CSE515_MWDB_Project/Phase 3/task_4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_feature_model = \"fc_fd\"\n",
    "\n",
    "t = int(input(\"Enter value of t: \"))\n",
    "if t < 1:\n",
    "    raise ValueError(\"t should be a positive integer\")\n",
    "\n",
    "num_layers = int(input(\"Enter the number of layers: \"))\n",
    "if num_layers < 1:\n",
    "    raise ValueError(\"num_layers should be a positive integer\")\n",
    "\n",
    "num_hashes_per_layer = int(input(\"Enter the number of hashes per layer: \"))\n",
    "if num_hashes_per_layer < 1:\n",
    "    raise ValueError(\"num_hashes_per_layer should be a positive integer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_images = list(fd_collection.find())\n",
    "all_images = sorted(all_images, key = lambda x: x[\"image_id\"])\n",
    "\n",
    "odd_image_ids = [img[\"image_id\"] for img in all_images if img[\"image_id\"] % 2 == 0]\n",
    "\n",
    "even_image_labels = [img[\"true_label\"] for img in all_images if img[\"image_id\"] % 2 == 0]\n",
    "odd_image_labels = [img[\"true_label\"] for img in all_images if img[\"image_id\"] % 2 != 0]\n",
    "\n",
    "feature_vectors = [np.array(img[selected_feature_model]).flatten() for img in all_images]\n",
    "\n",
    "total_len = len(feature_vectors)\n",
    "even_feature_vectors = []\n",
    "odd_feature_vectors = []\n",
    "\n",
    "for i in range(total_len):\n",
    "  if i % 2 == 0:\n",
    "    even_feature_vectors.append(feature_vectors[i])\n",
    "  else:\n",
    "    odd_feature_vectors.append(feature_vectors[i])\n",
    "\n",
    "even_feature_vectors = np.array(even_feature_vectors)\n",
    "odd_feature_vectors = np.array(odd_feature_vectors)\n",
    "\n",
    "odd_len = odd_feature_vectors.shape[0]\n",
    "even_len = even_feature_vectors.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "\n",
    "class LSHIndex:\n",
    "    def __init__(self, num_layers, num_hashes, dimensions):\n",
    "        self.num_layers = num_layers\n",
    "        self.num_hashes = num_hashes\n",
    "        self.dimensions = dimensions\n",
    "        self.index = [defaultdict(list) for _ in range(num_layers)]\n",
    "        self.hash_functions = self._generate_hash_functions()\n",
    "\n",
    "    def _generate_hash_functions(self):\n",
    "        hash_functions = []\n",
    "        for _ in range(self.num_layers):\n",
    "            layer_hashes = []\n",
    "            for _ in range(self.num_hashes):\n",
    "                random_projection = np.random.randn(self.dimensions)\n",
    "                random_projection /= np.linalg.norm(random_projection)\n",
    "                layer_hashes.append(random_projection)\n",
    "            hash_functions.append(layer_hashes)\n",
    "        return hash_functions\n",
    "\n",
    "    def hash_vector(self, vector):\n",
    "        hashed_values = []\n",
    "        for i in range(self.num_layers):\n",
    "            layer_hashes = self.hash_functions[i]\n",
    "            layer_hash = [int(np.dot(vector, h) > 0) for h in layer_hashes]\n",
    "            hashed_values.append(tuple(layer_hash))\n",
    "        return hashed_values\n",
    "\n",
    "    def add_vector(self, vector, image_id):\n",
    "        hashed = self.hash_vector(vector)\n",
    "        for i in range(self.num_layers):\n",
    "            self.index[i][hashed[i]].append((image_id, vector))\n",
    "\n",
    "    def query(self, query_vector):\n",
    "        hashed_query = self.hash_vector(query_vector)\n",
    "        candidates = set()\n",
    "        for i in range(self.num_layers):\n",
    "            candidates.update(self.index[i][hashed_query[i]])\n",
    "        return candidates\n",
    "\n",
    "    def query_t_similar(self, query_vector, t):\n",
    "        hashed_query = self.hash_vector(query_vector)\n",
    "        candidates = []\n",
    "        for i in range(self.num_layers):\n",
    "            candidates.extend(self.index[i][hashed_query[i]])\n",
    "\n",
    "        # Calculate Euclidean distance between query and candidate vectors\n",
    "        distances = [np.linalg.norm(candidate[0] - query_vector) for candidate in candidates]\n",
    "\n",
    "        # Sort candidates based on Euclidean distance and get top t similar vectors\n",
    "        similar_vectors = [candidate for _, candidate in sorted(zip(distances, candidates))][:t]\n",
    "\n",
    "        return similar_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 Most Similar Vectors:\n",
      "(20, [1.8676350116729736, -0.11808869242668152, -0.7870825529098511, -0.000766887329518795, -0.6891564130783081, -0.7333865761756897, -0.6145556569099426, 0.5133474469184875, -0.37114042043685913, -0.35024014115333557, 0.26625579595565796, -0.22692371904850006, -0.09660376608371735, -0.05850209295749664, 1.1124564409255981, 0.9980078339576721, -0.12832070887088776, 0.13986000418663025, 0.09640604257583618, 0.17902642488479614, -0.33510521054267883, 0.2255747765302658, -0.4015006422996521, -0.1690637767314911, -0.06382472068071365, 0.30264580249786377, -0.06097522750496864, 1.0156663656234741, 0.922686755657196, -0.020584329962730408, 0.2425142228603363, -0.19292673468589783, 0.8032246232032776, -0.5490302443504333, -0.006973720155656338, -0.3639185428619385, 1.0822560787200928, 0.6534451246261597, -0.18960878252983093, 1.1484874486923218, 0.6679640412330627, 0.6189641952514648, 0.1420220136642456, -0.5236685276031494, 0.6937704086303711, -0.1495991051197052, 1.1416597366333008, 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     ]
    }
   ],
   "source": [
    "num_vectors = even_feature_vectors.shape[0]\n",
    "vector_dimension = even_feature_vectors.shape[1]\n",
    "\n",
    "lsh = LSHIndex(num_layers, num_hashes_per_layer, vector_dimension)\n",
    "\n",
    "for index, vector in enumerate(even_feature_vectors):\n",
    "    lsh.add_vector(vector.tolist(), index * 2)\n",
    "\n",
    "query_vector = np.array(odd_feature_vectors[(1 // 2)])  # You can replace this with your query vector\n",
    "\n",
    "result_t = lsh.query_t_similar(query_vector, t)\n",
    "print(f\"{t} Most Similar Vectors:\")\n",
    "for result in result_t:\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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