1838 lines (1837 with data), 76.7 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"equal\n",
"5\n",
"VAE_FCTAE_EM:\t0.8493348 0.8093381 0.8123563 0.7995556 0.8052937\n",
"AE_FAETC_EM:\t0.7305929 0.7634619 0.7726853 0.7759933 0.7762746\n",
"AE_FCTAE_EM:\t0.9207857 0.9088667 0.885019 0.8641048 0.8987\n",
"DAE_FAETC_EM:\t0.7874571 0.7924825 0.7651413 0.7957556 0.7800365\n",
"DAE_FCTAE_EM:\t0.8781365 0.8675444 0.8450683 0.8852254 0.881327\n",
"SVAE_FCTAE_EM:\t0.2052535 0.1937893 0.2034376 0.2000667 0.2019203\n",
"MMDVAE_EM:\t0.6907236 0.6775745 0.710492 0.6831872 0.6926301\n",
"\n",
"10\n",
"VAE_FCTAE_EM:\t0.758355 0.7923074 0.7701208 0.7740536 0.8087662 0.8172028 0.7957532 0.7437403 0.7497229 0.7881303\n",
"AE_FAETC_EM:\t0.7393333 0.7186667 0.7486667 0.7683333 0.808 0.7509298 0.7136667 0.7466667 0.7242632 0.7206667\n",
"AE_FCTAE_EM:\t0.8967325 0.9245455 0.8673699 0.8439271 0.8613498 0.871089 0.8818107 0.843031 0.8663918 0.8250542\n",
"DAE_FAETC_EM:\t0.7773333 0.7272857 0.7086667 0.7266667 0.7826667 0.7343333 0.7893333 0.696 0.6953333 0.7278571\n",
"DAE_FCTAE_EM:\t0.8433333 0.8219167 0.8467436 0.8740769 0.8717576 0.8292857 0.8875833 0.8660256 0.8012632 0.856549\n",
"SVAE_FCTAE_EM:\t0.1591708 0.1614971 0.1591587 0.1671023 0.1534171 0.1651873 0.1553616 0.1658601 0.1576142 0.1600094\n",
"MMDVAE_EM:\t0.7594876 0.7254535 0.7869272 0.7070993 0.7475013 0.7432933 0.7412182 0.7654683 0.7333339 0.7375071\n",
"\n",
"15\n",
"VAE_FCTAE_EM:\t0.7709058 0.7611685 0.725512 0.808956 0.7473828 0.7762308 0.780849 0.7431538 0.7846158 0.736652 0.7923278 0.7640842 0.758185 0.7601735 0.7866136\n",
"AE_FAETC_EM:\t0.7297436 0.7172344 0.7522007 0.7530066 0.7293088 0.7411557 0.7702198 0.7845604 0.7910256 0.7465278 0.7661787 0.7752747 0.7341026 0.798315 0.7948022\n",
"AE_FCTAE_EM:\t0.813515 0.8480553 0.8243242 0.8718315 0.8912238 0.8817043 0.8846588 0.8738062 0.8711538 0.8817353 0.8577216 0.8634115 0.8764802 0.8667021 0.83273\n",
"DAE_FAETC_EM:\t0.727534 0.7280633 0.713112 0.735641 0.7145971 0.7652198 0.7589579 0.7907326 0.7353893 0.7689142 0.7683805 0.7324443 0.7603729 0.7205657 0.7750414\n",
"DAE_FCTAE_EM:\t0.7940128 0.7661172 0.8568864 0.8181319 0.8360806 0.8290476 0.8168864 0.8033124 0.8276374 0.8017611 0.8383736 0.7887949 0.847478 0.8298736 0.8756777\n",
"SVAE_FCTAE_EM:\t0.1765508 0.1663713 0.1715346 0.1762493 0.1786312 0.1758448 0.1786001 0.181078 0.1908793 0.1676178 0.1783602 0.1773119 0.1785067 0.1749671 0.1731573\n",
"MMDVAE_EM:\t0.6988397 0.6905553 0.7603651 0.7306667 0.7253751 0.7249014 0.7041905 0.7156468 0.7549524 0.7276808 0.6985091 0.7295652 0.7046456 0.7887875 0.7003095\n",
"\n",
"heterogeneous\n",
"5\n",
"VAE_FCTAE_EM:\t0.788704 0.8800345 0.845978 0.8114592 0.7931384\n",
"AE_FAETC_EM:\t0.6811634 0.6546725 0.7455464 0.7188325 0.7406956\n",
"AE_FCTAE_EM:\t0.9001578 0.8711772 0.9118168 0.8735622 0.8756521\n",
"DAE_FAETC_EM:\t0.7571631 0.7762026 0.7273021 0.7614543 0.7547438\n",
"DAE_FCTAE_EM:\t0.7839074 0.7187035 0.7828717 0.7842619 0.7574162\n",
"SVAE_FCTAE_EM:\t0.203859 0.2003706 0.2031883 0.2084795 0.1973851\n",
"MMDVAE_EM:\t0.7006904 0.681042 0.7057124 0.6715363 0.6967064\n",
"\n",
"10\n",
"VAE_FCTAE_EM:\t0.6597871 0.6756537 0.693256 0.7053431 0.7038487 0.6154055 0.6761671 0.7113576 0.6934843 0.7026042\n",
"AE_FAETC_EM:\t0.7606359 0.7215738 0.7564599 0.726074 0.7819293 0.7207249 0.6776939 0.6747369 0.7217847 0.7451345\n",
"AE_FCTAE_EM:\t0.8665326 0.8202176 0.840857 0.8251221 0.7967765 0.8630213 0.8476865 0.8508921 0.8323401 0.7981338\n",
"DAE_FAETC_EM:\t0.7272786 0.7072266 0.6741613 0.7518748 0.7218129 0.7299205 0.7320382 0.685665 0.6905846 0.7430155\n",
"DAE_FCTAE_EM:\t0.764177 0.7534933 0.7968361 0.7969159 0.8022471 0.8228534 0.7596997 0.8011668 0.853401 0.7809182\n",
"SVAE_FCTAE_EM:\t0.154768 0.1544365 0.1490643 0.1518639 0.1515257 0.1461104 0.1511859 0.1519876 0.1473031 0.1464361\n",
"MMDVAE_EM:\t0.651161 0.7064776 0.7003462 0.7564383 0.6819459 0.7054679 0.742896 0.7141084 0.72529 0.7240387\n",
"\n",
"15\n",
"VAE_FCTAE_EM:\t0.6225732 0.5895894 0.534353 0.563673 0.5873184 0.5686807 0.6257382 0.6199699 0.5934381 0.6576383 0.5762374 0.6266566 0.5999495 0.6454765 0.6205498\n",
"AE_FAETC_EM:\t0.6864116 0.7209304 0.6908687 0.6855 0.7564798 0.6772753 0.6749676 0.7431078 0.7049959 0.7238462 0.6737298 0.7303945 0.7581027 0.7177176 0.7307955\n",
"AE_FCTAE_EM:\t0.7819762 0.8145357 0.8139048 0.7872857 0.7963254 0.7849502 0.7927619 0.8021746 0.7340159 0.8054502 0.7569167 0.7982738 0.7606284 0.8176389 0.7734127\n",
"DAE_FAETC_EM:\t0.6111207 0.6625173 0.6101504 0.6825909 0.7029449 0.6546229 0.6301422 0.6386405 0.6269395 0.6753231 0.6376431 0.6977659 0.6822767 0.6368745 0.591305\n",
"DAE_FCTAE_EM:\t0.7528929 0.7683135 0.7399141 0.7691389 0.7325833 0.7500079 0.7704181 0.7674524 0.7853135 0.7489141 0.7745296 0.7397143 0.8132024 0.7545159 0.7559499\n",
"SVAE_FCTAE_EM:\t0.1592352 0.1555998 0.1569537 0.1621479 0.1506288 0.1664354 0.155096 0.1621046 0.1641762 0.1596278 0.1528357 0.1503384 0.154976 0.1440018 0.1500436\n",
"MMDVAE_EM:\t0.6598508 0.6063754 0.6759849 0.6587856 0.6742041 0.6590008 0.6307302 0.6935759 0.6922985 0.6769722 0.6256822 0.6842047 0.5814451 0.6493218 0.5932973\n",
"\n"
]
}
],
"source": [
"library(\"clusterCrit\")\n",
"datatypes<-c(\"equal\",\"heterogeneous\")\n",
"typenums<-c(5,10,15)\n",
"data_names<-c('VAE_FCTAE_EM','AE_FAETC_EM', 'AE_FCTAE_EM', 'DAE_FAETC_EM', 'DAE_FCTAE_EM','SVAE_FCTAE_EM','MMDVAE_EM')\n",
"\n",
"for(datatype in datatypes){\n",
" cat(datatype)\n",
" cat('\\n')\n",
" for(typenum in typenums){\n",
" cat(typenum)\n",
" cat('\\n')\n",
" for(data_name in data_names){\n",
" cat(data_name)\n",
" cat(':\\t')\n",
" number_cl=typenum\n",
" ind <- 0\n",
" JI_final <- matrix(data=NA, nrow=number_cl, ncol=1)\n",
" clpath=paste(\"../data/simulations_20210329212141/\",datatype,'/',typenum,'/','clusters.txt',sep='')\n",
" cl <- as.matrix(read.table(clpath, \n",
" sep=\"\\t\", row.names=1, header=TRUE))\n",
" #cat(\"cl:\",cl,\"\\t\")\n",
" cl2 <- as.matrix(as.numeric(cl[,2]))\n",
" rownames(cl2) <- cl[,1]\n",
" datapath=paste(\"../data/simulations/\",datatype,'/',typenum,'/',data_name,'_',typenum,'.txt',sep='')\n",
" factorization=read.table(datapath, sep=\" \", row.names=cl[,1],header=FALSE)\n",
"\n",
" factors <- factorization\n",
" \n",
" # Clustering by Kmeans\n",
" JI_good <- numeric(0)\n",
" for (run in 1:100) {\n",
" kmeans.out <- kmeans(factors, centers=number_cl) \n",
" clust_iCluster <- as.matrix(kmeans.out$cluster)\n",
"\n",
" ######creation sets of samples\n",
" JI_mat <- numeric(0)\n",
" for (p in 1:number_cl) {\n",
" x1 <- rownames(clust_iCluster)[which(clust_iCluster[,1]==p)]\n",
"\n",
"\n",
" row <- numeric(0)\n",
" for(j in 1:number_cl) {\n",
" x2 <- rownames(cl2)[which(cl2[,1]==j)]\n",
" I <- length(intersect(x1,x2))#交集\n",
" S <- I/(length(x1)+length(x2)-I)\n",
" row <- cbind(row,S)\n",
" }\n",
" JI_mat <- rbind(JI_mat,row)\n",
"\n",
"\n",
" }\n",
" JI_good <- rbind(JI_good,apply(JI_mat,1,max))\n",
" }\n",
" JI_final[1:number_cl] <- apply(JI_good,2,mean)\n",
" cat(JI_final)\n",
" cat('\\n')\n",
"\n",
" }\n",
" cat('\\n')\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"clust_iCluster: 3 9 2 8 3 3 10 2 9 3 3 3 6 10 9 3 4 5 7 7 8 9 10 8 8 3 6 9 2 7 5 7 5 8 9 7 5 6 4 5 7 4 5 4 3 7 9 3 3 8 3 5 4 8 10 7 4 1 8 8 1 8 3 4 5 4 8 1 5 8 9 8 3 3 7 9 4 5 4 3 2 9 10 1 3 3 1 1 1 3 8 9 9 8 9 9 3 9 2 7 \n",
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"[3,] 0 0 0.0 0.5 0.5 0 0 0 0.0 0\n",
"[4,] 0 0 0.0 0.0 0.0 1 0 0 0.0 0\n",
"[5,] 0 0 0.0 0.0 0.0 0 0 1 0.0 0\n",
"[1] \"subject13\" \"subject27\" \"subject38\"\n",
"x2: subject4 subject21 subject24 subject25 subject34 subject50 subject54 subject59 subject60 subject62 subject67 subject70 subject72 subject91 subject94 \n",
"I: 0 \n",
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"row: 0 0 0 0 0 0 0 0 \n",
"x2: subject13 subject27 subject38 subject58 subject61 subject68 subject84 subject87 subject88 subject89 \n",
"I: 3 \n",
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"row: 0 0 0 0 0 0 0 0 0.3 \n",
"x2: subject9 subject15 subject28 subject35 subject71 subject76 subject82 subject93 subject96 subject98 \n",
"I: 0 \n",
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"row: 0 0 0 0 0 0 0 0 0.3 0 \n",
"JI_mat: 0 0 0 0 0 0 0 0 0 0 0 0 0 0.5 0 0 0 0 0 0 0.5 0 0 0 0 0 0.5 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0.7 0 0 0 0 0.3 0 0 0 0 0 0 \n",
" S S S S S S S S S S\n",
"[1,] 0 0 0.0 0.0 0.0 0 0 0 0.7 0\n",
"[2,] 0 0 0.5 0.0 0.0 0 0 0 0.0 0\n",
"[3,] 0 0 0.0 0.5 0.5 0 0 0 0.0 0\n",
"[4,] 0 0 0.0 0.0 0.0 1 0 0 0.0 0\n",
"[5,] 0 0 0.0 0.0 0.0 0 0 1 0.0 0\n",
"[6,] 0 0 0.0 0.0 0.0 0 0 0 0.3 0\n",
" [1] \"subject19\" \"subject20\" \"subject30\" \"subject32\" \"subject36\" \n",
" [6] \"subject41\" \"subject46\" \"subject56\" \"subject75\" \"subject100\"\n",
"x2: subject4 subject21 subject24 subject25 subject34 subject50 subject54 subject59 subject60 subject62 subject67 subject70 subject72 subject91 subject94 \n",
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"JI_mat: 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0.5 0 0 0 0 0 0 0 0.5 0 0 0 0 0 0 0.5 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0.7 0 0 0 0 0.3 0 0 0 0 0 0 0 0 \n",
" S S S S S S S S S S\n",
"[1,] 0 0 0.0 0.0 0.0 0 0 0 0.7 0\n",
"[2,] 0 0 0.5 0.0 0.0 0 0 0 0.0 0\n",
"[3,] 0 0 0.0 0.5 0.5 0 0 0 0.0 0\n",
"[4,] 0 0 0.0 0.0 0.0 1 0 0 0.0 0\n",
"[5,] 0 0 0.0 0.0 0.0 0 0 1 0.0 0\n",
"[6,] 0 0 0.0 0.0 0.0 0 0 0 0.3 0\n",
"[7,] 0 1 0.0 0.0 0.0 0 0 0 0.0 0\n",
" [1] \"subject4\" \"subject21\" \"subject24\" \"subject25\" \"subject34\" \"subject50\"\n",
" [7] \"subject54\" \"subject59\" \"subject60\" \"subject62\" \"subject67\" \"subject70\"\n",
"[13] \"subject72\" \"subject91\" \"subject94\"\n",
"x2: subject4 subject21 subject24 subject25 subject34 subject50 subject54 subject59 subject60 subject62 subject67 subject70 subject72 subject91 subject94 \n",
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"x2: subject19 subject20 subject30 subject32 subject36 subject41 subject46 subject56 subject75 subject100 \n",
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"row: 1 0 0 0 0 0 0 \n",
"x2: subject18 subject31 subject33 subject37 subject40 subject43 subject52 subject65 subject69 subject78 \n",
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"row: 1 0 0 0 0 0 0 0 \n",
"x2: subject13 subject27 subject38 subject58 subject61 subject68 subject84 subject87 subject88 subject89 \n",
"I: 0 \n",
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"row: 1 0 0 0 0 0 0 0 0 \n",
"x2: subject9 subject15 subject28 subject35 subject71 subject76 subject82 subject93 subject96 subject98 \n",
"I: 0 \n",
"S: 0 \n",
"row: 1 0 0 0 0 0 0 0 0 0 \n",
"JI_mat: 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0.5 0 0 0 0 0 0 0 0 0.5 0 0 0 0 0 0 0 0.5 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0.7 0 0 0 0 0.3 0 0 0 0 0 0 0 0 0 0 \n",
" S S S S S S S S S S\n",
"[1,] 0 0 0.0 0.0 0.0 0 0 0 0.7 0\n",
"[2,] 0 0 0.5 0.0 0.0 0 0 0 0.0 0\n",
"[3,] 0 0 0.0 0.5 0.5 0 0 0 0.0 0\n",
"[4,] 0 0 0.0 0.0 0.0 1 0 0 0.0 0\n",
"[5,] 0 0 0.0 0.0 0.0 0 0 1 0.0 0\n",
"[6,] 0 0 0.0 0.0 0.0 0 0 0 0.3 0\n",
"[7,] 0 1 0.0 0.0 0.0 0 0 0 0.0 0\n",
"[8,] 1 0 0.0 0.0 0.0 0 0 0 0.0 0\n",
" [1] \"subject2\" \"subject9\" \"subject15\" \"subject22\" \"subject28\" \"subject35\"\n",
" [7] \"subject47\" \"subject71\" \"subject76\" \"subject82\" \"subject92\" \"subject93\"\n",
"[13] \"subject95\" \"subject96\" \"subject98\"\n",
"x2: subject4 subject21 subject24 subject25 subject34 subject50 subject54 subject59 subject60 subject62 subject67 subject70 subject72 subject91 subject94 \n",
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"row: 0 0 0 0 \n",
"x2: subject5 subject11 subject12 subject16 subject26 subject45 subject51 subject74 subject85 subject90 \n",
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"x2: subject17 subject39 subject42 subject44 subject53 subject57 subject64 subject66 subject77 subject79 \n",
"I: 0 \n",
"S: 0 \n",
"row: 0 0 0 0 0 0 \n",
"x2: subject2 subject22 subject47 subject92 subject95 \n",
"I: 5 \n",
"S: 0.3333333 \n",
"row: 0 0 0 0 0 0 0.3333333 \n",
"x2: subject18 subject31 subject33 subject37 subject40 subject43 subject52 subject65 subject69 subject78 \n",
"I: 0 \n",
"S: 0 \n",
"row: 0 0 0 0 0 0 0.3333333 0 \n",
"x2: subject13 subject27 subject38 subject58 subject61 subject68 subject84 subject87 subject88 subject89 \n",
"I: 0 \n",
"S: 0 \n",
"row: 0 0 0 0 0 0 0.3333333 0 0 \n",
"x2: subject9 subject15 subject28 subject35 subject71 subject76 subject82 subject93 subject96 subject98 \n",
"I: 10 \n",
"S: 0.6666667 \n",
"row: 0 0 0 0 0 0 0.3333333 0 0 0.6666667 \n",
"JI_mat: 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0.5 0 0 0 0 0 0 0 0 0 0.5 0 0 0 0 0 0 0 0 0.5 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3333333 0 0 0 0 1 0 0 0 0 0.7 0 0 0 0 0.3 0 0 0 0 0 0 0 0 0 0 0 0.6666667 \n",
" S S S S S S S S S S\n",
" [1,] 0 0 0.0 0.0 0.0 0 0.0000000 0 0.7 0.0000000\n",
" [2,] 0 0 0.5 0.0 0.0 0 0.0000000 0 0.0 0.0000000\n",
" [3,] 0 0 0.0 0.5 0.5 0 0.0000000 0 0.0 0.0000000\n",
" [4,] 0 0 0.0 0.0 0.0 1 0.0000000 0 0.0 0.0000000\n",
" [5,] 0 0 0.0 0.0 0.0 0 0.0000000 1 0.0 0.0000000\n",
" [6,] 0 0 0.0 0.0 0.0 0 0.0000000 0 0.3 0.0000000\n",
" [7,] 0 1 0.0 0.0 0.0 0 0.0000000 0 0.0 0.0000000\n",
" [8,] 1 0 0.0 0.0 0.0 0 0.0000000 0 0.0 0.0000000\n",
" [9,] 0 0 0.0 0.0 0.0 0 0.3333333 0 0.0 0.6666667\n",
"[1] \"subject7\" \"subject14\" \"subject23\" \"subject55\" \"subject83\"\n",
"x2: subject4 subject21 subject24 subject25 subject34 subject50 subject54 subject59 subject60 subject62 subject67 subject70 subject72 subject91 subject94 \n",
"I: 0 \n",
"S: 0 \n",
"row: 0 \n",
"x2: subject19 subject20 subject30 subject32 subject36 subject41 subject46 subject56 subject75 subject100 \n",
"I: 0 \n",
"S: 0 \n",
"row: 0 0 \n",
"x2: subject3 subject7 subject8 subject14 subject23 subject29 subject55 subject81 subject83 subject99 \n",
"I: 5 \n",
"S: 0.5 \n",
"row: 0 0 0.5 \n",
"x2: subject1 subject6 subject10 subject48 subject49 subject63 subject73 subject80 subject86 subject97 \n",
"I: 0 \n",
"S: 0 \n",
"row: 0 0 0.5 0 \n",
"x2: subject5 subject11 subject12 subject16 subject26 subject45 subject51 subject74 subject85 subject90 \n",
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"x2: subject17 subject39 subject42 subject44 subject53 subject57 subject64 subject66 subject77 subject79 \n",
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"x2: subject18 subject31 subject33 subject37 subject40 subject43 subject52 subject65 subject69 subject78 \n",
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"x2: subject13 subject27 subject38 subject58 subject61 subject68 subject84 subject87 subject88 subject89 \n",
"I: 0 \n",
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"row: 0 0 0.5 0 0 0 0 0 0 \n",
"x2: subject9 subject15 subject28 subject35 subject71 subject76 subject82 subject93 subject96 subject98 \n",
"I: 0 \n",
"S: 0 \n",
"row: 0 0 0.5 0 0 0 0 0 0 0 \n",
"JI_mat: 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0.5 0 0 0 0 0 0 0 0.5 0 0 0.5 0 0 0 0 0 0 0 0 0 0.5 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3333333 0 0 0 0 0 1 0 0 0 0 0 0.7 0 0 0 0 0.3 0 0 0 0 0 0 0 0 0 0 0 0 0.6666667 0 \n",
" S S S S S S S S S S\n",
" [1,] 0 0 0.0 0.0 0.0 0 0.0000000 0 0.7 0.0000000\n",
" [2,] 0 0 0.5 0.0 0.0 0 0.0000000 0 0.0 0.0000000\n",
" [3,] 0 0 0.0 0.5 0.5 0 0.0000000 0 0.0 0.0000000\n",
" [4,] 0 0 0.0 0.0 0.0 1 0.0000000 0 0.0 0.0000000\n",
" [5,] 0 0 0.0 0.0 0.0 0 0.0000000 1 0.0 0.0000000\n",
" [6,] 0 0 0.0 0.0 0.0 0 0.0000000 0 0.3 0.0000000\n",
" [7,] 0 1 0.0 0.0 0.0 0 0.0000000 0 0.0 0.0000000\n",
" [8,] 1 0 0.0 0.0 0.0 0 0.0000000 0 0.0 0.0000000\n",
" [9,] 0 0 0.0 0.0 0.0 0 0.3333333 0 0.0 0.6666667\n",
"[10,] 0 0 0.5 0.0 0.0 0 0.0000000 0 0.0 0.0000000\n",
"JI_good: 0.7 0.5 0.5 1 1 0.3 1 1 0.6666667 0.5 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7 0.5 0.5 1 1 0.3 1 1 0.6666667 0.5"
]
}
],
"source": [
"number_cl=10\n",
"ind <- 0\n",
"JI_final <- matrix(data=NA, nrow=number_cl, ncol=1)\n",
"#cat(JI_final,\"\\n\")\n",
"# Read clusters imposed on simulated data \n",
"cl <- as.matrix(read.table(\"../data/simulations_20210329212141/equal/10/clusters.txt\", \n",
" sep=\"\\t\", row.names=1, header=TRUE))\n",
"#cat(\"cl:\",cl,\"\\t\")\n",
"cl2 <- as.matrix(as.numeric(cl[,2]))\n",
"cat(\"cl2:\",cl2[,1],\"\\n\")\n",
"rownames(cl2) <- cl[,1]\n",
"\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/AE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/AE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/DAE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/DAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"factorization=read.table(\"../data/simulations_20210329212141/equal/10/VAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/LSTMVAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"\n",
"\n",
"\n",
"factors <- factorization\n",
"#factors <- factorizations[[i]][[1]]\n",
"\n",
"#print(factors)\n",
"# Clustering by Kmeans\n",
"JI_good <- numeric(0)\n",
"for (run in 1:1) {\n",
"kmeans.out <- kmeans(factors, centers=number_cl) \n",
"clust_iCluster <- as.matrix(kmeans.out$cluster)\n",
"cat(\"clust_iCluster:\",clust_iCluster,\"\\n\")\n",
"######creation sets of samples\n",
"JI_mat <- numeric(0)\n",
"for (p in 1:number_cl) {\n",
" x1 <- rownames(clust_iCluster)[which(clust_iCluster[,1]==p)]\n",
" print(x1)\n",
"\n",
" row <- numeric(0)\n",
" for(j in 1:number_cl) {\n",
" x2 <- rownames(cl2)[which(cl2[,1]==j)]\n",
" cat(\"x2:\",x2,\"\\n\")\n",
" I <- length(intersect(x1,x2))#交集\n",
" cat(\"I:\",I,\"\\n\")\n",
" S <- I/(length(x1)+length(x2)-I)\n",
" cat(\"S:\",S,\"\\n\")\n",
" row <- cbind(row,S)\n",
" cat(\"row:\",row,\"\\n\")\n",
" }\n",
" JI_mat <- rbind(JI_mat,row)\n",
" cat(\"JI_mat:\",JI_mat,\"\\n\")\n",
" print(JI_mat)\n",
"\n",
"\n",
" }\n",
" JI_good <- rbind(JI_good,apply(JI_mat,1,max))\n",
" cat(\"JI_good:\",JI_good,\"\\n\")\n",
"}\n",
"JI_final[1:number_cl] <- apply(JI_good,2,mean)\n",
"cat(JI_final)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cl2: 4 7 3 1 5 4 3 3 10 4 5 5 9 3 10 5 6 8 2 2 1 7 3 1 1 5 9 10 3 2 8 2 8 1 10 2 8 9 6 8 2 6 8 6 5 2 7 4 4 1 5 8 6 1 3 2 6 9 1 1 9 1 4 6 8 6 1 9 8 1 10 1 4 5 2 10 6 8 6 4 3 10 3 9 5 4 9 9 9 5 1 7 10 1 7 10 4 10 3 2 \n"
]
}
],
"source": [
"number_cl=10\n",
"ind <- 0\n",
"JI_final <- matrix(data=NA, nrow=number_cl, ncol=1)\n",
"#cat(JI_final,\"\\n\")\n",
"# Read clusters imposed on simulated data \n",
"cl <- as.matrix(read.table(\"../data/simulations_20210329212141/equal/10/clusters.txt\", \n",
" sep=\"\\t\", row.names=1, header=TRUE))\n",
"#cat(\"cl:\",cl,\"\\t\")\n",
"cl2 <- as.matrix(as.numeric(cl[,2]))\n",
"cat(\"cl2:\",cl2[,1],\"\\n\")\n",
"rownames(cl2) <- cl[,1]\n",
"\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/AE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/AE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/DAE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/DAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"factorization=read.table(\"../data/simulations_20210329212141/equal/10/VAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/LSTMVAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"\n",
"\n",
"\n",
"# factors <- factorization\n",
"\n",
"# # Clustering by Kmeans\n",
"# JI_good <- numeric(0)\n",
"# for (run in 1:100) {\n",
"# kmeans.out <- kmeans(factors, centers=number_cl) \n",
"# clust_iCluster <- as.matrix(kmeans.out$cluster)\n",
"# cat(\"clust_iCluster:\",clust_iCluster,\"\\n\")\n",
"# ######creation sets of samples\n",
"# JI_mat <- numeric(0)\n",
"# for (p in 1:number_cl) {\n",
"# x1 <- rownames(clust_iCluster)[which(clust_iCluster[,1]==p)]\n",
"# print(x1)\n",
"\n",
"# row <- numeric(0)\n",
"# for(j in 1:number_cl) {\n",
"# x2 <- rownames(cl2)[which(cl2[,1]==j)]\n",
"# #cat(\"x2:\",x2,\"\\n\")\n",
"# I <- length(intersect(x1,x2))#交集\n",
"# #cat(\"I:\",I,\"\\n\")\n",
"# S <- I/(length(x1)+length(x2)-I)\n",
"# #cat(\"S:\",S,\"\\n\")\n",
"# row <- cbind(row,S)\n",
"# #cat(\"row:\",row,\"\\n\")\n",
"# }\n",
"# JI_mat <- rbind(JI_mat,row)\n",
"# cat(\"JI_mat:\",JI_mat,\"\\n\")\n",
"# #print(JI_mat)\n",
"\n",
"\n",
"# }\n",
"# JI_good <- rbind(JI_good,apply(JI_mat,1,max))\n",
"# cat(\"JI_good:\",JI_good,\"\\n\")\n",
"# }\n",
"# JI_final[1:number_cl] <- apply(JI_good,2,mean)\n",
"# JI_final"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"number_cl=10\n",
"ind <- 0\n",
"JI_final <- matrix(data=NA, nrow=number_cl, ncol=1)\n",
"#cat(JI_final,\"\\n\")\n",
"# Read clusters imposed on simulated data \n",
"cl <- as.matrix(read.table(\"../data/simulations_20210329212141/equal/10/clusters.txt\", \n",
" sep=\"\\t\", row.names=1, header=TRUE))\n",
"#cat(\"cl:\",cl,\"\\t\")\n",
"cl2 <- as.matrix(as.numeric(cl[,2]))\n",
"cat(\"cl2:\",cl2[,1],\"\\n\")\n",
"rownames(cl2) <- cl[,1]\n",
"\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/AE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/AE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/DAE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/DAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/10/VAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"factorization=read.table(\"../data/simulations_20210329212141/equal/10/LSTMVAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"\n",
"\n",
"\n",
"factors <- factorization\n",
"#factors <- factorizations[[i]][[1]]\n",
"\n",
"#print(factors)\n",
"# Clustering by Kmeans\n",
"JI_good <- numeric(0)\n",
"for (run in 1:100) {\n",
"kmeans.out <- kmeans(factors, centers=number_cl) \n",
"clust_iCluster <- as.matrix(kmeans.out$cluster)\n",
"cat(\"clust_iCluster:\",clust_iCluster,\"\\n\")\n",
"######creation sets of samples\n",
"JI_mat <- numeric(0)\n",
"for (p in 1:number_cl) {\n",
" x1 <- rownames(clust_iCluster)[which(clust_iCluster[,1]==p)]\n",
" print(x1)\n",
"\n",
" row <- numeric(0)\n",
" for(j in 1:number_cl) {\n",
" x2 <- rownames(cl2)[which(cl2[,1]==j)]\n",
" cat(\"x2:\",x2,\"\\n\")\n",
" I <- length(intersect(x1,x2))#交集\n",
" cat(\"I:\",I,\"\\n\")\n",
" S <- I/(length(x1)+length(x2)-I)\n",
" cat(\"S:\",S,\"\\n\")\n",
" row <- cbind(row,S)\n",
" cat(\"row:\",row,\"\\n\")\n",
" }\n",
" JI_mat <- rbind(JI_mat,row)\n",
" cat(\"JI_mat:\",JI_mat,\"\\n\")\n",
" print(JI_mat)\n",
"\n",
"\n",
" }\n",
" JI_good <- rbind(JI_good,apply(JI_mat,1,max))\n",
" cat(\"JI_good:\",JI_good,\"\\n\")\n",
"}\n",
"JI_final[1:number_cl] <- apply(JI_good,2,mean)\n",
"JI_final"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"number_cl=15\n",
"ind <- 0\n",
"JI_final <- matrix(data=NA, nrow=number_cl, ncol=1)\n",
"#cat(JI_final,\"\\n\")\n",
"# Read clusters imposed on simulated data \n",
"cl <- as.matrix(read.table(\"../data/simulations_20210329212141/equal/15/clusters.txt\", \n",
" sep=\"\\t\", row.names=1, header=TRUE))\n",
"#cat(\"cl:\",cl,\"\\t\")\n",
"cl2 <- as.matrix(as.numeric(cl[,2]))\n",
"cat(\"cl2:\",cl2[,1],\"\\n\")\n",
"rownames(cl2) <- cl[,1]\n",
"\n",
"factorization=read.table(\"../data/simulations_20210329212141/equal/15/AE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/AE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/DAE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/DAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/VAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/LSTMVAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"\n",
"\n",
"\n",
"factors <- factorization\n",
"#factors <- factorizations[[i]][[1]]\n",
"\n",
"#print(factors)\n",
"# Clustering by Kmeans\n",
"JI_good <- numeric(0)\n",
"for (run in 1:1000) {\n",
"kmeans.out <- kmeans(factors, centers=number_cl) \n",
"clust_iCluster <- as.matrix(kmeans.out$cluster)\n",
"cat(\"clust_iCluster:\",clust_iCluster,\"\\n\")\n",
"######creation sets of samples\n",
"JI_mat <- numeric(0)\n",
"for (p in 1:number_cl) {\n",
" x1 <- rownames(clust_iCluster)[which(clust_iCluster[,1]==p)]\n",
" print(x1)\n",
"\n",
" row <- numeric(0)\n",
" for(j in 1:number_cl) {\n",
" x2 <- rownames(cl2)[which(cl2[,1]==j)]\n",
" #cat(\"x2:\",x2,\"\\n\")\n",
" I <- length(intersect(x1,x2))#交集\n",
" #cat(\"I:\",I,\"\\n\")\n",
" S <- I/(length(x1)+length(x2)-I)\n",
" #cat(\"S:\",S,\"\\n\")\n",
" row <- cbind(row,S)\n",
" #cat(\"row:\",row,\"\\n\")\n",
" }\n",
" JI_mat <- rbind(JI_mat,row)\n",
" cat(\"JI_mat:\",JI_mat,\"\\n\")\n",
" print(JI_mat)\n",
"\n",
"\n",
" }\n",
" JI_good <- rbind(JI_good,apply(JI_mat,1,max))\n",
" cat(\"JI_good:\",JI_good,\"\\n\")\n",
"}\n",
"JI_final[1:number_cl] <- apply(JI_good,2,mean)\n",
"JI_final"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cl2: 7 7 12 2 5 15 7 10 15 8 13 4 3 7 15 10 2 2 9 14 2 13 10 10 13 8 1 9 10 11 12 15 9 2 12 14 6 11 3 1 5 8 6 2 13 11 5 4 8 7 5 1 1 12 14 15 3 9 5 10 1 13 11 6 5 3 4 6 6 8 3 9 11 6 14 14 7 13 6 4 9 15 13 11 12 11 2 5 3 9 15 14 4 1 1 7 8 12 4 3 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOPub data rate exceeded.\n",
"The notebook server will temporarily stop sending output\n",
"to the client in order to avoid crashing it.\n",
"To change this limit, set the config variable\n",
"`--NotebookApp.iopub_data_rate_limit`.\n",
"\n",
"Current values:\n",
"NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
"NotebookApp.rate_limit_window=3.0 (secs)\n",
"\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
"<caption>A matrix: 15 × 1 of type dbl</caption>\n",
"<tbody>\n",
"\t<tr><td>0.7989517</td></tr>\n",
"\t<tr><td>0.8052115</td></tr>\n",
"\t<tr><td>0.8019024</td></tr>\n",
"\t<tr><td>0.8157678</td></tr>\n",
"\t<tr><td>0.7945885</td></tr>\n",
"\t<tr><td>0.8191150</td></tr>\n",
"\t<tr><td>0.8043023</td></tr>\n",
"\t<tr><td>0.8144615</td></tr>\n",
"\t<tr><td>0.8061806</td></tr>\n",
"\t<tr><td>0.8206591</td></tr>\n",
"\t<tr><td>0.8057421</td></tr>\n",
"\t<tr><td>0.7979146</td></tr>\n",
"\t<tr><td>0.8153197</td></tr>\n",
"\t<tr><td>0.8033851</td></tr>\n",
"\t<tr><td>0.8177235</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"A matrix: 15 × 1 of type dbl\n",
"\\begin{tabular}{l}\n",
"\t 0.7989517\\\\\n",
"\t 0.8052115\\\\\n",
"\t 0.8019024\\\\\n",
"\t 0.8157678\\\\\n",
"\t 0.7945885\\\\\n",
"\t 0.8191150\\\\\n",
"\t 0.8043023\\\\\n",
"\t 0.8144615\\\\\n",
"\t 0.8061806\\\\\n",
"\t 0.8206591\\\\\n",
"\t 0.8057421\\\\\n",
"\t 0.7979146\\\\\n",
"\t 0.8153197\\\\\n",
"\t 0.8033851\\\\\n",
"\t 0.8177235\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"A matrix: 15 × 1 of type dbl\n",
"\n",
"| 0.7989517 |\n",
"| 0.8052115 |\n",
"| 0.8019024 |\n",
"| 0.8157678 |\n",
"| 0.7945885 |\n",
"| 0.8191150 |\n",
"| 0.8043023 |\n",
"| 0.8144615 |\n",
"| 0.8061806 |\n",
"| 0.8206591 |\n",
"| 0.8057421 |\n",
"| 0.7979146 |\n",
"| 0.8153197 |\n",
"| 0.8033851 |\n",
"| 0.8177235 |\n",
"\n"
],
"text/plain": [
" [,1] \n",
" [1,] 0.7989517\n",
" [2,] 0.8052115\n",
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" [5,] 0.7945885\n",
" [6,] 0.8191150\n",
" [7,] 0.8043023\n",
" [8,] 0.8144615\n",
" [9,] 0.8061806\n",
"[10,] 0.8206591\n",
"[11,] 0.8057421\n",
"[12,] 0.7979146\n",
"[13,] 0.8153197\n",
"[14,] 0.8033851\n",
"[15,] 0.8177235"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"number_cl=15\n",
"ind <- 0\n",
"JI_final <- matrix(data=NA, nrow=number_cl, ncol=1)\n",
"#cat(JI_final,\"\\n\")\n",
"# Read clusters imposed on simulated data \n",
"cl <- as.matrix(read.table(\"../data/simulations_20210329212141/equal/15/clusters.txt\", \n",
" sep=\"\\t\", row.names=1, header=TRUE))\n",
"#cat(\"cl:\",cl,\"\\t\")\n",
"cl2 <- as.matrix(as.numeric(cl[,2]))\n",
"cat(\"cl2:\",cl2[,1],\"\\n\")\n",
"rownames(cl2) <- cl[,1]\n",
"\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/AE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"factorization=read.table(\"../data/simulations_20210329212141/equal/15/AE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/DAE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/DAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/VAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/LSTMVAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"\n",
"\n",
"\n",
"factors <- factorization\n",
"#factors <- factorizations[[i]][[1]]\n",
"\n",
"#print(factors)\n",
"# Clustering by Kmeans\n",
"JI_good <- numeric(0)\n",
"for (run in 1:1000) {\n",
"kmeans.out <- kmeans(factors, centers=number_cl) \n",
"clust_iCluster <- as.matrix(kmeans.out$cluster)\n",
"cat(\"clust_iCluster:\",clust_iCluster,\"\\n\")\n",
"######creation sets of samples\n",
"JI_mat <- numeric(0)\n",
"for (p in 1:number_cl) {\n",
" x1 <- rownames(clust_iCluster)[which(clust_iCluster[,1]==p)]\n",
" print(x1)\n",
"\n",
" row <- numeric(0)\n",
" for(j in 1:number_cl) {\n",
" x2 <- rownames(cl2)[which(cl2[,1]==j)]\n",
" cat(\"x2:\",x2,\"\\n\")\n",
" I <- length(intersect(x1,x2))#交集\n",
" cat(\"I:\",I,\"\\n\")\n",
" S <- I/(length(x1)+length(x2)-I)\n",
" cat(\"S:\",S,\"\\n\")\n",
" row <- cbind(row,S)\n",
" cat(\"row:\",row,\"\\n\")\n",
" }\n",
" JI_mat <- rbind(JI_mat,row)\n",
" cat(\"JI_mat:\",JI_mat,\"\\n\")\n",
" print(JI_mat)\n",
"\n",
"\n",
" }\n",
" JI_good <- rbind(JI_good,apply(JI_mat,1,max))\n",
" cat(\"JI_good:\",JI_good,\"\\n\")\n",
"}\n",
"JI_final[1:number_cl] <- apply(JI_good,2,mean)\n",
"JI_final"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cl2: 7 7 12 2 5 15 7 10 15 8 13 4 3 7 15 10 2 2 9 14 2 13 10 10 13 8 1 9 10 11 12 15 9 2 12 14 6 11 3 1 5 8 6 2 13 11 5 4 8 7 5 1 1 12 14 15 3 9 5 10 1 13 11 6 5 3 4 6 6 8 3 9 11 6 14 14 7 13 6 4 9 15 13 11 12 11 2 5 3 9 15 14 4 1 1 7 8 12 4 3 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOPub data rate exceeded.\n",
"The notebook server will temporarily stop sending output\n",
"to the client in order to avoid crashing it.\n",
"To change this limit, set the config variable\n",
"`--NotebookApp.iopub_data_rate_limit`.\n",
"\n",
"Current values:\n",
"NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
"NotebookApp.rate_limit_window=3.0 (secs)\n",
"\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
"<caption>A matrix: 15 × 1 of type dbl</caption>\n",
"<tbody>\n",
"\t<tr><td>0.7532231</td></tr>\n",
"\t<tr><td>0.7563619</td></tr>\n",
"\t<tr><td>0.7594553</td></tr>\n",
"\t<tr><td>0.7525584</td></tr>\n",
"\t<tr><td>0.7487293</td></tr>\n",
"\t<tr><td>0.7492135</td></tr>\n",
"\t<tr><td>0.7440258</td></tr>\n",
"\t<tr><td>0.7524720</td></tr>\n",
"\t<tr><td>0.7362969</td></tr>\n",
"\t<tr><td>0.7606681</td></tr>\n",
"\t<tr><td>0.7808652</td></tr>\n",
"\t<tr><td>0.7486006</td></tr>\n",
"\t<tr><td>0.7620518</td></tr>\n",
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"\t<tr><td>0.7502729</td></tr>\n",
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],
"text/latex": [
"A matrix: 15 × 1 of type dbl\n",
"\\begin{tabular}{l}\n",
"\t 0.7532231\\\\\n",
"\t 0.7563619\\\\\n",
"\t 0.7594553\\\\\n",
"\t 0.7525584\\\\\n",
"\t 0.7487293\\\\\n",
"\t 0.7492135\\\\\n",
"\t 0.7440258\\\\\n",
"\t 0.7524720\\\\\n",
"\t 0.7362969\\\\\n",
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"\t 0.7808652\\\\\n",
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"\t 0.7502729\\\\\n",
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],
"text/markdown": [
"\n",
"A matrix: 15 × 1 of type dbl\n",
"\n",
"| 0.7532231 |\n",
"| 0.7563619 |\n",
"| 0.7594553 |\n",
"| 0.7525584 |\n",
"| 0.7487293 |\n",
"| 0.7492135 |\n",
"| 0.7440258 |\n",
"| 0.7524720 |\n",
"| 0.7362969 |\n",
"| 0.7606681 |\n",
"| 0.7808652 |\n",
"| 0.7486006 |\n",
"| 0.7620518 |\n",
"| 0.7535825 |\n",
"| 0.7502729 |\n",
"\n"
],
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" [5,] 0.7487293\n",
" [6,] 0.7492135\n",
" [7,] 0.7440258\n",
" [8,] 0.7524720\n",
" [9,] 0.7362969\n",
"[10,] 0.7606681\n",
"[11,] 0.7808652\n",
"[12,] 0.7486006\n",
"[13,] 0.7620518\n",
"[14,] 0.7535825\n",
"[15,] 0.7502729"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"number_cl=15\n",
"ind <- 0\n",
"JI_final <- matrix(data=NA, nrow=number_cl, ncol=1)\n",
"#cat(JI_final,\"\\n\")\n",
"# Read clusters imposed on simulated data \n",
"cl <- as.matrix(read.table(\"../data/simulations_20210329212141/equal/15/clusters.txt\", \n",
" sep=\"\\t\", row.names=1, header=TRUE))\n",
"#cat(\"cl:\",cl,\"\\t\")\n",
"cl2 <- as.matrix(as.numeric(cl[,2]))\n",
"cat(\"cl2:\",cl2[,1],\"\\n\")\n",
"rownames(cl2) <- cl[,1]\n",
"\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/AE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/AE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"factorization=read.table(\"../data/simulations_20210329212141/equal/15/DAE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/DAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/VAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/LSTMVAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"\n",
"\n",
"\n",
"factors <- factorization\n",
"#factors <- factorizations[[i]][[1]]\n",
"\n",
"#print(factors)\n",
"# Clustering by Kmeans\n",
"JI_good <- numeric(0)\n",
"for (run in 1:1000) {\n",
"kmeans.out <- kmeans(factors, centers=number_cl) \n",
"clust_iCluster <- as.matrix(kmeans.out$cluster)\n",
"cat(\"clust_iCluster:\",clust_iCluster,\"\\n\")\n",
"######creation sets of samples\n",
"JI_mat <- numeric(0)\n",
"for (p in 1:number_cl) {\n",
" x1 <- rownames(clust_iCluster)[which(clust_iCluster[,1]==p)]\n",
" print(x1)\n",
"\n",
" row <- numeric(0)\n",
" for(j in 1:number_cl) {\n",
" x2 <- rownames(cl2)[which(cl2[,1]==j)]\n",
" cat(\"x2:\",x2,\"\\n\")\n",
" I <- length(intersect(x1,x2))#交集\n",
" cat(\"I:\",I,\"\\n\")\n",
" S <- I/(length(x1)+length(x2)-I)\n",
" cat(\"S:\",S,\"\\n\")\n",
" row <- cbind(row,S)\n",
" cat(\"row:\",row,\"\\n\")\n",
" }\n",
" JI_mat <- rbind(JI_mat,row)\n",
" cat(\"JI_mat:\",JI_mat,\"\\n\")\n",
" print(JI_mat)\n",
"\n",
"\n",
" }\n",
" JI_good <- rbind(JI_good,apply(JI_mat,1,max))\n",
" cat(\"JI_good:\",JI_good,\"\\n\")\n",
"}\n",
"JI_final[1:number_cl] <- apply(JI_good,2,mean)\n",
"JI_final"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cl2: 7 7 12 2 5 15 7 10 15 8 13 4 3 7 15 10 2 2 9 14 2 13 10 10 13 8 1 9 10 11 12 15 9 2 12 14 6 11 3 1 5 8 6 2 13 11 5 4 8 7 5 1 1 12 14 15 3 9 5 10 1 13 11 6 5 3 4 6 6 8 3 9 11 6 14 14 7 13 6 4 9 15 13 11 12 11 2 5 3 9 15 14 4 1 1 7 8 12 4 3 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOPub data rate exceeded.\n",
"The notebook server will temporarily stop sending output\n",
"to the client in order to avoid crashing it.\n",
"To change this limit, set the config variable\n",
"`--NotebookApp.iopub_data_rate_limit`.\n",
"\n",
"Current values:\n",
"NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
"NotebookApp.rate_limit_window=3.0 (secs)\n",
"\n"
]
},
{
"data": {
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"<tbody>\n",
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"\t<tr><td>0.8013052</td></tr>\n",
"\t<tr><td>0.8022179</td></tr>\n",
"\t<tr><td>0.7860330</td></tr>\n",
"\t<tr><td>0.8102876</td></tr>\n",
"\t<tr><td>0.7921890</td></tr>\n",
"\t<tr><td>0.7864014</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"A matrix: 15 × 1 of type dbl\n",
"\\begin{tabular}{l}\n",
"\t 0.7940330\\\\\n",
"\t 0.8056647\\\\\n",
"\t 0.7995865\\\\\n",
"\t 0.7820629\\\\\n",
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"\t 0.7954261\\\\\n",
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"\t 0.8022179\\\\\n",
"\t 0.7860330\\\\\n",
"\t 0.8102876\\\\\n",
"\t 0.7921890\\\\\n",
"\t 0.7864014\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"A matrix: 15 × 1 of type dbl\n",
"\n",
"| 0.7940330 |\n",
"| 0.8056647 |\n",
"| 0.7995865 |\n",
"| 0.7820629 |\n",
"| 0.7863840 |\n",
"| 0.7954261 |\n",
"| 0.7985005 |\n",
"| 0.7922929 |\n",
"| 0.7948400 |\n",
"| 0.8013052 |\n",
"| 0.8022179 |\n",
"| 0.7860330 |\n",
"| 0.8102876 |\n",
"| 0.7921890 |\n",
"| 0.7864014 |\n",
"\n"
],
"text/plain": [
" [,1] \n",
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" [5,] 0.7863840\n",
" [6,] 0.7954261\n",
" [7,] 0.7985005\n",
" [8,] 0.7922929\n",
" [9,] 0.7948400\n",
"[10,] 0.8013052\n",
"[11,] 0.8022179\n",
"[12,] 0.7860330\n",
"[13,] 0.8102876\n",
"[14,] 0.7921890\n",
"[15,] 0.7864014"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"number_cl=15\n",
"ind <- 0\n",
"JI_final <- matrix(data=NA, nrow=number_cl, ncol=1)\n",
"#cat(JI_final,\"\\n\")\n",
"# Read clusters imposed on simulated data \n",
"cl <- as.matrix(read.table(\"../data/simulations_20210329212141/equal/15/clusters.txt\", \n",
" sep=\"\\t\", row.names=1, header=TRUE))\n",
"#cat(\"cl:\",cl,\"\\t\")\n",
"cl2 <- as.matrix(as.numeric(cl[,2]))\n",
"cat(\"cl2:\",cl2[,1],\"\\n\")\n",
"rownames(cl2) <- cl[,1]\n",
"\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/AE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/AE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/DAE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"factorization=read.table(\"../data/simulations_20210329212141/equal/15/DAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/VAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/LSTMVAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"\n",
"\n",
"\n",
"factors <- factorization\n",
"#factors <- factorizations[[i]][[1]]\n",
"\n",
"#print(factors)\n",
"# Clustering by Kmeans\n",
"JI_good <- numeric(0)\n",
"for (run in 1:1000) {\n",
"kmeans.out <- kmeans(factors, centers=number_cl) \n",
"clust_iCluster <- as.matrix(kmeans.out$cluster)\n",
"cat(\"clust_iCluster:\",clust_iCluster,\"\\n\")\n",
"######creation sets of samples\n",
"JI_mat <- numeric(0)\n",
"for (p in 1:number_cl) {\n",
" x1 <- rownames(clust_iCluster)[which(clust_iCluster[,1]==p)]\n",
" print(x1)\n",
"\n",
" row <- numeric(0)\n",
" for(j in 1:number_cl) {\n",
" x2 <- rownames(cl2)[which(cl2[,1]==j)]\n",
" cat(\"x2:\",x2,\"\\n\")\n",
" I <- length(intersect(x1,x2))#交集\n",
" cat(\"I:\",I,\"\\n\")\n",
" S <- I/(length(x1)+length(x2)-I)\n",
" cat(\"S:\",S,\"\\n\")\n",
" row <- cbind(row,S)\n",
" cat(\"row:\",row,\"\\n\")\n",
" }\n",
" JI_mat <- rbind(JI_mat,row)\n",
" cat(\"JI_mat:\",JI_mat,\"\\n\")\n",
" print(JI_mat)\n",
"\n",
"\n",
" }\n",
" JI_good <- rbind(JI_good,apply(JI_mat,1,max))\n",
" cat(\"JI_good:\",JI_good,\"\\n\")\n",
"}\n",
"JI_final[1:number_cl] <- apply(JI_good,2,mean)\n",
"JI_final"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cl2: 7 7 12 2 5 15 7 10 15 8 13 4 3 7 15 10 2 2 9 14 2 13 10 10 13 8 1 9 10 11 12 15 9 2 12 14 6 11 3 1 5 8 6 2 13 11 5 4 8 7 5 1 1 12 14 15 3 9 5 10 1 13 11 6 5 3 4 6 6 8 3 9 11 6 14 14 7 13 6 4 9 15 13 11 12 11 2 5 3 9 15 14 4 1 1 7 8 12 4 3 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOPub data rate exceeded.\n",
"The notebook server will temporarily stop sending output\n",
"to the client in order to avoid crashing it.\n",
"To change this limit, set the config variable\n",
"`--NotebookApp.iopub_data_rate_limit`.\n",
"\n",
"Current values:\n",
"NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
"NotebookApp.rate_limit_window=3.0 (secs)\n",
"\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
"<caption>A matrix: 15 × 1 of type dbl</caption>\n",
"<tbody>\n",
"\t<tr><td>0.7365600</td></tr>\n",
"\t<tr><td>0.7399893</td></tr>\n",
"\t<tr><td>0.7380597</td></tr>\n",
"\t<tr><td>0.7495614</td></tr>\n",
"\t<tr><td>0.7558608</td></tr>\n",
"\t<tr><td>0.7513054</td></tr>\n",
"\t<tr><td>0.7519942</td></tr>\n",
"\t<tr><td>0.7433741</td></tr>\n",
"\t<tr><td>0.7291959</td></tr>\n",
"\t<tr><td>0.7450379</td></tr>\n",
"\t<tr><td>0.7317692</td></tr>\n",
"\t<tr><td>0.7382499</td></tr>\n",
"\t<tr><td>0.7398166</td></tr>\n",
"\t<tr><td>0.7516091</td></tr>\n",
"\t<tr><td>0.7268284</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"A matrix: 15 × 1 of type dbl\n",
"\\begin{tabular}{l}\n",
"\t 0.7365600\\\\\n",
"\t 0.7399893\\\\\n",
"\t 0.7380597\\\\\n",
"\t 0.7495614\\\\\n",
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"\t 0.7513054\\\\\n",
"\t 0.7519942\\\\\n",
"\t 0.7433741\\\\\n",
"\t 0.7291959\\\\\n",
"\t 0.7450379\\\\\n",
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"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"A matrix: 15 × 1 of type dbl\n",
"\n",
"| 0.7365600 |\n",
"| 0.7399893 |\n",
"| 0.7380597 |\n",
"| 0.7495614 |\n",
"| 0.7558608 |\n",
"| 0.7513054 |\n",
"| 0.7519942 |\n",
"| 0.7433741 |\n",
"| 0.7291959 |\n",
"| 0.7450379 |\n",
"| 0.7317692 |\n",
"| 0.7382499 |\n",
"| 0.7398166 |\n",
"| 0.7516091 |\n",
"| 0.7268284 |\n",
"\n"
],
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" [5,] 0.7558608\n",
" [6,] 0.7513054\n",
" [7,] 0.7519942\n",
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" [9,] 0.7291959\n",
"[10,] 0.7450379\n",
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]
},
"metadata": {},
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}
],
"source": [
"number_cl=15\n",
"ind <- 0\n",
"JI_final <- matrix(data=NA, nrow=number_cl, ncol=1)\n",
"#cat(JI_final,\"\\n\")\n",
"# Read clusters imposed on simulated data \n",
"cl <- as.matrix(read.table(\"../data/simulations_20210329212141/equal/15/clusters.txt\", \n",
" sep=\"\\t\", row.names=1, header=TRUE))\n",
"#cat(\"cl:\",cl,\"\\t\")\n",
"cl2 <- as.matrix(as.numeric(cl[,2]))\n",
"cat(\"cl2:\",cl2[,1],\"\\n\")\n",
"rownames(cl2) <- cl[,1]\n",
"\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/AE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/AE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/DAE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/DAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"factorization=read.table(\"../data/simulations_20210329212141/equal/15/VAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/LSTMVAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"\n",
"\n",
"\n",
"factors <- factorization\n",
"#factors <- factorizations[[i]][[1]]\n",
"\n",
"#print(factors)\n",
"# Clustering by Kmeans\n",
"JI_good <- numeric(0)\n",
"for (run in 1:1000) {\n",
"kmeans.out <- kmeans(factors, centers=number_cl) \n",
"clust_iCluster <- as.matrix(kmeans.out$cluster)\n",
"cat(\"clust_iCluster:\",clust_iCluster,\"\\n\")\n",
"######creation sets of samples\n",
"JI_mat <- numeric(0)\n",
"for (p in 1:number_cl) {\n",
" x1 <- rownames(clust_iCluster)[which(clust_iCluster[,1]==p)]\n",
" print(x1)\n",
"\n",
" row <- numeric(0)\n",
" for(j in 1:number_cl) {\n",
" x2 <- rownames(cl2)[which(cl2[,1]==j)]\n",
" cat(\"x2:\",x2,\"\\n\")\n",
" I <- length(intersect(x1,x2))#交集\n",
" cat(\"I:\",I,\"\\n\")\n",
" S <- I/(length(x1)+length(x2)-I)\n",
" cat(\"S:\",S,\"\\n\")\n",
" row <- cbind(row,S)\n",
" cat(\"row:\",row,\"\\n\")\n",
" }\n",
" JI_mat <- rbind(JI_mat,row)\n",
" cat(\"JI_mat:\",JI_mat,\"\\n\")\n",
" print(JI_mat)\n",
"\n",
"\n",
" }\n",
" JI_good <- rbind(JI_good,apply(JI_mat,1,max))\n",
" cat(\"JI_good:\",JI_good,\"\\n\")\n",
"}\n",
"JI_final[1:number_cl] <- apply(JI_good,2,mean)\n",
"JI_final"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cl2: 7 7 12 2 5 15 7 10 15 8 13 4 3 7 15 10 2 2 9 14 2 13 10 10 13 8 1 9 10 11 12 15 9 2 12 14 6 11 3 1 5 8 6 2 13 11 5 4 8 7 5 1 1 12 14 15 3 9 5 10 1 13 11 6 5 3 4 6 6 8 3 9 11 6 14 14 7 13 6 4 9 15 13 11 12 11 2 5 3 9 15 14 4 1 1 7 8 12 4 3 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOPub data rate exceeded.\n",
"The notebook server will temporarily stop sending output\n",
"to the client in order to avoid crashing it.\n",
"To change this limit, set the config variable\n",
"`--NotebookApp.iopub_data_rate_limit`.\n",
"\n",
"Current values:\n",
"NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
"NotebookApp.rate_limit_window=3.0 (secs)\n",
"\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
"<caption>A matrix: 15 × 1 of type dbl</caption>\n",
"<tbody>\n",
"\t<tr><td>0.4727026</td></tr>\n",
"\t<tr><td>0.4756525</td></tr>\n",
"\t<tr><td>0.4718643</td></tr>\n",
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"\t<tr><td>0.4793765</td></tr>\n",
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"\t<tr><td>0.4669012</td></tr>\n",
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"</table>\n"
],
"text/latex": [
"A matrix: 15 × 1 of type dbl\n",
"\\begin{tabular}{l}\n",
"\t 0.4727026\\\\\n",
"\t 0.4756525\\\\\n",
"\t 0.4718643\\\\\n",
"\t 0.4635589\\\\\n",
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"\t 0.4629963\\\\\n",
"\t 0.4653092\\\\\n",
"\t 0.4717958\\\\\n",
"\t 0.4745232\\\\\n",
"\t 0.4752839\\\\\n",
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],
"text/markdown": [
"\n",
"A matrix: 15 × 1 of type dbl\n",
"\n",
"| 0.4727026 |\n",
"| 0.4756525 |\n",
"| 0.4718643 |\n",
"| 0.4635589 |\n",
"| 0.4793765 |\n",
"| 0.4613873 |\n",
"| 0.4629963 |\n",
"| 0.4653092 |\n",
"| 0.4717958 |\n",
"| 0.4745232 |\n",
"| 0.4752839 |\n",
"| 0.4738429 |\n",
"| 0.4763239 |\n",
"| 0.4642153 |\n",
"| 0.4669012 |\n",
"\n"
],
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" [5,] 0.4793765\n",
" [6,] 0.4613873\n",
" [7,] 0.4629963\n",
" [8,] 0.4653092\n",
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"[10,] 0.4745232\n",
"[11,] 0.4752839\n",
"[12,] 0.4738429\n",
"[13,] 0.4763239\n",
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"[15,] 0.4669012"
]
},
"metadata": {},
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}
],
"source": [
"number_cl=15\n",
"ind <- 0\n",
"JI_final <- matrix(data=NA, nrow=number_cl, ncol=1)\n",
"#cat(JI_final,\"\\n\")\n",
"# Read clusters imposed on simulated data \n",
"cl <- as.matrix(read.table(\"../data/simulations_20210329212141/equal/15/clusters.txt\", \n",
" sep=\"\\t\", row.names=1, header=TRUE))\n",
"#cat(\"cl:\",cl,\"\\t\")\n",
"cl2 <- as.matrix(as.numeric(cl[,2]))\n",
"cat(\"cl2:\",cl2[,1],\"\\n\")\n",
"rownames(cl2) <- cl[,1]\n",
"\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/AE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/AE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/DAE_FAETC_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/DAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"#factorization=read.table(\"../data/simulations_20210329212141/equal/15/VAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"factorization=read.table(\"../data/simulations_20210329212141/equal/15/LSTMVAE_FCTAE_EM.txt\", sep=\" \", row.names=cl[,1],header=FALSE)\n",
"\n",
"\n",
"\n",
"factors <- factorization\n",
"#factors <- factorizations[[i]][[1]]\n",
"\n",
"#print(factors)\n",
"# Clustering by Kmeans\n",
"JI_good <- numeric(0)\n",
"for (run in 1:1000) {\n",
"kmeans.out <- kmeans(factors, centers=number_cl) \n",
"clust_iCluster <- as.matrix(kmeans.out$cluster)\n",
"cat(\"clust_iCluster:\",clust_iCluster,\"\\n\")\n",
"######creation sets of samples\n",
"JI_mat <- numeric(0)\n",
"for (p in 1:number_cl) {\n",
" x1 <- rownames(clust_iCluster)[which(clust_iCluster[,1]==p)]\n",
" print(x1)\n",
"\n",
" row <- numeric(0)\n",
" for(j in 1:number_cl) {\n",
" x2 <- rownames(cl2)[which(cl2[,1]==j)]\n",
" cat(\"x2:\",x2,\"\\n\")\n",
" I <- length(intersect(x1,x2))#交集\n",
" cat(\"I:\",I,\"\\n\")\n",
" S <- I/(length(x1)+length(x2)-I)\n",
" cat(\"S:\",S,\"\\n\")\n",
" row <- cbind(row,S)\n",
" cat(\"row:\",row,\"\\n\")\n",
" }\n",
" JI_mat <- rbind(JI_mat,row)\n",
" cat(\"JI_mat:\",JI_mat,\"\\n\")\n",
" print(JI_mat)\n",
"\n",
"\n",
" }\n",
" JI_good <- rbind(JI_good,apply(JI_mat,1,max))\n",
" cat(\"JI_good:\",JI_good,\"\\n\")\n",
"}\n",
"JI_final[1:number_cl] <- apply(JI_good,2,mean)\n",
"JI_final"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
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"name": "ir"
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"name": "R",
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}
Datasets
Models
