8aU23phmE\n",
        "downloaded = drive.CreateFile({'id': file_id})\n",
        "print('Downloaded content \"{}\"'.format(downloaded.GetContentString()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nlPxEJTXzRsZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "downloaded.GetContentFile('mobile_cleaned.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oCTK71OJyINc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EEOw0Pdsy10Q",
        "colab_type": "code",
        "outputId": "da4facea-9f20-48a6-9c5b-fceda9a62893",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "adc.json  mobile_cleaned.csv  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2WYTTffeyxN7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "file = pd.read_csv('mobile_cleaned.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qC9bo_ZQzgLS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "c4iT6mSS2W1D",
        "colab_type": "code",
        "outputId": "e8a017fe-fb12-4e8b-d77c-2536d14967a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "cell_type": "code",
      "source": [
        "file.head()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PhoneId</th>\n",
              "      <th>Pixel Density</th>\n",
              "      <th>Screen Size</th>\n",
              "      <th>Weight</th>\n",
              "      <th>RAM</th>\n",
              "      <th>Processor_frequency</th>\n",
              "      <th>Screen to Body Ratio (calculated)</th>\n",
              "      <th>Height</th>\n",
              "      <th>Internal Memory</th>\n",
              "      <th>Capacity</th>\n",
              "      <th>...</th>\n",
              "      <th>os_name_Tizen</th>\n",
              "      <th>os_name_iOS</th>\n",
              "      <th>SIM Slot(s)_Dual SIM, GSM+CDMA</th>\n",
              "      <th>SIM Slot(s)_Dual SIM, GSM+GSM</th>\n",
              "      <th>SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE</th>\n",
              "      <th>SIM Slot(s)_Single SIM, GSM</th>\n",
              "      <th>Sim1_2G</th>\n",
              "      <th>Sim1_3G</th>\n",
              "      <th>Sim1_4G</th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>403</td>\n",
              "      <td>6.26</td>\n",
              "      <td>182</td>\n",
              "      <td>4</td>\n",
              "      <td>1.8</td>\n",
              "      <td>80.68</td>\n",
              "      <td>157.9</td>\n",
              "      <td>64</td>\n",
              "      <td>4000</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>271</td>\n",
              "      <td>6.20</td>\n",
              "      <td>168</td>\n",
              "      <td>3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>80.85</td>\n",
              "      <td>156.2</td>\n",
              "      <td>32</td>\n",
              "      <td>4230</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>409</td>\n",
              "      <td>6.30</td>\n",
              "      <td>168</td>\n",
              "      <td>3</td>\n",
              "      <td>2.1</td>\n",
              "      <td>83.68</td>\n",
              "      <td>157.0</td>\n",
              "      <td>32</td>\n",
              "      <td>3500</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>411</td>\n",
              "      <td>6.00</td>\n",
              "      <td>169</td>\n",
              "      <td>4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>74.78</td>\n",
              "      <td>159.8</td>\n",
              "      <td>64</td>\n",
              "      <td>3300</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>396</td>\n",
              "      <td>6.50</td>\n",
              "      <td>175</td>\n",
              "      <td>4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>84.23</td>\n",
              "      <td>160.4</td>\n",
              "      <td>64</td>\n",
              "      <td>3750</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 88 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   PhoneId  Pixel Density  Screen Size  Weight  RAM  Processor_frequency  \\\n",
              "0        0            403         6.26     182    4                  1.8   \n",
              "1        1            271         6.20     168    3                  1.8   \n",
              "2        2            409         6.30     168    3                  2.1   \n",
              "3        4            411         6.00     169    4                  2.2   \n",
              "4        5            396         6.50     175    4                  2.2   \n",
              "\n",
              "   Screen to Body Ratio (calculated)  Height  Internal Memory  Capacity  \\\n",
              "0                              80.68   157.9               64      4000   \n",
              "1                              80.85   156.2               32      4230   \n",
              "2                              83.68   157.0               32      3500   \n",
              "3                              74.78   159.8               64      3300   \n",
              "4                              84.23   160.4               64      3750   \n",
              "\n",
              "    ...    os_name_Tizen  os_name_iOS  SIM Slot(s)_Dual SIM, GSM+CDMA  \\\n",
              "0   ...                0            0                               0   \n",
              "1   ...                0            0                               0   \n",
              "2   ...                0            0                               0   \n",
              "3   ...                0            0                               0   \n",
              "4   ...                0            0                               0   \n",
              "\n",
              "   SIM Slot(s)_Dual SIM, GSM+GSM  SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE  \\\n",
              "0                              0                                          1   \n",
              "1                              0                                          1   \n",
              "2                              0                                          1   \n",
              "3                              1                                          0   \n",
              "4                              0                                          1   \n",
              "\n",
              "   SIM Slot(s)_Single SIM, GSM  Sim1_2G  Sim1_3G  Sim1_4G  Rating  \n",
              "0                            0        0        0        1     4.5  \n",
              "1                            0        0        0        1     4.5  \n",
              "2                            0        0        0        1     4.4  \n",
              "3                            0        0        0        1     4.3  \n",
              "4                            0        0        0        1     4.4  \n",
              "\n",
              "[5 rows x 88 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "metadata": {
        "id": "uqRxvH7xBbaA",
        "colab_type": "code",
        "outputId": "5b22c055-8ab0-41c3-abf1-024bba9dccd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "cell_type": "code",
      "source": [
        "Y_real = file['Rating']\n",
        "Y_real.head()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4.5\n",
              "1    4.5\n",
              "2    4.4\n",
              "3    4.3\n",
              "4    4.4\n",
              "Name: Rating, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "metadata": {
        "id": "v26ACHiS2ZSB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "threshold = 4.2   #each time you change the threshold it will map all to 0 because values had already been mapped to 0 & 1 for previous threshold\n",
        "file['Rating'] = (file['Rating'] >= threshold).astype(np.int)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zmk-5ReV3I5I",
        "colab_type": "code",
        "outputId": "21958041-34c4-4185-f5b6-ff58663a8175",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "cell_type": "code",
      "source": [
        "file.head()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PhoneId</th>\n",
              "      <th>Pixel Density</th>\n",
              "      <th>Screen Size</th>\n",
              "      <th>Weight</th>\n",
              "      <th>RAM</th>\n",
              "      <th>Processor_frequency</th>\n",
              "      <th>Screen to Body Ratio (calculated)</th>\n",
              "      <th>Height</th>\n",
              "      <th>Internal Memory</th>\n",
              "      <th>Capacity</th>\n",
              "      <th>...</th>\n",
              "      <th>os_name_Tizen</th>\n",
              "      <th>os_name_iOS</th>\n",
              "      <th>SIM Slot(s)_Dual SIM, GSM+CDMA</th>\n",
              "      <th>SIM Slot(s)_Dual SIM, GSM+GSM</th>\n",
              "      <th>SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE</th>\n",
              "      <th>SIM Slot(s)_Single SIM, GSM</th>\n",
              "      <th>Sim1_2G</th>\n",
              "      <th>Sim1_3G</th>\n",
              "      <th>Sim1_4G</th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>403</td>\n",
              "      <td>6.26</td>\n",
              "      <td>182</td>\n",
              "      <td>4</td>\n",
              "      <td>1.8</td>\n",
              "      <td>80.68</td>\n",
              "      <td>157.9</td>\n",
              "      <td>64</td>\n",
              "      <td>4000</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>271</td>\n",
              "      <td>6.20</td>\n",
              "      <td>168</td>\n",
              "      <td>3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>80.85</td>\n",
              "      <td>156.2</td>\n",
              "      <td>32</td>\n",
              "      <td>4230</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>409</td>\n",
              "      <td>6.30</td>\n",
              "      <td>168</td>\n",
              "      <td>3</td>\n",
              "      <td>2.1</td>\n",
              "      <td>83.68</td>\n",
              "      <td>157.0</td>\n",
              "      <td>32</td>\n",
              "      <td>3500</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>411</td>\n",
              "      <td>6.00</td>\n",
              "      <td>169</td>\n",
              "      <td>4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>74.78</td>\n",
              "      <td>159.8</td>\n",
              "      <td>64</td>\n",
              "      <td>3300</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>396</td>\n",
              "      <td>6.50</td>\n",
              "      <td>175</td>\n",
              "      <td>4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>84.23</td>\n",
              "      <td>160.4</td>\n",
              "      <td>64</td>\n",
              "      <td>3750</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 88 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   PhoneId  Pixel Density  Screen Size  Weight  RAM  Processor_frequency  \\\n",
              "0        0            403         6.26     182    4                  1.8   \n",
              "1        1            271         6.20     168    3                  1.8   \n",
              "2        2            409         6.30     168    3                  2.1   \n",
              "3        4            411         6.00     169    4                  2.2   \n",
              "4        5            396         6.50     175    4                  2.2   \n",
              "\n",
              "   Screen to Body Ratio (calculated)  Height  Internal Memory  Capacity  \\\n",
              "0                              80.68   157.9               64      4000   \n",
              "1                              80.85   156.2               32      4230   \n",
              "2                              83.68   157.0               32      3500   \n",
              "3                              74.78   159.8               64      3300   \n",
              "4                              84.23   160.4               64      3750   \n",
              "\n",
              "    ...    os_name_Tizen  os_name_iOS  SIM Slot(s)_Dual SIM, GSM+CDMA  \\\n",
              "0   ...                0            0                               0   \n",
              "1   ...                0            0                               0   \n",
              "2   ...                0            0                               0   \n",
              "3   ...                0            0                               0   \n",
              "4   ...                0            0                               0   \n",
              "\n",
              "   SIM Slot(s)_Dual SIM, GSM+GSM  SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE  \\\n",
              "0                              0                                          1   \n",
              "1                              0                                          1   \n",
              "2                              0                                          1   \n",
              "3                              1                                          0   \n",
              "4                              0                                          1   \n",
              "\n",
              "   SIM Slot(s)_Single SIM, GSM  Sim1_2G  Sim1_3G  Sim1_4G  Rating  \n",
              "0                            0        0        0        1       1  \n",
              "1                            0        0        0        1       1  \n",
              "2                            0        0        0        1       1  \n",
              "3                            0        0        0        1       1  \n",
              "4                            0        0        0        1       1  \n",
              "\n",
              "[5 rows x 88 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "metadata": {
        "id": "z2uH0vSF6FWV",
        "colab_type": "code",
        "outputId": "6aa470b7-5dff-4d1d-8900-d357c3331ed6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "file.shape"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(341, 88)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "metadata": {
        "id": "rlSrlXkZ3QE7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(file['Rating'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mJbqSlQs3VHb",
        "colab_type": "code",
        "outputId": "ff0db6b5-ea95-4533-b623-9a89cf6df478",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "file['Rating'].value_counts(normalize = True)  #re run from file read each time u change threshold "
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.533724\n",
              "1    0.466276\n",
              "Name: Rating, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "metadata": {
        "id": "hEgaW35477_W",
        "colab_type": "code",
        "outputId": "8bdc72e0-2579-4fb6-e3a1-a042e5414d4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "cell_type": "code",
      "source": [
        "X = file.drop('Rating', axis = 1)\n",
        "X.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PhoneId</th>\n",
              "      <th>Pixel Density</th>\n",
              "      <th>Screen Size</th>\n",
              "      <th>Weight</th>\n",
              "      <th>RAM</th>\n",
              "      <th>Processor_frequency</th>\n",
              "      <th>Screen to Body Ratio (calculated)</th>\n",
              "      <th>Height</th>\n",
              "      <th>Internal Memory</th>\n",
              "      <th>Capacity</th>\n",
              "      <th>...</th>\n",
              "      <th>os_name_Other</th>\n",
              "      <th>os_name_Tizen</th>\n",
              "      <th>os_name_iOS</th>\n",
              "      <th>SIM Slot(s)_Dual SIM, GSM+CDMA</th>\n",
              "      <th>SIM Slot(s)_Dual SIM, GSM+GSM</th>\n",
              "      <th>SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE</th>\n",
              "      <th>SIM Slot(s)_Single SIM, GSM</th>\n",
              "      <th>Sim1_2G</th>\n",
              "      <th>Sim1_3G</th>\n",
              "      <th>Sim1_4G</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>403</td>\n",
              "      <td>6.26</td>\n",
              "      <td>182</td>\n",
              "      <td>4</td>\n",
              "      <td>1.8</td>\n",
              "      <td>80.68</td>\n",
              "      <td>157.9</td>\n",
              "      <td>64</td>\n",
              "      <td>4000</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>271</td>\n",
              "      <td>6.20</td>\n",
              "      <td>168</td>\n",
              "      <td>3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>80.85</td>\n",
              "      <td>156.2</td>\n",
              "      <td>32</td>\n",
              "      <td>4230</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>409</td>\n",
              "      <td>6.30</td>\n",
              "      <td>168</td>\n",
              "      <td>3</td>\n",
              "      <td>2.1</td>\n",
              "      <td>83.68</td>\n",
              "      <td>157.0</td>\n",
              "      <td>32</td>\n",
              "      <td>3500</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>411</td>\n",
              "      <td>6.00</td>\n",
              "      <td>169</td>\n",
              "      <td>4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>74.78</td>\n",
              "      <td>159.8</td>\n",
              "      <td>64</td>\n",
              "      <td>3300</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>396</td>\n",
              "      <td>6.50</td>\n",
              "      <td>175</td>\n",
              "      <td>4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>84.23</td>\n",
              "      <td>160.4</td>\n",
              "      <td>64</td>\n",
              "      <td>3750</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 87 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   PhoneId  Pixel Density  Screen Size  Weight  RAM  Processor_frequency  \\\n",
              "0        0            403         6.26     182    4                  1.8   \n",
              "1        1            271         6.20     168    3                  1.8   \n",
              "2        2            409         6.30     168    3                  2.1   \n",
              "3        4            411         6.00     169    4                  2.2   \n",
              "4        5            396         6.50     175    4                  2.2   \n",
              "\n",
              "   Screen to Body Ratio (calculated)  Height  Internal Memory  Capacity  \\\n",
              "0                              80.68   157.9               64      4000   \n",
              "1                              80.85   156.2               32      4230   \n",
              "2                              83.68   157.0               32      3500   \n",
              "3                              74.78   159.8               64      3300   \n",
              "4                              84.23   160.4               64      3750   \n",
              "\n",
              "    ...     os_name_Other  os_name_Tizen  os_name_iOS  \\\n",
              "0   ...                 0              0            0   \n",
              "1   ...                 0              0            0   \n",
              "2   ...                 0              0            0   \n",
              "3   ...                 0              0            0   \n",
              "4   ...                 0              0            0   \n",
              "\n",
              "   SIM Slot(s)_Dual SIM, GSM+CDMA  SIM Slot(s)_Dual SIM, GSM+GSM  \\\n",
              "0                               0                              0   \n",
              "1                               0                              0   \n",
              "2                               0                              0   \n",
              "3                               0                              1   \n",
              "4                               0                              0   \n",
              "\n",
              "   SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE  SIM Slot(s)_Single SIM, GSM  \\\n",
              "0                                          1                            0   \n",
              "1                                          1                            0   \n",
              "2                                          1                            0   \n",
              "3                                          0                            0   \n",
              "4                                          1                            0   \n",
              "\n",
              "   Sim1_2G  Sim1_3G  Sim1_4G  \n",
              "0        0        0        1  \n",
              "1        0        0        1  \n",
              "2        0        0        1  \n",
              "3        0        0        1  \n",
              "4        0        0        1  \n",
              "\n",
              "[5 rows x 87 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "metadata": {
        "id": "oaq75UeK9Vas",
        "colab_type": "code",
        "outputId": "4f181b29-9a32-42e0-d75d-8e743ac5bfb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "cell_type": "code",
      "source": [
        "Y = file['Rating']\n",
        "Y.head()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    1\n",
              "Name: Rating, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "metadata": {
        "id": "RX-bVE8UJDWu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Standardization\n",
        "\n",
        "z = (x - u)/s , where u is the mean & s is the standard deviation of dataset given for fit"
      ]
    },
    {
      "metadata": {
        "id": "LAgtL5vO9cuZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "O4r1DCnTJN3N",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "R = np.random.random((100, 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3AuoL8-UKP4V",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(R)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bVQ6s7QcMpdQ",
        "colab_type": "code",
        "outputId": "de7f8f28-b964-4796-f7c8-9c10a5c4a1a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(R)\n",
        "plt.show()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SjrYwdAP4QUgHPczPOrBMs0QQ5k9Rsq2ol370dLRJ09HE4jRrbJRFmTS6Kkh7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zRkUjxPuCYUswCw77d8c8JuoDV4HxQVZQAtSoP/RnSB4wlnDUfLEky4M4nLGCbtB1nD0dbjlZAn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\nsw2PAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "m7GsTUWSKWOs",
        "colab_type": "code",
        "outputId": "f2105153-98c8-4f77-9592-136dda70c929",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "np.mean(R)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5017620793581486"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "metadata": {
        "id": "JzWYcoLDKbMX",
        "colab_type": "code",
        "outputId": "28c6ce77-372f-432e-f2c8-9c583a160afc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "np.std(R)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2871295278187253"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "metadata": {
        "id": "UPvcKiX3KhHT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()  #object or instance formation of StandardScaler class"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ttd3QD2jLFZa",
        "colab_type": "code",
        "outputId": "234867f7-18e4-47a8-c01f-eec1bdf049c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "scaler.fit(R)  # fit takes the dataset from which it learns /  parameters learned in fit model are used to transform"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler(copy=True, with_mean=True, with_std=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "metadata": {
        "id": "uClL_rQ-LJNd",
        "colab_type": "code",
        "outputId": "77f43c80-7178-42ee-8ca1-26b27ffd36c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "scaler.mean_"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.50176208])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "metadata": {
        "id": "cT3vC7GDLSNp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "RT = scaler.transform(R)  #transform means the dataset will have a mean value of 0 & standard deviation of 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iDts04fYMHaC",
        "colab_type": "code",
        "outputId": "7fc99581-fbfb-4b16-a241-35c6ef44796d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "scaler.mean_  #it will only contain the mean of the fit model. For the mean of transformed model use numpy"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.50176208])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "metadata": {
        "id": "S00apDHqMKfR",
        "colab_type": "code",
        "outputId": "dcfcac73-90ac-4d44-e87c-aec349ef55d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "np.mean(RT)   # nearly equal to 0"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-6.88338275267597e-17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "metadata": {
        "id": "4673QAZbMcXk",
        "colab_type": "code",
        "outputId": "fb0e9fcf-b70b-4fb6-e4b0-a17783f548fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "np.std(RT)  # Standard deviation of 1"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "metadata": {
        "id": "zv7gFmkQMeMW",
        "colab_type": "code",
        "outputId": "b9dcdeb4-dc20-4125-9c6d-87a434cfe2dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(RT)\n",
        "plt.show()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cJ/P7VlPXnH/52haGXoB7330epmEUMmeet7Z5wtW1xw17h0PMzzYAIOWvXc01vE19tZng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\nYel6MwC0m1HwaE2AOdtF3tqhPKPAQ6fdQAi+v3bZ1iwReBkpMuP6tnO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v2VLF8nobW6bY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\nxpnD4XA4nIzBjTOHw+FwOBnj/wN/8fBT1KWWMgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "wK0tPPe0NDKS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Train_Test Split & Standardize\n",
        "\n",
        "Always split before standardizing data<br />\n",
        "Standardization refers to shifting the distribution of each attribute to have a mean of zero and a standard deviation of one (unit variance).<br />\n",
        "\n",
        "Normalization refers to rescaling real valued numeric attributes into the range 0 and 1."
      ]
    },
    {
      "metadata": {
        "id": "jAyLl5fBNLMK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wmbpkXSXTvl8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y_real, random_state = 0, stratify = Y)  #use binarised Y to arrange"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "alwYqmJ0V5rz",
        "colab_type": "code",
        "outputId": "3ab9932f-cb11-4168-8519-e215bc31b395",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print(X_train.shape, X_test.shape, Y_test.shape)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(255, 87) (86, 87) (86,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BIad5E6SWAD-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_jwfjwF7Wiet",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_scaled_train = scaler.fit_transform(X_train)  # fit using the train data & transform both using those same parameters learned from fit\n",
        "X_scaled_test = scaler.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1F81PNZbWqtm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "scaler.mean_  # it always shows the mean of fitting data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "koqpaiuOYriN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_scaled_test = pd.DataFrame(X_scaled_test)  #gotta convert in pandas dataframe to use head"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TUff2WZ5ZkSp",
        "colab_type": "code",
        "outputId": "82dd781f-8d5f-47ab-f7e5-4e4f3fbfb3ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        }
      },
      "cell_type": "code",
      "source": [
        "X_scaled_test.head()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>0</th>\n",
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              "      <td>1.981230</td>\n",
              "      <td>0.310285</td>\n",
              "      <td>0.517391</td>\n",
              "      <td>-0.102371</td>\n",
              "      <td>0.772151</td>\n",
              "      <td>-0.080730</td>\n",
              "      <td>1.044425</td>\n",
              "      <td>0.513703</td>\n",
              "      <td>-0.006540</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.141421</td>\n",
              "      <td>-0.062746</td>\n",
              "      <td>-0.258738</td>\n",
              "      <td>-0.062746</td>\n",
              "      <td>0.590937</td>\n",
              "      <td>-0.437709</td>\n",
              "      <td>-0.322329</td>\n",
              "      <td>-0.141421</td>\n",
              "      <td>-0.155230</td>\n",
              "      <td>0.212325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.310986</td>\n",
              "      <td>0.748945</td>\n",
              "      <td>1.145940</td>\n",
              "      <td>0.250136</td>\n",
              "      <td>-0.102371</td>\n",
              "      <td>0.661260</td>\n",
              "      <td>1.263849</td>\n",
              "      <td>0.766463</td>\n",
              "      <td>0.513703</td>\n",
              "      <td>0.304210</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.141421</td>\n",
              "      <td>-0.062746</td>\n",
              "      <td>-0.258738</td>\n",
              "      <td>-0.062746</td>\n",
              "      <td>-1.692228</td>\n",
              "      <td>2.284625</td>\n",
              "      <td>-0.322329</td>\n",
              "      <td>-0.141421</td>\n",
              "      <td>-0.155230</td>\n",
              "      <td>0.212325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.720004</td>\n",
              "      <td>0.748945</td>\n",
              "      <td>1.145940</td>\n",
              "      <td>0.250136</td>\n",
              "      <td>-0.120563</td>\n",
              "      <td>0.661260</td>\n",
              "      <td>1.263849</td>\n",
              "      <td>0.766463</td>\n",
              "      <td>-0.334843</td>\n",
              "      <td>0.304210</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.141421</td>\n",
              "      <td>-0.062746</td>\n",
              "      <td>-0.258738</td>\n",
              "      <td>-0.062746</td>\n",
              "      <td>-1.692228</td>\n",
              "      <td>2.284625</td>\n",
              "      <td>-0.322329</td>\n",
              "      <td>-0.141421</td>\n",
              "      <td>-0.155230</td>\n",
              "      <td>0.212325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.276981</td>\n",
              "      <td>0.121177</td>\n",
              "      <td>-1.778854</td>\n",
              "      <td>-1.175222</td>\n",
              "      <td>-0.138754</td>\n",
              "      <td>-0.669435</td>\n",
              "      <td>-1.526574</td>\n",
              "      <td>-2.235525</td>\n",
              "      <td>-0.759116</td>\n",
              "      <td>-1.456706</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.141421</td>\n",
              "      <td>-0.062746</td>\n",
              "      <td>-0.258738</td>\n",
              "      <td>-0.062746</td>\n",
              "      <td>-1.692228</td>\n",
              "      <td>-0.437709</td>\n",
              "      <td>3.102418</td>\n",
              "      <td>-0.141421</td>\n",
              "      <td>6.442049</td>\n",
              "      <td>-4.709758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.511776</td>\n",
              "      <td>-0.855351</td>\n",
              "      <td>1.006665</td>\n",
              "      <td>0.250136</td>\n",
              "      <td>-0.102371</td>\n",
              "      <td>-0.004088</td>\n",
              "      <td>0.987057</td>\n",
              "      <td>0.644160</td>\n",
              "      <td>0.513703</td>\n",
              "      <td>1.060368</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.141421</td>\n",
              "      <td>-0.062746</td>\n",
              "      <td>-0.258738</td>\n",
              "      <td>-0.062746</td>\n",
              "      <td>0.590937</td>\n",
              "      <td>-0.437709</td>\n",
              "      <td>-0.322329</td>\n",
              "      <td>-0.141421</td>\n",
              "      <td>-0.155230</td>\n",
              "      <td>0.212325</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 87 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         0         1         2         3         4         5         6   \\\n",
              "0  1.046444  1.981230  0.310285  0.517391 -0.102371  0.772151 -0.080730   \n",
              "1 -1.310986  0.748945  1.145940  0.250136 -0.102371  0.661260  1.263849   \n",
              "2 -1.720004  0.748945  1.145940  0.250136 -0.120563  0.661260  1.263849   \n",
              "3  1.276981  0.121177 -1.778854 -1.175222 -0.138754 -0.669435 -1.526574   \n",
              "4 -1.511776 -0.855351  1.006665  0.250136 -0.102371 -0.004088  0.987057   \n",
              "\n",
              "         7         8         9     ...           77        78        79  \\\n",
              "0  1.044425  0.513703 -0.006540    ...    -0.141421 -0.062746 -0.258738   \n",
              "1  0.766463  0.513703  0.304210    ...    -0.141421 -0.062746 -0.258738   \n",
              "2  0.766463 -0.334843  0.304210    ...    -0.141421 -0.062746 -0.258738   \n",
              "3 -2.235525 -0.759116 -1.456706    ...    -0.141421 -0.062746 -0.258738   \n",
              "4  0.644160  0.513703  1.060368    ...    -0.141421 -0.062746 -0.258738   \n",
              "\n",
              "         80        81        82        83        84        85        86  \n",
              "0 -0.062746  0.590937 -0.437709 -0.322329 -0.141421 -0.155230  0.212325  \n",
              "1 -0.062746 -1.692228  2.284625 -0.322329 -0.141421 -0.155230  0.212325  \n",
              "2 -0.062746 -1.692228  2.284625 -0.322329 -0.141421 -0.155230  0.212325  \n",
              "3 -0.062746 -1.692228 -0.437709  3.102418 -0.141421  6.442049 -4.709758  \n",
              "4 -0.062746  0.590937 -0.437709 -0.322329 -0.141421 -0.155230  0.212325  \n",
              "\n",
              "[5 rows x 87 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "metadata": {
        "id": "RLeEqzYmc76O",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler  # the output cannot be standardized because it may give -ve values out of 0 & 1 but sigmoid predicts results between 0 & 1 only"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TGOheg2nr346",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "minmax = MinMaxScaler()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rNA7o7dmsChe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_scaled_train = minmax.fit_transform(Y_train.reshape(-1,1))\n",
        "Y_scaled_test = minmax.transform(Y_test.reshape(-1,1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HkZ4OBcotS6_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_scaled_train = pd.DataFrame(Y_scaled_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iVFe07lnuLdh",
        "colab_type": "code",
        "outputId": "04062574-6021-4e05-89c9-bd59ffcc49a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "cell_type": "code",
      "source": [
        "Y_scaled_train.head()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\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>0.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.80</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      0\n",
              "0  0.68\n",
              "1  0.72\n",
              "2  0.72\n",
              "3  0.60\n",
              "4  0.80"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "metadata": {
        "id": "8NS1_RJsued9",
        "colab_type": "code",
        "outputId": "8c3113db-3324-4c54-f92f-a087407bf018",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "np.max(Y_scaled_test)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "metadata": {
        "id": "wk9_WhoQuiHm",
        "colab_type": "code",
        "outputId": "9ca16395-9f71-4469-a257-72032a9ff8a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "np.min(Y_scaled_train) "
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "metadata": {
        "id": "y0IT-k1VumNL",
        "colab_type": "code",
        "outputId": "56bbb9d8-21c9-4754-e3a5-c98e1b0ab0eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "np.min(Y_scaled_test)   # maybe min of test data won't be 0 because of train fit parameters we changed the test model"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.11999999999999988"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "metadata": {
        "id": "cUcG_-dlvFyT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "scaled_threshold = list(minmax.transform(np.asarray([threshold]).reshape(-1,1)))[0][0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MCRpxezyyTAx",
        "colab_type": "code",
        "outputId": "67b7b135-8ec6-4f72-d813-46a3f9713e1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print(scaled_threshold)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6800000000000002\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cves_QS3y2VU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_binary_train = (np.array(Y_scaled_train) > scaled_threshold).astype(np.int).ravel()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OLCl0Bh80tC9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(Y_binary_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "brRpLkqn1DG_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_binary_test = (Y_scaled_test > scaled_threshold).astype(np.int).ravel()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EYKLDyi3akg-",
        "colab_type": "code",
        "outputId": "f1bab091-870b-4be9-d1d3-79e3725c53df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "print(Y_binary_test)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 1 0 1 0 1 0 0 1 1 1 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0\n",
            " 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 1 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0\n",
            " 0 0 0 1 0 0 0 1 0 0 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "eK1j14oqIsoN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Sigmoid Class on Real Data"
      ]
    },
    {
      "metadata": {
        "id": "VfWcYn5NLM0W",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,mean_squared_error\n",
        "from tqdm import tqdm_notebook"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dpfM_01JI5hv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class sigmoid:\n",
        "  \n",
        "  def __init__(self):\n",
        "    self.w = None\n",
        "    self.b = None\n",
        "   \n",
        "  def perceptron(self, x):            # basically perceptron does that\n",
        "    return np.dot(self.w, x) + self.b\n",
        "  \n",
        "  def model(self, x):\n",
        "    return 1.0/(1.0 + np.exp(-x))\n",
        "  \n",
        "  def predict(self, X):\n",
        "    Y_pred = list()\n",
        "    for x in X:\n",
        "      y_pred = self.model(self.perceptron(x))\n",
        "      Y_pred.append(y_pred)\n",
        "      \n",
        "    return np.array(Y_pred)  \n",
        "  \n",
        "  def grad_w(self, x, y):\n",
        "    y_pred = self.model(self.perceptron(x)) \n",
        "    return (y_pred -y) * y_pred * (1 - y_pred) * x\n",
        "  \n",
        "  def grad_b(self, x, y):\n",
        "    y_pred = self.model(self.perceptron(x))\n",
        "    return (y_pred - y) * y_pred * (1 - y_pred)\n",
        "  \n",
        "  def fit(self, X, Y, epochs = 1, lr = 1, initialize = True,display_loss = False): # if w & b r already initialized then pass initialize = False\n",
        "    \n",
        "    wt_matrix = list()\n",
        "    \n",
        "    if initialize:      \n",
        "      self.w = np.random.randn(1, X.shape[1])  # 1 tells us that we want one set of values\n",
        "      self.b = 0\n",
        "      \n",
        "    if display_loss:\n",
        "      loss = {}\n",
        "      \n",
        "    for i in tqdm_notebook(range(epochs), total = epochs, unit = 'epoch'):\n",
        "      dw = 0\n",
        "      db = 0  #value of w & b should not be set to 0 each time epoch is run because that will make no sense to epochs\n",
        "      for x,y in zip(X,Y):\n",
        "        dw += self.grad_w(x,y)\n",
        "        db += self.grad_b(x,y)\n",
        "      self.w -= lr * dw    # we can keep changing w&b inside the loop just like perceptron but their we were changing w&b on basis of x value & here we r changing\n",
        "      self.b -= lr * db    # em on basis of dw & db value so it doesn't matters if we do small changes inside the loop or directly subtract em outside loop\n",
        "      wt_matrix.append(self.w.copy())  #.copy() otherwise only last value of w will be appended epoch number of times\n",
        "      \n",
        "      if display_loss:\n",
        "        Y_pred = self.predict(X)\n",
        "        loss[i] = mean_squared_error(Y_pred, Y)\n",
        "    if display_loss:\n",
        "      plt.plot(loss.values())\n",
        "      plt.xlabel('Epochs')\n",
        "      plt.ylabel('Mean Squared Error')\n",
        "      plt.show()\n",
        "      \n",
        "    return np.array(wt_matrix)  \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jj4dYBj3JgPK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sn = sigmoid()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DaF_WoLNJoPW",
        "colab_type": "code",
        "outputId": "120d9862-8dbb-4950-8706-dba417069cb0",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/colabwidgets/controls.css": {
              "data": "/* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /* We import all of these together in a single css file because the Webpack
loader sees only one file at a time. This allows postcss to see the variable
definitions when they are used. */

 /*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

 /*
This file is copied from the JupyterLab project to define default styling for
when the widget styling is compiled down to eliminate CSS variables. We make one
change - we comment out the font import below.
*/

 /**
 * The material design colors are adapted from google-material-color v1.2.6
 * https://github.com/danlevan/google-material-color
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/dist/palette.var.css
 *
 * The license for the material design color CSS variables is as follows (see
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/LICENSE)
 *
 * The MIT License (MIT)
 *
 * Copyright (c) 2014 Dan Le Van
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

 /*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.

Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:

* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations

Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/

 /*
 * Optional monospace font for input/output prompt.
 */

 /* Commented out in ipywidgets since we don't need it. */

 /* @import url('https://fonts.googleapis.com/css?family=Roboto+Mono'); */

 /*
 * Added for compabitility with output area
 */

 :root {

  /* Borders

  The following variables, specify the visual styling of borders in JupyterLab.
   */

  /* UI Fonts

  The UI font CSS variables are used for the typography all of the JupyterLab
  user interface elements that are not directly user generated content.
  */ /* Base font size */ /* Ensures px perfect FontAwesome icons */

  /* Use these font colors against the corresponding main layout colors.
     In a light theme, these go from dark to light.
  */

  /* Use these against the brand/accent/warn/error colors.
     These will typically go from light to darker, in both a dark and light theme
   */

  /* Content Fonts

  Content font variables are used for typography of user generated content.
  */ /* Base font size */


  /* Layout

  The following are the main layout colors use in JupyterLab. In a light
  theme these would go from light to dark.
  */

  /* Brand/accent */

  /* State colors (warn, error, success, info) */

  /* Cell specific styles */
  /* A custom blend of MD grey and blue 600
   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
  /* A custom blend of MD grey and orange 600
   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */

  /* Notebook specific styles */

  /* Console specific styles */

  /* Toolbar specific styles */
}

 /* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /*
 * We assume that the CSS variables in
 * https://github.com/jupyterlab/jupyterlab/blob/master/src/default-theme/variables.css
 * have been defined.
 */

 /* This file has code derived from PhosphorJS CSS files, as noted below. The license for this PhosphorJS code is:

Copyright (c) 2014-2017, PhosphorJS Contributors
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

*/

 /*
 * The following section is derived from https://github.com/phosphorjs/phosphor/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css 
 * We've scoped the rules so that they are consistent with exactly our code.
 */

 .jupyter-widgets.widget-tab > .p-TabBar {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
  margin: 0;
  padding: 0;
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  list-style-type: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] > .p-TabBar-content {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] > .p-TabBar-content {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
  -webkit-box-sizing: border-box;
          box-sizing: border-box;
  overflow: hidden;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
  -webkit-box-flex: 0;
      -ms-flex: 0 0 auto;
          flex: 0 0 auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel {
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  overflow: hidden;
  white-space: nowrap;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden {
  display: none !important;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab {
  position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab {
  left: 0;
  -webkit-transition: left 150ms ease;
  transition: left 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab {
  top: 0;
  -webkit-transition: top 150ms ease;
  transition: top 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging {
  -webkit-transition: none;
  transition: none;
}

 /* End tabbar.css */

 :root { /* margin between inline elements */

    /* From Material Design Lite */
}

 .jupyter-widgets {
    margin: 2px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    color: black;
    overflow: visible;
}

 .jupyter-widgets.jupyter-widgets-disconnected::before {
    line-height: 28px;
    height: 28px;
}

 .jp-Output-result > .jupyter-widgets {
    margin-left: 0;
    margin-right: 0;
}

 /* vbox and hbox */

 .widget-inline-hbox {
    /* Horizontal widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
    -webkit-box-align: baseline;
        -ms-flex-align: baseline;
            align-items: baseline;
}

 .widget-inline-vbox {
    /* Vertical Widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widget-box {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    margin: 0;
    overflow: auto;
}

 .widget-gridbox {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: grid;
    margin: 0;
    overflow: auto;
}

 .widget-hbox {
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
}

 .widget-vbox {
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 /* General Button Styling */

 .jupyter-button {
    padding-left: 10px;
    padding-right: 10px;
    padding-top: 0px;
    padding-bottom: 0px;
    display: inline-block;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
    text-align: center;
    font-size: 13px;
    cursor: pointer;

    height: 28px;
    border: 0px solid;
    line-height: 28px;
    -webkit-box-shadow: none;
            box-shadow: none;

    color: rgba(0, 0, 0, .8);
    background-color: #EEEEEE;
    border-color: #E0E0E0;
    border: none;
}

 .jupyter-button i.fa {
    margin-right: 4px;
    pointer-events: none;
}

 .jupyter-button:empty:before {
    content: "\200b"; /* zero-width space */
}

 .jupyter-widgets.jupyter-button:disabled {
    opacity: 0.6;
}

 .jupyter-button i.fa.center {
    margin-right: 0;
}

 .jupyter-button:hover:enabled, .jupyter-button:focus:enabled {
    /* MD Lite 2dp shadow */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
            box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .jupyter-button:active, .jupyter-button.mod-active {
    /* MD Lite 4dp shadow */
    -webkit-box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
            box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
    color: rgba(0, 0, 0, .8);
    background-color: #BDBDBD;
}

 .jupyter-button:focus:enabled {
    outline: 1px solid #64B5F6;
}

 /* Button "Primary" Styling */

 .jupyter-button.mod-primary {
    color: rgba(255, 255, 255, 1.0);
    background-color: #2196F3;
}

 .jupyter-button.mod-primary.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,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");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

/*# 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 */",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "text/css"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 756
        }
      },
      "cell_type": "code",
      "source": [
        "sn.fit(X_scaled_train, Y_scaled_train, 30000, 0.001, True, True)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d6a3f943c6c5468fa09a42a2563b3fae",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=30000), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Gg90imOhGNGs68budpVBpuzF36ig8dk8i3MRc6U9EZEtD/pZNTU1FamoqxGIx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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0.5897096 , -0.27160167, -0.00468142, ..., -0.236774  ,\n",
              "          0.47016653,  1.17345086]],\n",
              "\n",
              "       [[ 0.58970459, -0.27159819, -0.00468155, ..., -0.23677345,\n",
              "          0.47016713,  1.17345004]],\n",
              "\n",
              "       [[ 0.58969958, -0.27159472, -0.00468167, ..., -0.23677291,\n",
              "          0.47016773,  1.17344922]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[ 0.55871287, -0.2500865 , -0.00543847, ..., -0.23340016,\n",
              "          0.4738698 ,  1.1683855 ]],\n",
              "\n",
              "       [[ 0.55871251, -0.25008625, -0.00543848, ..., -0.23340012,\n",
              "          0.47386984,  1.16838544]],\n",
              "\n",
              "       [[ 0.55871215, -0.250086  , -0.00543849, ..., -0.23340008,\n",
              "          0.47386989,  1.16838538]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "metadata": {
        "id": "xr3mVtRrgXl2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_scaled_test = np.array(X_scaled_test)  # else there will be a 2d Y_pred_test. Always do calculations in numpy array"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8J1efiAhQKp_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_pred_train = sn.predict(X_scaled_train)\n",
        "Y_pred_test = sn.predict(X_scaled_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "O4w02-LAbWON",
        "colab_type": "code",
        "outputId": "699c5b3f-3efd-4f16-bb47-27d2b4fb45e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print(X_scaled_test.shape, Y_pred_train.shape, Y_pred_test.shape)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(86, 87) (255, 1) (86, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "d85s1bRPRB2g",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(Y_pred_test)   #output between 0 & 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zR11yFTERH3u",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_pred_binary_train = (Y_pred_train > scaled_threshold).astype(np.int).ravel()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yqk0DJdCSI0S",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_pred_binary_test = (Y_pred_test > scaled_threshold).astype(np.int).ravel()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8zsUVlEBSPGP",
        "colab_type": "code",
        "outputId": "33b6cc35-3991-44c4-9859-a71b255a5ca7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "print(Y_pred_binary_test)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 0 0 0 1 0 0 1 0 0 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0\n",
            " 0 0 0 0 1 0 0 0 0 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 0 1 0 0 0 0\n",
            " 1 0 1 0 0 0 1 1 1 0 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "23dtBVSxa2aQ",
        "colab_type": "code",
        "outputId": "8f6f3fb0-ba64-4143-f579-3608aadb9801",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_pred_binary_test.shape"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(86,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "metadata": {
        "id": "RnsPtiZRSSZy",
        "colab_type": "code",
        "outputId": "2d62a158-8403-4a33-daac-cfb74ec3482e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "accuracy_score(Y_pred_binary_train, Y_binary_train)  #accuracy can differ because maybe weights r random"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6588235294117647"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "metadata": {
        "id": "B55AMxryU-GQ",
        "colab_type": "code",
        "outputId": "fd35afa7-6320-4f90-9aae-196f491ef49e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "accuracy_score(Y_pred_binary_test, Y_binary_test)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5465116279069767"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    }
  ]
}