new_assignment_11_hidden_layer_three4.ipynb

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      "name": "new_assignment_11.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNF6F2ihlCXuS0kJ8EAnAfX",
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dlguswn3659/class-MachineLearning/blob/master/new_assignment_11_hidden_layer_three4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbdQEPZ_o5JX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import nltk\n",
        "import math\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from wordcloud import WordCloud,STOPWORDS\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.tokenize import word_tokenize,sent_tokenize\n",
        "from bs4 import BeautifulSoup\n",
        "import spacy\n",
        "import re,string,unicodedata\n",
        "from nltk.tokenize.toktok import ToktokTokenizer\n",
        "from nltk.stem import LancasterStemmer,WordNetLemmatizer\n",
        "from sklearn.linear_model import LogisticRegression,SGDClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.svm import SVC\n",
        "from textblob import TextBlob\n",
        "from textblob import Word\n",
        "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMRdc5kpo9vM",
        "colab_type": "code",
        "outputId": "255a80a7-61e0-4d86-977e-b08bcf03c53d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "import numpy as np\n",
        "import re\n",
        "import nltk\n",
        "from sklearn.datasets import load_files\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "import pickle\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.model_selection import train_test_split\n",
        "# from google.colab import files\n",
        "\n",
        "# uploaded = files.upload()\n",
        "\n",
        "# import zipfile\n",
        "# import io\n",
        "# zf = zipfile.ZipFile(io.BytesIO(uploaded['movie_review.zip']), \"r\")\n",
        "# zf.extractall()\n",
        "\n",
        "review_data = load_files(r\"movie_review\")\n",
        "X, y = review_data.data, review_data.target\n",
        "\n",
        "documents = []\n",
        "\n",
        "stemmer = WordNetLemmatizer()\n",
        "\n",
        "for sen in range(0, len(X)):\n",
        "    # Remove all the special characters\n",
        "    document = re.sub(r'\\W', ' ', str(X[sen]))\n",
        "    \n",
        "    # remove all single characters\n",
        "    document = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', document)\n",
        "    \n",
        "    # Remove single characters from the start\n",
        "    document = re.sub(r'\\^[a-zA-Z]\\s+', ' ', document) \n",
        "    \n",
        "    # Substituting multiple spaces with single space\n",
        "    document = re.sub(r'\\s+', ' ', document, flags=re.I)\n",
        "    \n",
        "    # Removing prefixed 'b'\n",
        "    document = re.sub(r'^b\\s+', '', document)\n",
        "    \n",
        "    # Converting to Lowercase\n",
        "    document = document.lower()\n",
        "    \n",
        "    # Lemmatization\n",
        "    document = document.split()\n",
        "    document = [stemmer.lemmatize(word) for word in document]\n",
        "    document = ' '.join(document)\n",
        "    \n",
        "    documents.append(document)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_uv_iLio-76",
        "colab_type": "code",
        "outputId": "8ebbbe5a-3dfa-4974-9960-6c6bd09bd79a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "vectorizer = CountVectorizer(max_features=40000, min_df=5, max_df=0.7, stop_words=stopwords.words('english'))\n",
        "# vectorizer = TfidfVectorizer()\n",
        "X = vectorizer.fit_transform(documents).toarray()\n",
        "print(X)\n",
        "\n",
        "vector_size = 1413\n",
        "\n",
        "###################################################\n",
        "\n",
        "# # tfidfconverter = TfidfTransformer()\n",
        "# # print(tfidfconverter)\n",
        "# # X = tfidfconverter.fit_transform(X).toarray()\n",
        "# # print(X)\n",
        "\n",
        "# X_train_tmp, X_test_tmp, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=False)\n",
        "\n",
        "# # print(X_train.shape)\n",
        "\n",
        "# X_pos_sum = np.zeros(12638)\n",
        "# X_neg_sum = np.zeros(12638)\n",
        "\n",
        "# X_train = np.zeros((1401, 897))\n",
        "# X_test = np.zeros((601, 897))\n",
        "\n",
        "# for i in range(0, 2002):\n",
        "#   if y[i] == 0:\n",
        "#     X_neg_sum = X_neg_sum + X[i]\n",
        "#   else:\n",
        "#     X_pos_sum = X_pos_sum + X[i]\n",
        "\n",
        "# pos_neg_voca = 0\n",
        "\n",
        "# for i in range(0, 12638):\n",
        "#   # if abs(X_neg_sum[i] - X_pos_sum[i]) >= 30:\n",
        "#   if (X_neg_sum[i]*4 <= X_pos_sum[i] and X_pos_sum[i] > 8) or (X_pos_sum[i]*4 <= X_neg_sum[i] and X_neg_sum[i] > 8):\n",
        "#     for j in range(0, 1401):\n",
        "#       X_train[j][pos_neg_voca] = X_train_tmp[j][i]\n",
        "#     for j in range(0, 601):\n",
        "#       X_test[j][pos_neg_voca] = X_test_tmp[j][i]\n",
        "#     pos_neg_voca  = pos_neg_voca + 1\n",
        "\n",
        "# # tfidfconverter = TfidfTransformer()\n",
        "# # X_train = tfidfconverter.fit_transform(X_train).toarray()\n",
        "# # X_test = tfidfconverter.fit_transform(X_test).toarray()\n",
        "\n",
        "# print(X_neg_sum)\n",
        "# print(X_pos_sum)\n",
        "# print(pos_neg_voca)\n",
        "\n",
        "#################################################\n",
        "\n",
        "list_x=X.tolist()\n",
        "negative=[]\n",
        "positive=[]\n",
        "for i in range(0,2002):\n",
        "  if y[i]==0:\n",
        "    negative.append(list_x[i])\n",
        "  else:\n",
        "    positive.append(list_x[i])\n",
        "negative=np.array(negative)\n",
        "positive=np.array(positive)\n",
        "total_sum=X.sum(axis=0)\n",
        "neg_sum=negative.sum(axis=0)\n",
        "pos_sum=positive.sum(axis=0)\n",
        "\n",
        "neg_ratio=neg_sum/total_sum\n",
        "pos_ratio=pos_sum/total_sum\n",
        "\n",
        "good=[]\n",
        "for i in range(0,len(pos_sum)):\n",
        "  if neg_ratio[i]>pos_ratio[i]*3 and neg_sum[i] > 7:\n",
        "    # print(neg_ratio[i])\n",
        "    good.append(i)\n",
        "  elif pos_ratio[i]>neg_ratio[i]*3 and pos_sum[i] > 7:\n",
        "    # print(pos_ratio[i])\n",
        "    good.append(i)\n",
        "print(\"good의 길이:   \" , len(good))\n",
        "new=np.ones((1,2002), dtype=float)\n",
        "for i in range(0,len(good)):\n",
        "  temp=X[:,good[i]]\n",
        "  temp=temp[np.newaxis]\n",
        "  new=np.vstack((new,temp))\n",
        "new=np.transpose(new)\n",
        "new=new[:,1:]\n",
        "# vectorizer = CountVectorizer(max_features=2, min_df=0, stop_words=stopwords.words('english'))\n",
        "# Xp = vectorizer.fit_transform(pluss).toarray()\n",
        "\n",
        "# tfidfconverter = TfidfTransformer()\n",
        "# X = tfidfconverter.fit_transform(X).toarray()\n",
        "X_train, X_test, y_train, y_test = train_test_split(new, y, test_size=0.3, shuffle=False)\n",
        "\n",
        "print(X_train)\n",
        "print(X_test)\n",
        "\n",
        "print(X_train[0])"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n",
            "good의 길이:    1412\n",
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [1. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "[0. 0. 0. ... 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlTm8tUMrWdL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#first hidden layer로 가기 위한 초기 fully connected u 생성\n",
        "w_tmp = np.ones(shape=(1401, 100, vector_size))\n",
        "v_tmp = np.ones(shape=(1401, 12, 100))\n",
        "u_tmp = np.ones(shape=(1401, 1, 12))\n",
        "\n",
        "matrix_u = np.zeros((100, vector_size))  #100줄\n",
        "matrix_u[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(1, 100):\n",
        "  for j in range(0, vector_size):\n",
        "    matrix_u[i][j] = np.random.normal()\n",
        "\n",
        "\n",
        "# matrix_u[196][0] = 0            #줄, 칸\n",
        "##############################################\n",
        "matrix_v = np.zeros((12, 100))  #12줄\n",
        "matrix_v[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(1, 12):\n",
        "  for j in range(0, 100):\n",
        "    matrix_v[i][j] = np.random.normal()\n",
        "\n",
        "\n",
        "# matrix_v[196][0] = 0            #줄, 칸\n",
        "##############################################\n",
        "matrix_w = np.zeros((1, 12))  #10줄\n",
        "# matrix_w[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(0, 1):\n",
        "  for j in range(0, 12):\n",
        "    matrix_w[i][j] = np.random.normal()\n",
        "\n",
        "w_tmp = matrix_w\n",
        "v_tmp = matrix_v\n",
        "u_tmp = matrix_u\n",
        "\n",
        "# matrix_w[196][0] = 0            #줄, 칸\n",
        "\n",
        "# print(matrix)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DrFN9qH2rXrj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import operator\n",
        "\n",
        "y_ = np.ones((100, 1))\n",
        "\n",
        "x_list = np.ones(shape=(1401, vector_size, 1))\n",
        "\n",
        "y__list = np.ones(shape=(1401, 100, 1))\n",
        "z__list = np.ones(shape=(1401, 12, 1))\n",
        "h__list = np.ones(shape=(1401, 1, 1))\n",
        "\n",
        "y_list = np.ones(shape=(1401, 100, 1))\n",
        "z_list = np.ones(shape=(1401, 12, 1))\n",
        "h_list = np.ones(shape=(1401, 1, 1))\n",
        "l_list = np.zeros(shape=(1401, 1, 1))\n",
        "\n",
        "y_list_tmp = np.ones(shape=(1401, 100, 1))\n",
        "z_list_tmp = np.ones(shape=(1401, 12, 1))\n",
        "h_list_tmp = np.ones(shape=(1401, 1, 1))\n",
        "y__list_tmp = np.ones(shape=(1401, 100, 1))\n",
        "z__list_tmp = np.ones(shape=(1401, 12, 1))\n",
        "h__list_tmp = np.ones(shape=(1401, 1, 1))\n",
        "\n",
        "\n",
        "#training set 1401개에 대해서\n",
        "for i in range(0, 1401):\n",
        "  bias   = [1]       #bias 1\n",
        "  im_vector = np.concatenate((bias, X_train[i]), axis = None)\n",
        "\n",
        "  im_matrix   = im_vector.reshape((vector_size, 1))\n",
        "  \n",
        "  x_list[i] = im_matrix\n",
        "\n",
        "  y_ = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "  y__list[i] = y_\n",
        "  \n",
        "  y_vector = np.ones((100,1)) #bias\n",
        "  for j in range (1, 100):\n",
        "    y_vector[j][0] = 1 / (1 + math.exp(-y_[j][0]))\n",
        "    # sigmoid(y_[j][0])  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  y   = y_vector.reshape((100, 1))\n",
        "  y_list[i] = y\n",
        "\n",
        "  z_  = np.dot(matrix_v, y)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "  z__list[i] = z_\n",
        "  z_vector = np.ones((12,1)) #bias\n",
        "  for j in range (1, 12):\n",
        "    z_vector[j][0] = 1 / (1 + math.exp(-z_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  z   = z_vector.reshape((12, 1))\n",
        "  z_list[i] = z\n",
        "  \n",
        "  # print(z)\n",
        "\n",
        "  h_  = np.dot(matrix_w, z)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "  h__list[i] = h_\n",
        "  \n",
        "  h_vector = np.ones((1,1)) #bias\n",
        "  for j in range (0, 1):\n",
        "    h_vector[j][0] = 1 / (1 + math.exp(-h_[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "  h   = h_vector.reshape((1, 1))\n",
        "  \n",
        "  h_list[i] = h\n",
        "\n",
        "  tmp_label = 0\n",
        "  # print(h)\n",
        "\n",
        "  if h >= 0.5:\n",
        "    tmp_label = 1\n",
        "\n",
        "  l_list[i][0] = tmp_label\n",
        "\n",
        "  y_list_tmp[i] = y_list[i]\n",
        "  z_list_tmp[i] = z_list[i]\n",
        "  h_list_tmp[i] = h_list[i]\n",
        "  y__list_tmp[i] = y__list[i]\n",
        "  z__list_tmp[i] = z__list[i]\n",
        "  h__list_tmp[i] = h__list[i]\n",
        "\n",
        "# print(l_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YB-qa4x8rZD7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import operator\n",
        "\n",
        "y_2 = np.ones((100, 1))\n",
        "\n",
        "x_list2 = np.ones(shape=(601, vector_size, 1))\n",
        "\n",
        "y__list2 = np.ones(shape=(601, 100, 1))\n",
        "z__list2 = np.ones(shape=(601, 12, 1))\n",
        "h__list2 = np.ones(shape=(601, 1, 1))\n",
        "\n",
        "y_list2 = np.ones(shape=(601, 100, 1))\n",
        "z_list2 = np.ones(shape=(601, 12, 1))\n",
        "h_list2 = np.ones(shape=(601, 1, 1))\n",
        "l_list2 = np.zeros(shape=(601, 1, 1))\n",
        "\n",
        "y_list_tmp2 = np.ones(shape=(601, 100, 1))\n",
        "z_list_tmp2 = np.ones(shape=(601, 12, 1))\n",
        "h_list_tmp2 = np.ones(shape=(601, 1, 1))\n",
        "y__list_tmp2 = np.ones(shape=(601, 100, 1))\n",
        "z__list_tmp2 = np.ones(shape=(601, 12, 1))\n",
        "h__list_tmp2 = np.ones(shape=(601, 1, 1))\n",
        "\n",
        "\n",
        "#training set 1000개에 대해서\n",
        "for i in range(0, 601):\n",
        "  bias2   = [1]       #bias 1\n",
        "  im_vector2 = np.concatenate((bias2, X_test[i]), axis = None)\n",
        "\n",
        "  # print(type(list_image[: , i]))\n",
        "  # print(list_image[:, i])\n",
        "  # print(im_vector)\n",
        "  im_matrix2   = im_vector2.reshape((vector_size, 1))\n",
        "  \n",
        "  x_list2[i] = im_matrix2\n",
        "\n",
        "  y_2 = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "  y__list2[i] = y_2\n",
        "  \n",
        "  y_vector2 = np.ones((100,1)) #bias\n",
        "  for j in range (1, 100):\n",
        "    y_vector2[j][0] = 1 / (1 + math.exp(-y_2[j][0]))\n",
        "    # sigmoid(y_[j][0])  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  y2   = y_vector2.reshape((100, 1))\n",
        "  y_list2[i] = y2\n",
        "\n",
        "  z_2  = np.dot(matrix_v, y2)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "  z__list2[i] = z_2\n",
        "  z_vector2 = np.ones((12,1)) #bias\n",
        "  for j in range (1, 12):\n",
        "    z_vector2[j][0] = 1 / (1 + math.exp(-z_2[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  z2   = z_vector2.reshape((12, 1))\n",
        "  z_list2[i] = z2\n",
        "  \n",
        "  # print(z)\n",
        "\n",
        "  h_2  = np.dot(matrix_w, z2)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "  h__list2[i] = h_2\n",
        "  \n",
        "  h_vector2 = np.ones((1,1)) #bias\n",
        "  for j in range (0, 1):\n",
        "    h_vector2[j][0] = 1 / (1 + math.exp(-h_2[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "  h2   = h_vector2.reshape((1, 1))\n",
        "  \n",
        "  h_list2[i] = h2\n",
        "\n",
        "  tmp_label = 0\n",
        "\n",
        "  if h2 >= 0.5:\n",
        "    tmp_label = 1\n",
        "\n",
        "  l_list2[i][0] = tmp_label\n",
        "\n",
        "  y_list_tmp2[i] = y_list2[i]\n",
        "  z_list_tmp2[i] = z_list2[i]\n",
        "  h_list_tmp2[i] = h_list2[i]\n",
        "  y__list_tmp2[i] = y__list2[i]\n",
        "  z__list_tmp2[i] = z__list2[i]\n",
        "  h__list_tmp2[i] = h__list2[i]\n",
        "\n",
        "# print(l_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j28x7DrlraXJ",
        "colab_type": "code",
        "outputId": "2be059fb-3d1f-456b-e1db-e0fff8b4edd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#이제 여기에다가\n",
        "\n",
        "#iteration에 따라\n",
        "#cost function 값을 배열에 저장하고\n",
        "#세타값을 업데이트 시키고(위의 세개 함수 호출)\n",
        "#업데이트 시킨값을 다시 cost함수에 넣고\n",
        "#업데이트 시키고\n",
        "#이것을 반복하는 함수를 짠다.\n",
        "\n",
        "#아직 해야하는 것 : h값에 따라 가장 큰값의 index로 L값정하는거 위에 추가해야함.\n",
        "\n",
        "iteration = 10000\n",
        "l_r = 0.2\n",
        "lambda_ = 10\n",
        "\n",
        "\n",
        "#####################################\n",
        "\n",
        "cost_history = np.zeros(iteration)    #about train\n",
        "cost_history2 = np.zeros(iteration)   #about test\n",
        "\n",
        "label_group = np.zeros(shape = (1401, 1))\n",
        "label_group2 = np.zeros(shape = (601, 1))\n",
        "\n",
        "accuracy_history = np.zeros(iteration)\n",
        "accuracy_history2 = np.zeros(iteration)\n",
        "\n",
        "for i in range(0, 1401):\n",
        "  label_group[i][0] = l_list[i][0]\n",
        "\n",
        "for i in range(0, 601):\n",
        "  label_group2[i][0] = l_list2[i][0]\n",
        "\n",
        "#############################################\n",
        "\n",
        "\n",
        "for t in range(0, iteration):\n",
        "  number_of_correct = 0\n",
        "  number_of_correct2 = 0\n",
        "  \n",
        "  print(t, end=' ')\n",
        "\n",
        "  parameter_sum = 0\n",
        "\n",
        "  for q in range(0, vector_size):\n",
        "    for w in range(1, 100):\n",
        "      parameter_sum = parameter_sum + matrix_u[w][q]**2\n",
        "\n",
        "  for q in range(0, 100):\n",
        "    for w in range(1, 12):\n",
        "      parameter_sum = parameter_sum + matrix_u[w][q]**2\n",
        "\n",
        "  for q in range(0, 12):\n",
        "    for w in range(0, 1):\n",
        "      parameter_sum = parameter_sum + matrix_u[w][q]**2\n",
        "\n",
        "  result = 0.0\n",
        "  for i in range(0, 1401):\n",
        "    for k in range(0, 1):\n",
        "      # if h_list_tmp[i][k][0] <= 0:\n",
        "      # print(h_list_tmp[i][k][0])\n",
        "      result = result + ((-1)*label_group[i][k]*math.log(h_list[i][k][0]) - (1-label_group[i][k])*math.log(1-h_list[i][k][0])) + ((lambda_)/(2 * 142512)) * parameter_sum\n",
        "  result = result / 1401\n",
        "  cost_history[t] = result\n",
        "\n",
        "\n",
        "\n",
        "##gradient descent\n",
        "  result_w = 0\n",
        "  result_v = 0\n",
        "  result_u = 0\n",
        "\n",
        "  matrix_w_lol = matrix_w[:,1:12]\n",
        "  matrix_v_lol = matrix_v[1:12,1:100]\n",
        "\n",
        "  y_list_tmp_lol = y_list_tmp[:,1:100,:]\n",
        "\n",
        "  z_list_lol = z_list[:,1:12,:]\n",
        "  y_list_lol = y_list[:,1:100,:]\n",
        "  x_list_lol = x_list[:,1:vector_size,:]\n",
        "\n",
        "  for i in range(0, 1401):\n",
        "    small_delta_2 = np.zeros(shape = (1, 1))\n",
        "    for j in range(0, 1):\n",
        "      small_delta_2[j][0] = h_list[i][j] - label_group[i][j]\n",
        "    # print(h_list[i])\n",
        "    # print(label_group[i])\n",
        "    # small_delta_2 = h_list[i] - label_group[i] #여기 label_group 다시 봐야함.\n",
        "\n",
        "\n",
        "\n",
        "    small_delta_1 = np.dot(np.transpose(matrix_w_lol), small_delta_2)\n",
        "    small_delta_0 = np.dot(np.transpose(matrix_v_lol), small_delta_1)*(y_list_tmp_lol[i]*(1 - y_list_tmp_lol[i]))\n",
        "\n",
        "    # print(small_delta_2)\n",
        "    result_w = result_w + np.dot(small_delta_2, np.transpose(z_list_lol[i]))\n",
        "    result_v = result_v + np.dot(small_delta_1, np.transpose(y_list_lol[i]))\n",
        "    result_u = result_u + np.dot(small_delta_0, np.transpose(x_list_lol[i]))\n",
        "\n",
        "    # print(result_u)\n",
        "\n",
        "  matrix_w[:, 1:12] = matrix_w[:, 1:12] - (result_w / 1401 *l_r)\n",
        "  matrix_v[1:12, 1:100] = matrix_v[1:12, 1:100] - (result_v / 1401 *l_r)\n",
        "  matrix_u[1:100, 1:vector_size] = matrix_u[1:100, 1:vector_size] - (result_u / 1401 *l_r)\n",
        "\n",
        "  matrix_u[0] = 0\n",
        "  matrix_u[0][0] = 1\n",
        "\n",
        "  matrix_v[0] = 0\n",
        "  matrix_v[0][0] = 1\n",
        "\n",
        "\n",
        "####################################################################\n",
        "\n",
        "  for k in range(0, 1401):\n",
        "    bias   = [1]       #bias 1\n",
        "    im_vector = np.concatenate((bias, X_train[k]), axis = None)\n",
        "\n",
        "    im_matrix   = im_vector.reshape((vector_size, 1))\n",
        "\n",
        "    y_ = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "    y__list[k] = y_\n",
        "    \n",
        "    y_vector = np.ones((100,1)) #bias\n",
        "    for j in range (1, 100):\n",
        "      y_vector[j][0] = 1 / (1 + math.exp(-y_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "    \n",
        "    # print(y_vector) #시그모이드 값 확인\n",
        "    y   = y_vector.reshape((100, 1))\n",
        "    y_list[k] = y\n",
        "\n",
        "    z_  = np.dot(matrix_v, y)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "    z__list[k] = z_\n",
        "    z_vector = np.ones((12,1)) #bias\n",
        "    for j in range (1, 12):\n",
        "      z_vector[j][0] = 1 / (1 + math.exp(-z_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "    z   = z_vector.reshape((12, 1))\n",
        "    z_list[k] = z\n",
        "    \n",
        "    # print(z)\n",
        "\n",
        "    h_  = np.dot(matrix_w, z)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "    h__list[k] = h_\n",
        "    \n",
        "    h_vector = np.ones((1,1)) #bias\n",
        "    for j in range (0, 1):\n",
        "      h_vector[j][0] = 1 / (1 + math.exp(-h_[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "    h   = h_vector.reshape((1, 1))\n",
        "\n",
        "    h_list[k] = h\n",
        "\n",
        "    tmp_label = 0\n",
        "\n",
        "    if h >= 0.5:\n",
        "      # print(h)\n",
        "      tmp_label = 1\n",
        "\n",
        "    l_list[k][0] = tmp_label\n",
        "\n",
        "    y_list_tmp[k] = y_list[k]\n",
        "    z_list_tmp[k] = z_list[k]\n",
        "    h_list_tmp[k] = h_list[k]\n",
        "    y__list_tmp[k] = y__list[k]\n",
        "    z__list_tmp[k] = z__list[k]\n",
        "    h__list_tmp[k] = h__list[k]\n",
        "\n",
        "    if l_list[k][0] == y_train[k]:\n",
        "      number_of_correct = number_of_correct + 1\n",
        "\n",
        "  accuracy_history[t] = number_of_correct / 1401 * 100\n",
        "  print(accuracy_history[t], end=' ')\n",
        "\n",
        "############################\n",
        "  #test data!\n",
        "  \n",
        "  # print(t, end=' ')\n",
        "\n",
        "  result2 = 0.0\n",
        "  for i in range(0, 601):\n",
        "    for k in range(0, 1):\n",
        "      result2 = result2 + ((-1)*label_group2[i][k]*math.log(h_list2[i][k][0]) - (1-label_group2[i][k])*math.log(1-h_list2[i][k][0])) + ((lambda_)/(2 * 142512)) * parameter_sum\n",
        "  result2 = result2 / 601\n",
        "  cost_history2[t] = result2\n",
        "\n",
        "\n",
        "  for k in range(0, 601):\n",
        "  # print(i)\n",
        "    bias2   = [1]       #bias 1\n",
        "    im_vector2 = np.concatenate((bias2, X_test[k]), axis = None)\n",
        "\n",
        "    im_matrix2   = im_vector2.reshape((vector_size, 1))\n",
        "\n",
        "    y_2 = np.dot(matrix_u, im_matrix2)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "    y__list2[k] = y_2\n",
        "    \n",
        "    y_vector2 = np.ones((100,1)) #bias\n",
        "    for j in range (1, 100):\n",
        "      y_vector2[j][0] = 1 / (1 + math.exp(-y_2[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "    \n",
        "    # print(y_vector) #시그모이드 값 확인\n",
        "    y2   = y_vector2.reshape((100, 1))\n",
        "    y_list2[k] = y2\n",
        "\n",
        "    z_2  = np.dot(matrix_v, y2)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "    z__list2[k] = z_2\n",
        "    z_vector2 = np.ones((12,1)) #bias\n",
        "    for j in range (1, 12):\n",
        "      z_vector2[j][0] = 1 / (1 + math.exp(-z_2[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "    z2   = z_vector2.reshape((12, 1))\n",
        "    z_list2[k] = z2\n",
        "    \n",
        "    # print(z)\n",
        "\n",
        "    h_2  = np.dot(matrix_w, z2)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "    h__list2[k] = h_2\n",
        "    \n",
        "    h_vector2 = np.ones((1,1)) #bias\n",
        "    for j in range (0, 1):\n",
        "      h_vector2[j][0] = 1 / (1 + math.exp(-h_2[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "    h2   = h_vector2.reshape((1, 1))\n",
        "\n",
        "    h_list2[k] = h2\n",
        "\n",
        "    tmp_label2 = 0\n",
        "\n",
        "    if h2 >= 0.5:\n",
        "      # print(h)\n",
        "      tmp_label2 = 1\n",
        "\n",
        "    l_list2[k][0] = tmp_label2\n",
        "\n",
        "    y_list_tmp2[k] = y_list2[k]\n",
        "    z_list_tmp2[k] = z_list2[k]\n",
        "    h_list_tmp2[k] = h_list2[k]\n",
        "    y__list_tmp2[k] = y__list2[k]\n",
        "    z__list_tmp2[k] = z__list2[k]\n",
        "    h__list_tmp2[k] = h__list2[k]\n",
        "\n",
        "    if l_list2[k][0] == y_test[k]:\n",
        "      number_of_correct2 = number_of_correct2 + 1\n",
        "\n",
        "  accuracy_history2[t] = number_of_correct2 / 601 * 100\n",
        "  print(accuracy_history2[t])\n",
        "\n",
        "  t = t + 1"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43m스트리밍 출력 내용이 길어서 마지막 5000줄이 삭제되었습니다.\u001b[0m\n",
            "4995 48.965024982155605 45.75707154742096\n",
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            "5000 48.965024982155605 45.75707154742096\n",
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      "metadata": {
        "id": "pMcbWdbh98uY",
        "colab_type": "text"
      },
      "source": [
        "# **Machine Learning Assignment09** #\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "Name : 이현주\n",
        "\n",
        "Student # : 20184060"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CwXihsOJgZ0R"
      },
      "source": [
        "# **1. Plot the loss curve [10pt]**\n",
        "\n",
        "- plot the training loss at every iteration of gradient descent using the training data in blue color (the first 6,000 images) [5pt]\n",
        "- plot the testing loss at every iteration of gradient descent using the testing data in red color (the rest 4,000 images) [5pt]\n",
        "- the both curves should be presented in one figure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_ZPJSBnroog",
        "colab_type": "code",
        "outputId": "896d33d0-5d2f-4c7e-9fb9-9faf5709b0c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "#그 코드를 바탕으로 training loss plotting 하기.\n",
        "real_iter = iteration\n",
        "\n",
        "fig,ax = plt.subplots(figsize=(12,8))\n",
        "\n",
        "cost_tmp = np.zeros(real_iter)\n",
        "\n",
        "for i in range(0, real_iter):\n",
        "  cost_tmp[i] = cost_history[i]\n",
        "\n",
        "\n",
        "# print(cost_history[1])\n",
        "\n",
        "ax.set_ylabel('J(Theta)')\n",
        "ax.set_xlabel('Iterations')\n",
        "_=ax.plot(range(real_iter),cost_tmp,'b.', color = 'blue')\n",
        "_=ax.plot(range(real_iter),cost_history2,'b.', color = 'red')"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OiKcfzyYgaCF"
      },
      "source": [
        "# **2. Plot the accuracy curve [10pt]**\n",
        "\n",
        "- plot the training accuracy (%) at every iteration of gradient descent using the training data in blue color (the first 6,000 images) [5pt]\n",
        "- plot the testing accuracy (%) at every iteration of gradient descent using the testing data in red color (the rest 4,000 images) [5pt]\n",
        "- the both curves should be presented in one figure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L688vFb8rqtO",
        "colab_type": "code",
        "outputId": "88c78a46-a537-4928-aea4-c4562d92d122",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "#그 코드를 바탕으로 training loss plotting 하기.\n",
        "fig,ax = plt.subplots(figsize=(12,8))\n",
        "\n",
        "# print(cost_history[1])\n",
        "\n",
        "ax.set_ylabel('accuracy')\n",
        "ax.set_xlabel('Iterations')\n",
        "_=ax.plot(range(iteration),accuracy_history,'b.', color = 'blue')\n",
        "_=ax.plot(range(iteration),accuracy_history2,'b.', color = 'red')\n",
        "# print(ac_tmp)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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py5bCmuag0gwpN/7ww9KsWdQ0AwAANDqS5jJ+8APpJz+RLlyQnnrKj4UlysWuvtpfCPjqq9I110gPPBBfnAAAAIgX5RkhNm2S7rtPOnvWJ827d/vbyE1k21de8dtv3+7PUAMAAKAxkTSH2LGjdCyq01+5bcMuDgQAAED9I2kOsXZt6VhUp79y24ZdHAgAAID6R01ziC1b/PQzn/HlGbfe6jv9TWTbz35WunjR34mj0lpoAAAA1B/ONJexZYv01rf6pPmRR3xNcyqVm0Zte9VV0rlz0uOP1yZeAAAAxIMzzWX09Ul79/rnzvmLAXfv9vPZaVgr7Z4e6dgx//zYMT9/9GiMwQIAACA2nGku48CB8suDugZmPfts+XkAAAA0DpLmMpYvL788qGtg1qJF5ecBAADQOEiay9izR1qxwj83kwYHpa1bc9Ow0gzJl2IsXuy3W7yY0gwAAIBGRtIcYc8eaf166corpXnzJrbte94jXXutnwIAAKBxcSFghOFh39FP8tPs86gLAbNdAaXcNHsrOgAAADQWzjRHKNfJr9yFgMVdAYO6BAIAAKAxkDRHKNfJr9yFgMVdAYO6BAIAAKAxUJ4RIdvJb+dOn0CvWuXPMK9bV/5CwGwpxo4dPmGmNAMAAKBxmXMu6Rgi9fb2un379iUdBgAAAJqYme13zvUGLaM8owLDw1J3t7RsmTR3rr+TxqZNSUcFAACAWqE8I0L+3TNOncqNc0cMAACA6YMzzRHK3T2DO2IAAABMDyTNEcrdPYM7YgAAAEwPlGdEyL97xhvfKP34x7419sgIpRkAAADTBUlzhV580dc0t7ZK11zj22MDAABgeoi9PMPMWs3s22b2tcz8bWZ2wMyeMrPHzewX445hKrIXAl665OcvXpSOHpU2bJBSqURDAwAAQI3Uoqb5HkmH8+YfkLTeOXejpP8u6f+sQQyTNtk22gAAAGgesSbNZrZQ0u2SHswbdpI6M8+vkPTjOGOYqsm20QYAAEDziLum+X5JGyV15I19QNL/MrPXJJ2WdFPQhmY2ImlEkhYtWhRzmOGyFwJ+8Yu+RCNb03zvveXbaAMAAKB5xHam2czukPS8c25/0aI/kPRO59xCSZ+X9Omg7Z1zKedcr3Oud968eXGFWZFt26Rf/3X//OJF6cSJRMMBAABAjcV5pvkWSe8ys3dKmiWp08z+RtIvOef2ZNb5sqSvxxhDVQwNSbt35+bPnvUXAkqcbQYAAJgOYjvT7Jy71zm30DnXI+kuSd+Q9G5JV5jZ9ZnVfkOFFwnWpfHx4HEuBAQAAJgeatoR0Dl3QdK/lTRqZt+R9NuSPlrLGCZj5crgcS4EBAAAmB5qkjQ758acc3dknj/snLvBOfc259yAc+6HtYhhKnbtkgYHc/OzZklbt1KaAQAAMF3U9ExzI7vxRumKK6TLL5dWrJBuuCHpiAAAAFArJM0V2LRJuu8+6aWXpJdflh57TFq1Skqnk44MAAAAtUDSXIEdO0rHLlyQxsZqHgoAAAASQNJcgbVrS8fa2qSBgZqHAgAAgASQNFdgyxZp48ZcTfOqVb5Eo78/6cgAAABQC3G30W4a114r9fX528xx1wwAAIDphaS5AqlUrgNgtjMgiTMAAMD0QXlGBYo7/9EJEAAAYHohaa5Acec/OgECAABML5RnVCBbijE6Sk0zAADAdETSXKGREZJlAACA6YryDAAAACACSTMAAAAQgaQZAAAAiEDSDAAAAEQgaQYAAAAikDQDAAAAEUiaAQAAgAgkzQAAAEAEkmYAAAAgAkkzAAAAEIGkGQAAAIhA0gwAAABEIGkGAAAAIpA0AwAAABFImgEAAIAIJM0AAABABJJmAAAAIAJJMwAAABCBpBkAAACIQNIMAAAARCBpBgAAACKQNAMAAAARSJoBAACACCTNAAAAQASSZgAAACACSTMAAAAQgaQZAAAAiEDSDAAAAEQgaQYAAAAikDQDAAAAEUiaAQAAgAgkzQAAAEAEkmYAAAAgAkkzAAAAEIGkGQAAAIhA0gwAAABEIGkGAAAAIpA0AwAAABFImgEAAIAIJM0AAABABJJmAAAAIAJJMwAAABCBpBkAAACIQNIMAAAARCBpBgAAACKQNAMAAAARSJoBAACACCTNAAAAQASSZgAAACACSTMAAAAQgaQZAAAAiNAW9wHMrFXSPknPOefuMLNxSR2ZxW+QtNc5tybuOAAAAIDJij1plnSPpMOSOiXJObcyu8DMRiX9VQ1iAAAAACYt1vIMM1so6XZJDwYs65R0q6SvxBkDAAAAMFVx1zTfL2mjpEsBy9ZIetQ5dzrmGAAAAIApiS1pNrM7JD3vnNsfssq/lvTFMtuPmNk+M9v3wgsvxBIjAAAAUImKkmYz22Fmt5vZRJLsWyS9y8yOSvqSpFvNbFtmf1dJWiHpb8I2ds6lnHO9zrneefPmTeCwAAAAQHVVmgT/Z0n/RtL3zexTZrYkagPn3L3OuYXOuR5Jd0n6hnNuOLP4tyR9zTl3djJBAwAAALVUUdLsnPtb59x6ScslHZX0t2b2pJn9jpnNmMRx71KZ0oyGkEpJfX3SnXdK6XTS0QAAACBGFd9yzsy6JQ1L+m1J35a0XdKvSnqfpIFy2zrnxiSN5c2XXb/upVLShg25+b/5G+lb35L6+5OLCQAAALGptKb5YUnjkuZI+k3n3Lucc192zn1Y0uVxBliXRkcL519/XRobSyQUAAAAxK/SmubPOOeWOec2O+eO5y9wzvXGEFd9W7eucH7GDGlgIJFQAAAAEL9Kk+ZlZjY3O2NmV5rZ78UUU/0bGZG2bpVWrJDWrKE0AwAAoMmZcy56JbOnnHM3Fo192zn39tgiy9Pb2+v27dtXi0MBAABgmjKz/WFVFJWeaW41M8vbYaukmdUIDgAAAKh3ld494+uSvmxmWzPzGzJjAAAAQNOrNGneJJ8ofzAz/4ikB2OJCAAAAKgzFSXNzrlLkh7IPAAAAIBppaKk2cyuk7RZ0jJJs7Ljzrk3xxQXAAAAUDcqvRDw8/JnmS9I+jVJX5C0La6gAAAAgHpSadI82zn3qPwt6o455z4h6fb4wmoACxZIZv4xNJR0NAAAAIhRpUnzOTNrkfR9M/t9M7tT07F9dtaCBdKJE7n53btJnAEAAJpYpUnzPZLmSPqIpH8haVjS++IKqu7lJ8xZ4+O1jwMAAAA1EXkhYKaRyXucc/9e0suSfif2qOrd/PmlifPKlcnEAgAAgNhFnml2zl2U9Ks1iKVxHD/uE+eswUFp167k4gEAAECsKm1u8m0z+6qk/ynpleygc25HLFE1guPHk44AAAAANVJp0jxL0klJt+aNOUnTN2kGAADAtFFpR0DqmAEAADBtVdoR8PPyZ5YLOOd+t+oRAQAAAHWm0vKMr+U9nyXpTkk/rn44AAAAQP2ptDxjNH/ezL4o6fFYIgIAAADqTKXNTYpdJ+kN1QwEAAAAqFcVJc1mdsbMTmcfkv5a0qZ4Q6tj6bR0zTWSWe7R05N0VAAAAIhJpeUZHXEH0jDSaenmm0vHjx3zifPRo7WOCAAAADGr9EzznWZ2Rd78XDNbE19YdWxsLHzZs8/WLAwAAADUTqU1zR93zr2UnXHOvSjp4/GEVOcGBsKXLVpUszAAAABQO5UmzUHrVXq7uubS3y89+aS0cGHh+OLFlGYAAAA0qUoT331m9mlJn83Mf0jS/nhCagD9/dI//3PSUQAAAKBGKj3T/GFJ5yV9WdKXJJ2VT5wBAACAplfp3TNekfSxmGMBAAAA6lKld894xMzm5s1faWa74gsLAAAAqB+VlmdclbljhiTJOfcz0REQAAAA00SlSfMlM/v5/dTMrEeSiyMgAAAAoN5UeveMP5b0uJl9S5JJWilpJLaoAAAAgDpS0Zlm59zXJfVKOiLpi5L+D0mvxRhXfRselrq7/TT/OQAAAJpSRWeazewDku6RtFDSU5JukpSWdGt8odWp4WFp+3b/PDvNf75tW+1jAgAAQKwqrWm+R9KvSDrmnPs1SW+X9GL5TZrUzp2TWwYAAICGVWnSfNY5d1aSzKzdOfePkpbEF1YdW716cssAAADQsCq9EPBHmfs0f0XSI2b2M0nH4gurjmXLL3buzCXJ2eeUZgAAADSlSjsC3pl5+gkz+6akKyR9Pbao6h3JMQAAwLRS6Znmn3POfSuOQAAAAIB6VWlNMwAAADBtkTQDAAAAEUiaAQAAgAgkzQAAAEAEkmYAAAAgAknzZAwPS93dfjo0JJnlHt3dUiqVdIQAAKAZpdOFeUc9PVpafHxNasK3nJv2hoel7dv98+w036lT0oYN/vnISO3iAgAAzS2dlm6+Oekowjnn43vySam/P+loqo4zzRO1c2dl642OxhsHAACYXsbGko6gMo0S5wSRNE9UtnV2lHXr4o0DAABMLwMDSUdQmUaJc4JImidq2zZp/Xqpq8tPBwcLl3d1SVu3UpoBAACqq7/flz7UK7OmLc2QqGmenG3bko4AAABMR/39vnYYNceZZgAAACACSTMAAAAQgaQZAAAAiEDSDAAAAEQgaQYAAAAikDQDAAAAEUiaJyOVkoaG/FTybS3vvFPq6ZHe8pbcOABgaoaH/b1f6/nR3V2d17psWW6fnZ3V2WejaGmZ/Ps/NFTbWNvakv+ZM5NaW2v7uqO0tlb/NS5YkPSrKsB9micqlZI2bPDPd++WfvAD6dOfli5cyK2TXU6DEwCYvOFhafv2pKOIduqUT5xPnpz8PpYtkw4fzs2fOeMT59Onpx5fvWtpmdp9h3fv9onzrl3ViylMW5t08WL8x6nEpUs+Ua2HeFpbfTzVduKET5yPH6/+vieBM80TNTpaOL9jR2HCHLYeAGBidu5MOoLKnTo1te2PHCkdO3NmavtsFNVo1DE+PvV9VKIeEtR8cSSqkxFnHCdOxLfvCSJpnqh16wrn1671//OMWg8AMDGrVycdQeW6uqa2/ZIlpWMdHVPbZ6Mwm/o+Vq6c+j4qUW8lES11ksbFGcf8+fHte4Jif7fNrNXMvm1mX8vMm5n9mZl9z8wOm9lH4o6hqkZGpK1bpcFBP92yRXrsMWnNGmnxYv8ntq1bKc0AgKnatk1avz7pKKJ1dU2tNEOSDh2Sli7NzXd0TI/SDMmfpZxK4jw4WJvSDMn/ZbleEueWlvo5833xYjyJ8/z5dVOaIUnmYu5fbmZ/KKlXUqdz7g4z+x1JvybpbufcJTN7g3Pu+XL76O3tdfv27Ys1TgAAAExvZrbfOdcbtCzWM81mtlDS7ZIezBv+oKT/yzl3SZKiEmYAAAAgaXGXZ9wvaaOk/ArxayW9x8z2mdlOM7su5hgAAACAKYktaTazOyQ975zbX7SoXdLZzKnv/yLpL0O2H8kk1vteeOGFuMIEAAAAIsV5pvkWSe8ys6OSviTpVjPbJulHknZk1nlY0luDNnbOpZxzvc653nnz5sUYJgAAAFBebEmzc+5e59xC51yPpLskfcM5NyzpK/IXAkrSOyR9L64YAAAAgGpI4gZ/n5K0zswOStos6QMJxDA5PT3+tjgzZhS2y16woLDtY19f+f1s2uT30dLib1EHAPUqlUq+XbCZNGtW0u9Eqb6+5N+XyTyK205PtVV5UK+CybyHE72VW2dnsu9jOj35142GFPst56qhLm4519MjHTtWOt7REdy1acUKac+e0vFNm6T77iscW7rU36MTAOpJKiVt2JB0FDnt7dLZs0lH4fX1SXv3Jh3F5GXvbVytVuWtrcHdccsJeg8rvfdwZ2d9dEx88kmpvz/pKFBFid1yrqk8+2zweNiX9sCB4PEdO0rHgtqnAkDSRkeTjqDQuXNJR5AT9m98o8i2na5Wq/LJNNkIeg8rbcdcDwmzJI2NJR0BaoikuVKLFgWPh7U5Xb48eHzt2tKxoPapAJC0deuSjqBQe3vSEeSE/RvfKLJtp6vVqnwyXfKC3sNKu8rVS4vxgYGkI0ANkTRX6uhR3yZb8vVb2XbZp0+X9kUPK82QfNvtjRv9PswozQBQv0ZG/L9z9aCeSjMk/2/8ihVJRzE5+W2nq9GqfDKlGVLpeziRttCnTyefOFOaMe1Q0wwAAACImmYAAABgSkiaAQAAgAgkzQAAAEAEkpEODIEAABnXSURBVGYAAAAgAkkzAAAAEIGkGQAAAIhA0lxOKiUNDfmp5Ft+trZKM2dKb3yjb4mdb2hImjMnt03+dMYMqbvbzy9bVti/PjuefywAjSN73/VmfsycmfS7jKnq7Cz/Gc+aNbH9pVLV+/nq7IznNQNVxH2aw6RS0oYNufnFi6Vjx0rX27jRNywZGpJ2767Osbdu9U0FANS/trbJtRBuRDNmSOfPJx0FJqOzs7LW05U2kSn+HVkNHR2+aQmQIO7TPBmjo4Xzzz4bvN6OHX46Ph7fsQHUr+mSMEvS668nHQEmq5KEWZLOnatsvTh+T1UaI5AQkuYw69YVzi9aFLze2rV+unJlfMcGUL9aW5OOoHZmzEg6AkxWpS2n29srWy+O31NJt8UGIpA0hxkZ8WUSg4N+evSotGKF1NLif3G84Q250gxJ2rXLrzt7dm6b/Glbm9TV5eeXLi08VnY8uz6lGUDjuHBheiTOlGY0ttOno5PSSkszpNzvyGqhNAMNgJpmAAAAQNQ0AwAAAFNC0gwAAABEIGkGAAAAIpA0AwAAABFImgEAAIAIJM0AAABABJLmclIp3x47lZLSaenOO6VZs/y9mpctK10/nZauv14yC360tEjd3X7a2iotWFC6TtB+ASQjnQ7/Pgc9UqmkIwaSMXMmv8fQ9LhPc5hUStqwITff0iJdulS4ztKl0qFD/nk6Ld1yi1SN9zN/vwCSkU5LN9888e1oUITpZubM0hbr/B5Dg+I+zZMxOlo4X5wwS9KRI7nnY2PVSZiL9wsgGWNjk9uu+N8OoNkVJ8wSv8fQlEiaw6xbVzjfEvBWLVmSez4w4P8sVQ35+wWQjIGByW1X/G8H0OxmzCgd4/cYmhBJc5iREf9n1sFBP338cWnNGqm93SfHxX966u+XnnhCuu668H2aSV1dufrm+fNL1+FPWkB96O+XnnxyYttQmoHp6Pz5wsSZ32NoUtQ0AwAAAKKmGQAAAJgSkmYAAAAgAkkzAAAAEIGkGQAAAIhA0gwAAABEIGkGAAAAIpA0lzM8LF12mdTdLc2Z4++v3NYmzZolDQ0Fb9PX59eLenR0SJs2ST090hvf6Le77jo/Vo9SKR/jnXf69sJobK2tpT+T3d1JR5WMBQuiv69tbUlHCQBIGPdpDjM8LG3fXn6dwUFp167cfF+ftHfv1I+9caO0ZcvU91MtqZS0YUNufsYM6Vvf8s0f0HhaW4Pbwku++c7Jk7WNJ0kLFkgnTlS2bmurdOFCvPEAABLFfZonY+fO6HXGxwvnDxyozrF37KjOfqpldLRw/vXXpbGxREJBFYQlzJJ06lTt4qgHlSbMknTxYnxxAADqHklzmNWro9dZubJwfvny6hx77drq7Kda1q0rnJ8xQxoYSCQUVEFLma99V1ft4qgHQa3sw7S2xhcHAKDuUagXZts2P334YV/D/Npr/tHa6usb3/GOwtIMSdqzp/ISjcsvl37v96Qvf9nvt6fHn+Vbu7a+SjMkaWTETz/3OekXfsGXj1Ca0bguXgwu0ZhupRmSdPx4ZSUalGYAwLRHTTMAAAAgapoBAACAKSFpBgAAACKQNAMAAAARSJoBAACACCTNAAAAQASSZgAAACACSXM5mzZJc+f6ZhBm/rFsWflthoakOXP8VJLSaWnzZj8dGvL3eJ4zx+873/Bw7hhm0pVXlq5TC6lUYRxhj87OytYrfqTT5Y+f3W9nZ21eb70YGprc+zmVR/7PNY/o7zYAYFrjPs1hNm2S7rsveNnSpdKhQ6XjQ0PS7t25+RUrpIMHpfPnfSOJ4vd640bfyGR4WNq+PfhY2XVqIZWSNmyI/zhPPhncHKWzUzpzJjff0SGdPh1/PEkr/rlBcsK+2wCAaYH7NE/Gjh3hy44cCR4fHy+cP3DAJ8wXL5YmzPnH2LlzcnFU2+hobY4zNhY8np8wB803q+KfGyQn7LsNAJj2SJrDrF0bvmzJkuDxlSsL55cvl2bO9C14zcKPsXr15OKotnXranOcgYHg8Y6O8vPNqvjnBskJ+24DAKY9kuYwW7b40ogrrihMeMv9+XbXLmlwUJo920/37JEefVT60z+VnnjCj7W2+uX5ZRfbtknr1xfua+7c2pZmSNLIiLR1a2XrTjahDSvNkHwpRna/06U0Q8r93NRa0H/kpjNKMwAAZVDTDAAAAIiaZgAAAGBKSJoBAACACCTNAAAAQASSZgAAACACSTMAAAAQgaQZAAAAiEDSXE46LV1/vb+fbUuL1NcX7/GWLfPHyn8UHzOdlu68048PDUnXXedbflfL0JA0Z46fFtu0SWpvL4yvra2y/ba1lb62Sh4LFlTvtU3VZF+DmW8RLvlpufUuuyzZ1wgAAAJxn+Yw6bR0yy2l7a9XrPBNS6pt2TLp8OHgZdljptPSqlXShQul61SjEcrQkLR7d25+cNA33pB8wnzffcHbtbYGx5TV1uZbiU/W/PnS8eOT374apvoaJmLOHOmVV2pzLAAA8HPcp3kyxsZKE2ZJOnAgnuMdORK+LHvMsbHw5HTHjqnHMD4ePl9u/1HJ5FSTzRMnprZ9NdQqYZakV1+t3bEAAEBFSJrDDAwEtxlevjye4y1ZEr4se8yBgfByiLVrpx7DypXh8+X239pafr9Ry6PMnz+17athqq9hIubMqd2xAABARUiaw/T3S0884WuGJZ9Ax1WaIUmHDklLl5aO5x+zv1967DFpzRo/Pjgo/eIvVqc0Q/KlGIOD0uzZhaUZkt//xo3SzJmF20SVZkh++WSTznoozZCm9hokqaPD/+Wio6P8epRmAABQl2KvaTazVkn7JD3nnLvDzP6rpHdIeimzyt3OuafK7SORmmYAAABMK+Vqmiu89cGU3CPpsKTOvLGPOuceqsGxAQAAgCmLtTzDzBZKul3Sg3EeBwAAAIhT3DXN90vaKOlS0fifmdnTZvYfzaw9aEMzGzGzfWa274UXXog5TAAAACBcbEmzmd0h6Xnn3P6iRfdK+iVJvyKpS1JgZw7nXMo51+uc6503b15cYQIAAACR4jzTfIukd5nZUUlfknSrmW1zzh133jlJn5e0IsYYAAAAgCmLLWl2zt3rnFvonOuRdJekbzjnhs1sgSSZmUlaI+m7ccUAAAAAVEMS92nebmYHJR2UdJWk/zuBGCozPCzNmiXNmCFdeaVvJV3LY3d3+6kk9fX5e0VHPbLrT1ZPj9TS4qdJWrasstdb/IiKe2jIf57d3VIqVXk8l13m9z9zpm9nXqmwz21oqPJ9AACAxMV+n+ZqSOQ+zcPD0vbtpePVaiQykWN3dUmnTlW+/fr10rZtEz9uT4907FhufvFi6ejRie9nqpYtkw4fnvz2YXEPDUm7dxeObd0qjYyU399ll5W2tn7ySd9sppy+Pmnv3vDlxQ1kAABAosrdp5mOgGF27gwe37Gj9seeSMIctH2lnn22/HytHDkyte3D4h4fLx0bHY3eX3HCLEljY9HbHThQfnlQPAAAoC6RNIdZvTp4fO3a2h+7q2tq21dq0aLy87WyZMnUtg+Le+XK0rF166L3N2dO6djAQPR2y5eXXx4UDwAAqEskzWG2bfNlDu3tUlubNHdubUoz8o/d1eWnJ09KKyq8ychkSzMkX9KweLGvuU2qNEOSDh2Sli6d3Lbl4t61y5dEtLX597aS0gxJeuWVXOI8Y0ZlpRmStGdP+OdGaQYAAA2FmmYAAABA1DQDAAAAU0LSDAAAAEQgaQYAAAAikDQDAAAAEUiaAQAAgAgkzQAAAEAEkuZy0mnpzjt9O+RUqrbH7uvz90vOPi67TNq82ccUJJXybaJTKb9udrvOzvLH6e4uPE72UevXO1U9PaWvoSXz4138Xk7lMTSU6MsEAADJ4D7NYdJpadUq6cKF3FilzTCmqq9P2rs3eNns2dKjjxY210ilpA0bwvfX0SGdPl063t1dvkV3rV7vVPX0SMeO1e54NCYBAKApcZ/myRgbK0yYJWl0tDbHPnAgfNn58z62fFFxnTkTPF4uYa5kv/Xi2Wdre7zx8doeDwAAJI6kOczAgG+3nG/dutoce/ny8GUzZ/rY8kXF1dERPN7VVX67Wr3eqVq0qLbHW7mytscDAACJI2kO098vPfaYtGaNtGJFbUsV9uzxx8w3Z470yU+WlmZIPq6tW33ZwNatft2ssNIMSTp5MjxxbpTSDEk6elRavLh03ExyrvS9nApKMwAAmJaoaQYAAABETTMAAAAwJSTNAAAAQASSZgAAACACSTMAAAAQgaQZAAAAiEDSDAAAAEQgaQ6TTkubN/sW1Zs3+/kk9PX5Jitz5khXXCENDwev193t70uc/7jxxvC4h4YK1+3ri+81JKG1NffaLrvMvz9h7x0AAECEtuhVpqF0WrrtNuncOenSJamlRWpvD24sEqe+PmnvXv/8tdf8Y/t2P79tW2697u7gltjf+Y7vXjc+Xhj30JC0e3fhunv3+uPt2VPd15CE1lb/uWW9+qp/BL13AAAAFeBMc5CxMen8+VzidemSnx8bq20cBw4Ej+/cWTgflDBnXbxYGvf4+MSO12jyE+Zixe8dAABABUiagwwMSDNn+jPMkp/OnOnHa2n58uDx1asL58NaYUv+rGtx3CtXTux4jaalzI918XsHAABQAdpoh0mn/Rna7m7p5EmfeNayNCOrr0/av98n7TNmSL/5m8HlBUElGm97m/TAA8FxF5dorFjRHKUZWfklGnPmSLNm+YSZ0gwAABCiXBttkmYAAABA5ZNmyjMAAACACCTNAAAAQASSZgAAACACSTMAAAAQgaQZAAAAiEDSHCaV8rdlS6WSjsS30a6k3fXQkL+92tBQaZvsZctK22ybSZ2dtXsdtbZgQXO3CgcAADVDG+0gqZS0YYN/nr2X8chIMrG0tfmufllh7a7z77tc3CJbkg4fDt7/mTM+cT59ujrx1osFC6QTJwrHmqlVOAAAqCnONAcZHS0/X0v5CXNWULvrsNbYlThzZvLb1qvihDmrWVqFAwCAmiJpDrJuXfn5WmptLR0Lancd1hq7Eh0dk9+2Xs2fHzzeLK3CAQBATZE0BxkZkbZulQYH/TSp0gxJunChMHEOa3e9a5ePd/ZsPx0cLFy+dGnw/js6mq80Q5KOHy9NnJutVTgAAKgZ2mgDAAAAoo02AAAAMCUkzQAAAEAEkmYAAAAgAkkzAAAAEIGkGQAAAIhA0gwAAABEIGluJt3dkpmfZs2a5ceyj5kzk4sPAACgQZE0N4vubunUKf/81Ck/P2uWdO5c4Xqvv07iDAAAMEFtSQeAKskmzGHz+V5/Pd5YAAAAmgxnmptFV1fpfHt78LozZsQfDwAAQBMhaW4WJ0/mEueuLj9/9mxp4jxjhnT+fO3jAwAAaGCUZzSTkydLx86erX0cAAAATYYzzQAAAEAEkmYAAAAgAkkzAAAAEIGkGQAAAIhA0gwAAABEIGkGAAAAIpA0AwAAABFImgEAAIAIJM0AAABABJJmAAAAIAJJMwAAABAh9qTZzFrN7Ntm9rWi8c+Y2ctxHx8AAACYqlqcab5H0uH8ATPrlXRlDY4NAAAATFmsSbOZLZR0u6QH88ZaJf0HSRvjPDYAAABQLXGfab5fPjm+lDf2+5K+6pw7HvOxAQAAgKqILWk2szskPe+c25839guS/jdJ/08F24+Y2T4z2/fCCy/EFSYAAAAQqS3Gfd8i6V1m9k5JsyR1SvoHSeckPWNmkjTHzJ5xzv1i8cbOuZSklCT19va6GOMEAAAAyortTLNz7l7n3ELnXI+kuyR9wzl3pXNuvnOuJzP+alDCDAAAANQT7tMcJp2WNm/2UwAAAExrcZZn/JxzbkzSWMD45bU4/oSl09Jtt0nnz0szZ0qPPir19ycdFQAAABLCmeYgY2M+Yb540U/HxpKOCAAAAAkiaQ4yMODPMLe2+unAQNIRAQAAIEE1Kc9oOP39viRjbMwnzJRmAAAATGskzWH6+0mWAQAAIInyDAAAACASSTMAAAAQgaQZAAAAiEDSDAAAAEQgaQYAAAAikDQDAAAAEUiaAQAAgAgkzQAAAEAEkmYAAAAgAkkzAAAAEIGkGQAAAIhA0gwAAABEIGkGAAAAIpA0AwAAABFImgEAAIAIJM0AAABABHPOJR1DJDN7QdKxBA59laSfJnBc1Baf8/TA5zw98Dk3Pz7j6SGpz3mxc25e0IKGSJqTYmb7nHO9SceBePE5Tw98ztMDn3Pz4zOeHurxc6Y8AwAAAIhA0gwAAABEIGkuL5V0AKgJPufpgc95euBzbn58xtND3X3O1DQDAAAAETjTDAAAAEQgaQ5hZv/SzI6Y2TNm9rGk40HlzOwaM/ummR0ys38ws3sy411m9oiZfT8zvTIzbmb2mcxn/bSZLc/b1/sy63/fzN6X1GtCODNrNbNvm9nXMvNvMrM9mc/zy2Y2MzPenpl/JrO8J28f92bGj5jZUDKvBGHMbK6ZPWRm/2hmh82sn+9zczGzP8j8e/1dM/uimc3iu9z4zOwvzex5M/tu3ljVvrtm9i/M7GBmm8+YmcX6gpxzPIoeklol/UDSmyXNlPQdScuSjotHxZ/fAknLM887JH1P0jJJ90n6WGb8Y5K2ZJ6/U9JOSSbpJkl7MuNdkn6YmV6ZeX5l0q+PR8nn/YeS/rukr2Xm/4ekuzLP/0LSBzPPf0/SX2Se3yXpy5nnyzLf8XZJb8p891uTfl08Cj7j/ybpA5nnMyXN5fvcPA9JV0v6J0mzM/P/Q9LdfJcb/yFplaTlkr6bN1a1766kvZl1LbPt6jhfD2eag62Q9Ixz7ofOufOSviTp3QnHhAo554475w5knp+RdFj+H+V3y//yVWa6JvP83ZK+4Ly/kzTXzBZIGpL0iHPulHPuZ5IekfQva/hSEMHMFkq6XdKDmXmTdKukhzKrFH/O2c//IUm3ZdZ/t6QvOefOOef+SdIz8v8GoA6Y2RXyv3g/J0nOufPOuRfF97nZtEmabWZtkuZIOi6+yw3POfeYpFNFw1X57maWdTrn/s75DPoLefuKBUlzsKsl/XPe/I8yY2gwmT/bvV3SHklvdM4dzyw6IemNmedhnzc/B/XvfkkbJV3KzHdLetE5dyEzn/+Z/fzzzCx/KbM+n3N9e5OkFyR9PlOG86CZXSa+z03DOfecpD+X9Kx8svySpP3iu9ysqvXdvTrzvHg8NiTNaFpmdrmkUUn/u3PudP6yzP9KuXVMAzOzOyQ975zbn3QsiFWb/J93H3DOvV3SK/J/0v05vs+NLVPT+m75/yD9gqTLxF8BpoVG++6SNAd7TtI1efMLM2NoEGY2Qz5h3u6c25EZ/knmzznKTJ/PjId93vwc1LdbJL3LzI7Kl1DdKuk/yf9Jry2zTv5n9vPPM7P8Ckknxedc734k6UfOuT2Z+Yfkk2i+z83j1yX9k3PuBefc65J2yH+/+S43p2p9d5/LPC8ejw1Jc7C/l3Rd5srdmfIXGnw14ZhQoUxt2+ckHXbOfTpv0VclZa+6fZ+kv8obf2/myt2bJL2U+dPRLkmDZnZl5kzIYGYMdcA5d69zbqFzrkf+O/oN59x6Sd+U9FuZ1Yo/5+zn/1uZ9V1m/K7MFflvknSd/MUlqAPOuROS/tnMlmSGbpN0SHyfm8mzkm4yszmZf7+znzHf5eZUle9uZtlpM7sp83Pz3rx9xaOWV1E20kP+Ks7vyV99+8dJx8NjQp/dr8r/uedpSU9lHu+Ur3l7VNL3Jf2tpK7M+ibps5nP+qCk3rx9/a78xSTPSPqdpF8bj9DPfEC5u2e8Wf4X5TOS/qek9sz4rMz8M5nlb87b/o8zn/8RxXz1NY9Jfb43StqX+U5/Rf4Ker7PTfSQ9CeS/lHSdyX9f/J3wOC73OAPSV+Ur1N/Xf6vRu+v5ndXUm/mZ+YHkv5fZZr2xfWgIyAAAAAQgfIMAAAAIAJJMwAAABCBpBkAAACIQNIMAAAARCBpBgAAACKQNANAgszs5cy0x8z+TZX3/UdF809Wc/8AMJ2QNANAfeiRNKGkOa9bWpiCpNk5d/MEYwIAZJA0A0B9+JSklWb2lJn9gZm1mtl/MLO/N7OnzWyDJJnZgJmNm9lX5bumycy+Ymb7zewfzGwkM/YpSbMz+9ueGcue1bbMvr9rZgfN7D15+x4zs4fM7B/NbHum05bM7FNmdigTy5/X/N0BgIRFnaUAANTGxyT9e+fcHZKUSX5fcs79ipm1S3rCzHZn1l0u6Zedc/+Umf9d59wpM5st6e/NbNQ59zEz+33n3I0Bx1or32XvbZKuymzzWGbZ2yW9RdKPJT0h6RYzOyzpTkm/5JxzZja36q8eAOocZ5oBoD4NSnqvmT0laY9869nrMsv25iXMkvQRM/uOpL+TdE3eemF+VdIXnXMXnXM/kfQtSb+St+8fOecuybeg75H0kqSzkj5nZmslvTrlVwcADYakGQDqk0n6sHPuxszjTc657JnmV36+ktmApF+X1O+ce5ukb0uaNYXjnst7flFSm3PugqQVkh6SdIekr09h/wDQkEiaAaA+nJHUkTe/S9IHzWyGJJnZ9WZ2WcB2V0j6mXPuVTP7JUk35S17Pbt9kXFJ78nUTc+TtErS3rDAzOxySVc45/6XpD+QL+sAgGmFmmYAqA9PS7qYKbP4r5L+k3xpxIHMxXgvSFoTsN3XJf27TN3xEfkSjayUpKfN7IBzbn3e+MOS+iV9R5KTtNE5dyKTdAfpkPRXZjZL/gz4H07uJQJA4zLnXNIxAAAAAHWN8gwAAAAgAkkzAAAAEIGkGQAAAIhA0gwAAABEIGkGAAAAIpA0AwAAABFImgEAAIAIJM0AAABAhP8fjkC6PS+ZIpUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "duWiLsZPR8Zc"
      },
      "source": [
        "# **3. Plot the quantitative results [6pt]**\n",
        "\n",
        "Training results\n",
        "\n",
        "- print the confusion matrix using the function confusion_matrix based on the training data [1pt]\n",
        "- print the classification report using the function classification_report based on the training data [1pt]\n",
        "- print the accuracy score using the function accuracy_score based on the training data [1pt]\n",
        "\n",
        "\n",
        "Testing results\n",
        "\n",
        "- print the confusion matrix using the function confusion_matrix based on the testing data [1pt]\n",
        "- print the classification report using the function classification_report based on the testing data [1pt]\n",
        "- print the accuracy score using the function accuracy_score based on the testing data [1pt]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbg3MT81r0Kk",
        "colab_type": "code",
        "outputId": "a350499c-61c0-4a77-a8ff-c7dcca5258fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "#Codes for Evaluation\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "l_list_tmp = l_list.reshape((1401))\n",
        "l_list2_tmp = l_list2.reshape((601))\n",
        "\n",
        "print(confusion_matrix(y_train,l_list_tmp))\n",
        "print(classification_report(y_train,l_list_tmp))\n",
        "print(accuracy_score(y_train, l_list_tmp))\n",
        "\n",
        "print(confusion_matrix(y_test,l_list2_tmp))\n",
        "print(classification_report(y_test,l_list2_tmp))\n",
        "print(accuracy_score(y_test, l_list2_tmp))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[381 318]\n",
            " [397 305]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.49      0.55      0.52       699\n",
            "           1       0.49      0.43      0.46       702\n",
            "\n",
            "    accuracy                           0.49      1401\n",
            "   macro avg       0.49      0.49      0.49      1401\n",
            "weighted avg       0.49      0.49      0.49      1401\n",
            "\n",
            "0.489650249821556\n",
            "[[220  82]\n",
            " [242  57]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.48      0.73      0.58       302\n",
            "           1       0.41      0.19      0.26       299\n",
            "\n",
            "    accuracy                           0.46       601\n",
            "   macro avg       0.44      0.46      0.42       601\n",
            "weighted avg       0.44      0.46      0.42       601\n",
            "\n",
            "0.46089850249584025\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xN8u62m6r0qk",
        "colab_type": "code",
        "outputId": "5fec7a9c-5658-4c9a-ae3e-02e904f912c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "class_1 = 0\n",
        "\n",
        "for i in range(0, 601):\n",
        "  if l_list2_tmp[i] == 1:\n",
        "    class_1 = class_1 + 1\n",
        "\n",
        "print(class_1)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "139\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}