assignment05-8.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled3.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOfTGnvyM7+7DfvziZVR6Sd",
      "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/dlguswn3659/class-MachineLearning/blob/master/assignment05-8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2tvbBN2FT3Q7",
        "colab_type": "text"
      },
      "source": [
        "# **Machine Learning Assignment05** #\n",
        "\n",
        "Name : 이현주\n",
        "\n",
        "Student # : 20184060"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kyJ8nEOjT31U",
        "colab_type": "text"
      },
      "source": [
        "# **1. Plot the training data [2pt]**\n",
        "\n",
        "- plot the training data points (x,y) with their labels / in colors (blue for label 0 and red for label 1)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EGMfwbsbT4EJ",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "98512357-1056-487b-c602-8030e38cbf2f"
      },
      "source": [
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "import seaborn as sns\n",
        "import matplotlib.animation as animation\n",
        "from scipy import stats \n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from sklearn.datasets.samples_generator import make_regression \n",
        "import csv\n",
        "import math\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "data    = np.genfromtxt(\"data.txt\", delimiter=',')\n",
        "\n",
        "x       = data[:, 0]\n",
        "y       = data[:, 1]\n",
        "label   = data[:, 2]\n",
        "\n",
        "x_label0    = x[label == 0]\n",
        "x_label1    = x[label == 1]\n",
        "\n",
        "y_label0    = y[label == 0]\n",
        "y_label1    = y[label == 1]\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.scatter(x_label0, y_label0, alpha=0.3, c='b')\n",
        "plt.scatter(x_label1, y_label1, alpha=0.3, c='r')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d3eb21ac-2873-4c04-b193-4f6eeba420dc\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-d3eb21ac-2873-4c04-b193-4f6eeba420dc\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Bu4s4L0UpMe",
        "colab_type": "text"
      },
      "source": [
        "# **2. Plot the estimated parameters [3pt]**\n",
        "\n",
        "- plot the estimated parameters (θ0,θ1,θ2) at every iteration of gradient descent until convergence\n",
        "- the colors for the parameters (θ0,θ1,θ2) should be red, green, blue, respectively"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xb3kaDt1U43m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "320fec64-1310-4527-e24c-437f415a8839"
      },
      "source": [
        "X = x #전체 x\n",
        "y = np.array(y,dtype=float)\n",
        "label = np.array(label,dtype=float)\n",
        "print(len(y))\n",
        "# print(X)\n",
        "# print(y)\n",
        "# print(label)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sX4wx6YtU5gM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gradient_descent(X,y,label,theta,learning_rate=0.0000001,iterations=10000):\n",
        "    \n",
        "    m = len(y)\n",
        "\n",
        "    cost_history = np.zeros(iterations)\n",
        "    theta_history = np.zeros((iterations,3))\n",
        "    for it in range(iterations):\n",
        "        pred = 0\n",
        "        pred0 = 0\n",
        "        pred1 = 0\n",
        "        pred2 = 0\n",
        "\n",
        "        for i in range(0, m):\n",
        "          # print(i)\n",
        "          #cost 함수\n",
        "          pred = pred + ((-1)*label[i]*math.log(1/(1 + math.exp((-1)*(theta[0][it] + theta[1][it]*X[i] + theta[2][it]*y[i]))))-((1-label[i])*math.log(1 - (-1)*(1/(1 + math.exp((-1)*(theta[0][it] + theta[1][it]*X[i] + theta[2][it]*y[i])))))))\n",
        "          \n",
        "          #  σ(z)   =    (1/(1 + math.exp((-1)*(theta[0][it] + theta[1][it]*X[i] + theta[2][it]*y[i]))))\n",
        "\n",
        "          #theta함수\n",
        "          pred0 = pred0 + ((1/(1 + math.exp((-1)*(theta[0][it] + theta[1][it]*X[i] + theta[2][it]*y[i])))) - label[i])\n",
        "          pred1 = pred1 + ((1/(1 + math.exp((-1)*(theta[0][it] + theta[1][it]*X[i] + theta[2][it]*y[i])))) - label[i])*X[i]\n",
        "          pred2 = pred2 + ((1/(1 + math.exp((-1)*(theta[0][it] + theta[1][it]*X[i] + theta[2][it]*y[i])))) - label[i])*y[i]\n",
        "        \n",
        "        theta[0][it + 1] = theta[0][it] - (1/m)*learning_rate*pred0\n",
        "        theta[1][it + 1] = theta[1][it] - (1/m)*learning_rate*pred1\n",
        "        theta[2][it + 1] = theta[2][it] - (1/m)*learning_rate*pred2\n",
        "\n",
        "        theta_history[it][0] = theta[0][it]\n",
        "        theta_history[it][1] = theta[1][it]\n",
        "        theta_history[it][2] = theta[2][it]\n",
        "        cost_history[it]  = (1/m) * pred\n",
        "        \n",
        "    return theta, cost_history, theta_history"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTFFpCxlhWE8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lr =0.0000001\n",
        "n_iter = 1000000\n",
        "\n",
        "theta = np.ones((3, n_iter + 1))\n",
        "theta[0][0] = -12.0\n",
        "theta[1][0] = 0.2\n",
        "theta[2][0] = -0.2\n",
        "\n",
        "X_b = np.c_[np.ones((len(X),0)),X]\n",
        "X_b = np.array(X_b,dtype=float)\n",
        "# print(X_b.shape)\n",
        "# print(X_b.dtype)\n",
        "# print(y.dtype)\n",
        "theta,cost_history,theta_history = gradient_descent(X_b,y,label,theta,lr,n_iter)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nP3lSeWm1qSc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "theta0_history = []\n",
        "theta1_history = []\n",
        "theta2_history = []\n",
        "\n",
        "for it in range(n_iter):\n",
        "  theta0_history.append(theta_history[it][0])\n",
        "  theta1_history.append(theta_history[it][1])\n",
        "  theta2_history.append(theta_history[it][2])\n",
        "\n",
        "# print(theta0_history)\n",
        "# print(n_iter)\n",
        "# print(theta_history[1000000][0])\n",
        "# print(theta_history[1000000][1])\n",
        "# print(theta_history[1000000][2])\n",
        "\n",
        "# theta0_best = theta_history[1000000][0]\n",
        "# theta1_best = theta_history[1000000][1]\n",
        "# theta2_best = theta_history[1000000][2]\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3i3kCIQk3KHn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "9dbfd533-0ae9-42d2-e166-d724cc067359"
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(12,8))\n",
        "\n",
        "theta0_history = []\n",
        "theta1_history = []\n",
        "theta2_history = []\n",
        "\n",
        "for it in range(n_iter):\n",
        "  theta0_history.append(theta_history[it][0])\n",
        "  theta1_history.append(theta_history[it][1])\n",
        "  theta2_history.append(theta_history[it][2])\n",
        "\n",
        "ax.set_ylabel('Theta')\n",
        "ax.set_xlabel('Iterations')\n",
        "_=ax.plot(range(n_iter),theta0_history,'b.', color = 'red')\n",
        "_=ax.plot(range(n_iter),theta1_history,'b.', color = 'green')\n",
        "_=ax.plot(range(n_iter),theta2_history,'b.', color = 'blue')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAAHgCAYAAACB2yrrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAZmElEQVR4nO3df7Dld13f8debrAYqENouTFqSdVESKQkQ9BL5UQtKhGiRlJ8GZRTJsC0jVEBkwCColZkI/hjaYmWVDOogFKEhGUGioWgsEsiGHyEJP5omAiFYCL+kMgSSvPvHPYHrcvfes3s/9/zY+3jM7Ow95/s957x3v7v3Pu/3fO451d0BAAC25g7zHgAAAI4GwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGCAXfMeYITdu3f33r175z0GAABHuSuuuOKm7r77etuOirDeu3dvDhw4MO8xAAA4ylXVxw+1zVIQAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADCCsAQBgAGENAAAD7Jr3ADtB/UrNe4SF1y/teY8AALAlwnogAX3kjpa/O98gAMDOJay34GiJQcY52v9N+MYBAA5NWB+hoz2gYD07/d+9bywA2IiwBpjSTv/GYrv5xgVYdsJ6xnzh+CaRAqzlcwJwuBatq4T1EeqX9oZfBBbtQC+io+XvSAzAkvrlr8erzsIyuy2VWqieqO7FGWatqjozySuTHJPk97v7vEPtu7Ky0gcOHJjZbLCT+MbhMAi1JePfNiy/W9M92/PEVXVFd6+st20hz1hX1TFJXpXkh5PckOTyqrqou6+Z72Sw89x+JqB2bIPcdhj77ti/pCXmmMHy6izayYyFDOskpye5truvS5KqekOSs5IIa+Zm54blTrdYn7TZDov5zC0wjcX6HL2oYX3PJJ9cc/mGJN8/p1mYgugElpdPYLCsFm1F86KG9aaqal+SfUmyZ8+eOU8zG+IVYHss2hdnYDktalh/KsmJay6fMLnuG7p7f5L9yeoPL85utDFEMhzdhBrAzrOoYX15kpOq6l5ZDeqzk/zEfEc6fOKZo5VoBIBvtZBh3d23VNWzklyc1ZfbO7+7r57zWBsS0TuLsAQADraQYZ0k3f22JG+b9xwbEdPrE50AwE60sGG96OYd1eIVAGCxCOsFIJIBAJafsJ4R8QwAcHQT1keo+9DLQUQ0AMDOI6y3QEADAHC7xXqDdQAAWFLCGgAABhDWAAAwgLAGAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADCCsAQBgAGENAAADCGsAABhAWAMAwADCGgAABhDWAAAwgLAGAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADCCsAQBgAGENAAADCGsAABhAWAMAwADCGgAABli4sK6qV1TVR6rqyqq6oKruNu+ZAABgMwsX1kn+Ismp3X3/JB9L8qI5zwMAAJtauLDu7j/v7lsmFy9LcsI85wEAgGksXFgf5OlJ/mzeQwAAwGZ2zeNBq+qSJMevs+nc7r5wss+5SW5J8rpD3Me+JPuSZM+ePds0KQAATGcuYd3dZ2y0vaqeluQxSR7Z3X2I+9ifZH+SrKysrLsPAADMylzCeiNVdWaSFyR5eHd/Zd7zAADANBZxjfV/TXKXJH9RVR+oqt+d90AAALCZhTtj3d33nvcMAABwuBbxjDUAACwdYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADCCsAQBgAGENAAADCGsAABhAWAMAwADCGgAABhDWAAAwgLAGAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADCCsAQBgAGENAAADCGsAABhAWAMAwADCGgAABhDWAAAwgLAGAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADLCwYV1VP19VXVW75z0LAABsZiHDuqpOTPKoJJ+Y9ywAADCNhQzrJL+d5AVJet6DAADANBYurKvqrCSf6u4PznsWAACY1q55PGhVXZLk+HU2nZvkF7O6DGSz+9iXZF+S7NmzZ+h8AABwuKp7cVZbVNX9krwjyVcmV52Q5MYkp3f33x3qdisrK33gwIEZTAgAwE5WVVd098p62+ZyxvpQuvtDSe5x++Wq+tskK91909yGAgCAKSzcGmsAAFhGC3XG+mDdvXfeMwAAwDScsQYAgAGENQAADCCsAQBgAGENAAADCGsAABhAWAMAwADCGgAABhDWAAAwgLAGAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADCCsAQBgAGENAAADCGsAABhAWAMAwADCGgAABhDWAAAwgLAGAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMsGuzHarqjknOSXJKkjvefn13P30b5wIAgKUyzRnrP0pyfJJHJ/mrJCck+fJ2DgUAAMtmmrC+d3f/UpJ/6O4/SPJvk3z/9o4FAADLZZqw/vrk9y9W1alJjktyj+0bCQAAls+ma6yT7K+qf5rkxUkuSnLnJL+0rVMBAMCSmSas39HdX0hyaZLvSpKqute2TgUAAEtmmqUgb17nujeNHgQAAJbZIc9YV9V9svoSe8dV1ePXbLpr1rzsHgAAsPFSkO9J8pgkd0vyY2uu/3KSZ2znUAAAsGwOGdbdfWGSC6vqId397hnOBAAAS2eaNdafq6p3VNVVSVJV96+qF2/zXAAAsFSmCevfS/KiTF7PuruvTHL2dg4FAADLZpqw/ifd/d6DrrtlO4YBAIBlNU1Y31RV352kk6Sqnpjk09s5VFU9u6o+UlVXV9XLt/OxAABghGneIOZnk+xPcp+q+lSS65M8dbsGqqofTHJWkgd0981V5e3TAQBYeJuGdXdfl+SMqvqOJHfo7i9v80zPTHJed988efzPbPPjAQDAlm0a1lV1bJInJNmbZFdVJUm6+1e3aaaTk/xAVb0syVeTPL+7L9+mxwIAgCGmWQpyYZIvJbkiyc0jHrSqLkly/Dqbzp3M9M+SPDjJg5K8saq+q7v7oPvYl2RfkuzZs2fEWAAAcMSmCesTuvvMkQ/a3WccaltVPTPJ/5iE9Hur6rYku5N89qD72J/Vtd9ZWVnpb7kjAACYoWleFeRvqup+2z7JN70lyQ8mSVWdnOTbk9w0w8cHAIDDdsgz1pN3Wrxtss/PVNV1WV0KUkm6u++/TTOdn+T8yeN/LclPH7wMBAAAFs1GS0HumeS0WQ1yu+7+Wrbx5fwAAGA7bBTW13f3x2c2CQAALLGNwvoeVfW8Q23s7t/ahnkAAGApbRTWxyS5c1bXVAMAABvYKKw/vY1vAgMAAEeVjV5uz5lqAACY0kZh/ciZTQEAAEvukGHd3Z+f5SAAALDMpnnnRQAAYBPCGgAABhDWAAAwgLAGAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADCCsAQBgAGENAAADCGsAABhAWAMAwADCGgAABhDWAAAwgLAGAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADCCsAQBgAGENAAADCGsAABhAWAMAwAALF9ZVdVpVXVZVH6iqA1V1+rxnAgCAzSxcWCd5eZJf6e7TkrxkchkAABbaIoZ1J7nr5OPjktw4x1kAAGAqu+Y9wDqek+TiqvqNrIb/Q9fbqar2JdmXJHv27JnddAAAsI65hHVVXZLk+HU2nZvkkUme291vrqonJ3lNkjMO3rG79yfZnyQrKyu9jeMCAMCm5hLW3f0toXy7qvrDJD83ufgnSX5/JkMBAMAWLOIa6xuTPHzy8Q8l+d9znAUAAKayiGusn5HklVW1K8lXM1lHDQAAi2zhwrq7/1eS75v3HAAAcDgWcSkIAAAsHWENAAADCGsAABhAWAMAwADCGgAABhDWAAAwgLAGAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADCCsAQBgAGENAAADCGsAABhAWAMAwADCGgAABhDWAAAwgLAGAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADCCsAQBgAGENAAADCGsAABhAWAMAwADCGgAABhDWAAAwgLAGAIAB5hLWVfWkqrq6qm6rqpWDtr2oqq6tqo9W1aPnMR8AAByuXXN63KuSPD7Jq9deWVX3TXJ2klOS/Mskl1TVyd196+xHBACA6c3ljHV3f7i7P7rOprOSvKG7b+7u65Ncm+T02U4HAACHb9HWWN8zySfXXL5hch0AACy0bVsKUlWXJDl+nU3ndveFA+5/X5J9SbJnz56t3h0AAGzJtoV1d59xBDf7VJIT11w+YXLdeve/P8n+JFlZWekjeCwAABhm0ZaCXJTk7Ko6tqruleSkJO+d80wAALCpeb3c3uOq6oYkD0ny1qq6OEm6++okb0xyTZK3J/lZrwgCAMAymMvL7XX3BUkuOMS2lyV52WwnAgCArVm0pSAAALCUhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADCCsAQBgAGENAAADCGsAABhAWAMAwADCGgAABhDWAAAwgLAGAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGENQAADCCsAQBgAGENAAADCGsAABhAWAMAwADCGgAABhDWAAAwgLAGAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAaYS1hX1ZOq6uqquq2qVtZc/8NVdUVVfWjy+w/NYz4AADhcu+b0uFcleXySVx90/U1Jfqy7b6yqU5NcnOSesx4OAAAO11zCurs/nCRVdfD1719z8eokd6qqY7v75hmON72D5gcAYIa65z3BP7LIa6yfkOR9ohoAgHUtWI9t2xnrqrokyfHrbDq3uy/c5LanJPn1JI/aYJ99SfYlyZ49e7YwKQAAbN22hXV3n3Ekt6uqE5JckOSnuvv/bHD/+5PsT5KVlZXFeh4AAIAdZ6GWglTV3ZK8NckLu/td855nQwu2pgcAYMdZsB6byw8vVtXjkvyXJHdP8taq+kB3PzrJs5LcO8lLquolk90f1d2fmcecm1qwgwkAwPzM61VBLsjqco+Dr/+1JL82+4kAAGBrFmopCAAALCthDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMICwBgCAAYQ1AAAMIKwBAGAAYQ0AAAMIawAAGEBYAwDAAMIaAAAGENYAADCAsAYAgAGqu+c9w5ZV1WeTfHxOD787yU1zemxmx3E++jnGO4PjvDM4zke/eR7j7+zuu6+34agI63mqqgPdvTLvOdhejvPRzzHeGRznncFxPvot6jG2FAQAAAYQ1gAAMICw3rr98x6AmXCcj36O8c7gOO8MjvPRbyGPsTXWAAAwgDPWAAAwgLCeUlWdWVUfraprq+qF62w/tqr++2T7e6pq7+ynZCumOMbPq6prqurKqnpHVX3nPOZkazY7zmv2e0JVdVUt3E+ds7lpjnNVPXnyf/rqqvrjWc/I1kzxOXtPVb2zqt4/+bz9o/OYk62pqvOr6jNVddUhtldV/efJv4Mrq+p7Zz3jWsJ6ClV1TJJXJfmRJPdN8pSquu9Bu52T5Avdfe8kv53k12c7JVsx5TF+f5KV7r5/kjcleflsp2SrpjzOqaq7JPm5JO+Z7YSMMM1xrqqTkrwoycO6+5Qkz5n5oByxKf8vvzjJG7v7gUnOTvI7s52SQV6b5MwNtv9IkpMmv/Yl+W8zmOmQhPV0Tk9ybXdf191fS/KGJGcdtM9ZSf5g8vGbkjyyqmqGM7I1mx7j7n5nd39lcvGyJCfMeEa2bpr/y0nyn7L6zfFXZzkcw0xznJ+R5FXd/YUk6e7PzHhGtmaaY9xJ7jr5+LgkN85wPgbp7kuTfH6DXc5K8oe96rIkd6uqfzGb6b6VsJ7OPZN8cs3lGybXrbtPd9+S5EtJ/vlMpmOEaY7xWuck+bNtnYjtsOlxnjyNeGJ3v3WWgzHUNP+fT05yclW9q6ouq6qNzoixeKY5xr+c5KlVdUOStyV59mxGY8YO9+v3tto1rweGZVVVT02ykuTh856FsarqDkl+K8nT5jwK229XVp86fkRWn326tKru191fnOtUjPSUJK/t7t+sqock+aOqOrW7b5v3YBy9nLGezqeSnLjm8gmT69bdp6p2ZfVpp8/NZDpGmOYYp6rOSHJuksd2980zmo1xNjvOd0lyapK/rKq/TfLgJBf5AcalM83/5xuSXNTdX+/u65N8LKuhzXKY5hifk+SNSdLd705yxyS7ZzIdszTV1+9ZEdbTuTzJSVV1r6r69qz+EMRFB+1zUZKfnnz8xCT/s71I+DLZ9BhX1QOTvDqrUW095nLa8Dh395e6e3d37+3uvVldS//Y7j4wn3E5QtN8zn5LVs9Wp6p2Z3VpyHWzHJItmeYYfyLJI5Okqv5VVsP6szOdklm4KMlPTV4d5MFJvtTdn57XMJaCTKG7b6mqZyW5OMkxSc7v7qur6leTHOjui5K8JqtPM12b1UX2Z89vYg7XlMf4FUnunORPJj+X+onufuzchuawTXmcWXJTHueLkzyqqq5JcmuSX+huzzIuiSmP8c8n+b2qem5Wf5DxaU54LZ+qen1WvwnePVkv/9Ik35Yk3f27WV0//6NJrk3ylSQ/M59JV3nnRQAAGMBSEAAAGEBYAwDAAMIaAAAGENYAADCAsAYA4KhXVedX1Weq6qop939yVV1TVVdX1R9PcxthDbDAqur/TX7fW1U/Mfi+f/Ggy38z8v4BFsxrk5w5zY5VdVKSFyV5WHefkuQ509xOWAMsh71JDiusJ+8Cu5F/FNbd/dDDnAlgaXT3pVl9r5FvqKrvrqq3V9UVVfXXVXWfyaZnJHlVd39hctup3hhOWAMsh/OS/EBVfaCqnltVx1TVK6rq8qq6sqr+fZJU1SMmXxwuSnLN5Lq3TL5oXF1V+ybXnZfkTpP7e93kutvPjtfkvq+qqg9V1Y+vue+/rKo3VdVHqup1NXm3pKo6b/KU6ZVV9Rsz/9sBODL7kzy7u78vyfOT/M7k+pOTnFxV76qqy6pqqjPd3nkRYDm8MMnzu/sxSTIJ5C9194Oq6tgk76qqP5/s+71JTu3u6yeXn97dn6+qOyW5vKre3N0vrKpndfdp6zzW45OcluQBSXZPbnPpZNsDk5yS5MYk70rysKr6cJLHJblPd3dV3W34nx5gsKq6c5KH5pvvqJwkx05+35XkpKy+6+MJSS6tqvt19xc3uk9hDbCcHpXk/lX1xMnl47L6ReBrSd67JqqT5D9W1eMmH5842W+jt+/+10le3923Jvm/VfVXSR6U5O8n931DklTVB7K6ROWyJF9N8pqq+tMkfzrgzwew3e6Q5IuHOMFwQ5L3dPfXk1xfVR/L6ufOyze7QwCWT2X16cvTJr/u1d23n7H+h2/sVPWIJGckeUh3PyDJ+5PccQuPe/Oaj29Nsqu7b0lyepI3JXlMkrdv4f4BZqK7/z6r0fyk5BvL4B4w2fyWrJ6tTlXtzurSkOs2u09hDbAcvpzkLmsuX5zkmVX1bUlSVSdX1Xesc7vjknyhu78y+aGcB6/Z9vXbb3+Qv07y45N13HdP8m+SvPdQg02eTj2uu9+W5LlZXUICsFCq6vVJ3p3ke6rqhqo6J8lPJjmnqj6Y5OokZ012vzjJ56rqmiTvTPIL3b3RM31JLAUBWBZXJrl18sn/tUlemdVlGO+b/ADhZ5P8u3Vu9/Yk/2GyDvqjWV22cbv9Sa6sqvd190+uuf6CJA9J8sEkneQF3f13a35a/mB3SXJhVd0xq2fSn3dkf0SA7dPdTznEpm/5wcTu7qx+Ljusz2e1ejsAAGArLAUBAIABhDUAAAwgrAEAYABhDQAAAwhrAAAYQFgDAMAAwhoAAAYQ1gAAMMD/B17IBSyPalYLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfEgwgp03g6e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# print(theta0_history[999999])\n",
        "# print(theta1_history[999999])\n",
        "# print(theta2_history[999999])\n",
        "\n",
        "# theta0_best = theta0_history[999999]\n",
        "# theta1_best = theta1_history[999999]\n",
        "# theta2_best = theta2_history[999999]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRx08Y7lq5GH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# X_b = np.c_[np.ones((100,1)),X]\n",
        "# theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)\n",
        "# print(theta_best)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HmY2rnBLU5zT",
        "colab_type": "text"
      },
      "source": [
        "# **3. Plot the training error [3pt]**\n",
        "\n",
        "* plot the training error J(θ0,θ1,θ2) at every iteration of gradient descent until convergence (in blue color)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "azbikC67-88N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lr =0.00001\n",
        "n_iter = 500\n",
        "\n",
        "theta = np.zeros((3, n_iter + 1))\n",
        "\n",
        "X_b = np.c_[np.ones((len(X),0)),X]\n",
        "X_b = np.array(X_b,dtype=float)\n",
        "# print(X_b.shape)\n",
        "# print(X_b.dtype)\n",
        "# print(y.dtype)\n",
        "theta,cost_history,theta_history = gradient_descent(X_b,y,label,theta,lr,n_iter)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ud9X_-9wVCmO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "f0ebc685-e432-42b8-d566-8a9208281799"
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(12,8))\n",
        "\n",
        "# n_iter = 50\n",
        "# cost_history = new_cost(X_b,y,theta,n_iter)\n",
        "\n",
        "ax.set_ylabel('J(Theta)')\n",
        "ax.set_xlabel('Iterations')\n",
        "_=ax.plot(range(n_iter),cost_history,'b.')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FaOR8fK0VDKF",
        "colab_type": "text"
      },
      "source": [
        "# **4. Plot the obtained classifier [4pt]**\n",
        "\n",
        "- plot the classifier σ(z) where z=θ0+θ1x+θ2y with x=[30:0.5:100] and y=[30:0.5:100]\n",
        "- [a:t:b] denotes a range of values from a to b with a stepsize t\n",
        "- use a colormap where blue is used for 0, red is used for 1, their weighted combination for a value between 0 and 1\n",
        "- plot the training data points (x,y) with their labels / in colors (blue for label 0 and red for label 1) superimposed on the classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mR8qBZrdERWV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#classifier 정의하기\n",
        "#  σ(z)   =    (1/(1 + math.exp((-1)*(theta[0][it] + theta[1][it]*X[i] + theta[2][it]*y[i]))))\n",
        "#  σ(z)   =    (1/(1 + math.exp((-1)*(theta[0][500000] + theta[1][500000]*X + theta[2][500000]*y))))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNJAVWvvVoQc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from itertools import product\n",
        "# import numpy as np\n",
        "# import matplotlib.pyplot as plt\n",
        "\n",
        "# from sklearn import datasets\n",
        "# from sklearn.tree import DecisionTreeClassifier\n",
        "# from sklearn.neighbors import KNeighborsClassifier\n",
        "# from sklearn.svm import SVC\n",
        "# from sklearn.ensemble import VotingClassifier\n",
        "\n",
        "# # Loading some example data\n",
        "# iris = datasets.load_iris()\n",
        "# X = iris.data[:, [1]]\n",
        "# y = iris.target\n",
        "\n",
        "# print(X)\n",
        "# print(y)\n",
        "\n",
        "# # Training classifiers\n",
        "# clf1 = DecisionTreeClassifier(max_depth=4)\n",
        "# clf2 = KNeighborsClassifier(n_neighbors=7)\n",
        "# clf3 = SVC(kernel='rbf', probability=True)\n",
        "# eclf = VotingClassifier(estimators=[('dt', clf1), ('knn', clf2),\n",
        "#                                     ('svc', clf3)],\n",
        "#                         voting='soft', weights=[2, 1, 2])\n",
        "\n",
        "# clf1.fit(X, y)\n",
        "# clf2.fit(X, y)\n",
        "# clf3.fit(X, y)\n",
        "# eclf.fit(X, y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxMlx8p9VosP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # Plotting decision regions\n",
        "# xx, yy = np.meshgrid(np.arange(0, 2, 0.01),\n",
        "#                      np.arange(0, 2, 0.01))\n",
        "# # xx, yy = np.meshgrid(np.arange(30, 100, 0.5),\n",
        "# #                      np.arange(30, 100, 0.5))\n",
        "\n",
        "# f, axarr = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10, 8))\n",
        "\n",
        "# for idx, clf, tt in zip(product([0, 1], [0, 1]),\n",
        "#                         [clf1, clf2, clf3, eclf],\n",
        "#                         ['Decision Tree (depth=4)', 'KNN (k=7)',\n",
        "#                          'Kernel SVM', 'Soft Voting']):\n",
        "\n",
        "#     Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "#     Z = Z.reshape(xx.shape)\n",
        "\n",
        "#     axarr[idx[0], idx[1]].contourf(xx, yy, Z, alpha=0.4)\n",
        "#     axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y,\n",
        "#                                   s=20, edgecolor='k')\n",
        "#     axarr[idx[0], idx[1]].set_title(tt)\n",
        "\n",
        "# plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jw9pwm3yRY0D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "a195ff04-3e73-4142-941e-83502245495d"
      },
      "source": [
        "#heatmap을 사용하면서 x축과 y축 값 증가 방향이 변화하여 점 분포가 기존에서 회전하고 뒤집어짐\n",
        "\n",
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "array = np.zeros((140, 140))\n",
        "# print(array)\n",
        "\n",
        "# theta0_best = -0.8059904497313255\n",
        "# theta0_best = -6.5\n",
        "# theta1_best = 0.016538778155258838\n",
        "# theta2_best = 0.006259696713467998\n",
        "\n",
        "#i를 X, j를 y라 한 상황\n",
        "for i in np.arange(30, 100, 0.5):\n",
        "  for j in np.arange(30, 100, 0.5):\n",
        "    # print((j - 30) * 2)\n",
        "    # tmp = (1/(1 + math.exp((-1)*((theta0_best) + theta1_best*i + theta2_best*j))))\n",
        "    # if(tmp >= 0.65):\n",
        "    #     tmp = 1\n",
        "    # else:\n",
        "    #     tmp = 0\n",
        "    \n",
        "    # array[(int((i - 30) * 2))][int((j - 30) * 2)] = (1/(1 + math.exp((-1)*((theta0_best) + theta1_best*x[(int((i - 30) * 2))] + theta2_best*y[(int((j - 30) * 2))]))))\n",
        "    array[(int((i - 30) * 2))][int((j - 30) * 2)] = (1/(1 + math.exp((-1)*((theta0_best) + theta1_best*i + theta2_best*j))))\n",
        "    # array[(int((i - 30) * 2))][int((j - 30) * 2)] = tmp\n",
        "\n",
        "# print(array)\n",
        "#비율 맞추기\n",
        "x_label0 = (x_label0 - 30)*2\n",
        "x_label1 = (x_label1 - 30)*2\n",
        "y_label0 = (y_label0 - 30)*2\n",
        "y_label1 = (y_label1 - 30)*2\n",
        "\n",
        "df_cm = pd.DataFrame(array, index = [i for i in np.arange(30,100,0.5)],\n",
        "                  columns = [i for i in np.arange(30,100,0.5)])\n",
        "# df_cm = pd.DataFrame(array)\n",
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "sn.heatmap(df_cm, cmap = \"coolwarm\", vmin=0, vmax=1)\n",
        "plt.scatter(x_label0, y_label0, alpha=0.3, c='b')\n",
        "plt.scatter(x_label1, y_label1, alpha=0.3, c='r')\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}