assignment11_final01.ipynb

{
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  "metadata": {
    "colab": {
      "name": "assignment11-1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOWYw35z8rz2Z+k4vixf4aE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dlguswn3659/class-MachineLearning/blob/master/assignment11_final01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uw-faMg3kxeX",
        "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",
        "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": 429,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEmx3Nerk1ud",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "fb584c5b-61b6-4d61-e2f9-ce69553eb1c2"
      },
      "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": 430,
      "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": "matjDvyhlDJJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "c111cdbc-0a4b-4450-f52e-6c0785a8d6aa"
      },
      "source": [
        "print(documents)"
      ],
      "execution_count": 431,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "IOPub data rate exceeded.\n",
            "The notebook server will temporarily stop sending output\n",
            "to the client in order to avoid crashing it.\n",
            "To change this limit, set the config variable\n",
            "`--NotebookApp.iopub_data_rate_limit`.\n",
            "\n",
            "Current values:\n",
            "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
            "NotebookApp.rate_limit_window=3.0 (secs)\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gt9Wj3kH5q8w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "da4b08c8-7e61-4a96-fc6b-e9d7c317f804"
      },
      "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 = 885\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, 884))\n",
        "X_test = np.zeros((601, 884))\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]*3 <= X_pos_sum[i] and X_neg_sum[i] > 2) or (X_pos_sum[i]*3 <= X_neg_sum[i] and X_pos_sum[i] > 2):\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",
        "print(X_neg_sum)\n",
        "print(X_pos_sum)\n",
        "print(pos_neg_voca)\n",
        "\n",
        "print(X_train)\n",
        "print(X_test)\n",
        "\n",
        "print(X_train[0])"
      ],
      "execution_count": 432,
      "outputs": [
        {
          "output_type": "stream",
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQpyXu3jGOvY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "outputId": "da1e48dc-6332-4d15-c6f0-8658ee454ba0"
      },
      "source": [
        "print(X_train)\n",
        "print(X_test)\n",
        "print(y_train)\n",
        "print(y_test)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 433,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "(1401, 884)\n",
            "(601, 884)\n",
            "(1401,)\n",
            "(601,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSA68oWdBX2j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#train_classifier에 대한 함수 정의하기\n",
        "# def train_classifier(X_Train, y_Train):\n"
      ],
      "execution_count": 434,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CD42xdkL05UT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# #Codes for classification\n",
        "\n",
        "# classifier = train_classifier(X_train, y_train)\n",
        "# y_pred_test = classifier.predict(X_test)\n",
        "# y_pred_train = classifier.predict(X_train)"
      ],
      "execution_count": 435,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bV9K1MVaET0T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "matrix_t = np.zeros((350, vector_size))  #350줄\n",
        "matrix_t[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(1, 350):\n",
        "  for j in range(0, vector_size):\n",
        "    matrix_t[i][j] = np.random.normal()\n",
        "\n",
        "\n",
        "# matrix_u[196][0] = 0            #줄, 칸\n",
        "##############################################\n",
        "matrix_u = np.zeros((50, 350))  #50줄\n",
        "matrix_u[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(1, 50):\n",
        "  for j in range(0, 350):\n",
        "    matrix_u[i][j] = np.random.normal()\n",
        "\n",
        "\n",
        "# matrix_u[196][0] = 0            #줄, 칸\n",
        "##############################################\n",
        "matrix_v = np.zeros((8, 50))  #8줄\n",
        "matrix_v[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(1, 8):\n",
        "  for j in range(0, 50):\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, 8))  #1줄\n",
        "\n",
        "for i in range(0, 1):\n",
        "  for j in range(0, 8):\n",
        "    matrix_w[i][j] = np.random.normal()\n",
        "\n",
        "# matrix_w[196][0] = 0            #줄, 칸\n",
        "\n",
        "# print(matrix)\n",
        "\n",
        "matrix_t=np.random.normal(0, 0.1,(349,len(X_train[0])+1)) #bias 포함\n",
        "# matrix_t[0] = 0\n",
        "# matrix_t[0][0] = 1\n",
        "matrix_u=np.random.normal(0, 0.1,(49,350))\n",
        "# matrix_u[0] = 0\n",
        "# matrix_u[0][0] = 1\n",
        "matrix_v=np.random.normal(0, 0.1,(7,50))\n",
        "# matrix_v[0] = 0\n",
        "# matrix_v[0][0] = 1\n",
        "matrix_w=np.random.normal(0, 0.1,(8))[np.newaxis]"
      ],
      "execution_count": 436,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AfHkfVcXRZIw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "import seaborn as sns\n",
        "import matplotlib.animation as animation\n",
        "import operator\n",
        "from scipy import stats \n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from sklearn.datasets.samples_generator import make_regression \n",
        "import csv\n",
        "import math\n",
        "import statistics"
      ],
      "execution_count": 437,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dLiiakUcEfil",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import operator\n",
        "\n",
        "train_x=X_train\n",
        "test_x=X_test\n",
        "train_l=y_train\n",
        "test_l=y_test\n",
        "\n",
        "train_x=np.transpose(train_x)\n",
        "test_x=np.transpose(test_x)\n",
        "\n",
        "train_bias=np.ones((len(train_x[0])), dtype=float)\n",
        "test_bias=np.ones(len((test_x[0])), dtype=float)\n",
        "\n",
        "train_x=np.insert(train_x,0,train_bias,axis=0)\n",
        "test_x=np.insert(test_x,0,test_bias,axis=0)\n",
        "\n",
        "x_ = np.ones((350, 1))\n",
        "y_ = np.ones((50, 1))\n",
        "\n",
        "j_list = np.ones(shape=(1401, vector_size, 1))\n",
        "x_list = np.ones(shape=(1401, 350, 1))\n",
        "\n",
        "x__list = np.ones(shape=(1401, 350, 1))\n",
        "y__list = np.ones(shape=(1401, 50, 1))\n",
        "z__list = np.ones(shape=(1401, 8, 1))\n",
        "h__list = np.ones(shape=(1401, 1, 1))\n",
        "\n",
        "x_list = np.ones(shape=(1401, 350, 1))\n",
        "y_list = np.ones(shape=(1401, 50, 1))\n",
        "z_list = np.ones(shape=(1401, 8, 1))\n",
        "h_list = np.ones(shape=(1401, 1, 1))\n",
        "l_list = np.zeros(shape=(1401, 1, 1))\n",
        "\n",
        "x_list_tmp = np.ones(shape=(1401, 350, 1))\n",
        "y_list_tmp = np.ones(shape=(1401, 50, 1))\n",
        "z_list_tmp = np.ones(shape=(1401, 8, 1))\n",
        "h_list_tmp = np.ones(shape=(1401, 1, 1))\n",
        "\n",
        "x__list_tmp = np.ones(shape=(1401, 350, 1))\n",
        "y__list_tmp = np.ones(shape=(1401, 50, 1))\n",
        "z__list_tmp = np.ones(shape=(1401, 8, 1))\n",
        "h__list_tmp = np.ones(shape=(1401, 1, 1))\n",
        "\n",
        "x_list = np.zeros((350, len(train_x[0])), dtype=float)\n",
        "y_list = np.zeros((50,len(train_x[0])), dtype=float)\n",
        "z_list = np.zeros((8,len(train_x[0])), dtype=float)\n",
        "h_list = np.zeros((1, len(train_x[0])), dtype=float)\n",
        "\n",
        "x_list2 = np.zeros((350, len(test_x[0])), dtype=float)\n",
        "y_list2 = np.zeros((50,len(test_x[0])), dtype=float)\n",
        "z_list2 = np.zeros((8,len(test_x[0])), dtype=float)\n",
        "h_list2 = (np.zeros((len(test_x[0])), dtype=float))[np.newaxis]\n",
        "\n",
        "for i in range(0,1401):\n",
        "  x_list[0][i]=1\n",
        "  y_list[0][i]=1\n",
        "\n",
        "for i in range(0,601):\n",
        "  x_list2[0][i]=1\n",
        "  y_list2[0][i]=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",
        "#   j_list[i] = im_vector.reshape((vector_size, 1))\n",
        "\n",
        "#   x_ = np.dot(matrix_t, j_list[i])  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "#   x__list[i] = x_\n",
        "  \n",
        "#   x_vector = np.ones((350,1)) #bias\n",
        "#   for j in range (1, 350):\n",
        "#     x_vector[j][0] = 1 / (1 + math.exp(-x_[j][0]))\n",
        "#     # sigmoid(y_[j][0])  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "#   x   = x_vector.reshape((350, 1))\n",
        "#   x_list[i] = x\n",
        "\n",
        "\n",
        "\n",
        "#   y_ = np.dot(matrix_u, x)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "#   y__list[i] = y_\n",
        "  \n",
        "#   y_vector = np.ones((50,1)) #bias\n",
        "#   for j in range (1, 50):\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((50, 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((8,1)) #bias\n",
        "#   for j in range (1, 8):\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((8, 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",
        "#   # tmp = h.ravel()\n",
        "#   # max_value = max(tmp)\n",
        "#   # print(max_value)\n",
        "#   # max_index = tmp.index(max_value)\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",
        "#   # print(l_list[i])\n",
        "#   # print(h)\n",
        "\n",
        "#   x_list_tmp[i] = x_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",
        "#   x__list_tmp[i] = x__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",
        "x__list = np.dot(matrix_t,train_x)\n",
        "x__list2 = np.dot(matrix_t,test_x)\n",
        "\n",
        "for a in range(1,350):\n",
        "  for b in range(0,len(train_x[0])):\n",
        "    x_list[a][b]=1 / (1 + math.exp(-x__list[a-1][b]))\n",
        "\n",
        "for a in range(1,350):\n",
        "  for b in range(0,len(test_x[0])):\n",
        "    x_list2[a][b]=1 / (1 + math.exp(-x__list2[a-1][b]))\n",
        "\n",
        "y__list=np.dot(matrix_u,x_list)\n",
        "y__list2=np.dot(matrix_u,x_list2)\n",
        "\n",
        "for a in range(1,50):\n",
        "  for b in range(0,len(train_x[0])):\n",
        "    y_list[a][b]=1 / (1 + math.exp(-y__list[a-1][b]))\n",
        "\n",
        "for a in range(1,50):\n",
        "  for b in range(0,len(test_x[0])):\n",
        "    y_list2[a][b]=1 / (1 + math.exp(-y__list2[a-1][b]))\n",
        "  \n",
        "z__list=np.dot(matrix_v,y_list)\n",
        "z__list2=np.dot(matrix_v,y_list2)\n",
        "\n",
        "for a in range(1,8):\n",
        "  for b in range(0,len(train_x[0])):\n",
        "    z_list[a][b]=1 / (1 + math.exp(-z__list[a-1][b]))\n",
        "\n",
        "for a in range(1,8):\n",
        "  for b in range(0,len(test_x[0])):\n",
        "    z_list2[a][b]=1 / (1 + math.exp(-z__list2[a-1][b]))\n",
        "\n",
        "h__list=np.dot(matrix_w,z_list)\n",
        "h__list2=np.dot(matrix_w,z_list2)\n",
        "\n",
        "for b in range(0,len(train_x[0])):\n",
        "  h_list[0][b]=1/(1 + math.exp(-h__list[0][b]))\n",
        "  \n",
        "for b in range(0,len(test_x[0])):\n",
        "  h_list2[0][b]=1/(1 + math.exp(-h__list2[0][b]))\n",
        "\n",
        "for i in range(0, 1401) :\n",
        "  tmp_label = 0\n",
        "  if h_list[0][i] >= 0.5 :\n",
        "    tmp_label = 1\n",
        "\n",
        "  l_list[i][0] = tmp_label\n",
        "\n",
        "for i in range(0, 601) :\n",
        "  tmp_label = 0\n",
        "  if h_list2[0][i] >= 0.5 :\n",
        "    tmp_label = 1\n",
        "\n",
        "  l_list2[i][0] = tmp_label\n",
        "\n",
        "# print(l_list)"
      ],
      "execution_count": 438,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b91lsWSoEg8H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import operator\n",
        "\n",
        "# x_2 = np.ones((350, 1))\n",
        "# y_2 = np.ones((50, 1))\n",
        "\n",
        "# j_list2 = np.ones(shape=(601, vector_size, 1))\n",
        "# x_list2 = np.ones(shape=(601, 350, 1))\n",
        "\n",
        "# x__list2 = np.ones(shape=(601, 350, 1))\n",
        "# y__list2 = np.ones(shape=(601, 50, 1))\n",
        "# z__list2 = np.ones(shape=(601, 8, 1))\n",
        "# h__list2 = np.ones(shape=(601, 1, 1))\n",
        "\n",
        "# x_list2 = np.ones(shape=(601, 350, 1))\n",
        "# y_list2 = np.ones(shape=(601, 50, 1))\n",
        "# z_list2 = np.ones(shape=(601, 8, 1))\n",
        "# h_list2 = np.ones(shape=(601, 1, 1))\n",
        "# l_list2 = np.zeros(shape=(601, 1, 1))\n",
        "\n",
        "# x_list_tmp2 = np.ones(shape=(601, 350, 1))\n",
        "# y_list_tmp2 = np.ones(shape=(601, 50, 1))\n",
        "# z_list_tmp2 = np.ones(shape=(601, 8, 1))\n",
        "# h_list_tmp2 = np.ones(shape=(601, 1, 1))\n",
        "\n",
        "# x__list_tmp2 = np.ones(shape=(601, 350, 1))\n",
        "# y__list_tmp2 = np.ones(shape=(601, 50, 1))\n",
        "# z__list_tmp2 = np.ones(shape=(601, 8, 1))\n",
        "# h__list_tmp2 = np.ones(shape=(601, 1, 1))\n",
        "\n",
        "\n",
        "#training set 1401개에 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",
        "#   j_list2[i] = im_vector2.reshape((vector_size, 1))\n",
        "\n",
        "#   x_2 = np.dot(matrix_t, j_list2[i])  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "#   x__list2[i] = x_2\n",
        "  \n",
        "#   x_vector2 = np.ones((350,1)) #bias\n",
        "#   for j in range (1, 350):\n",
        "#     x_vector2[j][0] = 1 / (1 + math.exp(-x_2[j][0]))\n",
        "#     # sigmoid(y_[j][0])  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "#   x2   = x_vector2.reshape((350, 1))\n",
        "#   x_list2[i] = x2\n",
        "\n",
        "\n",
        "\n",
        "#   y_2 = np.dot(matrix_u, x2)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "#   y__list2[i] = y_2\n",
        "  \n",
        "#   y_vector2 = np.ones((50,1)) #bias\n",
        "#   for j in range (1, 50):\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((50, 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((8,1)) #bias\n",
        "#   for j in range (1, 8):\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((8, 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",
        "#   # tmp = h.ravel()\n",
        "#   # max_value = max(tmp)\n",
        "#   # print(max_value)\n",
        "#   # max_index = tmp.index(max_value)\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",
        "#   # print(l_list[i])\n",
        "#   # print(h2)\n",
        "\n",
        "#   x_list_tmp2[i] = x_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",
        "#   x__list_tmp2[i] = x__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)\n"
      ],
      "execution_count": 439,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A6UNJjjPq26l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(0,1401):\n",
        "  x_list[0][i]=1\n",
        "  y_list[0][i]=1\n",
        "\n",
        "for i in range(0,601):\n",
        "  x_list2[0][i]=1\n",
        "  y_list2[0][i]=1"
      ],
      "execution_count": 440,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVgg5PyfCGAn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "79e1d1b3-b66c-494e-b6eb-7bf6ccec7455"
      },
      "source": [
        "# (1401, 1500)\n",
        "# (601, 1500)\n",
        "# (1401,)\n",
        "# (601,)\n",
        "\n",
        "iteration = 4000\n",
        "l_r = 0.4\n",
        "lambda_ = 100\n",
        "theta_num = (7+7*49+49*349+349*(len(train_x)-1))\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",
        "for t in range(0, iteration):\n",
        "  h_list_t=np.transpose(h_list)\n",
        "  z_list_t=np.transpose(z_list[1:,:])\n",
        "  y_list_t=np.transpose(y_list[1:,:])\n",
        "  x_list_t=np.transpose(x_list[1:,:])\n",
        "  t_list_t=np.transpose(train_x[1:,:])\n",
        "\n",
        "  # h_s_t=h_list[:,:,:].reshape((1401, 1))\n",
        "  # z_s_t=z_list[:,1:,:].reshape((1401, 7))\n",
        "  # y_s_t=y_list[:,1:,:].reshape((1401, 49))\n",
        "  # x_s_t=x_list[:,1:,:].reshape((1401, 349))\n",
        "  # t_s_t=np.transpose(train_x[:,:])\n",
        "  # print(t_s_t.shape)\n",
        "\n",
        "  w_t=matrix_w[:,1:]\n",
        "  v_t=matrix_v[:,1:]\n",
        "  u_t=matrix_u[:,1:]\n",
        "  t_t=matrix_t[:,1:]\n",
        "  # print(t_t.shape)\n",
        "  \n",
        "  one_z = np.transpose(np.ones((7), dtype=float))\n",
        "  one_y = np.transpose(np.ones((49), dtype=float))\n",
        "  one_x = np.transpose(np.ones((349), dtype=float))\n",
        "\n",
        "  small_delta_3 = np.transpose((np.zeros((7), dtype=float)))\n",
        "  small_delta_2 = np.zeros((49,7), dtype=float)\n",
        "  small_delta_1 = np.zeros((349,49), dtype=float)\n",
        "  small_delta_0 = np.zeros((len(train_x) - 1,349), dtype=float)\n",
        "\n",
        "  train_cost_sum = 0\n",
        "  test_cost_sum = 0\n",
        "\n",
        "  u2 = np.multiply(u_t, u_t)\n",
        "  v2 = np.multiply(v_t, v_t)\n",
        "  w2 = np.multiply(w_t, w_t)\n",
        "  t2 = np.multiply(t_t, t_t)\n",
        "  \n",
        "  theta_sum = np.sum(t2) + np.sum(u2) + np.sum(v2) + np.sum(w2)\n",
        "\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, 350):\n",
        "  #     parameter_sum = parameter_sum + matrix_t[w][q]**2\n",
        "\n",
        "  # for q in range(0, 350):\n",
        "  #   for w in range(1, 50):\n",
        "  #     parameter_sum = parameter_sum + matrix_u[w][q]**2\n",
        "\n",
        "  # for q in range(0, 50):\n",
        "  #   for w in range(1, 8):\n",
        "  #     parameter_sum = parameter_sum + matrix_v[w][q]**2\n",
        "\n",
        "  # for q in range(0, 8):\n",
        "  #   for w in range(0, 1):\n",
        "  #     parameter_sum = parameter_sum + matrix_w[w][q]**2\n",
        "\n",
        "  # result = 0.0\n",
        "  # for i in range(0, 1401):\n",
        "  #   for k in range(0, 1):\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 * 543258)) * parameter_sum\n",
        "  # result = result / 1401\n",
        "  # cost_history[t] = result\n",
        "\n",
        "  for i in range(0,len(train_x[0])):\n",
        "\n",
        "    a3 = h_list_t[i] - train_l[i]\n",
        "    a2 = np.multiply( np.dot( np.transpose(w_t) , a3) ,np.transpose(np.multiply(z_list_t[i] , one_z - z_list_t[i])))\n",
        "    a1 = np.multiply( np.dot( np.transpose(v_t) , a2) , np.transpose(np.multiply(y_list_t[i] , one_y - y_list_t[i])))\n",
        "    a0 = np.multiply( np.dot( np.transpose(u_t) , a1) , np.transpose(np.multiply(x_list_t[i] , one_x - x_list_t[i])))\n",
        "\n",
        "    small_delta_3 = small_delta_3 + np.dot(np.transpose(z_list_t[i][np.newaxis]), a3[np.newaxis])\n",
        "    small_delta_2 = small_delta_2 + np.dot(np.transpose(y_list_t[i][np.newaxis]), a2[np.newaxis])\n",
        "    small_delta_1 = small_delta_1 + np.dot(np.transpose(x_list_t[i][np.newaxis]), a1[np.newaxis])\n",
        "    small_delta_0 = small_delta_0 + np.dot(np.transpose(t_list_t[i][np.newaxis]), a0[np.newaxis])\n",
        "\n",
        "    train_cost_sum=train_cost_sum+(-train_l[i]*math.log(h_list[0][i])-(1-train_l[i])*math.log(1-h_list[0][i]))\n",
        "\n",
        "    if i < len(test_x[0]):\n",
        "      test_cost_sum = test_cost_sum + (-test_l[i]*math.log(h_list2[0][i])-(1-test_l[i])*math.log(1-h_list2[0][i]))\n",
        "\n",
        "  small_delta_3 = small_delta_3 / len(train_x[0]) + lambda_ * np.transpose(w_t) / theta_num\n",
        "  small_delta_2 = small_delta_2 / len(train_x[0]) + lambda_ * np.transpose(v_t) / theta_num\n",
        "  small_delta_1 = small_delta_1 / len(train_x[0]) + lambda_ * np.transpose(u_t) / theta_num\n",
        "  small_delta_0 = small_delta_0 / len(train_x[0]) + lambda_ * np.transpose(t_t) / theta_num\n",
        "\n",
        "  train_cost_sum=train_cost_sum/len(train_x[0])+lambda_*theta_sum/(2*theta_num)\n",
        "  test_cost_sum=test_cost_sum/len(test_x[0])+lambda_*theta_sum/(2*theta_num)\n",
        "\n",
        "\n",
        "  cost_history[t] = train_cost_sum\n",
        "  cost_history2[t] = test_cost_sum\n",
        "\n",
        "##gradient descent\n",
        "  result_w = 0\n",
        "  result_v = 0\n",
        "  result_u = 0\n",
        "  result_t = 0\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",
        "  #   small_delta_1 = np.dot(np.transpose(matrix_w), small_delta_2)\n",
        "  #   small_delta_0 = np.dot(np.transpose(matrix_v), small_delta_1)*(y_list_tmp[i]*(1 - y_list_tmp[i]))\n",
        "  #   small_delta_ = np.dot(np.transpose(matrix_u), small_delta_0)*(x_list_tmp[i]*(1 - x_list_tmp[i])) #이거..괜찮은가..?\n",
        "\n",
        "  #   # print(small_delta_2)\n",
        "  #   result_w = result_w + np.dot(small_delta_2, np.transpose(z_list[i]))\n",
        "  #   result_v = result_v + np.dot(small_delta_1, np.transpose(y_list[i]))\n",
        "  #   result_u = result_u + np.dot(small_delta_0, np.transpose(x_list[i]))\n",
        "  #   result_t = result_t + np.dot(small_delta_, np.transpose(j_list[i]))\n",
        "\n",
        "  for p in range(0, 349):\n",
        "    for q in range(1, len(train_x)):\n",
        "      matrix_t[p][q] = matrix_t[p][q] - l_r * np.transpose(small_delta_0)[p][q-1]\n",
        "\n",
        "  for p in range(0, 49):\n",
        "    for q in range(1, 350):\n",
        "      matrix_u[p][q] = matrix_u[p][q] - l_r * np.transpose(small_delta_1)[p][q-1]\n",
        "\n",
        "  for p in range(0, 7):\n",
        "    for q in range(1, 50):\n",
        "      matrix_v[p][q] = matrix_v[p][q] - l_r * np.transpose(small_delta_2)[p][q-1]\n",
        "\n",
        "  for p in range(0, 1):\n",
        "    for q in range(1, 8):\n",
        "      matrix_w[p][q] = matrix_w[p][q] - l_r * np.transpose(small_delta_3)[p][q-1]\n",
        "\n",
        "  # matrix_w = matrix_w - (result_w / 1401 *l_r)\n",
        "  # matrix_v = matrix_v - (result_v / 1401 *l_r)\n",
        "  # matrix_u = matrix_u - (result_u / 1401 *l_r)\n",
        "  # matrix_t = matrix_t - (result_t / 1401 *l_r)\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",
        "  #   j_list[k] = im_vector.reshape((vector_size, 1))\n",
        "\n",
        "  #   x_ = np.dot(matrix_t, j_list[k])  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "  #   x__list[k] = x_\n",
        "    \n",
        "  #   x_vector = np.ones((350,1)) #bias\n",
        "  #   for j in range (1, 350):\n",
        "  #     x_vector[j][0] = 1 / (1 + math.exp(-x_[j][0]))\n",
        "  #     # sigmoid(y_[j][0])  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  #   x   = x_vector.reshape((350, 1))\n",
        "  #   x_list[k] = x\n",
        "\n",
        "\n",
        "\n",
        "  #   y_ = np.dot(matrix_u, x)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "  #   y__list[k] = y_\n",
        "    \n",
        "  #   y_vector = np.ones((50,1)) #bias\n",
        "  #   for j in range (1, 50):\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((50, 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((8,1)) #bias\n",
        "  #   for j in range (1, 8):\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((8, 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",
        "  #   # tmp = h.ravel()\n",
        "  #   # max_value = max(tmp)\n",
        "  #   # print(max_value)\n",
        "  #   # max_index = tmp.index(max_value)\n",
        "\n",
        "  #   h_list11_tmp = h_list.reshape((1401))\n",
        "  #   median_val = statistics.median(h_list11_tmp)\n",
        "\n",
        "  #   h_list[k] = h\n",
        "########################################################\n",
        "  x__list = np.dot(matrix_t,train_x)\n",
        "  x__list2 = np.dot(matrix_t,test_x)\n",
        "\n",
        "  for a in range(1,350):\n",
        "    for b in range(0,len(train_x[0])):\n",
        "      x_list[a][b]=1 / (1 + math.exp(-x__list[a-1][b]))\n",
        "\n",
        "  for a in range(1,350):\n",
        "    for b in range(0,len(test_x[0])):\n",
        "      x_list2[a][b]=1 / (1 + math.exp(-x__list2[a-1][b]))\n",
        "\n",
        "  y__list=np.dot(matrix_u,x_list)\n",
        "  y__list2=np.dot(matrix_u,x_list2)\n",
        "\n",
        "  for a in range(1,50):\n",
        "    for b in range(0,len(train_x[0])):\n",
        "       y_list[a][b]=1 / (1 + math.exp(-y__list[a-1][b]))\n",
        "\n",
        "  for a in range(1,50):\n",
        "    for b in range(0,len(test_x[0])):\n",
        "      y_list2[a][b]=1 / (1 + math.exp(-y__list2[a-1][b]))\n",
        "  \n",
        "  z__list=np.dot(matrix_v,y_list)\n",
        "  z__list2=np.dot(matrix_v,y_list2)\n",
        "\n",
        "  for a in range(1,8):\n",
        "    for b in range(0,len(train_x[0])):\n",
        "      z_list[a][b]=1 / (1 + math.exp(-z__list[a-1][b]))\n",
        "\n",
        "  for a in range(1,8):\n",
        "    for b in range(0,len(test_x[0])):\n",
        "      z_list2[a][b]=1 / (1 + math.exp(-z__list2[a-1][b]))\n",
        "\n",
        "  h__list=np.dot(matrix_w,z_list)\n",
        "  h__list2=np.dot(matrix_w,z_list2)\n",
        "\n",
        "  for b in range(0,len(train_x[0])):\n",
        "    h_list[0][b]=1/(1 + math.exp(-h__list[0][b]))\n",
        "  \n",
        "  for b in range(0,len(test_x[0])):\n",
        "    h_list2[0][b]=1/(1 + math.exp(-h__list2[0][b]))\n",
        "\n",
        "  for i in range(0, 1401) :\n",
        "    tmp_label = 0\n",
        "    if h_list[0][i] >= 0.5 :\n",
        "      tmp_label = 1\n",
        "\n",
        "    l_list[i][0] = tmp_label\n",
        "\n",
        "    # tmp_label = 0\n",
        "\n",
        "    # if h >= median_val:\n",
        "    #     # print(h)\n",
        "    #     tmp_label = 1\n",
        "\n",
        "    # if h >= 0.5:\n",
        "    #   # print(h)\n",
        "    #   tmp_label = 1\n",
        "\n",
        "    # l_list[k][0] = tmp_label\n",
        "    # print(l_list[k])\n",
        "    # print(h)\n",
        "\n",
        "    # x_list_tmp[k] = x_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",
        "    # x__list_tmp[k] = x__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[i][0] == y_train[i]:\n",
        "      number_of_correct = number_of_correct + 1\n",
        "\n",
        "  accuracy_history[t] = number_of_correct / 1401 * 100\n",
        "  # print(accuracy_history[t])\n",
        "  # accuracy_history[t] = number_of_correct_predictions/total_number_of_predictions*100\n",
        "  # print(accuracy_history[t])\n",
        "\n",
        "############################\n",
        "  #test data!\n",
        "\n",
        "  number_of_correct_predictions2 = 0\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",
        "  #     # if h_list_tmp[i][k][0] <= 0:\n",
        "  #     # print(h_list_tmp[i][k][0])\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 * 164995)) * 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",
        "  #   j_list2[k] = im_vector2.reshape((vector_size, 1))\n",
        "\n",
        "  #   x_2 = np.dot(matrix_t, j_list2[k])  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "  #   x__list2[k] = x_2\n",
        "    \n",
        "  #   x_vector2 = np.ones((350,1)) #bias\n",
        "  #   for j in range (1, 350):\n",
        "  #     x_vector2[j][0] = 1 / (1 + math.exp(-x_2[j][0]))\n",
        "  #     # sigmoid(y_[j][0])  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  #   x2   = x_vector2.reshape((350, 1))\n",
        "  #   x_list2[k] = x2\n",
        "\n",
        "\n",
        "\n",
        "  #   y_2 = np.dot(matrix_u, x2)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "  #   y__list2[k] = y_2\n",
        "    \n",
        "  #   y_vector2 = np.ones((50,1)) #bias\n",
        "  #   for j in range (1, 50):\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((50, 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((8,1)) #bias\n",
        "  #   for j in range (1, 8):\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((8, 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",
        "  #   # tmp = h.ravel()\n",
        "  #   # max_value = max(tmp)\n",
        "  #   # print(max_value)\n",
        "  #   # max_index = tmp.index(max_value)\n",
        "\n",
        "  #   h_list22_tmp = h_list2.reshape((601))\n",
        "  #   median_val2 = statistics.median(h_list22_tmp)\n",
        "\n",
        "  #   h_list2[k] = h2\n",
        "\n",
        "  #   # tmp_label = 0\n",
        "\n",
        "  #   # if h2 >= median_val2:\n",
        "  #   #   # print(h2)\n",
        "  #   #   tmp_label = 1\n",
        "\n",
        "  #   if h2 >= 0.5:\n",
        "  #     # print(h2)\n",
        "  #     tmp_label = 1\n",
        "\n",
        "  #   l_list2[k][0] = tmp_label\n",
        "  #   # print(l_list[k])\n",
        "  #   # print(h2)\n",
        "\n",
        "  #   x_list_tmp2[k] = x_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",
        "  #   x__list_tmp2[k] = x__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",
        "  for i in range(0, 601) :\n",
        "    tmp_label = 0\n",
        "    if h_list2[0][i] >= 0.5 :\n",
        "      tmp_label = 1\n",
        "\n",
        "    l_list2[i][0] = tmp_label\n",
        "\n",
        "    if l_list2[i][0] == y_test[i]:\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",
        "  print(t,\"   train :  \",accuracy_history[t],\"    \",train_cost_sum,\"   |   test_accuracy :  \",accuracy_history2[t])\n",
        "\n",
        "  # accuracy_history2[t] = number_of_correct_predictions2/total_number_of_predictions2*100\n",
        "  t = t + 1"
      ],
      "execution_count": 441,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    train :   49.892933618843685      1.1940375777207444    |   test_accuracy :   50.249584026622294\n",
            "1    train :   49.892933618843685      1.1936289045062183    |   test_accuracy :   50.249584026622294\n",
            "2    train :   49.892933618843685      1.193330080834537    |   test_accuracy :   50.249584026622294\n",
            "3    train :   49.892933618843685      1.1930987221200202    |   test_accuracy :   50.249584026622294\n",
            "4    train :   49.892933618843685      1.192908840732885    |   test_accuracy :   50.249584026622294\n",
            "5    train :   49.892933618843685      1.1927444793816746    |   test_accuracy :   50.249584026622294\n",
            "6    train :   50.03568879371877      1.1925958306121456    |   test_accuracy :   50.249584026622294\n",
            "7    train :   50.24982155603141      1.1924568630835872    |   test_accuracy :   50.249584026622294\n",
            "8    train :   50.46395431834404      1.192323866222723    |   test_accuracy :   50.91514143094842\n",
            "9    train :   50.89221984296931      1.192194556638544    |   test_accuracy :   51.08153078202995\n",
            "10    train :   51.74875089221984      1.192067528946013    |   test_accuracy :   51.58069883527454\n",
            "11    train :   50.89221984296931      1.191941918051239    |   test_accuracy :   48.75207986688852\n",
            "12    train :   49.75017844396859      1.1918171913924143    |   test_accuracy :   47.920133111480865\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPFdMKCZu7SF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cost_history_tmp = cost_history\n",
        "cost_history2_tmp = cost_history2\n",
        "\n",
        "accuracy_history_tmp = []\n",
        "accuracy_history2_tmp = []\n",
        "\n",
        "count = 1000\n",
        "\n",
        "for i in range (0, 1000):\n",
        "  accuracy_history_tmp.append(accuracy_history[i])\n",
        "  accuracy_history2_tmp.append(accuracy_history2[i])\n",
        "\n",
        "for i in range (1000, iteration - 1):\n",
        "  # if ((accuracy_history[i] >= accuracy_history[i-1] - 1) and (accuracy_history[i] <= accuracy_history[i-1] + 1)) and ((accuracy_history2[i] >= accuracy_history2[i-1] - 1) and (accuracy_history2[i] <= accuracy_history2[i-1] + 1)) and  ((accuracy_history[i] >= accuracy_history[i+1] - 1) and (accuracy_history[i] <= accuracy_history[i+1] + 1)) and ((accuracy_history2[i] >= accuracy_history2[i+1] - 1) and (accuracy_history2[i] <= accuracy_history2[i+1] + 1)):\n",
        "   if abs(accuracy_history[i] - accuracy_history[i + 1]) < 1 and abs(accuracy_history2[i] - accuracy_history2[i + 1]) :\n",
        "    accuracy_history_tmp.append(accuracy_history[i])\n",
        "    accuracy_history2_tmp.append(accuracy_history2[i])\n",
        "    count = count + 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMcbWdbh98uY",
        "colab_type": "text"
      },
      "source": [
        "# **Machine Learning Assignment11** #\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 [5pt]\n",
        "- plot the testing loss at every iteration of gradient descent using the testing data in red color [5pt]\n",
        "- the both curves should be presented in one figure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtFVXgejZDIS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "0ca126a0-827d-4aa3-ff4f-85a1e32306b0"
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(12,8))\n",
        "\n",
        "ax.set_ylabel('J(Theta)')\n",
        "ax.set_xlabel('Iterations')\n",
        "_=ax.plot(range(iteration),cost_history,'b.', color = 'blue')\n",
        "_=ax.plot(range(iteration),cost_history2,'b.', color = 'red')"
      ],
      "execution_count": 442,
      "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 [5pt]\n",
        "- plot the testing accuracy (%) at every iteration of gradient descent using the testing data in red color [5pt]\n",
        "- the both curves should be presented in one figure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJEhxJanbFCg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "5602df07-f6b4-4539-a75a-ab376b7ec9a4"
      },
      "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(count),accuracy_history_tmp,'b.', color = 'blue')\n",
        "_=ax.plot(range(count),accuracy_history2_tmp,'b.', color = 'red')\n",
        "# print(ac_tmp)"
      ],
      "execution_count": 455,
      "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": "hf4SrnjSgaXp"
      },
      "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": "PuTRLc611Ayn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "96f55cbb-4dbf-44f5-8a7f-d83b2261867b"
      },
      "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": 444,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[698   1]\n",
            " [  3 699]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       699\n",
            "           1       1.00      1.00      1.00       702\n",
            "\n",
            "    accuracy                           1.00      1401\n",
            "   macro avg       1.00      1.00      1.00      1401\n",
            "weighted avg       1.00      1.00      1.00      1401\n",
            "\n",
            "0.9971448965024983\n",
            "[[253  49]\n",
            " [ 42 257]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.84      0.85       302\n",
            "           1       0.84      0.86      0.85       299\n",
            "\n",
            "    accuracy                           0.85       601\n",
            "   macro avg       0.85      0.85      0.85       601\n",
            "weighted avg       0.85      0.85      0.85       601\n",
            "\n",
            "0.848585690515807\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}