Solution_OSAP_LogisticRegression1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Solution_OSAP_LogisticRegression1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/afnanAlgognadi/Introduction-to-Machine-Learning/blob/main/Solution_OSAP_LogisticRegression1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QfFeTo_P-mTF"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plotClass(X,y,p):\n",
        "    plt.figure()\n",
        "    for i in range(y.shape[1]):\n",
        "        if y[0,i]==0:\n",
        "            plt.plot(X[0,i],X[1,i],'r'+p)\n",
        "        else:\n",
        "            plt.plot(X[0,i],X[1,i],'b'+p)\n"
      ],
      "metadata": {
        "id": "njYxETX4-qTk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_data=100 # data points per class\n",
        "\n",
        "x1=np.random.randn(2,num_data)+2\n",
        "x0=np.random.randn(2,num_data)\n",
        "\n",
        "y1=np.ones((1,num_data))\n",
        "y0=np.zeros((1,num_data))\n",
        "\n",
        "X=np.concatenate((x1,x0),axis=1)\n",
        "y=np.concatenate((y1,y0), axis=1)\n",
        "plotClass(X,y,'o')\n",
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "GKX3U7L0-rZl",
        "outputId": "df834eef-2956-4f80-c564-8283ca6ad851"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 200)\n",
            "(1, 200)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_z(W, X, b):\n",
        "  #w0+w1*x1+w2*x2\n",
        "  return W@X+b\n",
        "\n",
        "\n",
        "def get_sigmoid(z):\n",
        "  return 1/(1+np.exp(-z))\n",
        "\n",
        "\n",
        "def get_loss(y,yhat):\n",
        "  return np.sum(-y*np.log(yhat) -(1-y)*np.log(1-yhat), axis=1)/ y.shape[1]\n",
        "\n",
        "\n",
        "W = np.random.randn(1,X.shape[0])\n",
        "b = 0\n",
        "ls =[]\n",
        "lr=0.0001\n",
        "for i in range(1000):\n",
        "  z = get_z(W,X,b)\n",
        "  yhat = get_sigmoid(z)\n",
        "\n",
        "  loss = get_loss(y,yhat)\n",
        "  ls.append(loss)\n",
        "  # dZ=dJ/dy * dy/ dZ\n",
        "  dZ = yhat - y\n",
        "\n",
        "  dW = dZ @ X.T\n",
        "  db = np.sum(dZ)\n",
        "\n",
        "\n",
        "  W -=lr*dW\n",
        "  b -=lr*db\n",
        "\n",
        "\n",
        "plt.plot(ls)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "zwwaLXeqESQj",
        "outputId": "394e7a9b-5349-4f10-d652-82f80de80677",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc3ecc7a890>]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plotClass(X,y,'o')\n",
        "print(X.shape)\n",
        "print(y.shape)\n",
        "\n",
        "x1= np.linspace(-4,4,100)\n",
        "\n",
        "x2 = (-b -W[0,0]*x1)/W[0,1]\n",
        "plt.plot(x1,x2)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "FnmQ8xvfEXg4",
        "outputId": "eee0f3e0-0be7-4432-df17-e736c11d32c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 200)\n",
            "(1, 200)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plotClass(X, yhat>0.5,'o')\n",
        "plotClass(X,y,'x')"
      ],
      "metadata": {
        "id": "_LYBZL11EYqN",
        "outputId": "fb632f5f-d29c-4781-df5d-6689cfa2ec84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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F/dBnnLtWAByikJfgc44vkmCoNHqztDhm563Uu2qRvv765GnavXvq1tBWcZ0+1azFnG7nq6ZeMxFolxZ5H/vHQ8O7kAMYA9gGsL24uNhJpYOmj77ynphkee6RPPHMCr4W8yuumB8/ZXIZ8oRQW9emjUqZQHchtHTduMW7kKc/g7TI0/T1bu+RSWbiztDbpat4zTXJU3XNNfXPWWc6tjrLVce33c720UYJBQp5KPRI7HLpSSNV1cGoSYuj7mTUYj6X0aChklbtFtKLUE9+/mChkIdCSE+dKSaZntL/0ybZ6mrY9SphPM4P+SuqTjZSJLuslCpsxO8fT6K7JfLou40SAq6jVu4H8GMA/wvgeQAfLdt+0EIeIyYRN9ltdLLtCBNtNRGkI0fmq7qxkazPPXiqhYhNALsY0Ury4YAg0o50pE3RbAjaDF1dTfwSa2tRxss7f2nKcSLH5JKIreHpExRy0h4tQIcPzwmRUipRumy2RD1s0PRpt6miIbqxShTbZSeh7UsRU8PTJyjkpB0mFnn26c5mTzR52m2ae45Nx9riWFIe18LoIrKF0SndQyGPGVeWZZ1x6KY+8qxSaAu97Tj1pjhUyNrtRMH1fmZ8phNXhcmlMK0TLXI/UMhjpurpair0OuNT+rh5o03qRq1o9BDHlZXp2HXT8tk09xyajjYErUsPkI3Rn/SR+4NCHjtlT1fTJ2sySQRWp+/bv39ecNuWV1vr6WNXlS8Si1wTi4uhzqUoq1OIXQ9DgULeB8qerqaCNZlM0/cVJfFoQvZp12K+smIm4l36yFsok812wqVA1rmsdJuEC4U8ZEyeYJOnq2neVD2Ece9et0+tSfl8RK00bDxsuxisHS+n3vePE1989nyWLgXpCAp5yFQ9PSZPVxMzKu3yWF+fullcPLVtzLwu3uUblM9FsZpeppmy7Bzk4sYkKUsNNabbJGwo5KFT9gSbTtxY14zS49DT+y0szHd2tqWtmTeZJJ2mrqe2CcTZ3WZCCn1JL25M1ItyQD27Sv9IlpgbKwp5DDQVkqZ3Zld3dJvz6H3To0YXFpL5z2wKVCCO4TbFyO777GoYDVNoxOw+opB3QRvBCkRIgjNX0k+ZjknftctedE32HHnLTSi4jptHzhReXhvF0LbAPauB3E+BEsrjVhcKeRc0fRJDMhF8laWsAdG+/D17EhHXKQC6OHdTCq7jxY3J/6/Ozmqvl7N93KbF0Ke8ZzVxq1zcCOB+CphAPGm1oJC7ommYXdkx9HHynuBAO/6snTOvAZlMpgK+Z880TcDaWtiOzYLrqFdnE0Tq9eNx/Z945vKdOTPTYBgdYGDQIu+jkNtwiaQFSMdlZ61GGw9TVxazD3Ol6Okaj5W6/PKkPHqO07U1lT97Q7dU3joF1zGdsj1bZdOfOH3u9LRz6T5wavc8Ib0A14VCXkbbXzYtQDqEr8jcsnG3uDYnfJorKyuzwqffcN7xjuRa6vnXFhbsR600oPTWqbDI9eq8dDQmP0HMguST0LqB6kAhr6KteGkTKz2oJh1pYfsJc2UxTybTGX/1sotQv6Jza1+4jmfPhkjqeq+sNDtH3lM8HufPZmxY39xbx8BHrlT5LWIjLwrpFxRyE5qKo36a8pJDNcn+p6majdfF05vtgVNqmvzKpUpkfeJpyztrcrapd57AZgdCNTBt524dg6iVbBuZvQSmVY2x0440g0JeRVORqOqkayM8jkSn1rm7MvWywpe1vG36EfLq1qK+TXetaqdNqkqLfFhQyMtoIxImT2OelWv66p59UpuENDTFl6mXp062HZt5dWtQXxd+ascpYkjEuJ58+QYAPwTwDIBPVm0fnJC76P3I5u4ueoc2wVRgbNbDl6nXhTppv3u6btp9VLO+PjvOYu60I81wJuQALgXwTwB+DcBlAL4P4OqyfYIT8i5o67ox2S9PBNMdl0oVjzpJT4HetuFpg2t1yrqn9DVyFWFEiEVcCvm7ADycWj4J4GTZPoMUcqXqv7qnBcXUPZMV/ryOyzzRyhti2MQVFDrpgGt9nS6/XKkbb5zdri/1Jb3CpZD/PoAvpJZXAXwuZ7sxgG0A24uLix1VOyCaWORN3TPZBqNI3LNlGVrPGcM9SGR4F/L0Z3AWeVO/b97w/4WF8uH/RZ2jadGaTOYH3uhzZbfrq1U6tEaL9AK6VnzS1O9b5POumu4tvf3CQtKJl56Xc9+++c6+yaS7SSZ8Y7tDlb2OpCOKhPwStGcLwBtF5A0ichmAmwF81cJx+8OJE8BoNLtuNErWlzEaAefOAcePA6dOAceOAbt2AevrwN13A5ubs9tvbSXb63ONRsCnPgX88pdA0sgCL78MvPRSsv7226fHf/TRZBuRZDuR6T59I+86nTuXrDfl7Nnp9V9eTq7hXXdN1x8/nqwnpAvy1L3uB8D7APwISfTKbVXbD84ib4t2d6QnR85akUVWoY5GSQ+wKYtaGYprpa0Vnb3+LtMxELIDOCAoUrRgrKzMuzrSwlPmLjDxB6cjVdKdon0R8qJ0wzrHShP3Sva6tknHQIgBFPIYqevLzRNs02NkwxKzy7FT1H+g+w6aWtFl+WiJNdgNkUAhj5Emd282pM70GF1a5L6eyryGrk0Ioj5ezZTFFKX62O6fjhUK+RAocqHUUY4uYqt9PpXZPoCmVnSTwVo5u+Ytk3wYMUoh7z86NDFvxKZpOj3bT0rVPJxdP5Xpc7bNItnSrKYoNWPoY7jiFnK+i1ZTlUe8SjlcmIlVx+zyqcyeOzthhd6mw3tq6KJUFzZ+sQs530XNKbvby5SjqrFsO6jJdzqAwIwBilI9KAEJcQu5Urzz65An2G2vX5snqSj3y0CfyoFXvxGBtcPeiF/IleK7qAl5gm1LOZo0Bnn7BPxUdlG0gKtPAid+IadFXk2RYNucVahOYxqh6RlhkcmAiFvI+XSZ0cWkDHUa00hNT9oMJFTiFvJIBaFXDKwxpRePhEiRkEvyv25ZWlpS29vbnZ+XtODs2SSbXzqL4+ZmkjGwKotjZOjkhWtrSZLJdKJEQnwiIheUUkvZ9bt8FIYESl2xHo16p3BaxLV4j0azy4SEiI185KQv6LzaOs/2APNq20hV3pZ0qnPN5maynpA8KOQx4foJz05k0dYUjVCRms4BYhO2p6QuFHJTQhClLp7w0ShxDt9xR/Ldxp8QqyJ5/q1L29MQ7kMSHnk9oK4/USbN8hG1kRets7GRJMdyFRtnO/Yuxli+QCJ0ciNnxuP5ZF/pCTJIr0HU4Yeh0LUoFQmKq5loXAlYjLF8nhugwtNPJrMTYugJs2NoIElrKOS26FqUsk90dvIHmw+wi3j9GC1yjacGqLI9nUyS+Vuz87iS3uNEyAF8CMCTAF4BsGS6X7RCbkOU2sz6k53Y19egHNM6BOKiaITHBqjy8k4miXsNSL5juJ7ECq6E/DcAvAnAN3sv5LYTT5keJy0o+/bNz6E5Hs/7R12PejWtQ6wjckNugLRPfGEhf4IM0muculYGIeQ2RcnU2jMRFF+i09ZitXE9XTUUITdA2QkxtM+cnZ2DgEIeGib+17oujK7dADYmLm7TAIVsObsi5EaGOKexkAP4BoAncj4fUDWEHMAYwDaA7cXFxS7rHh4uhNd3J2yTOoRyDEIigRZ5KLiwIkMJi2xyXhsNUIzhjYQ0oEjIhzGyM6TRcLaTeaSzPN1++3RIYLq+tutvqw6bm0l6wfX15Dtbxq6OESsh3dfEL3nqbvoBcAzA8wB+DuA/ADxssl/nFnlbC9LEL+nLd2ly3hCjTGx02tX9XfvmXx5iH8HAweAHBLVxP4QcPWKKjj3Oi0XXQtZlHWwMNa8rzKH/Rk1gH8GgoJArZSfKouyBCf2h0kP7V1eT5bIGqUkdmgprl9cr9N+oCewjGAwUchsPsMkDE+pDpeu/uqqUyLxlnqZpHZpYvD6uV6i/URP62DCRQoYt5DZjlmO0yLP1zVrmeds2rUOd/WmRt6OPriJSyrCFvG0nV+w+8nT905b5vn1u6mBi8fq4XiH/Rk3oW+ctqWTYQt6WUKJWXDdINupgavH6ECEKH4kcCnkfaGtRHjkyn3RrYyNZH0L5CCGlFAn5MAYE9YW2c2p+4hPA6dOzU6+dPp2st0HeQKGbbgIeeGB2Ow5aIcQueeru+kOLvCWuwyhtQiudEGuAFnlPaDsk3ebkyqbna/oWwSHohBhBIY8Jk7wqJsfoOjdJ08ZjeXm2frr+y8vuykpIjOSZ6a4/dK00pIswShe0cef0Ke6bkJaAUSvES/idjcajTyMxCWlBkZDTtVJGzD7avLIvL8+nmh2NgBMn3JWjbcrbIaepJcSUPHV3/YnGIo854iLmsmv6UAdCLAJa5A1oG7ftk5jLrmFcOiFGUMir6DpczyYxlx1IXD7ZMt98M/Dgg/YiWWJ2nxGyA4W8iph9tDGXvYiyN40moswQR9IH8vwtrj/0kXdAzGU3IS+SpWmdGeJIIgH0kTfA9kTJXRJz2asoetNo2i8QuwuKkDx1d/2JxiIn4WFiddeNO6dFTiIBLixyEblTRJ4WkcdF5LyIXGmpfSEkn6o3jbr9AiZpD9ghSkInT91NPwB+D8Cunb/PADhjsh8tcjKDrRGnTXzkJufue38DiQa4HqIP4BiA+0y2pZCTGWwJpcsUBHS/kAAoEnJJ/tceEfk7AF9SSn2x4P9jAGMAWFxcvPa5556zcl7SE7SLY20tcYmEOHjp1KmkQ3R9PXHDENIxInJBKbWUXV/pIxeRb4jIEzmfD6S2uQ3AywDuKzqOUurzSqklpdTSwYMHm9aD9JXQI0eyvvdbbqHfnATDrqoNlFLvLfu/iHwEwI0AVpQt854Mj6xQjkbhiHm6Q1SX69ixJFXAQw8ly+ltCOmYSiEvQ0RuAHACwO8opV6yUyQyOPKEMqTcMHmRMufPJ0IeujuIDIJWQg7gcwBeBeAREQGA7yil/qh1qciwKAspDEEY89L86gbnNa+Z+s1DKCsZJNY6O+uwtLSktre3Oz8vIVaJoYOW9IrGnZ2EkBxszJ9KiCUo5IQ0oc+5bEh00LVCCCGRQNcKIYT0FAo5aQ+TShHiFQo5aQ9n2SHEK23jyAmZndCBoXiEdA4tcmKH0HOlENJjKOTEDn2c6JmQSKCQk/ZwcAwhXqGQk/ZwcAwhXuGAIEIIiQQOCCKEkJ5CISeEkMihkBNCSORQyAkhJHIo5IQQEjleolZE5EUAz1k85AEAP7F4vK6ItdxAvGWPtdxAvGWPtdxAeGX/VaXUwexKL0JuGxHZzgvJCZ1Yyw3EW/ZYyw3EW/ZYyw3EU3a6VgghJHIo5IQQEjl9EfLP+y5AQ2ItNxBv2WMtNxBv2WMtNxBJ2XvhIyeEkCHTF4ucEEIGC4WcEEIipxdCLiJ3isjTIvK4iJwXkSt9l8kUEfmQiDwpIq+ISPBhTiJyg4j8UESeEZFP+i6PKSJyj4i8ICJP+C5LHUTk9SKyKSI/2LlPbvVdJlNE5HIR+Z6IfH+n7J/2XaY6iMilIvKoiHzNd1mq6IWQA3gEwJuVUm8B8CMAJz2Xpw5PALgJwLd8F6QKEbkUwF8AOALgagB/ICJX+y2VMX8N4AbfhWjAywD+XCl1NYB3AvjjiK75zwG8Ryn1VgBvA3CDiLzTc5nqcCuAp3wXwoReCLlS6h+UUi/vLH4HwFU+y1MHpdRTSqkf+i6HIe8A8IxS6p+VUr8A8ACAD3gukxFKqW8B+E/f5aiLUurHSqmLO3//DxJheZ3fUpmhEn66s7h75xNFdIWIXAXgKIAv+C6LCb0Q8gx/CODrvgvRU14H4N9Sy88jElHpAyJyCMBvAviu35KYs+OeeAzACwAeUUrFUvbPAjgB4BXfBTFhl+8CmCIi3wDwKzn/uk0p9ZWdbW5D8ip6X5dlq8Kk7ISUISJXAPhbAH+qlPpv3+UxRSn1SwBv2+m3Oi8ib1ZKBd1PISI3AnhBKXVBRH7Xd3lMiEbIlVLvLfu/iHwEwI0AVlRgwfFVZY+Ifwfw+tTyVTvriENEZDcSEb9PKfWg7/I0QSn1XyKyiaSfImghB/BuAO8XkfcBuBzAgoh8USn1Yc/lKqQXrhURuQHJa9D7lVIv+S5Pj9kC8EYReYOIXAbgZhfwsIoAAADTSURBVABf9VymXiMiAuCvADyllLrLd3nqICIHdQSZiOwBcD2Ap/2Wqhql1Eml1FVKqUNI7vFJyCIO9ETIAXwOwKsBPCIij4nIX/oukCkickxEngfwLgB/LyIP+y5TETsdyn8C4GEknW7nlFJP+i2VGSJyP4B/BPAmEXleRD7qu0yGvBvAKoD37Nzbj+1YijHwWgCbIvI4EiPgEaVU8KF8McIh+oQQEjl9scgJIWSwUMgJISRyKOSEEBI5FHJCCIkcCjkhhEQOhZwQQiKHQk4IIZHzf0m5vqGYrTjLAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum(y==(yhat>0.5))/y.shape[1]"
      ],
      "metadata": {
        "id": "Kde5b4R-Gn1A",
        "outputId": "cb1e33db-e146-4de9-e134-0ddfe835a849",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.94"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1= np.linspace(-4,4,100)\n",
        "x2= np.linspace(-4,4,100)\n",
        "\n",
        "[X1, X2] = np.meshgrid(x1,x2)\n",
        "\n",
        "data = [X1.flatten(), X2.flatten()]\n",
        "data = np.array(data)\n",
        "\n",
        "z= get_z(W, data, b)\n",
        "yhat = z>0\n",
        "\n",
        "plotClass(data, yhat, '.')"
      ],
      "metadata": {
        "id": "eOpkhwHPITNN",
        "outputId": "a5b24f94-3fdf-4dbb-efb6-7237ffc2e9be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cj_HeluIJCVh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}