{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (1.19.2)\n",
      "Requirement already satisfied: seaborn in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (0.11.0)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (0.23.2)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (3.3.2)\n",
      "Requirement already satisfied: fasttext in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (0.9.2)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from seaborn) (1.1.3)\n",
      "Requirement already satisfied: scipy>=1.0 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from seaborn) (1.5.2)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from scikit-learn) (0.17.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from scikit-learn) (2.1.0)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from matplotlib) (2020.6.20)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from matplotlib) (8.0.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from matplotlib) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from matplotlib) (1.3.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: setuptools>=0.7.0 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from fasttext) (50.3.1.post20201107)\n",
      "Requirement already satisfied: pybind11>=2.2 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from fasttext) (2.6.2)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from pandas>=0.23->seaborn) (2020.1)\n",
      "Requirement already satisfied: six in c:\\users\\jedpc\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib) (1.15.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install numpy seaborn scikit-learn matplotlib fasttext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import copy\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "from scipy.cluster import hierarchy\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial import distance_matrix\n",
    "import fasttext\n",
    "import fasttext.util\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open(\"C:/Users/JedPC/Desktop/ISI/polish-urban-legends-public/dev-0/in.tsv\", encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_doc = []\n",
    "for line in file:\n",
    "    all_doc.append(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "document_vectors = vectorizer.fit_transform(all_doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5937322507759797"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(document_vectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ILOSC K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "K = 40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OBLICZANIE ILOSCI ZMIENNYCH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ELEMENTS = document_vectors.shape[0]\n",
    "SIZE = document_vectors.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ALGORYTM K SREDNICH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_random_centroids():\n",
    "    CENTROIDS = np.zeros((K, SIZE))\n",
    "    for i in range(K):\n",
    "        for j in range(SIZE):\n",
    "            CENTROIDS[i,j] = np.random.uniform(0,2)\n",
    "            if CENTROIDS[i,j] > 1:\n",
    "                CENTROIDS[i,j] = 0\n",
    "    return CENTROIDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "CENTROIDS = get_random_centroids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_data_to_labels(document_vectors, CENTROIDS):\n",
    "    LABELS = []\n",
    "    for POINT in document_vectors:\n",
    "        DISTANCES = [np.linalg.norm(POINT - CEN) for CEN in CENTROIDS]\n",
    "        \n",
    "        LABELS.append(np.argmin(DISTANCES))\n",
    "    return np.array(LABELS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "LABELS = assign_data_to_labels(document_vectors, CENTROIDS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_new_centroids(document_vectors, LABELS, CENTROIDS):\n",
    "    NEW_CENTROIDS = np.zeros_like(CENTROIDS)\n",
    "    for centroid_label in range(K):\n",
    "        CENT_DATA = document_vectors[LABELS == centroid_label]\n",
    "        NEW_CENTROIDS[centroid_label] = np.mean(CENT_DATA)            \n",
    "    return NEW_CENTROIDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JedPC\\anaconda3\\lib\\site-packages\\scipy\\sparse\\base.py:581: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return self.astype(np.float_)._mul_scalar(1./other)\n"
     ]
    }
   ],
   "source": [
    "NEW_CENTROIDS = get_new_centroids(document_vectors, LABELS, CENTROIDS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "CENTROIDS = NEW_CENTROIDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NEW_CENTROIDS[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LITERACJE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMBER = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(NUMBER):\n",
    "    LABELS = assign_data_to_labels(document_vectors, CENTROIDS)\n",
    "    CENTROIDS = get_new_centroids(document_vectors, LABELS, CENTROIDS)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n",
      "0\n",
      "1\n",
      "1\n",
      "30\n",
      "1\n",
      "0\n",
      "30\n",
      "30\n",
      "1\n",
      "0\n",
      "1\n",
      "30\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "30\n",
      "30\n",
      "0\n",
      "0\n",
      "1\n",
      "30\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "30\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "30\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "30\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "30\n",
      "30\n",
      "30\n",
      "0\n",
      "0\n",
      "30\n",
      "1\n",
      "30\n",
      "1\n",
      "0\n",
      "30\n",
      "1\n",
      "30\n"
     ]
    }
   ],
   "source": [
    "LABELS.shape[0]\n",
    "for i in range(LABELS.shape[0]):\n",
    "    print(LABELS[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30,  0,  1,  1, 30,  1,  0, 30, 30,  1,  0,  1, 30,  1,  1,  1,  1,\n",
       "        1,  0,  0,  1,  1,  0,  1,  1, 30, 30,  0,  0,  1, 30,  0,  0,  1,\n",
       "        1,  0,  1,  1, 30,  1,  0,  1,  1,  0,  0,  0,  1,  0, 30,  1,  0,\n",
       "        1,  0,  1,  0,  0,  0, 30,  0,  1,  0,  0,  0,  0,  0,  1,  0,  1,\n",
       "        1,  1,  0,  0,  0,  0, 30, 30, 30,  0,  0, 30,  1, 30,  1,  0, 30,\n",
       "        1, 30], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LABELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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