corpus_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(text):\n",
    "    \n",
    "    text = text.lower() # 모든 문자를 소문자로 바꿔주는 과정\n",
    "    text = text.replace(\".\", \" .\")\n",
    "    words = text.split()\n",
    "    word_to_id = {}\n",
    "    id_to_word = {}\n",
    "    \n",
    "    for word in words:\n",
    "        if word not in word_to_id:\n",
    "            new_id = len(word_to_id)\n",
    "            word_to_id[word] = new_id\n",
    "            id_to_word[new_id] = word\n",
    "        \n",
    "    corpus = np.array([word_to_id[w] for w in words])\n",
    "    \n",
    "    return corpus, word_to_id, id_to_word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_co_matrix(corpus, vocab_size, window_size=1):\n",
    "    \n",
    "    corpus_size = len(corpus)\n",
    "    co_matrix = np.zeros((vocab_size, vocab_size), dtype=np.int32)\n",
    "    \n",
    "    for idx, word_id in enumerate(corpus):\n",
    "        for i in range(1, window_size + 1):\n",
    "            left_idx = idx - i\n",
    "            right_idx = idx + i\n",
    "            \n",
    "            if left_idx >= 0:\n",
    "                left_word_id = corpus[left_idx]\n",
    "                co_matrix[word_id, left_word_id] += 1\n",
    "                \n",
    "            if right_idx < corpus_size:\n",
    "                right_word_id = corpus[right_idx]\n",
    "                co_matrix[word_id, right_word_id] += 1\n",
    "    \n",
    "    return co_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cos_similarity(x, y, eps=1e-8):\n",
    "    nx = x / np.sqrt(np.sum(x**2) + eps)\n",
    "    ny = y / np.sqrt(np.sum(y**2) + eps)\n",
    "    \n",
    "    return np.dot(nx, ny)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"you say goodbye and i say hello.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus, word_to_id, id_to_word = preprocessing(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 1 5 6]\n",
      "{'you': 0, 'say': 1, 'goodbye': 2, 'and': 3, 'i': 4, 'hello': 5, '.': 6}\n",
      "{0: 'you', 1: 'say', 2: 'goodbye', 3: 'and', 4: 'i', 5: 'hello', 6: '.'}\n"
     ]
    }
   ],
   "source": [
    "print(corpus)\n",
    "print(word_to_id)\n",
    "print(id_to_word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_size = len(word_to_id)\n",
    "co_matrix = create_co_matrix(corpus, vocab_size=vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 0 0 0 0 0]\n",
      " [1 0 1 0 1 1 0]\n",
      " [0 1 0 1 0 0 0]\n",
      " [0 0 1 0 1 0 0]\n",
      " [0 1 0 1 0 0 0]\n",
      " [0 1 0 0 0 0 1]\n",
      " [0 0 0 0 0 1 0]]\n"
     ]
    }
   ],
   "source": [
    "print(co_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = co_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 0 0 0]\n",
      "[0 1 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "c0 = C[word_to_id[\"you\"]]\n",
    "c1 = C[word_to_id[\"i\"]]\n",
    "print(c0)\n",
    "print(c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7071067758832467\n"
     ]
    }
   ],
   "source": [
    "print(cos_similarity(c0, c1))"
   ]
  }
 ],
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