Demo20200314.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Demo20200314.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c2rpjkqIF7WW",
        "colab_type": "text"
      },
      "source": [
        "##規則模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqU8JSvitHCs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sklearn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yaGXWpi-Afyc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ui5_Is0dAnGs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2f007f10-bd4d-451f-a85f-b19be994499f"
      },
      "source": [
        "print(iris.DESCR)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".. _iris_dataset:\n",
            "\n",
            "Iris plants dataset\n",
            "--------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 150 (50 in each of three classes)\n",
            "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
            "    :Attribute Information:\n",
            "        - sepal length in cm\n",
            "        - sepal width in cm\n",
            "        - petal length in cm\n",
            "        - petal width in cm\n",
            "        - class:\n",
            "                - Iris-Setosa\n",
            "                - Iris-Versicolour\n",
            "                - Iris-Virginica\n",
            "                \n",
            "    :Summary Statistics:\n",
            "\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "                    Min  Max   Mean    SD   Class Correlation\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
            "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
            "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
            "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "    :Class Distribution: 33.3% for each of 3 classes.\n",
            "    :Creator: R.A. Fisher\n",
            "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
            "    :Date: July, 1988\n",
            "\n",
            "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
            "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
            "Machine Learning Repository, which has two wrong data points.\n",
            "\n",
            "This is perhaps the best known database to be found in the\n",
            "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
            "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
            "data set contains 3 classes of 50 instances each, where each class refers to a\n",
            "type of iris plant.  One class is linearly separable from the other 2; the\n",
            "latter are NOT linearly separable from each other.\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
            "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
            "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
            "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
            "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
            "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
            "     Structure and Classification Rule for Recognition in Partially Exposed\n",
            "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
            "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
            "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
            "     on Information Theory, May 1972, 431-433.\n",
            "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
            "     conceptual clustering system finds 3 classes in the data.\n",
            "   - Many, many more ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGEu1T-TGeKe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3f97744a-79a7-42b8-bcc2-6878075483f2"
      },
      "source": [
        "a = [1,2,3]\n",
        "b = [2,3,4]\n",
        "a + b"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 2, 3, 4]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AmFBupDdGrtP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "20e643fc-74ef-43ef-8a94-e57d8a8f0025"
      },
      "source": [
        "for i in range(len(a)):\n",
        "  print(a[i] + b[i])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "5\n",
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ifgOsHI9HAg9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "db3a7695-86c3-41eb-d049-012059a68ab8"
      },
      "source": [
        "import numpy as np\n",
        "na = np.array(a)\n",
        "nb = np.array(b)\n",
        "type(na)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tQQBWNDH7Hz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9068c9ae-7281-46c2-adb9-b47484092b31"
      },
      "source": [
        "na + nb"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 5, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8GEtlA-iH-fl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "12557c42-0ab0-4603-eb8a-bc63c91e47b2"
      },
      "source": [
        "na * nb"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2,  6, 12])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUY0_z0CHQXk",
        "colab_type": "text"
      },
      "source": [
        "Numpy v.s. List\n",
        "\n",
        "\n",
        "*   List 可以混雜不同類型物件, Numpy 只允許同類型物件\n",
        "*   List 不方便直接做運算, Numpy 可以直接執行陣列與矩陣運算\n",
        "*   List 比較類似 Java 的 ArrayList, Numpy 比較類似Array\n",
        "*   List 可以做伸縮, Numpy 是固定大小\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_K9cChr_ArJe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "1bf5127f-d369-4f59-fdcd-fee762544dfb"
      },
      "source": [
        "# numpy_array[列,欄]\n",
        "iris.data[0:3,:]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.1, 3.5, 1.4, 0.2],\n",
              "       [4.9, 3. , 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.3, 0.2]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yeWLHOKRA0ZF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8124277d-d79f-442d-8fac-eb3f2b452aee"
      },
      "source": [
        "iris.target[0:3]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HhCNQTgSBIw5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d3bf0086-cd32-40e4-a0cf-f56bdceb6d1a"
      },
      "source": [
        "iris.target_names"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ZsqAjlkBb1K",
        "colab_type": "text"
      },
      "source": [
        "1. 結構化資料\n",
        "2. 裡面不能有缺失值\n",
        "3. 全部資料都為數值"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uq_Ifpr6BPVT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import tree\n",
        "clf = tree.DecisionTreeClassifier()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sK5jRYa5CFWP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "4f14d2ad-d6f1-4d91-b6d0-3567b76ff2c7"
      },
      "source": [
        "clf.fit(iris.data, iris.target)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUqevmE6CMmF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "0382b949-9bfa-466b-e28d-1312eed757d2"
      },
      "source": [
        "clf.predict(iris.data)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHoE3QcuCUAM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy\n",
        "a = numpy.array([[3,5,1,2]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LaxSyFLrCbuM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4d367ae7-49b7-43b8-d0e9-e549dcd41681"
      },
      "source": [
        "clf.predict(a)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ar4J4U7CdSf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import tree\n",
        "tree.export_graphviz(clf, out_file='tree.dot')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FaMxEP-JZSP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "a143eaa0-39c0-4792-e6fa-ec7c08c6ac81"
      },
      "source": [
        "! cat tree.dot"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "digraph Tree {\n",
            "node [shape=box] ;\n",
            "0 [label=\"X[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]\"] ;\n",
            "1 [label=\"gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]\"] ;\n",
            "0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n",
            "2 [label=\"X[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]\"] ;\n",
            "0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n",
            "3 [label=\"X[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]\"] ;\n",
            "2 -> 3 ;\n",
            "4 [label=\"X[3] <= 1.65\\ngini = 0.041\\nsamples = 48\\nvalue = [0, 47, 1]\"] ;\n",
            "3 -> 4 ;\n",
            "5 [label=\"gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]\"] ;\n",
            "4 -> 5 ;\n",
            "6 [label=\"gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]\"] ;\n",
            "4 -> 6 ;\n",
            "7 [label=\"X[3] <= 1.55\\ngini = 0.444\\nsamples = 6\\nvalue = [0, 2, 4]\"] ;\n",
            "3 -> 7 ;\n",
            "8 [label=\"gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]\"] ;\n",
            "7 -> 8 ;\n",
            "9 [label=\"X[2] <= 5.45\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 2, 1]\"] ;\n",
            "7 -> 9 ;\n",
            "10 [label=\"gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]\"] ;\n",
            "9 -> 10 ;\n",
            "11 [label=\"gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]\"] ;\n",
            "9 -> 11 ;\n",
            "12 [label=\"X[2] <= 4.85\\ngini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]\"] ;\n",
            "2 -> 12 ;\n",
            "13 [label=\"X[1] <= 3.1\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]\"] ;\n",
            "12 -> 13 ;\n",
            "14 [label=\"gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]\"] ;\n",
            "13 -> 14 ;\n",
            "15 [label=\"gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]\"] ;\n",
            "13 -> 15 ;\n",
            "16 [label=\"gini = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]\"] ;\n",
            "12 -> 16 ;\n",
            "}"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H01LzL1RJdHL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "5d413c22-ea75-45aa-f25d-e235fa0b0ec9"
      },
      "source": [
        "from sklearn import tree\n",
        "clf = tree.DecisionTreeClassifier(max_depth=2, random_state=123)\n",
        "clf.fit(iris.data, iris.target)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=2, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=123, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvkgOCpSKdiY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tree.export_graphviz(clf, out_file='tree2.dot')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KzK9knScKg3z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "cfcd83f7-6146-4743-ed1e-ad470b2f5a97"
      },
      "source": [
        "! cat tree2.dot"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "digraph Tree {\n",
            "node [shape=box] ;\n",
            "0 [label=\"X[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]\"] ;\n",
            "1 [label=\"gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]\"] ;\n",
            "0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n",
            "2 [label=\"X[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]\"] ;\n",
            "0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n",
            "3 [label=\"gini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]\"] ;\n",
            "2 -> 3 ;\n",
            "4 [label=\"gini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]\"] ;\n",
            "2 -> 4 ;\n",
            "}"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kboodkoBKj0E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgPDLPreL_7-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "bf82f7fe-2a67-4094-ae3d-df62a6360a34"
      },
      "source": [
        "from sklearn import tree\n",
        "clf = tree.DecisionTreeClassifier(max_depth=2, random_state=123)\n",
        "X = iris.data[:,[2,3]]\n",
        "y = iris.target\n",
        "clf.fit(X, y)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=2, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=123, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMVZPELyL3Xu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0NG4ZlDM0Ws",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8ada1e86-c08d-4156-c9d3-5bb1cf04553c"
      },
      "source": [
        "x_min, x_max"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 7.9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8AEXSCx9M4RQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ed8c839c-2fb4-4481-bc2e-a8f3b8d66e1a"
      },
      "source": [
        "y_min, y_max "
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.9, 3.5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJV_ErOdNDp9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xx ,yy = np.meshgrid(np.arange(1,3, 1), np.arange(2, 4, 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BgBjFHdNghO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "943c3d4c-2830-4376-e041-96059f326135"
      },
      "source": [
        "xx"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2],\n",
              "       [1, 2]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofADMjutNnFz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "82314373-7974-4d90-9da7-4bb971ff54a9"
      },
      "source": [
        "yy"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2, 2],\n",
              "       [3, 3]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vi2H6qjeM-kz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
        "np.arange(y_min, y_max, 0.1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2-8V6H2Nz28",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "2ad6f8b0-c475-484c-ae30-f173ab7a4f37"
      },
      "source": [
        "plt.scatter(X[:, 0], X[:, 1], c=y, alpha=1, cmap = plt.cm.RdYlBu)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f3ad85916d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DZC5EathMZH24D/MKuSNr2nnO3+r7hijb6jEnG4WKPO+/7thPTU0jjY1m3bnP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\nQhK6EEKkiP8Hz7SCfAWXpn8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQxyqNB0N3gj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
        "np.arange(y_min, y_max, 0.1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VbqIGxtO4lx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZkuyGqzLO6A6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d11aedc6-2529-462d-bc98-183924311b3d"
      },
      "source": [
        "Z"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "077RxZl9O6T4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Z = Z.reshape(xx.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cl4cm4DzO8XJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "9440b313-61b8-4e6b-d0da-6f660e7d37f4"
      },
      "source": [
        "Z"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 1, 1, 1],\n",
              "       [0, 0, 0, ..., 1, 1, 1],\n",
              "       [0, 0, 0, ..., 1, 1, 1],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 2, 2, 2],\n",
              "       [0, 0, 0, ..., 2, 2, 2],\n",
              "       [0, 0, 0, ..., 2, 2, 2]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79SwKi1JO9gJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "33d6fd52-1767-41ea-e45d-26accce55e19"
      },
      "source": [
        "plt.contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.rainbow)\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, alpha=1, cmap = plt.cm.RdYlBu)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f3ad58ddb70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+85lhZAJ7ELRTydsRZYSmqCRFK+HTQ7eS4BWdErovToYqujfSAmRweEZnMYnd\nzJCdnUbF9qYRNyKQ1ihPaW6Omgq4+NNw8aeP6XTc6ZfODo2tL5684w2ymc7VUm7G540XGtixcS9j\np1YX7F6mbwrVS+eyA+y/jqDbpjHDUrWkuk1FAEGtSAWZvAm/JyPovlwfBAk8lut6WSXp0Pv2Rk+j\ndgfC5tV78LLd2yGcaIQdGyzhFwMbaWtML+zVMjztXpQWoI2+zSffnOchoQrZXD18iyZC79sbKQ5+\n7vz+mPymkTix7inFc33GTrNkXwxs8jRTkqK4zJQGxtCCAjuoZp2OwSO8cXErtUymkUinBtigsfQ4\nWQ8EfWwatZxRkkQIRr82aCWjpY1orgfObi2nTFwqcqVvH4ho92oZD+FUeY0MDgrdRt5C/vdBbMI6\nrevrn6dPTn3XTJ760xt4rt/eIByNR5h13Fgr3RcJK+GbkiMoC2Qj42gmKj4x8ZlAE8dK90VC9skS\n5cVcA6yvweIhEYISfnuDKzBakkRy7x1RxkkLUfz2Y0ZJkgqync4JUyEuTm7qY6dLTEGD7P5GXZcI\n9VpLSqOoQlJjvKrj2cngLixfM6acT93wFmbNryPiCImKKCdfMpMPf+uEQY3D5GclfFNy6thLDLfT\noKiIQJlmGUkbjXkWEkkSb2+AncBuZktDp1J16AjYvoyaPcAxEQFV4QWdSkuHaqG1eadGHjzjZ47g\nE/932lCHYfKwEr4pOZWSJhrSEBpBqSLVq2uMprXQYR20qjyNvsbkYwnflJykxnFDGkJ9hLbQEbDd\nDeQ0wr2hQHKQG2XN8GdVOqbkNFDNTBrwO0wx7Cu4OOwm/2pPcVymy05G08K+ech6akztuj/smL6c\n42vQqDtXtuHgs5tK1msdaXsAmAOwEr4pOT4RXtBpNFERzCWv0EglL+q00NWkABw8Fsh6xtFEXDzK\nc6NZu9rXo6b9h+6jZf0Ox3RdFmXfOWl1ggZZjbBZa2jOLYziK6SJEkUplyxx8RhLMwtkQ26xE2Py\nsxK+KUlpYizTKe3TF+dL9PuMp4kofreG3o5EwFdhrY5mOzVMlwYm0Ny9YVeFp3UGinCirCXSZRSt\nKjRSwes6Hj/oBxTMr4NPHJdFsr7TOREBR30msoeNDG5XTDO8WAnflDQNJkw44HG10tarEa+OKDWS\nwiVKDakepkd2SRD+LSEiMIJUblI26XBehEoyuYdA2H1tXVjTM0v4xvRCG4nQqY678pT2ht+u0xTv\nIyhpYqSJhj5qVPOPkk0RC13T1u9wX2PysSodU2SUsexlsuwmisduKtmgdaGrQw20SlJMk11UkyJF\nNPdNYH+yDW1wRdiqwdqym6q9iCEAABNrSURBVHQUo6S108ApT4VGKsnkPs9OqqjTlk7fHnyEjTo6\nNKY2ErSQoFo7f3tQhC1qywSanlkJ3xSVGbKTObKNaklTLi4TaGKhrB/0BslqksyXjdTRQpm41EqK\nCNqpxL5vQXE/15japkG7wL7eMnspZ5VOJK1RfBV8FRqoZpXuX95vtY5nO9X4KngqpNXhVR1PExV5\nY1uuk9lFVe6+QlJjvKKT8yyqYsx+VsI3RSOKyyQaO5V2h6pBcqY0dKuzDxvxGlOfpTqdLA5uyD+n\nXVSxSyuJ4eER6bZYihLhdZ3AG4zDwc9NmtZzm4KLw0qdRAS/1+cYA1bCN0WkinRoA6ojykhpG9RY\nqns54laBSjKhyX4/IRv08cl7hE8kV23V+8Tdl3NMabOEb4pGhmjeBsnBnuo3k2fWzPBj7YuyGR7s\n/1RTNHpqkNzcY4Okzyx2ME6aEaBRK1jN+E6l7kpSzJVtVJDGw2GDjmILozpdZSStTJbdxHFpI05c\nXZweRsAGI14jzJAdRFF2aDVNlDNJgmbZZsrZqKNIWd26KRIFKeGLyE0iskNElufZLyLyYxFZIyLL\nRGRBIe5rDj3LdTJ7qGhvxMyowyqd0GlWyK6Ol3VMlCaiojiijJbWYEBTbg76KpIcJxuoIo0jEMNj\nljQwm63t15hII/NkM6OkjSrJUEsbiuDlpkL2VNhFJWmN4OUaYLM4RPGolRRVkmaa7ORY2cQYWqiU\nDONo4jhZT6VNcmaKRKFK+L8mWMLwljz7zwVm535OAH6W+21MJy4Or+gUYrhE8XMThOWvox5JC+W4\n3UazRlSZQQNvMI65sq19e8ffE2jmjdyUwl0baR0BX5XNjGSr1pIhikcEUMrIEsXjWNnU6RuAE9Ko\nK6rMlB1517U1ZjAVpISvqn8HdvdwyEXALRp4BqgVkQk9HG9KXJZorpthzw2SY9kbul0ERkowhXFF\nnoXCAWpppTJPY3FEYCRtJInnkj2AkCJOOdk8S6V0j2MENgLWFIfBarSdBGzq8L4+t60TEblSRJaK\nyNKmpoZBCs0MZ6k8X1JVIaPBvrCpCPafHyOLE9pYrLmJysIcTEOtexANwMYMpKLqpaOqN6jqQlVd\nWFMzZqjDMcPAJoIRqWFTGKwj+H9os47stn/fQuFtlJEiTkvI1Ak+Qr12btjdp4ly3Nygq67X7chT\nYVOeaxgz2AYr4W8GOlZiTs5tM6ZffCK8ohO7TUu8TuvYm1ukZD1j2KkVnfZnifC8Tm2/znKdRDPl\neCq4GsFVYY2OYU/eEa/CyzqFNuLt52RVaKIcP/feU2ELtWyhduD/EMb0wmB1y7wHuEZE/kDQWNuk\nqlsPcI4xvdJINU/obEbSShSlgSq6lmVWMoWouoymhVYStHRZscolyss6lQRZYni0Ee9xoBRAijjP\n6wzKyeDg00ICEGK4lJENHgZWnWOKSEESvojcCrwFqBOReuArEIyUUdXrgQeA84A1QBvwkULc15j9\nIjRS3eMRLlG2H6C0HcxieXCDvLrOYZMlOiSTvRlzIAX5v1JVLzvAfgWuLsS9jDHG9E1RNdoaY4wZ\nOJbwjTGmRFjCN8aYEmEJ3xhjSoQlfGOMKRGW8I0xpkRYwjfGmBJhCd8YY0qEJXxjjCkRlvCNMaZE\n2IQfw4C7bSNtT9yHJlspO/4M4m9agORb0cMYY/KwhF/kWh+9i6YbvgKeB55H219+T9lJb6f2mv+2\npG+MOShWpVPE/JamINln0uC5gKLpJKmnF5N++amhDs8YM8xYwi9i6Zf/gUS6fwnTdJLkk/cPQUTG\nmOHMEn4xc5zwNbxFkOjBzdlujDGW8ItY4thTQf3uO2IJIiPHsPeO60k9/xjqeYMfnDFm2ClIwheR\nc0RktYisEZF/D9n/YRFpEJGXcj9XFOK+h7pIWQUjP/djJFEOiQqIJyAWBxFa7/k1e//wYxq//xka\nPncJflvLUIdrjCly/e6lIyIO8H/A2UA9sERE7lHVlV0O/aOqXtPf+5WasvlvZtwvHif17MNoKknb\n43eTXbsC9YOSv6bacLeso/nWH1H70f8Y4miNMcWsECX8RcAaVV2rqhngD8BFBbiuyYlUjqDijHdS\nfsY7yK5bBX6Xah43S+rJ+4YmOGPMsFGIhD8J2NThfX1uW1fvFJFlInKHiEwJu5CIXCkiS0VkaVNT\nQwFCKx2qQx2BMabYDdbAq3uBW1U1LSJXATcDZ3Q9SFVvAG4AmDtr4SGZwjSdIrX0Efym3cSPPIHY\n1Nndjml97M+03v8bIhUjGHHVV4jW1JFa8jc0lSQ2/U1k167s3JgbjVH+5vM7XcPbs5PU0kdBlbLj\nz8CprRvoj2aMKXKFSPibgY4l9sm5be1UdVeHt78EvlOA+w472bUr2fnVD4Pnob4LCOUnn0Pt1d9C\nIhE8z2PHv5yGNu3/c+385LngOEgsEdTb+173njsRh6p3XNn+tvWRO2m64WtIJPgC13TjN6n5ly9R\neea7BuFTGmOKVSGqdJYAs0VkhojEgUuBezoeICITOry9EFhVgPsOK+r77Pr2J9DWZjTVGoyezaRI\nPr2Y5FMPAND88690SvbtPA9NtUEmBW62+37fo/W+W4JDd26l6Rdfg2waTSfRdBKyaZp+8Q3chi0D\n+RGNMUWu3wlfVV3gGmAxQSK/TVVXiMjXReTC3GGfEpEVIvIy8Cngw/2973CTXb8KbW3uviOdpO2v\ntwGQ7GvDq5sl+eifg2s8vTi8Ql+V1NOL+3Z9Y8whoSB1+Kr6APBAl21f7vD6C8AXCnGvYct1Ic9k\nZ5rNBC/8vjdbqOfu/921Fw+A+mjYtwNjTMmwkbaDJHbYPHC6P18lUY5Uj2Tnly7HGT2+bxd3opSf\n9HYAyo8/I5iSodsxDmWLzuzb9Y0xhwRL+INEnCiVF3yw23ZNJ8ksfZTMyiV42zbkv4ATDb4hxBIh\n+xyq3n01ANFJM6m6+F8gXgYSCX7iZVRd+FFikw8r1McxxgxDNh/+IPF9n5bbf9q3kyurqTr3/Wg6\nRea1l8m+vizorbOPQtvDf2TEpZ8CYMR7r6F80ZntjcHlJ59LbOYR/f0IxphhzhL+IEk/szhYxKQP\nxPdJHLGI+JsWsPXyhZ2TPUA2Tdtfb29P+ACxGYcTm3F4f0I2xhxirEpnkPhhPXR6TdBMKtd3P5xm\n0v24vjGmFFgJvx9UFXfdKtyGLcRmHkF0zMRux3jNu2n9y62oF9Jzprf3SSdpuuNn8NvvQd0E2L6p\n8wERh7LjOw9c1kya9IrnAEjMW4TEQ+r+jTElxRJ+H3nNjez+xhW4m9dCxEHdDOVv/idqP/6N9hGu\ne37zXdru+mX/b+Z7eGteyb/fiVJ92f7qnNSLT9D4vU/TvnqKKiM/+wPKFpzW/1iMMcOWVen00Z4f\n/RvZDauD0azJFshmSD51P62LbwUg89rLhUn2vSGQfPxeALym3TT+7yfRZCuabAl+Uq00/u+n8MJG\n8RpjSoYl/D7wW5tJL38mt7B4B+kUrQ/8BoDmP143eAFl0u0PmtQ/HgwfaIuSeurBwYvJGFN0LOH3\ngaaTQf/2sH3JVqC/jbQHT1O5+yZbwufbcbO2KpYxJc4Sfh9ERo7FqR3dfYfjkDj6ZNLLn6XsmJMG\nLyCJkDj2zUCwDq7Eui9wLrE4ifmnDl5MxpiiYwm/D0QkmNI4Ub5/uoR4AonGST71ALv/52pa7roJ\nnO6Jt4BR7L9vVQ0jPvDZ4O3MeZSdfF4Q274jE+WUnXQO8cOOHMB4jDHFznrp9FHiqBMZ8727aHng\nt3hb1iFVI0g99wi42f2TlEUcImMmgZuBiEPF296Lt2cnyYf+GAzCcqLgHdyEZjJqHLWf/xGZfyzG\nrV9DfO58Kt5+Gc6Ike3H1F79TdInnk1bbgbNirdcTGLhWwv22Y0xw5Ml/H6ITpjWvnB4w+ffGcxX\n35Hv4e9pYNzPH8OpGdW+eeQVXwJgy7t6PxJ2xEf/k6rzPtD+vnzOsXmPFRHKFr6VMkvyxpgOrEqn\nQPyWPaHbJeKEz4MPB7UQrbd7e1/CMsaYdpbwCyQx/7Tw6Y/jZTjjQtdsR6pqen39itMv6nNsxhgD\nBUr4InKOiKwWkTUi8u8h+xMi8sfc/mdFZHoh7ltMqt/9cSKVIyAWDzZIBEmUMeKqryJh89MDtZ/4\nZq+uHZu3iNiUWYUK1RhTovqd8EXEAf4POBc4ArhMRLrOxftRoFFVZwE/AP6nv/ctNs7IsYz54X3B\nvPNzjqXslHMZ/Y3fUpFbmCRM+QlnMfq/fk902pugrAJn4gxq//X7JE44GymvJFIzmqrLrmXM128Z\nxE9ijDlUFaLRdhGwRlXXAojIH4CLgJUdjrkI+Gru9R3AdSIiqgdRiT0MODWjGPG+a4Fre31O4vAF\njP3+XZ22VZx6XoEjM8aYwlTpTAI6Tt9Yn9sWekxu0fMmoNvIJRG5UkSWisjSpqaGAoRmjDFmn6Jq\ntFXVG1R1oaourKkZM9ThGGPMIaUQCX8z0LEbyuTcttBjRCQK1AA2daMxxgyiQiT8JcBsEZkhInHg\nUuCeLsfcA3wo9/pdwCOHWv29McYUu3432qqqKyLXAIsBB7hJVVeIyNeBpap6D3Aj8BsRWQPsJngo\nGGOMGUQFmVpBVR8AHuiy7csdXqeAdxfiXsYYY/qmqBptjTHGDBxL+MYYUyIs4RtjTImwhG+MMSXC\nEr4xxpQIS/jGGFMiLOEbY0yJsIRvjDElwhK+McaUCEv4xhhTIizhG2NMibCEb4wxJcISvjHGlAhL\n+MYYUyIs4RtjTImwhG+MMSWiXwlfREaJyMMi8nru98g8x3ki8lLup+vyh8YYYwZBf0v4/w78TVVn\nA3/LvQ+TVNVjcz8X9vOexhhj+qC/Cf8i4Obc65uBi/t5PWOMMQOkvwl/nKpuzb3eBozLc1yZiCwV\nkWdExB4KxhgzBA64iLmI/BUYH7LrPzq+UVUVEc1zmWmqullEZgKPiMgrqvpGyL2uBK4EGFc39YDB\nG2OM6b0DJnxVPSvfPhHZLiITVHWriEwAduS5xubc77Ui8hgwH+iW8FX1BuAGgLmzFuZ7eBhjjOmD\n/lbp3AN8KPf6Q8DdXQ8QkZEiksi9rgNOAVb2877GGGMOUn8T/reBs0XkdeCs3HtEZKGI/DJ3zOHA\nUhF5GXgU+LaqWsI3xphBdsAqnZ6o6i7gzJDtS4Ercq//ARzVn/sYY4zpPxtpa4wxJcISvjHGlAhL\n+MYYUyIs4RtjTImwhG+MMSXCEr4xxpQIS/jGGFMiLOEbY0yJsIRvjDElwhK+McaUCEv4xhhTIizh\nG2NMibCEb4wxJcISvjHGlAhL+MYYUyJEtThXEhSRvcDqoY6jl+qAnUMdRC9ZrAPDYh0YFuvBm6aq\nY8J29GsBlAG2WlUXDnUQvSEiSy3WwrNYB4bFOjCGQ6xWpWOMMSXCEr4xxpSIYk74Nwx1AAfBYh0Y\nFuvAsFgHRtHHWrSNtsYYYwqrmEv4xhhjCsgSvjHGlIiiTPgico6IrBaRNSLy70MdTz4icpOI7BCR\n5UMdy4GIyBQReVREVorIChG5dqhjykdEykTkORF5ORfr14Y6pp6IiCMiL4rIfUMdy4GIyHoReUVE\nXhKRpUMdT09EpFZE7hCRV0VklYicNNQxhRGRubm/576fZhH59FDHFabo6vBFxAFeA84G6oElwGWq\nunJIAwshIqcBLcAtqnrkUMfTExGZAExQ1RdEpBp4Hri4SP+uAlSqaouIxIAngWtV9ZkhDi2UiHwG\nWAiMUNULhjqenojIemChqhbDAKEeicjNwBOq+ksRiQMVqrpnqOPqSS5/bQZOUNUNQx1PV8VYwl8E\nrFHVtaqaAf4AXDTEMYVS1b8Du4c6jt5Q1a2q+kLu9V5gFTBpaKMKp4GW3NtY7qe4SiY5IjIZOB/4\n5VDHcigRkRrgNOBGAFXNFHuyzzkTeKMYkz0UZ8KfBGzq8L6eIk1Mw5WITAfmA88ObST55apJXgJ2\nAA+rarHG+kPg3wB/qAPpJQUeEpHnReTKoQ6mBzOABuBXueqyX4pI5VAH1QuXArcOdRD5FGPCNwNI\nRKqAPwGfVtXmoY4nH1X1VPVYYDKwSESKrspMRC4Adqjq80Mdy0E4VVUXAOcCV+eqJYtRFFgA/ExV\n5wOtQNG25wHkqp0uBG4f6ljyKcaEvxmY0uH95Nw200+5+vA/Ab9T1TuHOp7eyH2NfxQ4Z6hjCXEK\ncGGuXvwPwBki8tuhDalnqro593sH8GeCKtRiVA/Ud/hmdwfBA6CYnQu8oKrbhzqQfIox4S8BZovI\njNwT81LgniGOadjLNYTeCKxS1e8PdTw9EZExIlKbe11O0ID/6tBG1Z2qfkFVJ6vqdIL/Tx9R1Q8M\ncVh5iUhlrsGeXPXI24Ci7GGmqtuATSIyN7fpTKDoOhh0cRlFXJ0DRThbpqq6InINsBhwgJtUdcUQ\nhxVKRG4F3gLUiUg98BVVvXFoo8rrFOBy4JVc3TjAF1X1gSGMKZ8JwM25Hg8R4DZVLfouj8PAOODP\nwbOfKPB7Vf3L0IbUo08Cv8sV/NYCHxniePLKPUDPBq4a6lh6UnTdMo0xxgyMYqzSMcYYMwAs4Rtj\nTImwhG+MMSXCEr4xxpQIS/jGGFMiLOEbY0yJsIRvjDEl4v8DlT7oPtYIXngAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rGXKQ62PEsS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "42b571e8-1185-4e90-da9e-5e4aa4d09c1c"
      },
      "source": [
        "plt.contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.rainbow)\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, alpha=1, cmap = plt.cm.RdYlBu)\n",
        "plt.title('Decision Tree')\n",
        "plt.xlabel('Petal.Length')\n",
        "plt.ylabel('Petal.Width')\n",
        "plt.show()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xUscqHZXhyMImhrFJhxHA5VhZ1ZokWgRFmcgOdmp+EkUq6fLSP9aQjHfs15CI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\nWaIwxhiT1f8H4dYlk42PtJgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vt-s2_9JSXWz",
        "colab_type": "text"
      },
      "source": [
        "##邏輯回歸分析"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vN_S3XEWQGEK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "outputId": "e35b50a1-ec75-41e4-8a1b-e0a52dd0a2b1"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "clf = LogisticRegression()\n",
        "clf.fit(iris.data, iris.target)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0rFNX3LSt5P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "222bf8eb-62c8-4154-e917-90533028e063"
      },
      "source": [
        "clf.predict(iris.data)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V52LrZavSxst",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "7dcd97f6-1df1-4135-ce98-bd630152c610"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "iris = load_iris()\n",
        "X = iris.data[:, [2, 3]]\n",
        "y = iris.target\n",
        "clf = LogisticRegression()\n",
        "clf.fit(X, y)\n"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzjjSW1sS-eS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "17212d16-763f-4f92-8707-0fd8cc80d2f5"
      },
      "source": [
        "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
        "np.arange(y_min, y_max, 0.1))\n",
        "\n",
        "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z = Z.reshape(xx.shape)\n",
        "plt.plot()\n",
        "plt.contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.RdYlBu)\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap = plt.cm.brg)\n",
        "plt.title('Logistic Regression')\n",
        "plt.xlabel('Petal.Length')\n",
        "plt.ylabel('Petal.Width')\n",
        "plt.show()\n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VmWs367stoyrBKKQQIb7sshZDKG7ZnUEXk/aeHrUtIH42dF3FtuyNTa590Zlp\nrielOoF4cy7W79rJX06ZzrbC6nC/w8N0OeigKqdh5I/N2Os7qsGaTBevc/una61V5sbum8LIAxNY\n0/3z+jkZnkA2J+86l/7V1pwYHz5+ety5lOQU19+nm68nXntNeI3sAH1qBnLzyocpqLVGw3ttNfxx\n1M9Y2nM+zpCLgM3H10ou5bLiX9b3BazrtpR7xlxTP8zVHnLgDLoISABjC+EKuunuy+PcbQ0t2At7\nzeOBEddb63xLiPzaQn618mH61gxqxU8/82mgUKqTiA0Wv3IfawUJob4RuConENUgHCJIyAgT90zF\nZ/Nx0FnBpi7rCIabmhBrxNPOnE0MqxqNDRs3r3yYj3q/wQe9X8FhnEzbcTHHR/yKv2PsVVaQiLhP\nhWtf1Ott2Ru4ZdxM/v7ZXAThoeG3srTnfPx2b31q8beKnqZPzUCm75hBleMAt427nBpH9FwIdyCL\nKXtOZ797D8eWncL07d8iJ9gVgJLsL/nDqJ9GZaHdnrWJm8dfyj8+/QB7XQ+40kChVGdSFyxC/Xuw\nLndvUum/A7YA03ZezKjy45h54gkNQSLMZ6/lpQH/qJ8hbTcOpu46n6m7zo9bhpU9FyQxuzvEflcp\n/+u2gsFVI5jf5w38Nl/UMV57Da8M+CfTd8xgfu834i6LKgjj95/I6Tu/0Wjf3L7PNJo3YWyGakcl\nq3MXMnb/5Ljl74y0j0KpTsZDgL6eQ0tPvjNrCwcdFdhM/F/ZB5z74m6PJ9l+ABs2DjjLqLUfTHhO\npbO8/v7xUoD7bF7KXWVxz93r3t0o6NU5kOCczkoDhVIdSA21rJWNlNN0kgOHzYXHm/w4/9PWHMOA\nL31xZ27bQjbGxnRUNyUrkFwuKr/4GV4xjq7+XHr4ejXaLyFhVPlEAEaVH4cn2HjdbWfIxaj9x8W9\n/rFlJ8VdqzsgAUaUH5tUGTsLDRRKdQAGwz322RS4J3GC62IK3VOY6bgBL764x3sIcO+WK63OaFN/\nkejX4W3T3s/mmzMu5dSrzuPBH4Db70aMFWTsIQdZwS58a9NPki7r1etvi3tfW7ChtuIOZnHB1ivo\n4c9DEK5efzvuoAcJRdw3lMN3i63U3qPLj2PEgWNxRwQLdyCLo/efwFEJZoR/pfRs+tQOCI/uajhn\n+vYZ5Hv7Jv1+OoOk0oyLyHDgF8BAIvo1jDGnJjwpRTTNeOaz71hLj/EFOEYM1uyxSXrK9hrfd95C\ntTQk4ssyHi4NnsdDgdsTnvfC9qf5RZc/sKt7DQV7spi2+nI+O34hG7uvxBX08LPf+7jlllpsEd8T\nn5zk5vqnJ7Gz225GlR/Hhc0zxAQAACAASURBVFuuIs/X55DKuzR3PrOH38Zezy5yvflc8uW17M7a\nzoL8OXQJdOfskss4fm/0pLiNXVfz0oCH2Z69mREHjuGCrVfRu7aofn9A/MwrfIF3C19EsDFtxzc4\nddcFCedTgLXO9ltFT/NxwVtkBbM5q+QSJu+Z3qFmVSerqTTjyQaKFcBDWGtG1M/AMcYsaa1CthYN\nFJlPA8WhG+36GmttjVcH9Bg3+7yf46HxanB1Eq1r0efjuYy795c4q6NHEgVcbtbP/ClfXnxFq78P\nlTqtsR5FwBjz91Ysk1KqFe2WvXG3GwwVVDUZKOqHzQ6w1o6oW9diUFkptkDjzl6Hz0tW6Y5WKbfK\nDE32UYhITxHpCbwuIj8QkcK6beHtSqk0MCk0rr7fIFIu3ehFbvTGiirYWQoRrQnxUpWv7HMkRhp/\nRQSysikfcTSevbsg2L4pxH02L2WuXXHTgau201yNYgnRuSJ/EbHPAEe0RaGUUofmd4Gf86F8SrXN\nR92S09leO38K3Yit7st+XznMugHmfQwi0Ccf/n4nnNIwYikyVXkxsPeYyfRa+gkOrzX0NOh0EXS6\nGPvHX4EIQbeHNbNuoGT619v0/QUlwL+G3MOcfs8A1mimS4p/xlk7vtOm91WWJmsUxpjBxpgjgBHh\n5/UPYGT7FFEp1ZzRL29i0SThm8/B4C/htHfgjfNtXHz9qoaDLphlBQmfH7w+2LIdLroaNlirCXoI\n/0oPVRAMBRgyqoBnf3A36678BRWDhlNVNJiKQcNx1NZg93mxe2txVZQz5i+3UbDowzZ9f08c8Xvm\n9nvGSl9ur+Wgs4LHht3DJ/lvt+l9lSXZ4bGfJrlNKZUKv3uQo5Z7efoSKB4K75wJp8zxwyPPWEFh\n9XpY8z8rSETy+eBvT9a/jE0gWBkS3p1yIR/+800++vvLdNuyAbsvel1ph7eGYU/9tc3eml98vF30\nb7z26Al1XnsNzw5uu/uqBk02PYlIH6w1qrNEZDwNTVDdgPZbgUQp1bSSXfG3hwyUV8DWHeCIM6s6\nEIT/Ra9PHi+BYHFpFaOD+zG2+L8ts3a3Xed2taMybnoOgL3uBO9btarm+ijOBGZiLUEauQpdJdYi\nQ0qptnSwGtwucDTzv+oxo+HdTxpv75INOVkwYkjj2gSAxw0nNZ657CndTVG37lDUFchn+ertLLc5\nONlmBZugDWqyIOcgGBHKj0p+oZ9D1dWfS1YgB78rZvKggSGVo9rsvqpBc30UjxtjpgIzjTFTIx7n\nJljj+pCIyKMiUioiqxPsP0VEDojI8vDjlpbeU6mM8P4CGHMG9JkABcfAtbdZTUiJ3Paz+MuA5WRB\n4UQY91XolQv2mP/lPW64ckbD64efhh6jYdBkPD1HkX3OZP4ydCZ3fvskrv/mVI5Z2o1ZD9vI3Q89\n98HAzfDSN5ysn3lta7zruGzYmLnxhqhZ1xhr6da6mdmqbTXX9PSzeM/rNLHWdbIeA/4KPNHEMfON\nMWe38D5KZY4V6+Ci70NNuE0+GIQnXrJGLT1xf/xzZt0Qf42hreEmoSDxm6d8fqisgrxc+O8H8NOG\nWdxBG5z8eAlbXCUEw6nI1wzbwZph1AelkgFw6VPCzavKOLoNF447bdeFdPfn8p/Bf2GPZztHVI7i\n0uKfM6RKaxTtobmmp67hv0cCE4HXwq/PARa19ObGmI9EZFBLr6NUh/KHhxvXHmpr4fV5sHsv9G6c\nII+1Gw7vXn6/1Zl9701w/d1Ru96ZBqW9IeCM2Bin1uJ1eHlm0F8arWDX2iaUTWVC2dQ2vYeKr8lA\nYYy5DUBEPgKOMcZUhl/fCrzZ5qWznBBOIbIDuM4Ysyb2ABGZBcwC6Ncrzv9ESmWSL4ohFKfz1u22\nhrTGCxSHyx+ANeEgszN6ne2NQ8GfZO6GndlbWq9MKu0kOzy2N0SlofSFt7W1pcBAY8xY4C/AK/EO\nMsbMNsZMMMZMyOvarR2KpdRhSCKvGgATjgZ7nBFKXi8MG9R4e7ygkiy3CyaNs8o2ZGDUrqNXgiOJ\niddihMGVIw6/DBF0zer0lGygeAJYJCK3hmsTC7H6F9qUMabCGFMVfv4W4BQRrTKozPJFMXz1u9Bt\nJPQaCz/+jTWaKZHrZkFWTG6m7Cy46luQ29167fNZ18w5ErqMsGZaHw4D/Ot5yDnKataK8JX5MGoN\nuCOmL4iRRqlCXCE33/nyp4d3/7DNOeu5cfy3uWDqUXzzpLHMHnZ73IWIVGokFSiMMXcC3wP2hx/f\nM8b8ri0LBtY8DhHr/wAROQ6rvLr0lMocu/fC1G/CR4sgGILqWnjyZfj69xOfM2QgvPcMnDbFGrVU\nVAi3/hTuvqHhmBMvgg8XRqzpEOeXeDLBw+eDXXus57v2gMsFXXOs02023vndZK6WS8kzPehuuvDd\n4HncE/gVA4IDcQezGbh7HN9/d3aLOpXLXLu44dgZrO3xOUZC1Dqqmdv3Oe4e/cPDvqZqXc2Neupm\njKkIJwDcHH7U7etpjEl+/cP41/8PcArQS0RKgN8ATgBjzEPARcDVIhIAaoAZJpm86Eqli7qZ0ZH/\nbL1eWLwCVn0BY46Kf97oI+H1R+Pv27bTmmkdT598+PJj+MtjcOv9DSOnkuXzQVEf2L0UgC7AfQbu\n894cddg1we9RUpPHutKDrNi9k2JnFUMKklu5LtYbRU/iF19UR7nf7mV17kK2Z22iX83gw7quaj3N\ndVX9GzibhuSAdYRWSApojPlWM/v/ijV8VqnMtGId1Hobb7fb4X9fJg4UTVmyMvG+veExqiu/OPQg\nUWfr9mYPqcs2ayUQLKR4zaGtwR3py65rCdgbzxFxhJyU5HypgSINNDfq6ezwX/0vpdThGDcS3pnf\nOFgEg3DkkMO75sQmZkHnh7P/jx0BL/8XqmsSH5vIwH7JHxuqYERRN6AgYbBorqYxtHI0a3oswh8T\nLAK2AEUHNUF1Omiu6ekVrOR/nwCfG2OamBqqVCewdx/cdC+8+g7YBL7xNfjtddC9a/zjr5gBf34s\nuvnJ6bCGpR53rvXa7YKTj7dmY/sDkOWBc6fBnA+tPE02m5VmY0epVQuJvEYsjxvyxkJ+3uG9vyyP\nNcs7CXU5oUoOVhAMGYaMKiDP44w6ZtGS5msnZ5VcwltFTxEwfoxYn5Er6GbM/klam0gTzXVm/xPo\nAdwJ7BaRT0XkDyJygYi0x/BYpdKHzwenfBOefd2azXygEp54EU7/duIhqgV58OGzcOpkKylfl2wr\nEV/kl7zXB3PnN2yrqbXuUV5hvQ6F4IPPGoIExA8SAJu2Wedv3d64NmETyPY0vO7eFX5wKfTvawWj\nwf1h9t1w4VeT/khis82W1fqjHkNGFVBcWtXkNfJ8vblnyXOM3n8ctpCdrEAO07d/ixtWa6tzumiu\n6ekN4A0AEbED47E6n38PDAbiDPZWqoN6fR6U7o3+kvb5rUlw8z6GM06Kf97wIxo6pv/2BFx3Z9uX\nNZ5QeA2y+S/AsWMatv/h5oSnJCMq26wtJ2rf6q0HqPIHKS5turN7wMFh3LH8qRaVQ7WdZuddhuct\nTA4/JgEeYB6woG2LplSaWfkFVMWZ/1DrtUYhJQoUkf7btgv8NEvEKmtkoGgF9cEidvR6TKrywx0Z\npVKruT6KDcAB4EVgDnBH3QQ4pTqdYYMhJ7vxZDmPG4YMSu4ax4+zah+pIsDQQW1y6foV8iLEW9dC\ng0Xmaa6P4lFgO/B14CrgeyIyIdwMpVTncuF0K1BELt5jt0PP7nDWKYnP21kKV98EA0+AJ1ucnf/w\nORyQk2Nlmh1wAlz5SyjZ2aa3rO/DGNCd0Ufn1zdDqczS3HoUvzPGnGeMmQDcirVg0VXAShFJcR1a\nqXaWnWV1TJ90nBUgHHaYdqI1i9rpjH/OgUqYciE8/Srs2Wel/bbFmTEduU6ETeLPqva4o5/Hri3h\ncloJA+126NYVrr7EqsHYbdYoqb69oaLS6vDeuw+efQMmX2A9b0N1cy5GD+jO2HGFABosMkxSuSFF\n5AjgOOB4rH6KAmBTkycp1RENLIK3HrdGQIkkDhB1HnveChaBiGaZUJzkAi4X3HotfOcCuOU+azRV\nIM7IpuL51hf/yNOslCCRgkE4dQr89XZryG1dzcfrszLDjj8rOn15MGj1ufzjP3Bj26fLaJigl3jO\nhUpPTdYoRORlEdkBvAWcijVDe4YxJl8XE1KdmsvVfJAA+HRJcjOka2phwTLo2QMWLosfJFxOa/Gh\n0rL42WWDIVi03JoLEdk85nbB2o3W31i1XvhkcfPla6H6/otQBcFQIKlhsyp9NFej+BdwlTFmbzPH\nKaXiGT7Y+oKPt151JJerIYX4kIHWQkSxac18fujX2/rCT3S9I/rH3z6wX/zg43BYw3fbQeQw2tUl\ngaSGzar00FwfxWuJgoSI9GmbIil1GIyxJqlNuRBGnGqtMV2XFbW9rfwCvvlDOHKqVaNIJour02Gl\nEQcrzbgnJs242w3TvmL1M+TlwnlnND4mywO/vDr+9UcNt/JKuWJqQS6nNemunURO0NPO7cyR7HoU\n8TzSaqVQqqVu+SP88NewbI01Ae5fz8Gk89u8o7aRz1fCqTPgjXdh2w5YsLTxsqYiUFhgfUm7nFat\n481/WenEwVq46In7raDgdlmPC6fDv/7QcI2H7oIZ5zTs750P/7wHpkxIXLaXZsP0k8P3dVkzsV96\nuM2GyyaiwSLzSEfL2j32iCFmzh13pboYqgXsO9bSY3wBjhGDoefQ5k/Yuw+Gn9I48Z7Hbf06v+ma\nNilnXNO+k1ybv8cNH78ABb2gV8/4xxhj9Ud0zbFGXMVTUwsVVVYyQFuSv/uqDkKNF3rlHv6CR62g\nFoeVqrzkICuW76SL067NUCl03mnDloRHuDbSXGd2z6YebVNcpQ7Ryi8aN6mAFTje+7R9y7Ks0ZLu\n8Tkd1sp3iYIEWF/ivXslDhJgNTf17pV8kADokmMFlhQGCWgYNjuiKKd+2KxKT811ZtetQxHvX1SL\n16NQqlX07R0/SZ7NZg1nbU/5Pa2mr+YYY5VbNUpVrrWK9NNcZ/ZgY8wR4b+xDw0SKj0cNcRaEc4Z\n87vH7YIfzUx8XiAAP7oFCo6xUnN//f8a92ksWwOTzoPcMdbM6gcfb3ydeR/DOZfDxHOskUtZnsbH\nRLLboWsX+NXvrX6Uex+CTz6H7/wEJpwN378Jirck884zXl1/Rd2wWe2vSE9JTbgDEJFcYBhWUkAA\njDEfteTmIvIo1gp6pcaY0XH2C/AAcBZQDcw0xixtyT1VB/XSw3DZz6z+AYfd+rL+6x3WwkGJjP8q\nFG9teP32B9ZIpW0LIDsbFq+Eky9uGKa6Zx/84i5Y/T/4ezgD7IOPw2/ub0jpvWGzFbCyPNbEuEDQ\nSjG+dLXVlxAMQm53K4X4gvA/5XUbG4auGgPrv4SX3rZmfI8+sjU/pbQUlX1Wc0KlpaQaNkXkSuAj\nrMSAt4X/3toK938MmN7E/q9iBadhwCzg761wT9UR5eXCG/+C/30IC16BTZ/AuacnPn7uR9FBok5N\nLdxyv/V81g2N5zIAPP4CVFdbyQEjgwRYM7YDAZj1bfj4RdjyKbzwEGz8CBa+Cu/+xwoSkZPw/H7r\nPnX3qpsxfdO9h/45ZCgdCZXeku0B+wkwEdhijJmKtS5FeUtvHq6RNDV+8TzgCWP5DOghItrrpRLL\n72kN94w3cznS828m3jc3XFHe2ETzz/ufwZr/WbWXWF4fvP+pNZGta/hXsc1mTaT7cmvjJrJEFnSu\nyrMmEExfyQaKWmNMLYCIuI0xXwDtUSfuB2yLeF0S3hZFRGaJyGIRWVxWWdEOxVJtrWbTZoKlJbBv\nY9vcoH/fxPv65Ft/s9yJjxnc31puNNFKc/0SzEctyLOGgSQjr0eSB3YcmkAwPSUbKEpEpAfwCvCO\niLwKpE1vmzFmtjFmgjFmQl7XbqkujmqhYN+ReMuzMWX+tgsW112VeHjob39u/b06wYzlXj1h5DAr\nWIwd2biGkJ0FP/5e/HNPnGj1UTQ3nDU7C35yedPHdGB1w2aHjCpIdVEUSQYKY8wFxphyY8ytwK+x\nZmWf15YFC9sORCavKQpvUx1csO9IypeVYsqayZF0uLKz4cWHor/kReC2n8Lx463Xt/4UzpoafV5u\nd/gkYk2J5x6EiWOtCXRdu1jrVdx9PZw8Kf59bTb47xPWSK1sjzWnoUc3a0a12wXduljXumIGfP+S\n1n3PGUITCKafZNOMP2mMuRTAGPNh3TagrZPEvAZcIyLPYKU4P2CMaduVVlRaKV9WSg/AMYLkZmkf\niumnwP5VVp/EgSq44AwrtUWkFx6yZkfP+RBGDLXSa0Tq1RPm/Ru2brdGRY0c1vzw2MH9YfEbsGGT\n1Wk95kgrOd/uvVbaj6GDrODRiWkCwfSS7PDYUZEvwivcHdvSm4vIf4BTgF4iUgL8BnACGGMewkpv\nfhawEWt4bIL6vOqIgn1HYt+xlppNm8nOc2KH1g8WNpsVMJpSkAeXXtj0MQP6WY9DMWxw9OvevayH\nAnTYbDppbs3sG4GbgCwRqaBhhrYPmN3SmxtjvtXMfgO0/YoqKm0F+47Eu2MtWWV+a7ks1alosEgP\nySyF2hX4vTGmmzGma/iRZ4y5sZ3KqJTqxCKHzY4dV6jDZlMg2VFPvxKRS0Tk1wAi0l9EjmvDciml\nVD1NIJhayQaKB4ETgG+HX1eFtymlVLuJHDartYr2k2ygON4Y80OgFsAYsx+IswCvUkq1jdhhs9oE\n1X6SDRT+8EgnAyAi+UCozUqllFJxaE6o1Eh2eOyfgZeBAhG5E7gIuLnNSqWUUgkkGgkVS0dGtZ6k\nAoUx5mkRWQKchjVE9nxjzLo2LZlSSiUQGyzstuivshXLd+ow2lbU3DwKD/B9YCiwCnjYGJMgC5pS\nSrWf+mAxIK/RvmBI51y0puZqFI8DfmA+1toQI4Br27pQSimVjLphs42Eg4cGi9bRXKAYaYwZAyAi\njwCL2r5ISimVvPrRUBGKsspgQB52m4PiNaUaLFqouUBRn7rTGBOQRGmZVVravDuHlz8dyMFaB9OO\n2cFxw/cmzKytVEdSX9MoygMKKF5TmuoiZbTmAsXYcI4nsDqxI3M+GWNM505xmcaemz+Q6x+ZQDAk\nBII2Hn1nGGcft40H/m+RBgvVeYQqGFHUjbpgobWKw9Ncrid7OMdTXZ4nR8RzDRJpqvygk+sfmUCt\n34E/aMcg1HgdvLmoPx+u6p3q4inVLurmXOgEvZZLdsKdyiAfreqDw954vc1qr51XFgxIQYmUSg2d\noNc6NFB0QHZ7/EnzIganQyfUq85Fg0XLaaDogKaO2UWocYUCjzNE7x61/OmVEcxbVkgwpJ0VqnOI\nTFWuweLQpTRQiMh0EVkvIhtF5IY4+2eKyB4RWR5+XJmKcmaabE+Qf/zkU7JcAbLdfjzOAC5HEBHD\nQ28dye9fGM33/3oC0246g8rqZLO4KJXZ6kZC1a1rAWiwSFLKviXCSQYfBKYBJcDnIvKaMWZtzKHP\nGmOuafcCZrhTx+5i2V9f4+3FRVR77bwwfxArNuUSMtZvg4O1Nop3duWeF8Zwx3eXpbi0SrUfHTZ7\n6FJZozgO2GiM+dIY4wOeAc5LYXk6nO45fmacvIkZJ21i1ZaGIFHHF7Dzyqfaua06j9hU5bquRXJS\nGSj6AdsiXpeEt8X6uoisFJEXRKR/vAuJyCwRWSwii8sqK+IdohIwaD+F6lwiO7d12Gxy0r2B+nXg\nP8YYr4j8H1buqVNjDzLGzAZmA4w9YkicbtzMV+OzM3dJX8oq3UwZWcqRRY0D4vMfDeQfc4bTPdvH\n3ZcvIb+7l/8u6UeN186oAftYtblnVK3C5QhywQlboq6x54CbuUv7YQyceex28rt72/y9KdXeEqUq\n1wl58aUyUGwHImsIReFt9Ywxkdm+/gnc2w7lSjurNvfgG3dODc+yFhA45/ht/GnWImw2CAZh/DXn\nsKciq/6cE687C7sthMcZImiEYEjCI6Ea4qjNZvjRuQ1dQs9+NIjrH52AzWYdc/MTx3DXzCV8+5RN\n7fVWlWo3GiySl8qmp8+BYSIyWERcwAzgtcgDRCRyFfVzgU63BkYoBJf98SscqHZRVeuk1u+g1mfN\nsn71M6t/4ZePTggHCYl6BEN2Dnqd1Poc+AP2RvtDIWH2f48EYHtZFjc8eixev50ar4MarwOv385N\njx1Dyd7sVLx1pdpc5LDZseMKtRkqgZQFivC6FtcAc7ACwHPGmDUicruInBs+7MciskZEVgA/Bmam\nprSps3pLLhXVzkbbq70OnnrvCABeTnq2dXR/hC9g5/n5gwB4Y1F/4rXZGSO8sajoEEqsVGapGzY7\noiinftisipbSPgpjzFvAWzHbbol4fiNwY3uXK50EgpKwu9kXsAMQCtXVEg6dP2j9VggEbOHrRAsZ\n8Ad0XqbqBDSBYEL6DZDmjh68P27ajSxXgNwutVx4xykU5lZD3PpArOhjnPYgZx9nDTw789jt2G2N\nr+GwGc48dnuj7Up1JJpAsGkaKNKcw2646sz1WF/ydV/khhqfnXeW9WPBugI2l3at3x79COGwBxFC\nuJ2BiGOsv3ab4WcXrAFgaN9KfnjOF3hcAWwSwiYhslwBrv7aFwzvV9ku71WpVNKcUIml+/DYTi8U\ngj++PDr8SiL+GqKbm2JfQ7dsH5efsYEar4MlG/NYVtyTYMhefw0DPPneEH55kRUsrvv6GqYfu51X\nP7MGo507aRtjBpW3yftSKh3pSKj4NFCkuTcW9Q8n74vtP2juNYSMcMKIvUwcvpcjr7yQYCi6Aun1\nO/j3+w2BAmD0oHJGa3BQnVh9sNB1t+tp01OaizfiKXlCjddOMCgJezBq/fYEe5TqvDSBYDStUaSA\nMbB6Sw9K9uYwZtB+inpVNzqmrMLF4/OGEkq4fETzTU+1tcIDrxzFXc+OoV/PCrbs6R51jN0W4oxj\nojuqa302FnxRgDEweUQpHpeuX6E6L00gaNFA0c7KKl18++6T2bizG3ZbCF/AzoWTN/OHKxdjC9fv\n7vjPGB58Y0TMmXV1AiGyQ7qp1wFjZ9mXveJcxzrGYQ9x/UWr6ve8t6IP//eXydTVP4wRHvrRp5w2\nbleL37dSmcZDgFoc4ZFQxkog2EmHzWrTUzv70d8msXZbd6q9DiprXHj9dl5ZMJDH5g0FYMnG3HCQ\nkJgHh/k38TVE4MVPBgKwt8LNlX+aQlWNk8oaF5U11kzwqx6Ywt4D7tZ6+0plFE0gaNFA0Y4OHHTy\n8doCAsHofoEan4NH5gwD4I8vjo53KofemR1vAl5M05TPwWPzrPu+/lncxLwYA68tjL9Pqc5Ah81q\noGhXNT47tgQTqA/WWp3WBw62pPP60B2stVofK2uc+OLMwPYHbS3sUFcq83X2YKGBoh317lFLfvfa\nRtvt9hAnjd7Fp2vzOWn07iSv1vJs6jYJMfXonQCccvQuXHFmgLscIaaO1T4KpTpzAkENFO1IBP40\naxFZrgAOu/Wl7HEFcDuCvPrZAGbedyJ/f/MonPYQ0YEgcrY1Cf7G7o+/TQha93UG6NHFx69mrASs\nVCHnTtpKtttff9dst5+zj9/G2MH7W+kTUCqzddYEgjrqqZ1NGVXKu7+bwyNzh/Hlzq50z/EyZ0k/\nfAF7fZI/uwTp36sKX8COzWa49NRi9h5w8+R7QwkEBYc9hD9YlzYcGs/UjhwJBWAozK3mnz+Zz+uL\nBrF+ezcmDivju6dvJK+rr/6o+2d9ztcmlvDs/MEAXPyVzUwbv6NtPxClMlDssNmOPhJKA0UKDO5T\nxR3fXQbAmTdPo8YX3QcQNHZ2l2ez5C+v06tbwwpzd85cDkDfSy4muc5sw52XLeXyMzbWbz1m2IqE\n5RKBacfsZNoxOw/1LSnVacQOm61rgurIwUKbnlJsf5Ur7naH3STsRDaH0D2xc19W8wcppQ5JZ+vc\n1hpFip06didPv39EoyGzHleQgQUH457TI8dH+UEXyaxBcdGJm1uhlEqpWIkSCMbqCDWNlNYoRGS6\niKwXkY0ickOc/W4ReTa8f6GIDGr/Uratn56/lu45flwOq5O5Lr33vZd/Hnd9CID7Zy0KP0vUmW1t\nmzxiN0cWaYpwpdpKbM1iyKiCqEdHqWmkrEYhInbgQWAaUAJ8LiKvGWPWRhx2BbDfGDNURGYA9wDf\nbP/Stp3eubV8cM9/eeS/w5i/pjcDCg7yf2etb3Kk0fQJO3j1lne58bFj2bK7C316VnPdhat4beFA\n5q/ug8cV4IozN3Dt+Z1uiXGl2l1ktlmnvXFT8orlOzO+D0PMoTR4t+aNRU4AbjXGnBl+fSOAMeZ3\nEcfMCR+zQEQcwC4g3zRR6LFHDDFz7rirbQuv2pV9x1p6jC/AMWIw9Bya6uIoFVctDkpq8hptX731\nAKtX7qGL057WweK804YtMcZMiLcvlX0U/YBtEa9LgOMTHWOMCYjIASAP2Bt5kIjMAmYB9OsVmwRP\nKaXaR1FWWeONA/Kw2xwUrynN2JpFh+jMNsbMBmaDVaNIcXGUUp2Qh0Dc7R0hVXkqO7O3A5HZ5orC\n2+IeE2566g7ECdlKKZXGQhWMKMqxUpVnYOd2KgPF58AwERksIi5gBvBazDGvAZeFn18EvNdU/4RS\nSqWbupFR1gS9zExVnrJAYYwJANcAc4B1wHPGmDUicruInBs+7BEgT0Q2Aj8DGg2hVUqpdJfpE/RS\n2kdhjHkLeCtm2y0Rz2uBb7R3uVQa27dRRz6pjJRogl4mdG5rCg+V9oJ9R1K+rJTAuk2pLopSLRKZ\nqjyTahYdYtST6hxqNm0mC3CMQGsVKmPVpSrPpGGzWqNQGSHYdyTe8mxqNm0mWFpiNUEplcHq1rUY\nMqog1UVplgYKlTHqgkX14g0aLFRGq59zER4Jle7DZjVQqIxSFyxMmb/5g5VKY5EjodJ92KwGCqWU\nSpFMGTargUIppVIo0OOn5wAACTlJREFUE4KFBgqllEqxyGGzY8cVpl2w0OGxSimVBuqHzRblAYVp\nlUBQaxRKKZVO0jCBoAYKpZRKE+maQFADhVJKpZF07NzWQKGUUmkm3YKFBgqllEpD6ZRAUAOFUkql\nqbqRUHXDZoGUBAsdHquUUmku1etup6RGISI9ReQdEdkQ/pub4LigiCwPP2KXSVVKqQ4vHRIIpqrp\n6QbgXWPMMOBdEi9xWmOMGRd+nJvgGKWU6tBSnUAwVYHiPODx8PPHgfNTVA6llMoIqRwJlapA0dsY\nszP8fBfQO8FxHhFZLCKfiYgGE6VUp5aqYNFmndkiMg/oE2fXryJfGGOMiJgElxlojNkuIkcA74nI\nKmNMcZx7zQJmAfTr1auFJVdKqfRlBQvql1JdsXxn3GDRmkurtlmgMMacnmifiOwWkUJjzE4RKQTi\nduMbY7aH/34pIh8A44FGgcIYMxuYDTD2iCGJgo5SSnUIsQkE8zzOqP2Llmxv1XW4U9X09BpwWfj5\nZcCrsQeISK6IuMPPewFTgLXtVkKllEpzdetul9X6ox5DRhW0arNUquZR3A08JyJXAFuAiwFEZALw\nfWPMlcAI4GERCWEFtLuNMRoolFIKq1ZRiyOcbbZbnCNaL1V5SgKFMaYMOC3O9sXAleHnnwJj2rlo\nSimVMer7KyiL2l5y0BkOHtYEvZY2QWkKD6WUymAeAo0erZ2qXAOFUkp1MK09jFYDhVJKdUCtGSw0\nUCilVAfVWqnKNVAopVQH1hqpyjXNuFJKdXDRE/QOPVW51iiUUqqzCFUwoijnkFOVa6BQSqlOoCXD\nZjVQKKVUJ3G4I6G0j0IppTqR+tncRU4gn9Ur9zQbLDRQKKVUJxOZqhxg9co9TR6vgUIppTqh+pFQ\n4XUtmqKBQimlOrGGYbOJaWe2Ukp1Uh4C1pNQRZPHaY1CKaU6sYZU5YmJMR1r5VARqQTWp7ocSeoF\n7E11IZKkZW0bWta2oWU9dAONMfnxdnTEGsV6Y8yEVBciGSKyWMva+rSsbUPL2jYyoazaR6GUUqpJ\nGiiUUko1qSMGitmpLsAh0LK2DS1r29Cyto20L2uH68xWSinVujpijUIppdT/t3enMXaNcRzHvz8t\n6WILSkobrdgj0tVWmoktSmNJJAheIHat8kIQiXgnkSASodKioiqqiC0tsURFUFPV1VqVToOKpVoa\npX5enGfqqnvPTC/Nc+bO/5PczL3nnnvOL5OZ+Z/nf848538UhSKEEEKplioUkk6T9ImkzyXdnDtP\nI5IelrRW0tLcWboiaaikNyQtl7RM0vW5MzUiqZ+k9yV9lLLekTtTGUl9JH0o6cXcWboiaZWkJZIW\nSfogd54yknaX9LSkjyWtkHRs7kz1SDokfT87Hz9LmpI7Vz0tc45CUh/gU+AUoANYAFxge3nWYHVI\nGg9sAB6zfUTuPGUkDQYG214oaRegHTi7ot9XAQNtb5C0I/A2cL3tdzNHq0vSjcAYYFfbE3PnKSNp\nFTDGdhX+MayUpBnAfNvTJO0EDLD9U+5cZdLfrzXA0ba/yp1na600ojgK+Nz2StubgCeBszJnqsv2\nW8APuXN0h+2vbS9Mz9cDK4D98qaqz4XOifV3TI9KHglJGgKcAUzLnaWVSNoNGA9MB7C9qepFIjkJ\n+KKKRQJaq1DsB6yued1BRf+g9VSShgEjgffyJmkstXMWAWuBV21XNeu9wE3An7mDdJOBVyS1S7oi\nd5gSw4HvgEdSW2+apC5mMqqE84FZuUM00kqFImxHknYG5gBTbJdPNZmR7c22RwBDgKMkVa61J2ki\nsNZ2e+4s2+B426OACcC1qX1aRX2BUcADtkcCvwCVPV8JkNpjZwKzc2dppJUKxRpgaM3rIWlZ+I9S\nv38OMNP2M7nzdEdqN7wBnJY7Sx3jgDNT3/9J4ERJj+eNVM72mvR1LfAsRau3ijqAjpqR5NMUhaPK\nJgALbX+bO0gjrVQoFgAHSRqeKvT5wPOZM/V46QTxdGCF7btz5ykjaZCk3dPz/hQXNnycN9W/2b7F\n9hDbwyh+Tl+3fVHmWA1JGpguZCC1cU4FKnnFnu1vgNWSDkmLTgIqd+HFVi6gwm0naKHZY23/Iek6\nYB7QB3jY9rLMseqSNAtoA/aS1AHcbnt63lQNjQMuBpak3j/ArbZfzpipkcHAjHQFyQ7AU7Yrf+lp\nD7AP8GxxzEBf4Anbc/NGKjUJmJkOGFcCl2TO01AqvKcAV+bOUqZlLo8NIYSwfbRS6ymEEMJ2EIUi\nhBBCqSgUIYQQSkWhCCGEUCoKRQghhFJRKEKvI2lzmq1zqaTZkgZ0sf6t3dzuKkl71Vn+pqQxzebt\nxn7bJB1X8/pRSedur/2F3icKReiNNtoekWbu3QRc1cX63SoUGbUBx3W1UgjNikIRerv5wIEAki5K\n97NYJGlqmmDwTqB/WjYzrfdcmhxvWbMT5KVt3yVpgaTFkq5My9vSCKTzfgoz03/HI+n0tKxd0n2S\nXkwTNV4F3JAynpB2MV7SO5JWxugi/FdRKEKvJakvxTw7SyQdBpwHjEuTCm4GLrR9M3+PQC5MH73U\n9miKe0lMlrRnE7u/DFhneywwFrhc0vD03khgCnA4cAAwTlI/YCowIe17EIDtVcCDwD0p4/y0jcHA\n8cBE4M4m8oWwRctM4RHCNuhfMx3JfIq5rK4ARgML0gF8f4qpyuuZLOmc9HwocBDw/TZmOBU4suZo\nf7e0nU3A+7Y7AFLOYRQ3ulpp+8u0/qyUuZHnbP8JLJe0zzZmC+EfolCE3mhjGjVskdo7M2zfUvZB\nSW3AycCxtn+V9CbQr4kMAibZnldn+7/VLNpMc7+ntdtQE58PYYtoPYVQeA04V9LeAJL2kLR/eu/3\nNNU6FEf+P6YicShwTJP7mwdc3bldSQd3cYOdT4AD0jkJKNpkndYDuzSZI4QuRaEIAUj3AL+N4i5u\ni4FXKfr8AA8Bi9PJ7LlAX0krKHr/de/HLellSfvWLHpJUkd6zKa4BepyYKGkpRTnHxqOHGxvBK4B\n5kpqpygO69LbLwDnbHUyO4T/TcweG0IPIWln2xtSm+x+4DPb9+TOFVpfjChC6DkuTye3l1G0wKZm\nzhN6iRhRhBBCKBUjihBCCKWiUIQQQigVhSKEEEKpKBQhhBBKRaEIIYRQ6i9uQpAJaHZKLQAAAABJ\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sa5o17nOphp2",
        "colab_type": "text"
      },
      "source": [
        "## Numpy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-nyE8PuTGki",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = [1,3,5,7,9]\n",
        "b = [2,4,6,8,10]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skn6rNcbpqU-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0bc739eb-7b64-43a7-96d4-1be281eaf2fb"
      },
      "source": [
        "len(a)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjfSPQyhprz0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "c4b7bc59-ff6c-4e81-8655-143a9247c456"
      },
      "source": [
        "for i in range(len(a)):\n",
        "  print(i)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJ4VBvEGpvt8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "170d9171-455c-4013-e157-cb8b59295131"
      },
      "source": [
        "for i in range(len(a)):\n",
        "  print(a[i] * b[i])"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "12\n",
            "30\n",
            "56\n",
            "90\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-w_ull0p0Wp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "9ee6ec7f-f32b-4f2c-f02f-ce68175debfb"
      },
      "source": [
        "for i in zip(a, b):\n",
        "  print(i)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 2)\n",
            "(3, 4)\n",
            "(5, 6)\n",
            "(7, 8)\n",
            "(9, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NiLKYeUGp6Lf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "2f2ca734-e2d8-4d72-d250-c5474610789b"
      },
      "source": [
        "for i,j in zip(a, b):\n",
        "  print(i,j)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 2\n",
            "3 4\n",
            "5 6\n",
            "7 8\n",
            "9 10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nreKDfiNqBc3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "99c2cf11-4d9f-4f09-d0da-32952158bda6"
      },
      "source": [
        "for i,j in zip(a, b):\n",
        "  print(i*j)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "12\n",
            "30\n",
            "56\n",
            "90\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-LGz_n2RqEsz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "na = np.array(a)\n",
        "nb = np.array(b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tcykXszeqQXA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ff96a88c-c56b-4f10-8353-890dc54612b9"
      },
      "source": [
        "a"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 3, 5, 7, 9]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRQXLg2xqSPF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8577450f-8aa8-494d-f7ca-4f2c63c39961"
      },
      "source": [
        "type(a)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qg_7qnJBqUhj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b4855013-9fe8-4ded-e0f3-d3655814a0f4"
      },
      "source": [
        "na"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 3, 5, 7, 9])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "euJ9hQ26qVeB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "11c6d97b-7aed-46bc-e0e0-c58adf7ee92d"
      },
      "source": [
        "type(na)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rFSbknLCqXZt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "abc489fe-7e54-48a9-ced8-2086e8cb4ccb"
      },
      "source": [
        "na * nb"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2, 12, 30, 56, 90])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1f4Fc8xhqaFs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "na = np.array([1, 2, 3, 4, 5])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2B9MQBEqrAj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2b8144ae-8551-43c7-a25f-1dc1e7f9ac18"
      },
      "source": [
        "na.sum()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s1oVFfbIqs2r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "64178702-2cc5-43b1-96c6-46e77747f75f"
      },
      "source": [
        "na[0]"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qwnu1-SeqvJt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e034a507-3711-4afa-cc90-c1424bee1f87"
      },
      "source": [
        "na[-1]"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GREG01QqxU1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6fff91da-d8fe-4e04-b36c-9eaf2ea48304"
      },
      "source": [
        "na[1:3]"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MX-uR-xjqzVc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "764b7357-5ce7-4564-a6e4-05e58254aaee"
      },
      "source": [
        "na > 3"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False, False,  True,  True])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaKy-3hcq6BA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "452641d9-b5e2-49b9-f394-414e0d6af74f"
      },
      "source": [
        "na[na > 3]"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SS-8eZN1rAKj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3179196c-56a6-4000-b71e-f9f7890cc3ae"
      },
      "source": [
        "na = numpy.array([[1,2,3,4,5],[6,7,8,9,10]])\n",
        "na.shape"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I6CEUaZyrKfX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "90950739-2d38-4219-ac78-dcc3fb7c4929"
      },
      "source": [
        "na[0,0]"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_pVOwYXrRDa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d5e74483-cfe8-496d-dde1-950c32dc2123"
      },
      "source": [
        "na[0,:]"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXjlAXUarUQ4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a9e97c87-4820-4d38-f915-c8fe6fed2645"
      },
      "source": [
        "na[:,0]"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 6])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9jHpbpCraio",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "bfd37933-dccc-4699-cc73-084505ec03f9"
      },
      "source": [
        "na.T"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  6],\n",
              "       [ 2,  7],\n",
              "       [ 3,  8],\n",
              "       [ 4,  9],\n",
              "       [ 5, 10]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JISWWUWXrdg8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "house   = np.array([20,300,10])\n",
        "weights = np.array([[100], [-1], [-10]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDcxS15br_8I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "400b02bd-c264-480e-c293-0e8a0ffcaf64"
      },
      "source": [
        "house.shape"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jDBw2Cn7sA30",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "afa2b5d6-1e65-47c7-d412-a9eef8546ec1"
      },
      "source": [
        "weights.shape"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPUKrf4SsCP6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cf00be3b-96de-4d59-c74a-ea3d7c468191"
      },
      "source": [
        "np.dot(house, weights)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1600])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eCJTlrjpsVhB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "house2 = np.random.randn(4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n0u09PPzsu5r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "40ab8063-57df-4208-9863-79189ca2c4a0"
      },
      "source": [
        "house2"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.16910272,  0.16901037, -1.10260588,  1.27661528])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-KmhV88Esvpb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w1 = np.random.randn(4,5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9i7PlK7Ds2im",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "10fb82e3-a0eb-4596-c25c-ba9a6775dc58"
      },
      "source": [
        "w1.shape"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPTClKjDs6G8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "780cff55-0d2b-4e8e-f646-f840aaeca99f"
      },
      "source": [
        "w1"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.50044946,  0.09002163,  0.17296555,  0.77495291, -0.7327864 ],\n",
              "       [-0.73660002, -1.51105186,  0.01041411,  0.39577903,  2.83919618],\n",
              "       [-0.05442528,  0.6178581 , -0.28308135,  2.23727232, -0.14769081],\n",
              "       [-0.15024735,  0.3219798 ,  0.49935448, -0.77460247, -0.34634119]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVDA6UY8s7i2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "o1 = np.dot(house2, w1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9m4WIKnwtAG4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "512a9eab-e8bc-46fa-a8c5-4a0e007ad8ce"
      },
      "source": [
        "o1"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.17166412, -0.54081598,  0.92212188, -3.51985485,  0.32447007])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NI6hyWQEtCfo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w2 = np.random.randn(5,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cc0ER25FtGsF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "4c3449c9-e477-482a-bb0d-7c420599f2c2"
      },
      "source": [
        "w2"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.25978347],\n",
              "       [0.15521198],\n",
              "       [0.8868397 ],\n",
              "       [0.92065142],\n",
              "       [0.67296609]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9IwDCmjqtIOH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "o2 = np.dot(o1, w2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWJhUGZStLXX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b3a15a62-e09f-4fef-f750-d77ba045fdc1"
      },
      "source": [
        "o2"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-2.50462847])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWXvhZDwtfzk",
        "colab_type": "text"
      },
      "source": [
        "##Activation Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQ-n00gKtL_h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def threshold_function(x):\n",
        "  y = x > 0\n",
        "  return y.astype(int)\n",
        "\n",
        "a = np.array([-1,1,2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcR_xAl0toN1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4edb9886-5b57-44ed-dc0a-94b62fd2886a"
      },
      "source": [
        "a > 0"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False,  True,  True])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nx5HC4nBtrnb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "469c22f3-e28d-46bb-c3c1-bead70947082"
      },
      "source": [
        "(a > 0).astype(int)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6U1VOWmtzgU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e9adf3bc-7f38-49be-cc85-f8cede787f97"
      },
      "source": [
        "threshold_function(a)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAGtgBjtt4ET",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sigmoid_function(x):\n",
        "  return 1/ (1 + np.exp(-x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxtmkveAuDai",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1802d56d-927e-4fa5-d247-5231c1540271"
      },
      "source": [
        "sigmoid_function(a)"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.26894142, 0.73105858, 0.88079708])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-mJp1cJSuFvt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tangent_function(x):\n",
        "  return (1 - np.exp(-2*x)) / (1 +np.exp(-2*x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JE0q72R5ut2v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "726d85bc-4061-488c-b994-61fbb7d1b5c3"
      },
      "source": [
        "tangent_function(a)"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.76159416,  0.76159416,  0.96402758])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXU6QZeAuvFV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "200d626e-18b3-4190-dde8-f43e140fe23b"
      },
      "source": [
        "np.tanh(a)"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.76159416,  0.76159416,  0.96402758])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJ3HcxgBvLU1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1ea46918-a37a-40f6-b6ed-7aef8b4ae284"
      },
      "source": [
        "def relu_function(x):\n",
        "  return np.maximum(0,x)\n",
        "x = np.array([-1,1,2])\n",
        "relu_function(x)"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "egg8IiR8vhA_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "X = np.array([1,2])\n",
        "W = np.array([[1,3,5],[2,4,6]])\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHBTY-AWwAbq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fc9ea11e-a145-4298-ea3d-9db37b95c7da"
      },
      "source": [
        "X.shape, W.shape"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2,), (2, 3))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSEMO6vUwCWH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4f9c0375-3eda-4989-bcf1-33c04f9fc0af"
      },
      "source": [
        "Y = np.dot(X,W)\n",
        "Y"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 5, 11, 17])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yk7awsJqwM4F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "network = {}\n",
        "network['w1'] = np.array([[0.1,0.3,0.5],[0.2,0.4,0.6]])\n",
        "network['w2'] = np.array([[0.1,0.4],[0.2,0.5],[0.3,0.6]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFxLe_imwgOd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1b542d43-5d09-4b29-8c8d-17d14e94a4b5"
      },
      "source": [
        "x = np.array([1,0.5])\n",
        "a = np.dot(x, network['w1'])\n",
        "y = np.dot(a, network['w2']) \n",
        "y\n"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.36, 0.81])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HKAM5jtrwmRW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "network = {}\n",
        "network['w1'] = np.array([[0.1,0.3,0.5],[0.2,0.4,0.6]])\n",
        "network['b1'] = np.array([0.1,0.2,0.3])\n",
        "network['w2'] = np.array([[0.1,0.4],[0.2,0.5],[0.3,0.6]])\n",
        "network['b2'] = np.array([0.1,0.2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbFaOwjHw3lD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "887537ed-65c1-41ea-aa3e-60da9e884b9d"
      },
      "source": [
        "x = np.array([1,0.5])\n",
        "a = np.dot(x, network['w1']) + network['b1']\n",
        "z = sigmoid_function(a)\n",
        "y = np.dot(z, network['w2']) + network['b2']\n",
        "y"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.51615984, 1.21402696])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RlYBVpfxPwM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "45853328-aca3-4c2c-8729-56272e573c8a"
      },
      "source": [
        "x = np.array([1,0.5])\n",
        "a = np.dot(x, network['w1']) + network['b1']\n",
        "z = sigmoid_function(a)\n",
        "y = np.dot(z, network['w2']) + network['b2']\n",
        "sigmoid_function(y)"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.62624937, 0.7710107 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-F7169pxa9D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def softmax_function(x):\n",
        "  return np.exp(x) / np.sum(np.exp(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xn7s9F9qx1dZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2d66154c-587e-4b60-8bf6-e93498e74640"
      },
      "source": [
        "softmax_function(y)"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.33228528, 0.66771472])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_l5_PWB3Fx1",
        "colab_type": "text"
      },
      "source": [
        "##Training Nueral Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-p86RTlx36G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def mean_squared_err(y_hat, y):\n",
        "  return 0.5 * np.sum((y_hat - y) ** 2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBvxRwlm3Oi8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "46604ccc-9e3d-40fb-9265-03d27c658f58"
      },
      "source": [
        "y = np.array([20,30])\n",
        "y_hat = np.array([10,15])\n",
        "mean_squared_err(y_hat, y)"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "162.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aNJJAfSR3b4r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cross_entropy_err(y_hat, y):\n",
        "  delta = 1e-8\n",
        "  return -np.sum(y*np.log(y_hat + delta)) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hO0rwToC398y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def func(x):\n",
        "  return x ** 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nr-W3zEF57MU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "60664918-470c-4b51-e9c1-25f0d807e267"
      },
      "source": [
        "x = np.arange(-6,6, 0.01)\n",
        "y= func(x)\n",
        "plt.plot(x,y)"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3ad52d43c8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gQH/uWpzNyZpGqyMpD1BcVc/3X80mKjSIebNG62QdN6QF7iPiu3dh/u2jKays\nZ+6Szbopsjqv+iYbP3htM+W1Tbw0O4Po8GCrI6mz0AL3IaOSI/nzTcPJOnySX320U0emqLOy2w2P\nvpfD1qPlPPvdEQyN180Z3NUFZ2Iq7zI9PYEDRdU8v/wgyT1CuVcX31dneOrLfXy8/TiPThvItDSd\naenOtMB90MNTB5B/qpanvthHbHgI3x6daHUk5SZeXZ/HvJWHmDUumbmT+lodR12AFrgP8vMT/nzT\nCEqrG3hsaQ49w4OZNEDXavd1X+0q5PGPdzFlcAyPXztUR5x4AL0G7qOCAvyYN2s0/WPDmbtkMzsK\nKqyOpCy05egpfvjWVoYldudvOk3eY+i/kg8LDwlk8Z1jiAwN4s5XNnK0TDdG9kX7i6qY88omYsJD\nWDg7g9AgfWHuKbTAfVxMRAiL52TSbDfMWphFUaXu5uNLjpbVMmtBFkH+frx2VyY9w3S4oCfRAlf0\niwlj0R1jKKtuYOaCLMqqG6yOpDpBYUU9ty7YQKPNzpLvjaV3D92YwdNogSsARiZHsvCOMRScqmXW\nwo1U1DZZHUm5UFl1A7MWZlFe28TiOzMZEBtudSTVDlrg6n+N69ODf96WwaHiamYv2kh1g64j7o0q\n6pqYvWgj+SdrWTA7gxFJ3a2OpNqpQwUuInkiskNEtolItrNCKetMGhDN87eOZMexCua8skk3g/Ay\n5bWNzFqQxb7CKubNGs24Pj2sjqQ6wBln4JcZY9KNMRlOeCzlBq4YGsez300nO+8ks1/eSFW9Xk7x\nBqdqGpnpKO/5t2Vw2aAYqyOpDtJLKOqsrhsRz99mjGTL0XJuW7iRijotcU92sqaRWxdkcaC4mvm3\nj9by9hIdLXADfCUim0Xk7rPdQUTuFpFsEckuKSnp4OFUZ7pmeDwvzBzFruMVzFywgVO6DK1HKqtu\n4NaXNpBbUs2C2zO4dKCWt7foaIFfZIwZBVwF3Ccil5x5B2PMfGNMhjEmIzpap2t7miuHxvHP20az\nv6iaGS9toFSHGHqUglO13DxvPXllNSycPYZLdMkEr9KhAjfGHHP8WQx8AGQ6I5RyL5MHxbJwdgZ5\nZTV8+8V1HCmrsTqSaoX9RVXc9OJ6SqsbeO2usVzUv6fVkZSTtbvARaSriIR/8zlwBbDTWcGUe7m4\nfzRvfH8clXVNfPvFdbp2ipvbcvQUN89bj90Y3v7BeMak6EbE3qgjZ+CxwBoR2Q5sBD41xnzhnFjK\nHY1KjuS9uRMIDvDnu/PXs2q/vqfhjpbvLWbmS1l0Dw1k6dwJDO4VYXUk5SLtLnBjTK4xZoTjY6gx\n5vfODKbcU9/oMN6/dwK9e5C+90MAAApxSURBVHRlziubWLq5wOpIysEYw6K1h7lr8Sb6RHflvXsm\nkBQVanUs5UI6jFC1WWxECG//YBxj+0TxyLvbefLzvdjtuj2blZptdn710S6e+GQ3UwbH8u4943Uf\nSx+gBa7aJSIkkFfuzGTm2GTmrTzE3a9l69R7i1TWNzFncTavbTjCDy7pw7xZo3VJWB+hBa7aLdDf\nj9/fMIzfTh/K8n0l3PjCWl1TvJPtL6ri+ufXsu5gKU/eOIyfXj0YPz/dScdXaIGrDrttfAqvzsmk\nqLKBa59fw9d7iqyO5BM+3n6c6c+vpaqhmde/N5ZbMpOtjqQ6mRa4coqJ/Xry8f0TSYzswl2Ls/nj\nZ3tostmtjuWVmmx2nvhkFz98cytpCRF8+sBFjNVFqXySFrhymt49urJ07gRmjUvmn6tymTF/Aycq\n6qyO5VXySmu4ed56Fq3NY87EVN74/jhiIkKsjqUsogWunCok0J/fXT+M525JZ/eJSq56bjWf5pyw\nOpbHM8bwTnY+V/9tNbkl1fzj1lH86tohBOrmwz5N36pWLjE9PYFhCd146J3t3PfGFr7aHc9vrkuj\nW2ig1dE8TnltIz/7YAef7ShkXJ8onvlOOvHdu1gdS7kBLXDlMn2iw1h6z3heWHGIv319gKzck/zp\npuFM0gWVWsUYw+c7C/nVR7uoqGvkJ1cN4vsX98FfR5koB339pVwqwN+PH17enw/vm0h4SACzX97I\nD9/cSnFVvdXR3FphRT13v7aZe1/fQly3YD68byL3TOqr5a3+DzGm82bQZWRkmOxs3XnNV9U32Zi3\n8hAvLD9EcKAfj04bxK2ZyVpKp2my2Xl9wxGe/mo/jTY7D08dwF0XpRKg17p9mohsPtuuZ1rgqtPl\nllTzy492svZgGSMSu/Hzbw0hM1VXy1t9oITffLKbA8XVXNSvJ7+7Po2Unl2tjqXcgBa4civGGD7a\ndpw/fr6HosoGrhgSy2NXDaJvdJjV0Trd/qIqnvpiL//eU0xyVCi/+NZgpg6JRURfmagWWuDKLdU1\n2li4JpcXVxyivtnOd8ckMXdSX59YRS+3pJrnvj7Ax9uPExroz/2T+zPnohSCA/ytjqbcjBa4cmul\n1Q089+8DvLXpKMbADSMTuPeyfqR64SWEA0VV/HNVLh9sPUagvzB7Qgo/uKQvUV2DrI6m3JQWuPII\nJyrq+OfKXN7ceJQmm51paXHcPj6FsalRHn1JwRjDukNlvLQ6lxX7SggO8OPWscnMvbQvMeE6k1Kd\nnxa48ijFVfUsXHOYtzbmU1HXxMDYcG6f0JvrRsQTHuI5k4FO1jTy4dZjvJOdz97CKnqGBXH7+BRm\njk2mR5iu161aRwtceaS6RhufbD/OK+vy2H2ikuAAP6YMjuX6kQlMGhBNUID7Da+rb7Kx+kApH2wt\nYNnuIppshhGJ3bh1bDLT0xMICdRr3KpttMCVRzPGsC2/nA+3HuOTnBOcrGmkW5dAJg2IZvKgGCYN\niCbSwmvIp2oaWXWghC93FbJiXwm1jTaiugZxw8gEbs5IZFCc7kup2s8lBS4i04DnAH9ggTHmyfPd\nXwtcOUOTzc6aA6V8knOclftKKKtpxE9gRFJ3xqREMSo5koyUSHq66BKFMYaiygZyCsrJOnySdYfK\n2FtYiTEQHR7MFUNiuXJoHOP69HDLVwjK8zi9wEXEH9gPTAUKgE3ADGPM7nP9jBa4cja73ZBzrILl\ne4tZe7CUnIIKGh3rkMdFhNA/NowBseH0iwkjvnsXenULITYihIiQgPO+KWqzGyrrmiiraST/VC35\nJ2s5WlbLgeJqdh2voLS6EYCgAD8yekcyvk8PJvTrycik7rojjnK6cxV4RxazygQOGmNyHQd4C5gO\nnLPAlXI2Pz8hPak76UndeWjqABqabew8VsHmI6fYe6KKfUVVLNlwhIbm/7u5hL+fEBrkT9egALoE\n+WM3BpvdYLcbahptVNY3cea5TUigH6k9w7hsYAxpCd0YGh9BWkI3vaatLNORAk8A8k/7ugAYe+ad\nRORu4G6A5GTd8km5VnCAP6N7RzG69/+fmm+zG46X11FUWc+JinoKK+qpqGuiprGZ2gYbtU02/KXl\nl4G/CF2C/OkeGkRkaCCRoUEkRnYhOSqU6PBgjx7KqLyPy5eTNcbMB+ZDyyUUVx9PqTP5+wlJUaE+\nMbtT+ZaOvMNyDEg67etEx21KKaU6QUcKfBPQX0RSRSQIuAX42DmxlFJKXUi7L6EYY5pF5H7gS1qG\nEb5sjNnltGRKKaXOq0PXwI0xnwGfOSmLUkqpNtBZBkop5aG0wJVSykNpgSullIfSAldKKQ/VqasR\nikgJcKSdP94TKHViHCvpc3E/3vI8QJ+Lu+rIc+ltjIk+88ZOLfCOEJHssy3m4on0ubgfb3keoM/F\nXbniueglFKWU8lBa4Eop5aE8qcDnWx3AifS5uB9veR6gz8VdOf25eMw1cKWUUv+XJ52BK6WUOo0W\nuFJKeSiPK3AReUBE9orILhF5yuo8HSUij4iIEZGeVmdpDxH5s+PfI0dEPhCR7lZnaisRmSYi+0Tk\noIj8xOo87SUiSSKyXER2O/5//MjqTB0hIv4islVE/mV1lo4Qke4i8p7j/8keERnvrMf2qAIXkcto\n2XdzhDFmKPAXiyN1iIgkAVcAR63O0gHLgDRjzHBaNrn+qcV52sSxOfc/gKuAIcAMERlibap2awYe\nMcYMAcYB93nwcwH4EbDH6hBO8BzwhTFmEDACJz4njypwYC7wpDGmAcAYU2xxno56FngU8Nh3ko0x\nXxljmh1fbqBlZyZP8r+bcxtjGoFvNuf2OMaYE8aYLY7Pq2gpigRrU7WPiCQC3wIWWJ2lI0SkG3AJ\nsBDAGNNojCl31uN7WoEPAC4WkSwRWSkiY6wO1F4iMh04ZozZbnUWJ5oDfG51iDY62+bcHll6pxOR\nFGAkkGVtknb7Ky0nN3arg3RQKlACLHJcDlogIl2d9eAu39S4rUTk30DcWb71c1ryRtHy8nAM8I6I\n9DFuOhbyAs/lZ7RcPnF753sexpiPHPf5OS0v4V/vzGzqv4lIGLAUeNAYU2l1nrYSkWuAYmPMZhG5\n1Oo8HRQAjAIeMMZkichzwE+AXzrrwd2KMWbKub4nInOB9x2FvVFE7LQsEFPSWfna4lzPRUSG0fKb\nebuIQMtlhy0ikmmMKezEiK1yvn8TABG5A7gGuNxdf5meh1dtzi0igbSU9+vGmPetztNOE4HrRORq\nIASIEJElxphZFudqjwKgwBjzzSuh92gpcKfwtEsoHwKXAYjIACAID1ypzBizwxgTY4xJMcak0PKP\nPMody/tCRGQaLS91rzPG1Fqdpx28ZnNuaTkbWAjsMcY8Y3We9jLG/NQYk+j4v3EL8B8PLW8c/6fz\nRWSg46bLgd3Oeny3OwO/gJeBl0VkJ9AIzPbAMz5v8zwQDCxzvJrYYIy5x9pIredlm3NPBG4DdojI\nNsdtP3PsXaus8wDwuuMEIRe401kPrFPplVLKQ3naJRSllFIOWuBKKeWhtMCVUspDaYErpZSH0gJX\nSikPpQWulFIeSgtcKaU81P8DzkimbgL9IjYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsugToT06Ibh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def dfunc(f, x):\n",
        "  h = 1e-4\n",
        "  return (f(x+h) - f(x)) / (h)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cB9lnItH6cwJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "06263941-4354-4b1d-ee0a-8315e57ad5a3"
      },
      "source": [
        "dfunc(func, 3)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.000100000012054"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9Nh188M6gmB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tfunc(f, x, t):\n",
        "    d = dfunc(f, x)\n",
        "    y = f(x) - d*x\n",
        "    return d*t + y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKK1g9Ug65UC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "8d927571-cd23-43ec-bfd6-7272649d32f3"
      },
      "source": [
        "x = np.arange(-6, 6, 0.01)\n",
        "y = func(x)\n",
        "plt.plot(x, y)\n",
        "\n",
        "x2 = np.arange(0, 5, 0.01)\n",
        "y2 = tfunc(func, 3, x2)\n",
        "plt.plot(x2, y2)\n",
        "plt.show()"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ujR3afaSAke/+zEfr9nHDha2ZP7o54Yuus54eOfEbCIgwHVHYkJS8sKlebQNZ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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbkwFqSc7BjX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def dfunc(f, x):\n",
        "  h = 1e-4\n",
        "  return (f(x+h) - f(x-h)) / (2*h)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DK7sKklh7Yp5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ef8dab78-109b-4962-e247-68a9936ca915"
      },
      "source": [
        "dfunc(func, 3)"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.000000000012662"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YstGIdeT7a3i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "97b57f10-4a0c-4820-cd0a-da32cf736ffa"
      },
      "source": [
        "3 - dfunc(func, 3)"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-3.000000000012662"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MC1pySkI71jX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "c2035e0d-f614-468b-fe19-ed1dacff2eba"
      },
      "source": [
        "x = 3\n",
        "for i in range(10):\n",
        "  x = x - dfunc(func, x)\n",
        "  print(x)\n",
        "print(x)"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-3.000000000012662\n",
            "3.0000000000177636\n",
            "-3.0000000000304254\n",
            "3.000000000035527\n",
            "-3.000000000048189\n",
            "3.0000000000532907\n",
            "-3.0000000000659526\n",
            "3.0000000000710543\n",
            "-3.000000000083716\n",
            "3.000000000088818\n",
            "3.000000000088818\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PTt1Skk8B9Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gradient_descent(func, init_x, epochs = 10):    \n",
        "    x = init_x       \n",
        "    res = [x]\n",
        "    for i in range(epochs):         \n",
        "        grad = dfunc(func,x)        \n",
        "        x = x - grad\n",
        "        res.append(x)\n",
        "    return np.array(res)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kao64kF28ahy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "bf4fd690-37e1-4132-ac01-9b60d9ffb0c1"
      },
      "source": [
        "x = gradient_descent(func, 3)\n",
        "t = np.arange(-6.0, 6.0, 0.08)\n",
        "plt.plot(t, func(t), c='b')\n",
        "plt.plot(x, func(x), c='r')    \n",
        "plt.scatter(x, func(x), c='r')    \n",
        "plt.show()"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1ydCPWrKiQQNfkvSee2D6dC8qkyfHTlW8Vq+GYcN8dMlGG8Err2jRsVKkAi5Z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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "40W4X9P88hpe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gradient_descent(func, init_x, lr = 0.3, epochs = 10):    \n",
        "    x = init_x       \n",
        "    res = [x]\n",
        "    for i in range(epochs):         \n",
        "        grad = dfunc(func,x)        \n",
        "        x = x - grad * lr\n",
        "        res.append(x)\n",
        "    return np.array(res)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLW1GnYs9FLJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "2ecc4372-3f9b-436a-84c5-82873af541cf"
      },
      "source": [
        "x = gradient_descent(func, -5, 0.8)\n",
        "t = np.arange(-6.0, 6.0, 0.08)\n",
        "plt.plot(t, func(t), c='b')\n",
        "plt.plot(x, func(x), c='r')    \n",
        "plt.scatter(x, func(x), c='r')    \n",
        "plt.show()"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lI/awcccSuE8NHAj/+pdOe/qwqsyEU2KiTp/MmKF1p717a3158+bac2XCBJ2D\nC5O33tILgoEDtQGjCZ+8nIk5VkQ2ichPh912oojMEJFfA/+eEN4wzdEKFdIpz8KFtU48jH+Pxs+y\nzu1MT9fmOn/8AT17QrVqurEgPT2kD7dypS60n3OOFsuY8MrLCPwNoP1Rt90LfOGcqwN8Efi/ibCq\nVbW3xKJFMGCA19GYqHb88Vo3/ssvuqOmRQstEUlOhssv112fQRY07N0LV1yhg4pJkzxdP40buSZw\n59wcYOtRN3cGxgXeHwd0CXFcJo86dYJbb9WOhR9+6HU0JuolJEC7dvrLsnIl3H47zJwJ552nu21e\nfRV27y7QXQ8YoIOJ11/XAb4Jv4LOgVdwzq0PvL8BqJDTJ4pIHxFJEZGUjIyMAj6cOZYhQ6BxYz0Y\nPUab2JlwqFlTz+1MS9PEDTr/Ubky3HWX9mTJow8/1EFEv346/W4iI+hFTKeF5DleeznnRjvnmjrn\nmiYlJQX7cCYbRYvqno4DB/QSdt8+ryMyvlKihHaaWrxYp1LatdNWt7VrQ8eOenROZmaOX75qlRa5\nNGqkrwcmcgqawDeKSCWAwL+RLzQ1R6hTR7u8ffutbswzJt9EDp3bmZoK998PCxbAhRfqYuiLL+qp\n26D1q9Wrs0dK8J9TlsK+vUyerIMJEzkFTeAfAr0C7/cCpoYmHBOMrl3hzjv1UGSrDzdBqVxZz+1c\ns0bLDk88URdbKlfWEfr110NqKn15kcX7T+PNg1dR8xv7pYu0XLfSi8hbwPlAOWAj8BAwBXgHqAak\nApc7545e6PwH20offvv3awvplBTdynzGGV5HZGJGSoqWJI7T+oV0TqIzU7mIT3iMB7WiZfVqb2OM\nUTltpc+10Mc5d2UOH7og6KhMyBUurFfAjRtrD/GUFNsJZ0Jg3To91++wI6Eqs45BDKYTgfInW0GP\nOKvUjEGVKsE77+hJXD176q5qa+Np8mXjRpg9W88+mzVLD2EGKFOGvcVK8+Rft/E1ZzOJq0gksMBp\ntYMRZwk8RrVsqW2ib71VN9wNHux1RCaqbdlyZMLOOvrpuOPg3HO1vLBVK/ad2oC2jbbw3fLj+IqW\nlMvaIlKihP2SecASeAzr2xeWLNGGdPXra4mhMYAeADFnDnz5pSbsJUv09pIltcnO1VfrJVzjxn9v\nqXQO+t4AXy0vz6Sb59L0482wRnTkPXgwdO/u4ROKT9YPPMbt26eLmgsXwldfQZMmXkdkPLFjh/4C\nZI2wv/9ea7uLFYOzz9Zk3bo1NGumCynZeOklHRQMHKiDAhM5OS1iWgKPA5s2QdOmOoL67js9xMXE\nuF27YN68Qwk7JQUOHtTDVIATiTgAABDNSURBVM88UxN2q1b6fh6Kt7/8UqsHO3SwNRUvFLgKxfhf\n+fK61fmcc6BLF/17Ll7c66hMSO3ZA998cyhhL1igNaWFCmkb2Xvv1YR91lk6X50Py5fDZZfBySdr\nSbgl7+hhCTxONGyom3suvVRPSnn3XftD9LV9+7TQPythf/ONtgNMSNB5sjvu0IR9zjlQqlSBHyYj\nQ0fdiYkwbZo2NTTRwxJ4HOnSRStT+vfXg1uGDvU6IpNn+/frNEhWwp43T0fdIvrqfMstmrBbtgxZ\n4f+ePdrtct06LVCpWTMkd2tCyBJ4nLntNm0+9OyzemTiLbd4HZHJ1sGD2ps1K2HPnasHTYJur+3d\nWxP2uefqNvcQy8zUPQTz58Pkydo+3EQfS+BxaNgw3fF8661aAdaxo9cRGTIztZQvK2HPmQN//qkf\nO+WUQ2V9550HEejqec898N57+rty6aVhfzhTQJbA41Biop6Ycv75ehjLjBla+msiyDlYuvRQwv7f\n/2BrYFNM7dr6g2nVSn9IlSpFNLRnntErtL59dbrNRC9L4HGqZEmYPl0T98UX64Cvfn2vo4phzmk5\nR1bCnj1bVwgBqlfXUxCySvuqVPEszLFjdfR9xRXaElzEs1BMHlgCj2NJSTr6PuccrfGdNw9q1fI6\nqhjhnC42ZCXsWbNgfeAQqypVoH37Qwm7enVPQ83ywQc6td6uHYwff0TfKhOlLIHHuWrV9MCVli2h\nbVtN4hG+Yo8dqalHJuy1a/X2ChUOJetWrXSKJMqGtrNmQbduWjL+/vu638dEP0vghlNPhU8+0dzS\ntq3+Mdvpd3mQnn5kwv79d729XDmdu87aPHPKKVGXsA/3zTdaLlinDnz8sU6vGX+wBG4AbYExbZpu\n2mjTRrdOly3rdVRR5vAWq19+Cb/+qrefcIJWh/Tvrwn7tNN8s0tqwQKdzalYUa/EwlCRaMLIErj5\nW6tWuuW+Y0dN4l98Eed/0Js3a3VITi1Wb7hBv2kNGvhywnjhQp3vLldOn95JJ3kdkckvS+DmCG3b\narOizp31j3vmTChTxuuoImTbtiMTdh5arPrV4sX6sy5TRi8mPCx8MUEI6rdQRFYDO4CDwIHsumUZ\n/2nfXjdxXHqpJvFPP43RkXhWi9Wsntjff6/VI8WKaWnO449rwj5Gi1U/WrRIf66lSunTTk72OiJT\nUKEYRrRyzm0Owf2YKHLxxZrEu3bV9bgZM7SYwtdya7H64IPaE7tFizy1WPWjefN0nSNr5F2jhtcR\nmWD4+zrQhFXHjvDRR9oE69xzdTqlalWvo8qHvLZYPfvsuOivO3OmTo1VrqzrG776WZpsBXWgg4j8\nDvwBOGCUc250Np/TB+gDUK1atSapqakFfjzjjblz4d//1mKLmTO1jDkq7d2rSTprSuTbbw+1WG3a\n9FAddpAtVv3oww8P9fT+/HM71MNvwnIij4hUds6li0h5YAbQzzk3J6fPtxN5/GvhQrjwQp0Knj4d\nGjXyOiJyb7GadUxYy5Zx3cj6jTd0h2XjxlrvH5PrGTEuLCfyOOfSA/9uEpEPgOZAjgnc+FeTJtov\npX17nU6ZPFkTekR53GLVb5yDxx6Dhx7Sc1Hffz+uX8diUoETuIiUBBKcczsC77cDHg1ZZCbq1Kun\nsxIdOuiUyujRcO21YXzArBarWVMic+bA9u36sVNPjXiLVT/Zvx9uugnGjNFv06uv2vb4WBTMCLwC\n8IHoFuFCwCTn3KchicpErZNO0jzatStcdx2sWaMjvJDsFM+txeoVV+iUyPnn2yTuMezYod+qTz6B\n+++HRx+N6p38JggFTuDOuVVAgxDGYnzi+OO1Z0afPvDII7pB8fXXC9BDwyctVv3kt9/027ZsGYwa\npT8jE7usjNAUSOHC2ju6Xj0YMEDbgkz5vykkD++vw/Jq1WDwYOje/dAXOacZ5vCEHeUtVv1k5kw9\nBwJ081WbNt7GY8LPErgpMBG4+25dP+z2n3007X8Ok0nmPFK1tWqfPtpPpHTp7Fustm59KGHXqmXX\n+QXkHLzwAtx5pzY+nDrV+rrHi6DKCPPLyghj14rKrei07hVWUYP36MrFfMQR6TirxWpWwo7yFqt+\nsWOH9tR66y3dcDV+vPbaMrElLGWExmSpu/5/zKcFT3MPHfnoyA/WrAmXXKJJ++STrU9tiHz/vU6Z\nrFqlbVsGDvRNF1sTIpbATWhUq0bp1FQGcz//5TI+40IasZgri35AuRIlYMQI3RWZpUwZTeZHv9Wu\nrc2kTI6cg1degTvu0NfCWbO09N3EH5tCMaExcaLOee/eDcAiGnG5vMtqqcHA+xJ44L6DFNmwRqtO\nDn9btgzWrTt0PyK6gJldcj/ppLifdtm4UadMpk7VNd/x460EPh7YFIoJr6xqk0GDYM0aGlfbyqJB\nKfSbW4vHH4dp0xIZP74G9dvX0MxzuB07YMWKfyb3OXP+fkEAtH9J3br/TOx168bFOWDvvqubc3bu\nhKFD4fbbbcok3tkI3ITdhx/q4HzrVnj4Ya1cyVN77cxMPXfy6MS+fLmWKh7+u1u1avaj9qpVfZ/l\ntmyBvn3h7be1Nfm4cboR1cSPsDSzyi9L4PFr82a45RZ45x0tO3zlFW0KWGB79mjxeXbJPWu7PWib\n2Dp1sk/uUd4YJDNTN0gNGKBP6aGH9H2fHwZkCsASuIkKU6bArbdqOfg118CQIVphGDLOwYYN2Sf2\n33/XrJilYsXsE3v16p5nySVLdLrk66/1NLeXX9YXPhOfLIGbqLFrl/bnGDZMa5YffhhuvDECzZb2\n7tWdoNkl96yeK6CB1KqVfXIPcwlkRoZ2EHz5Ze2//swz0KtX3K/dxj1L4CbqLF2qo/Evv9RS8cGD\nta7ZkynrzZuzT+wrV8KBA4c+r1y57BN7zZpBvQLt2qUvaM88o+u2vXvr98O64hqwBG6ilHPw2Wc6\nt7tkifYdf/RRuOiiKBl1HjigUy/Llv0zuW/adOjzEhM1iWeX3MuXP/LJTJz4d7XOnip1GNt6Ao9/\n1owNG3Q35ZNP6p4nY7JYAjdR7eBBmDQJHnhA26g0aKA7C7t21dwYlbZty37U/uuvR25aKl36UDL/\n6y+YOpWd+wrxMjczjDvZSEX+VXcTT79enrPP9u7pmOhlCdz4wr59msiffloHvbVrQ//+0KOH5sGo\n5ZwWaG/cqBuTvvtO+5l/9ZUm+mw8w13MpA0DeZLzqq1GUldHNmbjG5bAja9kZmrFytNP6znFJUrA\nVVfpYmeTJhEMZNcurWrZuFHfst4/+t+NG4/cdJQlIQGXlMT24hVYubMiSzeXZyMVKE8GjVnIGSzV\nzxM5skLGmMNYAje+lZICI0fqyHzPHqhfX0+cueKKQNvUw+aUs+1DfrTdu3NOyEfftmvXP79eRBcz\nK1bUtrhZ/x72fmb5iixMq8Ckz8vxznuJrFsHlSpB793Pc/2fQ6lK2pH3mZwMq1eH8ttmYoglcON7\n27bBhAmayL/5Rm9rUmMLl6S9yIX7p9GI70nEaTVIz56aFLNL0jt2ZP8AZcv+MykfnpwXLdL6vvR0\nve+jXih279YZk88+00Of166FokV1QbZnT+jYEQq/c2TPGEAvL0aPPvaLjolrOSVwnHMFfgPaA8uB\nlcC9uX1+kyZNnDGhkJrq3NChzjUr8r0D52ZxnnM6E523tyJFnKtd27nSpfX/CQnZ/5uc7NyECfpW\nosQR9/FH8Upu5r0z3BNPOHfBBXqXWXd98cXOvfmmc3/+mU3wEybo/Yocun9jjgFIcdnk1AKPwEUk\nEVgBtAXSgO+AK51zP+f0NTYCNyGXkMAmV46fOJ1tlGEjFRAcx7Od0rKdSiW3U67Ido7bu5kSuzZT\nmH0kENxV52zOozevspI6f992xhnQrh20bQstW+qg2phQCUc3wubASqeHGyMibwOdgRwTuDEhV60a\n5VNTac0sABywhmqklLuIuddPIiUFfvoJNgQ2WjZgMYtpFNRDnsU31GcJ1zKWpiyk8ebP7YwK44lg\nEnhlYO1h/08DWhz9SSLSB+gDUK1atSAezphsDB58xJyyAMklNpP8XEv+c9iU8i4pxSpq8DvVWUo9\nkshgP4WowCYKcTDbu36THmwiiePZTjXWUItVJJNKUfbxHl31k5KTwZK38UjYO/Y450YDo0GnUML9\neCbOHNWHPKcqlJLJ5Tgj9SfO4Kc833VPJhz6T9myWgKz+7Bt9SVK6GMZ45Fguk6kA1UP+3+VwG3G\nRFb37lqCl5mp/2ZXzTF4cMEnpkuUgOef10qR5GQtI0xOtsoR47lgRuDfAXVEpAaauLsBV4UkKmNC\nLbuReocOMH267t1PTNT9/Ef/e3S5oCVsE0UKnMCdcwdEpC/wGZAIjHXOLQ1ZZMaEWvfuloBNTAlq\nDtw5Nx2YHqJYjDHG5IO/Dws0xpg4ZgncGGN8yhK4Mcb4lCVwY4zxKUvgxhjjU5bAjTHGpyyBG2OM\nT0X0QAcRyQBSC/jl5YDNIQzHS/ZcopM9l+gVS8+nIM8l2TmXdPSNEU3gwRCRlOz64fqRPZfoZM8l\nesXS8wnlc7EpFGOM8SlL4MYY41N+SuCjvQ4ghOy5RCd7LtErlp5PyJ6Lb+bAjTHGHMlPI3BjjDGH\nsQRujDE+5bsELiL9RGSZiCwVkSFexxMsEblTRJyIlPM6loISkWcCP5MlIvKBiJTxOqb8EpH2IrJc\nRFaKyL1ex1NQIlJVRGaJyM+Bv5HbvI4pWCKSKCLfi8hHXscSDBEpIyKTA38rv4jIWcHep68SuIi0\nAjoDDZxzpwFDPQ4pKCJSFWgHrPE6liDNAE53ztUHVgADPY4nX0QkEXgJuAioB1wpIvW8jarADgB3\nOufqAWcCt/j4uWS5DfjF6yBC4HngU+fcKUADQvCcfJXAgZuAp5xzewGcc5s8jidYw4F7AF+vJDvn\nPnfOZR3X/i16wLWfNAdWOudWOef2AW+jAwXfcc6td84tCry/A00Slb2NquBEpArwb+A1r2MJhoiU\nBs4FxgA45/Y557YFe79+S+B1gZYiMl9E/icizbwOqKBEpDOQ7pz7wetYQuxa4BOvg8inysDaw/6f\nho+TXhYRqQ40AuZ7G0lQnkMHOZleBxKkGkAG8HpgOug1ESkZ7J0GdSZmOIjITKBiNh8ahMZ7Inpp\n2Ax4R0RquiithczludyHTp/4wrGei3NuauBzBqGX8BMjGZv5JxEpBbwH9HfObfc6noIQkYuBTc65\nhSJyvtfxBKkQ0Bjo55ybLyLPA/cCDwR7p1HFOdcmp4+JyE3A+4GEvUBEMtHGMBmRii8/cnouInIG\n+or8g4iATjksEpHmzrkNEQwxz471cwEQkf8DLgYuiNYX1GNIB6oe9v8qgdt8SUQKo8l7onPufa/j\nCcI5QCcR6QAUA44XkQnOuR4ex1UQaUCacy7ramgymsCD4rcplClAKwARqQsUwYcdypxzPzrnyjvn\nqjvnqqM/3MbRmrxzIyLt0cvcTs653V7HUwDfAXVEpIaIFAG6AR96HFOBiI4IxgC/OOeGeR1PMJxz\nA51zVQJ/I92AL32avAn8ba8VkZMDN10A/Bzs/UbdCDwXY4GxIvITsA/o5cPRXiwaARQFZgSuKL51\nzt3obUh555w7ICJ9gc+ARGCsc26px2EV1DlAT+BHEVkcuO0+59x0D2Myqh8wMTBIWAVcE+wd2lZ6\nY4zxKb9NoRhjjAmwBG6MMT5lCdwYY3zKErgxxviUJXBjjPEpS+DGGONTlsCNMcan/h9C98+b0vx6\n0gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LhVIWPxH9Nos",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "e2d598a6-99cd-469f-d77d-f79eba139d06"
      },
      "source": [
        "x = gradient_descent(func, -5, 1)\n",
        "t = np.arange(-6.0, 6.0, 0.08)\n",
        "plt.plot(t, func(t), c='b')\n",
        "plt.plot(x, func(x), c='r')    \n",
        "plt.scatter(x, func(x), c='r')    \n",
        "plt.show()"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Tga1CCPNTX1oAbFXN9/Q1swozq1i8eHEGUQtP06behPLCC/DMM7HTiMiNN8J//lM6w3wt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\n2tjulw7tSAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JO3-UOA59kQ8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "3582146d-4c00-48fe-cc77-29c3050ade69"
      },
      "source": [
        "x = gradient_descent(func, -5, 0.01)\n",
        "t = np.arange(-6.0, 6.0, 0.08)\n",
        "plt.plot(t, func(t), c='b')\n",
        "plt.plot(x, func(x), c='r')    \n",
        "plt.scatter(x, func(x), c='r')    \n",
        "plt.show()"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T7qNi40bo08evazJ2rMb3Z5oKd2KaWW2gknNuS+zjPsDQhCWTtFKlCvz5z99P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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-z1bkTv9ner",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def dfunc(f, x):\n",
        "    h = 1e-4 \n",
        "    grad = np.zeros_like(x)\n",
        "    it = np.nditer(x, flags=['multi_index'])\n",
        "    while not it.finished:     \n",
        "        idx = it.multi_index\n",
        "        tmp_val = x[idx]\n",
        "        x[idx] = float(tmp_val) + h\n",
        "        fxh1 = f(x) # f(x+h)\n",
        "        x[idx] = tmp_val - h \n",
        "        fxh2 = f(x) # f(x-h)\n",
        "        grad[idx] = (fxh1 - fxh2) / (2*h)\n",
        "        x[idx] = tmp_val\n",
        "        it.iternext()   \n",
        "    return grad"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_3yaJzL-wdP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "477d87e2-a427-4ca0-c609-2dd7ff262edc"
      },
      "source": [
        " np.random.seed(42)\n",
        " np.random.randint(1,42,6)"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([39, 29, 15,  8, 21, 39])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aE0lZ8Br-o5l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x  = np.array([0.6, 0.9])\n",
        "\n",
        "np.random.seed(42)\n",
        "weight   = np.random.randn(2,3)\n",
        "z        = np.dot(x, weight)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-psmvFII_LaZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "077300c4-c944-468e-f3dd-cffbf078be2b"
      },
      "source": [
        "y_hat    = softmax_function(z)\n",
        "y_hat"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.73222998, 0.10288817, 0.16488185])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9YdQIs_e_MJa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "666d376f-5360-487e-b8f5-b88b22345d06"
      },
      "source": [
        "y        = np.array([0, 0, 1])\n",
        "cross_entropy_err(y_hat, y)"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.8025260716416405"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxZrbFg9_TJg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "50099a82-cf7a-433b-9e11-767e2c0617a7"
      },
      "source": [
        "def predict(x):\n",
        "    return np.dot(x, weight)\n",
        "\n",
        "def loss(x, y):\n",
        "    z = predict(x)\n",
        "    y_hat = softmax_function(z)\n",
        "    loss = cross_entropy_err(y_hat, y)\n",
        "    return loss\n",
        "\n",
        "func = lambda w: loss(x, y)\n",
        "dfunc(func, weight)"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.43933796,  0.0617329 , -0.50107086],\n",
              "       [ 0.65900694,  0.09259935, -0.75160629]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGhajomRB56D",
        "colab_type": "text"
      },
      "source": [
        "##訓練神經網路"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wE99Ijn-_vKM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRVbFpMYB_WF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def softmax_function(x):\n",
        "    return np.exp(x) / np.sum(np.exp(x))\n",
        "\n",
        "def sigmoid_function(x):\n",
        "    return 1/ (1 + np.exp(-x))\n",
        "\n",
        "def dfunc(f, x):\n",
        "    h = 1e-4 \n",
        "    grad = np.zeros_like(x)\n",
        "    it = np.nditer(x, flags=['multi_index'])\n",
        "    while not it.finished:     \n",
        "        idx = it.multi_index\n",
        "        tmp_val = x[idx]\n",
        "        x[idx] = float(tmp_val) + h\n",
        "        fxh1 = f(x) # f(x+h)\n",
        "        x[idx] = tmp_val - h \n",
        "        fxh2 = f(x) # f(x-h)\n",
        "        grad[idx] = (fxh1 - fxh2) / (2*h)\n",
        "        x[idx] = tmp_val\n",
        "        it.iternext()   \n",
        "    return grad\n",
        "\n",
        "def cross_entropy_err(y_hat, y):\n",
        "    delta = 1e-8\n",
        "    return -np.sum(y*np.log(y_hat + delta))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXDigNzfCTSJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ANN(object):\n",
        "    def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):\n",
        "        self.params = {}\n",
        "        self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)\n",
        "        self.params['b1'] = np.zeros(hidden_size)\n",
        "        self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)\n",
        "        self.params['b2'] = np.zeros(output_size)\n",
        " \n",
        "    def predict(self, x):\n",
        "        W1, W2 = self.params['W1'], self.params['W2']\n",
        "        b1, b2 = self.params['b1'], self.params['b2']\n",
        "        a1 = np.dot(x, W1) + b1\n",
        "        z1 = sigmoid_function(a1)\n",
        "        a2 = np.dot(z1, W2) + b2\n",
        "        y = softmax_function(a2)\n",
        "        return y\n",
        "        \n",
        "    def loss(self, x, y):\n",
        "        y_hat = self.predict(x)\n",
        "        return cross_entropy_err(y_hat, y)\n",
        "        \n",
        "    def numerical_gradient(self, x, y):\n",
        "        loss_W = lambda W: self.loss(x, y)\n",
        "        grads = {}\n",
        "        grads['W1'] = dfunc(loss_W, self.params['W1'])\n",
        "        grads['b1'] = dfunc(loss_W, self.params['b1'])\n",
        "        grads['W2'] = dfunc(loss_W, self.params['W2'])\n",
        "        grads['b2'] = dfunc(loss_W, self.params['b2'])\n",
        "        return grads"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQ_nz7K_DFJu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = ANN(input_size=4, hidden_size=5, output_size=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9sselNsDMog",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "71f1beff-b1cd-4ea2-ac62-fbb2051a323e"
      },
      "source": [
        "net.params['W1']"
      ],
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.0007201 ,  0.01003533,  0.00361636, -0.0064512 ,  0.00361396],\n",
              "       [ 0.01538037, -0.00035826,  0.01564644, -0.02619745,  0.00821903],\n",
              "       [ 0.00087047, -0.00299007,  0.00091761, -0.01987569, -0.00219672],\n",
              "       [ 0.00357113,  0.01477894, -0.0051827 , -0.00808494, -0.00501757]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JPghSDjeDORq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ed2ad648-1d22-47bf-8d27-ed9f0274b7a8"
      },
      "source": [
        "net.params['b1']"
      ],
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTMDHwEWDedA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "95077261-4030-4271-f962-48e2974783d3"
      },
      "source": [
        "net.params['W1'].shape"
      ],
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "el7FppUADf3Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "5b8fc21e-2969-4922-d8fe-dbd34b3aebaa"
      },
      "source": [
        "net.params['W2']"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 9.15402118e-03,  3.28751110e-03, -5.29760204e-03],\n",
              "       [ 5.13267433e-03,  9.70775493e-04,  9.68644991e-03],\n",
              "       [-7.02053094e-03, -3.27662147e-03, -3.92108153e-03],\n",
              "       [-1.46351495e-02,  2.96120277e-03,  2.61055272e-03],\n",
              "       [ 5.11345664e-05, -2.34587133e-03, -1.41537074e-02]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYmAOSKTDj9m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cddd107a-3e54-4a60-913e-78db59678d4a"
      },
      "source": [
        "net.params['W2'].shape"
      ],
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Emoin3PnDmBk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()\n",
        "x= iris.data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGklQt5iDpTG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Shx7KnlTDukt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_hat = net.predict(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huSATN-qEGjc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "c837914a-f24f-4403-dd96-bfe3f3e9fcc0"
      },
      "source": [
        "y_hat[0:3,:]"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00222135, 0.00222958, 0.00221521],\n",
              "       [0.00222123, 0.00222961, 0.0022153 ],\n",
              "       [0.00222124, 0.00222961, 0.00221526]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KB4N_TXPD8pb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "82e4a234-f3cd-4793-c890-6354d1228b68"
      },
      "source": [
        "iris.target"
      ],
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ang0IRSOEnmq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "e34d4030-56e4-4cb2-93ac-4f39fb23ac86"
      },
      "source": [
        "a = [7,2,4]\n",
        "for i in enumerate(a):\n",
        "  print(i)"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0, 7)\n",
            "(1, 2)\n",
            "(2, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r3zXStH5EF9N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = np.zeros((len(iris.target), 3))\n",
        "for idx, val in enumerate(iris.target):\n",
        "    y[idx, val] = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "owE61oN9D9jW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "2864d2b3-cceb-4f19-c821-edc801ec2cf4"
      },
      "source": [
        "y[0:3,:]"
      ],
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvU9V_lkE20B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#y - y_hat"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vip1SARqFACx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4ad5fb4b-d5cc-4363-cb06-db8fc1787acf"
      },
      "source": [
        "import time\n",
        "epochs = 3000\n",
        "lr     = 0.01\n",
        "a = time.clock()\n",
        "train_loss = []\n",
        "for i in range(epochs):         \n",
        "    grad = net.numerical_gradient(x,y)\n",
        "    for key in ('W1', 'b1', 'W2', 'b2'):\n",
        "        net.params[key] = net.params[key] - lr * grad[key]\n",
        "    loss = net.loss(x, y)\n",
        "    train_loss.append(loss)\n",
        "time.clock() - a"
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.609500999999995"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4cj4b8-dFcNV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "0ef5ee9a-79da-4f82-dae0-d735dec88dd6"
      },
      "source": [
        "net.params['W1']"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.87528243, -4.1601032 ,  1.12563981,  4.49336522, -0.82310418],\n",
              "       [ 0.95757907, -4.84599824,  1.96425022,  4.85830651, -2.56640712],\n",
              "       [-2.69765164,  6.25651091, -4.02817191, -7.30876271,  4.26029221],\n",
              "       [-3.23609277,  9.0640642 , -2.52128215, -7.29588234,  2.0641366 ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWfN2WV3Fmzk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_hat2 = net.predict(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5jUJ7H-lFqnG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "1845c9be-f8d1-4ebe-eb9f-3360c14906ec"
      },
      "source": [
        "y_hat2[0:3,:]"
      ],
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7.82548298e-03, 3.20592126e-06, 1.36467314e-09],\n",
              "       [7.71111652e-03, 3.25624535e-06, 1.38006127e-09],\n",
              "       [7.78798755e-03, 3.22161149e-06, 1.36976721e-09]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjmFIKwrFvL-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predicted = np.argmax(y_hat2, axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aT-OYPIMGDBm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8d3be4c1-8ea2-4307-8733-c7440cd32662"
      },
      "source": [
        "(predicted == iris.target).sum() / len(iris.target)"
      ],
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.98"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-YhqpvuGPUX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "2ee05fd8-3de4-4b15-ee16-22972c35464b"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(range(0,3000),train_loss)\n",
        "plt.xlabel('Iteration')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training Loss')\n",
        "plt.show()"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EN7OWMZ7Lqpa7sFzbalT/QjVDX0eOakbCauSoZuT1ZmZ5Gs+RxYQ7my/P/gWPmjKzVlYx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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T06dnRK9Gsuh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "e1344f2a-416f-44c9-9cc5-d015a638fafb"
      },
      "source": [
        "#iris.target\n",
        "#predicted"
      ],
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gi_6u9MOLC5N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "15ddac81-1fda-4f9b-fdf9-7073f35c2936"
      },
      "source": [
        "(iris.target == predicted).sum()"
      ],
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "147"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vrDu97gyLNBB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5e251cba-056c-45cc-90ed-dba03bc77cc3"
      },
      "source": [
        "(iris.target == predicted).sum() / len(iris.target)"
      ],
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.98"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XidvcDmjLXie",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "73e63595-984d-4f8b-867a-31ea4c228b22"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(iris.target, predicted)"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.98"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYWDukP4LhDR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "81808580-9292-421e-ec93-ec88e08723c1"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(iris.target, predicted)"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[50,  0,  0],\n",
              "       [ 0, 48,  2],\n",
              "       [ 0,  1, 49]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PbmmwgVBMHGm",
        "colab_type": "text"
      },
      "source": [
        "## Decision Boundary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6Bgq8irMUNs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = ANN(input_size=2, hidden_size=5, output_size=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSXbW80cLtIw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()\n",
        "x= iris.data[:,[2,3]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6XNl3ofpMQlN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = np.zeros((len(iris.target), 3))\n",
        "for idx, val in enumerate(iris.target):\n",
        "    y[idx, val] = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nrzwkv2JMbVg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "epochs = 3000\n",
        "lr     = 0.01\n",
        "\n",
        "train_loss = []\n",
        "for i in range(epochs):         \n",
        "    grad = net.numerical_gradient(x,y)\n",
        "    for key in ('W1', 'b1', 'W2', 'b2'):\n",
        "        net.params[key] = net.params[key] - lr * grad[key]\n",
        "    loss = net.loss(x, y)\n",
        "    train_loss.append(loss)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gP0wuSfzMfdz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1\n",
        "y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1\n",
        "\n",
        "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
        "                     np.arange(y_min, y_max, 0.1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZMt-2DjMyUp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Z = numpy.argmax(net.predict(np.c_[xx.ravel(), yy.ravel()]), axis = 1)\n",
        "Z = Z.reshape(xx.shape)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1A10-iFM3eM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "170da9c3-305b-4af8-bff1-fdeeeef87a66"
      },
      "source": [
        "plt.plot()\n",
        "plt.contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.RdYlBu)\n",
        "plt.scatter(x[:, 0], x[:, 1], c=y, cmap = plt.cm.brg)\n",
        "plt.title('Neural Network')\n",
        "plt.xlabel('Petal.Length')\n",
        "plt.ylabel('Petal.Width')\n",
        "plt.show()"
      ],
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/EoHLL92LDev9yftUVccJhwTLcuIwRo6Ocs3Nmxk+whHXcEj41S0jWPROOT6f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\nFGZmVup/nr/BI8O6cgkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2h4AGaMOXCh",
        "colab_type": "text"
      },
      "source": [
        "## Stochastic Gradient Descent"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPmo87_0M5SK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cross_entropy_err(y_hat, y):\n",
        "    y      = y.reshape(1, y.size)\n",
        "    y_hat  = y_hat.reshape(1, y_hat.size)\n",
        "    batch_size = y_hat.shape[0]\n",
        "    return -np.sum(y * np.log(y_hat)) / batch_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2nRWCOvO1Ct",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8cfb3b0f-9a0c-4b60-c6c5-3353b21d624e"
      },
      "source": [
        "import numpy as np\n",
        "net = ANN(input_size=4, hidden_size=5, output_size=3)\n",
        "\n",
        "epochs      = 3000\n",
        "lr          = 0.01\n",
        "batch_size  = 30\n",
        "\n",
        "t0 = time.clock()\n",
        "train_loss = []\n",
        "for i in range(epochs): \n",
        "    idx = np.random.choice(iris.data.shape[0], batch_size)\n",
        "    x_batch   = iris.data[idx]\n",
        "    y_batch   = y[idx]\n",
        "    grad = net.numerical_gradient(x_batch,y_batch)\n",
        "    for key in ('W1', 'b1', 'W2', 'b2'):\n",
        "        net.params[key] = net.params[key] - lr * grad[key]\n",
        "    loss = net.loss(x_batch, y_batch)\n",
        "    train_loss.append(loss)\n",
        "time.clock() - t0"
      ],
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13.382220000000004"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBnZR-NSPSTl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "0ad828cb-21bb-43a0-c4a2-85f29e827906"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(range(0,3000),train_loss)\n",
        "plt.xlabel('Iteration')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training Loss')\n",
        "plt.show()"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x3ep6ezK88NLeGbWITLdVtNZDmAO7X3ix5GO/f9R8fvn0ZGwTQ1RTTjnhE0k1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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyXfhGoePgZ0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predicted = np.argmax(net.predict(iris.data), axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CjVPO13_PwFv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "76cf8e30-dee2-4bf4-a7fa-669cba2696b9"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(iris.target, predicted)"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.98"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ahKZtJSGP-MT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "d436d0dd-e3ec-49ea-a58f-8b73bac3471d"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(iris.target, predicted)"
      ],
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[50,  0,  0],\n",
              "       [ 0, 47,  3],\n",
              "       [ 0,  0, 50]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_wiZDOEQoWW",
        "colab_type": "text"
      },
      "source": [
        "## 0307 Homework\n",
        "\n",
        "1. 請使用 pip 將 jieba 安裝在自己的電腦上\n",
        "2. 閱讀jieba 相關的文件，了解jieba 的用法\n",
        "3. 請將蔡英文演說文 (https://raw.githubusercontent.com/ywchiu/tibamedl/master/Data/speech_tsai.txt) 下載存入到自己電腦\n",
        "4. 請使用python 的檔案操作方法讀取新聞\n",
        "5. 請使用 jieba 將演說文切詞\n",
        "6. 請統計該演說文最常出現的十大字詞\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HCMcn5nQDFR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "511e979b-df75-4b79-961b-1db6b8978ba0"
      },
      "source": [
        "! pip install jieba"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: jieba in /usr/local/lib/python3.6/dist-packages (0.42.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ibeLYypeQ97b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "71f6ba68-4345-41bb-c599-ab07bbb329d3"
      },
      "source": [
        "s = '今天天氣真好'\n",
        "import jieba\n",
        "seg_list = jieba.cut(s)\n",
        "\", \".join(seg_list)"
      ],
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Building prefix dict from the default dictionary ...\n",
            "Dumping model to file cache /tmp/jieba.cache\n",
            "Loading model cost 0.971 seconds.\n",
            "Prefix dict has been built successfully.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'今天, 天氣, 真好'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5RnAaUbaSQaL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "10e39147-4ef8-4c3c-ec04-d1c8a2a2945a"
      },
      "source": [
        "list(jieba.cut(s))"
      ],
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['今天', '天氣', '真好']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKlH2oQpSayt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "86fe74c3-e0ae-4a55-8f0b-01be7e472e3d"
      },
      "source": [
        "! wget https://raw.githubusercontent.com/ywchiu/tibamedl/master/Data/speech_tsai.txt"
      ],
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-14 09:04:50--  https://raw.githubusercontent.com/ywchiu/tibamedl/master/Data/speech_tsai.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 6579 (6.4K) [text/plain]\n",
            "Saving to: ‘speech_tsai.txt’\n",
            "\n",
            "\rspeech_tsai.txt       0%[                    ]       0  --.-KB/s               \rspeech_tsai.txt     100%[===================>]   6.42K  --.-KB/s    in 0s      \n",
            "\n",
            "2020-03-14 09:04:50 (67.8 MB/s) - ‘speech_tsai.txt’ saved [6579/6579]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVFmabqVSffV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('speech_tsai.txt', 'r') as f:\n",
        "  speech = f.read()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxX3GF_1Sl1B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "2299f3f6-8c91-4e07-ec29-09a12f996655"
      },
      "source": [
        "speech"
      ],
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'大會主席蘇嘉全院長、現場各位貴賓、電視機前和網路上的全體國人同胞，大家早安，大家好。\\n\\n今天，是中華民國一百零八年的國慶日，謝謝來自世界各地的好朋友，跟我們共度這個值得紀念的日子。\\n\\n去年，也是在國慶日，我和大家說，台灣正處在變局當中。世界貿易局勢的變動、國際政治情勢的變化，都讓未來充滿挑戰。我們必須「求穩、應變、進步」，厚植實力、壯大台灣。\\n\\n轉眼又過了一年，世界依然快速變化，甚至更加劇烈。美中貿易戰的持續進行；距離我們不遠的香港，因為「一國兩制」的失敗，正處於失序邊緣。\\n\\n儘管如此，中國依然以「一國兩制台灣方案」，不斷威脅我們，並採取各種文攻武嚇，強烈挑戰了區域的穩定及和平。\\n\\n各位國人同胞，當自由民主受到挑戰，當中華民國的生存發展受到脅迫，我們就必須站出來捍衛。拒絕「一國兩制」，是兩千三百萬台灣人民不分黨派、不分立場，彼此間最大的共識。\\n\\n中華民國已經在台灣屹立超過七十年，一旦接受「一國兩制」，中華民國就沒有生存的空間。身為總統，站出來守護國家主權，不是挑釁，而是我最基本的責任。\\n\\n七十年來，我們共同經歷過種種嚴峻的挑戰。每一次挑戰，不但沒有擊倒我們，反而讓我們更強壯、更堅定。\\n\\n我們一起走過八二三砲戰，一起度過九六年台海危機。一次又一次文攻武嚇，從來就沒有讓台灣人民屈服。我們共同捍衛住腳下這塊土地，也捍衛住國家的主權。\\n\\n我們經歷過退出聯合國的恐慌，也承受過一次又一次的斷交壓力。但台灣人民想要走向世界的決心，不曾有任何改變。\\n\\n一九七零年代的石油危機，一九九七年的亞洲金融風暴，千禧年的網路泡沫，還有十年前的金融海嘯。經濟總是充滿挑戰，但一卡皮箱走遍天下的台商，加上勤奮又有創意的台灣人民，讓我們一次又一次，把危機化做轉機，讓台灣的經濟持續往前走。\\n\\n我們也經歷過八七水災、九二一地震、SARS風暴、八八風災。天災病變的磨難，沒有擊敗過台灣人民奮鬥生存的意志。家園毀壞了，我們重建；土地受傷了，我們復原。擦乾眼淚，重新站起，明天又是充滿希望的一天。\\n\\n這些共同的記憶，彰顯了台灣人民的韌性。因為有這樣的韌性，我們成為亞洲四小龍的一員。因為這樣的韌性，我們走過了民主化的艱鉅歷程，成為世界上重要的民主典範。\\n\\n我們共同走過這一段路，無論是哪個黨派，只要是生活在這塊土地上的人民，都不能分割彼此。中華民國不是誰的專利，台灣也不是誰能夠獨佔。「中華民國台灣」六個字，既不是藍色、也不是綠色，這就是整個社會最大的共識。\\n\\n展望未來，前方的挑戰還有很多，等著我們一一克服。\\n\\n我們看見中國崛起跟擴張，以威權體制，結合民族主義和經濟力量，挑戰自由民主的價值和世界秩序。也因此，處於印太地區戰略前緣的台灣，成為了守護民主價值的第一道防線。\\n\\n中國利用「銳實力」步步進逼，但我們很清楚，作為區域的重要成員，台灣要善盡國際責任。我們不挑釁、不冒進，結合理念相近的國家，確保台海和平穩定的現狀，不會被片面改變。\\n\\n要做到這些，我們必須要團結。儘管台灣社會曾經因為族群、世代、信仰、黨派的差異，而出現爭執，但透過對話，我們一定可以找到彼此可以接受的最大公約數。經驗證明，衝突、對話、團結進步，會引導國家往正確的方向前進。\\n\\n我們也必須堅守自由民主的價值。台灣人民曾經一起走過民主化的艱辛道路，民主偶有紛擾，但只有民主制度，能夠保障得來不易的自由，也才能讓下一代保有決定未來的權利。\\n\\n我們也必須持續壯大台灣。三年多來，我們努力調整經濟結構，引導產業升級轉型、推動國際多元布局。我們迎來投資大爆發，在全球經濟的變動中，站穩了腳步，方向是對的，我們就要繼續走。\\n\\n三年多來，我們努力維護社會公平，加薪減稅、全面照顧，讓全民分享經濟成長的果實。未來，推動長照2.0升級，推動幼托補助擴大，減輕民眾負擔，使人人可以享有優質的照顧，是政府要繼續努力的方向。\\n\\n三年多來，我們推動國防自主、採購先進武器，提升國軍士氣，強化充足戰力。國造高教機的原型機剛剛出廠，國造艦艇也將陸續加入建軍行列。捍衛國土、堅守自由民主，國軍責無旁貸。\\n\\n三年多來，我們積極參與國際，負責任、肯貢獻，成為維護區域和平穩定，不可或缺的良善力量。我們會持續和理念相近的國家攜手，爭取更多實質合作的機會。\\n\\n未來的路線很清楚，目標也很明確。\\n\\n第一，讓國人繼續團結在自由民主的旗幟下，捍衛國家主權。\\n\\n第二，持續壯大台灣，強化經濟實力，讓民富而國強。\\n\\n第三，積極走向世界，克服挑戰，讓中華民國台灣在國際舞台上抬頭挺胸，勇敢自信。\\n\\n上一季，我們的經濟成長率，已經重返亞洲四小龍之首。世界經濟論壇評比我們是四大「超級創新國」之一。我們的高科技和創新產業，也走在世界的最前端。\\n\\n我們的體育選手、我們的技能國手，無數充滿創意的設計師、藝術家，在國際舞台發光發熱，帶給台灣榮耀和驕傲。\\n\\n我們自製的福衛五號，以及福衛七號接連升空，展現太空科技實力。就連人類歷史上第一次觀測到的黑洞影像，也有台灣科學團隊的參與。\\n\\n當我們可以上太空，可以看見五千五百萬光年外的黑洞，那麼，眼前還有什麼挑戰，是我們沒有勇氣面對的？\\n\\n歷史的磨難，會因為我們的韌性，而被轉化為成長力量。天災的挑戰，會因為我們的努力，而成為再生的契機。這塊土地上每一個人的努力，都讓我們的國家，一天一天變得更好、更進步。\\n\\n在國慶日這一天，這塊土地上的所有人民，都團結在自由民主的旗幟下，樂觀面對未來、堅定克服挑戰。天佑台灣，台灣加油，中華民國加油。謝謝大家。\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "24RIsML3Sm0l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "words = list(jieba.cut(speech))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1maqsk3jSvRK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dic = {}\n",
        "for w in words:\n",
        "  if w not in dic:\n",
        "    dic[w] = 1\n",
        "  else:\n",
        "    dic[w] = dic[w] + 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6e69_D_1S9eB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "swd = sorted(dic.items(), key = lambda e: e[1], reverse = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7pnD8gu4TCuF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "7cec06b3-df44-486e-a00c-faf72013e644"
      },
      "source": [
        "swd[0:10]"
      ],
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('，', 140),\n",
              " ('的', 84),\n",
              " ('。', 67),\n",
              " ('\\n', 65),\n",
              " ('我們', 43),\n",
              " ('、', 27),\n",
              " ('台灣', 19),\n",
              " ('也', 14),\n",
              " ('挑戰', 12),\n",
              " ('在', 11)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ePDQYkh2TLc0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "952a7cc5-f2da-459d-aed7-2a030492327c"
      },
      "source": [
        "for k, v in swd[0:30]:\n",
        "  if len(k) >=2:\n",
        "    print(k,v)"
      ],
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "我們 43\n",
            "台灣 19\n",
            "挑戰 12\n",
            "人民 9\n",
            "經濟 8\n",
            "中華民國 7\n",
            "一次 7\n",
            "未來 6\n",
            "自由民主 6\n",
            "世界 5\n",
            "國際 5\n",
            "必須 5\n",
            "持續 5\n",
            "因為 5\n",
            "不是 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfxTExnBTTCi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "a761f347-3e83-440f-8d57-4e2add8237bf"
      },
      "source": [
        "from collections import Counter\n",
        "c = Counter(words)\n",
        "for k,v in c.most_common(30):\n",
        "  if len(k) >=2:\n",
        "    print(k,v)"
      ],
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "我們 43\n",
            "台灣 19\n",
            "挑戰 12\n",
            "人民 9\n",
            "經濟 8\n",
            "中華民國 7\n",
            "一次 7\n",
            "未來 6\n",
            "自由民主 6\n",
            "世界 5\n",
            "國際 5\n",
            "必須 5\n",
            "持續 5\n",
            "因為 5\n",
            "不是 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ylMd1p8UGV-",
        "colab_type": "text"
      },
      "source": [
        "## 0314 作業\n",
        "\n",
        "有一糖尿病資料集 (X: 'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
        "       'BMI', 'DiabetesPedigreeFunction', 'Age' ;目標y: outcome),請使用以下方法建立模型,並且比較個模型的準確度\n",
        "\n",
        "1. 使用sklearn 的決策樹建立分類模型\n",
        "2. 使用sklearn 的邏輯式回歸建立分類模型\n",
        "3. 使用ANN 的類神經網路建立分類模型\n",
        "4. 比較個模型的準確度"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFMrlyEQTjXu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas\n",
        "diabetes = pandas.read_csv('https://raw.githubusercontent.com/ywchiu/cdc_course/master/data/diabetes.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7JR8zxQiUzmY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "db1e02cc-00e8-4d79-fc0b-82f556be1a89"
      },
      "source": [
        "diabetes.columns"
      ],
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
              "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGxOmzSDU0qs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = diabetes.iloc[:,:-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-cn2rofVJaG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = diabetes.iloc[:,-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EA7E7RMLVQE1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ab494d19-eb89-40fd-b789-cdadafdbecc0"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dylQgUVSVRdH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "6ca07beb-4692-4175-9cbc-2e798517d529"
      },
      "source": [
        "y"
      ],
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      0\n",
              "2      1\n",
              "3      0\n",
              "4      1\n",
              "      ..\n",
              "763    0\n",
              "764    0\n",
              "765    0\n",
              "766    1\n",
              "767    0\n",
              "Name: Outcome, Length: 768, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZapUs7LVTy6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yxI67wvaVcDD",
        "colab_type": "text"
      },
      "source": [
        "### 標準化\n",
        "(x - mu ) /std"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1fdIkLyVbOL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "de54d348-db28-47fe-8c55-6b2f23724076"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "a = numpy.array([[50],[60],[70]])\n",
        "sc = StandardScaler()\n",
        "sc.fit_transform(a)"
      ],
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.22474487],\n",
              "       [ 0.        ],\n",
              "       [ 1.22474487]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61i9tx_iVl9-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "d605693d-eaaa-46d5-c09a-9270be150084"
      },
      "source": [
        "(a - a.mean()) /a.std()"
      ],
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.22474487],\n",
              "       [ 0.        ],\n",
              "       [ 1.22474487]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 275
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JX643wGfV7Pj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X = sc.fit_transform(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7YT5JnsWFzQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "d964c645-1df8-427d-eb74-e83180455daa"
      },
      "source": [
        "X"
      ],
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.63994726,  0.84832379,  0.14964075, ...,  0.20401277,\n",
              "         0.46849198,  1.4259954 ],\n",
              "       [-0.84488505, -1.12339636, -0.16054575, ..., -0.68442195,\n",
              "        -0.36506078, -0.19067191],\n",
              "       [ 1.23388019,  1.94372388, -0.26394125, ..., -1.10325546,\n",
              "         0.60439732, -0.10558415],\n",
              "       ...,\n",
              "       [ 0.3429808 ,  0.00330087,  0.14964075, ..., -0.73518964,\n",
              "        -0.68519336, -0.27575966],\n",
              "       [-0.84488505,  0.1597866 , -0.47073225, ..., -0.24020459,\n",
              "        -0.37110101,  1.17073215],\n",
              "       [-0.84488505, -0.8730192 ,  0.04624525, ..., -0.20212881,\n",
              "        -0.47378505, -0.87137393]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 277
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OawAsrOWGZy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}