c191f21_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "c191f21.1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPvMIqDOiauymq4HkB1AiEA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/everestso/Fall2021/blob/main/c191f21_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZScUdO2ZEp9M"
      },
      "source": [
        "# Simple Colab Example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjHvDPa-FBuS"
      },
      "source": [
        "### Includes\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "\n"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8ecyTzwEjVf",
        "outputId": "ec5520fa-5257-479c-a7b1-2db69eb48480"
      },
      "source": [
        "Weights = [5, 2] # F(X) = 5x_0 + 2x_1\n",
        "F = lambda W, X: sum([w*x for w,x in zip(W,X)])\n",
        "\n",
        "dataX1 = range(-10, 11, 1)\n",
        "dataX0 = [1]*len(dataX1)\n",
        "\n",
        "data = (list(zip(dataX0, dataX1)))\n",
        "print (data)\n",
        "Y = [F(Weights, X) for X in data]\n",
        "print (Y)\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(1, -10), (1, -9), (1, -8), (1, -7), (1, -6), (1, -5), (1, -4), (1, -3), (1, -2), (1, -1), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10)]\n",
            "[-15, -13, -11, -9, -7, -5, -3, -1, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "7isVVuw5H-eI",
        "outputId": "e2175e6c-7821-4d74-e38f-401d687869de"
      },
      "source": [
        "plt.plot(dataX1, Y)\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "Np3kyXjRIJ4w",
        "outputId": "997faa69-7270-442e-ff93-371bcde09ab6"
      },
      "source": [
        "dataX2 = [x**2 for x in dataX1]\n",
        "data = list(zip(dataX0, dataX1, dataX2))\n",
        "print (data)\n",
        "\n",
        "Weights = [0, 0, 3]\n",
        "Y = [F(Weights, X) for X in data]\n",
        "plt.plot(dataX1, Y, \"g\")\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(1, -10, 100), (1, -9, 81), (1, -8, 64), (1, -7, 49), (1, -6, 36), (1, -5, 25), (1, -4, 16), (1, -3, 9), (1, -2, 4), (1, -1, 1), (1, 0, 0), (1, 1, 1), (1, 2, 4), (1, 3, 9), (1, 4, 16), (1, 5, 25), (1, 6, 36), (1, 7, 49), (1, 8, 64), (1, 9, 81), (1, 10, 100)]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XiXq706M1Yro"
      },
      "source": [
        "# y = 3*cos(x)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "BKgqrpyI1jaM",
        "outputId": "b29d9403-5a19-435e-f90a-f4ec0b2897c8"
      },
      "source": [
        "print(math.pi)\n",
        "dataX1=[math.pi/30.0*i for i in range(61)]\n",
        "dataX0 = [1]*len(dataX1)\n",
        "dataX2 = [math.cos(x) for x in dataX1]\n",
        "data = list(zip(dataX0, dataX1, dataX2))\n",
        "print (data)\n",
        "\n",
        "Weights = [0, 0, 3]\n",
        "Y = [F(Weights, X) for X in data]\n",
        "plt.plot(dataX1, Y, \"g\")\n",
        "plt.show()\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.141592653589793\n",
            "[(1, 0.0, 1.0), (1, 0.10471975511965977, 0.9945218953682733), (1, 0.20943951023931953, 0.9781476007338057), (1, 0.3141592653589793, 0.9510565162951535), (1, 0.41887902047863906, 0.9135454576426009), (1, 0.5235987755982988, 0.8660254037844387), (1, 0.6283185307179586, 0.8090169943749475), (1, 0.7330382858376183, 0.7431448254773944), (1, 0.8377580409572781, 0.6691306063588582), (1, 0.9424777960769379, 0.5877852522924731), (1, 1.0471975511965976, 0.5000000000000001), (1, 1.1519173063162573, 0.4067366430758004), (1, 1.2566370614359172, 0.30901699437494745), (1, 1.361356816555577, 0.20791169081775945), (1, 1.4660765716752366, 0.10452846326765368), (1, 1.5707963267948966, 6.123233995736766e-17), (1, 1.6755160819145563, -0.10452846326765333), (1, 1.780235837034216, -0.20791169081775912), (1, 1.8849555921538759, -0.30901699437494734), (1, 1.9896753472735356, -0.40673664307580004), (1, 2.0943951023931953, -0.4999999999999998), (1, 2.199114857512855, -0.587785252292473), (1, 2.3038346126325147, -0.6691306063588579), (1, 2.4085543677521746, -0.743144825477394), (1, 2.5132741228718345, -0.8090169943749473), (1, 2.617993877991494, -0.8660254037844385), (1, 2.722713633111154, -0.9135454576426008), (1, 2.827433388230814, -0.9510565162951535), (1, 2.9321531433504733, -0.9781476007338056), (1, 3.036872898470133, -0.9945218953682733), (1, 3.141592653589793, -1.0), (1, 3.2463124087094526, -0.9945218953682734), (1, 3.3510321638291125, -0.9781476007338057), (1, 3.4557519189487724, -0.9510565162951536), (1, 3.560471674068432, -0.9135454576426011), (1, 3.665191429188092, -0.8660254037844388), (1, 3.7699111843077517, -0.8090169943749476), (1, 3.874630939427411, -0.7431448254773946), (1, 3.979350694547071, -0.6691306063588585), (1, 4.084070449666731, -0.5877852522924732), (1, 4.1887902047863905, -0.5000000000000004), (1, 4.29350995990605, -0.4067366430758009), (1, 4.39822971502571, -0.30901699437494756), (1, 4.50294947014537, -0.2079116908177598), (1, 4.607669225265029, -0.10452846326765423), (1, 4.71238898038469, -1.8369701987210297e-16), (1, 4.817108735504349, 0.10452846326765299), (1, 4.921828490624009, 0.20791169081775857), (1, 5.026548245743669, 0.30901699437494723), (1, 5.1312680008633285, 0.40673664307579976), (1, 5.235987755982988, 0.49999999999999933), (1, 5.340707511102648, 0.5877852522924729), (1, 5.445427266222308, 0.6691306063588578), (1, 5.550147021341967, 0.7431448254773937), (1, 5.654866776461628, 0.8090169943749473), (1, 5.759586531581287, 0.8660254037844384), (1, 5.864306286700947, 0.9135454576426005), (1, 5.969026041820607, 0.9510565162951535), (1, 6.073745796940266, 0.9781476007338056), (1, 6.178465552059926, 0.9945218953682733), (1, 6.283185307179586, 1.0)]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "WAlTRj7Q8hPP",
        "outputId": "6f1db800-3e32-444c-9918-703926a41960"
      },
      "source": [
        "dataX1 = [2*math.pi/30.0*i for i in range(31)]\n",
        "dataX0 = [1]*len(dataX1)\n",
        "dataX2 = [math.cos(x) for x in dataX1]\n",
        "dataX3 = [math.sin(x) for x in dataX1]\n",
        "data = list(zip(dataX0, dataX1, dataX2, dataX3))\n",
        "print (data)\n",
        "\n",
        "Weights = [10, 0, 5, 3]\n",
        "Y = [F(Weights, X) for X in data]\n",
        "plt.plot(dataX2, Y, \"g\")\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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        {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWselZfW_gDe"
      },
      "source": [
        "# Now Learning w/ Line"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "imbiUmhG_ktc",
        "outputId": "12bd2967-b5d7-4e7f-b6f1-b9d8e7265c4c"
      },
      "source": [
        "Weights = [5, 2] # F(X) = 5x_0 + 2x_1\n",
        "F = lambda W, X: sum([w*x for w,x in zip(W,X)])\n",
        "\n",
        "dataX1 = range(-10, 11, 1)\n",
        "dataX0 = [1]*len(dataX1)\n",
        "\n",
        "data = (list(zip(dataX0, dataX1)))\n",
        "print (data)\n",
        "Y = [F(Weights, X) for X in data]\n",
        "print (Y)\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(1, -10), (1, -9), (1, -8), (1, -7), (1, -6), (1, -5), (1, -4), (1, -3), (1, -2), (1, -1), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10)]\n",
            "[-15, -13, -11, -9, -7, -5, -3, -1, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "0SJ2_L2h_y3u",
        "outputId": "74a3ef82-2dbe-4963-8981-802bb3712d74"
      },
      "source": [
        "## Add Noise\n",
        "import random\n",
        "import numpy as np\n",
        "EPS = 100\n",
        "yNoise = [y+random.random()*EPS-EPS/2 for y in Y]\n",
        "\n",
        "plt.plot(dataX1, Y, \"g\")\n",
        "plt.plot(dataX1, yNoise, \"g+\")\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5gSd0aj3AvQW",
        "outputId": "99e1db09-7c20-422a-9a82-b7e6d2003608"
      },
      "source": [
        "cv =np.cov(dataX1, yNoise, bias=True, rowvar=False)\n",
        "w1 = cv[1][0]/cv[0][0]\n",
        "print(w1)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.083952567967879\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PcYqoGM_BHH5",
        "outputId": "cd6740cf-74ca-41f8-d858-21462b07dad5"
      },
      "source": [
        "Xbar, Ybar = np.mean(dataX1), np.mean(yNoise)\n",
        "w0 = Ybar - w1*Xbar\n",
        "print(w0,w1)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6.614123882619294 1.083952567967879\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "9STi8YY2CBtI",
        "outputId": "3a9372ba-1f61-4359-ff60-dac2d6c24118"
      },
      "source": [
        "yPredicted = [F([w0,w1], x) for x in data]\n",
        "plt.plot(dataX1, Y, \"b\")\n",
        "plt.plot(dataX1, yPredicted, \"g\")\n",
        "plt.plot(dataX1, yNoise, \"g+\")\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zqFwjkkINTQ18dOCjdj3zjQc3tgrzaXnTKCksoby4PDI8cXrBdAb2HdjuNY8ehd+GQ/2ZZ/y0Av36+Vkc77kHbr4Zxo9P9Xcq0jGFvGS9042no2Ee0zPfeGAjTa4JiIZ5qDDELdNviZRZOgrzWLt2wVNP+R77yy/7hTby8vy6pmVlfh72oRrdKGTmqCCVayRrtIR5bJDX7Kth08FNkTDvZb2YNnJapMzScit/cX7xOcO8hXOwbl20DNPylpw2zZdhysv9Mnh91EWSDKFyjWSVU42nfJi3mQK3bZifn3c+JYUl3Fpya/QCaEExA/oM6PIxz5zxy+C1LKyxbZuvpV95JTz8sO+xz5ih+rpkH4W8pM2pxlNs2L+h3Rqgmw5uotk1A9DbekfC/LaS2yIXQC/Iv6BbYR7r8GE/PW9lJTz7rH88YAAsXAgPPujLMWPGJOI7FUkfhbwk3anGU3y4/8PIBdCWMN9cv7lVmBflF3HhqAv5i9BfRC6AXpB/Af379E9YW3bsiPbWq6t9D76wED7zGV+GWbDAz8cuEhQKeUmYk2dOsuHAhnZ3gG6p39IuzC8efTGfu/BzkZ55UV5RQsO8hXOwZk20vv7++357cTF85zu+DDN7NvTunfBDi2QEhbx02ckzJ6M985g7QGPDvE+vPhTlFXHpmEv5/IWfj1wAvSD/Avr17pfU9jU0+F56S7Dv3Olr6XPmwKOP+mAvLk5qE0QyhkJeOnTizIlWZZaWceZb6rfg8KOy+vTqwwX5F3DpmEv5wkVfiJRZivKLkh7mserrfV29qgqee86PZx80yA9vfOghuPFGX5YRyTWBCflMnP0tW5w4c4L1devbjTPfWr+1XZjPHDuTL178xchollSHeaytW6PL4L32ml8Gb/RouP1231u//noY2LlRkyKBFZhx8lpk4NyONxxn/f717Xrm2w5ti4R53159uSD/gnbjzIvyiujbO70TrzQ3w+rV0TLMuvAyd6GQD/XycrjiCujVK63NFEm5tI6TN7PFwI+A3sDPnHOPJPuYue5Yw7G4PfNth7ZF9unbqy/FBcXMGj+LOy+9M9IzPz/v/LSHeaxTp/xdppWV/q7T3bv9RdKrr4bHHvPhPm1aulspkrmSGvJm1hv4d2AhsBN418yqnHO1iXj9TJ/9LdlawrztHaDbD2+P7NOvdz+K84uZPWE2X730q5Ge+fl559OnV2ZW6/bv9/PCVFXB88/D8eMwZIifnresDJYsgfz8dLdSJDsktVxjZlcBFc65G8KPHwBwzj0cb3+Va+I7evoo6/evb3cHaNswn14wPdIjb/k4LW9axoZ5rI0bo/X1N9/0pZnx44ksqnHttdA/8SMsRQIhneWa8cDHMY93AlfG7mBmS4GlAJNyfHmco6ePUltX267MsuPwjsg+/Xv3Z3rBdOZMnMPXZ36dUGGI0KgQ5408LyvCvEVTE6xcGa2vr1/vt19yib/btLwcZs7UNAIiPZX2VHDOPQ48Dr4n393XycTZ3zpy5PSRuGWWj49Efx8O6DOA6QXTmTtpbqve+dSRU7MqzGOdOAEvvuhD/amnYN8+P8nX/PnwzW/6HvvkyelupUiwJDstdgETYx5PCG9LuEyswR8+dThumaVtmM8omME1k6+JhvmoEFNHTKV3r+y/DXPvXnj6aR/sK1bAyZMwbJivq5eX+zr7iBHpbqVIcCU75N8FisxsKj7cbwc+n+RjptzhU4fblVhq62rZeWRnZJ+WMJ83ZV6rnvmUEVMCEeYtnIMNG3wZprIS3n7bb5s0Cb7+9egyeP3SM7ReukD3ngRD0sfJm9kS4If4IZQ/d859v6N9M30++UOnDkVr5jHjzHcdjf5xMrDPQGYUzmh3ATRoYR6rqQneeit64XTjRr/98suj49cvvlj19WwT5MEMQZPWcfLOuWeBZ5N9nESqP1kf9wLoJ0c/iewzsM9ASgpLuHbqtf7iZ/gC6OThkwMb5rGOHfPll8pKX445cMAvUn3ddX7ir5tvhgkT0t1KEcnOK3gJUn+yPhLiT7z3BMMGDKNmXw27j+2O7DOo7yBKCktYcN6CVr3zySMm08ty69bK3bv9BdOqKn8B9fRpGDnSzwtTVgY33ODr7ZK9cv3ekyAKzLQGZ3Pw5MF2c5nX1NWw59ie1scfV9oqyEOjQkwaPinnwryFc1BTEy3DrFzpt0+d6kswZWUwd67vwUvwqFyTPXJm+b8DJw7EvQAaG+aD+w6mpLCExecvbjU3y9QfTeXdu95NY+szQ2MjvP56dGGNLVv89lmz4Pvf98EeCqm+LpItAhHyVRuqWPrUUvYe3xvZNqTfkEiYx/bOJw6fGOmZV1RXcOP/vjHyNbn6p+mRI376gJZl8Orr/d2lCxbAfff5+vrYselupaRaNt17Ih0LRLnm/d3v868r/7XVOPOJwyZiXehu5tqfpjt3RnvrL7/sl8HLz/frmpaX+3VOhwxJdytFpDMCX665bOxl/Lz85+luRkZzDj74IFpff+89v72oCL71LV+GmTNHy+CJBE0gQj4RgvinaUMDvPpqtMe+Y4evpV91FTzyiO+xFxervi4SZAr5sKDU4A8d8svfVVb6j0eO+NWRFi2CZcv8cMfRo9PdShFJFYV8AGzbFu2tv/qqHyEzahTcdpvvrV9/vV/vVERyj0I+Cznnl8Frqa+vXeu3z5gB99zj6+tXXqll8EREIZ81Tp+GV16Jzr/+ySc+xOfOhR/8wAd7UVG6WykimUYhn8EOHPDj1isr/Tj2Y8dg8GA/fUB5uZ+ut6Ag3a0UkUymkM8wmzdHyzBvvOFneBw7Fr7wBd9bv+46GDAg3a0UkWyhkE+z5mZ4991oGaamxm+/6CK4/37fY7/8ctXXRaR7FPJpcPIkvPRSdBm8PXv8TUjXXAN33eWnETjvvHS3UkSCQCGfInV18Mwzvsf+wgt+vdOhQ/3ydy319ZEj091KEQkahXwSbdgQHb/+1lu+NDNhAtx5pw/2efP8RGAiIsmikE+gpia/pmlLfX3DBr/9ssvg7//eXzi97DJNIyAiqaOQ76Hjx/0yeFVVfhm8ujq/iMb8+fBXf+Xr65MmpbuVIpKrFPLdsGePD/TKSr8M3qlTMHx4dBm8xYv9YxGRdFPId4JzUFsbra+/847fNmUKLF3qg/2aa7QMnohkHoV8Bxob4c03o/X1zZv99iuugO99z184vfBC1ddFJLMp5GMcPeqnD6iq8sMdDx6Efv38LI5/+7d+1aTx49PdShGRzsv5kN+1y9+QVFnpl8FraIC8PB/oZWV+HvahQ9PdShGR7sm5kHcO1q2LlmFalpSdNg3uvtuXYebMgT45d2ZEJIhyIsrOnIHXXoteON22zdfSZ8+Ghx/2PfYZM1RfF5HgCWzIHz7sl7+rqvLT9R4+7GdvXLgQHnzQl2PGjEl3K0VEkitQIb9jR3Sa3upqP0KmsBA+8xlfhlmwwM/HLiKSKwIR8i+95Je9W7PGPy4uhu9+15dhZs/2MzyKiOSiQIT80KEwZAg8+qgP9uLidLdIRCQzBCLkZ82C119PdytERDKP1hsSEQkwhbyISIAp5EVEAkwhLyISYAp5EZEAU8iLiASYQl5yRkV1RbqbIJJyPQp5M/tnM/vQzNaa2R/MbETMcw+Y2SYz22BmN/S8qSI9s/zV5elugkiHktUJ6WlPfgVwoXPuYuAj4AEAMysBbgdCwGLgP8xMkwuIiHQgWZ2QHoW8c+4F51xj+OHbwITw5+XAr51zp51zW4FNwKyeHEukOyqqK7Dlhi3380i3fK7SjeQKc84l5oXMngKedM79t5n9G/C2c+6/w889ATznnPttnK9bCiwFmDRp0uXbt29PSHtE2rLlhluWmPe7SCJUVFfE7cEvm7eMivkVnX4dM1vtnCuN99w5564xsxeBeDOvP+icqwzv8yDQCPyq060Kc849DjwOUFpaqv+BIpIzKuZXRMI8WZ2Qc4a8c27B2Z43szuBm4DrXfTPgl3AxJjdJoS3iVBRXdGlXkqiLJu3LOXHFEm3no6uWQzcC5Q5507EPFUF3G5m/c1sKlAErOzJsSQ40jXKJR2/WEQ6K1mdkJ5ONfxvQH9ghfkFUt92zn3DOVdjZr8BavFlnL90zjX18FgiIoGVrE5Ij0LeOXf+WZ77PvD9nry+BEfbC0wto126eoFJRLomYaNrEqG0tNStWrUq3c2QJNMoF5HEOtvoGk1rICISYAp5STmNchFJHYW8pJxq8KmjO3tFIS8SYJqUTRTyIiIBppAXCRhNyiaxNIRSJMA0XDU3aAiliEiOUsiLBJiGq4pCXiTANFxVFPIiIgGmkBcRCTCFvIhIgCnkRUQCTCEvIhJgCnkRkQBTyIuIBJhCXkQkwBTyIiIBppAXEQkwhbyISIAp5EVEAkwhLyISYAp5EZEAU8iLiASYQl5EJMAU8iIiAaaQFxEJMIW8iEiAKeRFRAJMIS8iEmAKeRGRAFPIi4gEmEI+ASqqK9LdBBGRuBTyCbD81eXpboKISFwKeRGRAEtIyJvZ35iZM7OC8GMzsx+b2SYzW2tmMxNxnExSUV2BLTdsuQFEPlfpRkQySZ+evoCZTQQWATtiNn8KKAr/uxL4SfhjYFTMr6BifgXgA94tc+ltkIhIHInoyf9P4F4gNuXKgV86721ghJmNTcCxAkc9fxFJph6FvJmVA7uccx+0eWo88HHM453hbfFeY6mZrTKzVXV1dT1pTtosm7es21+ri7YikkznLNeY2YvAmDhPPQj8Hb5U023OuceBxwFKS0uzsubRUrYREck05+zJO+cWOOcubPsP2AJMBT4ws23ABOA9MxsD7AImxrzMhPA2QRdtRSR1zLnEdJ7DQV/qnNtvZjcCdwNL8Bdcf+ycm3Wu1ygtLXWrVq1KSHuyhS7aikhPmdlq51xpvOd6PLqmA8/iA34TcAL4SpKOIyIiZ5GwkHfOTYn53AF/majXDrKeXLQVETkX3fGaZrpoKyLJpJAXEQkwhbyISIAp5EVEAkwhLyISYAp5EZEAS9jNUIlgZnXA9m5+eQGwP4HNSZRMbRdkbtvUrq5Ru7omiO2a7JwrjPdERoV8T5jZqo7u+EqnTG0XZG7b1K6uUbu6JtfapXKNiEiAKeRFRAIsSCH/eLob0IFMbRdkbtvUrq5Ru7omp9oVmJq8iIi0F6SevIiItKGQFxEJsKwKeTO7zcxqzKzZzErbPPeAmW0ysw1mdkMHXz/VzN4J7/ekmfVLQhufNLM14X/bzGxNB/ttM7N14f2SvlKKmVWY2a6Yti3pYL/F4XO4yczuT0G7/tnMPjSztWb2BzMb0cF+KTlf5/r+zax/+Ge8KfxempKstsQcc6KZvWJmteH3/7fi7DPfzA7H/Hz/Idntijn2WX825v04fM7WmtnMFLSpOOZcrDGzI2b27Tb7pOScmdnPzWyfmf0pZluema0ws43hjyM7+No7wvtsNLM7utUA51zW/ANmAMVANX4VqpbtJcAHQH/8koSbgd5xvv43wO3hz38KfDPJ7f0X4B86eG4bUJDCc1cB3HOOfXqHz915QL/wOS1JcrsWAX3Cn/8T8E/pOl+d+f6B/wH8NPz57cCTKfjZjQVmhj8fCnwUp13zgadT9X7qys8Gv4DQc4ABs4F3Uty+3sAe/A1DKT9nwDXATOBPMdseBe4Pf35/vPc9kIdfZjUPGBn+fGRXj59VPXnn3Hrn3IY4T5UDv3bOnXbObcWvSNVquUEzM+A64LfhTb8AbklWW8PH+yzwf5J1jCSYBWxyzm1xzjUAv8af26Rxzr3gnGsMP3wbvx5wunTm+y/Hv3fAv5euD/+sk8Y5t9s5917486PAemB8Mo+ZYOXAL533NjDCzMam8PjXA5udc929m75HnHOvAQfbbI59H3WURTcAK5xzB51z9cAKYHFXj59VIX8W44GPYx7vpP1/gnzgUEygxNsnka4G9jrnNnbwvANeMLPVZrY0ie2IdXf4z+Wfd/DnYWfOYzJ9Fd/jiycV56sz339kn/B76TD+vZUS4fLQZcA7cZ6+ysw+MLPnzCyUqjZx7p9Nut9Xt9NxZytd52y0c253+PM9wOg4+yTkvCVrjdduM7MXgTFxnnrQOVeZ6vbE08EKb4kAAALSSURBVMk2fo6z9+LnOud2mdkoYIWZfRj+jZ+UdgE/AR7C/4d8CF9K+mpPjpeIdrWcLzN7EGgEftXByyT8fGUbMxsC/A74tnPuSJun38OXI46Fr7f8P6AoRU3L2J9N+LpbGfBAnKfTec4inHPOzJI2lj3jQt45t6AbX7YLmBjzeEJ4W6wD+D8T+4R7YPH2SUgbzawP8Bng8rO8xq7wx31m9gd8qaBH/zE6e+7M7D+Bp+M81ZnzmPB2mdmdwE3A9S5cjIzzGgk/X3F05vtv2Wdn+Oc8HP/eSioz64sP+F85537f9vnY0HfOPWtm/2FmBc65pE/E1YmfTVLeV530KeA959zetk+k85wBe81srHNud7h0tS/OPrvw1w1aTMBfj+ySoJRrqoDbwyMfpuJ/G6+M3SEcHq8At4Y33QEk6y+DBcCHzrmd8Z40s8FmNrTlc/zFxz/F2zdR2tRAP93B8d4FisyPQuqH/zO3KsntWgzcC5Q55050sE+qzldnvv8q/HsH/Hvp5Y5+MSVKuOb/BLDeOfdYB/uMabk2YGaz8P+3U/HLpzM/myrgy+FRNrOBwzGlimTr8C/qdJ2zsNj3UUdZ9DywyMxGhsuri8LbuibZV5YT+Q8fTjuB08Be4PmY5x7Ej4zYAHwqZvuzwLjw5+fhw38T8H+B/klq5/8CvtFm2zjg2Zh2fBD+V4MvWyT73P0XsA5YG36DjW3brvDjJfjRG5tT1K5N+LrjmvC/n7ZtVyrPV7zvH/ge/pcQwIDwe2dT+L10XgrO0Vx8mW1tzHlaAnyj5X0G3B0+Nx/gL2DPSXa7zvazadM2A/49fE7XETMyLsltG4wP7eEx21J+zvC/ZHYDZ8L59TX8dZyXgI3Ai0BeeN9S4GcxX/vV8HttE/CV7hxf0xqIiARYUMo1IiISh0JeRCTAFPIiIgGmkBcRCTCFvIhIgCnkRUQCTCEvIhJg/x+WSEiR6VjoXQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xp8GEPXEG1XC"
      },
      "source": [
        "# Class Assignment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aW6kqE37Gw97",
        "outputId": "fc0b6e03-3b07-4091-978d-bcae2ed02ae4"
      },
      "source": [
        "Weights = [5, 2] # F(X) = 5x_0 + 2x_1\n",
        "F = lambda W, X: sum([w*x for w,x in zip(W,X)])\n",
        "\n",
        "dataX1 = range(-2, 11, 1)\n",
        "dataX0 = [1]*len(dataX1)\n",
        "\n",
        "data = (list(zip(dataX0, dataX1)))\n",
        "print (data)\n",
        "Y = [F(Weights, X) for X in data]\n",
        "print (Y)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(1, -2), (1, -1), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10)]\n",
            "[1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qaJYSM9QHA7K",
        "outputId": "03173989-ae4d-41bc-d51c-9389130735ec"
      },
      "source": [
        "TrainingData1 = zip(data, Y)\n",
        "\n",
        "print (list(TrainingData1))\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[((1, -2), 1), ((1, -1), 3), ((1, 0), 5), ((1, 1), 7), ((1, 2), 9), ((1, 3), 11), ((1, 4), 13), ((1, 5), 15), ((1, 6), 17), ((1, 7), 19), ((1, 8), 21), ((1, 9), 23), ((1, 10), 25)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "hUWy3PubH9qG",
        "outputId": "e0598b5c-8192-4447-d984-fcb26363a109"
      },
      "source": [
        "## Add Noise\n",
        "import random\n",
        "import numpy as np\n",
        "EPS = 30\n",
        "yNoise = [round(y+random.random()*EPS-EPS/2,2) for y in Y]\n",
        "TrainingData2 = zip(data, yNoise)\n",
        "print(list(TrainingData2))\n",
        "\n",
        "plt.plot(dataX1, Y, \"g\")\n",
        "plt.plot(dataX1, yNoise, \"g+\")\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[((1, -2), 7.3), ((1, -1), 1.78), ((1, 0), 14.19), ((1, 1), 1.19), ((1, 2), 14.68), ((1, 3), 23.79), ((1, 4), 12.46), ((1, 5), 4.42), ((1, 6), 10.39), ((1, 7), 27.38), ((1, 8), 10.39), ((1, 9), 19.98), ((1, 10), 36.35)]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5aLPcCY9YJ2C"
      },
      "source": [
        "# Testing Data\n",
        "\n",
        "## Training Data 1 (No Noise):\n",
        "[((1, -2), 1), ((1, -1), 3), ((1, 0), 5), ((1, 1), 7), ((1, 2), 9), ((1, 3), 11), ((1, 4), 13), ((1, 5), 15), ((1, 6), 17), ((1, 7), 19), ((1, 8), 21), ((1, 9), 23), ((1, 10), 25)]\n",
        "\n",
        "## Training Data 2 (Noise):\n",
        "[((1, -2), 6.39), ((1, -1), 16.51), ((1, 0), -3.11), ((1, 1), 10.79), ((1, 2), 11.62), ((1, 3), 23.24), ((1, 4), 18.27), ((1, 5), 27.58), ((1, 6), 22.21), ((1, 7), 5.12), ((1, 8), 8.86), ((1, 9), 10.69), ((1, 10), 14.82)]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KZmWdMNIkwO",
        "outputId": "efd15f40-c789-459e-e9e4-a2d5b5f5be6c"
      },
      "source": [
        "data = [((1, -2), 1), ((1, -1), 3), ((1, 0), 5), ((1, 1), 7), ((1, 2), 9), ((1, 3), 11), ((1, 4), 13), ((1, 5), 15), ((1, 6), 17), ((1, 7), 19), ((1, 8), 21), ((1, 9), 23), ((1, 10), 25)]\n",
        "print(data)\n",
        "X = [d for d,_ in data]\n",
        "Y = [y for _, y in data]\n",
        "print(X)\n",
        "print(Y)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[((1, -2), 1), ((1, -1), 3), ((1, 0), 5), ((1, 1), 7), ((1, 2), 9), ((1, 3), 11), ((1, 4), 13), ((1, 5), 15), ((1, 6), 17), ((1, 7), 19), ((1, 8), 21), ((1, 9), 23), ((1, 10), 25)]\n",
            "[(1, -2), (1, -1), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10)]\n",
            "[1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OeN-2kRYI8vI"
      },
      "source": [
        "alpha=0.01\n",
        "model=[0, 0]\n",
        "Errors = []"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aLSOgxBtMXF_",
        "outputId": "7b45e39e-9dcf-4ef0-dcd9-e55a0a2728bb"
      },
      "source": [
        "# Calculate Predicted\n",
        "\n",
        "yPredicted = [F(model, x) for x in X]\n",
        "print(yPredicted)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DIYJtXs5JgtM",
        "outputId": "85851c83-6d54-4516-fcdf-57a038a2abb7"
      },
      "source": [
        "# Evaluate Model\n",
        "Error = sum([(r-y)**2.0 for r,y in zip(Y, yPredicted)])\n",
        "print(\"With Model=\", model, \"Error=\", Error)\n",
        "Errors.append([model, Error])\n",
        "print (\"History w/ Alpha=\", alpha, \"=\")\n",
        "print (Errors)\n",
        "print(\"Next\")\n",
        "for i in range(len(Errors)):\n",
        "  print(\"Iterations=\",i+1,\":=\",Errors[i])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "With Model= [0, 0] Error= 2925.0\n",
            "History w/ Alpha= 0.01 =\n",
            "[[[0, 0], 2925.0]]\n",
            "Next\n",
            "Iterations= 1 := [[0, 0], 2925.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dx7wY_2CKAh8",
        "outputId": "c464df4c-cbdb-45fd-f2b6-3545a9b07a51"
      },
      "source": [
        "deltaX0 = 1/len(Y)*sum( [(-2*(r-y)*x[0]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "deltaX1 = 1/len(Y)*sum( [(-2*(r-y)*x[1]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "\n",
        "print(deltaX0)\n",
        "print(deltaX1)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-26.0\n",
            "-160.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olKN1ZJJL7ZV",
        "outputId": "440dbd9a-2eee-40ac-d374-6ee5f322e151"
      },
      "source": [
        "model = [model[0]-alpha*deltaX0, model[1]-alpha*deltaX1]\n",
        "print(model)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.26, 1.6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vE6QgohmdVvT"
      },
      "source": [
        "# Plot Learned Model with Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "WegUgbPMdVJE",
        "outputId": "e69bdfb9-460e-4f71-f094-00a6df543287"
      },
      "source": [
        "dataX1 = [x[1] for x, y in data]\n",
        "plt.plot(dataX1, yPredicted, \"g\")\n",
        "plt.plot(dataX1, Y, \"g+\")\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wg1gxjIY2WAC"
      },
      "source": [
        "### Cubic function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xno2wCK32d78"
      },
      "source": [
        "Weights = [0.5, 0.2, 0.25, 0.5] # F(X) = 0.5x_0 + 0.2x_1, 0.25x_1^2 + 0.5x_1^3\n",
        "F = lambda W, X: sum([w*x for w,x in zip(W,X)])\n",
        "\n",
        "dataX1 = [i*0.01 for i in range(-500, 501, 10)]\n",
        "dataX0 = [1]*len(dataX1)\n",
        "dataX2 = [x**2 for x in dataX1]\n",
        "dataX3 = [x**3 for x in dataX1]\n",
        "\n",
        "data = list(zip(dataX0, dataX1, dataX2, dataX3))\n",
        "\n",
        "Y = [F(Weights, X) for X in data]\n",
        "TrainingData1 = list(zip(data, Y)) \n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "5T8Ley6q240W",
        "outputId": "e53f8c71-33af-484d-97a9-060b5ce09a19"
      },
      "source": [
        "plt.plot(dataX1, Y)\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJZJxHzF3vnZ"
      },
      "source": [
        "data = TrainingData1\n",
        "X = [(d[0], d[1], d[2], d[3]) for d,_ in data]\n",
        "Y = [y for _, y in data]\n",
        "\n",
        "alpha=0.0001\n",
        "model=[0, 0, 0, 0]\n",
        "Errors = []\n",
        "iteration = 0"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "MjEJL6d-C4Qx",
        "outputId": "7b954248-0882-47c7-d4c8-15fa121a1714"
      },
      "source": [
        "dataX1 = [x[1] for x,y in data]\n",
        "yPredicted = [F(model, x) for x in X]\n",
        "plt.plot(dataX1, yPredicted, \"g\")\n",
        "plt.plot(dataX1, Y, \"g+\")\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bnQl_ygO4TpU",
        "outputId": "22bb2f9c-8c88-42a1-ea76-b99fb81dbc42"
      },
      "source": [
        "yPredicted = [F(model, x) for x in X]\n",
        "Error = sum([(r-y)**2.0 for r,y in zip(Y, yPredicted)])\n",
        "if iteration%1==0:\n",
        "  print(\"Iteration: \", iteration, \"Error=\", Error)\n",
        "\n",
        "deltaX0 = 1/len(Y)*sum( [(-2*(r-y)*x[0]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "deltaX1 = 1/len(Y)*sum( [(-2*(r-y)*x[1]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "deltaX2 = 1/len(Y)*sum( [(-2*(r-y)*x[2]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "deltaX3 = 1/len(Y)*sum( [(-2*(r-y)*x[3]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "\n",
        "print(deltaX0, deltaX1, deltaX2, deltaX3)\n",
        "\n",
        "model = [model[0]-alpha*deltaX0, model[1]-alpha*deltaX1, model[1]-alpha*deltaX2, model[1]-alpha*deltaX3]\n",
        "print(model)\n",
        "iteration += 1"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  0 Error= 63509.65092500001\n",
            "-5.250000000000003 -133.433 -73.5165000000001 -2419.852249999999\n",
            "[0.0005250000000000003, 0.0133433, 0.00735165000000001, 0.24198522499999991]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QeGSpZfN_Uyb",
        "outputId": "663e8d3f-9c0f-4b0a-d3ed-d2834659a3f6"
      },
      "source": [
        "for _ in range(8):\n",
        "  yPredicted = [F(model, x) for x in X]\n",
        "  Error = sum([(r-y)**2.0 for r,y in zip(Y, yPredicted)])\n",
        "  if iteration%1==0:\n",
        "    print(\"Iteration: \", iteration, \"Error=\", Error)\n",
        "    plt.plot(dataX1, yPredicted, \"g\")\n",
        "    plt.plot(dataX1, Y, \"g+\")\n",
        "    plt.show()\n",
        "  deltaX0 = 1/len(Y)*sum( [(-2*(r-y)*x[0]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "  deltaX1 = 1/len(Y)*sum( [(-2*(r-y)*x[1]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "  deltaX2 = 1/len(Y)*sum( [(-2*(r-y)*x[2]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "  deltaX3 = 1/len(Y)*sum( [(-2*(r-y)*x[3]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "\n",
        "  model = [model[0]-alpha*deltaX0, model[1]-alpha*deltaX1, model[1]-alpha*deltaX2, model[1]-alpha*deltaX3]\n",
        "  iteration += 1"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  1 Error= 18222.197480258525\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  2 Error= 33565.72877680522\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  3 Error= 24449.66387134854\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  4 Error= 26877.51244075408\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  5 Error= 24309.3360557008\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  6 Error= 24056.23582701846\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  7 Error= 22825.741439090732\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  8 Error= 22085.42267443668\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-toqLYNzExRH",
        "outputId": "84f4d38c-5907-4b63-faf0-e202104bd146"
      },
      "source": [
        "for _ in range(101):\n",
        "  yPredicted = [F(model, x) for x in X]\n",
        "  Error = sum([(r-y)**2.0 for r,y in zip(Y, yPredicted)])\n",
        "  if iteration%10==0:\n",
        "    print(\"Iteration: \", iteration, \"Error=\", Error)\n",
        "    plt.plot(dataX1, yPredicted, \"g\")\n",
        "    plt.plot(dataX1, Y, \"g+\")\n",
        "    plt.show()\n",
        "  deltaX0 = 1/len(Y)*sum( [(-2*(r-y)*x[0]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "  deltaX1 = 1/len(Y)*sum( [(-2*(r-y)*x[1]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "  deltaX2 = 1/len(Y)*sum( [(-2*(r-y)*x[2]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "  deltaX3 = 1/len(Y)*sum( [(-2*(r-y)*x[3]) for r,y,x in zip(Y, yPredicted, X)] )\n",
        "\n",
        "  model = [model[0]-alpha*deltaX0, model[1]-alpha*deltaX1, model[1]-alpha*deltaX2, model[1]-alpha*deltaX3]\n",
        "  iteration += 1"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  10 Error= 20389.752528789715\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  20 Error= 13784.33924274274\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tP24D4MBqB3JGgzM4q8FZtG/UnvYN23PoAYfG4dtKRSjkRZKsLEFdkQAvS5hH+2HoD6zcupLW/2xNZsdMlm9ezltfvsW2H7exJ38PADXSa3DKUafQ+pjWtKvfjnb129HiqBZUS1N1t7JK6o+GmFlXM/vMzFaa2ZB4jyep4ydXVAZ0HK/zRm9alve4JDbcigz4Wf1m8WyPZwHod2o/2jdsD0CtB2vR+p+tC8dYsH4Bbeu35bZf3caESybw4R8+ZPvQ7WQPyObpHk/z+za/p9UxrRTwVVhcV/Jmlg58DpwHrAM+AH7r7suKer9W8qktXivc8hzH67wVOS6v2jVr8/3u73/2fKNDG9HksCZ89+N39G7Rm6Z1mtLsiGaccMQJHFT9oHKPI5VPMvvk2wEr3X1VZCKTgJ5AkSEv4VWW0I7u/ijLcUncnTzPA2BH7o7C4y07txQef/3D1+R7PgBrvl1DvucXPv58y+eFx59s+qTweNGGReTlF3z+3bXvFh7PXjWbvfl7AXhp+UuFpY/i7qdeluOyaFqnKUcffDTz186nf6v+1KtVj/fXv8+gMwfRsHZDxi4ey73n3luuc0q4xHsl3wvo6u7XRR73A85w95uKer9W8uFS0qp78182s3XXVk548gRmXjmTb3/8lt4v9uaRLo/wQ+4PDMsaxsC2A9mxZwfjPxzPhc0uZNfeXbz55ZtJ/EbBqVWjFgdWP5BNOzbR4sgWHFzjYBasX0CP5j2oVaMWE5dOZGiHoRxa81CGzBnC5F6TqXNgHTo/35m1t67liAOP4KAHDvrJ7QTK8o+fhFOlvuLVzAYAAwAaNWqU5NlIRWRmZTKs4zA279zMXW/eRY/mPVj/w3qGzx3Oxu0b2bhjIwDNn2xOzo4cAOo+XLfw810mdCk8vm3mbYXHo7JHFR6/tuK1Ise+5pfXUDO9JqMXjua2M2+jWlo1Hnr3IYZ1HEa1tGr8z1v/w8jOI0m3dP4868883vVx0iyNm6bfxD+7/5M0S+P3r/6esT3GkmZpmBlXv3w1Ey+diGFcMfUKJveajJlx2QuXMe3yaaRZGhf970XM6DuD9LR0znv+PN655h3SLZ2zxp7F4usXUz2tOi2fasnqW1ZTI70G9R6px/ah26lZrSbV763O90MLyio23Ph44MeFx9MunwbAxKUTeaDTAwDs2ruLy1pcBhT0qjeo3aDwuPC/gQJeiuPucfsD/Ap4I+rxUGBoce9v06aNS+W1ffd2X7Jhib/wyQveeXxnv/7V6/28585zMvED7zvQySTmP1t3bvU9eXucTArHTeRxvM477K1hFT4WKQ2Q7cXkarxX8h8AzcysCbAeuBy4Is5jSoy+3/09H2/6mKUbl7IsZxnLNy9nWc4y1v+w/ifvW7JxCU0OawLAwNMH0ujQRtwy45Ziz1uWzcbDDzy8zPOMXskGdRyv80avtMt7LBKLuPfJm1k34FEgHRjr7vcX917V5BNvy84tfPD1Byz8eiGLvlnE4g2L+fLbLwtfr55WnVbHtOKkuifx/EfP8+9e/6ZZnWacNua0Mp2/PB0l5e2uEZECuhhKAMjLz2PppqXM/2o+7657l/fXvc8X274ofL1pnaa0PqY1W3ZuYdCZgzjl6FNo8liTCl8WX94AF5GKUcinqLz8PBZ/s5is1Vm8tfot5n01r7CPul6tepzZ4EzOqH8Gp9c7nTe+eIMRnUcAsd3vpLhgF5H4qdTdNRKstd+tZcbKGcxcNZM5q+YU3nvkxLon8tuWv+XXjX5Nh0YdaHRoI4bPHc7g9oMBOPe5cwtDHsp3h8Ky1KFFJDm0kq/i8j2fD9Z/wLTPpvHaitf4aONHANSvVZ8uv+hC5+M7c07jczi21rE/+2wsK3at0kUqD63kQ2Zv/l7eXvM2Ly57kWmfTePrH74m3dLp0KgDD3V+iG7NunHykSf/7H7eRQVzRVfsCniRqkEhX0Xkez7vrn2Xfy39F1OWT2HTjk0cVP0gLmh6AZeceAndmnUrsv1w/9sGRP+9PwW7SPioXFPJrdiygvEfjmfi0oms/nY1B1Y7kO4ndKdPiz5c0OyCUm8wVZEbcakUI1K1qFxTxezcs5MXPnmBZxc/yztfvUOapdH5+M7ck3EPl5x0CYfUOKTEz+8f0kXdAKukC4AU8CLhoZCvRD7d/Cmjs0czbsk4vtv9Hc3qNOPBTg9yVaurqFerXomf3b8sU1pJ5mefV7CLhJLKNUmW7/m8vuJ1Hnv/MWavmk31tOr0OrkX17e5nrOPO7vMP4Zckfumi0g4qFxTCe3cs5NxS8bx6HuPsmLrChrUbsAD5z7Ataddy1EHH1Wmc5SlLBNt/xKNiISfVvIJtnXXVv6x4B88vuBxNu/czBn1z+DWM2/l0pMupXp69XKdqyy97dpEFQk/reQrgZwdOfztv3/jHx/8g+252+l+QncGnzWYDo06lLkkAz8P7dJ+Ok4BL5LaFPJxtnnnZkbOG8mo7FHs2rOLPi37cOev76TlUS0rdL79N1VVlhGRkijk4+S7H7/jkf8+wt/f+zvbc7fT99S+3PnrOzmx7onlPldZVu+ZWf/3ulbvIrKPavIBy83L5akPnuLet+9ly64t/Oak33DPOfdw8pEnV/icZam9i0jqUk0+AdydKcunMGT2EL7Y9gWdmnRiZOeRtKnXJpjzl7J6FxEpikI+AEu+WcItM27h7TVv0/KolkzvO53zf3F+uTZU95cxLoO5a+YWPi5qNa+yjIiURiEfg627tnLnnDsZs2gMhx9wOKMvHM11p11Help6zOeeu2auVu8iErO0ZE+gKsr3fMYtGUfzJ5vz9KKnubndzay4eQXXt70+poAvS4Br9S4i5aGVfDl9uvlTBrw6gHe+eoezGp7FqG6jaHVMq0DOXVx7ZMfjOgZyfhFJPQr5MsrNy2XkvJHc9859HFz9YJ656BmuaX0NaRbb/wyVpT1SRKSi1EJZBos2LOLql6/m400f06dFHx7r+hhHH3J0IOdWe6SIxEotlBWUm5fL/W/fz/3v3M/RhxzNK5e/wkXNLwp8HG2wiki8xBTyZvYwcBGQC3wBXOPu30ZeGwpcC+QBf3T3N2Kca0Ity1nGlVOvZPE3i+l3aj8e6/pYkT+vVxFqjxSRRIm1u2YW0NLdTwU+B4YCmNnJwOVAC6ArMMrMYu8rTAB358kFT9JmTBvWfb+Ol/q8xHOXPBdYwMP/tUfuW7XvO9Y9Z0QkaDGt5N19ZtTD94BekeOewCR33w18aWYrgXbAf2MZL95yduTQf1p/Xl/xOhc0vYCxPcdyzCHHJGx8rd5FJGhB1uR/B/w7clyfgtDfZ13kuZ8xswHAAIBGjRoFOJ3yyVqdxRVTrmDrrq083vVxbmp3U0xXrO6vuBKN2iNFJJ5KDXkzmw0UtZy9092nRd5zJ7AXmFjeCbj7GGAMFHTXlPfzscrLz+O+t+/jnrfvoWmdpkzvOz2wvvdoxV3BKiIST6WGvLt3Lul1M+sPdAc6+f/1Y64HGka9rUHkuUpl887N9J3al5lfzKTfqf0YdeEoDqlxSLKnJSISmFi7a7oCg4GO7r4z6qVXgH+Z2SNAPaAZsCCWsYK2YP0Cek3uxcYdGxnTfQzXnXZdoOUZUIlGRJIv1pr8k0BNYFYkIN9z9z+4+ydmNhlYRkEZ50Z3z4txrMCMXTyWG167gWMPOZb5v5tP23pFXkMQM5VoRCTZYu2uaVrCa/cD98dy/qDtydvDn2b+iScWPEHn4zsz6TeTOOKgI5I9LRGRuEmZK1637trKZS9cxptfvsltZ97GyPNGUi0t+K+vEo2IVCYpEfIrtqzgwn9dyJrv1jD+4vFc1eqquI2lEo2IVCahD/ms1Vlc+u9LSU9L582r3qR9o/bJnpKISMKEOuQnfDSB3037HU3rNOU/V/yH4w8/Pi7jZGZl6j7wIlIphTLk3Z0R80Zwx5t3cE7jc5jaZyqHHXBYfMdUiUZEKqHQ/fxfXn4eN75+I3e8eQdXnHIFM66cEfeAj17Fi4hUJqFaye/eu5u+U/syZfkUBp81mAc7PxjzLzeVl+4kKSKVSWhW8ttzt9P9f7szZfkUHunyCCPPGxnXgM8Yl4ENt8L6e/SxiEhlEYqV/JadW+j2r24s/Hoh43qO4+pfXh33MdUqKSJVQShCftaqWXy08SOm9plKj+Y9kj0dEZFKIxQhf3nLy+nQqAMNajeI6zhqlRSRqiYUIQ/EPeD3UYlGRKqS0Gy8JoJaJUWkqlHIV5BaJUWkKlDIlyIzK1OtkiJSZYWmJh9PqsOLSFWllXwpVIcXkapMIV8OqsOLSFWjkC+C6vAiEhaqyRdDdXgRCQOt5IugOryIhIVCvhSqw4tIVRZIyJvZn8zMzaxu5LGZ2eNmttLMPjKz04IYJ55UhxeRMIq5Jm9mDYEuwFdRT18ANIv8OQN4KvJ3paY6vIiETRAr+b8Dg4HoVOwJPOcF3gMOM7NjAxgrblSHF5EwiinkzawnsN7dP9zvpfrA2qjH6yLPFXWOAWaWbWbZOTk5sUwnMKrDi0hYlBryZjbbzD4u4k9P4A7g7lgm4O5j3L2tu7c98sgjYzlVuakOLyJhZ+4Vqz2b2SnAHGBn5KkGwNdAO2A4kOXu/xt572dAhrtvKOmcbdu29ezs7ArNJ1aqw4tIVWVmC929bVGvVbhc4+5L3f0od2/s7o0pKMmc5u7fAK8AV0W6bM4Evist4JMhMysz2VMQEYmrePXJvw6sAlYCTwMD4zROTKI3W1WHF5EwCuy2BpHV/L5jB24M6tyJkJmRmewpiIgELuWueC1us1WlGxEJo5S8QZkuehKRVJFyK3ld9CQiqSTlQj6aNltFJOxSIuR10ZOIpKoKXwwVD4m4GEp1eBEJm7hcDFWVqHNGRFJVSoS8LnoSkVSVEiEfTRc9iUgqCW3I66InEZEU2XjVZquIhFnKb7yKiKSqUIb8/iUZbbaKSKoKZcjvf+sCbbaKSKoKZciLiEiB0IS8umlERH4ulN016qYRkVSSEt01WrGLiPxcaEJety4QEfm50IR8NHXTiIgUqNIhr81WEZGShWbjVZutIpKq4rrxamY3m9mnZvaJmT0U9fxQM1tpZp+Z2fmxjiMiIuVXLZYPm9k5QE+glbvvNrOjIs+fDFwOtADqAbPN7AR3z4t1wsXRZquIyM/FupK/ARjh7rsB3H1T5PmewCR33+3uXwIrgXYxjlUibbaKiPxcrCF/AvBrM3vfzOaa2emR5+sDa6Pety7y3M+Y2QAzyzaz7JycnBinIyIi0Uot15jZbOCYIl66M/L5OsCZwOnAZDM7vjwTcPcxwBgo2Hgtz2dFRKRkpYa8u3cu7jUzuwGY6gUtOgvMLB+oC6wHGka9tUHkORERSaBYyzUvA+cAmNkJQA1gM/AKcLmZ1TSzJkAzYEGMY4mISDnF1F0DjAXGmtnHQC5wdWRV/4mZTQaWAXuBG+PZWSMiIkWrVBdDmVkOsCbZ86iAuhT8H0wq0XcOv1T7vlB1v/Nx7n5kUS9UqpCvqswsu7irzcJK3zn8Uu37Qji/c5W+d42IiJRMIS8iEmIK+WCMSfYEkkDfOfxS7ftCCL+zavIiIiGmlbyISIgp5EVEQkwhHzAz+5OZuZnVTfZc4snMHo78jsBHZvaSmR2W7DnFi5l1jfwuwkozG5Ls+cSbmTU0s7fMbFnkdyJuSfacEsXM0s1ssZn9J9lzCYpCPkBm1hDoAnyV7LkkwCygpbufCnwODE3yfOLCzNKBfwAXACcDv438XkKY7QX+5O4nU3DzwRtT4DvvcwuwPNmTCJJCPlh/BwYDod/NdveZ7r438vA9Cm5CF0btgJXuvsrdc4FJFPxeQmi5+wZ3XxQ5/oGC0CvyVuFhYmYNgAuBZ5I9lyAp5ANiZj2B9e7+YbLnkgS/A6YnexJxUubfRggjM2sMtAbeT+5MEuJRChZp+cmeSJBivUFZSinl3vp3UFCqCY2Svq+7T4u8504K/vd+YiLnJvFnZocAU4BB7v59sucTT2bWHdjk7gvNLCPZ8wmSQr4ciru3vpmdAjQBPjQzKChdLDKzdu7+TQKnGKiSfksAwMz6A92BTh7eCy5S8rcRzKw6BQE/0d2nJns+CdAe6GeFD60AAAC5SURBVGFm3YADgNpmNsHdr0zyvGKmi6HiwMxWA23dvSreza5MzKwr8AjQ0d1D+7uNZlaNgo3lThSE+wfAFe7+SVInFkdWsFIZD2x190HJnk+iRVbyf3b37smeSxBUk5eKehKoBcwysyVmNjrZE4qHyObyTcAbFGxATg5zwEe0B/oB50b+2y6JrHClCtJKXkQkxLSSFxEJMYW8iEiIKeRFREJMIS8iEmIKeRGREFPIi4iEmEJeRCTE/j9Qf5HP77GkSwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  30 Error= 9341.60238144066\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  40 Error= 6355.69775072316\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  50 Error= 4354.026764703041\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  60 Error= 3016.407430824035\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  70 Error= 2126.073794631106\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  80 Error= 1536.402529361122\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  90 Error= 1148.3224327997343\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  100 Error= 894.9808977820261\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "332Yj3DZ_lEU",
        "outputId": "3eff7f8d-0408-4ae3-c295-e08061f6a59e"
      },
      "source": [
        "dataX1 = [x[1] for x,y in data]\n",
        "plt.plot(dataX1, yPredicted, \"g\")\n",
        "plt.plot(dataX1, Y, \"g+\")\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AL7mdX5BPRnYGmcszuXXhrQDUurNW8dICj7z7CLlf5JZqh4I99cQ95M2sq5l9ZGZrzOzWeF9PUkc0wVnedrw+t6ygrmy7oLCAjTs2AjDpvUmMXDgSgNPHn87h9xwOwIAZA4ofoxcp/478UnegRlKwpxB3j9sPUBtYCzQH6gLLgNblHX/22We7pK5Rr4+q0jYhYr4dr88tub1l5xZfuWWlE8KnLZ/mYxePdUL4gBcHeIcnOzghqvxT2XUj/+4kWIAcLydX492Tbwescfd17r4XmAb0ivM1JQnFo7cbCx5e1uO7/O/4ds+3bP9uOwBffPsFG3ZsAGD1V6tZ/p/lQNEzSvdPO5z10SymfzgdgMdyHuOhxQ8BMHLhSK5/5XoALMOK11CP3D7+geNp/ffWAPR7sR83zLsBgKnLp5K9PrtUO9f9YR35d+QXtTmiZ15ZL109donr2jVm1gfo6u6/Db/+NfATdx9a1vFauyZYItc+iXwaUEXbe27fw7bvtnHigyfy9tVv8/V/v6Z7Zncm9JjAt3u+5Y8L/piQ7xILA84awDHfO4ZxS8fxSLdHOO6w4+g/oz8rrlvBCd8/gQb3Nziov6OytiP/riX1JPXj/8xsEDAIoEmTJglujRyKyICJ3M7IzjggeFblrWL9jvUA3JV9F5u/LVpzPPJpQfX+Uq94+7zJ5xVvD549uMxrtzy2JYfVOYx//+ffnH/K+dSrXY8F6xZw2emXUbd2XaatmMbVP7yaOrXrMCF3Ajf85AbSaqXxwOIHuP1nt5NWK41QdojRnUeTViuNtFpp3DjvRh67+DHSaqXx25d/S+ZlmaTVSuOXL/ySuQPmUrd2XTpP6UzOtTnUS6vHWY+exec3fc5hdQ7jmPuOKTeQn7nsGQDGLR3H0HZF/Zz+M/pzxvFnVPj3W17PXL10OSjl1XFi8QOcC8yLeD0SGFne8arJ1xxl1cZ37tnphPCp/57qd/zrjkOqK9+ZdaePf3e8E8LnfjLXF29c7ITwjTs2+tfffR33enm8Preq4w2qn0tVUEFNPt4hnwasA5rxv4HXM8o7XiGf3PYHT2FhoRPCZ66aWRzmzf/W3C1kFQb41H9P9UXrFzkh/L/5/3X3xAVnRYEar88ViZeKQj7u68mbWXdgLEUzbSa7+93lHauafPIJZYUY0X4ESzYtodOUTvQ8rSfvbHqHvN15B3X+odaVD2ZbRIpUVJPXQ0OklNv/dTudmnVi4bqFjH5zNHVq1SG/ML/Cc2IR4CJyaBTyUqlhc4fR4tgWzPlkDvPWzgMgrVYa+wr3lXuOZnyIJIeknl0jieHuXDfnOhoc3oCZq2eyMm8lAK2Oa1V8TFkBHxnm+2nGh0jyUsinmFV5q5i6fCrTVkxj7fa11LJadDilQ3HIf7T1o1LnVBbskWGuYBdJLgr5FLB191amLp/K6EWj+XLXl9SyWlzY/ELWbl9LoRfy+mevlzqnKsEuIslLIR9Q7k7WZ1kMnTuUNdvWsLdgLwAXnXoR89bOY/7a+aXPUbCLBI5CPmC+3fMtU5ZN4e85fy8uwQz7yTCubns1bR5rw6tXvFp8bMkB0/0U7CLBofXkA2LzN5u5deGtNBnbhKFzh3J4ncN5steTAPxtyd9o81gb4H+LZJVcXlfBLhJM6snXcGu3rWX0m6N58v0nwaD36b15fuXz5Hyew8CXBpY6vqxnd4KCXSSoFPI11MdbP+auN+4ic3kmdWrVoZBC1l2/jmbHNKvw7tJICnaR4FPI1zAbdmzgzuw7eeqDpzAzbjznRm4+92ZOGnNS8aPfoHSgQ+kHM4tI8OmO1xpi+3fbuXvR3Tzy7iMA/D7994xdMrbc43XXqUjqqOiOVw28Jrn8gnweWfIILR5pwZjFY2jdoDWfXP8JD3UtegpRZU8JUsCLpDaVa5LY65++ztC5Q1mZt5JOzTrxYJcHaTuhLaeMPaX4GJVlRKQiCvkk9Pm3n3Pz/JuZtmIazY5uRr8z+pHZOxOzokAvbzGw/dR7F5H9VJNPIoVeyBPvPcHwBcPZs28PI386khHtR3D4PYeXe055UyJFJHVoFcoa4OOtH3Pty9fyxvo36NSsExN6TKDFsS2K91fWexcRKYtCPsEKvZBx747j1oW3Ui+tHpMumcTAHw7kgqcvIHt9dvFxZdXeVZYRkcoo5BNo/dfrGfjSQF7/7HW6tejGE5c8wUn1TwIge322eu8iEjVNoUyQ5z98njaPtWHp50t5vOfjzOk/h4m5Eys9T713EakK9eSr2a69u7jh1Rt44v0n+MnJPyGzdybNjym6UzUjO4OM7IziY/eXaDqc0iEhbRWRmk89+Wq0+qvVtHuiHZPen8TIn45k0cBFTFk25YBjyrq5KeuqrAS0VkSCQFMoq8n0D6dzzaxrOCztMDJ7Z3Jh8wuBsgdU99P0SBE5GJpCmUD7CvcxfP5wxi4Zy7mNzmX65dNpdGSjA47RAKuIxEtUIW9m9wM9gb3AWmCgu38d3jcSuAYoAP7g7vOibGuNs+27bfR9oS8L1y3k+nbX80CXB6hbuy4dn+qo6ZEiUi2i7ckvAEa6+z4zuw8YCdxiZq2BfsAZwEnAQjM7zd0LorxejbEybyWXPHsJG7/ZyORLJjOw7f8e4KHpkSJSXaIaeHX3+e6+L/zyHWB/HaIXMM3d97j7p8AaoF0016pJFqxdwLmTzmXn3p1kXZl1QMBXRL13EYm1WNbkrwaeC2+fTFHo77cp/F4pZjYIGATQpEmTGDYnMSa9N4nfzfkdpzc4nTn959D4qMYA5ZZoND1SROKp0pA3s4XACWXsus3dXwofcxuwD5ha1Qa4+0RgIhTNrqnq+cnC3bn9X7dzz5v3cNGpFzH98ukcWe/I4v3llWhEROKp0pB39wsr2m9mVwE9gM7+v/mYm4HGEYc1Cr8XSPsK9zH45cFM/mAy1/7oWsZ3H0+d2nUS3SwRkahn13QFRgAd3H13xK5ZQKaZjaFo4LUl8G4010pW3+V/R78X+zHro1mM6jCKUR1GFa/7rhKNiCRatDX5cUA9YEE42N5x99+5+4dmNh1YSVEZZ0gQZ9Z8s+cbej7bk0XrFzGu2ziGtBtywH6VaEQk0aIKeXdvUcG+u4G7o/n8ZLb9u+10ndqV9754j8zemfQ7s1+imyQiUorueD0Eebvy6PJMF1bmreSFy1+g1w96Fe9TiUZEkolCvoq27NpCp6c7sXb7Wl7q9xJdW3Q9YL9KNCKSTBTyVfDV7q/oPKUz67avY07/OXRq1inRTRIRqZBC/iBt3b2VzlM6s2bbGmb/avYBAR/KCmkdeBFJSgr5g/D1f7/m5//4OR999RGzfjWLzs07lzpGJRoRSUZ6aEgldu3dRY/MHqzYsoKZfWfS5dQupY6J7MWLiCQT9eQrsLdgL72n92bxpsU81+c5urXsVuk5ozqMqoaWiYgcHPXky1FQWMAVM65g3tp5PN7zcfq07nPA/o5PdcQyrLj+HrktIpIs1JMvg7sz7NVhPL/yeR7s8iBXt7261DGaKikiNYF68mW47637GL90PMPPG85N596U6OaIiBwy9eRLmLJsCiNfG0n/s/pz74X3HrBPUyVFpKZRyEd4bd1rXDPrGjo368yTvZ6klpX+j45KNCJSk6hcE7b6q9X0nt6bVse14sVfvkjd2nVLHaOpkiJS0yjkKVqu4OLMi6mXVo/Z/Wdz1PeOqvQcTZUUkZog5UN+z749/OK5X7D5m8281O8lmh7d9ID9oayQpkqKSI2V0jV5d2fIK0N4c8ObTOs9jXManVP2carDi0gNldI9+b8v/TuT3p/EbT+7jb5n9i3zGNXhRaQmS9mQz/osi2GvDqPHaT2484I7D+oc1eFFpKZJyZDfsGMDlz9/OS2Pa8kzv3im1FRJ1eFFJChSria/Z98e+kzvw559e/hn33+WO5NGdXgRCYKU68nfOO9Gln6+lKcvfZpWDVqVeYzq8CISFCkV8v9Y9g8ezXmU4ecN5xen/+KgzlEdXkRqspiEvJndbGZuZg3Cr83MHjazNWb2bzP7USyuE40VW1YwePZgzj/lfO7pfE+p/arDi0gQRV2TN7PGQBdgQ8Tb3YCW4Z+fAI+G/0yI3fm76ftCX+rXq8+03tNIq1X211YdXkSCJhY9+YeAEUBkKvYCpniRd4CjzezEGFzrkPxh7h9YlbeKqZdN5cT6ZTdDdXgRCaKoQt7MegGb3X1ZiV0nAxsjXm8Kv1fWZwwysxwzy8nLy4umOWXKXJ7JpPcnMfKnI7mw+YUHdY7q8CISFJWGvJktNLMVZfz0Av4E/DmaBrj7RHdPd/f0hg0bRvNRpazZtobBswfTvnF7Mi4o3VNXHV5Egq7Smry7l9n9NbOzgGbAMjMDaAS8Z2btgM1A44jDG4Xfqzb5BfkMmDGAOrXq8GzvZ8usw4c6hgh1DAGqw4tIMB1yucbdl7v78e7e1N2bUlSS+ZG7fwnMAn4TnmVzDrDD3b+ITZMPzl1v3MW7m99lQo8JND6qcZnHhLJC1dkkEZFqF6958q8A64A1wOPA7+N0nTK9teEt7l50N1e2uZLLz7i83OMiB1tVhxeRIIrZsgbh3vz+bQeGxOqzq+KbPd9wxcwraHp0Ux7u9vBBn7e/bCMiEiSBu+P1xldvZMOODTzzi2c4st6RpfaXN9iq0o2IBFGgFiib8/EcJn8wmZE/Hcm5jc8t9zjd9CQiqSIwPfmtu7fy25d/y1nHn1VhfV03PYlIKglMT/76udfz1e6veKX/K9RLq3dQ52iwVUSCLhA9+RdXvsizK57lz+f/mbYnti21Xzc9iUiqsqKJMMkhPT3dc3Jyqnze599+zoNvP8i9F95Lndp1KjxWdXgRCRozy3X39LL2BaInf1L9k3jwogfLDXjNnBGRVBWIkK+MbnoSkVSVEiEfSTc9iUgqCWzI66YnEZGADLxWRoOtIhJkgR94FRGRsgUy5EuWZDTYKiKpKpAhX3LpAg22ikiqCmTIi4hIkcCEvGbTiIiUFsjZNZpNIyKpJCVm16jHLiJSWmBCXksXiIiUFpiQj6TZNCIiRWp0yGuwVUSkYoEZeNVgq4ikqrgOvJrZ9Wa22sw+NLO/Rrw/0szWmNlHZnZRtNcREZGqi+oZr2Z2AdALaOPue8zs+PD7rYF+wBnAScBCMzvN3QuibXB5NNgqIlJatD3564B73X0PgLtvCb/fC5jm7nvc/VNgDdAuymtVSIOtIiKlRRvypwE/M7MlZpZtZj8Ov38ysDHiuE3h90oxs0FmlmNmOXl5eVE2R0REIlVarjGzhcAJZey6LXz+scA5wI+B6WbWvCoNcPeJwEQoGnityrkiIlKxSkPe3S8sb5+ZXQfM8KIpOu+aWSHQANgMNI44tFH4PRERqUbRlmv+CVwAYGanAXWBr4BZQD8zq2dmzYCWwLtRXktERKooqtk1wGRgspmtAPYCV4Z79R+a2XRgJbAPGBLPmTUiIlK2pLoZyszygPWJbschaEDR/2BSib5z8KXa94Wa+51PcfeGZe1IqpCvqcwsp7y7zYJK3zn4Uu37QjC/c41eu0ZERCqmkBcRCTCFfGxMTHQDEkDfOfhS7ftCAL+zavIiIgGmnryISIAp5EVEAkwhH2NmdrOZuZk1SHRb4snM7g8/R+DfZjbTzI5OdJvixcy6hp+LsMbMbk10e+LNzBqb2etmtjL8nIhhiW5TdTGz2mb2vpnNTnRbYkUhH0Nm1hjoAmxIdFuqwQLgTHf/f8DHwMgEtycuzKw2MB7oBrQGfhV+XkKQ7QNudvfWFC0+OCQFvvN+w4BViW5ELCnkY+shYAQQ+NFsd5/v7vvCL9+haBG6IGoHrHH3de6+F5hG0fMSAsvdv3D398Lb31IUemUuFR4kZtYIuBh4ItFtiSWFfIyYWS9gs7svS3RbEuBqYG6iGxEnB/1shCAys6ZAW2BJYltSLcZS1EkrTHRDYinaBcpSSiVr6/+JolJNYFT0fd39pfAxt1H03/up1dk2iT8z+z7wInCDu3+T6PbEk5n1ALa4e66ZdUx0e2JJIV8F5a2tb2ZnAc2AZWYGRaWL98ysnbt/WY1NjKmKniUAYGZXAT2Azh7cGy5S8tkIZlaHooCf6u4zEt2eaj3UjzIAAAC9SURBVNAeuMTMugPfA440s2fc/YoEtytquhkqDszsMyDd3WvianYHxcy6AmOADu4e2Oc2mlkaRQPLnSkK96VAf3f/MKENiyMr6qk8DWxz9xsS3Z7qFu7J/9HdeyS6LbGgmrwcqnFAfWCBmX1gZo8lukHxEB5cHgrMo2gAcnqQAz6sPfBroFP43/aDcA9XaiD15EVEAkw9eRGRAFPIi4gEmEJeRCTAFPIiIgGmkBcRCTCFvIhIgCnkRUQC7P8DemFkPHvQ0R8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TeHfKilqijVh"
      },
      "source": [
        "## Perceptron"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JRXU8zuVil5Q"
      },
      "source": [
        "G = lambda w, x: 1 if F(w,x)>=0 else 0\n",
        "\n",
        "w=0, 0, 0\n",
        "\n",
        "data = [((1,0,0), 0), ((1,0,1), 1), ((1,1,0), 1), ((1,1,1), 1)]\n",
        "Errors=[]"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VC3Dkz7PjWAL"
      },
      "source": [
        "X = [(d[0], d[1], d[2]) for d,_ in data]\n",
        "Y = [y for _, y in data]\n",
        "\n"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OHmri3rzjrh0",
        "outputId": "da0bc894-26da-46bf-81b1-b541487560c6"
      },
      "source": [
        "errors = 0\n",
        "print (\"w=\", w)\n",
        "for x, y in zip(X, Y):\n",
        "  if G(w, x) != y and y==1:                ### Predicted Wrong\n",
        "    errors += 1                            ### Actual is 1\n",
        "    w = [w1 + x1 for w1, x1 in zip(w,x)]   ### Add input to weights\n",
        "    Errors.append([w, errors])\n",
        "  elif G(w, x) != y and y==0:              ### Predicted Wrong\n",
        "    errors += 1                            ### Actual is 0\n",
        "    w = [w1 - x1 for w1, x1 in zip(w,x)]   ### Subtract input from weights\n",
        "    Errors.append([w, errors])\n",
        "print (\"errors=\", errors)\n",
        "for i in range(len(Errors)):\n",
        "  print(\"Updates=\",i+1,\":=\",Errors[i])   "
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "w= [-1, 1, 1]\n",
            "errors= 0\n",
            "Updates= 1 := [[-1, 0, 0], 1]\n",
            "Updates= 2 := [[0, 0, 1], 2]\n",
            "Updates= 3 := [[-1, 0, 1], 1]\n",
            "Updates= 4 := [[0, 1, 1], 2]\n",
            "Updates= 5 := [[-1, 1, 1], 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X3DDo8H_k8SV",
        "outputId": "38ea3757-40bf-4741-d3ee-83fdf354e990"
      },
      "source": [
        "print(\"Model=\", w)\n",
        "for x, y in zip(X, Y):\n",
        "  print((\"x,y,predicted=\", x,y,G(w,x)))"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model= [-1, 1, 1]\n",
            "('x,y,predicted=', (1, 0, 0), 0, 0)\n",
            "('x,y,predicted=', (1, 0, 1), 1, 1)\n",
            "('x,y,predicted=', (1, 1, 0), 1, 1)\n",
            "('x,y,predicted=', (1, 1, 1), 1, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOz218Cn6cT9"
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Yd60XqX6mDh"
      },
      "source": [
        "sigmoid = lambda y: 1/(1+math.exp(-y))\n",
        "classify = lambda y: 1 if y>0.5 else 0\n",
        "data = [((1,2,3),0), ((1,3,4),0), ((1,4,5),0), ((1,5,6),1), ((1,6,7),1), ((1,8,9),1) ]\n",
        "\n",
        "\n",
        "X = [(d[0], d[1], d[2]) for d,_ in data]\n",
        "Y = [y for _, y in data]"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "veeHtiqt6aRI",
        "outputId": "5abfef69-0252-4e8d-fabd-8a707245d6b2"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "log_reg = LogisticRegression(random_state=42, solver='sag', penalty='none', max_iter=10000, fit_intercept=False)\n",
        "#log_reg = LogisticRegression(random_state=42, penalty='none', verbose=1, fit_intercept=False)\n",
        "log_reg.fit(X, Y)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=False,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=10000,\n",
              "                   multi_class='auto', n_jobs=None, penalty='none',\n",
              "                   random_state=42, solver='sag', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MqFgceIi7Tq2",
        "outputId": "6e803cdf-d6be-42c3-fd20-2d8396c0e7be"
      },
      "source": [
        "print (log_reg.intercept_, log_reg.coef_)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.] [[-10.49840862   6.83664725  -3.66176137]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0DfCC4nt95ZW",
        "outputId": "a644ba28-910c-4775-9f4c-c3758cec797f"
      },
      "source": [
        "w=[round(c,2) for c in log_reg.coef_[0]]\n",
        "print(w)\n",
        "\n",
        "for x,y in zip(X, Y):\n",
        "  print (\"x,r,y=\", x, y, sigmoid(F(w,x)), round(sigmoid(F(w,x)), 3), classify(sigmoid(F(w,x))))"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-10.5, 6.84, -3.66]\n",
            "x,r,y= (1, 2, 3) 0 0.00040956716498605005 0.0 0\n",
            "x,r,y= (1, 3, 4) 0 0.009756665624551348 0.01 0\n",
            "x,r,y= (1, 4, 5) 0 0.1915453485614673 0.192 0\n",
            "x,r,y= (1, 5, 6) 1 0.8506870654691565 0.851 1\n",
            "x,r,y= (1, 6, 7) 1 0.9927537604041685 0.993 1\n",
            "x,r,y= (1, 8, 9) 1 0.9999873772854231 1.0 1\n"
          ]
        }
      ]
    }
  ]
}