curremt_version_movie_review_layer_4.ipynb

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  "metadata": {
    "colab": {
      "name": "assignment11-1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMgOZs3sQBgfPEnHutOHFkn",
      "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/curremt_version_movie_review_layer_4.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": 268,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEmx3Nerk1ud",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "25f1680a-6d6a-4506-ca34-9f7aa2d0718e"
      },
      "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": 269,
      "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": 192
        },
        "outputId": "995f74a0-c161-4dbc-e53b-4880cb580dcd"
      },
      "source": [
        "print(documents)"
      ],
      "execution_count": 270,
      "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": "cb4ecd6e-e4a5-4464-a4fb-37f5f18e9cd6"
      },
      "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": 271,
      "outputs": [
        {
          "output_type": "stream",
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQpyXu3jGOvY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "outputId": "d5a6887d-61ed-42b1-f25a-d55873517973"
      },
      "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": 272,
      "outputs": [
        {
          "output_type": "stream",
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          "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": 273,
      "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": 274,
      "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": 275,
      "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": 276,
      "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": 277,
      "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": 278,
      "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": 279,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVgg5PyfCGAn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "22341ebc-889f-4ac1-f703-9c355628f604"
      },
      "source": [
        "# (1401, 1500)\n",
        "# (601, 1500)\n",
        "# (1401,)\n",
        "# (601,)\n",
        "\n",
        "iteration = 1000\n",
        "l_r = 0.4\n",
        "lambda_ = 100\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_s_t=np.transpose(h_list)\n",
        "  z_s_t=np.transpose(z_list[1:,:])\n",
        "  y_s_t=np.transpose(y_list[1:,:])\n",
        "  x_s_t=np.transpose(x_list[1:,:])\n",
        "  t_s_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",
        "  delta5=np.transpose((np.zeros((7), dtype=float)))\n",
        "  delta4=np.zeros((49,7), dtype=float)\n",
        "  delta3=np.zeros((349,49), dtype=float)\n",
        "  delta2=np.zeros((len(train_x)-1,349), dtype=float)\n",
        "\n",
        "  J_sum = 0\n",
        "  theta_sum = 0\n",
        "  Jt_sum=0\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",
        "    lable=train_l[i]\n",
        "    temp5=h_s_t[i]-train_l[i]\n",
        "    temp4=np.multiply( np.dot( np.transpose(w_t ) , temp5 ) ,np.transpose(np.multiply(z_s_t[i] , one_z - z_s_t[i])))\n",
        "    temp3=np.multiply( np.dot( np.transpose(v_t) , temp4) , np.transpose(np.multiply(y_s_t[i] , one_y - y_s_t[i])))\n",
        "    temp2=np.multiply( np.dot( np.transpose( u_t ) , temp3) , np.transpose(np.multiply(x_s_t[i] , one_x - x_s_t[i])))\n",
        "\n",
        "    delta5=delta5+ np.dot( np.transpose(z_s_t[i][np.newaxis]),temp5[np.newaxis])\n",
        "    delta4=delta4 + np.dot( np.transpose(y_s_t[i][np.newaxis]), temp4[np.newaxis])\n",
        "    delta3=delta3+ np.dot( np.transpose(x_s_t[i][np.newaxis]), temp3[np.newaxis] )\n",
        "    delta2=delta2+ np.dot(np.transpose(t_s_t[i][np.newaxis]), temp2[np.newaxis])\n",
        "\n",
        "    J_sum=J_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",
        "      Jt_sum=Jt_sum+(-test_l[i]*math.log(h_list2[0][i])-(1-test_l[i])*math.log(1-h_list2[0][i]))\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",
        "  theta_sum=np.sum(t2)+np.sum(u2)+np.sum(v2)+np.sum(w2)\n",
        "\n",
        "  delta5=delta5/len(train_x[0])+lambda_*np.transpose(w_t)/(7+7*49+49*349+349*(len(train_x)-1))\n",
        "  delta4=delta4/len(train_x[0])+lambda_*np.transpose(v_t)/(7+7*49+49*349+349*(len(train_x)-1))\n",
        "  delta3=delta3/len(train_x[0])+lambda_*np.transpose(u_t)/(7+7*49+49*349+349*(len(train_x)-1))\n",
        "  delta2=delta2/len(train_x[0])+lambda_*np.transpose(t_t)/(7+7*49+49*349+349*(len(train_x)-1))\n",
        "\n",
        "  J_sum=J_sum/len(train_x[0])+lambda_*theta_sum/(2*(7+7*49+49*349+349*(len(train_x)-1)))\n",
        "  Jt_sum=Jt_sum/len(test_x[0])+lambda_*theta_sum/(2*(7+7*49+49*349+349*(len(train_x)-1)))\n",
        "\n",
        "\n",
        "  cost_history[t] = J_sum\n",
        "  cost_history2[t] = Jt_sum\n",
        "\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 a in range(0, 349):\n",
        "    for b in range(1,len(train_x)):\n",
        "      if a<1 and b<8:\n",
        "        matrix_w[a][b]=matrix_w[a][b]-l_r*(np.transpose(delta5)[a][b-1])\n",
        "      if a<7 and b<50:\n",
        "        matrix_v[a][b]=matrix_v[a][b]-l_r*(np.transpose(delta4)[a][b-1])\n",
        "      if a<49 and b<350:\n",
        "        matrix_u[a][b]=matrix_u[a][b]-l_r*np.transpose(delta3)[a][b-1]\n",
        "      matrix_t[a][b]=matrix_t[a][b]-l_r*np.transpose(delta2)[a][b-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",
        "  print(t,\"train:::acccuracy  \",accuracy_history[t],\"   J   \",J_sum,\"::::: test accuracy\",accuracy_history2[t],\"   J   \",Jt_sum)\n",
        "\n",
        "  # accuracy_history2[t] = number_of_correct_predictions2/total_number_of_predictions2*100\n",
        "  t = t + 1"
      ],
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 train:::acccuracy   49.892933618843685    J    1.2020599005470183 ::::: test accuracy 50.249584026622294    J    1.2011329351519038\n",
            "1 train:::acccuracy   49.892933618843685    J    1.198996758722013 ::::: test accuracy 50.249584026622294    J    1.1982328948094674\n",
            "2 train:::acccuracy   49.892933618843685    J    1.19685349582122 ::::: test accuracy 50.249584026622294    J    1.1962235792833629\n",
            "3 train:::acccuracy   49.892933618843685    J    1.1953319259331971 ::::: test accuracy 50.249584026622294    J    1.1948127176412515\n",
            "4 train:::acccuracy   49.892933618843685    J    1.1942356090242892 ::::: test accuracy 50.249584026622294    J    1.1938083193688767\n",
            "5 train:::acccuracy   49.892933618843685    J    1.1934326996358566 ::::: test accuracy 50.249584026622294    J    1.1930819772155135\n",
            "6 train:::acccuracy   49.892933618843685    J    1.1928334839733943 ::::: test accuracy 50.249584026622294    J    1.1925466909236189\n",
            "7 train:::acccuracy   49.892933618843685    J    1.1923763039257147 ::::: test accuracy 50.249584026622294    J    1.1921429790748708\n",
            "8 train:::acccuracy   49.892933618843685    J    1.1920184769297435 ::::: test accuracy 50.249584026622294    J    1.1918299266694552\n",
            "9 train:::acccuracy   49.892933618843685    J    1.1917303031171127 ::::: test accuracy 50.249584026622294    J    1.1915792822653701\n",
            "10 train:::acccuracy   49.892933618843685    J    1.1914910400305927 ::::: test accuracy 50.249584026622294    J    1.1913714982465462\n",
            "11 train:::acccuracy   49.892933618843685    J    1.1912861630211045 ::::: test accuracy 50.249584026622294    J    1.1911930404038\n",
            "12 train:::acccuracy   49.892933618843685    J    1.191105482408952 ::::: test accuracy 50.249584026622294    J    1.1910345429462375\n",
            "13 train:::acccuracy   49.892933618843685    J    1.190941840228942 ::::: test accuracy 50.249584026622294    J    1.1908895349882744\n",
            "14 train:::acccuracy   49.892933618843685    J    1.1907902034951952 ::::: test accuracy 50.249584026622294    J    1.1907535575759072\n",
            "15 train:::acccuracy   49.892933618843685    J    1.1906470310072141 ::::: test accuracy 50.249584026622294    J    1.1906235497215594\n",
            "16 train:::acccuracy   49.892933618843685    J    1.1905098300063648 ::::: test accuracy 50.249584026622294    J    1.1904974207734094\n",
            "17 train:::acccuracy   50.03568879371877    J    1.1903768451751409 ::::: test accuracy 50.249584026622294    J    1.1903737523478166\n",
            "18 train:::acccuracy   50.17844396859387    J    1.1902468401813522 ::::: test accuracy 50.582362728785355    J    1.1902515905757347\n",
            "19 train:::acccuracy   50.53533190578159    J    1.1901189440826414 ::::: test accuracy 50.74875207986689    J    1.1901303013979694\n",
            "20 train:::acccuracy   50.60670949321913    J    1.189992543261182 ::::: test accuracy 49.417637271214645    J    1.1900094699067767\n",
            "21 train:::acccuracy   50.24982155603141    J    1.1898672053557093 ::::: test accuracy 48.0865224625624    J    1.1898888304612587\n",
            "22 train:::acccuracy   46.96645253390435    J    1.1897426256984718 ::::: test accuracy 48.0865224625624    J    1.1897682182940095\n",
            "23 train:::acccuracy   46.18129907209136    J    1.1896185895898546 ::::: test accuracy 46.089850249584025    J    1.1896475361125671\n",
            "24 train:::acccuracy   47.89436117059244    J    1.1894949457231574 ::::: test accuracy 45.75707154742096    J    1.1895267311484332\n",
            "25 train:::acccuracy   48.3226266952177    J    1.189371587461658 ::::: test accuracy 46.75540765391015    J    1.18940577947161\n",
            "26 train:::acccuracy   48.53675945753034    J    1.1892484396466765 ::::: test accuracy 48.0865224625624    J    1.1892846753454012\n",
            "27 train:::acccuracy   49.321912919343326    J    1.1891254493020884 ::::: test accuracy 47.920133111480865    J    1.189163424066904\n",
            "28 train:::acccuracy   49.46466809421842    J    1.1890025790841343 ::::: test accuracy 48.91846921797005    J    1.1890420372086503\n",
            "29 train:::acccuracy   49.892933618843685    J    1.18887980266556 ::::: test accuracy 49.08485856905158    J    1.1889205295061624\n",
            "30 train:::acccuracy   50.17844396859387    J    1.188757101482808 ::::: test accuracy 48.91846921797005    J    1.1887989168667092\n",
            "31 train:::acccuracy   50.32119914346895    J    1.1886344624436884 ::::: test accuracy 49.25124792013311    J    1.188677215135696\n",
            "32 train:::acccuracy   50.32119914346895    J    1.1885118763119207 ::::: test accuracy 49.08485856905158    J    1.1885554393697764\n",
            "33 train:::acccuracy   50.32119914346895    J    1.1883893365686578 ::::: test accuracy 48.91846921797005    J    1.1884336034441596\n",
            "34 train:::acccuracy   50.32119914346895    J    1.1882668386101605 ::::: test accuracy 48.91846921797005    J    1.1883117198762694\n",
            "35 train:::acccuracy   50.32119914346895    J    1.1881443791823663 ::::: test accuracy 49.25124792013311    J    1.1881897997857478\n",
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            "37 train:::acccuracy   50.39257673090649    J    1.187899567377702 ::::: test accuracy 49.25124792013311    J    1.1879458878287266\n",
            "38 train:::acccuracy   50.32119914346895    J    1.1877772122082046 ::::: test accuracy 49.25124792013311    J    1.1878239118065401\n",
            "39 train:::acccuracy   50.32119914346895    J    1.1876548896467591 ::::: test accuracy 49.25124792013311    J    1.1877019311846353\n",
            "40 train:::acccuracy   50.32119914346895    J    1.187532599100877 ::::: test accuracy 49.25124792013311    J    1.1875799513663725\n",
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            "42 train:::acccuracy   50.24982155603141    J    1.1872881124639245 ::::: test accuracy 49.25124792013311    J    1.1873360118785532\n",
            "43 train:::acccuracy   50.24982155603141    J    1.1871659158339025 ::::: test accuracy 49.25124792013311    J    1.1872140594462641\n",
            "44 train:::acccuracy   50.24982155603141    J    1.1870437500828888 ::::: test accuracy 49.25124792013311    J    1.1870921224718238\n",
            "45 train:::acccuracy   50.24982155603141    J    1.1869216150812012 ::::: test accuracy 49.25124792013311    J    1.1869702033282423\n",
            "46 train:::acccuracy   50.24982155603141    J    1.1867995107279325 ::::: test accuracy 49.25124792013311    J    1.1868483040163247\n",
            "47 train:::acccuracy   50.24982155603141    J    1.1866774369424649 ::::: test accuracy 49.25124792013311    J    1.186726426220546\n",
            "48 train:::acccuracy   50.24982155603141    J    1.1865553936584812 ::::: test accuracy 49.25124792013311    J    1.1866045713570854\n",
            "49 train:::acccuracy   50.24982155603141    J    1.1864333808197598 ::::: test accuracy 49.25124792013311    J    1.186482740614969\n",
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            "57 train:::acccuracy   50.32119914346895    J    1.1854583690092868 ::::: test accuracy 49.25124792013311    J    1.1855090542678346\n",
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            "59 train:::acccuracy   50.32119914346895    J    1.1852149177578277 ::::: test accuracy 49.25124792013311    J    1.1852659158422099\n",
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            "61 train:::acccuracy   50.32119914346895    J    1.184971586654443 ::::: test accuracy 49.25124792013311    J    1.1850228946013646\n",
            "62 train:::acccuracy   50.32119914346895    J    1.1848499660778717 ::::: test accuracy 49.25124792013311    J    1.1849014282219987\n",
            "63 train:::acccuracy   50.32119914346895    J    1.184728375442077 ::::: test accuracy 49.25124792013311    J    1.184779991449359\n",
            "64 train:::acccuracy   50.32119914346895    J    1.1846068147151871 ::::: test accuracy 49.25124792013311    J    1.184658584345735\n",
            "65 train:::acccuracy   50.32119914346895    J    1.1844852838654005 ::::: test accuracy 49.25124792013311    J    1.1845372069590392\n",
            "66 train:::acccuracy   50.32119914346895    J    1.184363782860963 ::::: test accuracy 49.25124792013311    J    1.1844158593251142\n",
            "67 train:::acccuracy   50.32119914346895    J    1.1842423116701657 ::::: test accuracy 49.25124792013311    J    1.184294541469665\n",
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            "71 train:::acccuracy   50.32119914346895    J    1.1837567244104341 ::::: test accuracy 49.25124792013311    J    1.183809568076176\n",
            "72 train:::acccuracy   50.32119914346895    J    1.1836354018133903 ::::: test accuracy 49.25124792013311    J    1.1836883992444065\n",
            "73 train:::acccuracy   50.32119914346895    J    1.1835141088403978 ::::: test accuracy 49.25124792013311    J    1.1835672602075655\n",
            "74 train:::acccuracy   50.32119914346895    J    1.183392845459944 ::::: test accuracy 49.25124792013311    J    1.18344615095396\n",
            "75 train:::acccuracy   50.32119914346895    J    1.183271611640539 ::::: test accuracy 49.25124792013311    J    1.1833250714694117\n",
            "76 train:::acccuracy   50.32119914346895    J    1.1831504073507053 ::::: test accuracy 49.417637271214645    J    1.1832040217376512\n",
            "77 train:::acccuracy   50.32119914346895    J    1.183029232558988 ::::: test accuracy 49.417637271214645    J    1.1830830017406602\n",
            "78 train:::acccuracy   50.32119914346895    J    1.1829080872339475 ::::: test accuracy 49.417637271214645    J    1.1829620114589514\n",
            "79 train:::acccuracy   50.32119914346895    J    1.1827869713441577 ::::: test accuracy 49.417637271214645    J    1.182841050871812\n",
            "80 train:::acccuracy   50.32119914346895    J    1.1826658848582077 ::::: test accuracy 49.417637271214645    J    1.1827201199574944\n",
            "81 train:::acccuracy   50.32119914346895    J    1.1825448277447002 ::::: test accuracy 49.417637271214645    J    1.1825992186933891\n",
            "82 train:::acccuracy   50.32119914346895    J    1.1824237999722507 ::::: test accuracy 49.417637271214645    J    1.1824783470561695\n",
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            "86 train:::acccuracy   50.32119914346895    J    1.1819399816657141 ::::: test accuracy 49.417637271214645    J    1.181995156290302\n",
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            "108 train:::acccuracy   50.46395431834404    J    1.1792872739440536 ::::: test accuracy 49.417637271214645    J    1.1793459995955395\n",
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            "147 train:::acccuracy   50.53533190578159    J    1.1746184274948948 ::::: test accuracy 49.750415973377706    J    1.1746839788638277\n",
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            "705 train:::acccuracy   71.3775874375446    J    1.110068840426169 ::::: test accuracy 69.05158069883528    J    1.1107486118311218\n",
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            "708 train:::acccuracy   71.23483226266953    J    1.1096932182147623 ::::: test accuracy 69.38435940099833    J    1.1103865256952752\n",
            "709 train:::acccuracy   71.3775874375446    J    1.1095676436285749 ::::: test accuracy 69.38435940099833    J    1.1102655444884517\n",
            "710 train:::acccuracy   71.59172019985725    J    1.1094418812838496 ::::: test accuracy 69.21797004991681    J    1.110144416985516\n",
            "711 train:::acccuracy   71.5203426124197    J    1.1093159286259364 ::::: test accuracy 69.21797004991681    J    1.110023141113062\n",
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            "721 train:::acccuracy   72.23411848679515    J    1.108045345494146 ::::: test accuracy 69.55074875207987    J    1.1088017436637507\n",
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            "723 train:::acccuracy   72.30549607423269    J    1.1077886699172117 ::::: test accuracy 69.7171381031614    J    1.1085554603208427\n",
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            "729 train:::acccuracy   72.66238401142041    J    1.1070130898219497 ::::: test accuracy 69.88352745424294    J    1.107812248914711\n",
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            "731 train:::acccuracy   72.73376159885797    J    1.10675262518546 ::::: test accuracy 69.88352745424294    J    1.1075629874558863\n",
            "732 train:::acccuracy   72.73376159885797    J    1.1066220142372696 ::::: test accuracy 69.88352745424294    J    1.1074380585109918\n",
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            "745 train:::acccuracy   73.23340471092077    J    1.1048993103297975 ::::: test accuracy 70.38269550748753    J    1.1057944344087756\n",
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            "758 train:::acccuracy   74.44682369735904    J    1.1031243267821869 ::::: test accuracy 70.88186356073211    J    1.1041093889085787\n",
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            "833 train:::acccuracy   77.4446823697359    J    1.091132974298871 ::::: test accuracy 74.70881863560732    J    1.0929840165743872\n",
            "834 train:::acccuracy   77.51605995717344    J    1.0909413251977294 ::::: test accuracy 74.87520798668885    J    1.092810037784333\n",
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            "838 train:::acccuracy   77.58743754461099    J    1.090162238477485 ::::: test accuracy 74.70881863560732    J    1.0921039951490719\n",
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            "844 train:::acccuracy   77.58743754461099    J    1.08895364145342 ::::: test accuracy 74.87520798668885    J    1.091012503140173\n",
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            "847 train:::acccuracy   77.87294789436116    J    1.088329931814684 ::::: test accuracy 75.04159733777038    J    1.0904509953643848\n",
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            "857 train:::acccuracy   78.15845824411134    J    1.0861461276446662 ::::: test accuracy 75.04159733777038    J    1.088494132998941\n",
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            "878 train:::acccuracy   78.8722341184868    J    1.0809094745368937 ::::: test accuracy 75.20798668885192    J    1.0838547130985634\n",
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            "884 train:::acccuracy   79.22912205567452    J    1.0792089108869052 ::::: test accuracy 75.37437603993344    J    1.0823623444878887\n",
            "885 train:::acccuracy   79.22912205567452    J    1.078914948756389 ::::: test accuracy 75.04159733777038    J    1.0821050047162386\n",
            "886 train:::acccuracy   79.22912205567452    J    1.0786178139805778 ::::: test accuracy 75.04159733777038    J    1.0818450703143498\n",
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            "888 train:::acccuracy   79.22912205567452    J    1.0780137683348765 ::::: test accuracy 75.04159733777038    J    1.0813172046477666\n",
            "889 train:::acccuracy   79.15774446823698    J    1.0777067246608347 ::::: test accuracy 75.20798668885192    J    1.0810491637991917\n",
            "890 train:::acccuracy   79.08636688079943    J    1.0773962426596275 ::::: test accuracy 75.04159733777038    J    1.0807783090907028\n",
            "891 train:::acccuracy   79.22912205567452    J    1.0770822520370726 ::::: test accuracy 75.04159733777038    J    1.0805045824919004\n",
            "892 train:::acccuracy   79.22912205567452    J    1.0767646808728333 ::::: test accuracy 74.87520798668885    J    1.0802279246186093\n",
            "893 train:::acccuracy   79.22912205567452    J    1.0764434555805305 ::::: test accuracy 74.87520798668885    J    1.0799482746992233\n",
            "894 train:::acccuracy   79.22912205567452    J    1.0761185008668832 ::::: test accuracy 74.87520798668885    J    1.079665570540255\n",
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            "897 train:::acccuracy   79.15774446823698    J    1.0751204807777155 ::::: test accuracy 74.87520798668885    J    1.0787984886161093\n",
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            "908 train:::acccuracy   79.3718772305496    J    1.0711309430030596 ::::: test accuracy 75.20798668885192    J    1.0753484867508503\n",
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            "911 train:::acccuracy   79.5146324054247    J    1.06994153709325 ::::: test accuracy 75.20798668885192    J    1.0743243826185305\n",
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            "951 train:::acccuracy   80.65667380442541    J    1.047848903007964 ::::: test accuracy 76.2063227953411    J    1.0555296365932854\n",
            "952 train:::acccuracy   80.65667380442541    J    1.0470929863595457 ::::: test accuracy 76.2063227953411    J    1.054891246569608\n",
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            "954 train:::acccuracy   80.72805139186295    J    1.045541395259129 ::::: test accuracy 76.37271214642263    J    1.0535814267466779\n",
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            "957 train:::acccuracy   80.65667380442541    J    1.0431103264303054 ::::: test accuracy 76.2063227953411    J    1.0515304670319063\n",
            "958 train:::acccuracy   80.72805139186295    J    1.0422711442755235 ::::: test accuracy 76.2063227953411    J    1.0508228207633485\n",
            "959 train:::acccuracy   80.7994289793005    J    1.0414170447109081 ::::: test accuracy 76.2063227953411    J    1.0501027495007802\n",
            "960 train:::acccuracy   80.87080656673804    J    1.0405477180363363 ::::: test accuracy 76.2063227953411    J    1.0493699898176245\n",
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            "963 train:::acccuracy   81.15631691648822    J    1.0378452104621398 ::::: test accuracy 76.37271214642263    J    1.0470928840530305\n",
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            "968 train:::acccuracy   81.44182726623839    J    1.0330063106486422 ::::: test accuracy 77.3710482529118    J    1.0430181828210892\n",
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            "970 train:::acccuracy   81.44182726623839    J    1.0309457158778303 ::::: test accuracy 77.3710482529118    J    1.0412837112424072\n",
            "971 train:::acccuracy   81.44182726623839    J    1.029887236518179 ::::: test accuracy 77.3710482529118    J    1.0403928680803423\n",
            "972 train:::acccuracy   81.51320485367594    J    1.028809497790501 ::::: test accuracy 77.3710482529118    J    1.039485879682039\n",
            "973 train:::acccuracy   81.51320485367594    J    1.0277121433751228 ::::: test accuracy 77.3710482529118    J    1.03856243776527\n",
            "974 train:::acccuracy   81.44182726623839    J    1.026594815140772 ::::: test accuracy 77.3710482529118    J    1.0376222319328792\n",
            "975 train:::acccuracy   81.44182726623839    J    1.0254571535212431 ::::: test accuracy 77.3710482529118    J    1.036664949964893\n",
            "976 train:::acccuracy   81.51320485367594    J    1.024298797921157 ::::: test accuracy 77.3710482529118    J    1.0356902781348993\n",
            "977 train:::acccuracy   81.51320485367594    J    1.023119387151944 ::::: test accuracy 77.3710482529118    J    1.0346979015517022\n",
            "978 train:::acccuracy   81.44182726623839    J    1.021918559899084 ::::: test accuracy 77.3710482529118    J    1.033687504527244\n",
            "979 train:::acccuracy   81.51320485367594    J    1.0206959552216714 ::::: test accuracy 77.3710482529118    J    1.0326587709717796\n",
            "980 train:::acccuracy   81.5845824411135    J    1.0194512130852598 ::::: test accuracy 77.53743760399334    J    1.0316113848172617\n",
            "981 train:::acccuracy   81.5845824411135    J    1.0181839749289296 ::::: test accuracy 77.70382695507487    J    1.0305450304698232\n",
            "982 train:::acccuracy   81.5845824411135    J    1.0168938842674227 ::::: test accuracy 77.87021630615641    J    1.029459393292268\n",
            "983 train:::acccuracy   81.5845824411135    J    1.0155805873291417 ::::: test accuracy 77.87021630615641    J    1.0283541601173563\n",
            "984 train:::acccuracy   81.5845824411135    J    1.0142437337306518 ::::: test accuracy 77.87021630615641    J    1.0272290197926253\n",
            "985 train:::acccuracy   81.5845824411135    J    1.012882977188289 ::::: test accuracy 77.87021630615641    J    1.0260836637574446\n",
            "986 train:::acccuracy   81.5845824411135    J    1.0114979762672915 ::::: test accuracy 77.87021630615641    J    1.0249177866528563\n",
            "987 train:::acccuracy   81.65596002855104    J    1.0100883951687722 ::::: test accuracy 77.87021630615641    J    1.023731086964687\n",
            "988 train:::acccuracy   81.65596002855104    J    1.008653904554639 ::::: test accuracy 77.87021630615641    J    1.0225232677002738\n",
            "989 train:::acccuracy   81.87009279086367    J    1.0071941824104687 ::::: test accuracy 78.03660565723793    J    1.0212940370990413\n",
            "990 train:::acccuracy   81.94147037830122    J    1.005708914946053 ::::: test accuracy 78.03660565723793    J    1.0200431093769808\n",
            "991 train:::acccuracy   81.87009279086367    J    1.0041977975332175 ::::: test accuracy 78.53577371048253    J    1.0187702055049546\n",
            "992 train:::acccuracy   81.87009279086367    J    1.0026605356801785 ::::: test accuracy 78.70216306156405    J    1.0174750540205217\n",
            "993 train:::acccuracy   82.08422555317631    J    1.0010968460415677 ::::: test accuracy 78.70216306156405    J    1.0161573918728317\n",
            "994 train:::acccuracy   82.08422555317631    J    0.9995064574628518 ::::: test accuracy 78.86855241264558    J    1.01481696529987\n",
            "995 train:::acccuracy   82.22698072805139    J    0.9978891120577121 ::::: test accuracy 79.03494176372712    J    1.0134535307371197\n",
            "996 train:::acccuracy   82.22698072805139    J    0.9962445663165334 ::::: test accuracy 79.03494176372712    J    1.0120668557564791\n",
            "997 train:::acccuracy   82.22698072805139    J    0.9945725922438765 ::::: test accuracy 78.86855241264558    J    1.0106567200339183\n",
            "998 train:::acccuracy   82.22698072805139    J    0.9928729785224824 ::::: test accuracy 79.03494176372712    J    1.009222916344191\n",
            "999 train:::acccuracy   82.29835831548894    J    0.9911455317009175 ::::: test accuracy 79.03494176372712    J    1.0077652515804958\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtFVXgejZDIS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "c72ee7ea-5128-4d37-ec3e-71f34366d298"
      },
      "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": 281,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vw9u4c1kX/zJpKfePnlN2RxtgQjrAOc/v4LUrl3Bg1EQOTD4POt3RliTVl3qtuU7AtyPi3ogou0UVER0RsTUitu7bt2+EliepEgYG7X8ev5RtHB+0C8tGAM7oPcAZPTtIS5awb8xkHpljfbYkqT7Ua7i+LKX0JuBtwB/nSkyKSil1ppQuSildNGnSpJFboaSKWrQiw+8/s5qDXdv42Pwu7nhFNmgXNkIWC9pnHunpa4Tcc8p0g7YkqabqMlynlB7PfX0KWAdcXNsVSRopmQws35Th2uezQfvTi7r4j9MX8TjtZXe0B04c2T9ushNHJEkjru7CdUS8PCJOy38PXAkUnTgiqbllMrBsXYbLf76Ox7r28qlFXXzn1OODNhwftCcc7OmbONLz8ulOHJEkjYhqTgv5MrAQOBN4ErgZGAOQUrolItqBrcDpZM+QeAGYnXv9utyPGQ38vymlvxrK7+m0EKk1dHbC3au6ue7hm5h7ePNxU0ZKHVbjxBFJUiV4QqOkppUf7Xf2czuYcnAnU+jpdz8ftAf+SfdM2wR4VTsTPn4DdBi0JUlDZ7iW1BK6u+H77+3kbQ+t4lU8yaSCaaCldrMBnh7dzvPnzfXodUnSkHj8uaSWkMnAR3Z28HzXDm5eup+/PHsNjzB10NF+hRNHnhztaD9J0slz51pSU+vuhu+t7OZX/2Mlr39uC2fRU/JUyMI/DXuBp0e184s3uqMtSerPshBJ4ljQnvPtlcz+5YkF7WdPaYdL5jJxuUFbklqdZSGSxLHRflf+Yh3fWLOXD8w6NtoPys/QdrSfJGko3LmW1PK6u2Hb9Z28+f5VTDjavxESSu9oP9nWztNnvI4zf3027csWu6MtSS3CshBJGqKhBu2Bf3L2AgemzGHSO+fCYoO2JDUzw7UknYShjvaD42u0DdqS1LwM15I0DN3dsHYtTP7XTq7Z8wmms7tkIyQcH7SfnDGfs9YuN2RLUpMwXEtSheQnjlyyeSVTn93GOb27+3WGlwravcDeU2fyymlncNoN13kqpCQ1MMO1JFXJ+hu7Gff3K5n54jamUTpoD/yTdt8pU3jZuVMM2pLUgAzXklRl3d3wtZu6mb9lJa8/ZNCWpGZmuJakEVQYtH/l0Bam0NPvfrmg/fipMxh95RWO9pOkOma4lqQayY/2u+SBWznzyBO8hj397pc7FdKJI5JUnwzXklQHOjthz82dvGvfrUw6eoJB+xXTaHvTHI9fl6Q6YLiWpDpTLmgPOkPboC1JNWW4lqQ6lg/a73lqFa/rfXBIo/0gG7Rfmjabl3/0BhshJWkEGa4lqUH0H+33KG0Fcbpc0HbiiCSNHMO1JDWY/MSRN/5gLbNf2MIctpc8FdLRfpI0sgzXktTAhhO0nx01gTSpnQkft3REkirFcC1JTSIftGfds5a5L27gdTzU737ZoH1qO70XzbURUpKGyXAtSU3I0X6SVBuGa0lqcoVB+7SjzzCDh4Z0/LpBW5JOnOFaklpIZyfcvaqbdz+ykl85uIWz6OkL14ON9nv2lHa4xNIRSSqnXLhuK3ZRktS4Ojrg1h0ZfuuldXxjzV6umdrFJuaToN8DsmE7/2gDJhzsYcLm9fTOm8dzE6dnk7okacgM15LUxDo64Mu7MpzStYmPze/ijlcsZRtzjgvaif5BO4DTDuwiLVnCM6MncmDyeQZtSRoCy0IkqcUUjvY7+4UdzGAnU+jp95p8+Yij/STpeNZcS5KK6u6GtWth8r928tt7VvEqnmQSB/ruDzbar+11r+OVc2fD4sXWaEtqGYZrSdKg8kF7wlc7ed++TzCd3UM+rCYBz18wn1euXm7IltT0bGiUJA0qk4HVq+Gvnupgw5pdfGBWF988ZRGP0z5ofXYAp/9wM0fnzWP/uMnsX3B1Nq1LUotx51qSVFa50X5Qfoa2o/0kNSPLQiRJFdHZCd/9RDe/u3slF7CNaewe0mE1CXjxtHZefvlcWGbQltTYDNeSpIrK12f/ckM3Vz20kovZUnLiCBwftPdPmcOkd861EVJSQzJcS5KqZrCJIzDI8esGbUkNxnAtSRoRwwnaCfjljDm84gqDtqT6ZriWJI24wtF+79lXfoY2GLQlNQ7DtSSppjo74dZb4YqfZYP2LB4s2QgJx5eOvDRtNi//qKdCSqoPhmtJUt3IH78+f8tKXn+o/MQR6B+0nx8/hdNfNwWuu86gLalmDNeSpLrU3Q3/eH03V2w/sdF+YNCWVDuGa0lSXRs42i8btB+lrSBSG7Ql1QvDtSSpYeSDdmzp5pKfrGXuixt4HQ/1e025oP3ilBmc+s4rbISUVDWGa0lSw+rshEc+2smiA7cymSd4DXv63S83Q/tQ+zReNneOp0JKqijDtSSp4XV3w8qVcO6GTn7neYO2pNoxXEuSmspgQXuwGdpt06bBHIO2pJNTLly3FbsoSVI9y2Rg3Tr41HMdpK67WTz/MTpYw4+YRS9Bgr4HZMN24SPt2kVav57eefPgvPOytSeSVAHuXEuSmsLARsjzX9zCHLb328V2hrakSrAsRJLUcjo74buf6OYte9cy51D5oD3w34SHT5vA2HPa4QZPhZR0PMO1JKmldXbCv9/czVt61nIJJxa0D01o55T5c63PltTHcC1JEscaIQ9v7ubtB9byZoY+Q9tGSEl5hmtJkgYYOHHkDJ5hBg8N6fh1g7bU2gzXkiSVUbij/b4DK7mYLZxFT1+4Hmy036H2aYz7+Eetz5ZahKP4JEkqIz/a71/3Z3h6zTqunLWXt53WxSbm9xvrV2q03yk9u0hLlnDo9ImO9pNanOFakqQCHR2wYwd867kMO9ds4nfau7iFpWxjznFBO3EsYAOMef4AaccOg7bUwiwLkSRpEAMbIWexgxnsZAo9fa8pN3Ek2tthrhNHpGZhzbUkSRWSD9rbtsFbdndyE59gOruHNNrPRkipORiuJUmqgsEaIWGQoO2OttSQbGiUJKkKSjVCrmMRj9M+aCNk6ukhrV9P77x5MH269dlSEzBcS5JUAYWNkE+vWcdlU/dyKcUnjhQ2QvYF7V3ZiSNMtBFSamSWhUiSVCVDaYSEY6UjNkJKjcGaa0mSaiwftLdsgXf0dHIDq3gVTzKJA32vsRFSagzWXEuSVGP5+uy9e+HX1nTwX2btYNaE/XSwhkeYOrSykXx99uTJcPXV2cQuqa4YriVJGmH5+uz9+7NB+/Kpu7iUE2+ExEZIqe4YriVJqqGODti1C/6mK8M/LlrHr7UPvREyYSOkVG+suZYkqc50d8NNN8Ghzd0sxkZIqd7Y0ChJUgPq7oa1a7MlJDt3nngjZAAYtKWKs6FRkqQGlMnA6tWwadOxRsh3TN3BqxlaI2TC+mxppBmuJUlqEPn67K4ueHpRB5e1l26ELFuf7bQRqWoM15IkNZjCsX5/05Xhb+ev4xz29gvaUDxoQ5HdbIO2VDGGa0mSGlgmky0b6eqCOUsz/OWcbNDuYA0/Yhb7mAAMspudD9pOG5GGzXAtSVITyNdnb9t2rGzkLe2l67OhSNDescOxftIwGa4lSWoyhWUjXV2wc34HM9hVcn42DCgbOXCAtGMHWJ8tnTDDtSRJTWxg2UjHzE1cShe3sJRtzDmxaSPuZkuDcs61JEktprMTVq2CBx+EuWQPqrmELVzA9n67bsXmZwfAjBlwxRWweLGzs9WSPERGkiQdp7sbVq7M1mk/+ihckrr5CCu5mC2cRU9fuC4ZsgHmzMkeUmPQVgvxEBlJknScfG32rl1w113ZspGbZh4b61esPrtfbTaQtm+HW26xbETKMVxLkqS+aSM7d8KaNfDzWRl+g2P12TuZCZRpgoTsOe1LlsDMmfDBD9oEqZY0aLiOiLaIuDAi3h4Rb46IV43EwiRJUm10dGRzclcXtC/K8Bftq3kDO/tmZ/dSfNoI+WsPP3xsN3vBAkO2WkrJcB0R50ZEJ/AwsBx4D3A98H8iYktE/FFEuPMtSVKTKhzpt2YNfGdqB+ezg8tyJ0E+wlR6ibJlI2zefOwkSEtG1AJKNjRGxJeB1cD30oAX5Xav/wB4JqX0paqvcohsaJQkqbq6u+Gmm7KZGY5NG3kzG3gdD/W9rmQTZHt7tgFy2TIbINWwnBYiSZIqqrsb1q6FLVtg+/bstffTyQ2sYhYPFh3pd9ykkdmz4YYbsnUoUgMZdriOiDcCs4Fx+WsppbUVW2GFGK4lSRp5xXaz8yP9ptDT97qBu9l9z93NVoMZ1ii+iLgZ+Pvc4zeAlcC7KrpCSZLUsApPgVy6FPbPzPBusiP9OljDI0wt3wDZ0wOeAqkmMZSGxN8BLgd6Ukp/BFwAvLKqq5IkSQ1n4Di/WbPg83Qwg11DnpvtOD81uqGE6xdTSr3AkYg4HXgKOKe6y5IkSY2scJzfokWwq73/3OxtzOk30g8GhGzH+alBDSVcb42I8cDngHuBHwCD/j88Im6LiKci4oES998QEd0RcTAiPjzg3lsj4icR8XBE3DSENUqSpDo0cJzf3qkZrmc1v8o2Liuzm53Xb5zfhRe6m626d0LTQiJiGnB6Sum+Ibx2PvACsDal9MYi918FTAUWkR3p96nc9VHATuAtwB7gHuA9KaUdg/2eNjRKklT/urth5crspJGenqGP8yt8zvz5sHy5DZCqieE2NG7If59S2pVSuq/wWikppc3AgTL3n0op3QMcHnDrYuDhlNIjKaVDwD8BVw32+0mSpMZQuJu9bBncHdnd7HKnQMbAH5LfzbZkRHWm3AmN4yJiAnBmRJwRERNyj2nAlCquaQrwWMHzPVX+/SRJUo2sWAF33ZWdMjJzZrYBsvAUyMdpB/rXZveTD9lOGVGdKLdzvYRsjfUbyNZZ35t7fA34X9Vf2tBEREdEbI2Irfv27av1ciRJ0gkqNmVkC/3H+RXbze7HKSOqEyXDdUrpb1NK04EPp5SmFzwuSClVM1w/Tv9pJGfnrpVaZ2dK6aKU0kWTJk2q4rIkSVK1DZwy0t5efje7UL8pI5deCjfeONLLl4Y0LeS2iPgfEdEJEBEzI+IdVVzTPcDMiJgeEWOB3wfurOLvJ0mS6szAKSNTp/bfzV7OMnqJ0nXZKWW7JidPhquvdidbI2ZI4Ro4BMzLPX8c+H8G+0UR8WWyI/teHxF7IuK6iFgaEUtz99sjYg/wZ8D/yL3m9JTSEeBDwLeAB4H/nVL60Qn/k0mSpKbQ0QG7dh0L2QAfYwWXcRe3sJSdzARKlIsUnv5o86NGwKCj+CJia0rpoojYllK6MHfthymlC0ZkhSfAUXySJDW/7m64/nrYvv3YtffTyQ2sYhYP9u0cHjdhJG/2bLjhhmxql07CsEbxAYci4mXk/mMwIs4FDlZwfZIkSUOWycC2bceaH6F/XfYm5g+t+XHOHHeyVXFDCdc3A/8OnBMRdwAbgGVVXZUkSdIgCpsf58/PXttC9pj1wZofAfjhD7PlItOnO8ZPFTOkExojYiIwl+zfsGxJKT1d7YWdDMtCJElqXcXKRQA+wY0s45O0FUTsoiUj06bBRz9quYgGNdyyEIBxwDPAc8Ds3NHmkiRJdWNguUjkEvSQmx937bJcRMM2lIbGFcDvAT8CenOXU0rpXVVe2wlz51qSJOV1d2en8a1f3//6++nkJj7Ba9ndd63oTvb8+bB8eTa1SwWGu3O9CHh9SuntKaV35h51F6wlSZIK5WdlF9ZkQ7b5cQa76GANjzC1dONj/mh1a7J1AoYSrh8BxlR7IZIkSdWQycCmTdmQPWfOsev5kH0pXWwje6NsuYghW0NQMlxHxN9HxN8BvwS2R8SaiPi7/GPklihJkjR8xUb4QXbCyK+yrW8nG6zJ1skrt3O9FbiX7NHjfwl05Z7nH5IkSQ2n2Ag/OL5cBEqE7PwIP098VBElGxoj4osppT8c2eUMjw2NkiTpRJUa4ZdvfJzO7r6Gx6KNj4sWwbJlNj62kJNtaPyVKq1HkiSpbhSWi0ydeuz6kGuy16+Hyy6zHltA+XB9akRcGBFvKvYYsRVKkiSNgI6ObFn1sgHnUJeqye6ntzdbj22pSMsrVxbyPHAPxf8GJKWU3lzNhZ0My0IkSVIllCoVgeNPfLRUpPWUKwspF663pZQurOrKKsxwLSbbL4sAABlKSURBVEmSKqmzEz7xCdi9u//1uXTzEVbyLr7GqFIhOwI+8hFYsWJE1qqRU4njzyVJklpOYalIFKTnLWR4N+v4de5iE9mRI8dtV6aUPSLSUpGWUi5c3zhiq5AkSapjK1bAXXdlqz0KbSHDb7CJ5Syjlyh90uOll8KNRqtWUC5c/7eIeGdEHHc6Y0S8NiL+IiLeV8W1SZIk1Y38cepr1kDbgAT1MVZwGXexjkUcpchUkfwutqc8Nr1y4foDwK8DP46IeyLiGxHx3Yj4GbAG+EFK6bYRWaUkSVKd6OiA738/u4tdvFSki03ML3/K47XXjtyCNaJKNjT2e1HENKAdeBHYmVJ6sbrLOjk2NEqSpJHU3Z3dkF6//vh776eT1SxlFKn4RJH582H5cieKNKCTamiMiOcj4rmIeA64D/gO8D3gyYjYFxFbIuLy6ixZkiSp/pUrFfk8HX0Nj72UqcW++mobHptIyXCdUjotpXR6waPvOdld7CXA347YSiVJkupUYalIoXzDY/6Ux6ITRTzhsamc1Ci+lNLRlNIPgb+v8HokSZIaUrld7Pwpj9mpIkXkT3h0F7vhDWvOdUppTaUWIkmS1Azyu9jz5x9/72OsYAlrONJ3vuMA7mI3PA+RkSRJqrBMBjZtOv7wGcjXYn+fdSwqvYu9dKkBu0EZriVJkqokf/jMwF3s/Ni+krvYKWXLRDx4puEYriVJkqoov4t9zTXH3xt0F3vlSmdiNxjDtSRJ0gi4/fZss+PUqf2v53exl7KG3igSze64AxYssNGxQRiuJUmSRkhHR/aQxmXLjr/3OTq4NH2fn55dpBNy82YbHRuE4VqSJGmErViR3cUe2Oy4hQwz9mzie1OL1JDY6NgQDNeSJEk10NEBt9xy/ExsgPm7b+eOs5fZ6NiADNeSJEk1Um4m9rV7sjOxe4njb65caR12nTJcS5Ik1VC5aSKfo4Ol3FK80dE67LpkuJYkSaoDt99eutHxsvR9nphZZHvbOuy6Y7iWJEmqE6UaHbvJcPbDm9h2ZZH0bR12XTFcS5Ik1ZFSjY4pwa9+ZwWbrimSvsEDZ+qE4VqSJKnO5BsdZ8/ufz0lWHhHB195S4kxI3fc4Q52jRmuJUmS6lAmA5//fPEM/bvf7uAf3lNizMjKlQbsGjJcS5Ik1alMBlavLh6wP3RHhhvnlhgzYolIzRiuJUmS6li5WdgrV8K1lBgzYolITRiuJUmS6lx+FnbJDM2K4jctERlxhmtJkqQGsaJchmZF6RIRT3McMYZrSZKkBrKiTIa+cUqJEpHNmw3YI8RwLUmS1GBKnea4ciV0nltie/vwYbjppuovrsUZriVJkhpQqRKRpUvLBOzNm63BrjLDtSRJUoNaseL4KSIpFQTsNWuO/0U2OVaV4VqSJKmBLV8OY8b0v5YSfPCD0H1+h1NERpjhWpIkqYHlx/QNPCq9txfe/37oXlSiROSTn7TBsQoM15IkSQ2u1FHpO3bkhoQUC9gp5dK3AbuSDNeSJElNIH9UekT/64cPZ6tAihZo96VvA3alGK4lSZKaREcH3HLL8QF7/fpcifXy5TBqVP+bfelblWC4liRJaiKlAvbKlXDj+gx89rPH3/za16Czc+QW2cQM15IkSU2mVMD+5CdzE0RuuaX/jb75fQbs4TJcS5IkNaGODvjIR/pf6+thPL8DFi06/ub111t/PUyGa0mSpCZVrofx/rctO35A9tGj1l8Pk+FakiSpiZXqYfyf3ywxINv662ExXEuSJDWxTIkexjvvhG6KDMi2/npYDNeSJElNrqNID2Nvb64CJJOBd72r/03rr0+a4VqSJKkFdBTpYeybf73M+utKMVxLkiS1iGXLjq+/7pt/bf11RRiuJUmSWkSp+utPfrKg/rowfVsecsIM15IkSS2k1Pzrvvrrgenb8pATYriWJElqMcXmX/dVgHR0wFVX9b95553uXg+R4VqSJKkFDZx/3a8CZNmy/uP5entzRzsasAdjuJYkSWpBZStAio3nyx/taMAuy3AtSZLUospWgBQbLXL4sPXXgzBcS5IktbBiFSAlmxvB+utBGK4lSZJaWLEKkH7NjSWPdlQxhmtJkqQWN7ACpF9zY7GjHT1cpiTDtSRJUosbdLx1sfT9oQ9ZHlKE4VqSJElFmxv7Nqjz6buwOPvIEdi4cSSX2BAM15IkSQKGUB7y4Q/3v/nssyO+xnpnuJYkSRIwhPKQ8eP73/z0py0NGcBwLUmSpD5lZ18vXNi/NKRf8hYYriVJkjRA2dnX73xn/xc7OaQfw7UkSZL6KTv7umxhtgzXkiRJOk7JDM1ghdmtzXAtSZKk45Rqbly7lkEKs1ub4VqSJElFFcvQPT25b4oVZq9dO2Jrq1eGa0mSJJW0bBmMGXPs+b/9W26Dulhhdl/ybl2Ga0mSJJWUycDb337s+eHDA45FL0zeX/96y08OMVxLkiSprPb2/s/7HYt+3XXHbhw92vKTQwzXkiRJKmvx4jLT9wbebPHJIYZrSZIklVV2ckixg2W+/vWW3b02XEuSJGlQg04OKdy9buHJIYZrSZIkDUnZySGf/eyxgJ0S3HprS+5eVy1cR8RtEfFURDxQ4n5ExN9FxMMRcV9EvKng3tGI2J573FmtNUqSJGnoyk4O6ejoXx7S72brqObO9ReBt5a5/zZgZu7RAawuuPdiSmlO7vGuor9akiRJI27g5JB+5dVlb7aGqoXrlNJm4ECZl1wFrE1ZW4DxETG5WuuRJEnS8A0cDtKvvLrYzY0bR3J5NVfLmuspwGMFz/fkrgGMi4itEbElIhaN/NIkSZJUTNny6kwG/vzPj704JXj22Zqss1bqtaFxakrpIuAPgFURcW6pF0ZERy6Ib923b9/IrVCSJKlFlS2vHj++/8y+T32qpU5trGW4fhw4p+D52blrpJTyXx8BNgIXlvohKaXOlNJFKaWLJk2aVL3VSpIkqU/J8uqFC48vDfnQh1qm9rqW4fpOYHFuashc4Ocppb0RcUZEnAIQEWcClwI7arhOSZIkDVCyvDqTgX/4B2griJlHjrRM7XU1R/F9GegGXh8ReyLiuohYGhFLcy/5BvAI8DDwOeD63PVZwNaI+CHwXWB5SslwLUmSVEfKlld3dMCHP1ziZnMbXa0fnFJ6zyD3E/DHRa53AedXa12SJEmqjHx5dUrZ55/5DCxalA3e5W82r3ptaJQkSVKdG1hefeRIwVi+YjdboDTEcC1JkqSTki+vLjmW78/+7NiLW6Q0xHAtSZKkk1ZsLF/f7vXAsXyf+UzTTw0xXEuSJGlYBo7l6+nJfVO2bqQ5Ga4lSZI0LIsXw5gxx55//eu5c2PK1o00J8O1JEmShiWTgeuuO/b86NGCc2PK1o00H8O1JEmShm3xYhhdMOS533CQknUjzcdwLUmSpGErOxxkYN3Iv/1b05aGGK4lSZJUESWHg2Qy8Pa3H7vRxKUhhmtJkiRVRNlzY1qkNMRwLUmSpIo4odKQb36zKUtDDNeSJEmqmLKlIdddd+zmoUNNWRpiuJYkSVLFlD03pnD3uklnXhuuJUmSVDHFzo35whcKdq9/67eOvbgJGxsN15IkSaqojg74wAeOPT98uHUaGw3XkiRJqrgLLzz2fW9v6zQ2Gq4lSZJUcfv3t2Zjo+FakiRJFVd25vXAxsa+ouzGZ7iWJElSxRWbeT1xYsHN973v2M1+RdmNzXAtSZKkqhg/HtpyaTMCtm0ruDmwKLsveTc2w7UkSZKqYuFCGD06+/1x1R/795dJ3o3LcC1JkqSqyFd/FO1dHJi8m+RAGcO1JEmSqqZk72KTHihjuJYkSVLVlO1dbMIDZQzXkiRJqqqSvYtNeKCM4VqSJElVVbJ3sQkPlDFcS5IkqarK9i422YEyhmtJkiRVVdnexSY7UMZwLUmSpKor27vYRAfKGK4lSZJUdWV7F5voQBnDtSRJkqqubO9i2aMcG4vhWpIkSSOi7IEyTVJ3bbiWJEnSiCiboZuk7tpwLUmSpBFTMkM3Sd214VqSJEkjpmSGbpK6a8O1JEmSRkzJDJ2vGWnw0xoN15IkSRoxAzP0kSMFdddNcFqj4VqSJEkjavFiGDcuWx4SUVB3XTZ5NwbDtSRJkkZUJgOrVsGoUdmmxj/5k4IN6vzudUT2BQsX1nKpJ8xwLUmSpBG3fz8cPZoN1wcPDtigzu9c5782EMO1JEmSRtzEidlgDQNG8m3cmC0HSakhmxoN15IkSRpxZUfyjRqV/b4BmxoN15IkSRpxg47ky2uwo9AN15IkSRpxzXoUuuFakiRJNdGMR6EbriVJklQTzXgUuuFakiRJNTHko9Ab6DAZw7UkSZJqYtCj0Ise41jfDNeSJEmqmbJHoZc8xrF+Ga4lSZJUM2UzdNljHOuT4VqSJEk1VTJDlzzGsX4ZriVJklRTJTN0A47kM1xLkiSpppppJJ/hWpIkSTXVTCP5DNeSJEmqqUFH8o0Zk705alQ2idcxw7UkSZJqrmyGzqfu/Nc6ZriWJElSXSiaoTduzG5lpwSHDsHatbVY2pAZriVJklRzJTP0woXZrWxoiKZGw7UkSZJqrmSGbrCmRsO1JEmSam7QpsaiZ6TXH8O1JEmS6kLJDF32jPT6YriWJElSXSiboUuekV5fDNeSJEmqGyUzdMkz0uuL4VqSJEl1o2SGLjwjva0t+7wOGa4lSZJUN0pm6IUL4ZRTshfb2ty5liRJkgZTMkM3SFOj4VqSJEl1Y9Cmxt7e7OOll+rytEbDtSRJkupKYYY+dKigqbEBTms0XEuSJKmu5DN0RPbrwoW5Gw1wWqPhWpIkSXUnn5/zX/ssXgxjxhRJ3vXBcC1JkqS6snFjdlM6pWxZyHGl1SWTd+0ZriVJklRXypZWFyZvy0IkSZKk8sqWVi9cCGPHZuf0RdTdvGvDtSRJkurO4sUwblyRDF3n864N15IkSao7Q5533W9WX+0ZriVJklSXys67Hjs2m7zHjq2riSGja70ASZIkqZh8Y2Nvb5F51xs2ZNP2woXZ53XCcC1JkqS6VXLqXiZTV6E6z7IQSZIk1aU6n7pXlOFakiRJdanOp+4VZbiWJElSXarzqXtFGa4lSZJUt/bvh6NHs+H64MH6Lw0xXEuSJKluTZyYDdaQ/VrvpSGGa0mSJNWt/fuzNdeQ/bp/f23XMxjDtSRJkurWwoVwyinZYN3W1uI71xFxW0Q8FREPlLgfEfF3EfFwRNwXEW8quPfeiHgo93hvNdcpSZKk+tRoTY3V3rn+IvDWMvffBszMPTqA1QARMQG4GbgEuBi4OSLOqOpKJUmSVJdKHoNeh6oarlNKm4EDZV5yFbA2ZW0BxkfEZOA3ge+klA6klJ4BvkP5kC5JkqQm1Ujzrmtdcz0FeKzg+Z7ctVLXjxMRHRGxNSK27tu3r2oLlSRJUm00UmlIrcP1sKWUOlNKF6WULpo0aVKtlyNJkqQqaJTSkFqH68eBcwqen527Vuq6JEmSWtDChdmd64js14ULa72i4modru8EFuemhswFfp5S2gt8C7gyIs7INTJembsmSZKkFhXR/2s9Gl3NHx4RXwYWAmdGxB6yE0DGAKSUbgG+AfwW8DDwS+CPcvcORMRfAvfkftRfpJTKNUZKkiSpiW3cCEeOQErZrxs3Zmux601Vw3VK6T2D3E/AH5e4dxtwWzXWJUmSpMaSnxhy8GB9TwypdVmIJEmSNKhGmRhiuJYkSVJDaISJIYZrSZIkNYR8acioUdmv9TgxpKo115IkSVKlZDKwYQOsXVvrlZTmzrUkSZIaype+BJ/7HFx+ef3VXRuuJUmS1DA2bszWWx89Wp9114ZrSZIkNYx6P6nRcC1JkqSGUs8nNRquJUmS1DCKndRYTwzXkiRJahj5cXxtbfV5UqPhWpIkSQ2j3k9qNFxLkiSpodTzSY2Ga0mSJDWUej6p0RMaJUmS1FDyJzVu3JgN1plMrVd0jOFakiRJDSeTqa9QnWdZiCRJklQhhmtJkiSpQgzXkiRJUoUYriVJkqQKMVxLkiRJFWK4liRJkirEcC1JkiRViOFakiRJqhDDtSRJklQhhmtJkiSpQgzXkiRJUoUYriVJkqQKMVxLkiRJFWK4liRJkirEcC1JkiRViOFakiRJqpBIKdV6DRUTEfuA3TX4rc8Enq7B76uR5fvcGnyfW4Pvc/PzPW4NtXqfp6aUJhW70VThulYiYmtK6aJar0PV5fvcGnyfW4Pvc/PzPW4N9fg+WxYiSZIkVYjhWpIkSaoQw3VldNZ6ARoRvs+twfe5Nfg+Nz/f49ZQd++zNdeSJElShbhzLUmSJFWI4XqYIuKtEfGTiHg4Im6q9Xp0ciLinIj4bkTsiIgfRcQNuesTIuI7EfFQ7usZuesREX+Xe9/vi4g31fafQCciIkZFxLaI+Nfc8+kRcXfu/fzniBibu35K7vnDufvTarluDV1EjI+Ir0TEjyPiwYjI+HluPhHxp7k/sx+IiC9HxDg/z40vIm6LiKci4oGCayf8+Y2I9+Ze/1BEvHek1m+4HoaIGAX8A/A2YDbwnoiYXdtV6SQdAf48pTQbmAv8ce69vAnYkFKaCWzIPYfsez4z9+gAVo/8kjUMNwAPFjxfAXwmpTQDeAa4Lnf9OuCZ3PXP5F6nxvC3wL+nlN4AXED2/fbz3EQiYgrw34GLUkpvBEYBv4+f52bwReCtA66d0Oc3IiYANwOXABcDN+cDebUZrofnYuDhlNIjKaVDwD8BV9V4TToJKaW9KaUf5L5/nuy/iKeQfT+/lHvZl4BFue+vAtamrC3A+IiYPMLL1kmIiLOBtwOfzz0P4M3AV3IvGfg+59//rwCX516vOhYRrwTmA7cCpJQOpZSexc9zMxoNvCwiRgOnAnvx89zwUkqbgQMDLp/o5/c3ge+klA6klJ4BvsPxgb0qDNfDMwV4rOD5ntw1NbDcXxVeCNwNvDqltDd3qwd4de573/vGtQpYBvTmnk8Enk0pHck9L3wv+97n3P2f516v+jYd2Ad8IVf+8/mIeDl+nptKSulx4FPAo2RD9c+Be/Hz3KxO9PNbs8+14VoqEBGvAL4K/ElK6bnCeyk7WsfxOg0sIt4BPJVSurfWa1FVjQbeBKxOKV0I/IJjf4UM+HluBrm/4r+K7H9MnQW8nBHamVRt1fvn13A9PI8D5xQ8Pzt3TQ0oIsaQDdZ3pJT+JXf5yfxfD+e+PpW77nvfmC4F3hURu8iWcb2ZbG3u+NxfK0P/97Lvfc7dfyWwfyQXrJOyB9iTUro79/wrZMO2n+fmcgXws5TSvpTSYeBfyH7G/Tw3pxP9/Nbsc224Hp57gJm5zuSxZBsp7qzxmnQScnV3twIPppT+puDWnUC+w/i9wNcKri/OdSnPBX5e8NdVqlMppY+mlM5OKU0j+3n9j5TSNcB3gd/JvWzg+5x//38n9/q63S1RVkqpB3gsIl6fu3Q5sAM/z83mUWBuRJya+zM8/z77eW5OJ/r5/RZwZUSckftbjitz16rOQ2SGKSJ+i2wN5yjgtpTSX9V4SToJEXEZ8D3gfo7V4n6MbN31/wZeA+wG/ktK6UDuD/L/RfavIH8J/FFKaeuIL1wnLSIWAh9OKb0jIl5Ldid7ArANuDaldDAixgH/SLYG/wDw+ymlR2q1Zg1dRMwh27Q6FngE+COyG0p+nptIRHwc+D2yE5+2Ae8nW1fr57mBRcSXgYXAmcCTZKd+rOcEP78R8T6y/y4H+KuU0hdGZP2Ga0mSJKkyLAuRJEmSKsRwLUmSJFWI4VqSJEmqEMO1JEmSVCGGa0mSJKlCDNeS1AAi4oXc12kR8QcV/tkfG/C8q5I/X5JaieFakhrLNOCEwnXBaXWl9AvXKaV5J7gmSVKO4VqSGsty4NcjYntE/GlEjIqIT0bEPRFxX0QsgewhORHxvYi4k+ypdUTE+oi4NyJ+FBEduWvLgZflft4duWv5XfLI/ewHIuL+iPi9gp+9MSK+EhE/jog7cgc5EBHLI2JHbi2fGvH/dSSpxgbbzZAk1ZebyJ0sCZALyT9PKf1aRJwC3BUR38699k3AG1NKP8s9f1/uRLOXAfdExFdTSjdFxIdSSnOK/F6/DcwBLiB7Uto9EbE5d+9C4DzgCeAu4NKIeBC4GnhDSilFxPiK/9NLUp1z51qSGtuVwOKI2A7cDUwEZubu/WdBsAb47xHxQ2ALcE7B60q5DPhySuloSulJYBPwawU/e09KqRfYTrZc5efAS8CtEfHbZI8ilqSWYriWpMYWwH9LKc3JPaanlPI717/oe1HEQuAKIJNSugDYBowbxu97sOD7o8DolNIR4GLgK8A7gH8fxs+XpIZkuJakxvI8cFrB828BH4yIMQAR8bqIeHmRX/dK4JmU0i8j4g3A3IJ7h/O/foDvAb+Xq+ueBMwH/rPUwiLiFcArU0rfAP6UbDmJJLUUa64lqbHcBxzNlXd8EfhbsiUZP8g1Fe4DFhX5df8OLM3VRf+EbGlIXidwX0T8IKV0TcH1dUAG+CGQgGUppZ5cOC/mNOBrETGO7I76n53cP6IkNa5IKdV6DZIkSVJTsCxEkiRJqhDDtSRJklQhhmtJkiSpQgzXkiRJUoUYriVJkqQKMVxLkiRJFWK4liRJkirEcC1JkiRVyP8PyKeIiTGsgzQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJEhxJanbFCg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "dcacebd0-9d16-40c1-aeee-301aa5750a23"
      },
      "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": 282,
      "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": "code",
      "metadata": {
        "id": "PuTRLc611Ayn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "3b7806d8-6c9b-4cd6-a6b5-91bd11c0101a"
      },
      "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": 283,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[601  98]\n",
            " [150 552]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.86      0.83       699\n",
            "           1       0.85      0.79      0.82       702\n",
            "\n",
            "    accuracy                           0.82      1401\n",
            "   macro avg       0.82      0.82      0.82      1401\n",
            "weighted avg       0.82      0.82      0.82      1401\n",
            "\n",
            "0.8229835831548894\n",
            "[[248  54]\n",
            " [ 72 227]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.82      0.80       302\n",
            "           1       0.81      0.76      0.78       299\n",
            "\n",
            "    accuracy                           0.79       601\n",
            "   macro avg       0.79      0.79      0.79       601\n",
            "weighted avg       0.79      0.79      0.79       601\n",
            "\n",
            "0.7903494176372712\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HDpSEICHtNlB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "782e9e17-35a9-433d-af2d-4d1420ad7729"
      },
      "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": 284,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "281\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbPP4IIn3lMu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "9a38f73d-fd35-4962-b00c-772bdb4c90f9"
      },
      "source": [
        "print(y_train)\n",
        "print(l_list_tmp)"
      ],
      "execution_count": 285,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 1 ... 1 0 1]\n",
            "[0. 0. 1. ... 1. 0. 1.]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}