From 9e05228e941c8e741b6731f5e44e3bf01e65d8df Mon Sep 17 00:00:00 2001 From: Kaushik Narayan R Date: Wed, 11 Oct 2023 16:10:35 -0700 Subject: [PATCH] kmeans impl. (incomplete) --- Phase 2/task_3.ipynb | 1171 +++++++++++++++++++++++++++++++++++++++++- Phase 2/utils.py | 124 ++++- 2 files changed, 1269 insertions(+), 26 deletions(-) diff --git a/Phase 2/task_3.ipynb b/Phase 2/task_3.ipynb index 477e772..628bd77 100644 --- a/Phase 2/task_3.ipynb +++ b/Phase 2/task_3.ipynb @@ -2,7 +2,17 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -22,36 +32,1157 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Applying lda on the fc_fd space to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n" + "Applying kmeans on the fc_fd space to get 101 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n", + "Initialized centroids\n", + "Iteration 0\n", + "Iteration 1\n", + "Iteration 2\n", + "Iteration 3\n", + "Iteration 4\n", + "Iteration 5\n", + "Iteration 6\n", + "Iteration 7\n", + "Iteration 8\n", + "Iteration 9\n", + "Iteration 10\n", + "Iteration 11\n", + "Iter 11 - Converged\n", + "Latent semantic no. 0\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 1\n", + "Image_ID\t6694\t-\tWeight\t86.24777040384905\n", + "Image_ID\t5370\t-\tWeight\t86.41176825659043\n", + "Image_ID\t5416\t-\tWeight\t86.42286099974446\n", + "Image_ID\t1884\t-\tWeight\t86.43132487919141\n", + "Image_ID\t4112\t-\tWeight\t86.44410314063354\n", + "Image_ID\t5418\t-\tWeight\t86.46202808091046\n", + "Image_ID\t5824\t-\tWeight\t86.49420117741343\n", + "Image_ID\t5372\t-\tWeight\t86.51492694134762\n", + "Image_ID\t8000\t-\tWeight\t86.54565063060723\n", + "Image_ID\t3378\t-\tWeight\t86.5500002443244\n", + "Latent semantic no. 2\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 3\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 4\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 5\n", + "Image_ID\t7352\t-\tWeight\t90.64757095773723\n", + "Image_ID\t7376\t-\tWeight\t90.71067468608301\n", + "Image_ID\t7386\t-\tWeight\t90.76267419712356\n", + "Image_ID\t4120\t-\tWeight\t90.7761953741958\n", + "Image_ID\t4098\t-\tWeight\t90.78511260904212\n", + "Image_ID\t4106\t-\tWeight\t90.83692910810315\n", + "Image_ID\t4164\t-\tWeight\t90.84554230329758\n", + "Image_ID\t4176\t-\tWeight\t90.87149236402715\n", + "Image_ID\t4060\t-\tWeight\t90.88059248834472\n", + "Image_ID\t7346\t-\tWeight\t90.91838962828639\n", + "Latent semantic no. 6\n", + "Image_ID\t2474\t-\tWeight\t87.40025737206862\n", + "Image_ID\t6606\t-\tWeight\t87.4597300269568\n", + "Image_ID\t6598\t-\tWeight\t87.49991118656628\n", + "Image_ID\t4838\t-\tWeight\t87.55518151536396\n", + "Image_ID\t6642\t-\tWeight\t87.55523931323744\n", + "Image_ID\t2368\t-\tWeight\t87.56413489447696\n", + "Image_ID\t6628\t-\tWeight\t87.57817539885589\n", + "Image_ID\t7408\t-\tWeight\t87.57850346166208\n", + "Image_ID\t6626\t-\tWeight\t87.5806398870456\n", + "Image_ID\t2476\t-\tWeight\t87.58129887989053\n", + "Latent semantic no. 7\n", + "Image_ID\t942\t-\tWeight\t85.98229317331767\n", + "Image_ID\t920\t-\tWeight\t85.99513494588004\n", + "Image_ID\t1046\t-\tWeight\t86.00408434902852\n", + "Image_ID\t1020\t-\tWeight\t86.00891567744765\n", + "Image_ID\t936\t-\tWeight\t86.02546639856742\n", + "Image_ID\t1060\t-\tWeight\t86.05504306499252\n", + "Image_ID\t960\t-\tWeight\t86.05867898471652\n", + "Image_ID\t1042\t-\tWeight\t86.07480399707968\n", + "Image_ID\t944\t-\tWeight\t86.07798745777\n", + "Image_ID\t934\t-\tWeight\t86.09227433231904\n", + "Latent semantic no. 8\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 9\n", + "Image_ID\t2944\t-\tWeight\t89.04868094397185\n", + "Image_ID\t2936\t-\tWeight\t89.06083637784863\n", + "Image_ID\t2912\t-\tWeight\t89.1068240103825\n", + "Image_ID\t2368\t-\tWeight\t89.12895236355644\n", + "Image_ID\t2406\t-\tWeight\t89.13133936211321\n", + "Image_ID\t2624\t-\tWeight\t89.15467293158144\n", + "Image_ID\t2696\t-\tWeight\t89.17392195826152\n", + "Image_ID\t2404\t-\tWeight\t89.20766371443642\n", + "Image_ID\t2198\t-\tWeight\t89.21491969138262\n", + "Image_ID\t2542\t-\tWeight\t89.2182757832318\n", + "Latent semantic no. 10\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 11\n", + "Image_ID\t4022\t-\tWeight\t87.41753881066812\n", + "Image_ID\t3238\t-\tWeight\t87.52799948430496\n", + "Image_ID\t5156\t-\tWeight\t87.59855885463972\n", + "Image_ID\t7700\t-\tWeight\t87.60052382803585\n", + "Image_ID\t3222\t-\tWeight\t87.62636215044996\n", + "Image_ID\t4728\t-\tWeight\t87.69293881061323\n", + "Image_ID\t7704\t-\tWeight\t87.71714690058255\n", + "Image_ID\t4680\t-\tWeight\t87.75786740558478\n", + "Image_ID\t4828\t-\tWeight\t87.76226145849695\n", + "Image_ID\t7726\t-\tWeight\t87.80492647988473\n", + "Latent semantic no. 12\n", + "Image_ID\t4436\t-\tWeight\t86.67681607776012\n", + "Image_ID\t6218\t-\tWeight\t86.69967018736763\n", + "Image_ID\t5156\t-\tWeight\t86.81702458893257\n", + "Image_ID\t7720\t-\tWeight\t86.88467103268661\n", + "Image_ID\t8018\t-\tWeight\t86.90994797716274\n", + "Image_ID\t5164\t-\tWeight\t86.91181467438722\n", + "Image_ID\t6432\t-\tWeight\t86.96600194651431\n", + "Image_ID\t6448\t-\tWeight\t86.97061278957824\n", + "Image_ID\t4270\t-\tWeight\t86.97954158868984\n", + "Image_ID\t5310\t-\tWeight\t87.0191494868232\n", + "Latent semantic no. 13\n", + "Image_ID\t3198\t-\tWeight\t89.31644919831085\n", + "Image_ID\t4816\t-\tWeight\t89.37038132683568\n", + "Image_ID\t7704\t-\tWeight\t89.41332029542151\n", + "Image_ID\t3346\t-\tWeight\t89.48035710097939\n", + "Image_ID\t8056\t-\tWeight\t89.51973616499744\n", + "Image_ID\t7340\t-\tWeight\t89.52525208876524\n", + "Image_ID\t4828\t-\tWeight\t89.5413501480498\n", + "Image_ID\t4826\t-\tWeight\t89.55601898263475\n", + "Image_ID\t4930\t-\tWeight\t89.55797770138608\n", + "Image_ID\t4784\t-\tWeight\t89.5642232429917\n", + "Latent semantic no. 14\n", + "Image_ID\t8064\t-\tWeight\t88.45902740459917\n", + "Image_ID\t8388\t-\tWeight\t88.55823365104827\n", + "Image_ID\t8030\t-\tWeight\t88.6383474641654\n", + "Image_ID\t8012\t-\tWeight\t88.67329988585138\n", + "Image_ID\t8000\t-\tWeight\t88.69723161557924\n", + "Image_ID\t8070\t-\tWeight\t88.71323154069493\n", + "Image_ID\t8056\t-\tWeight\t88.73224186664692\n", + "Image_ID\t8044\t-\tWeight\t88.74005605651269\n", + "Image_ID\t8038\t-\tWeight\t88.74112633874262\n", + "Image_ID\t4900\t-\tWeight\t88.74360021219267\n", + "Latent semantic no. 15\n", + "Image_ID\t4836\t-\tWeight\t89.93597669387725\n", + "Image_ID\t4804\t-\tWeight\t89.95222317881516\n", + "Image_ID\t4834\t-\tWeight\t89.95861895776788\n", + "Image_ID\t4784\t-\tWeight\t89.96717048757617\n", + "Image_ID\t4792\t-\tWeight\t89.97718459210286\n", + "Image_ID\t4796\t-\tWeight\t90.00291005824353\n", + "Image_ID\t4838\t-\tWeight\t90.00789776028019\n", + "Image_ID\t4786\t-\tWeight\t90.01069854022265\n", + "Image_ID\t4622\t-\tWeight\t90.03274790898605\n", + "Image_ID\t5418\t-\tWeight\t90.04014897318623\n", + "Latent semantic no. 16\n", + "Image_ID\t4356\t-\tWeight\t88.71442930545756\n", + "Image_ID\t7408\t-\tWeight\t88.83832522639833\n", + "Image_ID\t4340\t-\tWeight\t88.8476687632454\n", + "Image_ID\t5080\t-\tWeight\t88.8710063812181\n", + "Image_ID\t7386\t-\tWeight\t88.89401963700948\n", + "Image_ID\t2368\t-\tWeight\t88.91836637444757\n", + "Image_ID\t1994\t-\tWeight\t88.9279831620539\n", + "Image_ID\t7338\t-\tWeight\t88.93450044727868\n", + "Image_ID\t7336\t-\tWeight\t88.94343388001364\n", + "Image_ID\t5146\t-\tWeight\t88.94496346681454\n", + "Latent semantic no. 17\n", + "Image_ID\t5802\t-\tWeight\t92.88369959056688\n", + "Image_ID\t5156\t-\tWeight\t92.89628938747803\n", + "Image_ID\t5804\t-\tWeight\t92.91763657448669\n", + "Image_ID\t5870\t-\tWeight\t92.92775097577334\n", + "Image_ID\t5824\t-\tWeight\t92.93267145413694\n", + "Image_ID\t5822\t-\tWeight\t92.94855741716044\n", + "Image_ID\t4728\t-\tWeight\t92.96073257561997\n", + "Image_ID\t5820\t-\tWeight\t92.9736508717931\n", + "Image_ID\t5854\t-\tWeight\t92.97705991756656\n", + "Image_ID\t4838\t-\tWeight\t92.9951395534961\n", + "Latent semantic no. 18\n", + "Image_ID\t3206\t-\tWeight\t91.25042380610218\n", + "Image_ID\t2368\t-\tWeight\t91.26183510352539\n", + "Image_ID\t5602\t-\tWeight\t91.29297056963065\n", + "Image_ID\t7700\t-\tWeight\t91.35213902271698\n", + "Image_ID\t2312\t-\tWeight\t91.36075159865251\n", + "Image_ID\t7704\t-\tWeight\t91.36574897091297\n", + "Image_ID\t2944\t-\tWeight\t91.38350810633338\n", + "Image_ID\t1936\t-\tWeight\t91.3943608122145\n", + "Image_ID\t2474\t-\tWeight\t91.41546986639196\n", + "Image_ID\t2052\t-\tWeight\t91.41978571312745\n", + "Latent semantic no. 19\n", + "Image_ID\t7958\t-\tWeight\t87.6541449823757\n", + "Image_ID\t4570\t-\tWeight\t87.69308950212205\n", + "Image_ID\t7944\t-\tWeight\t87.71886300978065\n", + "Image_ID\t3416\t-\tWeight\t87.73885213647506\n", + "Image_ID\t4456\t-\tWeight\t87.7609829890399\n", + "Image_ID\t7964\t-\tWeight\t87.80942071356425\n", + "Image_ID\t6210\t-\tWeight\t87.8216368006498\n", + "Image_ID\t6912\t-\tWeight\t87.82696946580515\n", + "Image_ID\t2936\t-\tWeight\t87.86269147697347\n", + "Image_ID\t4388\t-\tWeight\t87.8744868378565\n", + "Latent semantic no. 20\n", + "Image_ID\t3346\t-\tWeight\t87.96218777986847\n", + "Image_ID\t5376\t-\tWeight\t88.11918284335448\n", + "Image_ID\t6650\t-\tWeight\t88.13026176275342\n", + "Image_ID\t4120\t-\tWeight\t88.15491138527892\n", + "Image_ID\t6678\t-\tWeight\t88.16708541805143\n", + "Image_ID\t6652\t-\tWeight\t88.20034021241193\n", + "Image_ID\t7368\t-\tWeight\t88.20944919088015\n", + "Image_ID\t3348\t-\tWeight\t88.23635534928184\n", + "Image_ID\t3358\t-\tWeight\t88.27092228656878\n", + "Image_ID\t4550\t-\tWeight\t88.27510400346307\n", + "Latent semantic no. 21\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 22\n", + "Image_ID\t4140\t-\tWeight\t89.86484204287082\n", + "Image_ID\t4510\t-\tWeight\t90.14647492802428\n", + "Image_ID\t2856\t-\tWeight\t90.18503256940957\n", + "Image_ID\t4504\t-\tWeight\t90.27216505362344\n", + "Image_ID\t5362\t-\tWeight\t90.30097111048967\n", + "Image_ID\t4520\t-\tWeight\t90.34232000159137\n", + "Image_ID\t4494\t-\tWeight\t90.34374772059383\n", + "Image_ID\t4984\t-\tWeight\t90.37459222705623\n", + "Image_ID\t2902\t-\tWeight\t90.37860441225983\n", + "Image_ID\t2764\t-\tWeight\t90.38613195624941\n", + "Latent semantic no. 23\n", + "Image_ID\t7902\t-\tWeight\t88.58910581231534\n", + "Image_ID\t8064\t-\tWeight\t88.85415812036153\n", + "Image_ID\t5308\t-\tWeight\t88.90816257797974\n", + "Image_ID\t5930\t-\tWeight\t88.96367059278609\n", + "Image_ID\t6296\t-\tWeight\t88.97553574815636\n", + "Image_ID\t6922\t-\tWeight\t88.98785316676987\n", + "Image_ID\t6916\t-\tWeight\t88.99043864848856\n", + "Image_ID\t5910\t-\tWeight\t88.99232343464888\n", + "Image_ID\t6294\t-\tWeight\t89.0021868755143\n", + "Image_ID\t8050\t-\tWeight\t89.00803677320208\n", + "Latent semantic no. 24\n", + "Image_ID\t7244\t-\tWeight\t86.16184519315041\n", + "Image_ID\t7402\t-\tWeight\t86.20902836482321\n", + "Image_ID\t1740\t-\tWeight\t86.31403299459166\n", + "Image_ID\t5322\t-\tWeight\t86.3321154227239\n", + "Image_ID\t7332\t-\tWeight\t86.33423027472288\n", + "Image_ID\t7352\t-\tWeight\t86.35094188643379\n", + "Image_ID\t2398\t-\tWeight\t86.35985802738831\n", + "Image_ID\t2794\t-\tWeight\t86.36883399536076\n", + "Image_ID\t5290\t-\tWeight\t86.36936064973959\n", + "Image_ID\t4068\t-\tWeight\t86.37966504091469\n", + "Latent semantic no. 25\n", + "Image_ID\t8336\t-\tWeight\t87.80333602701593\n", + "Image_ID\t1916\t-\tWeight\t87.91519966834713\n", + "Image_ID\t3214\t-\tWeight\t88.04996134485624\n", + "Image_ID\t1884\t-\tWeight\t88.11666972703097\n", + "Image_ID\t5088\t-\tWeight\t88.19212889526298\n", + "Image_ID\t4728\t-\tWeight\t88.19847786264056\n", + "Image_ID\t1880\t-\tWeight\t88.20676731088574\n", + "Image_ID\t7720\t-\tWeight\t88.21361155637078\n", + "Image_ID\t4382\t-\tWeight\t88.24764243310348\n", + "Image_ID\t4270\t-\tWeight\t88.2514399463752\n", + "Latent semantic no. 26\n", + "Image_ID\t1074\t-\tWeight\t88.447684452512\n", + "Image_ID\t1810\t-\tWeight\t88.57188442187989\n", + "Image_ID\t1104\t-\tWeight\t88.63634914342056\n", + "Image_ID\t1860\t-\tWeight\t88.64804502830324\n", + "Image_ID\t1070\t-\tWeight\t88.6810145996332\n", + "Image_ID\t1128\t-\tWeight\t88.72549519914838\n", + "Image_ID\t1764\t-\tWeight\t88.73195353569196\n", + "Image_ID\t1758\t-\tWeight\t88.73995883129075\n", + "Image_ID\t1108\t-\tWeight\t88.7429170143801\n", + "Image_ID\t1840\t-\tWeight\t88.74939503190697\n", + "Latent semantic no. 27\n", + "Image_ID\t6694\t-\tWeight\t85.84086155636386\n", + "Image_ID\t5852\t-\tWeight\t85.98726558014859\n", + "Image_ID\t5850\t-\tWeight\t86.14750950339153\n", + "Image_ID\t7196\t-\tWeight\t86.1502407332287\n", + "Image_ID\t6418\t-\tWeight\t86.16540492083459\n", + "Image_ID\t3364\t-\tWeight\t86.17914217200325\n", + "Image_ID\t6702\t-\tWeight\t86.18075440814934\n", + "Image_ID\t5156\t-\tWeight\t86.18120446274955\n", + "Image_ID\t5872\t-\tWeight\t86.19376729186416\n", + "Image_ID\t6174\t-\tWeight\t86.20112095944498\n", + "Latent semantic no. 28\n", + "Image_ID\t7704\t-\tWeight\t89.44989721646836\n", + "Image_ID\t7700\t-\tWeight\t89.50767906345772\n", + "Image_ID\t8030\t-\tWeight\t89.58917111648921\n", + "Image_ID\t7716\t-\tWeight\t89.61482359453626\n", + "Image_ID\t4696\t-\tWeight\t89.63209910812765\n", + "Image_ID\t8298\t-\tWeight\t89.68288100514273\n", + "Image_ID\t6680\t-\tWeight\t89.70668926565826\n", + "Image_ID\t4946\t-\tWeight\t89.71422694720974\n", + "Image_ID\t7720\t-\tWeight\t89.71903526779427\n", + "Image_ID\t8068\t-\tWeight\t89.76427261697894\n", + "Latent semantic no. 29\n", + "Image_ID\t7700\t-\tWeight\t88.72682746659379\n", + "Image_ID\t7704\t-\tWeight\t88.86391083966221\n", + "Image_ID\t3214\t-\tWeight\t89.03164366659627\n", + "Image_ID\t8030\t-\tWeight\t89.04148340876165\n", + "Image_ID\t7738\t-\tWeight\t89.10421390387106\n", + "Image_ID\t4790\t-\tWeight\t89.1047061529149\n", + "Image_ID\t3238\t-\tWeight\t89.13054774800906\n", + "Image_ID\t8044\t-\tWeight\t89.13097968424894\n", + "Image_ID\t7748\t-\tWeight\t89.1422817278473\n", + "Image_ID\t7154\t-\tWeight\t89.16602873249813\n", + "Latent semantic no. 30\n", + "Image_ID\t5376\t-\tWeight\t87.66695149283436\n", + "Image_ID\t2238\t-\tWeight\t87.75075169312677\n", + "Image_ID\t2674\t-\tWeight\t87.75372167792074\n", + "Image_ID\t5156\t-\tWeight\t87.77725248693956\n", + "Image_ID\t2214\t-\tWeight\t87.82561452947769\n", + "Image_ID\t5384\t-\tWeight\t87.83698193071733\n", + "Image_ID\t2556\t-\tWeight\t87.85147065058716\n", + "Image_ID\t2474\t-\tWeight\t87.85478688001746\n", + "Image_ID\t2710\t-\tWeight\t87.86152864699527\n", + "Image_ID\t7204\t-\tWeight\t87.87318447034271\n", + "Latent semantic no. 31\n", + "Image_ID\t1108\t-\tWeight\t88.5917516851893\n", + "Image_ID\t1782\t-\tWeight\t88.60934529582849\n", + "Image_ID\t2024\t-\tWeight\t88.61898861779629\n", + "Image_ID\t1096\t-\tWeight\t88.62688775799296\n", + "Image_ID\t4494\t-\tWeight\t88.62964254172651\n", + "Image_ID\t1492\t-\tWeight\t88.63145376195882\n", + "Image_ID\t1110\t-\tWeight\t88.64822567672941\n", + "Image_ID\t1998\t-\tWeight\t88.6634040708559\n", + "Image_ID\t1616\t-\tWeight\t88.67521521436682\n", + "Image_ID\t1676\t-\tWeight\t88.68097863695839\n", + "Latent semantic no. 32\n", + "Image_ID\t2606\t-\tWeight\t90.99386997243806\n", + "Image_ID\t1988\t-\tWeight\t91.02616576603171\n", + "Image_ID\t2312\t-\tWeight\t91.04047075268295\n", + "Image_ID\t5654\t-\tWeight\t91.04971709387992\n", + "Image_ID\t5104\t-\tWeight\t91.09871344270306\n", + "Image_ID\t2408\t-\tWeight\t91.10462932034581\n", + "Image_ID\t2380\t-\tWeight\t91.13105272803249\n", + "Image_ID\t2406\t-\tWeight\t91.13494416096799\n", + "Image_ID\t2228\t-\tWeight\t91.13567145611566\n", + "Image_ID\t2424\t-\tWeight\t91.14592621004473\n", + "Latent semantic no. 33\n", + "Image_ID\t4022\t-\tWeight\t89.84642594543604\n", + "Image_ID\t5156\t-\tWeight\t89.91893102748803\n", + "Image_ID\t6612\t-\tWeight\t89.92551827570942\n", + "Image_ID\t4008\t-\tWeight\t89.92963716156265\n", + "Image_ID\t934\t-\tWeight\t89.92986266457386\n", + "Image_ID\t5174\t-\tWeight\t89.93092248136756\n", + "Image_ID\t946\t-\tWeight\t89.9564425258312\n", + "Image_ID\t6628\t-\tWeight\t89.95814851287538\n", + "Image_ID\t940\t-\tWeight\t89.95952669019012\n", + "Image_ID\t3948\t-\tWeight\t89.97251244573975\n", + "Latent semantic no. 34\n", + "Image_ID\t2558\t-\tWeight\t88.75651028065141\n", + "Image_ID\t2310\t-\tWeight\t88.87846666554044\n", + "Image_ID\t2368\t-\tWeight\t88.9751128762646\n", + "Image_ID\t2312\t-\tWeight\t89.02049956710398\n", + "Image_ID\t4086\t-\tWeight\t89.06274084592813\n", + "Image_ID\t922\t-\tWeight\t89.07397383309979\n", + "Image_ID\t2476\t-\tWeight\t89.07711153933982\n", + "Image_ID\t2438\t-\tWeight\t89.09158649258974\n", + "Image_ID\t5370\t-\tWeight\t89.10384981516941\n", + "Image_ID\t5376\t-\tWeight\t89.1083871777014\n", + "Latent semantic no. 35\n", + "Image_ID\t1778\t-\tWeight\t89.02356310708977\n", + "Image_ID\t1076\t-\tWeight\t89.03001005257754\n", + "Image_ID\t1810\t-\tWeight\t89.04003429133581\n", + "Image_ID\t1562\t-\tWeight\t89.0480773571983\n", + "Image_ID\t1154\t-\tWeight\t89.07240737428626\n", + "Image_ID\t1122\t-\tWeight\t89.09182542057793\n", + "Image_ID\t7728\t-\tWeight\t89.13325686551619\n", + "Image_ID\t1114\t-\tWeight\t89.13874161232562\n", + "Image_ID\t1664\t-\tWeight\t89.14097291989448\n", + "Image_ID\t1074\t-\tWeight\t89.14554857160557\n", + "Latent semantic no. 36\n", + "Image_ID\t7704\t-\tWeight\t86.54593960909813\n", + "Image_ID\t4838\t-\tWeight\t86.57103078166504\n", + "Image_ID\t8030\t-\tWeight\t86.61953918473453\n", + "Image_ID\t7720\t-\tWeight\t86.61967158626888\n", + "Image_ID\t4828\t-\tWeight\t86.6720108971525\n", + "Image_ID\t7758\t-\tWeight\t86.69835706802114\n", + "Image_ID\t4340\t-\tWeight\t86.71992969866406\n", + "Image_ID\t4382\t-\tWeight\t86.75642522723395\n", + "Image_ID\t8068\t-\tWeight\t86.75729307265075\n", + "Image_ID\t7686\t-\tWeight\t86.76332518198649\n", + "Latent semantic no. 37\n", + "Image_ID\t5310\t-\tWeight\t86.90985037169483\n", + "Image_ID\t4236\t-\tWeight\t86.97643941827066\n", + "Image_ID\t5032\t-\tWeight\t86.99965217743606\n", + "Image_ID\t6598\t-\tWeight\t87.01740457929483\n", + "Image_ID\t5150\t-\tWeight\t87.03703663917709\n", + "Image_ID\t6642\t-\tWeight\t87.05613721169718\n", + "Image_ID\t6600\t-\tWeight\t87.06308993510669\n", + "Image_ID\t3182\t-\tWeight\t87.07630583366654\n", + "Image_ID\t7546\t-\tWeight\t87.1087689192526\n", + "Image_ID\t5286\t-\tWeight\t87.11812354402787\n", + "Latent semantic no. 38\n", + "Image_ID\t6882\t-\tWeight\t92.62966233237255\n", + "Image_ID\t5384\t-\tWeight\t92.64528919150807\n", + "Image_ID\t7720\t-\tWeight\t92.6649318398373\n", + "Image_ID\t5364\t-\tWeight\t92.67201666901403\n", + "Image_ID\t4170\t-\tWeight\t92.68652146031363\n", + "Image_ID\t5376\t-\tWeight\t92.70385463208531\n", + "Image_ID\t4126\t-\tWeight\t92.7227575880467\n", + "Image_ID\t6232\t-\tWeight\t92.73401059049777\n", + "Image_ID\t5372\t-\tWeight\t92.75406540439869\n", + "Image_ID\t5368\t-\tWeight\t92.75965506120552\n", + "Latent semantic no. 39\n", + "Image_ID\t2894\t-\tWeight\t87.90707389888186\n", + "Image_ID\t2368\t-\tWeight\t87.93705322166254\n", + "Image_ID\t6628\t-\tWeight\t87.9815627259868\n", + "Image_ID\t4728\t-\tWeight\t88.00628896116119\n", + "Image_ID\t4708\t-\tWeight\t88.04550367249361\n", + "Image_ID\t4686\t-\tWeight\t88.09533348353604\n", + "Image_ID\t4942\t-\tWeight\t88.11214399523702\n", + "Image_ID\t7704\t-\tWeight\t88.13631048352592\n", + "Image_ID\t4820\t-\tWeight\t88.13763138722983\n", + "Image_ID\t4126\t-\tWeight\t88.15193056591507\n", + "Latent semantic no. 40\n", + "Image_ID\t1214\t-\tWeight\t87.34418344061751\n", + "Image_ID\t1212\t-\tWeight\t87.39000435521427\n", + "Image_ID\t1840\t-\tWeight\t87.39974094603078\n", + "Image_ID\t4330\t-\tWeight\t87.4121208594063\n", + "Image_ID\t7902\t-\tWeight\t87.47983133092741\n", + "Image_ID\t3432\t-\tWeight\t87.51329158111787\n", + "Image_ID\t4656\t-\tWeight\t87.51849204229575\n", + "Image_ID\t3460\t-\tWeight\t87.5309302478826\n", + "Image_ID\t7908\t-\tWeight\t87.53185663276665\n", + "Image_ID\t2792\t-\tWeight\t87.53805225740376\n", + "Latent semantic no. 41\n", + "Image_ID\t1916\t-\tWeight\t88.28915191575848\n", + "Image_ID\t896\t-\tWeight\t88.59319131983725\n", + "Image_ID\t986\t-\tWeight\t88.62391726238216\n", + "Image_ID\t1010\t-\tWeight\t88.6265258035925\n", + "Image_ID\t6172\t-\tWeight\t88.65120552686204\n", + "Image_ID\t910\t-\tWeight\t88.65596393892446\n", + "Image_ID\t976\t-\tWeight\t88.66401155899828\n", + "Image_ID\t900\t-\tWeight\t88.66531862985444\n", + "Image_ID\t4616\t-\tWeight\t88.6696594363229\n", + "Image_ID\t972\t-\tWeight\t88.67366010406501\n", + "Latent semantic no. 42\n", + "Image_ID\t6922\t-\tWeight\t87.1202785027324\n", + "Image_ID\t7616\t-\tWeight\t87.12485595667385\n", + "Image_ID\t6912\t-\tWeight\t87.22593048258594\n", + "Image_ID\t7902\t-\tWeight\t87.27811082023815\n", + "Image_ID\t7624\t-\tWeight\t87.33271903737663\n", + "Image_ID\t6932\t-\tWeight\t87.33307451489563\n", + "Image_ID\t6924\t-\tWeight\t87.34472141346441\n", + "Image_ID\t6190\t-\tWeight\t87.38478156098549\n", + "Image_ID\t1286\t-\tWeight\t87.3850488972728\n", + "Image_ID\t7696\t-\tWeight\t87.42143784009906\n", + "Latent semantic no. 43\n", + "Image_ID\t1778\t-\tWeight\t86.24437944252624\n", + "Image_ID\t4728\t-\tWeight\t86.27099686180829\n", + "Image_ID\t1428\t-\tWeight\t86.30162087211455\n", + "Image_ID\t1294\t-\tWeight\t86.31855429701581\n", + "Image_ID\t1122\t-\tWeight\t86.35353793941226\n", + "Image_ID\t1560\t-\tWeight\t86.3698780882323\n", + "Image_ID\t1862\t-\tWeight\t86.38591004062131\n", + "Image_ID\t1072\t-\tWeight\t86.40424868346773\n", + "Image_ID\t1510\t-\tWeight\t86.41537192037187\n", + "Image_ID\t1374\t-\tWeight\t86.42111798596154\n", + "Latent semantic no. 44\n", + "Image_ID\t3762\t-\tWeight\t90.00132768346799\n", + "Image_ID\t5852\t-\tWeight\t90.00495750045143\n", + "Image_ID\t5872\t-\tWeight\t90.06431527323468\n", + "Image_ID\t4102\t-\tWeight\t90.06850236421991\n", + "Image_ID\t7402\t-\tWeight\t90.09395697709277\n", + "Image_ID\t4098\t-\tWeight\t90.10630608462922\n", + "Image_ID\t5802\t-\tWeight\t90.10750969701621\n", + "Image_ID\t7332\t-\tWeight\t90.12791644438683\n", + "Image_ID\t7398\t-\tWeight\t90.15071016937546\n", + "Image_ID\t7342\t-\tWeight\t90.15530607531313\n", + "Latent semantic no. 45\n", + "Image_ID\t7704\t-\tWeight\t87.28285550316976\n", + "Image_ID\t1212\t-\tWeight\t87.33217458317203\n", + "Image_ID\t1750\t-\tWeight\t87.39208778944099\n", + "Image_ID\t7720\t-\tWeight\t87.39809776716828\n", + "Image_ID\t1560\t-\tWeight\t87.39928570308041\n", + "Image_ID\t1074\t-\tWeight\t87.40735179314476\n", + "Image_ID\t1348\t-\tWeight\t87.41785529265846\n", + "Image_ID\t7700\t-\tWeight\t87.43530081687966\n", + "Image_ID\t1758\t-\tWeight\t87.45808728174451\n", + "Image_ID\t1374\t-\tWeight\t87.45934975840045\n", + "Latent semantic no. 46\n", + "Image_ID\t7704\t-\tWeight\t87.94576484623228\n", + "Image_ID\t3246\t-\tWeight\t88.00506652473103\n", + "Image_ID\t4600\t-\tWeight\t88.0179837072344\n", + "Image_ID\t6342\t-\tWeight\t88.04403529595196\n", + "Image_ID\t6352\t-\tWeight\t88.06876231706778\n", + "Image_ID\t6922\t-\tWeight\t88.08363683859432\n", + "Image_ID\t5852\t-\tWeight\t88.08671515145315\n", + "Image_ID\t7386\t-\tWeight\t88.08962795310308\n", + "Image_ID\t6920\t-\tWeight\t88.11985674391767\n", + "Image_ID\t7398\t-\tWeight\t88.12268684896914\n", + "Latent semantic no. 47\n", + "Image_ID\t7704\t-\tWeight\t88.1788850927885\n", + "Image_ID\t3240\t-\tWeight\t88.2753203397972\n", + "Image_ID\t976\t-\tWeight\t88.27870968247419\n", + "Image_ID\t872\t-\tWeight\t88.28861978376635\n", + "Image_ID\t8520\t-\tWeight\t88.30280742818479\n", + "Image_ID\t7758\t-\tWeight\t88.3167711306483\n", + "Image_ID\t1750\t-\tWeight\t88.31778957234057\n", + "Image_ID\t1778\t-\tWeight\t88.3200527252172\n", + "Image_ID\t896\t-\tWeight\t88.32478952914494\n", + "Image_ID\t7720\t-\tWeight\t88.35298489373174\n", + "Latent semantic no. 48\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 49\n", + "Image_ID\t7720\t-\tWeight\t90.15184868438516\n", + "Image_ID\t3940\t-\tWeight\t90.1798815360305\n", + "Image_ID\t3222\t-\tWeight\t90.20844781978592\n", + "Image_ID\t4584\t-\tWeight\t90.23677487768238\n", + "Image_ID\t3220\t-\tWeight\t90.25535658444292\n", + "Image_ID\t8512\t-\tWeight\t90.28313184974597\n", + "Image_ID\t3238\t-\tWeight\t90.28613120802542\n", + "Image_ID\t7016\t-\tWeight\t90.33732013969987\n", + "Image_ID\t6606\t-\tWeight\t90.34398692221934\n", + "Image_ID\t4764\t-\tWeight\t90.34913766391256\n", + "Latent semantic no. 50\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 51\n", + "Image_ID\t2474\t-\tWeight\t90.09059535786318\n", + "Image_ID\t2078\t-\tWeight\t90.23725908334548\n", + "Image_ID\t2312\t-\tWeight\t90.23738248730997\n", + "Image_ID\t2116\t-\tWeight\t90.24657883963151\n", + "Image_ID\t2412\t-\tWeight\t90.29423718416923\n", + "Image_ID\t2068\t-\tWeight\t90.29945426396222\n", + "Image_ID\t2650\t-\tWeight\t90.32283048949566\n", + "Image_ID\t2602\t-\tWeight\t90.33017440020221\n", + "Image_ID\t2438\t-\tWeight\t90.33953852945602\n", + "Image_ID\t2406\t-\tWeight\t90.3422241869283\n", + "Latent semantic no. 52\n", + "Image_ID\t6936\t-\tWeight\t86.14625388908418\n", + "Image_ID\t4278\t-\tWeight\t86.2253469545425\n", + "Image_ID\t6912\t-\tWeight\t86.31263452148897\n", + "Image_ID\t4246\t-\tWeight\t86.3597289626618\n", + "Image_ID\t7980\t-\tWeight\t86.36390515722906\n", + "Image_ID\t7720\t-\tWeight\t86.36657631122799\n", + "Image_ID\t7738\t-\tWeight\t86.3792389558034\n", + "Image_ID\t2940\t-\tWeight\t86.40549094656707\n", + "Image_ID\t3214\t-\tWeight\t86.4234994269869\n", + "Image_ID\t4236\t-\tWeight\t86.44272657937451\n", + "Latent semantic no. 53\n", + "Image_ID\t2902\t-\tWeight\t89.93428788763727\n", + "Image_ID\t4140\t-\tWeight\t89.94536699372794\n", + "Image_ID\t2896\t-\tWeight\t89.96478884463608\n", + "Image_ID\t2890\t-\tWeight\t90.0113966259351\n", + "Image_ID\t3182\t-\tWeight\t90.05134780498115\n", + "Image_ID\t7376\t-\tWeight\t90.1389236606174\n", + "Image_ID\t5386\t-\tWeight\t90.19207138307499\n", + "Image_ID\t4120\t-\tWeight\t90.20494091666205\n", + "Image_ID\t2872\t-\tWeight\t90.21936463499995\n", + "Image_ID\t3132\t-\tWeight\t90.22526129646967\n", + "Latent semantic no. 54\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 55\n", + "Image_ID\t3090\t-\tWeight\t89.77420421836791\n", + "Image_ID\t4980\t-\tWeight\t90.10942253431193\n", + "Image_ID\t5678\t-\tWeight\t90.25174771623084\n", + "Image_ID\t4942\t-\tWeight\t90.25880234747584\n", + "Image_ID\t6660\t-\tWeight\t90.26300822331702\n", + "Image_ID\t6246\t-\tWeight\t90.28280094646746\n", + "Image_ID\t5658\t-\tWeight\t90.30941268411487\n", + "Image_ID\t6476\t-\tWeight\t90.34150020433678\n", + "Image_ID\t6764\t-\tWeight\t90.34868084077216\n", + "Image_ID\t6218\t-\tWeight\t90.3545699149964\n", + "Latent semantic no. 56\n", + "Image_ID\t4270\t-\tWeight\t88.77582076583622\n", + "Image_ID\t6184\t-\tWeight\t88.81635809203968\n", + "Image_ID\t6170\t-\tWeight\t88.96682709755494\n", + "Image_ID\t6146\t-\tWeight\t89.00006292369699\n", + "Image_ID\t4236\t-\tWeight\t89.00108968348162\n", + "Image_ID\t4248\t-\tWeight\t89.07963941062201\n", + "Image_ID\t4242\t-\tWeight\t89.08260449904508\n", + "Image_ID\t5156\t-\tWeight\t89.08498530558549\n", + "Image_ID\t4254\t-\tWeight\t89.10782701957987\n", + "Image_ID\t7630\t-\tWeight\t89.11177262266172\n", + "Latent semantic no. 57\n", + "Image_ID\t6218\t-\tWeight\t89.10654660127496\n", + "Image_ID\t7152\t-\tWeight\t89.16003688857154\n", + "Image_ID\t6924\t-\tWeight\t89.19536795404636\n", + "Image_ID\t7948\t-\tWeight\t89.19825168690419\n", + "Image_ID\t4164\t-\tWeight\t89.21420212008154\n", + "Image_ID\t942\t-\tWeight\t89.24477027639863\n", + "Image_ID\t7966\t-\tWeight\t89.25213643020135\n", + "Image_ID\t8052\t-\tWeight\t89.25561528912483\n", + "Image_ID\t4106\t-\tWeight\t89.26491227286999\n", + "Image_ID\t3198\t-\tWeight\t89.26634770851963\n", + "Latent semantic no. 58\n", + "Image_ID\t6244\t-\tWeight\t87.9252644621365\n", + "Image_ID\t2770\t-\tWeight\t87.98087876574613\n", + "Image_ID\t6650\t-\tWeight\t88.1100667257138\n", + "Image_ID\t3352\t-\tWeight\t88.1266279470227\n", + "Image_ID\t4026\t-\tWeight\t88.20730740364812\n", + "Image_ID\t5376\t-\tWeight\t88.23468328570743\n", + "Image_ID\t4584\t-\tWeight\t88.25711982796575\n", + "Image_ID\t3940\t-\tWeight\t88.25759908198268\n", + "Image_ID\t4838\t-\tWeight\t88.28928235246106\n", + "Image_ID\t6652\t-\tWeight\t88.29298227566578\n", + "Latent semantic no. 59\n", + "Image_ID\t3806\t-\tWeight\t90.97637085357731\n", + "Image_ID\t7704\t-\tWeight\t90.99904730028013\n", + "Image_ID\t8530\t-\tWeight\t91.04293199305144\n", + "Image_ID\t4104\t-\tWeight\t91.08425861611666\n", + "Image_ID\t3940\t-\tWeight\t91.09073792441117\n", + "Image_ID\t7738\t-\tWeight\t91.09676622810753\n", + "Image_ID\t7700\t-\tWeight\t91.09734966294592\n", + "Image_ID\t2278\t-\tWeight\t91.09801149990307\n", + "Image_ID\t3240\t-\tWeight\t91.10263037512594\n", + "Image_ID\t5654\t-\tWeight\t91.10884748994746\n", + "Latent semantic no. 60\n", + "Image_ID\t6916\t-\tWeight\t89.45111211641026\n", + "Image_ID\t1058\t-\tWeight\t89.53732001791982\n", + "Image_ID\t942\t-\tWeight\t89.558886510572\n", + "Image_ID\t958\t-\tWeight\t89.58184035277407\n", + "Image_ID\t4270\t-\tWeight\t89.63543626811098\n", + "Image_ID\t2424\t-\tWeight\t89.64331533702523\n", + "Image_ID\t926\t-\tWeight\t89.66518009102234\n", + "Image_ID\t1020\t-\tWeight\t89.68027623386692\n", + "Image_ID\t4494\t-\tWeight\t89.7094794545812\n", + "Image_ID\t1040\t-\tWeight\t89.7181002390123\n", + "Latent semantic no. 61\n", + "Image_ID\t5852\t-\tWeight\t88.48262420173582\n", + "Image_ID\t1810\t-\tWeight\t88.48303023585287\n", + "Image_ID\t1166\t-\tWeight\t88.50826486505805\n", + "Image_ID\t1286\t-\tWeight\t88.51021491197656\n", + "Image_ID\t1530\t-\tWeight\t88.52019252210945\n", + "Image_ID\t1752\t-\tWeight\t88.54497273106028\n", + "Image_ID\t6600\t-\tWeight\t88.54679003993495\n", + "Image_ID\t1560\t-\tWeight\t88.54840216325297\n", + "Image_ID\t1608\t-\tWeight\t88.54858482853388\n", + "Image_ID\t1644\t-\tWeight\t88.5535998099343\n", + "Latent semantic no. 62\n", + "Image_ID\t4022\t-\tWeight\t92.27440795041889\n", + "Image_ID\t8000\t-\tWeight\t92.28637110855257\n", + "Image_ID\t8050\t-\tWeight\t92.31462543020213\n", + "Image_ID\t6872\t-\tWeight\t92.33651759015696\n", + "Image_ID\t3432\t-\tWeight\t92.34357206483118\n", + "Image_ID\t8064\t-\tWeight\t92.37993338749051\n", + "Image_ID\t7832\t-\tWeight\t92.41841951013765\n", + "Image_ID\t8032\t-\tWeight\t92.46776567109228\n", + "Image_ID\t7900\t-\tWeight\t92.4828320262045\n", + "Image_ID\t4838\t-\tWeight\t92.49027862234693\n", + "Latent semantic no. 63\n", + "Image_ID\t5852\t-\tWeight\t90.48822706956535\n", + "Image_ID\t7200\t-\tWeight\t90.48843376280308\n", + "Image_ID\t4802\t-\tWeight\t90.50078603470266\n", + "Image_ID\t904\t-\tWeight\t90.50192631399365\n", + "Image_ID\t5872\t-\tWeight\t90.51742131155257\n", + "Image_ID\t896\t-\tWeight\t90.5208007569666\n", + "Image_ID\t5074\t-\tWeight\t90.54071406451389\n", + "Image_ID\t5802\t-\tWeight\t90.55009209608625\n", + "Image_ID\t5850\t-\tWeight\t90.55476524344512\n", + "Image_ID\t1052\t-\tWeight\t90.55828740209857\n", + "Latent semantic no. 64\n", + "Image_ID\t938\t-\tWeight\t91.35890750364672\n", + "Image_ID\t3958\t-\tWeight\t91.3767116513224\n", + "Image_ID\t5148\t-\tWeight\t91.45220550661134\n", + "Image_ID\t4642\t-\tWeight\t91.48126744290404\n", + "Image_ID\t4622\t-\tWeight\t91.48166894535946\n", + "Image_ID\t6344\t-\tWeight\t91.49938995271499\n", + "Image_ID\t5150\t-\tWeight\t91.53229419670643\n", + "Image_ID\t5156\t-\tWeight\t91.54440014118005\n", + "Image_ID\t5234\t-\tWeight\t91.54475439323207\n", + "Image_ID\t934\t-\tWeight\t91.55435253474316\n", + "Latent semantic no. 65\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 66\n", + "Image_ID\t1884\t-\tWeight\t88.36009327400762\n", + "Image_ID\t5310\t-\tWeight\t88.47013814030365\n", + "Image_ID\t6922\t-\tWeight\t88.4760999916122\n", + "Image_ID\t6012\t-\tWeight\t88.47786367443865\n", + "Image_ID\t7956\t-\tWeight\t88.52357048216508\n", + "Image_ID\t7560\t-\tWeight\t88.5333135114252\n", + "Image_ID\t4102\t-\tWeight\t88.53392803917055\n", + "Image_ID\t6912\t-\tWeight\t88.54242161811551\n", + "Image_ID\t6920\t-\tWeight\t88.55605549481041\n", + "Image_ID\t7958\t-\tWeight\t88.56523521375891\n", + "Latent semantic no. 67\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 68\n", + "Image_ID\t6922\t-\tWeight\t88.97555965474324\n", + "Image_ID\t5614\t-\tWeight\t89.13221941287263\n", + "Image_ID\t6936\t-\tWeight\t89.14087554314409\n", + "Image_ID\t5678\t-\tWeight\t89.16807126770114\n", + "Image_ID\t2436\t-\tWeight\t89.22999031228645\n", + "Image_ID\t6932\t-\tWeight\t89.29127671281968\n", + "Image_ID\t7148\t-\tWeight\t89.30696440167635\n", + "Image_ID\t2474\t-\tWeight\t89.33881574577734\n", + "Image_ID\t6924\t-\tWeight\t89.37218662272257\n", + "Image_ID\t7144\t-\tWeight\t89.38231015357783\n", + "Latent semantic no. 69\n", + "Image_ID\t4812\t-\tWeight\t86.14114514185201\n", + "Image_ID\t4846\t-\tWeight\t86.15242673989768\n", + "Image_ID\t6628\t-\tWeight\t86.15768125101238\n", + "Image_ID\t1656\t-\tWeight\t86.16944094059217\n", + "Image_ID\t1740\t-\tWeight\t86.18092758726853\n", + "Image_ID\t6618\t-\tWeight\t86.18683990619505\n", + "Image_ID\t4834\t-\tWeight\t86.18986646352988\n", + "Image_ID\t7340\t-\tWeight\t86.20216511954754\n", + "Image_ID\t6612\t-\tWeight\t86.20367322749482\n", + "Image_ID\t1758\t-\tWeight\t86.2127921411654\n", + "Latent semantic no. 70\n", + "Image_ID\t3220\t-\tWeight\t89.13454402672143\n", + "Image_ID\t2940\t-\tWeight\t89.2956550248684\n", + "Image_ID\t5054\t-\tWeight\t89.35874005244268\n", + "Image_ID\t5654\t-\tWeight\t89.38367810130735\n", + "Image_ID\t5376\t-\tWeight\t89.40485139804943\n", + "Image_ID\t5644\t-\tWeight\t89.44009855314272\n", + "Image_ID\t6352\t-\tWeight\t89.49456877594085\n", + "Image_ID\t4038\t-\tWeight\t89.52817714691476\n", + "Image_ID\t7328\t-\tWeight\t89.53658515364435\n", + "Image_ID\t5628\t-\tWeight\t89.54272627928619\n", + "Latent semantic no. 71\n", + "Image_ID\t6026\t-\tWeight\t87.24597880019654\n", + "Image_ID\t6010\t-\tWeight\t87.29872566750541\n", + "Image_ID\t6008\t-\tWeight\t87.30556394272789\n", + "Image_ID\t5996\t-\tWeight\t87.30701046727573\n", + "Image_ID\t6012\t-\tWeight\t87.33474974121008\n", + "Image_ID\t8098\t-\tWeight\t87.3394104062729\n", + "Image_ID\t5990\t-\tWeight\t87.36125375420274\n", + "Image_ID\t4728\t-\tWeight\t87.39662087580174\n", + "Image_ID\t6014\t-\tWeight\t87.43733443137002\n", + "Image_ID\t5992\t-\tWeight\t87.46484608387125\n", + "Latent semantic no. 72\n", + "Image_ID\t2436\t-\tWeight\t89.53691473971136\n", + "Image_ID\t2078\t-\tWeight\t89.55764931274295\n", + "Image_ID\t5852\t-\tWeight\t89.57147007684355\n", + "Image_ID\t2554\t-\tWeight\t89.57648061221118\n", + "Image_ID\t2068\t-\tWeight\t89.59106767845208\n", + "Image_ID\t2424\t-\tWeight\t89.60136439622522\n", + "Image_ID\t1984\t-\tWeight\t89.61020589256563\n", + "Image_ID\t2642\t-\tWeight\t89.61901925226833\n", + "Image_ID\t2472\t-\tWeight\t89.6198969834998\n", + "Image_ID\t5802\t-\tWeight\t89.62167852540945\n", + "Latent semantic no. 73\n", + "Image_ID\t2310\t-\tWeight\t89.67248957259147\n", + "Image_ID\t5362\t-\tWeight\t89.80911704035512\n", + "Image_ID\t2368\t-\tWeight\t89.96110943381042\n", + "Image_ID\t3346\t-\tWeight\t89.96381471920319\n", + "Image_ID\t2186\t-\tWeight\t89.97288681935248\n", + "Image_ID\t5384\t-\tWeight\t90.0078579314617\n", + "Image_ID\t2180\t-\tWeight\t90.01041847886842\n", + "Image_ID\t3356\t-\tWeight\t90.03077653201424\n", + "Image_ID\t4494\t-\tWeight\t90.04703159559632\n", + "Image_ID\t2788\t-\tWeight\t90.05244058235918\n", + "Latent semantic no. 74\n", + "Image_ID\t1074\t-\tWeight\t87.85872356502425\n", + "Image_ID\t1738\t-\tWeight\t87.91843644085242\n", + "Image_ID\t1758\t-\tWeight\t88.02017510479632\n", + "Image_ID\t1608\t-\tWeight\t88.0216288013008\n", + "Image_ID\t1718\t-\tWeight\t88.03048086125212\n", + "Image_ID\t1778\t-\tWeight\t88.03458845467921\n", + "Image_ID\t1808\t-\tWeight\t88.03937482063795\n", + "Image_ID\t1158\t-\tWeight\t88.04461387129828\n", + "Image_ID\t1286\t-\tWeight\t88.04913205157611\n", + "Image_ID\t1852\t-\tWeight\t88.05885322109293\n", + "Latent semantic no. 75\n", + "Image_ID\t4356\t-\tWeight\t89.61837266938768\n", + "Image_ID\t7704\t-\tWeight\t89.7968945636183\n", + "Image_ID\t7720\t-\tWeight\t89.80297896733283\n", + "Image_ID\t7700\t-\tWeight\t89.84551820651306\n", + "Image_ID\t4270\t-\tWeight\t89.86313422848194\n", + "Image_ID\t4404\t-\tWeight\t89.9192836535801\n", + "Image_ID\t4236\t-\tWeight\t90.03511956006274\n", + "Image_ID\t4254\t-\tWeight\t90.05821902170833\n", + "Image_ID\t4038\t-\tWeight\t90.06256053828315\n", + "Image_ID\t4348\t-\tWeight\t90.06323957917324\n", + "Latent semantic no. 76\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 77\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 78\n", + "Image_ID\t4728\t-\tWeight\t88.38731876425989\n", + "Image_ID\t3958\t-\tWeight\t88.47691303131506\n", + "Image_ID\t6912\t-\tWeight\t88.50332360424315\n", + "Image_ID\t4236\t-\tWeight\t88.53826591513592\n", + "Image_ID\t4724\t-\tWeight\t88.6403349698232\n", + "Image_ID\t4402\t-\tWeight\t88.66895123888668\n", + "Image_ID\t4360\t-\tWeight\t88.68633546068656\n", + "Image_ID\t7402\t-\tWeight\t88.69146857656513\n", + "Image_ID\t4398\t-\tWeight\t88.69256515272716\n", + "Image_ID\t4366\t-\tWeight\t88.71748707174505\n", + "Latent semantic no. 79\n", + "Image_ID\t0\t-\tWeight\tnan\n", + "Image_ID\t2\t-\tWeight\tnan\n", + "Image_ID\t4\t-\tWeight\tnan\n", + "Image_ID\t6\t-\tWeight\tnan\n", + "Image_ID\t8\t-\tWeight\tnan\n", + "Image_ID\t10\t-\tWeight\tnan\n", + "Image_ID\t12\t-\tWeight\tnan\n", + "Image_ID\t14\t-\tWeight\tnan\n", + "Image_ID\t16\t-\tWeight\tnan\n", + "Image_ID\t18\t-\tWeight\tnan\n", + "Latent semantic no. 80\n", + "Image_ID\t5370\t-\tWeight\t87.27439095454655\n", + "Image_ID\t5382\t-\tWeight\t87.29759580760152\n", + "Image_ID\t5156\t-\tWeight\t87.3092193068339\n", + "Image_ID\t5150\t-\tWeight\t87.35534374495286\n", + "Image_ID\t5162\t-\tWeight\t87.39015435557953\n", + "Image_ID\t5418\t-\tWeight\t87.43006401719745\n", + "Image_ID\t5410\t-\tWeight\t87.4309007618408\n", + "Image_ID\t5170\t-\tWeight\t87.43537134290779\n", + "Image_ID\t5424\t-\tWeight\t87.4363040184357\n", + "Image_ID\t5372\t-\tWeight\t87.43822670987063\n", + "Latent semantic no. 81\n", + "Image_ID\t2180\t-\tWeight\t87.46318688043219\n", + "Image_ID\t2186\t-\tWeight\t87.56167125860551\n", + "Image_ID\t2168\t-\tWeight\t87.60065161780193\n", + "Image_ID\t1778\t-\tWeight\t87.62269469713135\n", + "Image_ID\t2278\t-\tWeight\t87.64914169380536\n", + "Image_ID\t6922\t-\tWeight\t87.66193461412291\n", + "Image_ID\t2146\t-\tWeight\t87.66620955072564\n", + "Image_ID\t1614\t-\tWeight\t87.74725990056837\n", + "Image_ID\t1994\t-\tWeight\t87.75949255192486\n", + "Image_ID\t2178\t-\tWeight\t87.79233225185834\n", + "Latent semantic no. 82\n", + "Image_ID\t5654\t-\tWeight\t87.70808012360229\n", + "Image_ID\t5674\t-\tWeight\t87.96135849637315\n", + "Image_ID\t4270\t-\tWeight\t88.00482615715241\n", + "Image_ID\t5038\t-\tWeight\t88.01969361810276\n", + "Image_ID\t5502\t-\tWeight\t88.09635610416296\n", + "Image_ID\t5046\t-\tWeight\t88.14065062344345\n", + "Image_ID\t4838\t-\tWeight\t88.14921302388126\n", + "Image_ID\t5520\t-\tWeight\t88.17296443358039\n", + 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"Image_ID\t1096\t-\tWeight\t87.19192032167649\n", + "Image_ID\t1078\t-\tWeight\t87.26534446106028\n", + "Image_ID\t1808\t-\tWeight\t87.283696859818\n", + "Image_ID\t1110\t-\tWeight\t87.28383551265503\n", + "Image_ID\t1108\t-\tWeight\t87.31985374260492\n", + "Image_ID\t1758\t-\tWeight\t87.32309647614431\n", + "Image_ID\t1208\t-\tWeight\t87.32585794789108\n", + "Image_ID\t1490\t-\tWeight\t87.33313342081311\n", + "Image_ID\t1524\t-\tWeight\t87.33945025202613\n", + "Latent semantic no. 93\n", + "Image_ID\t1334\t-\tWeight\t87.16772811242393\n", + "Image_ID\t8044\t-\tWeight\t87.24769679658849\n", + "Image_ID\t8074\t-\tWeight\t87.25344971860738\n", + "Image_ID\t5216\t-\tWeight\t87.29565717770485\n", + "Image_ID\t1154\t-\tWeight\t87.29645031841564\n", + "Image_ID\t6452\t-\tWeight\t87.30648343845141\n", + "Image_ID\t8050\t-\tWeight\t87.32628339088339\n", + "Image_ID\t8012\t-\tWeight\t87.3432507520708\n", + "Image_ID\t7992\t-\tWeight\t87.35348293351674\n", + 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"Object of type ndarray is not JSON serializable", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Phase 2\\task_3.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[0;32m 7\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mk should be a positive integer\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 9\u001b[0m selected_dim_reduction_method \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(\n\u001b[0;32m 10\u001b[0m \u001b[39minput\u001b[39m(\n\u001b[0;32m 11\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mEnter dimensionality reduction method - one of \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 12\u001b[0m \u001b[39m+\u001b[39m \u001b[39mstr\u001b[39m(\u001b[39mlist\u001b[39m(valid_dim_reduction_methods\u001b[39m.\u001b[39mkeys()))\n\u001b[0;32m 13\u001b[0m )\n\u001b[0;32m 14\u001b[0m )\n\u001b[1;32m---> 16\u001b[0m extract_latent_semantics(\n\u001b[0;32m 17\u001b[0m fd_collection,\n\u001b[0;32m 18\u001b[0m k,\n\u001b[0;32m 19\u001b[0m selected_feature_model,\n\u001b[0;32m 20\u001b[0m selected_dim_reduction_method,\n\u001b[0;32m 21\u001b[0m top_images\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m,\n\u001b[0;32m 22\u001b[0m )\n", - "File \u001b[1;32mc:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Phase 2\\utils.py:674\u001b[0m, in \u001b[0;36mextract_latent_semantics\u001b[1;34m(fd_collection, k, feature_model, dim_reduction_method, top_images)\u001b[0m\n\u001b[0;32m 669\u001b[0m \u001b[39m# unsupervised LDA to extract topics (Latent Dirichlet Allocation)\u001b[39;00m\n\u001b[0;32m 670\u001b[0m \u001b[39m# Note: LDA takes a bit of time\u001b[39;00m\n\u001b[0;32m 671\u001b[0m \u001b[39mcase\u001b[39;00m \u001b[39m3\u001b[39m:\n\u001b[0;32m 672\u001b[0m \u001b[39m# LDA requires non-negative input data\u001b[39;00m\n\u001b[0;32m 673\u001b[0m \u001b[39m# so shift the input by subtracting the smallest value\u001b[39;00m\n\u001b[1;32m--> 674\u001b[0m min_value \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mmin(feature_vectors)\n\u001b[0;32m 675\u001b[0m feature_vectors_shifted \u001b[39m=\u001b[39m feature_vectors \u001b[39m-\u001b[39m min_value\n\u001b[0;32m 677\u001b[0m model \u001b[39m=\u001b[39m LatentDirichletAllocation(n_components\u001b[39m=\u001b[39mk, learning_method\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39monline\u001b[39m\u001b[39m\"\u001b[39m, verbose\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\base.py:1151\u001b[0m, in \u001b[0;36m_fit_context..decorator..wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1144\u001b[0m estimator\u001b[39m.\u001b[39m_validate_params()\n\u001b[0;32m 1146\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[0;32m 1147\u001b[0m skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[0;32m 1148\u001b[0m prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 1149\u001b[0m )\n\u001b[0;32m 1150\u001b[0m ):\n\u001b[1;32m-> 1151\u001b[0m \u001b[39mreturn\u001b[39;00m fit_method(estimator, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\decomposition\\_lda.py:665\u001b[0m, in \u001b[0;36mLatentDirichletAllocation.fit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m 663\u001b[0m \u001b[39mif\u001b[39;00m learning_method \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39monline\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m 664\u001b[0m \u001b[39mfor\u001b[39;00m idx_slice \u001b[39min\u001b[39;00m gen_batches(n_samples, batch_size):\n\u001b[1;32m--> 665\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_em_step(\n\u001b[0;32m 666\u001b[0m X[idx_slice, :],\n\u001b[0;32m 667\u001b[0m total_samples\u001b[39m=\u001b[39;49mn_samples,\n\u001b[0;32m 668\u001b[0m batch_update\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m 669\u001b[0m parallel\u001b[39m=\u001b[39;49mparallel,\n\u001b[0;32m 670\u001b[0m )\n\u001b[0;32m 671\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 672\u001b[0m \u001b[39m# batch update\u001b[39;00m\n\u001b[0;32m 673\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_em_step(\n\u001b[0;32m 674\u001b[0m X, total_samples\u001b[39m=\u001b[39mn_samples, batch_update\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, parallel\u001b[39m=\u001b[39mparallel\n\u001b[0;32m 675\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\decomposition\\_lda.py:524\u001b[0m, in \u001b[0;36mLatentDirichletAllocation._em_step\u001b[1;34m(self, X, total_samples, batch_update, parallel)\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"EM update for 1 iteration.\u001b[39;00m\n\u001b[0;32m 498\u001b[0m \n\u001b[0;32m 499\u001b[0m \u001b[39mupdate `_component` by batch VB or online VB.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 520\u001b[0m \u001b[39m Unnormalized document topic distribution.\u001b[39;00m\n\u001b[0;32m 521\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 523\u001b[0m \u001b[39m# E-step\u001b[39;00m\n\u001b[1;32m--> 524\u001b[0m _, suff_stats \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_e_step(\n\u001b[0;32m 525\u001b[0m X, cal_sstats\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, random_init\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, parallel\u001b[39m=\u001b[39;49mparallel\n\u001b[0;32m 526\u001b[0m )\n\u001b[0;32m 528\u001b[0m \u001b[39m# M-step\u001b[39;00m\n\u001b[0;32m 529\u001b[0m \u001b[39mif\u001b[39;00m batch_update:\n", - "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\decomposition\\_lda.py:467\u001b[0m, in \u001b[0;36mLatentDirichletAllocation._e_step\u001b[1;34m(self, X, cal_sstats, random_init, parallel)\u001b[0m\n\u001b[0;32m 465\u001b[0m \u001b[39mif\u001b[39;00m parallel \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 466\u001b[0m parallel \u001b[39m=\u001b[39m Parallel(n_jobs\u001b[39m=\u001b[39mn_jobs, verbose\u001b[39m=\u001b[39m\u001b[39mmax\u001b[39m(\u001b[39m0\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m))\n\u001b[1;32m--> 467\u001b[0m results \u001b[39m=\u001b[39m parallel(\n\u001b[0;32m 468\u001b[0m delayed(_update_doc_distribution)(\n\u001b[0;32m 469\u001b[0m X[idx_slice, :],\n\u001b[0;32m 470\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mexp_dirichlet_component_,\n\u001b[0;32m 471\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdoc_topic_prior_,\n\u001b[0;32m 472\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmax_doc_update_iter,\n\u001b[0;32m 473\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmean_change_tol,\n\u001b[0;32m 474\u001b[0m cal_sstats,\n\u001b[0;32m 475\u001b[0m random_state,\n\u001b[0;32m 476\u001b[0m )\n\u001b[0;32m 477\u001b[0m \u001b[39mfor\u001b[39;49;00m idx_slice \u001b[39min\u001b[39;49;00m gen_even_slices(X\u001b[39m.\u001b[39;49mshape[\u001b[39m0\u001b[39;49m], n_jobs)\n\u001b[0;32m 478\u001b[0m )\n\u001b[0;32m 480\u001b[0m \u001b[39m# merge result\u001b[39;00m\n\u001b[0;32m 481\u001b[0m doc_topics, sstats_list \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mresults)\n", - "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\utils\\parallel.py:65\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m 60\u001b[0m config \u001b[39m=\u001b[39m get_config()\n\u001b[0;32m 61\u001b[0m iterable_with_config \u001b[39m=\u001b[39m (\n\u001b[0;32m 62\u001b[0m (_with_config(delayed_func, config), args, kwargs)\n\u001b[0;32m 63\u001b[0m \u001b[39mfor\u001b[39;00m delayed_func, args, kwargs \u001b[39min\u001b[39;00m iterable\n\u001b[0;32m 64\u001b[0m )\n\u001b[1;32m---> 65\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(iterable_with_config)\n", - "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\joblib\\parallel.py:1863\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m 1861\u001b[0m output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_sequential_output(iterable)\n\u001b[0;32m 1862\u001b[0m \u001b[39mnext\u001b[39m(output)\n\u001b[1;32m-> 1863\u001b[0m \u001b[39mreturn\u001b[39;00m output \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreturn_generator \u001b[39melse\u001b[39;00m \u001b[39mlist\u001b[39;49m(output)\n\u001b[0;32m 1865\u001b[0m \u001b[39m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[0;32m 1866\u001b[0m \u001b[39m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[0;32m 1867\u001b[0m \u001b[39m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[0;32m 1868\u001b[0m \u001b[39m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[0;32m 1869\u001b[0m \u001b[39m# callback.\u001b[39;00m\n\u001b[0;32m 1870\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n", - "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\joblib\\parallel.py:1792\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m 1790\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_dispatched_batches \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m 1791\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_dispatched_tasks \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m-> 1792\u001b[0m res \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1793\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_completed_tasks \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m 1794\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprint_progress()\n", - "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\utils\\parallel.py:127\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 125\u001b[0m config \u001b[39m=\u001b[39m {}\n\u001b[0;32m 126\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mconfig):\n\u001b[1;32m--> 127\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfunction(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\site-packages\\sklearn\\decomposition\\_lda.py:144\u001b[0m, in \u001b[0;36m_update_doc_distribution\u001b[1;34m(X, exp_topic_word_distr, doc_topic_prior, max_doc_update_iter, mean_change_tol, cal_sstats, random_state)\u001b[0m\n\u001b[0;32m 140\u001b[0m last_d \u001b[39m=\u001b[39m doc_topic_d\n\u001b[0;32m 142\u001b[0m \u001b[39m# The optimal phi_{dwk} is proportional to\u001b[39;00m\n\u001b[0;32m 143\u001b[0m \u001b[39m# exp(E[log(theta_{dk})]) * exp(E[log(beta_{dw})]).\u001b[39;00m\n\u001b[1;32m--> 144\u001b[0m norm_phi \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mdot(exp_doc_topic_d, exp_topic_word_d) \u001b[39m+\u001b[39m eps\n\u001b[0;32m 146\u001b[0m doc_topic_d \u001b[39m=\u001b[39m exp_doc_topic_d \u001b[39m*\u001b[39m np\u001b[39m.\u001b[39mdot(cnts \u001b[39m/\u001b[39m norm_phi, exp_topic_word_d\u001b[39m.\u001b[39mT)\n\u001b[0;32m 147\u001b[0m \u001b[39m# Note: adds doc_topic_prior to doc_topic_d, in-place.\u001b[39;00m\n", - "File \u001b[1;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mdot\u001b[1;34m(*args, **kwargs)\u001b[0m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Phase 2\\task_3.ipynb Cell 4\u001b[0m line \u001b[0;36m1\n\u001b[0;32m 7\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mk should be a positive integer\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 9\u001b[0m selected_dim_reduction_method \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(\n\u001b[0;32m 10\u001b[0m \u001b[39minput\u001b[39m(\n\u001b[0;32m 11\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mEnter dimensionality reduction method - one of \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 12\u001b[0m \u001b[39m+\u001b[39m \u001b[39mstr\u001b[39m(\u001b[39mlist\u001b[39m(valid_dim_reduction_methods\u001b[39m.\u001b[39mkeys()))\n\u001b[0;32m 13\u001b[0m )\n\u001b[0;32m 14\u001b[0m )\n\u001b[1;32m---> 16\u001b[0m extract_latent_semantics(\n\u001b[0;32m 17\u001b[0m fd_collection,\n\u001b[0;32m 18\u001b[0m k,\n\u001b[0;32m 19\u001b[0m selected_feature_model,\n\u001b[0;32m 20\u001b[0m selected_dim_reduction_method,\n\u001b[0;32m 21\u001b[0m top_images\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m,\n\u001b[0;32m 22\u001b[0m )\n", + "File \u001b[1;32mc:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Phase 2\\utils.py:834\u001b[0m, in \u001b[0;36mextract_latent_semantics\u001b[1;34m(fd_collection, k, feature_model, dim_reduction_method, top_images)\u001b[0m\n\u001b[0;32m 827\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mImage_ID\u001b[39m\u001b[39m\\t\u001b[39;00m\u001b[39m{\u001b[39;00mimage_id\u001b[39m}\u001b[39;00m\u001b[39m\\t\u001b[39;00m\u001b[39m-\u001b[39m\u001b[39m\\t\u001b[39;00m\u001b[39mWeight\u001b[39m\u001b[39m\\t\u001b[39;00m\u001b[39m{\u001b[39;00mweight\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 829\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(\n\u001b[0;32m 830\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mfeature_model\u001b[39m}\u001b[39;00m\u001b[39m-\u001b[39m\u001b[39m{\u001b[39;00mdim_reduction_method\u001b[39m}\u001b[39;00m\u001b[39m-\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m-semantics.json\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m 831\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mw\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m 832\u001b[0m encoding\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m 833\u001b[0m ) \u001b[39mas\u001b[39;00m output_file:\n\u001b[1;32m--> 834\u001b[0m json\u001b[39m.\u001b[39;49mdump(all_latent_semantics, output_file, ensure_ascii\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n", + "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\json\\__init__.py:179\u001b[0m, in \u001b[0;36mdump\u001b[1;34m(obj, fp, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[0;32m 173\u001b[0m iterable \u001b[39m=\u001b[39m \u001b[39mcls\u001b[39m(skipkeys\u001b[39m=\u001b[39mskipkeys, ensure_ascii\u001b[39m=\u001b[39mensure_ascii,\n\u001b[0;32m 174\u001b[0m check_circular\u001b[39m=\u001b[39mcheck_circular, allow_nan\u001b[39m=\u001b[39mallow_nan, indent\u001b[39m=\u001b[39mindent,\n\u001b[0;32m 175\u001b[0m separators\u001b[39m=\u001b[39mseparators,\n\u001b[0;32m 176\u001b[0m default\u001b[39m=\u001b[39mdefault, sort_keys\u001b[39m=\u001b[39msort_keys, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkw)\u001b[39m.\u001b[39miterencode(obj)\n\u001b[0;32m 177\u001b[0m \u001b[39m# could accelerate with writelines in some versions of Python, at\u001b[39;00m\n\u001b[0;32m 178\u001b[0m \u001b[39m# a debuggability cost\u001b[39;00m\n\u001b[1;32m--> 179\u001b[0m \u001b[39mfor\u001b[39;00m chunk \u001b[39min\u001b[39;00m iterable:\n\u001b[0;32m 180\u001b[0m fp\u001b[39m.\u001b[39mwrite(chunk)\n", + "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\json\\encoder.py:431\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode\u001b[1;34m(o, _current_indent_level)\u001b[0m\n\u001b[0;32m 429\u001b[0m \u001b[39myield from\u001b[39;00m _iterencode_list(o, _current_indent_level)\n\u001b[0;32m 430\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(o, \u001b[39mdict\u001b[39m):\n\u001b[1;32m--> 431\u001b[0m \u001b[39myield from\u001b[39;00m _iterencode_dict(o, _current_indent_level)\n\u001b[0;32m 432\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 433\u001b[0m \u001b[39mif\u001b[39;00m markers \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\json\\encoder.py:405\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode_dict\u001b[1;34m(dct, _current_indent_level)\u001b[0m\n\u001b[0;32m 403\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 404\u001b[0m chunks \u001b[39m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[1;32m--> 405\u001b[0m \u001b[39myield from\u001b[39;00m chunks\n\u001b[0;32m 406\u001b[0m \u001b[39mif\u001b[39;00m newline_indent \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 407\u001b[0m _current_indent_level \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\json\\encoder.py:325\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode_list\u001b[1;34m(lst, _current_indent_level)\u001b[0m\n\u001b[0;32m 323\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 324\u001b[0m chunks \u001b[39m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[1;32m--> 325\u001b[0m \u001b[39myield from\u001b[39;00m chunks\n\u001b[0;32m 326\u001b[0m \u001b[39mif\u001b[39;00m newline_indent \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 327\u001b[0m _current_indent_level \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\json\\encoder.py:438\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode\u001b[1;34m(o, _current_indent_level)\u001b[0m\n\u001b[0;32m 436\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mCircular reference detected\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 437\u001b[0m markers[markerid] \u001b[39m=\u001b[39m o\n\u001b[1;32m--> 438\u001b[0m o \u001b[39m=\u001b[39m _default(o)\n\u001b[0;32m 439\u001b[0m \u001b[39myield from\u001b[39;00m _iterencode(o, _current_indent_level)\n\u001b[0;32m 440\u001b[0m \u001b[39mif\u001b[39;00m markers \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\rknar\\.pyenv\\pyenv-win\\versions\\3.10.5\\lib\\json\\encoder.py:179\u001b[0m, in \u001b[0;36mJSONEncoder.default\u001b[1;34m(self, o)\u001b[0m\n\u001b[0;32m 160\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdefault\u001b[39m(\u001b[39mself\u001b[39m, o):\n\u001b[0;32m 161\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Implement this method in a subclass such that it returns\u001b[39;00m\n\u001b[0;32m 162\u001b[0m \u001b[39m a serializable object for ``o``, or calls the base implementation\u001b[39;00m\n\u001b[0;32m 163\u001b[0m \u001b[39m (to raise a ``TypeError``).\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 177\u001b[0m \n\u001b[0;32m 178\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 179\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mObject of type \u001b[39m\u001b[39m{\u001b[39;00mo\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m 180\u001b[0m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mis not JSON serializable\u001b[39m\u001b[39m'\u001b[39m)\n", + "\u001b[1;31mTypeError\u001b[0m: Object of type ndarray is not JSON serializable" ] } ], diff --git a/Phase 2/utils.py b/Phase 2/utils.py index 1c70c5c..3e822bd 100644 --- a/Phase 2/utils.py +++ b/Phase 2/utils.py @@ -1,6 +1,7 @@ # All imports # Math import math +import random import cv2 import numpy as np from scipy.stats import pearsonr @@ -8,7 +9,8 @@ from scipy.sparse.linalg import svds from sklearn.decomposition import NMF from sklearn.decomposition import LatentDirichletAllocation from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -from sklearn.cluster import KMeans + +# from sklearn.cluster import KMeans # Torch import torch @@ -582,6 +584,97 @@ valid_dim_reduction_methods = { } +class KMeans: + def __init__(self, n_clusters, tol=0.001, max_iter=300, verbose=0): + self.n_clusters = n_clusters + self.max_iter = max_iter + self.tol = tol + self.cluster_centers_ = {} + self.verbose = verbose + + def fit(self, data): + """Iterative fitting clusters on data of `(n_samples,n_features)` dimensions""" + + # Randomly select centroid start points with uniform distribution from dataset + min_, max_ = np.min(data, axis=0), np.max(data, axis=0) + self.cluster_centers_ = { + i: np.random.uniform(min_, max_) for i in range(self.n_clusters) + } + + if self.verbose > 0: + print("Initialized centroids") + for itr in range(self.max_iter): + print(f"Iteration {itr}") + self.clusters = {} + + for j in range(self.n_clusters): + self.clusters[j] = [] + + for feature_set in data: + # TODO: Should this be modified to use different distance measures + # based on the feature set? + distances = [ + np.linalg.norm(feature_set - self.cluster_centers_[i]) + for i in range(len(self.cluster_centers_)) + ] + + # Put data point into closest cluster + cluster = np.argmin(distances) + self.clusters[cluster].append(feature_set) + + prev_centroids = self.cluster_centers_ + + for c in self.cluster_centers_: + if isinstance(self.cluster_centers_[c], np.ndarray): + if np.isnan(self.cluster_centers_[c]).any(): + # Reinitialize centroid to a random point in the dataset + self.cluster_centers_[c] = np.random.uniform(min_, max_) + else: + # Compute the mean of non-empty cluster + self.cluster_centers_[c] = np.mean(self.clusters[c], axis=0) + elif np.isnan(self.cluster_centers_[c]): + # Reinitialize centroid to a random point in the dataset + self.cluster_centers_[c] = np.random.uniform(min_, max_) + + # Check if centroids have converged + optimized = True + for c in self.cluster_centers_: + prev_centroid = prev_centroids[c] + current_centroid = self.cluster_centers_[c] + convergence_tol = np.sum(abs( + (prev_centroid - current_centroid) / prev_centroid * 100.0 + )) + if convergence_tol > self.tol: + optimized = False + if self.verbose > 0: + print(f"Iter {itr} - Not converged yet") + break + + if itr > 10 and optimized: + if self.verbose > 0: + print(f"Iter {itr} - Converged") + break + + return self + + def transform(self, data): + """Transform data of `(n_samples,n_features)` dimensions to `(n_samples,n_clusters)` using fitted model""" + + Y = np.empty((len(data), self.n_clusters)) + + for idx, feature_set in enumerate(data): + # TODO: Could this be modified to use different distance measures + # based on the feature set? + Y[idx] = np.array( + [ + np.linalg.norm(feature_set - self.cluster_centers_[i]) + for i in range(len(self.cluster_centers_)) + ] + ) + + return Y + + def extract_latent_semantics( fd_collection, k, feature_model, dim_reduction_method, top_images=None ): @@ -659,7 +752,10 @@ def extract_latent_semantics( W = model.transform(feature_vectors_shifted) H = model.components_ - all_latent_semantics = {"image-semantic": W, "semantic-feature": H} + all_latent_semantics = { + "image-semantic": W.tolist(), + "semantic-feature": H.tolist(), + } # for each latent semantic, sort imageID-weight pairs by weights in descending order displayed_latent_semantics = [ @@ -689,7 +785,10 @@ def extract_latent_semantics( # X (4339 x k) is the other factor matrix for image ID-latent semantic pairs X = model.transform(feature_vectors_shifted) - all_latent_semantics = {"image-semantic": X, "semantic-feature": K} + all_latent_semantics = { + "image-semantic": X.tolist(), + "semantic-feature": K.tolist(), + } # for each latent semantic, sort imageID-weight pairs by weights in descending order displayed_latent_semantics = [ @@ -703,11 +802,24 @@ def extract_latent_semantics( # k-means clustering to reduce to k clusters/dimensions case 4: - model = KMeans(n_clusters=k).fit(feature_vectors) + model = KMeans(n_clusters=k, verbose=2).fit(feature_vectors) CC = model.cluster_centers_ - U = model.transform(feature_vectors) + Y = model.transform(feature_vectors) - all_latent_semantics = {"image-semantic": U, "semantic_feature": CC} + all_latent_semantics = { + "image-semantic": Y.tolist(), + "semantic-feature": list(CC.values()), + } + + # for each latent semantic, sort imageID-weight pairs by weights in descending order + displayed_latent_semantics = [ + sorted( + list(zip(feature_ids, latent_semantic)), + key=lambda x: x[1], + reverse=False, + )[:top_images] + for latent_semantic in Y.T + ] for idx, latent_semantic in enumerate(displayed_latent_semantics): print(f"Latent semantic no. {idx}")