assignment11_final_ver5.ipynb

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  "metadata": {
    "colab": {
      "name": "assignment11-1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP2fnGPgb4n4Bi715CvP5vd",
      "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_final_ver5.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": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEmx3Nerk1ud",
        "colab_type": "code",
        "colab": {
          "resources": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 148
        },
        "outputId": "2a782a02-5bb1-4887-ce51-526a1582cabf"
      },
      "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": 5,
      "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"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5ece4120-ed43-4905-a264-d474478a8707\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5ece4120-ed43-4905-a264-d474478a8707\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving movie_review.zip to movie_review.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "matjDvyhlDJJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "5ef2b912-d1aa-4d19-d8b8-64121acb791d"
      },
      "source": [
        "print(documents)"
      ],
      "execution_count": 6,
      "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": "b2e7acc0-efff-41a0-8e11-f82ed6108e47"
      },
      "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": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n",
            "[ 7. 60.  4. ...  3.  8.  5.]\n",
            "[ 2. 55.  6. ...  7.  6. 12.]\n",
            "884\n",
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [1. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
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            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 2. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 2. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 2. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQpyXu3jGOvY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "outputId": "d7e19713-f7e4-40fb-e55f-5530f4a516fa"
      },
      "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": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [1. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "[0 0 1 ... 1 0 1]\n",
            "[1 1 0 1 1 1 1 0 0 0 0 1 1 0 0 1 1 1 0 0 0 0 1 1 1 0 0 1 1 1 0 0 0 1 1 0 1\n",
            " 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 1 1 0 0 0 0\n",
            " 0 0 1 1 1 0 1 1 1 0 1 1 0 1 1 0 1 1 0 0 0 1 0 0 0 1 1 1 0 0 1 1 1 1 1 0 0\n",
            " 0 1 1 0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 0 1 0 1 0\n",
            " 1 1 1 1 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1 1 0 1 0 0 1 1 1 0\n",
            " 1 1 1 1 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 0 1\n",
            " 1 1 1 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 1 0 0 0 1 0 0 1 0 0 1 1 1 1 1 1 1 0 0\n",
            " 0 1 1 0 0 0 0 0 1 0 1 1 1 0 0 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 0 0 0 1 0 0\n",
            " 0 0 0 1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 0 0 1 0 0 1 1 0 1 1 1 0 0 1 1 1 1 0 1\n",
            " 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1\n",
            " 0 0 0 1 0 1 1 1 0 0 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 0 0 1 1 0 0 1 0\n",
            " 1 0 1 1 0 1 1 0 1 1 0 1 0 1 0 0 1 1 1 1 0 0 1 1 0 1 0 0 0 0 1 0 0 1 1 1 1\n",
            " 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 1 1 1 1\n",
            " 1 0 1 1 0 1 0 0 0 0 0 1 1 1 1 1 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1\n",
            " 0 0 1 0 0 1 1 1 1 0 0 0 0 1 1 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0\n",
            " 1 0 0 1 1 0 0 0 1 1 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 0 1\n",
            " 1 1 1 0 0 1 1 0 0]\n",
            "(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": 9,
      "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": 10,
      "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": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AfHkfVcXRZIw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "d2394aa8-9e07-4f8a-8205-8367e63c229d"
      },
      "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": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.datasets.samples_generator module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "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",
        "l_list2 = np.zeros(shape=(601, 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": 13,
      "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": 14,
      "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": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVgg5PyfCGAn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "143430d2-2d83-46b6-b506-dacd2b4f3bb4"
      },
      "source": [
        "# (1401, 1500)\n",
        "# (601, 1500)\n",
        "# (1401,)\n",
        "# (601,)\n",
        "\n",
        "iteration = 5000\n",
        "l_r = 0.5\n",
        "lambda_ = 100\n",
        "theta_num = (7+7*49+49*349+349*(len(train_x)-1))\n",
        "max_acc = 0\n",
        "l_list2_max = np.zeros(shape=(601, 1, 1))\n",
        "l_list1_max = np.zeros(shape=(1401, 1, 1))\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",
        "\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",
        "  if max_acc < accuracy_history2[t]:\n",
        "    max_acc = accuracy_history2[t]\n",
        "\n",
        "    l_list2_max = l_list2\n",
        "    l_list_max = l_list\n",
        "\n",
        "  # accuracy_history2[t] = number_of_correct_predictions2/total_number_of_predictions2*100\n",
        "  t = t + 1"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43m스트리밍 출력 내용이 길어서 마지막 5000줄이 삭제되었습니다.\u001b[0m\n",
            "1    train :   50.107066381156315      1.192826873863109    |   test_accuracy :   49.750415973377706\n",
            "2    train :   50.107066381156315      1.192312326195172    |   test_accuracy :   49.750415973377706\n",
            "3    train :   50.107066381156315      1.1919756775079213    |   test_accuracy :   49.750415973377706\n",
            "4    train :   50.107066381156315      1.1917288997151867    |   test_accuracy :   49.750415973377706\n",
            "5    train :   50.107066381156315      1.1915276178009793    |   test_accuracy :   49.750415973377706\n",
            "6    train :   50.107066381156315      1.191349403339939    |   test_accuracy :   49.750415973377706\n",
            "7    train :   50.32119914346895      1.1911829032085792    |   test_accuracy :   49.750415973377706\n",
            "8    train :   50.24982155603141      1.191022365370146    |   test_accuracy :   49.58402662229617\n",
            "9    train :   50.60670949321913      1.1908648739473362    |   test_accuracy :   49.08485856905158\n",
            "10    train :   50.46395431834404      1.190708950257769    |   test_accuracy :   48.91846921797005\n",
            "11    train :   50.24982155603141      1.190553844245772    |   test_accuracy :   49.08485856905158\n",
            "12    train :   50.60670949321913      1.1903991753666507    |   test_accuracy :   48.75207986688852\n",
            "13    train :   51.10635260528195      1.1902447505058271    |   test_accuracy :   49.25124792013311\n",
            "14    train :   50.96359743040685      1.19009047163425    |   test_accuracy :   48.58569051580699\n",
            "15    train :   50.96359743040685      1.1899362889659524    |   test_accuracy :   48.91846921797005\n",
            "16    train :   51.32048536759457      1.1897821771937769    |   test_accuracy :   48.41930116472546\n",
            "17    train :   51.034975017844395      1.1896281234319979    |   test_accuracy :   48.41930116472546\n",
            "18    train :   50.89221984296931      1.1894741210982722    |   test_accuracy :   48.91846921797005\n",
            "19    train :   50.74946466809421      1.189320166809087    |   test_accuracy :   48.91846921797005\n",
            "20    train :   50.678087080656674      1.1891662588043062    |   test_accuracy :   48.91846921797005\n",
            "21    train :   50.678087080656674      1.1890123961476573    |   test_accuracy :   48.58569051580699\n",
            "22    train :   50.74946466809421      1.188858578320967    |   test_accuracy :   48.58569051580699\n",
            "23    train :   50.74946466809421      1.1887048050182414    |   test_accuracy :   48.58569051580699\n",
            "24    train :   50.678087080656674      1.1885510760411404    |   test_accuracy :   48.58569051580699\n",
            "25    train :   50.74946466809421      1.188397391245936    |   test_accuracy :   48.58569051580699\n",
            "26    train :   50.74946466809421      1.1882437505165901    |   test_accuracy :   48.58569051580699\n",
            "27    train :   50.74946466809421      1.1880901537510804    |   test_accuracy :   48.58569051580699\n",
            "28    train :   50.82084225553176      1.1879366008544663    |   test_accuracy :   48.58569051580699\n",
            "29    train :   50.89221984296931      1.1877830917353633    |   test_accuracy :   48.58569051580699\n",
            "30    train :   51.034975017844395      1.1876296263041501    |   test_accuracy :   48.75207986688852\n",
            "31    train :   51.034975017844395      1.1874762044720608    |   test_accuracy :   48.75207986688852\n",
            "32    train :   51.034975017844395      1.1873228261507216    |   test_accuracy :   48.75207986688852\n",
            "33    train :   51.24910778015703      1.1871694912518995    |   test_accuracy :   48.75207986688852\n",
            "34    train :   51.24910778015703      1.1870161996873985    |   test_accuracy :   48.91846921797005\n",
            "35    train :   51.24910778015703      1.1868629513689821    |   test_accuracy :   48.91846921797005\n",
            "36    train :   51.32048536759457      1.1867097462083365    |   test_accuracy :   48.91846921797005\n",
            "37    train :   51.32048536759457      1.1865565841170553    |   test_accuracy :   48.91846921797005\n",
            "38    train :   51.32048536759457      1.1864034650066293    |   test_accuracy :   48.75207986688852\n",
            "39    train :   51.32048536759457      1.1862503887884221    |   test_accuracy :   48.75207986688852\n",
            "40    train :   51.32048536759457      1.1860973553736835    |   test_accuracy :   48.75207986688852\n",
            "41    train :   51.39186295503212      1.1859443646735301    |   test_accuracy :   48.75207986688852\n",
            "42    train :   51.53461812990721      1.1857914165989418    |   test_accuracy :   48.75207986688852\n",
            "43    train :   51.53461812990721      1.185638511060756    |   test_accuracy :   48.91846921797005\n",
            "44    train :   51.53461812990721      1.185485647969665    |   test_accuracy :   48.91846921797005\n",
            "45    train :   51.605995717344754      1.1853328272362076    |   test_accuracy :   48.91846921797005\n",
            "46    train :   51.605995717344754      1.1851800487707678    |   test_accuracy :   49.08485856905158\n",
            "47    train :   51.67737330478231      1.1850273124835595    |   test_accuracy :   49.08485856905158\n",
            "48    train :   51.53461812990721      1.1848746182846357    |   test_accuracy :   49.08485856905158\n",
            "49    train :   51.605995717344754      1.184721966083871    |   test_accuracy :   49.08485856905158\n",
            "50    train :   51.67737330478231      1.1845693557909622    |   test_accuracy :   49.08485856905158\n",
            "51    train :   51.67737330478231      1.184416787315421    |   test_accuracy :   49.08485856905158\n",
            "52    train :   51.67737330478231      1.184264260566569    |   test_accuracy :   49.08485856905158\n",
            "53    train :   51.67737330478231      1.1841117754535289    |   test_accuracy :   49.08485856905158\n",
            "54    train :   51.67737330478231      1.1839593318852264    |   test_accuracy :   49.08485856905158\n",
            "55    train :   51.67737330478231      1.18380692977038    |   test_accuracy :   49.08485856905158\n",
            "56    train :   51.67737330478231      1.1836545690174913    |   test_accuracy :   49.08485856905158\n",
            "57    train :   51.67737330478231      1.1835022495348488    |   test_accuracy :   49.08485856905158\n",
            "58    train :   51.67737330478231      1.183349971230514    |   test_accuracy :   49.08485856905158\n",
            "59    train :   51.74875089221984      1.1831977340123214    |   test_accuracy :   49.08485856905158\n",
            "60    train :   51.74875089221984      1.1830455377878688    |   test_accuracy :   49.25124792013311\n",
            "61    train :   51.74875089221984      1.1828933824645151    |   test_accuracy :   49.25124792013311\n",
            "62    train :   51.82012847965739      1.1827412679493698    |   test_accuracy :   49.25124792013311\n",
            "63    train :   51.74875089221984      1.1825891941492934    |   test_accuracy :   49.25124792013311\n",
            "64    train :   51.74875089221984      1.1824371609708886    |   test_accuracy :   49.25124792013311\n",
            "65    train :   51.74875089221984      1.1822851683204898    |   test_accuracy :   49.25124792013311\n",
            "66    train :   51.74875089221984      1.18213321610417    |   test_accuracy :   49.25124792013311\n",
            "67    train :   51.74875089221984      1.1819813042277172    |   test_accuracy :   49.25124792013311\n",
            "68    train :   51.74875089221984      1.181829432596645    |   test_accuracy :   49.25124792013311\n",
            "69    train :   51.74875089221984      1.1816776011161765    |   test_accuracy :   49.25124792013311\n",
            "70    train :   51.74875089221984      1.181525809691244    |   test_accuracy :   49.417637271214645\n",
            "71    train :   51.74875089221984      1.1813740582264785    |   test_accuracy :   49.417637271214645\n",
            "72    train :   51.74875089221984      1.1812223466262055    |   test_accuracy :   49.417637271214645\n",
            "73    train :   51.67737330478231      1.1810706747944406    |   test_accuracy :   49.417637271214645\n",
            "74    train :   51.67737330478231      1.1809190426348817    |   test_accuracy :   49.417637271214645\n",
            "75    train :   51.67737330478231      1.1807674500509027    |   test_accuracy :   49.417637271214645\n",
            "76    train :   51.74875089221984      1.1806158969455467    |   test_accuracy :   49.417637271214645\n",
            "77    train :   51.67737330478231      1.1804643832215231    |   test_accuracy :   49.417637271214645\n",
            "78    train :   51.74875089221984      1.1803129087811983    |   test_accuracy :   49.417637271214645\n",
            "79    train :   51.74875089221984      1.1801614735265855    |   test_accuracy :   49.417637271214645\n",
            "80    train :   51.82012847965739      1.1800100773593503    |   test_accuracy :   49.417637271214645\n",
            "81    train :   51.67737330478231      1.179858720180793    |   test_accuracy :   49.417637271214645\n",
            "82    train :   51.67737330478231      1.1797074018918445    |   test_accuracy :   49.58402662229617\n",
            "83    train :   51.74875089221984      1.1795561223930653    |   test_accuracy :   49.58402662229617\n",
            "84    train :   51.74875089221984      1.1794048815846336    |   test_accuracy :   49.417637271214645\n",
            "85    train :   51.82012847965739      1.1792536793663388    |   test_accuracy :   49.417637271214645\n",
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            "4955    train :   99.92862241256245      0.16795894244337847    |   test_accuracy :   83.52745424292846\n",
            "4956    train :   99.92862241256245      0.16792078670256713    |   test_accuracy :   83.52745424292846\n",
            "4957    train :   99.92862241256245      0.16788264519621257    |   test_accuracy :   83.52745424292846\n",
            "4958    train :   99.92862241256245      0.16784451791359356    |   test_accuracy :   83.52745424292846\n",
            "4959    train :   99.92862241256245      0.1678064048440195    |   test_accuracy :   83.52745424292846\n",
            "4960    train :   99.92862241256245      0.16776830597683018    |   test_accuracy :   83.52745424292846\n",
            "4961    train :   99.92862241256245      0.16773022130139617    |   test_accuracy :   83.52745424292846\n",
            "4962    train :   99.92862241256245      0.16769215080711786    |   test_accuracy :   83.52745424292846\n",
            "4963    train :   99.92862241256245      0.1676540944834261    |   test_accuracy :   83.52745424292846\n",
            "4964    train :   99.92862241256245      0.1676160523197814    |   test_accuracy :   83.52745424292846\n",
            "4965    train :   99.92862241256245      0.1675780243056743    |   test_accuracy :   83.52745424292846\n",
            "4966    train :   99.92862241256245      0.16754001043062486    |   test_accuracy :   83.52745424292846\n",
            "4967    train :   99.92862241256245      0.16750201068418244    |   test_accuracy :   83.52745424292846\n",
            "4968    train :   99.92862241256245      0.1674640250559261    |   test_accuracy :   83.52745424292846\n",
            "4969    train :   99.92862241256245      0.167426053535464    |   test_accuracy :   83.52745424292846\n",
            "4970    train :   99.92862241256245      0.16738809611243316    |   test_accuracy :   83.52745424292846\n",
            "4971    train :   99.92862241256245      0.16735015277649984    |   test_accuracy :   83.52745424292846\n",
            "4972    train :   99.92862241256245      0.16731222351735864    |   test_accuracy :   83.52745424292846\n",
            "4973    train :   99.92862241256245      0.16727430832473325    |   test_accuracy :   83.52745424292846\n",
            "4974    train :   99.92862241256245      0.1672364071883755    |   test_accuracy :   83.52745424292846\n",
            "4975    train :   99.92862241256245      0.16719852009806563    |   test_accuracy :   83.52745424292846\n",
            "4976    train :   99.92862241256245      0.16716064704361225    |   test_accuracy :   83.52745424292846\n",
            "4977    train :   99.92862241256245      0.1671227880148518    |   test_accuracy :   83.52745424292846\n",
            "4978    train :   99.92862241256245      0.16708494300164883    |   test_accuracy :   83.52745424292846\n",
            "4979    train :   99.92862241256245      0.16704711199389569    |   test_accuracy :   83.52745424292846\n",
            "4980    train :   99.92862241256245      0.16700929498151204    |   test_accuracy :   83.52745424292846\n",
            "4981    train :   99.92862241256245      0.16697149195444552    |   test_accuracy :   83.52745424292846\n",
            "4982    train :   99.92862241256245      0.16693370290267082    |   test_accuracy :   83.52745424292846\n",
            "4983    train :   99.92862241256245      0.16689592781619003    |   test_accuracy :   83.52745424292846\n",
            "4984    train :   99.92862241256245      0.1668581666850322    |   test_accuracy :   83.52745424292846\n",
            "4985    train :   99.92862241256245      0.16682041949925364    |   test_accuracy :   83.52745424292846\n",
            "4986    train :   99.92862241256245      0.1667826862489371    |   test_accuracy :   83.52745424292846\n",
            "4987    train :   99.92862241256245      0.1667449669241924    |   test_accuracy :   83.52745424292846\n",
            "4988    train :   99.92862241256245      0.16670726151515583    |   test_accuracy :   83.52745424292846\n",
            "4989    train :   99.92862241256245      0.16666957001198995    |   test_accuracy :   83.52745424292846\n",
            "4990    train :   99.92862241256245      0.16663189240488396    |   test_accuracy :   83.52745424292846\n",
            "4991    train :   99.92862241256245      0.166594228684053    |   test_accuracy :   83.52745424292846\n",
            "4992    train :   99.92862241256245      0.16655657883973832    |   test_accuracy :   83.52745424292846\n",
            "4993    train :   99.92862241256245      0.16651894286220725    |   test_accuracy :   83.52745424292846\n",
            "4994    train :   99.92862241256245      0.1664813207417528    |   test_accuracy :   83.52745424292846\n",
            "4995    train :   99.92862241256245      0.16644371246869383    |   test_accuracy :   83.52745424292846\n",
            "4996    train :   99.92862241256245      0.1664061180333745    |   test_accuracy :   83.52745424292846\n",
            "4997    train :   99.92862241256245      0.16636853742616456    |   test_accuracy :   83.52745424292846\n",
            "4998    train :   99.92862241256245      0.1663309706374592    |   test_accuracy :   83.52745424292846\n",
            "4999    train :   99.92862241256245      0.16629341765767855    |   test_accuracy :   83.52745424292846\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPFdMKCZu7SF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cost_history_tmp = []\n",
        "cost_history2_tmp = []\n",
        "\n",
        "accuracy_history_tmp = []\n",
        "accuracy_history2_tmp = []\n",
        "\n",
        "count_acc = 0\n",
        "max_tmp_acc1 = accuracy_history[999]\n",
        "max_tmp_acc2 = accuracy_history2[999]\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",
        "  cost_history_tmp.append(cost_history[i])\n",
        "  cost_history2_tmp.append(cost_history2[i])\n",
        "  count_acc = count_acc + 1\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(max_tmp_acc1 - accuracy_history[i]) < 2) and (abs(max_tmp_acc2 - accuracy_history[i])) < 2 ) and ((abs(accuracy_history[i] - accuracy_history[i + 1]) < 2) and (abs(accuracy_history2[i] - accuracy_history2[i + 1]) < 2)) and ((abs(accuracy_history[i] - accuracy_history[i - 1]) < 2) and (abs(accuracy_history2[i] - accuracy_history2[i - 1]) < 2)) :\n",
        "    accuracy_history_tmp.append(accuracy_history[i])\n",
        "    accuracy_history2_tmp.append(accuracy_history2[i])\n",
        "\n",
        "    cost_history_tmp.append(cost_history[i])\n",
        "    cost_history2_tmp.append(cost_history2[i])\n",
        "\n",
        "    max_tmp_acc1 = accuracy_history[i]\n",
        "    max_tmp_acc2 = accuracy_history2[i]\n",
        "\n",
        "    count_acc = count_acc + 1\n",
        "  \n",
        "###############################################\n",
        "\n",
        "# for i in range (0, 1000):\n",
        "#   cost_history_tmp.append(cost_history[i])\n",
        "#   cost_history2_tmp.append(cost_history2[i])\n",
        "#   count_acc = count_acc + 1\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]) < 1)) and ((abs(accuracy_history[i] - accuracy_history[i - 1]) < 1) and (abs(accuracy_history2[i] - accuracy_history2[i - 1]) < 1)) :\n",
        "#     accuracy_history_tmp.append(accuracy_history[i])\n",
        "#     accuracy_history2_tmp.append(accuracy_history2[i])\n",
        "#     count_acc = count_acc + 1"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhokQqGN1B-X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cost_history_tmp2 = []\n",
        "cost_history2_tmp2 = []\n",
        "\n",
        "accuracy_history_tmp2 = []\n",
        "accuracy_history2_tmp2 = []\n",
        "\n",
        "for i in range(800, count_acc):\n",
        "  cost_history_tmp2.append(cost_history_tmp[i])\n",
        "  cost_history2_tmp2.append(cost_history2_tmp[i])\n",
        "  accuracy_history_tmp2.append(accuracy_history_tmp[i])\n",
        "  accuracy_history2_tmp2.append(accuracy_history2_tmp[i])\n",
        "\n",
        "count_acc = count_acc - 800\n",
        "\n"
      ],
      "execution_count": 29,
      "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": 499
        },
        "outputId": "2c1e92cb-223a-4260-ba75-03ac26224b26"
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(12,8))\n",
        "\n",
        "ax.set_ylabel('J(Theta)')\n",
        "ax.set_xlabel('Iterations')\n",
        "_=ax.plot(range(count_acc),cost_history_tmp2,'b.', color = 'blue')\n",
        "_=ax.plot(range(count_acc),cost_history2_tmp2,'b.', color = 'red')"
      ],
      "execution_count": 30,
      "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": "9rU03NpN1y6W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "aaf2a5c8-a327-4133-a8ec-5f90c2286727"
      },
      "source": [
        "print(max_acc)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "87.35440931780366\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": "067fa551-2694-492e-eedb-ee2051fca6ef"
      },
      "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_acc),accuracy_history_tmp2,'b.', color = 'blue')\n",
        "_=ax.plot(range(count_acc),accuracy_history2_tmp2,'b.', color = 'red')\n",
        "# print(ac_tmp)"
      ],
      "execution_count": 31,
      "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": "d1166631-fe09-4ad9-f4f3-749bfdb2d344"
      },
      "source": [
        "#Codes for Evaluation\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "l_list_tmp = l_list_max.reshape((1401))\n",
        "l_list2_tmp = l_list2_max.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": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[698   1]\n",
            " [  0 702]]\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",
            "0.9992862241256245\n",
            "[[249  53]\n",
            " [ 46 253]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.82      0.83       302\n",
            "           1       0.83      0.85      0.84       299\n",
            "\n",
            "    accuracy                           0.84       601\n",
            "   macro avg       0.84      0.84      0.84       601\n",
            "weighted avg       0.84      0.84      0.84       601\n",
            "\n",
            "0.8352745424292846\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oaqn0Eko45v3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b978e89d-dc80-409c-aa7e-1dccbd8cbf75"
      },
      "source": [
        "print(\"MAX accuracy score of test : \", max_acc, \"%\")"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAX accuracy score of test :  87.35440931780366 %\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}