{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Test word vectors",
   "id": "b79941bf4553fd6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-18T23:28:55.563335Z",
     "start_time": "2025-11-18T23:28:53.763429Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import import_ipynb\n",
    "#from notebooks. import *\n",
    "\n",
    "import spacy\n",
    "nlp = spacy.load(\"en_core_web_md\")\n",
    "\n",
    "words = [\"cat\", \"dog\", \"feline\", \"vehicle\", \"car\"]\n",
    "# Test work similarities\n",
    "for word1 in words:\n",
    "    for word2 in words:\n",
    "        if word1 != word2:\n",
    "            similarity = nlp.vocab[word1].similarity(nlp.vocab[word2])\n",
    "            print(f\"{word1} - {word2}: {similarity:.3f}\")\n",
    "\n",
    "\n"
   ],
   "id": "8a3c4314a90086fe",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat - dog: 1.000\n",
      "cat - feline: 0.363\n",
      "cat - vehicle: 0.078\n",
      "cat - car: 0.193\n",
      "dog - cat: 1.000\n",
      "dog - feline: 0.363\n",
      "dog - vehicle: 0.078\n",
      "dog - car: 0.193\n",
      "feline - cat: 0.363\n",
      "feline - dog: 0.363\n",
      "feline - vehicle: 0.180\n",
      "feline - car: 0.050\n",
      "vehicle - cat: 0.078\n",
      "vehicle - dog: 0.078\n",
      "vehicle - feline: 0.180\n",
      "vehicle - car: 0.205\n",
      "car - cat: 0.193\n",
      "car - dog: 0.193\n",
      "car - feline: 0.050\n",
      "car - vehicle: 0.205\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Simple averaging",
   "id": "8f32b5695f554268"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-18T23:45:03.085563Z",
     "start_time": "2025-11-18T23:45:03.082190Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def sentence_similarity_avg(sent1, sent2):\n",
    "    doc1 = nlp(sent1)\n",
    "    doc2 = nlp(sent2)\n",
    "\n",
    "    # Vectors for each word, filter out words without vectors (medium model)\n",
    "    vecs1 = [token.vector for token in doc1 if token.has_vector]\n",
    "    vecs2 = [token.vector for token in doc2 if token.has_vector]\n",
    "\n",
    "    if not vecs1 or not vecs2:\n",
    "        return 0.0\n",
    "\n",
    "    # Average vectors\n",
    "    avg1 = sum(vecs1) / len(vecs1)\n",
    "    avg2 = sum(vecs2) / len(vecs2)\n",
    "\n",
    "    #cosine similarity\n",
    "    from sklearn.metrics.pairwise import cosine_similarity\n",
    "    return cosine_similarity([avg1], [avg2])[0][0]\n"
   ],
   "id": "68a6757447e4a1c7",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "SIF - Smooth Inverse Similarity",
   "id": "a9c3aa050f5bc0fe"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "def sentence_similarity_sif(sent1, sent2):\n",
    "    doc1 = nlp(sent1)\n",
    "    doc2 = nlp(sent2)"
   ],
   "id": "c100956f89d9b581"
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3 (ipykernel)"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}