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start kmeans task 9

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pranavbrkr 2023-10-13 11:27:59 -07:00
parent 934e8420d7
commit d8490c7c4b
4 changed files with 364 additions and 222 deletions
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205
Phase 2/task_3.ipynb
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@ -29,120 +29,119 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Applying kmeans on the resnet_fd space to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n",
"Applying kmeans on the cm_fd space to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n",
"Initialized centroids\n",
"Iteration 56 - Converged\n",
"Note: for K-Means we display distances, in ascending order\n",
"Latent semantic no. 0\n",
"Image_ID\t440\t-\tDistance\t10.640763416796371\n",
"Image_ID\t700\t-\tDistance\t11.159224514655602\n",
"Image_ID\t654\t-\tDistance\t11.395135539610168\n",
"Image_ID\t486\t-\tDistance\t11.550858382118225\n",
"Image_ID\t462\t-\tDistance\t11.61044182679253\n",
"Image_ID\t652\t-\tDistance\t11.818427599783789\n",
"Image_ID\t676\t-\tDistance\t11.925768133017636\n",
"Image_ID\t584\t-\tDistance\t11.93319861884516\n",
"Image_ID\t692\t-\tDistance\t11.979693069110743\n",
"Image_ID\t6\t-\tDistance\t12.137562566975056\n",
"Image_ID\t2406\t-\tDistance\t2.4329297906521914\n",
"Image_ID\t2624\t-\tDistance\t2.4610601036735282\n",
"Image_ID\t7112\t-\tDistance\t2.5837781069798633\n",
"Image_ID\t5390\t-\tDistance\t2.60890832624663\n",
"Image_ID\t4782\t-\tDistance\t2.6300363909906017\n",
"Image_ID\t4218\t-\tDistance\t2.6526211985836103\n",
"Image_ID\t4210\t-\tDistance\t2.6581936664893533\n",
"Image_ID\t944\t-\tDistance\t2.7472085431102213\n",
"Image_ID\t6600\t-\tDistance\t2.788716977448917\n",
"Image_ID\t2398\t-\tDistance\t2.797045487845613\n",
"Latent semantic no. 1\n",
"Image_ID\t3602\t-\tDistance\t13.563162479981145\n",
"Image_ID\t2414\t-\tDistance\t14.192224338224467\n",
"Image_ID\t3560\t-\tDistance\t14.205420291205272\n",
"Image_ID\t3600\t-\tDistance\t14.389262503144405\n",
"Image_ID\t2228\t-\tDistance\t14.4828087393621\n",
"Image_ID\t3636\t-\tDistance\t14.497503774497243\n",
"Image_ID\t3614\t-\tDistance\t14.591251785931954\n",
"Image_ID\t2090\t-\tDistance\t14.620114150279178\n",
"Image_ID\t2328\t-\tDistance\t14.69159730598465\n",
"Image_ID\t2448\t-\tDistance\t14.774950728597261\n",
"Image_ID\t5826\t-\tDistance\t1.7730956906058473\n",
"Image_ID\t3944\t-\tDistance\t1.8750448829509372\n",
"Image_ID\t968\t-\tDistance\t1.9655862567434115\n",
"Image_ID\t1068\t-\tDistance\t1.9677696006956515\n",
"Image_ID\t5664\t-\tDistance\t2.0908245587325114\n",
"Image_ID\t7392\t-\tDistance\t2.1187697478953686\n",
"Image_ID\t3304\t-\tDistance\t2.154265483459674\n",
"Image_ID\t1008\t-\tDistance\t2.2197924178276014\n",
"Image_ID\t908\t-\tDistance\t2.237300492325052\n",
"Image_ID\t2940\t-\tDistance\t2.2377555386247865\n",
"Latent semantic no. 2\n",
"Image_ID\t4838\t-\tDistance\t12.261260721990451\n",
"Image_ID\t7302\t-\tDistance\t12.880136852617754\n",
"Image_ID\t7978\t-\tDistance\t13.077993711608961\n",
"Image_ID\t8600\t-\tDistance\t13.305290839761437\n",
"Image_ID\t7292\t-\tDistance\t13.334716062864114\n",
"Image_ID\t7720\t-\tDistance\t13.37155798887382\n",
"Image_ID\t7958\t-\tDistance\t13.430323190148206\n",
"Image_ID\t4600\t-\tDistance\t13.45781162474979\n",
"Image_ID\t4270\t-\tDistance\t13.491427681265899\n",
"Image_ID\t4828\t-\tDistance\t13.539053205319615\n",
"Image_ID\t2406\t-\tDistance\t2.1258319537256445\n",
"Image_ID\t6922\t-\tDistance\t2.2011613151345975\n",
"Image_ID\t2624\t-\tDistance\t2.2289354011778006\n",
"Image_ID\t6484\t-\tDistance\t2.2515469285749545\n",
"Image_ID\t5390\t-\tDistance\t2.451999872498352\n",
"Image_ID\t4222\t-\tDistance\t2.4690306175362067\n",
"Image_ID\t5038\t-\tDistance\t2.4722970669139785\n",
"Image_ID\t3196\t-\tDistance\t2.475614158419068\n",
"Image_ID\t462\t-\tDistance\t2.49778761746267\n",
"Image_ID\t7380\t-\tDistance\t2.5265238831399635\n",
"Latent semantic no. 3\n",
"Image_ID\t1758\t-\tDistance\t5.030040634300718\n",
"Image_ID\t1562\t-\tDistance\t5.3329050871004755\n",
"Image_ID\t1586\t-\tDistance\t5.583507266395663\n",
"Image_ID\t1362\t-\tDistance\t6.017196001905923\n",
"Image_ID\t1626\t-\tDistance\t6.045998053427588\n",
"Image_ID\t1208\t-\tDistance\t6.051540458349612\n",
"Image_ID\t1374\t-\tDistance\t6.178242313742901\n",
"Image_ID\t1112\t-\tDistance\t6.249956790411116\n",
"Image_ID\t1710\t-\tDistance\t6.310688634541122\n",
"Image_ID\t1490\t-\tDistance\t6.376123320547912\n",
"Image_ID\t2412\t-\tDistance\t1.9079653649524306\n",
"Image_ID\t2138\t-\tDistance\t1.9508782175940445\n",
"Image_ID\t2290\t-\tDistance\t1.9526171427482104\n",
"Image_ID\t2302\t-\tDistance\t1.9769105940849563\n",
"Image_ID\t2640\t-\tDistance\t2.0476236872823406\n",
"Image_ID\t2634\t-\tDistance\t2.058811198055415\n",
"Image_ID\t2648\t-\tDistance\t2.0779524915237726\n",
"Image_ID\t2628\t-\tDistance\t2.1411367238671497\n",
"Image_ID\t2630\t-\tDistance\t2.156701968346356\n",
"Image_ID\t2502\t-\tDistance\t2.1813059883906454\n",
"Latent semantic no. 4\n",
"Image_ID\t8282\t-\tDistance\t10.506907762007522\n",
"Image_ID\t8348\t-\tDistance\t10.647963471647738\n",
"Image_ID\t8380\t-\tDistance\t10.715093501411761\n",
"Image_ID\t8228\t-\tDistance\t10.879515968086416\n",
"Image_ID\t8240\t-\tDistance\t10.896279105885796\n",
"Image_ID\t8340\t-\tDistance\t10.952943877775777\n",
"Image_ID\t8174\t-\tDistance\t11.012538653878869\n",
"Image_ID\t8368\t-\tDistance\t11.01584931675634\n",
"Image_ID\t8176\t-\tDistance\t11.074708303511043\n",
"Image_ID\t8386\t-\tDistance\t11.090905861600216\n",
"Image_ID\t2528\t-\tDistance\t1.985388167407023\n",
"Image_ID\t2570\t-\tDistance\t2.020441033596718\n",
"Image_ID\t7000\t-\tDistance\t2.0389617509774554\n",
"Image_ID\t2544\t-\tDistance\t2.0461546917978493\n",
"Image_ID\t6946\t-\tDistance\t2.087028769480915\n",
"Image_ID\t5070\t-\tDistance\t2.093563899781913\n",
"Image_ID\t3884\t-\tDistance\t2.12383247213783\n",
"Image_ID\t6662\t-\tDistance\t2.133611417276695\n",
"Image_ID\t5584\t-\tDistance\t2.134813594870179\n",
"Image_ID\t7592\t-\tDistance\t2.1350058409043253\n",
"Latent semantic no. 5\n",
"Image_ID\t7400\t-\tDistance\t9.07340282234228\n",
"Image_ID\t7332\t-\tDistance\t9.27997555888011\n",
"Image_ID\t6626\t-\tDistance\t9.490015364667478\n",
"Image_ID\t7990\t-\tDistance\t9.619812101313876\n",
"Image_ID\t7392\t-\tDistance\t9.640980435311661\n",
"Image_ID\t7404\t-\tDistance\t9.6738734363643\n",
"Image_ID\t7980\t-\tDistance\t9.710518881249477\n",
"Image_ID\t7410\t-\tDistance\t9.778693486707565\n",
"Image_ID\t7950\t-\tDistance\t9.785247539262517\n",
"Image_ID\t7346\t-\tDistance\t9.806294880503\n",
"Image_ID\t2406\t-\tDistance\t1.7192989054765462\n",
"Image_ID\t7736\t-\tDistance\t1.8415960899814483\n",
"Image_ID\t2624\t-\tDistance\t1.890325981685572\n",
"Image_ID\t4782\t-\tDistance\t1.947887574583758\n",
"Image_ID\t2434\t-\tDistance\t2.012480907684106\n",
"Image_ID\t5658\t-\tDistance\t2.0159295631755936\n",
"Image_ID\t5632\t-\tDistance\t2.0209799503972894\n",
"Image_ID\t5390\t-\tDistance\t2.054049699587572\n",
"Image_ID\t3762\t-\tDistance\t2.0632381421057997\n",
"Image_ID\t6922\t-\tDistance\t2.1324100407425832\n",
"Latent semantic no. 6\n",
"Image_ID\t8542\t-\tDistance\t11.232961895055158\n",
"Image_ID\t6014\t-\tDistance\t11.304802835945505\n",
"Image_ID\t8566\t-\tDistance\t11.443919577851908\n",
"Image_ID\t7200\t-\tDistance\t11.484387898391537\n",
"Image_ID\t6626\t-\tDistance\t11.48886846539337\n",
"Image_ID\t6620\t-\tDistance\t11.578369802598303\n",
"Image_ID\t6636\t-\tDistance\t11.662783932711658\n",
"Image_ID\t8056\t-\tDistance\t11.74943673802499\n",
"Image_ID\t7700\t-\tDistance\t11.769992973787971\n",
"Image_ID\t6622\t-\tDistance\t11.780162710805048\n",
"Image_ID\t7244\t-\tDistance\t2.0882730827827514\n",
"Image_ID\t7256\t-\tDistance\t2.2363345183902643\n",
"Image_ID\t6946\t-\tDistance\t2.2626049811136104\n",
"Image_ID\t7232\t-\tDistance\t2.3287228186618827\n",
"Image_ID\t7260\t-\tDistance\t2.432017355562297\n",
"Image_ID\t4942\t-\tDistance\t2.5360228464626915\n",
"Image_ID\t3194\t-\tDistance\t2.652196198820196\n",
"Image_ID\t4946\t-\tDistance\t2.707800015244559\n",
"Image_ID\t6972\t-\tDistance\t2.772167403532193\n",
"Image_ID\t3822\t-\tDistance\t2.7757540939652245\n",
"Latent semantic no. 7\n",
"Image_ID\t2646\t-\tDistance\t7.514711553618432\n",
"Image_ID\t2260\t-\tDistance\t7.633993639248322\n",
"Image_ID\t2460\t-\tDistance\t7.685809907469392\n",
"Image_ID\t2660\t-\tDistance\t7.701780256364207\n",
"Image_ID\t2418\t-\tDistance\t7.716363257255012\n",
"Image_ID\t2240\t-\tDistance\t7.74734521250179\n",
"Image_ID\t2430\t-\tDistance\t7.784825198465868\n",
"Image_ID\t2264\t-\tDistance\t7.828411523843045\n",
"Image_ID\t2242\t-\tDistance\t7.878806112518542\n",
"Image_ID\t2196\t-\tDistance\t7.918897962650677\n",
"Image_ID\t1234\t-\tDistance\t2.5103511852585627\n",
"Image_ID\t1406\t-\tDistance\t2.5905943688502\n",
"Image_ID\t1582\t-\tDistance\t2.64691846983913\n",
"Image_ID\t1844\t-\tDistance\t2.741629768608531\n",
"Image_ID\t1638\t-\tDistance\t2.7657226276060536\n",
"Image_ID\t1154\t-\tDistance\t2.8386700997389043\n",
"Image_ID\t1286\t-\tDistance\t2.8446264818255877\n",
"Image_ID\t1848\t-\tDistance\t2.8793700988824398\n",
"Image_ID\t1284\t-\tDistance\t2.879846330398362\n",
"Image_ID\t1592\t-\tDistance\t2.8822966091246407\n",
"Latent semantic no. 8\n",
"Image_ID\t562\t-\tDistance\t8.552732623243445\n",
"Image_ID\t796\t-\tDistance\t9.316343355329956\n",
"Image_ID\t612\t-\tDistance\t9.451362646413244\n",
"Image_ID\t476\t-\tDistance\t9.458717454426738\n",
"Image_ID\t798\t-\tDistance\t9.853412912988212\n",
"Image_ID\t460\t-\tDistance\t9.859458462429464\n",
"Image_ID\t190\t-\tDistance\t10.065071186269668\n",
"Image_ID\t462\t-\tDistance\t10.065893471754435\n",
"Image_ID\t456\t-\tDistance\t10.099056881970604\n",
"Image_ID\t828\t-\tDistance\t10.29276769283984\n",
"Image_ID\t7686\t-\tDistance\t2.3114266143360425\n",
"Image_ID\t4286\t-\tDistance\t2.3193670377796534\n",
"Image_ID\t7974\t-\tDistance\t2.410584599384146\n",
"Image_ID\t7668\t-\tDistance\t2.4392449505107026\n",
"Image_ID\t3262\t-\tDistance\t2.4432361382128236\n",
"Image_ID\t7856\t-\tDistance\t2.484388558904672\n",
"Image_ID\t6250\t-\tDistance\t2.5139181727884887\n",
"Image_ID\t6982\t-\tDistance\t2.522220046130116\n",
"Image_ID\t4032\t-\tDistance\t2.5671693188571254\n",
"Image_ID\t8610\t-\tDistance\t2.592334945993663\n",
"Latent semantic no. 9\n",
"Image_ID\t3124\t-\tDistance\t12.500361886870435\n",
"Image_ID\t8064\t-\tDistance\t12.967833703429173\n",
"Image_ID\t4270\t-\tDistance\t13.225230811650766\n",
"Image_ID\t7720\t-\tDistance\t13.340802785257075\n",
"Image_ID\t8050\t-\tDistance\t13.601572206798334\n",
"Image_ID\t8074\t-\tDistance\t13.693355761074226\n",
"Image_ID\t8042\t-\tDistance\t13.72102497292387\n",
"Image_ID\t6450\t-\tDistance\t13.750626256669166\n",
"Image_ID\t8018\t-\tDistance\t13.768703250806348\n",
"Image_ID\t6628\t-\tDistance\t13.784107713433421\n"
"Image_ID\t8656\t-\tDistance\t0.0\n",
"Image_ID\t5314\t-\tDistance\t7.545361629760217\n",
"Image_ID\t7854\t-\tDistance\t7.706317148014618\n",
"Image_ID\t712\t-\tDistance\t7.812246024712053\n",
"Image_ID\t8170\t-\tDistance\t7.940921127343809\n",
"Image_ID\t496\t-\tDistance\t7.95303740274659\n",
"Image_ID\t662\t-\tDistance\t7.976573111687378\n",
"Image_ID\t3188\t-\tDistance\t7.9858733547811935\n",
"Image_ID\t3116\t-\tDistance\t8.012971090439164\n",
"Image_ID\t3078\t-\tDistance\t8.023521594743528\n"
]
}
],
@ -195,7 +194,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
"version": "3.11.4"
}
},
"nbformat": 4,

251
Phase 2/task_5.ipynb
View File

@ -2,18 +2,9 @@
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"outputs": [],
"source": [
"from utils import *\n",
"warnings.filterwarnings('ignore')\n",
@ -22,7 +13,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
@ -31,124 +22,156 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Applying svd on the given similarity matrix to get 10 latent semantics (showing only top 10 label-weight pairs for each latent semantic)...\n",
"Applying kmeans on the given similarity matrix to get 10 latent semantics (showing only top 10 label-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",
"label\t28\t-\tWeight\t0.2583354411312026\n",
"label\t29\t-\tWeight\t0.2301362547676974\n",
"label\t33\t-\tWeight\t0.2129183683279978\n",
"label\t9\t-\tWeight\t0.17625685452423093\n",
"label\t95\t-\tWeight\t0.16277551497836534\n",
"label\t47\t-\tWeight\t0.1424860388015467\n",
"label\t39\t-\tWeight\t0.1349747704005884\n",
"label\t30\t-\tWeight\t0.13251434767496492\n",
"label\t52\t-\tWeight\t0.12669069496270755\n",
"label\t8\t-\tWeight\t0.1257730807471899\n",
"label\t84\t-\tWeight\t16.953715652557495\n",
"label\t34\t-\tWeight\t17.25164883471016\n",
"label\t1\t-\tWeight\t17.379970016799952\n",
"label\t72\t-\tWeight\t17.439397073433092\n",
"label\t32\t-\tWeight\t17.447297173030393\n",
"label\t31\t-\tWeight\t17.448932606262144\n",
"label\t40\t-\tWeight\t17.561159943630802\n",
"label\t79\t-\tWeight\t17.572813876633166\n",
"label\t5\t-\tWeight\t17.721278660592027\n",
"label\t56\t-\tWeight\t17.731177291838822\n",
"Latent semantic no. 1\n",
"label\t96\t-\tWeight\t0.2666765976054894\n",
"label\t97\t-\tWeight\t0.19087869496500426\n",
"label\t25\t-\tWeight\t0.17776094778851348\n",
"label\t3\t-\tWeight\t0.1759798805642099\n",
"label\t98\t-\tWeight\t0.16951497899752574\n",
"label\t22\t-\tWeight\t0.1667032655640346\n",
"label\t24\t-\tWeight\t0.16034180060184824\n",
"label\t19\t-\tWeight\t0.15345532912389587\n",
"label\t52\t-\tWeight\t0.13271640119612757\n",
"label\t29\t-\tWeight\t0.12856388746021633\n",
"label\t84\t-\tWeight\t19.27643729221191\n",
"label\t5\t-\tWeight\t19.449814613173483\n",
"label\t32\t-\tWeight\t19.684592406270944\n",
"label\t63\t-\tWeight\t19.911988624963808\n",
"label\t79\t-\tWeight\t19.930151237028223\n",
"label\t38\t-\tWeight\t19.948477661871497\n",
"label\t89\t-\tWeight\t19.965086791647906\n",
"label\t94\t-\tWeight\t19.990956583854018\n",
"label\t72\t-\tWeight\t19.99680017871235\n",
"label\t45\t-\tWeight\t20.058898160614795\n",
"Latent semantic no. 2\n",
"label\t46\t-\tWeight\t0.21813474254675366\n",
"label\t79\t-\tWeight\t0.19091788352587957\n",
"label\t55\t-\tWeight\t0.1871080482210247\n",
"label\t56\t-\tWeight\t0.18322792605578184\n",
"label\t78\t-\tWeight\t0.17506936966351683\n",
"label\t98\t-\tWeight\t0.1733164832137484\n",
"label\t22\t-\tWeight\t0.17114312653027375\n",
"label\t38\t-\tWeight\t0.16928636840289424\n",
"label\t45\t-\tWeight\t0.1567042877228484\n",
"label\t4\t-\tWeight\t0.15108693899889344\n",
"label\t0\t-\tWeight\tnan\n",
"label\t1\t-\tWeight\tnan\n",
"label\t2\t-\tWeight\tnan\n",
"label\t3\t-\tWeight\tnan\n",
"label\t4\t-\tWeight\tnan\n",
"label\t5\t-\tWeight\tnan\n",
"label\t6\t-\tWeight\tnan\n",
"label\t7\t-\tWeight\tnan\n",
"label\t8\t-\tWeight\tnan\n",
"label\t9\t-\tWeight\tnan\n",
"Latent semantic no. 3\n",
"label\t96\t-\tWeight\t0.2736613529052896\n",
"label\t98\t-\tWeight\t0.218185914155306\n",
"label\t22\t-\tWeight\t0.1963451355822489\n",
"label\t3\t-\tWeight\t0.17627732148468614\n",
"label\t39\t-\tWeight\t0.1728992502839298\n",
"label\t52\t-\tWeight\t0.15597562436756945\n",
"label\t51\t-\tWeight\t0.1291470561734402\n",
"label\t30\t-\tWeight\t0.12453129554714541\n",
"label\t18\t-\tWeight\t0.1236867360720947\n",
"label\t38\t-\tWeight\t0.12184856229773917\n",
"label\t0\t-\tWeight\tnan\n",
"label\t1\t-\tWeight\tnan\n",
"label\t2\t-\tWeight\tnan\n",
"label\t3\t-\tWeight\tnan\n",
"label\t4\t-\tWeight\tnan\n",
"label\t5\t-\tWeight\tnan\n",
"label\t6\t-\tWeight\tnan\n",
"label\t7\t-\tWeight\tnan\n",
"label\t8\t-\tWeight\tnan\n",
"label\t9\t-\tWeight\tnan\n",
"Latent semantic no. 4\n",
"label\t6\t-\tWeight\t0.23875690719216863\n",
"label\t67\t-\tWeight\t0.21007869938490106\n",
"label\t63\t-\tWeight\t0.18822840034389135\n",
"label\t14\t-\tWeight\t0.18738002200878218\n",
"label\t87\t-\tWeight\t0.17508576062247283\n",
"label\t23\t-\tWeight\t0.167492867766091\n",
"label\t15\t-\tWeight\t0.15522709562173342\n",
"label\t61\t-\tWeight\t0.13244353806854162\n",
"label\t45\t-\tWeight\t0.12833204093005665\n",
"label\t68\t-\tWeight\t0.12622315521729294\n",
"label\t32\t-\tWeight\t18.607843379925203\n",
"label\t89\t-\tWeight\t18.671771165930238\n",
"label\t84\t-\tWeight\t18.83858895833768\n",
"label\t79\t-\tWeight\t18.84775924713071\n",
"label\t55\t-\tWeight\t18.88614269359777\n",
"label\t34\t-\tWeight\t18.891433443455583\n",
"label\t11\t-\tWeight\t19.034715149675442\n",
"label\t63\t-\tWeight\t19.042445031693624\n",
"label\t5\t-\tWeight\t19.075471660855772\n",
"label\t59\t-\tWeight\t19.096232354525338\n",
"Latent semantic no. 5\n",
"label\t30\t-\tWeight\t0.17385975982344382\n",
"label\t25\t-\tWeight\t0.14655711054814133\n",
"label\t39\t-\tWeight\t0.13307896633493813\n",
"label\t68\t-\tWeight\t0.12851498788897622\n",
"label\t24\t-\tWeight\t0.12828250585375986\n",
"label\t0\t-\tWeight\t0.12500243174429157\n",
"label\t1\t-\tWeight\t0.12371257574727512\n",
"label\t77\t-\tWeight\t0.12370279647800499\n",
"label\t89\t-\tWeight\t0.12233344688386875\n",
"label\t83\t-\tWeight\t0.11445596984835589\n",
"label\t88\t-\tWeight\t17.332684081151356\n",
"label\t100\t-\tWeight\t17.414638052725692\n",
"label\t89\t-\tWeight\t17.64193670680817\n",
"label\t46\t-\tWeight\t17.663677856892257\n",
"label\t5\t-\tWeight\t17.750606635854105\n",
"label\t11\t-\tWeight\t17.921812162626082\n",
"label\t17\t-\tWeight\t17.99728875058849\n",
"label\t64\t-\tWeight\t18.20535869665654\n",
"label\t84\t-\tWeight\t18.280826365832894\n",
"label\t59\t-\tWeight\t18.48939095974247\n",
"Latent semantic no. 6\n",
"label\t17\t-\tWeight\t0.2335282879255542\n",
"label\t48\t-\tWeight\t0.19418795795666355\n",
"label\t21\t-\tWeight\t0.19013440200231033\n",
"label\t85\t-\tWeight\t0.17503295059460947\n",
"label\t11\t-\tWeight\t0.14933372636956993\n",
"label\t1\t-\tWeight\t0.1384254243377172\n",
"label\t0\t-\tWeight\t0.13078647401074162\n",
"label\t57\t-\tWeight\t0.11374248801163754\n",
"label\t10\t-\tWeight\t0.10468223841103744\n",
"label\t99\t-\tWeight\t0.10191451131216464\n",
"label\t0\t-\tWeight\tnan\n",
"label\t1\t-\tWeight\tnan\n",
"label\t2\t-\tWeight\tnan\n",
"label\t3\t-\tWeight\tnan\n",
"label\t4\t-\tWeight\tnan\n",
"label\t5\t-\tWeight\tnan\n",
"label\t6\t-\tWeight\tnan\n",
"label\t7\t-\tWeight\tnan\n",
"label\t8\t-\tWeight\tnan\n",
"label\t9\t-\tWeight\tnan\n",
"Latent semantic no. 7\n",
"label\t82\t-\tWeight\t0.23372455436757703\n",
"label\t95\t-\tWeight\t0.21795238756371887\n",
"label\t60\t-\tWeight\t0.18080422229063045\n",
"label\t16\t-\tWeight\t0.1806105172209771\n",
"label\t27\t-\tWeight\t0.17365150902149876\n",
"label\t59\t-\tWeight\t0.17250044548228938\n",
"label\t26\t-\tWeight\t0.1661853291143862\n",
"label\t13\t-\tWeight\t0.16331211225170805\n",
"label\t34\t-\tWeight\t0.1523080193090529\n",
"label\t67\t-\tWeight\t0.13577900574984025\n",
"label\t59\t-\tWeight\t19.676597202857955\n",
"label\t72\t-\tWeight\t19.687934144875545\n",
"label\t89\t-\tWeight\t19.830805124280474\n",
"label\t90\t-\tWeight\t20.021426354120276\n",
"label\t77\t-\tWeight\t20.05776182294002\n",
"label\t34\t-\tWeight\t20.058245159709028\n",
"label\t70\t-\tWeight\t20.117786048649382\n",
"label\t68\t-\tWeight\t20.139598145778074\n",
"label\t88\t-\tWeight\t20.185751240083068\n",
"label\t38\t-\tWeight\t20.208902223231863\n",
"Latent semantic no. 8\n",
"label\t53\t-\tWeight\t0.2259481751468642\n",
"label\t37\t-\tWeight\t0.21583443408756542\n",
"label\t76\t-\tWeight\t0.20483376297311964\n",
"label\t44\t-\tWeight\t0.1690198227623472\n",
"label\t68\t-\tWeight\t0.1650723880318989\n",
"label\t28\t-\tWeight\t0.15689929414378492\n",
"label\t14\t-\tWeight\t0.1564371673909956\n",
"label\t54\t-\tWeight\t0.1553627917623035\n",
"label\t51\t-\tWeight\t0.14380435363337046\n",
"label\t36\t-\tWeight\t0.13510425005259438\n",
"label\t0\t-\tWeight\tnan\n",
"label\t1\t-\tWeight\tnan\n",
"label\t2\t-\tWeight\tnan\n",
"label\t3\t-\tWeight\tnan\n",
"label\t4\t-\tWeight\tnan\n",
"label\t5\t-\tWeight\tnan\n",
"label\t6\t-\tWeight\tnan\n",
"label\t7\t-\tWeight\tnan\n",
"label\t8\t-\tWeight\tnan\n",
"label\t9\t-\tWeight\tnan\n",
"Latent semantic no. 9\n",
"label\t19\t-\tWeight\t0.11741024839079275\n",
"label\t40\t-\tWeight\t0.11107319334138463\n",
"label\t53\t-\tWeight\t0.11058750626248925\n",
"label\t51\t-\tWeight\t0.10794606425819818\n",
"label\t96\t-\tWeight\t0.10735468567860716\n",
"label\t55\t-\tWeight\t0.10731282010915796\n",
"label\t50\t-\tWeight\t0.10703093662670059\n",
"label\t1\t-\tWeight\t0.10651036503732043\n",
"label\t79\t-\tWeight\t0.10640855392103846\n",
"label\t47\t-\tWeight\t0.10594110421348357\n"
"label\t0\t-\tWeight\tnan\n",
"label\t1\t-\tWeight\tnan\n",
"label\t2\t-\tWeight\tnan\n",
"label\t3\t-\tWeight\tnan\n",
"label\t4\t-\tWeight\tnan\n",
"label\t5\t-\tWeight\tnan\n",
"label\t6\t-\tWeight\tnan\n",
"label\t7\t-\tWeight\tnan\n",
"label\t8\t-\tWeight\tnan\n",
"label\t9\t-\tWeight\tnan\n"
]
},
{
"ename": "TypeError",
"evalue": "Object of type ndarray is not JSON serializable",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32me:\\Fall 23\\CSE 515 - Multimedia and web databases\\CSE515_MWDB_Project\\Phase 2\\task_5.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m selected_dim_reduction_method \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39minput\u001b[39m(\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mEnter dimensionality reduction method - one of \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=11'>12</a>\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 <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m )\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m )\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m label_sim_matrix \u001b[39m=\u001b[39m find_label_label_similarity(fd_collection,selected_feature_model)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m extract_latent_semantics_from_sim_matrix(\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m label_sim_matrix,\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m selected_feature_model,\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m \u001b[39m\"\u001b[39;49m\u001b[39mlabel\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m k,\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m selected_dim_reduction_method,\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m top_images\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m,\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Fall%2023/CSE%20515%20-%20Multimedia%20and%20web%20databases/CSE515_MWDB_Project/Phase%202/task_5.ipynb#W6sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m )\n",
"File \u001b[1;32me:\\Fall 23\\CSE 515 - Multimedia and web databases\\CSE515_MWDB_Project\\Phase 2\\utils.py:1193\u001b[0m, in \u001b[0;36mextract_latent_semantics_from_sim_matrix\u001b[1;34m(sim_matrix, feature_model, sim_type, k, dim_reduction_method, top_images)\u001b[0m\n\u001b[0;32m 1187\u001b[0m \u001b[39mfor\u001b[39;00m label \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_labels):\n\u001b[0;32m 1188\u001b[0m \u001b[39m# get representative vectors for the label\u001b[39;00m\n\u001b[0;32m 1189\u001b[0m label_mean_vectors\u001b[39m.\u001b[39mappend(\n\u001b[0;32m 1190\u001b[0m calculate_label_representatives(fd_collection, label, feature_model)\n\u001b[0;32m 1191\u001b[0m )\n\u001b[1;32m-> 1193\u001b[0m label_sim_matrix \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros((num_labels, num_labels))\n\u001b[0;32m 1195\u001b[0m \u001b[39m# Calculate half and fill the other\u001b[39;00m\n\u001b[0;32m 1196\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_labels):\n",
"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\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;49;00m chunk \u001b[39min\u001b[39;49;00m iterable:\n\u001b[0;32m 180\u001b[0m fp\u001b[39m.\u001b[39;49mwrite(chunk)\n",
"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:432\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode\u001b[1;34m(o, _current_indent_level)\u001b[0m\n\u001b[0;32m 430\u001b[0m \u001b[39myield from\u001b[39;00m _iterencode_list(o, _current_indent_level)\n\u001b[0;32m 431\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(o, \u001b[39mdict\u001b[39m):\n\u001b[1;32m--> 432\u001b[0m \u001b[39myield from\u001b[39;00m _iterencode_dict(o, _current_indent_level)\n\u001b[0;32m 433\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 434\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\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:406\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode_dict\u001b[1;34m(dct, _current_indent_level)\u001b[0m\n\u001b[0;32m 404\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 405\u001b[0m chunks \u001b[39m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[1;32m--> 406\u001b[0m \u001b[39myield from\u001b[39;00m chunks\n\u001b[0;32m 407\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 408\u001b[0m _current_indent_level \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n",
"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:326\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode_list\u001b[1;34m(lst, _current_indent_level)\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 325\u001b[0m chunks \u001b[39m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[1;32m--> 326\u001b[0m \u001b[39myield from\u001b[39;00m chunks\n\u001b[0;32m 327\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 328\u001b[0m _current_indent_level \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n",
"File \u001b[1;32mc:\\Users\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:439\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode\u001b[1;34m(o, _current_indent_level)\u001b[0m\n\u001b[0;32m 437\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 438\u001b[0m markers[markerid] \u001b[39m=\u001b[39m o\n\u001b[1;32m--> 439\u001b[0m o \u001b[39m=\u001b[39m _default(o)\n\u001b[0;32m 440\u001b[0m \u001b[39myield from\u001b[39;00m _iterencode(o, _current_indent_level)\n\u001b[0;32m 441\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\\Pranav\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\json\\encoder.py:180\u001b[0m, in \u001b[0;36mJSONEncoder.default\u001b[1;34m(self, o)\u001b[0m\n\u001b[0;32m 161\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdefault\u001b[39m(\u001b[39mself\u001b[39m, o):\n\u001b[0;32m 162\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 163\u001b[0m \u001b[39m a serializable object for ``o``, or calls the base implementation\u001b[39;00m\n\u001b[0;32m 164\u001b[0m \u001b[39m (to raise a ``TypeError``).\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 178\u001b[0m \n\u001b[0;32m 179\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 180\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 181\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"
]
}
],

128
Phase 2/task_6.ipynb
View File

@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@ -13,7 +13,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@ -22,9 +22,127 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Applying svd on the given similarity matrix to get 10 latent semantics (showing only top 10 image-weight pairs for each latent semantic)...\n",
"Latent semantic no. 0\n",
"image\t4327\t-\tWeight\t0.06387300915798859\n",
"image\t1653\t-\tWeight\t0.06225930582406118\n",
"image\t4309\t-\tWeight\t0.06095954690299202\n",
"image\t4329\t-\tWeight\t0.05889167793151601\n",
"image\t4318\t-\tWeight\t0.057637806985593974\n",
"image\t2325\t-\tWeight\t0.05612114732712442\n",
"image\t106\t-\tWeight\t0.0553241190050714\n",
"image\t4315\t-\tWeight\t0.05421665176601695\n",
"image\t3627\t-\tWeight\t0.05399516331236024\n",
"image\t4310\t-\tWeight\t0.053878520320048216\n",
"Latent semantic no. 1\n",
"image\t926\t-\tWeight\t0.05969523866379485\n",
"image\t900\t-\tWeight\t0.05738047297133547\n",
"image\t826\t-\tWeight\t0.05737642454934881\n",
"image\t868\t-\tWeight\t0.05734058503897999\n",
"image\t895\t-\tWeight\t0.05679076771674271\n",
"image\t904\t-\tWeight\t0.05665906456098433\n",
"image\t894\t-\tWeight\t0.05659729687888854\n",
"image\t892\t-\tWeight\t0.05637734281187336\n",
"image\t896\t-\tWeight\t0.05626000549868052\n",
"image\t901\t-\tWeight\t0.05621099897239924\n",
"Latent semantic no. 2\n",
"image\t3685\t-\tWeight\t0.03814215214744671\n",
"image\t3049\t-\tWeight\t0.036692586102556525\n",
"image\t4152\t-\tWeight\t0.03586290113404638\n",
"image\t4219\t-\tWeight\t0.03489203423252102\n",
"image\t3208\t-\tWeight\t0.034624460148066645\n",
"image\t4015\t-\tWeight\t0.034128851806262454\n",
"image\t3838\t-\tWeight\t0.03287941968649245\n",
"image\t3544\t-\tWeight\t0.03241416766297603\n",
"image\t4313\t-\tWeight\t0.03231321369753675\n",
"image\t3999\t-\tWeight\t0.0320836528785244\n",
"Latent semantic no. 3\n",
"image\t3892\t-\tWeight\t0.06648490667589399\n",
"image\t3827\t-\tWeight\t0.06563860391481106\n",
"image\t4285\t-\tWeight\t0.06444388987867274\n",
"image\t2076\t-\tWeight\t0.0632874253246352\n",
"image\t3745\t-\tWeight\t0.059842032954945085\n",
"image\t2563\t-\tWeight\t0.058523031976053054\n",
"image\t3884\t-\tWeight\t0.05599712556112116\n",
"image\t3890\t-\tWeight\t0.05593428984607223\n",
"image\t1402\t-\tWeight\t0.055899080338839564\n",
"image\t2665\t-\tWeight\t0.05546907037179164\n",
"Latent semantic no. 4\n",
"image\t3915\t-\tWeight\t0.046851797776368324\n",
"image\t474\t-\tWeight\t0.04434329032960532\n",
"image\t1536\t-\tWeight\t0.04432346480047559\n",
"image\t475\t-\tWeight\t0.04072813117892643\n",
"image\t4255\t-\tWeight\t0.040667016325110085\n",
"image\t3406\t-\tWeight\t0.04040216466729356\n",
"image\t525\t-\tWeight\t0.04029068846509459\n",
"image\t470\t-\tWeight\t0.03997152944980991\n",
"image\t1384\t-\tWeight\t0.03934508125909288\n",
"image\t3357\t-\tWeight\t0.038333999492323516\n",
"Latent semantic no. 5\n",
"image\t3956\t-\tWeight\t0.060011760982094924\n",
"image\t2767\t-\tWeight\t0.056207067311306175\n",
"image\t2775\t-\tWeight\t0.05358938347103485\n",
"image\t3902\t-\tWeight\t0.04905728202908321\n",
"image\t3099\t-\tWeight\t0.048312802387355414\n",
"image\t2794\t-\tWeight\t0.04816327335760051\n",
"image\t3638\t-\tWeight\t0.047326856090526045\n",
"image\t2951\t-\tWeight\t0.04694598325565932\n",
"image\t2493\t-\tWeight\t0.04693933314957065\n",
"image\t1519\t-\tWeight\t0.04692793968385709\n",
"Latent semantic no. 6\n",
"image\t899\t-\tWeight\t0.0557700158434989\n",
"image\t901\t-\tWeight\t0.05385589776037553\n",
"image\t903\t-\tWeight\t0.05326609068648323\n",
"image\t892\t-\tWeight\t0.05278963837571468\n",
"image\t893\t-\tWeight\t0.05252069078249831\n",
"image\t895\t-\tWeight\t0.052452644917562574\n",
"image\t894\t-\tWeight\t0.050699354240581404\n",
"image\t896\t-\tWeight\t0.049699837882682285\n",
"image\t898\t-\tWeight\t0.04862913600225998\n",
"image\t821\t-\tWeight\t0.048576878816213136\n",
"Latent semantic no. 7\n",
"image\t1171\t-\tWeight\t0.03313529748848967\n",
"image\t1350\t-\tWeight\t0.0331120998877209\n",
"image\t1069\t-\tWeight\t0.03307893455959305\n",
"image\t1145\t-\tWeight\t0.032922013181510126\n",
"image\t1324\t-\tWeight\t0.03250158367280327\n",
"image\t1320\t-\tWeight\t0.03214994190354176\n",
"image\t1251\t-\tWeight\t0.0320119250948106\n",
"image\t1353\t-\tWeight\t0.031686689071375326\n",
"image\t1341\t-\tWeight\t0.031629828629460004\n",
"image\t1314\t-\tWeight\t0.03156526413086752\n",
"Latent semantic no. 8\n",
"image\t4291\t-\tWeight\t0.030379982764735483\n",
"image\t3659\t-\tWeight\t0.030303639415262738\n",
"image\t4062\t-\tWeight\t0.03009699185922372\n",
"image\t3645\t-\tWeight\t0.030070839535056917\n",
"image\t4186\t-\tWeight\t0.029988779115247072\n",
"image\t3651\t-\tWeight\t0.02991186566754093\n",
"image\t4306\t-\tWeight\t0.029842728762290226\n",
"image\t4290\t-\tWeight\t0.029821657616221558\n",
"image\t4295\t-\tWeight\t0.029776560377156956\n",
"image\t4063\t-\tWeight\t0.029718537108632898\n",
"Latent semantic no. 9\n",
"image\t3461\t-\tWeight\t-0.01124924934362133\n",
"image\t3690\t-\tWeight\t-0.011359970957367781\n",
"image\t3677\t-\tWeight\t-0.011388302213245766\n",
"image\t2274\t-\tWeight\t-0.011401573573016426\n",
"image\t2695\t-\tWeight\t-0.011431074774005161\n",
"image\t3868\t-\tWeight\t-0.011463898013717732\n",
"image\t1137\t-\tWeight\t-0.011497915659232156\n",
"image\t1647\t-\tWeight\t-0.01152169864389044\n",
"image\t1203\t-\tWeight\t-0.011532017314265241\n",
"image\t2391\t-\tWeight\t-0.011548629237775063\n"
]
}
],
"source": [
"selected_feature_model = valid_feature_models[\n",
" str(input(\"Enter feature model - one of \" + str(list(valid_feature_models.keys()))))\n",
@ -70,7 +188,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
"version": "3.11.4"
}
},
"nbformat": 4,

2
Phase 2/task_9.ipynb
View File

@ -120,6 +120,8 @@
" comparison_feature_space = np.array(data['image-semantic'])\n",
" comparison_vector = np.matmul(label_rep, np.transpose(H))\n",
"\n",
" case \"\"\n",
"\n",
" print(comparison_feature_space.shape)\n",
" n = len(comparison_feature_space)\n",
" \n",