Tweets-DP-BMM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacoda\\lib\\site-packages\\gensim\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "from collections import defaultdict\n",
    "import gensim\n",
    "from InitKmeans import InitKmeans\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.mixture import GaussianMixture\n",
    "     \n",
    "def pre_processData(newsgroups_train):\n",
    "    tokenizer = RegexpTokenizer(r'\\w+')\n",
    "    for i in range(len(newsgroups_train)):\n",
    "        newsgroups_train[i] = newsgroups_train[i].lower()\n",
    "        #newsgroups_train[i] = tokenizer.tokenize(newsgroups_train[i])\n",
    "    #newsgroups_train = [[token for token in doc if not token.isdigit()] for doc in newsgroups_train]\n",
    "    newsgroups_train = [doc.split(' ') for doc in newsgroups_train]\n",
    "    return newsgroups_train\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "from collections import defaultdict\n",
    "import gensim\n",
    "from InitKmeans import InitKmeans\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.mixture import GaussianMixture\n",
    "import sys  \n",
    "import json\n",
    "if __name__ == \"__main__\":\n",
    "    newsgroups_train = list()\n",
    "    news_labels = {}\n",
    "    docID_username = {}\n",
    "    \n",
    "    i = 0\n",
    "    file_path=\"D:/part_news4_biterm.txt\"\n",
    "    with open(file_path) as fp:\n",
    "        lines = fp.read().split(\"\\n\")\n",
    "        for line in lines:\n",
    "            if line:\n",
    "                text = json.loads(line)[\"bitermText\"].strip()\n",
    "                label = json.loads(line)[\"clusterNo\"]\n",
    "                newsgroups_train.append(text)\n",
    "                news_labels[i] = label\n",
    "                i+=1\n",
    "    fp.close()\n",
    "    \n",
    "    corpus = pre_processData(newsgroups_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_biterm = {}\n",
    "for docID in range(len(corpus)):\n",
    "    words = corpus[docID]\n",
    "    duplicate_biterm = []\n",
    "    doc_biterm[docID] = {}\n",
    "    for biterm in words:\n",
    "        hash_biterm = set(biterm.split(','))\n",
    "        if hash_biterm not in duplicate_biterm:\n",
    "            duplicate_biterm.append(hash_biterm)\n",
    "            doc_biterm[docID][biterm] = 1\n",
    "        else:\n",
    "            if biterm in doc_biterm[docID].keys():\n",
    "                doc_biterm[docID][biterm] += 1\n",
    "            else:\n",
    "                doc_biterm[docID][biterm] = 1\n",
    "doc_words = {}\n",
    "i_biterm = 0\n",
    "for docID in range(len(corpus)):\n",
    "    doc_words[docID] = {}\n",
    "    words = corpus[docID]\n",
    "    for biterm in words:\n",
    "        for word in biterm.split(','):\n",
    "            if word not in doc_words[docID].keys():\n",
    "                doc_words[docID][word] = 0\n",
    "            doc_words[docID][word] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  0  ,docID  0\n",
      "len(Topics)  0\n",
      "iter:  0  ,docID  1000\n",
      "len(Topics)  97\n",
      "iter:  0  ,docID  2000\n",
      "len(Topics)  121\n",
      "iter:  0  ,docID  3000\n",
      "len(Topics)  132\n",
      "iter:  0  ,docID  4000\n",
      "len(Topics)  141\n",
      "iter:  0  ,docID  5000\n",
      "len(Topics)  144\n",
      "iter:  0  ,docID  6000\n",
      "len(Topics)  149\n",
      "iter:  0  ,docID  7000\n",
      "len(Topics)  151\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-a2d2800c3230>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     62\u001b[0m                                     \u001b[0mn_z\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m                             \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdoc_biterm\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdocID\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mbiterm\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m                                 \u001b[0mpro_k\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mn_z\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0meach_word\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mn_z\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0meach_word\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mbeta\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m \u001b[0mn_w\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mV\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mbeta\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     65\u001b[0m                                 \u001b[0mi\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "alpha_NMI = {}\n",
    "alpha_topics = {}\n",
    "for alpha in np.arange(0.5,5.5,0.5):\n",
    "    alpha = np.around(alpha,decimals=1)\n",
    "    docID_assign_z = {}\n",
    "    m_z = {}\n",
    "    n_z = {}\n",
    "    n_w = {}\n",
    "    n_b = {}\n",
    "    Topics = []\n",
    "    V = set()\n",
    "    D = set()\n",
    "    #alpha = 1\n",
    "    beta = 0.02\n",
    "    total_iter = 10\n",
    "    for iter in range(total_iter):\n",
    "        for docID in range(len(corpus)):\n",
    "            if docID % 1000 == 0:\n",
    "                print(\"iter: \",iter,\" ,docID \",docID)\n",
    "                print(\"len(Topics) \",len(Topics))\n",
    "            words = corpus[docID]\n",
    "            D.discard(docID)\n",
    "            if docID in docID_assign_z.keys():\n",
    "                before_k = docID_assign_z[docID]\n",
    "                m_z[before_k].discard(docID)\n",
    "                for biterm in words:\n",
    "                    for word in biterm.split(','):\n",
    "                        n_z[before_k][word] -= 1\n",
    "                        n_w[before_k] -=1\n",
    "            else:\n",
    "                before_k = -1\n",
    "            if len(D) == 0 and len(V) == 0:\n",
    "                choose_k = 0\n",
    "                D.add(docID)\n",
    "                docID_assign_z[docID] = choose_k\n",
    "                if choose_k not in m_z.keys():\n",
    "                    m_z[choose_k] = set()\n",
    "                m_z[choose_k].add(docID)\n",
    "                for biterm in words:\n",
    "                    for word in biterm.split(','):\n",
    "                        if choose_k not in n_w.keys():\n",
    "                            n_w[choose_k] = 0\n",
    "                        if choose_k not in n_z.keys():\n",
    "                            n_z[choose_k] = {}\n",
    "                        if word not in n_z[choose_k].keys():\n",
    "                            n_z[choose_k][word] = 0\n",
    "                        n_z[choose_k][word] += 1\n",
    "                        n_w[choose_k] += 1\n",
    "                        V.add(word)\n",
    "                if choose_k not in Topics:\n",
    "                    Topics.append(choose_k)\n",
    "            else:\n",
    "                log_pro = []\n",
    "                for k in Topics:\n",
    "                    pro_k = len(m_z[k])\n",
    "                    if pro_k != 0:\n",
    "                        i = 0\n",
    "                        for biterm in words:\n",
    "                            each_word = biterm.strip().split(',')\n",
    "                            for word in each_word:\n",
    "                                if word not in n_z[k].keys():\n",
    "                                    n_z[k][word] = 0\n",
    "                            for j in range(doc_biterm[docID][biterm]):\n",
    "                                pro_k *= (n_z[k][each_word[0]] + n_z[k][each_word[-1]] + beta + j) / ( n_w[k] + len(V)*beta + i) \n",
    "                                i += 1\n",
    "\n",
    "                    if pro_k == 0:\n",
    "                        pro_k = sys.float_info.min\n",
    "                    log_pro.append(pro_k)\n",
    "\n",
    "                pro_new_k = alpha*(len(D))\n",
    "                i = 0\n",
    "                for biterm in words:\n",
    "                    for j in range(doc_biterm[docID][biterm]):\n",
    "                        pro_new_k *= ( beta + j) / ( len(V)*beta + i) \n",
    "                        i += 1\n",
    "\n",
    "                if pro_new_k == 0:\n",
    "                    pro_new_k = sys.float_info.min        \n",
    "                log_pro.append(pro_new_k)\n",
    "\n",
    "                sum_pro=sum(log_pro)\n",
    "\n",
    "                normalized_posterior = [i/sum_pro for i in log_pro]    \n",
    "                select_k = None\n",
    "                if iter == (total_iter - 1):\n",
    "                    select_k = normalized_posterior.index(max(normalized_posterior))\n",
    "\n",
    "                else:\n",
    "                    select_k = np.random.choice( (len(Topics)+1) , 1, p=normalized_posterior)[0]  \n",
    "\n",
    "                if select_k == len(Topics):\n",
    "                    choose_k = np.max(Topics) + 1\n",
    "                else:\n",
    "                    choose_k = Topics[select_k]\n",
    "\n",
    "                D.add(docID)\n",
    "                docID_assign_z[docID] = choose_k\n",
    "                if choose_k not in m_z.keys():\n",
    "                    m_z[choose_k] = set()\n",
    "                m_z[choose_k].add(docID)\n",
    "                for biterm in words:\n",
    "                    for word in biterm.split(','):\n",
    "                        if choose_k not in n_w.keys():\n",
    "                            n_w[choose_k] = 0\n",
    "                        if choose_k not in n_z.keys():\n",
    "                            n_z[choose_k] = {}\n",
    "                        if word not in n_z[choose_k].keys():\n",
    "                            n_z[choose_k][word] = 0\n",
    "                        n_z[choose_k][word] += 1\n",
    "                        n_w[choose_k] += 1\n",
    "                        V.add(word)\n",
    "                if choose_k not in Topics:\n",
    "                    Topics.append(choose_k)\n",
    "\n",
    "            count_k = []\n",
    "            for k in Topics:\n",
    "                if k in m_z.keys() and len(m_z[k]) == 0:\n",
    "                    m_z.pop(k, None)\n",
    "                    n_z.pop(k, None)\n",
    "                    n_w.pop(k, None)\n",
    "                    count_k.append(k)\n",
    "            for k in count_k:\n",
    "                Topics.remove(k)   \n",
    "\n",
    "        from sklearn.metrics.cluster import normalized_mutual_info_score\n",
    "        nmi_sample = []\n",
    "        nmi_result = []\n",
    "        for key, value in news_labels.items():\n",
    "                nmi_sample.append(value)\n",
    "                nmi_result.append(docID_assign_z[key])\n",
    "        print(\"alpha \", alpha, \"Topics \",len(Topics), \" iter \",iter,\" \",normalized_mutual_info_score(np.array(nmi_sample), np.array(nmi_result)))\n",
    "        alpha_NMI[alpha] = normalized_mutual_info_score(np.array(nmi_sample), np.array(nmi_result))\n",
    "        alpha_topics[alpha] = len(Topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(alpha_NMI)\n",
    "print(alpha_topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.5: 0.8680239094660858, 1.0: 0.8706042742918579, 1.5: 0.8757233687872634, 2.0: 0.8461538635964255}\n",
      "{0.5: 308, 1.0: 539, 1.5: 411, 2.0: 372}\n"
     ]
    }
   ],
   "source": [
    "print(alpha_NMI)\n",
    "print(alpha_topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  0  ,docID  0\n",
      "len(Topics)  0\n",
      "iter:  0  ,docID  1000\n",
      "len(Topics)  95\n",
      "iter:  0  ,docID  2000\n",
      "len(Topics)  115\n",
      "iter:  0  ,docID  3000\n",
      "len(Topics)  125\n",
      "iter:  0  ,docID  4000\n",
      "len(Topics)  130\n",
      "iter:  0  ,docID  5000\n",
      "len(Topics)  131\n",
      "iter:  0  ,docID  6000\n",
      "len(Topics)  132\n",
      "iter:  0  ,docID  7000\n",
      "len(Topics)  134\n",
      "iter:  0  ,docID  8000\n",
      "len(Topics)  136\n",
      "iter:  0  ,docID  9000\n",
      "len(Topics)  146\n",
      "iter:  0  ,docID  10000\n",
      "len(Topics)  150\n",
      "iter:  0  ,docID  11000\n",
      "len(Topics)  154\n",
      "iter:  0  ,docID  12000\n",
      "len(Topics)  156\n",
      "iter:  0  ,docID  13000\n",
      "len(Topics)  159\n",
      "iter:  0  ,docID  14000\n",
      "len(Topics)  161\n",
      "iter:  0  ,docID  15000\n",
      "len(Topics)  164\n",
      "iter:  0  ,docID  16000\n",
      "len(Topics)  165\n",
      "iter:  0  ,docID  17000\n",
      "len(Topics)  165\n",
      "iter:  0  ,docID  18000\n",
      "len(Topics)  168\n",
      "iter:  0  ,docID  19000\n",
      "len(Topics)  168\n",
      "iter:  0  ,docID  20000\n",
      "len(Topics)  172\n",
      "iter:  0  ,docID  21000\n",
      "len(Topics)  174\n",
      "iter:  0  ,docID  22000\n",
      "len(Topics)  175\n",
      "iter:  0  ,docID  23000\n",
      "len(Topics)  176\n",
      "iter:  0  ,docID  24000\n",
      "len(Topics)  180\n",
      "iter:  0  ,docID  25000\n",
      "len(Topics)  186\n",
      "iter:  0  ,docID  26000\n",
      "len(Topics)  188\n",
      "iter:  0  ,docID  27000\n",
      "len(Topics)  191\n",
      "iter:  0  ,docID  28000\n",
      "len(Topics)  194\n",
      "iter:  0  ,docID  29000\n",
      "len(Topics)  198\n",
      "iter:  0  ,docID  30000\n",
      "len(Topics)  199\n",
      "alpha  0.1 Topics  199  iter  0   0.7833861607735505\n",
      "iter:  1  ,docID  0\n",
      "len(Topics)  199\n",
      "iter:  1  ,docID  1000\n",
      "len(Topics)  201\n",
      "iter:  1  ,docID  2000\n",
      "len(Topics)  204\n",
      "iter:  1  ,docID  3000\n",
      "len(Topics)  207\n",
      "iter:  1  ,docID  4000\n",
      "len(Topics)  209\n",
      "iter:  1  ,docID  5000\n",
      "len(Topics)  209\n",
      "iter:  1  ,docID  6000\n",
      "len(Topics)  210\n",
      "iter:  1  ,docID  7000\n",
      "len(Topics)  211\n",
      "iter:  1  ,docID  8000\n",
      "len(Topics)  212\n",
      "iter:  1  ,docID  9000\n",
      "len(Topics)  213\n",
      "iter:  1  ,docID  10000\n",
      "len(Topics)  213\n",
      "iter:  1  ,docID  11000\n",
      "len(Topics)  213\n",
      "iter:  1  ,docID  12000\n",
      "len(Topics)  213\n",
      "iter:  1  ,docID  13000\n",
      "len(Topics)  214\n",
      "iter:  1  ,docID  14000\n",
      "len(Topics)  214\n",
      "iter:  1  ,docID  15000\n",
      "len(Topics)  216\n",
      "iter:  1  ,docID  16000\n",
      "len(Topics)  217\n",
      "iter:  1  ,docID  17000\n",
      "len(Topics)  219\n",
      "iter:  1  ,docID  18000\n",
      "len(Topics)  219\n",
      "iter:  1  ,docID  19000\n",
      "len(Topics)  219\n",
      "iter:  1  ,docID  20000\n",
      "len(Topics)  217\n",
      "iter:  1  ,docID  21000\n",
      "len(Topics)  217\n",
      "iter:  1  ,docID  22000\n",
      "len(Topics)  218\n",
      "iter:  1  ,docID  23000\n",
      "len(Topics)  218\n",
      "iter:  1  ,docID  24000\n",
      "len(Topics)  220\n",
      "iter:  1  ,docID  25000\n",
      "len(Topics)  220\n",
      "iter:  1  ,docID  26000\n",
      "len(Topics)  221\n",
      "iter:  1  ,docID  27000\n",
      "len(Topics)  220\n",
      "iter:  1  ,docID  28000\n",
      "len(Topics)  221\n",
      "iter:  1  ,docID  29000\n",
      "len(Topics)  220\n",
      "iter:  1  ,docID  30000\n",
      "len(Topics)  220\n",
      "alpha  0.1 Topics  220  iter  1   0.8251123991465243\n",
      "iter:  2  ,docID  0\n",
      "len(Topics)  220\n",
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      "len(Topics)  232\n",
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      "len(Topics)  232\n",
      "alpha  0.1 Topics  232  iter  2   0.8393318738972675\n",
      "iter:  3  ,docID  0\n",
      "len(Topics)  232\n",
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      "len(Topics)  242\n",
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      "len(Topics)  242\n",
      "alpha  0.1 Topics  243  iter  3   0.8484725853547591\n",
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      "len(Topics)  243\n",
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      "len(Topics)  246\n",
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      "len(Topics)  251\n",
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      "len(Topics)  250\n",
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      "len(Topics)  250\n",
      "iter:  4  ,docID  30000\n",
      "len(Topics)  251\n",
      "alpha  0.1 Topics  251  iter  4   0.8531468632017877\n",
      "iter:  5  ,docID  0\n",
      "len(Topics)  251\n",
      "iter:  5  ,docID  1000\n",
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      "len(Topics)  249\n",
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      "len(Topics)  249\n",
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      "len(Topics)  249\n",
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      "len(Topics)  249\n",
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      "len(Topics)  251\n",
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      "len(Topics)  251\n",
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      "len(Topics)  251\n",
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      "len(Topics)  252\n",
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      "len(Topics)  254\n",
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      "len(Topics)  255\n",
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      "len(Topics)  255\n",
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      "len(Topics)  255\n",
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      "len(Topics)  255\n",
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      "len(Topics)  255\n",
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      "len(Topics)  255\n",
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      "len(Topics)  255\n",
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      "len(Topics)  253\n",
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      "len(Topics)  254\n",
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      "len(Topics)  253\n",
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      "len(Topics)  253\n",
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      "len(Topics)  254\n",
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      "len(Topics)  255\n",
      "iter:  5  ,docID  30000\n",
      "len(Topics)  255\n",
      "alpha  0.1 Topics  255  iter  5   0.8545846951904252\n",
      "iter:  6  ,docID  0\n",
      "len(Topics)  255\n",
      "iter:  6  ,docID  1000\n",
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      "len(Topics)  258\n",
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      "len(Topics)  258\n",
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      "len(Topics)  258\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  6  ,docID  9000\n",
      "len(Topics)  259\n",
      "iter:  6  ,docID  10000\n",
      "len(Topics)  261\n",
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      "len(Topics)  264\n",
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      "len(Topics)  264\n",
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      "len(Topics)  268\n",
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      "len(Topics)  268\n",
      "iter:  6  ,docID  30000\n",
      "len(Topics)  268\n",
      "alpha  0.1 Topics  268  iter  6   0.8572711290024694\n",
      "iter:  7  ,docID  0\n",
      "len(Topics)  268\n",
      "iter:  7  ,docID  1000\n",
      "len(Topics)  270\n",
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      "len(Topics)  274\n",
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      "len(Topics)  273\n",
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      "len(Topics)  273\n",
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      "len(Topics)  271\n",
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      "len(Topics)  271\n",
      "alpha  0.1 Topics  271  iter  7   0.8599490185510605\n",
      "iter:  8  ,docID  0\n",
      "len(Topics)  271\n",
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      "len(Topics)  272\n",
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      "len(Topics)  272\n",
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      "len(Topics)  272\n",
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      "len(Topics)  272\n",
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      "len(Topics)  272\n",
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      "len(Topics)  274\n",
      "alpha  0.1 Topics  274  iter  8   0.8632895198129188\n",
      "iter:  9  ,docID  0\n",
      "len(Topics)  274\n",
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      "len(Topics)  266\n",
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      "len(Topics)  266\n",
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      "len(Topics)  266\n",
      "iter:  9  ,docID  30000\n",
      "len(Topics)  266\n",
      "alpha  0.1 Topics  266  iter  9   0.8667865634503141\n",
      "iter:  0  ,docID  0\n",
      "len(Topics)  0\n",
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      "len(Topics)  199\n",
      "alpha  0.2 Topics  199  iter  0   0.7896482766676832\n",
      "iter:  1  ,docID  0\n",
      "len(Topics)  199\n",
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      "len(Topics)  214\n",
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      "len(Topics)  214\n",
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      "len(Topics)  213\n",
      "iter:  1  ,docID  30000\n",
      "len(Topics)  213\n",
      "alpha  0.2 Topics  213  iter  1   0.8329376628400074\n",
      "iter:  2  ,docID  0\n",
      "len(Topics)  213\n",
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      "len(Topics)  217\n",
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      "len(Topics)  218\n",
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      "len(Topics)  218\n",
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      "len(Topics)  218\n",
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      "len(Topics)  222\n",
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      "len(Topics)  226\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  2  ,docID  18000\n",
      "len(Topics)  226\n",
      "iter:  2  ,docID  19000\n",
      "len(Topics)  226\n",
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      "len(Topics)  229\n",
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      "len(Topics)  229\n",
      "alpha  0.2 Topics  229  iter  2   0.8434212460488048\n",
      "iter:  3  ,docID  0\n",
      "len(Topics)  229\n",
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      "len(Topics)  250\n",
      "alpha  0.2 Topics  250  iter  3   0.8484884244704447\n",
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      "len(Topics)  250\n",
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      "len(Topics)  263\n",
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      "len(Topics)  263\n",
      "alpha  0.2 Topics  263  iter  4   0.8528885556794712\n",
      "iter:  5  ,docID  0\n",
      "len(Topics)  263\n",
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      "len(Topics)  272\n",
      "alpha  0.2 Topics  272  iter  5   0.8566118883138489\n",
      "iter:  6  ,docID  0\n",
      "len(Topics)  272\n",
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      "len(Topics)  280\n",
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      "len(Topics)  282\n",
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      "len(Topics)  284\n",
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      "len(Topics)  284\n",
      "iter:  6  ,docID  30000\n",
      "len(Topics)  284\n",
      "alpha  0.2 Topics  284  iter  6   0.8586673331884584\n",
      "iter:  7  ,docID  0\n",
      "len(Topics)  284\n",
      "iter:  7  ,docID  1000\n",
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      "len(Topics)  288\n",
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      "len(Topics)  288\n",
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      "len(Topics)  289\n",
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      "len(Topics)  289\n",
      "iter:  7  ,docID  8000\n",
      "len(Topics)  288\n",
      "iter:  7  ,docID  9000\n",
      "len(Topics)  288\n",
      "iter:  7  ,docID  10000\n",
      "len(Topics)  288\n",
      "iter:  7  ,docID  11000\n",
      "len(Topics)  289\n",
      "iter:  7  ,docID  12000\n",
      "len(Topics)  288\n",
      "iter:  7  ,docID  13000\n",
      "len(Topics)  288\n",
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      "len(Topics)  288\n",
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      "len(Topics)  289\n",
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      "len(Topics)  289\n",
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      "len(Topics)  291\n",
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      "len(Topics)  291\n",
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      "len(Topics)  294\n",
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      "len(Topics)  295\n",
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      "len(Topics)  295\n",
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      "len(Topics)  295\n",
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      "len(Topics)  295\n",
      "iter:  7  ,docID  30000\n",
      "len(Topics)  295\n",
      "alpha  0.2 Topics  295  iter  7   0.8607696815428707\n",
      "iter:  8  ,docID  0\n",
      "len(Topics)  295\n",
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      "len(Topics)  291\n",
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      "len(Topics)  291\n",
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      "len(Topics)  293\n",
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      "len(Topics)  293\n",
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      "len(Topics)  293\n",
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      "len(Topics)  294\n",
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      "len(Topics)  292\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  8  ,docID  26000\n",
      "len(Topics)  290\n",
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      "len(Topics)  291\n",
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      "len(Topics)  293\n",
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      "len(Topics)  293\n",
      "alpha  0.2 Topics  293  iter  8   0.8610843023385276\n",
      "iter:  9  ,docID  0\n",
      "len(Topics)  293\n",
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      "len(Topics)  289\n",
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      "alpha  0.2 Topics  281  iter  9   0.8656853471524877\n",
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      "alpha  0.3 Topics  225  iter  0   0.7873130239981275\n",
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      "alpha  0.3 Topics  249  iter  1   0.8269780454178439\n",
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      "len(Topics)  281\n",
      "alpha  0.3 Topics  281  iter  2   0.8405942842188178\n",
      "iter:  3  ,docID  0\n",
      "len(Topics)  281\n",
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      "len(Topics)  282\n",
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      "len(Topics)  282\n",
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      "len(Topics)  285\n",
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      "len(Topics)  288\n",
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      "len(Topics)  289\n",
      "alpha  0.3 Topics  289  iter  3   0.8493606459092005\n",
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      "len(Topics)  289\n",
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      "len(Topics)  302\n",
      "alpha  0.3 Topics  301  iter  4   0.855312824053501\n",
      "iter:  5  ,docID  0\n",
      "len(Topics)  301\n",
      "iter:  5  ,docID  1000\n",
      "len(Topics)  303\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  5  ,docID  2000\n",
      "len(Topics)  303\n",
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      "len(Topics)  324\n",
      "iter:  5  ,docID  30000\n",
      "len(Topics)  321\n",
      "alpha  0.3 Topics  321  iter  5   0.8566220075687614\n",
      "iter:  6  ,docID  0\n",
      "len(Topics)  321\n",
      "iter:  6  ,docID  1000\n",
      "len(Topics)  323\n",
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      "len(Topics)  325\n",
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      "len(Topics)  325\n",
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      "len(Topics)  322\n",
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      "len(Topics)  323\n",
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      "len(Topics)  323\n",
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      "len(Topics)  323\n",
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      "len(Topics)  326\n",
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      "len(Topics)  326\n",
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      "len(Topics)  328\n",
      "alpha  0.3 Topics  326  iter  6   0.8586184952960894\n",
      "iter:  7  ,docID  0\n",
      "len(Topics)  326\n",
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      "len(Topics)  328\n",
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      "len(Topics)  329\n",
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      "len(Topics)  328\n",
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      "len(Topics)  328\n",
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      "len(Topics)  328\n",
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      "len(Topics)  329\n",
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      "len(Topics)  329\n",
      "alpha  0.3 Topics  329  iter  7   0.8592509887032332\n",
      "iter:  8  ,docID  0\n",
      "len(Topics)  329\n",
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      "len(Topics)  338\n",
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      "len(Topics)  339\n",
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      "len(Topics)  337\n",
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      "len(Topics)  339\n",
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      "len(Topics)  337\n",
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      "len(Topics)  338\n",
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      "len(Topics)  337\n",
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      "len(Topics)  336\n",
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      "len(Topics)  336\n",
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      "len(Topics)  336\n",
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      "len(Topics)  335\n",
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      "len(Topics)  335\n",
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      "len(Topics)  334\n",
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      "len(Topics)  335\n",
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      "len(Topics)  333\n",
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      "len(Topics)  335\n",
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      "len(Topics)  336\n",
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      "len(Topics)  338\n",
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      "len(Topics)  338\n",
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      "len(Topics)  339\n",
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      "len(Topics)  339\n",
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      "len(Topics)  339\n",
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      "len(Topics)  340\n",
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      "len(Topics)  341\n",
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      "len(Topics)  339\n",
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      "len(Topics)  340\n",
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      "len(Topics)  340\n",
      "iter:  8  ,docID  30000\n",
      "len(Topics)  340\n",
      "alpha  0.3 Topics  339  iter  8   0.860783340624375\n",
      "iter:  9  ,docID  0\n",
      "len(Topics)  339\n",
      "iter:  9  ,docID  1000\n",
      "len(Topics)  339\n",
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      "len(Topics)  335\n",
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      "len(Topics)  335\n",
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      "len(Topics)  335\n",
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      "len(Topics)  334\n",
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      "len(Topics)  334\n",
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      "len(Topics)  333\n",
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      "len(Topics)  334\n",
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      "len(Topics)  334\n",
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      "len(Topics)  334\n",
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      "len(Topics)  333\n",
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      "len(Topics)  332\n",
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      "len(Topics)  331\n",
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      "len(Topics)  331\n",
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      "len(Topics)  331\n",
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      "len(Topics)  332\n",
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      "len(Topics)  330\n",
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      "len(Topics)  330\n",
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      "len(Topics)  327\n",
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      "len(Topics)  325\n",
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      "len(Topics)  324\n",
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      "len(Topics)  324\n",
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      "len(Topics)  324\n",
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      "len(Topics)  323\n",
      "iter:  9  ,docID  30000\n",
      "len(Topics)  322\n",
      "alpha  0.3 Topics  321  iter  9   0.8665974423500961\n",
      "iter:  0  ,docID  0\n",
      "len(Topics)  0\n",
      "iter:  0  ,docID  1000\n",
      "len(Topics)  94\n",
      "iter:  0  ,docID  2000\n",
      "len(Topics)  111\n",
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      "len(Topics)  120\n",
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      "len(Topics)  130\n",
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      "len(Topics)  141\n",
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      "len(Topics)  146\n",
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      "len(Topics)  158\n",
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      "len(Topics)  166\n",
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      "len(Topics)  172\n",
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      "len(Topics)  177\n",
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      "len(Topics)  179\n",
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      "len(Topics)  182\n",
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      "len(Topics)  183\n",
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      "len(Topics)  184\n",
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      "len(Topics)  189\n",
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      "len(Topics)  191\n",
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      "len(Topics)  192\n",
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      "len(Topics)  192\n",
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      "len(Topics)  194\n",
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      "len(Topics)  195\n",
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      "len(Topics)  196\n",
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      "len(Topics)  206\n",
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      "len(Topics)  218\n",
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      "len(Topics)  224\n",
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      "len(Topics)  226\n",
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      "len(Topics)  229\n",
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      "len(Topics)  231\n",
      "iter:  0  ,docID  30000\n",
      "len(Topics)  232\n",
      "alpha  0.4 Topics  233  iter  0   0.793362495797266\n",
      "iter:  1  ,docID  0\n",
      "len(Topics)  233\n",
      "iter:  1  ,docID  1000\n",
      "len(Topics)  236\n",
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      "len(Topics)  242\n",
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      "len(Topics)  246\n",
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      "len(Topics)  251\n",
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      "len(Topics)  254\n",
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      "len(Topics)  253\n",
      "iter:  1  ,docID  9000\n",
      "len(Topics)  256\n",
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      "len(Topics)  258\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  1  ,docID  11000\n",
      "len(Topics)  260\n",
      "iter:  1  ,docID  12000\n",
      "len(Topics)  260\n",
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      "len(Topics)  266\n",
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      "len(Topics)  282\n",
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      "len(Topics)  284\n",
      "iter:  1  ,docID  30000\n",
      "len(Topics)  284\n",
      "alpha  0.4 Topics  285  iter  1   0.8343162117854795\n",
      "iter:  2  ,docID  0\n",
      "len(Topics)  285\n",
      "iter:  2  ,docID  1000\n",
      "len(Topics)  289\n",
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      "len(Topics)  300\n",
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      "len(Topics)  301\n",
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      "len(Topics)  306\n",
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      "len(Topics)  307\n",
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      "len(Topics)  307\n",
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      "len(Topics)  310\n",
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      "len(Topics)  309\n",
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      "len(Topics)  308\n",
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      "len(Topics)  309\n",
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      "len(Topics)  309\n",
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      "len(Topics)  309\n",
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      "len(Topics)  312\n",
      "iter:  2  ,docID  25000\n",
      "len(Topics)  312\n",
      "iter:  2  ,docID  26000\n",
      "len(Topics)  312\n",
      "iter:  2  ,docID  27000\n",
      "len(Topics)  311\n",
      "iter:  2  ,docID  28000\n",
      "len(Topics)  309\n",
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      "len(Topics)  309\n",
      "iter:  2  ,docID  30000\n",
      "len(Topics)  310\n",
      "alpha  0.4 Topics  309  iter  2   0.8462720623294504\n",
      "iter:  3  ,docID  0\n",
      "len(Topics)  309\n",
      "iter:  3  ,docID  1000\n",
      "len(Topics)  305\n",
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      "len(Topics)  307\n",
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      "len(Topics)  312\n",
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      "len(Topics)  315\n",
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      "len(Topics)  314\n",
      "iter:  3  ,docID  8000\n",
      "len(Topics)  313\n",
      "iter:  3  ,docID  9000\n",
      "len(Topics)  311\n",
      "iter:  3  ,docID  10000\n",
      "len(Topics)  312\n",
      "iter:  3  ,docID  11000\n",
      "len(Topics)  312\n",
      "iter:  3  ,docID  12000\n",
      "len(Topics)  312\n",
      "iter:  3  ,docID  13000\n",
      "len(Topics)  313\n",
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      "len(Topics)  312\n",
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      "len(Topics)  313\n",
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      "len(Topics)  312\n",
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      "len(Topics)  314\n",
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      "len(Topics)  314\n",
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      "len(Topics)  314\n",
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      "len(Topics)  315\n",
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      "len(Topics)  318\n",
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      "len(Topics)  316\n",
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      "len(Topics)  318\n",
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      "len(Topics)  320\n",
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      "len(Topics)  323\n",
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      "len(Topics)  325\n",
      "iter:  3  ,docID  27000\n",
      "len(Topics)  328\n",
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      "len(Topics)  328\n",
      "iter:  3  ,docID  29000\n",
      "len(Topics)  328\n",
      "iter:  3  ,docID  30000\n",
      "len(Topics)  326\n",
      "alpha  0.4 Topics  326  iter  3   0.8540512675839602\n",
      "iter:  4  ,docID  0\n",
      "len(Topics)  326\n",
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      "len(Topics)  334\n",
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      "len(Topics)  338\n",
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      "len(Topics)  340\n",
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      "len(Topics)  340\n",
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      "len(Topics)  343\n",
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      "len(Topics)  342\n",
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      "len(Topics)  343\n",
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      "len(Topics)  344\n",
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      "len(Topics)  347\n",
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      "len(Topics)  345\n",
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      "len(Topics)  346\n",
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      "len(Topics)  346\n",
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      "len(Topics)  346\n",
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      "len(Topics)  347\n",
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      "len(Topics)  347\n",
      "iter:  4  ,docID  30000\n",
      "len(Topics)  349\n",
      "alpha  0.4 Topics  349  iter  4   0.8589494932486529\n",
      "iter:  5  ,docID  0\n",
      "len(Topics)  349\n",
      "iter:  5  ,docID  1000\n",
      "len(Topics)  350\n",
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      "len(Topics)  343\n",
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      "len(Topics)  342\n",
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      "len(Topics)  341\n",
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      "len(Topics)  340\n",
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      "len(Topics)  340\n",
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      "len(Topics)  339\n",
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      "len(Topics)  340\n",
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      "len(Topics)  336\n",
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      "len(Topics)  337\n",
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      "len(Topics)  338\n",
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      "len(Topics)  339\n",
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      "len(Topics)  339\n",
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      "len(Topics)  340\n",
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      "len(Topics)  341\n",
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      "len(Topics)  341\n",
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      "len(Topics)  341\n",
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      "len(Topics)  341\n",
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      "len(Topics)  341\n",
      "iter:  5  ,docID  30000\n",
      "len(Topics)  340\n",
      "alpha  0.4 Topics  340  iter  5   0.860424604182586\n",
      "iter:  6  ,docID  0\n",
      "len(Topics)  340\n",
      "iter:  6  ,docID  1000\n",
      "len(Topics)  341\n",
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      "len(Topics)  347\n",
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      "len(Topics)  349\n",
      "iter:  6  ,docID  8000\n",
      "len(Topics)  348\n",
      "iter:  6  ,docID  9000\n",
      "len(Topics)  348\n",
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      "len(Topics)  348\n",
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      "len(Topics)  350\n",
      "iter:  6  ,docID  12000\n",
      "len(Topics)  350\n",
      "iter:  6  ,docID  13000\n",
      "len(Topics)  348\n",
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      "len(Topics)  349\n",
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      "len(Topics)  349\n",
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      "len(Topics)  349\n",
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      "len(Topics)  349\n",
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      "len(Topics)  350\n",
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      "len(Topics)  352\n",
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      "len(Topics)  351\n",
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      "len(Topics)  351\n",
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      "len(Topics)  352\n",
      "iter:  6  ,docID  24000\n",
      "len(Topics)  355\n",
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      "len(Topics)  355\n",
      "iter:  6  ,docID  26000\n",
      "len(Topics)  358\n",
      "iter:  6  ,docID  27000\n",
      "len(Topics)  358\n",
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      "len(Topics)  358\n",
      "iter:  6  ,docID  29000\n",
      "len(Topics)  359\n",
      "iter:  6  ,docID  30000\n",
      "len(Topics)  358\n",
      "alpha  0.4 Topics  358  iter  6   0.8632524334029404\n",
      "iter:  7  ,docID  0\n",
      "len(Topics)  358\n",
      "iter:  7  ,docID  1000\n",
      "len(Topics)  361\n",
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      "len(Topics)  364\n",
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      "len(Topics)  357\n",
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      "len(Topics)  357\n",
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      "len(Topics)  353\n",
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      "len(Topics)  351\n",
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      "len(Topics)  351\n",
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      "len(Topics)  350\n",
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      "len(Topics)  350\n",
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      "len(Topics)  348\n",
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      "len(Topics)  346\n",
      "iter:  7  ,docID  18000\n",
      "len(Topics)  345\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  7  ,docID  19000\n",
      "len(Topics)  345\n",
      "iter:  7  ,docID  20000\n",
      "len(Topics)  344\n",
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      "len(Topics)  343\n",
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      "len(Topics)  341\n",
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      "len(Topics)  342\n",
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      "len(Topics)  340\n",
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      "len(Topics)  339\n",
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      "len(Topics)  339\n",
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      "len(Topics)  340\n",
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      "len(Topics)  341\n",
      "iter:  7  ,docID  30000\n",
      "len(Topics)  341\n",
      "alpha  0.4 Topics  341  iter  7   0.866017636481465\n",
      "iter:  8  ,docID  0\n",
      "len(Topics)  341\n",
      "iter:  8  ,docID  1000\n",
      "len(Topics)  341\n",
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      "len(Topics)  353\n",
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      "len(Topics)  361\n",
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      "len(Topics)  361\n",
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      "len(Topics)  361\n",
      "iter:  8  ,docID  30000\n",
      "len(Topics)  364\n",
      "alpha  0.4 Topics  364  iter  8   0.8674047153221497\n",
      "iter:  9  ,docID  0\n",
      "len(Topics)  364\n",
      "iter:  9  ,docID  1000\n",
      "len(Topics)  367\n",
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      "len(Topics)  364\n",
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      "len(Topics)  364\n",
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      "len(Topics)  360\n",
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      "len(Topics)  354\n",
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      "len(Topics)  351\n",
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      "len(Topics)  344\n",
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      "len(Topics)  344\n",
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      "len(Topics)  341\n",
      "alpha  0.4 Topics  341  iter  9   0.8709520495334826\n",
      "iter:  0  ,docID  0\n",
      "len(Topics)  0\n",
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      "len(Topics)  100\n",
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      "len(Topics)  242\n",
      "iter:  0  ,docID  30000\n",
      "len(Topics)  242\n",
      "alpha  0.5 Topics  243  iter  0   0.7865721588475333\n",
      "iter:  1  ,docID  0\n",
      "len(Topics)  243\n",
      "iter:  1  ,docID  1000\n",
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      "len(Topics)  257\n",
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      "len(Topics)  258\n",
      "iter:  1  ,docID  8000\n",
      "len(Topics)  259\n",
      "iter:  1  ,docID  9000\n",
      "len(Topics)  260\n",
      "iter:  1  ,docID  10000\n",
      "len(Topics)  259\n",
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      "len(Topics)  259\n",
      "iter:  1  ,docID  12000\n",
      "len(Topics)  261\n",
      "iter:  1  ,docID  13000\n",
      "len(Topics)  262\n",
      "iter:  1  ,docID  14000\n",
      "len(Topics)  262\n",
      "iter:  1  ,docID  15000\n",
      "len(Topics)  262\n",
      "iter:  1  ,docID  16000\n",
      "len(Topics)  261\n",
      "iter:  1  ,docID  17000\n",
      "len(Topics)  262\n",
      "iter:  1  ,docID  18000\n",
      "len(Topics)  262\n",
      "iter:  1  ,docID  19000\n",
      "len(Topics)  262\n",
      "iter:  1  ,docID  20000\n",
      "len(Topics)  264\n",
      "iter:  1  ,docID  21000\n",
      "len(Topics)  264\n",
      "iter:  1  ,docID  22000\n",
      "len(Topics)  264\n",
      "iter:  1  ,docID  23000\n",
      "len(Topics)  264\n",
      "iter:  1  ,docID  24000\n",
      "len(Topics)  268\n",
      "iter:  1  ,docID  25000\n",
      "len(Topics)  270\n",
      "iter:  1  ,docID  26000\n",
      "len(Topics)  271\n",
      "iter:  1  ,docID  27000\n",
      "len(Topics)  271\n",
      "iter:  1  ,docID  28000\n",
      "len(Topics)  270\n",
      "iter:  1  ,docID  29000\n",
      "len(Topics)  271\n",
      "iter:  1  ,docID  30000\n",
      "len(Topics)  271\n",
      "alpha  0.5 Topics  271  iter  1   0.828843912167567\n",
      "iter:  2  ,docID  0\n",
      "len(Topics)  271\n",
      "iter:  2  ,docID  1000\n",
      "len(Topics)  274\n",
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      "len(Topics)  274\n",
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      "len(Topics)  273\n",
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      "len(Topics)  277\n",
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      "len(Topics)  278\n",
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      "len(Topics)  280\n",
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      "len(Topics)  280\n",
      "iter:  2  ,docID  8000\n",
      "len(Topics)  283\n",
      "iter:  2  ,docID  9000\n",
      "len(Topics)  283\n",
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      "len(Topics)  284\n",
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      "len(Topics)  286\n",
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      "len(Topics)  286\n",
      "iter:  2  ,docID  13000\n",
      "len(Topics)  288\n",
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      "len(Topics)  288\n",
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      "len(Topics)  287\n",
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      "len(Topics)  287\n",
      "iter:  2  ,docID  17000\n",
      "len(Topics)  289\n",
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      "len(Topics)  289\n",
      "iter:  2  ,docID  19000\n",
      "len(Topics)  290\n",
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      "len(Topics)  290\n",
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      "len(Topics)  291\n",
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      "len(Topics)  290\n",
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      "len(Topics)  291\n",
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      "len(Topics)  293\n",
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      "len(Topics)  293\n",
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      "len(Topics)  292\n",
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      "len(Topics)  292\n",
      "iter:  2  ,docID  28000\n",
      "len(Topics)  295\n",
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      "len(Topics)  295\n",
      "iter:  2  ,docID  30000\n",
      "len(Topics)  297\n",
      "alpha  0.5 Topics  297  iter  2   0.842228503330089\n",
      "iter:  3  ,docID  0\n",
      "len(Topics)  297\n",
      "iter:  3  ,docID  1000\n",
      "len(Topics)  299\n",
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      "len(Topics)  300\n",
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      "len(Topics)  299\n",
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      "len(Topics)  301\n",
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      "len(Topics)  304\n",
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      "len(Topics)  304\n",
      "iter:  3  ,docID  7000\n",
      "len(Topics)  304\n",
      "iter:  3  ,docID  8000\n",
      "len(Topics)  303\n",
      "iter:  3  ,docID  9000\n",
      "len(Topics)  304\n",
      "iter:  3  ,docID  10000\n",
      "len(Topics)  304\n",
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      "len(Topics)  304\n",
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      "len(Topics)  304\n",
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      "len(Topics)  303\n",
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      "len(Topics)  303\n",
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      "len(Topics)  303\n",
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      "len(Topics)  304\n",
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      "len(Topics)  303\n",
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      "len(Topics)  303\n",
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      "len(Topics)  304\n",
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      "len(Topics)  304\n",
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      "len(Topics)  304\n",
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      "len(Topics)  308\n",
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      "len(Topics)  307\n",
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      "len(Topics)  309\n",
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      "len(Topics)  309\n",
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      "len(Topics)  314\n",
      "iter:  3  ,docID  27000\n",
      "len(Topics)  316\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  3  ,docID  28000\n",
      "len(Topics)  317\n",
      "iter:  3  ,docID  29000\n",
      "len(Topics)  317\n",
      "iter:  3  ,docID  30000\n",
      "len(Topics)  315\n",
      "alpha  0.5 Topics  315  iter  3   0.8486097167881156\n",
      "iter:  4  ,docID  0\n",
      "len(Topics)  315\n",
      "iter:  4  ,docID  1000\n",
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      "len(Topics)  315\n",
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      "len(Topics)  316\n",
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      "len(Topics)  316\n",
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      "len(Topics)  317\n",
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      "len(Topics)  319\n",
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      "len(Topics)  322\n",
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      "len(Topics)  323\n",
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      "len(Topics)  323\n",
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      "len(Topics)  325\n",
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      "len(Topics)  325\n",
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      "len(Topics)  325\n",
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      "len(Topics)  324\n",
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      "len(Topics)  325\n",
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      "len(Topics)  326\n",
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      "len(Topics)  326\n",
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      "len(Topics)  326\n",
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      "len(Topics)  327\n",
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      "len(Topics)  330\n",
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      "len(Topics)  327\n",
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      "len(Topics)  327\n",
      "iter:  4  ,docID  28000\n",
      "len(Topics)  328\n",
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      "len(Topics)  328\n",
      "iter:  4  ,docID  30000\n",
      "len(Topics)  329\n",
      "alpha  0.5 Topics  329  iter  4   0.8540094431940697\n",
      "iter:  5  ,docID  0\n",
      "len(Topics)  329\n",
      "iter:  5  ,docID  1000\n",
      "len(Topics)  326\n",
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      "len(Topics)  326\n",
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      "len(Topics)  328\n",
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      "len(Topics)  328\n",
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      "len(Topics)  328\n",
      "iter:  5  ,docID  7000\n",
      "len(Topics)  328\n",
      "iter:  5  ,docID  8000\n",
      "len(Topics)  328\n",
      "iter:  5  ,docID  9000\n",
      "len(Topics)  328\n",
      "iter:  5  ,docID  10000\n",
      "len(Topics)  327\n",
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      "len(Topics)  327\n",
      "iter:  5  ,docID  12000\n",
      "len(Topics)  329\n",
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      "len(Topics)  330\n",
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      "len(Topics)  333\n",
      "iter:  5  ,docID  15000\n",
      "len(Topics)  334\n",
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      "len(Topics)  335\n",
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      "len(Topics)  337\n",
      "iter:  5  ,docID  18000\n",
      "len(Topics)  339\n",
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      "len(Topics)  339\n",
      "iter:  5  ,docID  20000\n",
      "len(Topics)  341\n",
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      "len(Topics)  342\n",
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      "len(Topics)  341\n",
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      "len(Topics)  342\n",
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      "len(Topics)  342\n",
      "iter:  5  ,docID  25000\n",
      "len(Topics)  343\n",
      "iter:  5  ,docID  26000\n",
      "len(Topics)  343\n",
      "iter:  5  ,docID  27000\n",
      "len(Topics)  343\n",
      "iter:  5  ,docID  28000\n",
      "len(Topics)  345\n",
      "iter:  5  ,docID  29000\n",
      "len(Topics)  344\n",
      "iter:  5  ,docID  30000\n",
      "len(Topics)  345\n",
      "alpha  0.5 Topics  345  iter  5   0.8577101360097288\n",
      "iter:  6  ,docID  0\n",
      "len(Topics)  345\n",
      "iter:  6  ,docID  1000\n",
      "len(Topics)  346\n",
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      "len(Topics)  347\n",
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      "len(Topics)  347\n",
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      "len(Topics)  347\n",
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      "len(Topics)  346\n",
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      "len(Topics)  347\n",
      "iter:  6  ,docID  7000\n",
      "len(Topics)  345\n",
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      "len(Topics)  345\n",
      "iter:  6  ,docID  9000\n",
      "len(Topics)  344\n",
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      "len(Topics)  342\n",
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      "len(Topics)  341\n",
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      "len(Topics)  342\n",
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      "len(Topics)  346\n",
      "iter:  6  ,docID  30000\n",
      "len(Topics)  346\n",
      "alpha  0.5 Topics  346  iter  6   0.8590551189197234\n",
      "iter:  7  ,docID  0\n",
      "len(Topics)  346\n",
      "iter:  7  ,docID  1000\n",
      "len(Topics)  347\n",
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      "len(Topics)  350\n",
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      "len(Topics)  353\n",
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      "len(Topics)  352\n",
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      "len(Topics)  350\n",
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      "len(Topics)  351\n",
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      "len(Topics)  351\n",
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      "len(Topics)  352\n",
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      "len(Topics)  353\n",
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      "len(Topics)  353\n",
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      "len(Topics)  352\n",
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      "len(Topics)  351\n",
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      "len(Topics)  352\n",
      "iter:  7  ,docID  21000\n",
      "len(Topics)  353\n",
      "iter:  7  ,docID  22000\n",
      "len(Topics)  354\n",
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      "len(Topics)  353\n",
      "iter:  7  ,docID  24000\n",
      "len(Topics)  352\n",
      "iter:  7  ,docID  25000\n",
      "len(Topics)  353\n",
      "iter:  7  ,docID  26000\n",
      "len(Topics)  354\n",
      "iter:  7  ,docID  27000\n",
      "len(Topics)  354\n",
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      "len(Topics)  354\n",
      "iter:  7  ,docID  29000\n",
      "len(Topics)  355\n",
      "iter:  7  ,docID  30000\n",
      "len(Topics)  353\n",
      "alpha  0.5 Topics  353  iter  7   0.858652955322389\n",
      "iter:  8  ,docID  0\n",
      "len(Topics)  353\n",
      "iter:  8  ,docID  1000\n",
      "len(Topics)  355\n",
      "iter:  8  ,docID  2000\n",
      "len(Topics)  352\n",
      "iter:  8  ,docID  3000\n",
      "len(Topics)  352\n",
      "iter:  8  ,docID  4000\n",
      "len(Topics)  356\n",
      "iter:  8  ,docID  5000\n",
      "len(Topics)  357\n",
      "iter:  8  ,docID  6000\n",
      "len(Topics)  357\n",
      "iter:  8  ,docID  7000\n",
      "len(Topics)  354\n",
      "iter:  8  ,docID  8000\n",
      "len(Topics)  355\n",
      "iter:  8  ,docID  9000\n",
      "len(Topics)  355\n",
      "iter:  8  ,docID  10000\n",
      "len(Topics)  354\n",
      "iter:  8  ,docID  11000\n",
      "len(Topics)  354\n",
      "iter:  8  ,docID  12000\n",
      "len(Topics)  353\n",
      "iter:  8  ,docID  13000\n",
      "len(Topics)  352\n",
      "iter:  8  ,docID  14000\n",
      "len(Topics)  354\n",
      "iter:  8  ,docID  15000\n",
      "len(Topics)  355\n",
      "iter:  8  ,docID  16000\n",
      "len(Topics)  358\n",
      "iter:  8  ,docID  17000\n",
      "len(Topics)  358\n",
      "iter:  8  ,docID  18000\n",
      "len(Topics)  358\n",
      "iter:  8  ,docID  19000\n",
      "len(Topics)  359\n",
      "iter:  8  ,docID  20000\n",
      "len(Topics)  357\n",
      "iter:  8  ,docID  21000\n",
      "len(Topics)  357\n",
      "iter:  8  ,docID  22000\n",
      "len(Topics)  357\n",
      "iter:  8  ,docID  23000\n",
      "len(Topics)  360\n",
      "iter:  8  ,docID  24000\n",
      "len(Topics)  359\n",
      "iter:  8  ,docID  25000\n",
      "len(Topics)  357\n",
      "iter:  8  ,docID  26000\n",
      "len(Topics)  358\n",
      "iter:  8  ,docID  27000\n",
      "len(Topics)  358\n",
      "iter:  8  ,docID  28000\n",
      "len(Topics)  358\n",
      "iter:  8  ,docID  29000\n",
      "len(Topics)  359\n",
      "iter:  8  ,docID  30000\n",
      "len(Topics)  360\n",
      "alpha  0.5 Topics  359  iter  8   0.8609892678748667\n",
      "iter:  9  ,docID  0\n",
      "len(Topics)  359\n",
      "iter:  9  ,docID  1000\n",
      "len(Topics)  359\n",
      "iter:  9  ,docID  2000\n",
      "len(Topics)  359\n",
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      "len(Topics)  359\n",
      "iter:  9  ,docID  4000\n",
      "len(Topics)  358\n",
      "iter:  9  ,docID  5000\n",
      "len(Topics)  358\n",
      "iter:  9  ,docID  6000\n",
      "len(Topics)  357\n",
      "iter:  9  ,docID  7000\n",
      "len(Topics)  356\n",
      "iter:  9  ,docID  8000\n",
      "len(Topics)  356\n",
      "iter:  9  ,docID  9000\n",
      "len(Topics)  356\n",
      "iter:  9  ,docID  10000\n",
      "len(Topics)  354\n",
      "iter:  9  ,docID  11000\n",
      "len(Topics)  354\n",
      "iter:  9  ,docID  12000\n",
      "len(Topics)  354\n",
      "iter:  9  ,docID  13000\n",
      "len(Topics)  355\n",
      "iter:  9  ,docID  14000\n",
      "len(Topics)  354\n",
      "iter:  9  ,docID  15000\n",
      "len(Topics)  353\n",
      "iter:  9  ,docID  16000\n",
      "len(Topics)  350\n",
      "iter:  9  ,docID  17000\n",
      "len(Topics)  348\n",
      "iter:  9  ,docID  18000\n",
      "len(Topics)  348\n",
      "iter:  9  ,docID  19000\n",
      "len(Topics)  347\n",
      "iter:  9  ,docID  20000\n",
      "len(Topics)  348\n",
      "iter:  9  ,docID  21000\n",
      "len(Topics)  347\n",
      "iter:  9  ,docID  22000\n",
      "len(Topics)  345\n",
      "iter:  9  ,docID  23000\n",
      "len(Topics)  344\n",
      "iter:  9  ,docID  24000\n",
      "len(Topics)  345\n",
      "iter:  9  ,docID  25000\n",
      "len(Topics)  344\n",
      "iter:  9  ,docID  26000\n",
      "len(Topics)  341\n",
      "iter:  9  ,docID  27000\n",
      "len(Topics)  339\n",
      "iter:  9  ,docID  28000\n",
      "len(Topics)  338\n",
      "iter:  9  ,docID  29000\n",
      "len(Topics)  338\n",
      "iter:  9  ,docID  30000\n",
      "len(Topics)  337\n",
      "alpha  0.5 Topics  337  iter  9   0.8640038818629493\n",
      "iter:  0  ,docID  0\n",
      "len(Topics)  0\n",
      "iter:  0  ,docID  1000\n",
      "len(Topics)  98\n",
      "iter:  0  ,docID  2000\n",
      "len(Topics)  121\n",
      "iter:  0  ,docID  3000\n",
      "len(Topics)  134\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  0  ,docID  4000\n",
      "len(Topics)  147\n",
      "iter:  0  ,docID  5000\n",
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      "alpha  0.6 Topics  240  iter  0   0.7931459866706035\n",
      "iter:  1  ,docID  0\n",
      "len(Topics)  240\n",
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      "len(Topics)  254\n",
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      "len(Topics)  253\n",
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      "len(Topics)  253\n",
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      "len(Topics)  254\n",
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      "len(Topics)  255\n",
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      "len(Topics)  258\n",
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      "len(Topics)  258\n",
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      "len(Topics)  259\n",
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      "len(Topics)  258\n",
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      "len(Topics)  259\n",
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      "len(Topics)  260\n",
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      "len(Topics)  260\n",
      "alpha  0.6 Topics  261  iter  1   0.8327440176816073\n",
      "iter:  2  ,docID  0\n",
      "len(Topics)  261\n",
      "iter:  2  ,docID  1000\n",
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      "len(Topics)  266\n",
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      "len(Topics)  269\n",
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      "len(Topics)  269\n",
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      "len(Topics)  271\n",
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      "len(Topics)  271\n",
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      "len(Topics)  271\n",
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      "len(Topics)  271\n",
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      "len(Topics)  272\n",
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      "len(Topics)  272\n",
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      "len(Topics)  281\n",
      "alpha  0.6 Topics  281  iter  2   0.8458212312021147\n",
      "iter:  3  ,docID  0\n",
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      "len(Topics)  304\n",
      "alpha  0.6 Topics  305  iter  3   0.8512371449772637\n",
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      "len(Topics)  305\n",
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      "len(Topics)  311\n",
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      "len(Topics)  312\n",
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      "len(Topics)  311\n",
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      "len(Topics)  312\n",
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      "len(Topics)  313\n",
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      "len(Topics)  317\n",
      "iter:  4  ,docID  30000\n",
      "len(Topics)  317\n",
      "alpha  0.6 Topics  317  iter  4   0.8554482477999786\n",
      "iter:  5  ,docID  0\n",
      "len(Topics)  317\n",
      "iter:  5  ,docID  1000\n",
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      "len(Topics)  322\n",
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      "len(Topics)  334\n",
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      "len(Topics)  334\n",
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      "len(Topics)  334\n",
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      "len(Topics)  333\n",
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      "len(Topics)  335\n",
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      "len(Topics)  334\n",
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      "len(Topics)  335\n",
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      "len(Topics)  335\n",
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      "len(Topics)  335\n",
      "iter:  5  ,docID  22000\n",
      "len(Topics)  336\n",
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      "len(Topics)  335\n",
      "iter:  5  ,docID  24000\n",
      "len(Topics)  336\n",
      "iter:  5  ,docID  25000\n",
      "len(Topics)  337\n",
      "iter:  5  ,docID  26000\n",
      "len(Topics)  337\n",
      "iter:  5  ,docID  27000\n",
      "len(Topics)  335\n",
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      "len(Topics)  336\n",
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      "len(Topics)  336\n",
      "iter:  5  ,docID  30000\n",
      "len(Topics)  337\n",
      "alpha  0.6 Topics  336  iter  5   0.8575361831174775\n",
      "iter:  6  ,docID  0\n",
      "len(Topics)  336\n",
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      "len(Topics)  333\n",
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      "len(Topics)  331\n",
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      "len(Topics)  331\n",
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      "len(Topics)  330\n",
      "iter:  6  ,docID  8000\n",
      "len(Topics)  329\n",
      "iter:  6  ,docID  9000\n",
      "len(Topics)  329\n",
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      "len(Topics)  330\n",
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      "len(Topics)  330\n",
      "iter:  6  ,docID  12000\n",
      "len(Topics)  330\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  6  ,docID  13000\n",
      "len(Topics)  330\n",
      "iter:  6  ,docID  14000\n",
      "len(Topics)  327\n",
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      "len(Topics)  326\n",
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      "len(Topics)  327\n",
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      "len(Topics)  326\n",
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      "len(Topics)  324\n",
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      "len(Topics)  325\n",
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      "len(Topics)  325\n",
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      "len(Topics)  328\n",
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      "len(Topics)  328\n",
      "iter:  6  ,docID  30000\n",
      "len(Topics)  329\n",
      "alpha  0.6 Topics  329  iter  6   0.8591464387160724\n",
      "iter:  7  ,docID  0\n",
      "len(Topics)  329\n",
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      "len(Topics)  332\n",
      "iter:  7  ,docID  8000\n",
      "len(Topics)  333\n",
      "iter:  7  ,docID  9000\n",
      "len(Topics)  333\n",
      "iter:  7  ,docID  10000\n",
      "len(Topics)  332\n",
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      "len(Topics)  332\n",
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      "len(Topics)  331\n",
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      "len(Topics)  331\n",
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      "len(Topics)  333\n",
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      "len(Topics)  335\n",
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      "len(Topics)  336\n",
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      "len(Topics)  337\n",
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      "len(Topics)  337\n",
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      "len(Topics)  338\n",
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      "len(Topics)  340\n",
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      "len(Topics)  338\n",
      "iter:  7  ,docID  28000\n",
      "len(Topics)  336\n",
      "iter:  7  ,docID  29000\n",
      "len(Topics)  336\n",
      "iter:  7  ,docID  30000\n",
      "len(Topics)  335\n",
      "alpha  0.6 Topics  335  iter  7   0.8588769649667991\n",
      "iter:  8  ,docID  0\n",
      "len(Topics)  335\n",
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      "len(Topics)  336\n",
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      "len(Topics)  336\n",
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      "len(Topics)  335\n",
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      "len(Topics)  336\n",
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      "len(Topics)  336\n",
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      "len(Topics)  334\n",
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      "len(Topics)  333\n",
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      "len(Topics)  333\n",
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      "len(Topics)  332\n",
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      "len(Topics)  333\n",
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      "len(Topics)  333\n",
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      "len(Topics)  334\n",
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      "len(Topics)  334\n",
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      "len(Topics)  336\n",
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      "len(Topics)  337\n",
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      "len(Topics)  338\n",
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      "len(Topics)  339\n",
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      "len(Topics)  340\n",
      "alpha  0.6 Topics  340  iter  8   0.8604889204706501\n",
      "iter:  9  ,docID  0\n",
      "len(Topics)  340\n",
      "iter:  9  ,docID  1000\n",
      "len(Topics)  342\n",
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      "len(Topics)  346\n",
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      "len(Topics)  346\n",
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      "len(Topics)  345\n",
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      "len(Topics)  346\n",
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      "len(Topics)  346\n",
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      "len(Topics)  345\n",
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      "len(Topics)  345\n",
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      "len(Topics)  344\n",
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      "len(Topics)  343\n",
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      "len(Topics)  342\n",
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      "len(Topics)  342\n",
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      "len(Topics)  342\n",
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      "len(Topics)  342\n",
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      "len(Topics)  341\n",
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      "len(Topics)  342\n",
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      "len(Topics)  341\n",
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      "len(Topics)  340\n",
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      "len(Topics)  340\n",
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      "len(Topics)  340\n",
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      "len(Topics)  340\n",
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      "len(Topics)  339\n",
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      "len(Topics)  337\n",
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      "len(Topics)  337\n",
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      "len(Topics)  336\n",
      "iter:  9  ,docID  30000\n",
      "len(Topics)  336\n",
      "alpha  0.6 Topics  336  iter  9   0.8655290262237872\n",
      "iter:  0  ,docID  0\n",
      "len(Topics)  0\n",
      "iter:  0  ,docID  1000\n",
      "len(Topics)  100\n",
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      "len(Topics)  156\n",
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      "len(Topics)  188\n",
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      "len(Topics)  196\n",
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      "len(Topics)  234\n",
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      "len(Topics)  248\n",
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      "len(Topics)  252\n",
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      "len(Topics)  255\n",
      "alpha  0.7 Topics  256  iter  0   0.7975280652726093\n",
      "iter:  1  ,docID  0\n",
      "len(Topics)  256\n",
      "iter:  1  ,docID  1000\n",
      "len(Topics)  259\n",
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      "len(Topics)  276\n",
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      "len(Topics)  282\n",
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      "len(Topics)  282\n",
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      "len(Topics)  283\n",
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      "len(Topics)  282\n",
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      "len(Topics)  282\n",
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      "len(Topics)  282\n",
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      "len(Topics)  281\n",
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      "len(Topics)  285\n",
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      "len(Topics)  285\n",
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      "len(Topics)  285\n",
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      "len(Topics)  285\n",
      "iter:  1  ,docID  20000\n",
      "len(Topics)  287\n",
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      "len(Topics)  287\n",
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      "len(Topics)  290\n",
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      "len(Topics)  290\n",
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      "len(Topics)  292\n",
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      "len(Topics)  294\n",
      "iter:  1  ,docID  26000\n",
      "len(Topics)  292\n",
      "iter:  1  ,docID  27000\n",
      "len(Topics)  291\n",
      "iter:  1  ,docID  28000\n",
      "len(Topics)  292\n",
      "iter:  1  ,docID  29000\n",
      "len(Topics)  293\n",
      "iter:  1  ,docID  30000\n",
      "len(Topics)  292\n",
      "alpha  0.7 Topics  291  iter  1   0.83798787355029\n",
      "iter:  2  ,docID  0\n",
      "len(Topics)  291\n",
      "iter:  2  ,docID  1000\n",
      "len(Topics)  293\n",
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      "len(Topics)  294\n",
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      "len(Topics)  295\n",
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      "len(Topics)  297\n",
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      "len(Topics)  298\n",
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      "len(Topics)  298\n",
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      "len(Topics)  297\n",
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      "len(Topics)  298\n",
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      "len(Topics)  306\n",
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      "len(Topics)  307\n",
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      "len(Topics)  307\n",
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      "len(Topics)  310\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  2  ,docID  22000\n",
      "len(Topics)  310\n",
      "iter:  2  ,docID  23000\n",
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      "len(Topics)  312\n",
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      "len(Topics)  313\n",
      "alpha  0.7 Topics  313  iter  2   0.8512770076624816\n",
      "iter:  3  ,docID  0\n",
      "len(Topics)  313\n",
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      "len(Topics)  336\n",
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      "len(Topics)  337\n",
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      "len(Topics)  336\n",
      "alpha  0.7 Topics  336  iter  3   0.8565523018389373\n",
      "iter:  4  ,docID  0\n",
      "len(Topics)  336\n",
      "iter:  4  ,docID  1000\n",
      "len(Topics)  340\n",
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      "len(Topics)  345\n",
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      "len(Topics)  347\n",
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      "len(Topics)  347\n",
      "iter:  4  ,docID  9000\n",
      "len(Topics)  349\n",
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      "len(Topics)  348\n",
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      "len(Topics)  348\n",
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      "len(Topics)  349\n",
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      "len(Topics)  348\n",
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      "len(Topics)  347\n",
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      "len(Topics)  349\n",
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      "len(Topics)  349\n",
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      "len(Topics)  351\n",
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      "len(Topics)  350\n",
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      "len(Topics)  350\n",
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      "len(Topics)  349\n",
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      "len(Topics)  352\n",
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      "len(Topics)  357\n",
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      "len(Topics)  358\n",
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      "len(Topics)  357\n",
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      "len(Topics)  354\n",
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      "len(Topics)  356\n",
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      "len(Topics)  356\n",
      "alpha  0.7 Topics  356  iter  4   0.8585917847298021\n",
      "iter:  5  ,docID  0\n",
      "len(Topics)  356\n",
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      "len(Topics)  368\n",
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      "len(Topics)  370\n",
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      "len(Topics)  370\n",
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      "len(Topics)  375\n",
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      "len(Topics)  379\n",
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      "len(Topics)  380\n",
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      "len(Topics)  380\n",
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      "len(Topics)  381\n",
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      "len(Topics)  381\n",
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      "len(Topics)  382\n",
      "iter:  5  ,docID  30000\n",
      "len(Topics)  378\n",
      "alpha  0.7 Topics  378  iter  5   0.8610951640665286\n",
      "iter:  6  ,docID  0\n",
      "len(Topics)  378\n",
      "iter:  6  ,docID  1000\n",
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      "len(Topics)  382\n",
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      "len(Topics)  384\n",
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      "len(Topics)  382\n",
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      "len(Topics)  382\n",
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      "len(Topics)  382\n",
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      "len(Topics)  384\n",
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      "len(Topics)  384\n",
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      "len(Topics)  388\n",
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      "len(Topics)  386\n",
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      "len(Topics)  387\n",
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      "len(Topics)  387\n",
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      "len(Topics)  387\n",
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      "len(Topics)  390\n",
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      "len(Topics)  388\n",
      "alpha  0.7 Topics  388  iter  6   0.8630491767508348\n",
      "iter:  7  ,docID  0\n",
      "len(Topics)  388\n",
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      "len(Topics)  391\n",
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      "len(Topics)  392\n",
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      "len(Topics)  394\n",
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      "len(Topics)  392\n",
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      "len(Topics)  390\n",
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      "len(Topics)  391\n",
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      "len(Topics)  389\n",
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      "len(Topics)  390\n",
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      "len(Topics)  392\n",
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      "len(Topics)  395\n",
      "alpha  0.7 Topics  394  iter  7   0.8646014773845394\n",
      "iter:  8  ,docID  0\n",
      "len(Topics)  394\n",
      "iter:  8  ,docID  1000\n",
      "len(Topics)  397\n",
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      "len(Topics)  396\n",
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      "len(Topics)  398\n",
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      "len(Topics)  397\n",
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      "len(Topics)  392\n",
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      "len(Topics)  392\n",
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      "len(Topics)  394\n",
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      "len(Topics)  393\n",
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      "len(Topics)  394\n",
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      "len(Topics)  396\n",
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      "len(Topics)  394\n",
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      "len(Topics)  396\n",
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      "len(Topics)  396\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  8  ,docID  30000\n",
      "len(Topics)  394\n",
      "alpha  0.7 Topics  394  iter  8   0.8661652694153419\n",
      "iter:  9  ,docID  0\n",
      "len(Topics)  394\n",
      "iter:  9  ,docID  1000\n",
      "len(Topics)  396\n",
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      "len(Topics)  392\n",
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      "len(Topics)  390\n",
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      "len(Topics)  389\n",
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      "len(Topics)  387\n",
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      "len(Topics)  388\n",
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      "len(Topics)  386\n",
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      "len(Topics)  384\n",
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      "alpha  0.7 Topics  374  iter  9   0.870667958105711\n",
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      "len(Topics)  0\n",
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      "len(Topics)  267\n",
      "alpha  0.8 Topics  267  iter  0   0.7912162758798807\n",
      "iter:  1  ,docID  0\n",
      "len(Topics)  267\n",
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      "len(Topics)  307\n",
      "alpha  0.8 Topics  306  iter  1   0.8295287049266158\n",
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      "len(Topics)  306\n",
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      "len(Topics)  329\n",
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      "len(Topics)  329\n",
      "alpha  0.8 Topics  329  iter  2   0.8414009116902934\n",
      "iter:  3  ,docID  0\n",
      "len(Topics)  329\n",
      "iter:  3  ,docID  1000\n",
      "len(Topics)  333\n",
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      "len(Topics)  335\n",
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      "len(Topics)  335\n",
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      "len(Topics)  338\n",
      "iter:  3  ,docID  10000\n",
      "len(Topics)  340\n",
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      "len(Topics)  340\n",
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      "len(Topics)  343\n",
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      "len(Topics)  343\n",
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      "len(Topics)  342\n",
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      "len(Topics)  348\n",
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      "len(Topics)  349\n",
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      "len(Topics)  350\n",
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      "len(Topics)  349\n",
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      "len(Topics)  350\n",
      "iter:  3  ,docID  30000\n",
      "len(Topics)  351\n",
      "alpha  0.8 Topics  351  iter  3   0.8466019624536272\n",
      "iter:  4  ,docID  0\n",
      "len(Topics)  351\n",
      "iter:  4  ,docID  1000\n",
      "len(Topics)  352\n",
      "iter:  4  ,docID  2000\n",
      "len(Topics)  353\n",
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      "len(Topics)  353\n",
      "iter:  4  ,docID  4000\n",
      "len(Topics)  354\n",
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      "len(Topics)  352\n",
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      "len(Topics)  352\n",
      "iter:  4  ,docID  7000\n",
      "len(Topics)  354\n",
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      "len(Topics)  354\n",
      "iter:  4  ,docID  9000\n",
      "len(Topics)  355\n",
      "iter:  4  ,docID  10000\n",
      "len(Topics)  354\n",
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      "len(Topics)  355\n",
      "iter:  4  ,docID  12000\n",
      "len(Topics)  355\n",
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      "len(Topics)  356\n",
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      "len(Topics)  357\n",
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      "len(Topics)  358\n",
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      "len(Topics)  358\n",
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      "len(Topics)  357\n",
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      "len(Topics)  357\n",
      "iter:  4  ,docID  19000\n",
      "len(Topics)  358\n",
      "iter:  4  ,docID  20000\n",
      "len(Topics)  361\n",
      "iter:  4  ,docID  21000\n",
      "len(Topics)  360\n",
      "iter:  4  ,docID  22000\n",
      "len(Topics)  360\n",
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      "len(Topics)  360\n",
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      "len(Topics)  360\n",
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      "len(Topics)  360\n",
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      "len(Topics)  360\n",
      "iter:  4  ,docID  27000\n",
      "len(Topics)  360\n",
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      "len(Topics)  360\n",
      "iter:  4  ,docID  29000\n",
      "len(Topics)  359\n",
      "iter:  4  ,docID  30000\n",
      "len(Topics)  361\n",
      "alpha  0.8 Topics  361  iter  4   0.8520785421375058\n",
      "iter:  5  ,docID  0\n",
      "len(Topics)  361\n",
      "iter:  5  ,docID  1000\n",
      "len(Topics)  360\n",
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      "len(Topics)  360\n",
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      "len(Topics)  361\n",
      "iter:  5  ,docID  4000\n",
      "len(Topics)  364\n",
      "iter:  5  ,docID  5000\n",
      "len(Topics)  364\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  5  ,docID  6000\n",
      "len(Topics)  365\n",
      "iter:  5  ,docID  7000\n",
      "len(Topics)  364\n",
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      "len(Topics)  363\n",
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      "len(Topics)  365\n",
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      "len(Topics)  367\n",
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      "len(Topics)  370\n",
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      "len(Topics)  369\n",
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      "len(Topics)  372\n",
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      "len(Topics)  373\n",
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      "len(Topics)  372\n",
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      "len(Topics)  370\n",
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      "len(Topics)  371\n",
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      "len(Topics)  370\n",
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      "len(Topics)  370\n",
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      "len(Topics)  368\n",
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      "len(Topics)  370\n",
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      "len(Topics)  370\n",
      "alpha  0.8 Topics  371  iter  5   0.8547757560946139\n",
      "iter:  6  ,docID  0\n",
      "len(Topics)  371\n",
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      "len(Topics)  369\n",
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      "len(Topics)  373\n",
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      "len(Topics)  374\n",
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      "len(Topics)  377\n",
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      "len(Topics)  380\n",
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      "len(Topics)  381\n",
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      "len(Topics)  383\n",
      "iter:  6  ,docID  19000\n",
      "len(Topics)  384\n",
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      "len(Topics)  383\n",
      "iter:  6  ,docID  21000\n",
      "len(Topics)  382\n",
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      "len(Topics)  383\n",
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      "len(Topics)  384\n",
      "iter:  6  ,docID  24000\n",
      "len(Topics)  384\n",
      "iter:  6  ,docID  25000\n",
      "len(Topics)  383\n",
      "iter:  6  ,docID  26000\n",
      "len(Topics)  385\n",
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      "len(Topics)  386\n",
      "iter:  6  ,docID  28000\n",
      "len(Topics)  387\n",
      "iter:  6  ,docID  29000\n",
      "len(Topics)  386\n",
      "iter:  6  ,docID  30000\n",
      "len(Topics)  386\n",
      "alpha  0.8 Topics  386  iter  6   0.8571138037271758\n",
      "iter:  7  ,docID  0\n",
      "len(Topics)  386\n",
      "iter:  7  ,docID  1000\n",
      "len(Topics)  388\n",
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      "len(Topics)  390\n",
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      "len(Topics)  391\n",
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      "len(Topics)  390\n",
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      "len(Topics)  392\n",
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      "len(Topics)  393\n",
      "iter:  7  ,docID  9000\n",
      "len(Topics)  390\n",
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      "len(Topics)  391\n",
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      "len(Topics)  388\n",
      "iter:  7  ,docID  13000\n",
      "len(Topics)  387\n",
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      "len(Topics)  387\n",
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      "len(Topics)  385\n",
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      "len(Topics)  385\n",
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      "len(Topics)  387\n",
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      "len(Topics)  386\n",
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      "len(Topics)  386\n",
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      "len(Topics)  387\n",
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      "len(Topics)  388\n",
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      "len(Topics)  387\n",
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      "len(Topics)  387\n",
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      "len(Topics)  386\n",
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      "len(Topics)  387\n",
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      "len(Topics)  387\n",
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      "len(Topics)  385\n",
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      "len(Topics)  385\n",
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      "len(Topics)  385\n",
      "iter:  7  ,docID  30000\n",
      "len(Topics)  383\n",
      "alpha  0.8 Topics  385  iter  7   0.8577900347906002\n",
      "iter:  8  ,docID  0\n",
      "len(Topics)  385\n",
      "iter:  8  ,docID  1000\n",
      "len(Topics)  386\n",
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      "len(Topics)  382\n",
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      "len(Topics)  382\n",
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      "len(Topics)  382\n",
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      "len(Topics)  379\n",
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      "len(Topics)  378\n",
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      "len(Topics)  380\n",
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      "len(Topics)  381\n",
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      "len(Topics)  381\n",
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      "len(Topics)  382\n",
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      "len(Topics)  381\n",
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      "len(Topics)  379\n",
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      "len(Topics)  387\n",
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      "len(Topics)  387\n",
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      "len(Topics)  389\n",
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      "len(Topics)  390\n",
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      "len(Topics)  389\n",
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      "len(Topics)  392\n",
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      "len(Topics)  392\n",
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      "len(Topics)  393\n",
      "iter:  8  ,docID  30000\n",
      "len(Topics)  395\n",
      "alpha  0.8 Topics  393  iter  8   0.8579566832526718\n",
      "iter:  9  ,docID  0\n",
      "len(Topics)  393\n",
      "iter:  9  ,docID  1000\n",
      "len(Topics)  396\n",
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      "len(Topics)  399\n",
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      "len(Topics)  399\n",
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      "len(Topics)  398\n",
      "iter:  9  ,docID  10000\n",
      "len(Topics)  398\n",
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      "len(Topics)  394\n",
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      "len(Topics)  393\n",
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      "len(Topics)  393\n",
      "iter:  9  ,docID  15000\n",
      "len(Topics)  389\n",
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      "len(Topics)  391\n",
      "iter:  9  ,docID  17000\n",
      "len(Topics)  390\n",
      "iter:  9  ,docID  18000\n",
      "len(Topics)  389\n",
      "iter:  9  ,docID  19000\n",
      "len(Topics)  388\n",
      "iter:  9  ,docID  20000\n",
      "len(Topics)  388\n",
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      "len(Topics)  386\n",
      "iter:  9  ,docID  22000\n",
      "len(Topics)  383\n",
      "iter:  9  ,docID  23000\n",
      "len(Topics)  383\n",
      "iter:  9  ,docID  24000\n",
      "len(Topics)  384\n",
      "iter:  9  ,docID  25000\n",
      "len(Topics)  381\n",
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      "len(Topics)  381\n",
      "iter:  9  ,docID  27000\n",
      "len(Topics)  378\n",
      "iter:  9  ,docID  28000\n",
      "len(Topics)  377\n",
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      "len(Topics)  376\n",
      "iter:  9  ,docID  30000\n",
      "len(Topics)  374\n",
      "alpha  0.8 Topics  374  iter  9   0.8614436874163404\n",
      "iter:  0  ,docID  0\n",
      "len(Topics)  0\n",
      "iter:  0  ,docID  1000\n",
      "len(Topics)  105\n",
      "iter:  0  ,docID  2000\n",
      "len(Topics)  132\n",
      "iter:  0  ,docID  3000\n",
      "len(Topics)  143\n",
      "iter:  0  ,docID  4000\n",
      "len(Topics)  152\n",
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      "len(Topics)  159\n",
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      "len(Topics)  164\n",
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      "len(Topics)  167\n",
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      "len(Topics)  172\n",
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      "len(Topics)  180\n",
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      "len(Topics)  205\n",
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      "len(Topics)  209\n",
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      "len(Topics)  213\n",
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      "len(Topics)  226\n",
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      "len(Topics)  227\n",
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      "len(Topics)  232\n",
      "iter:  0  ,docID  20000\n",
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      "len(Topics)  237\n",
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      "len(Topics)  243\n",
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      "len(Topics)  245\n",
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      "len(Topics)  255\n",
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      "len(Topics)  260\n",
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      "len(Topics)  268\n",
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      "len(Topics)  272\n",
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      "len(Topics)  276\n",
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      "len(Topics)  279\n",
      "iter:  0  ,docID  30000\n",
      "len(Topics)  283\n",
      "alpha  0.9 Topics  284  iter  0   0.7964680384719433\n",
      "iter:  1  ,docID  0\n",
      "len(Topics)  284\n",
      "iter:  1  ,docID  1000\n",
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      "len(Topics)  291\n",
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      "len(Topics)  298\n",
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      "len(Topics)  302\n",
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      "len(Topics)  306\n",
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      "len(Topics)  313\n",
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      "len(Topics)  313\n",
      "iter:  1  ,docID  9000\n",
      "len(Topics)  316\n",
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      "len(Topics)  318\n",
      "iter:  1  ,docID  11000\n",
      "len(Topics)  317\n",
      "iter:  1  ,docID  12000\n",
      "len(Topics)  319\n",
      "iter:  1  ,docID  13000\n",
      "len(Topics)  319\n",
      "iter:  1  ,docID  14000\n",
      "len(Topics)  323\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  1  ,docID  15000\n",
      "len(Topics)  325\n",
      "iter:  1  ,docID  16000\n",
      "len(Topics)  329\n",
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      "len(Topics)  333\n",
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      "len(Topics)  332\n",
      "iter:  1  ,docID  19000\n",
      "len(Topics)  334\n",
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      "len(Topics)  335\n",
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      "len(Topics)  340\n",
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      "len(Topics)  349\n",
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      "len(Topics)  349\n",
      "iter:  1  ,docID  24000\n",
      "len(Topics)  353\n",
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      "len(Topics)  353\n",
      "iter:  1  ,docID  26000\n",
      "len(Topics)  354\n",
      "iter:  1  ,docID  27000\n",
      "len(Topics)  356\n",
      "iter:  1  ,docID  28000\n",
      "len(Topics)  355\n",
      "iter:  1  ,docID  29000\n",
      "len(Topics)  356\n",
      "iter:  1  ,docID  30000\n",
      "len(Topics)  352\n",
      "alpha  0.9 Topics  352  iter  1   0.8405573505797204\n",
      "iter:  2  ,docID  0\n",
      "len(Topics)  352\n",
      "iter:  2  ,docID  1000\n",
      "len(Topics)  357\n",
      "iter:  2  ,docID  2000\n",
      "len(Topics)  360\n",
      "iter:  2  ,docID  3000\n",
      "len(Topics)  364\n",
      "iter:  2  ,docID  4000\n",
      "len(Topics)  366\n",
      "iter:  2  ,docID  5000\n",
      "len(Topics)  366\n",
      "iter:  2  ,docID  6000\n",
      "len(Topics)  365\n",
      "iter:  2  ,docID  7000\n",
      "len(Topics)  364\n",
      "iter:  2  ,docID  8000\n",
      "len(Topics)  362\n",
      "iter:  2  ,docID  9000\n",
      "len(Topics)  365\n",
      "iter:  2  ,docID  10000\n",
      "len(Topics)  367\n",
      "iter:  2  ,docID  11000\n",
      "len(Topics)  369\n",
      "iter:  2  ,docID  12000\n",
      "len(Topics)  369\n",
      "iter:  2  ,docID  13000\n",
      "len(Topics)  370\n",
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      "len(Topics)  370\n",
      "iter:  2  ,docID  15000\n",
      "len(Topics)  371\n",
      "iter:  2  ,docID  16000\n",
      "len(Topics)  373\n",
      "iter:  2  ,docID  17000\n",
      "len(Topics)  376\n",
      "iter:  2  ,docID  18000\n",
      "len(Topics)  379\n",
      "iter:  2  ,docID  19000\n",
      "len(Topics)  379\n",
      "iter:  2  ,docID  20000\n",
      "len(Topics)  382\n",
      "iter:  2  ,docID  21000\n",
      "len(Topics)  382\n",
      "iter:  2  ,docID  22000\n",
      "len(Topics)  383\n",
      "iter:  2  ,docID  23000\n",
      "len(Topics)  385\n",
      "iter:  2  ,docID  24000\n",
      "len(Topics)  385\n",
      "iter:  2  ,docID  25000\n",
      "len(Topics)  385\n",
      "iter:  2  ,docID  26000\n",
      "len(Topics)  388\n",
      "iter:  2  ,docID  27000\n",
      "len(Topics)  390\n",
      "iter:  2  ,docID  28000\n",
      "len(Topics)  392\n",
      "iter:  2  ,docID  29000\n",
      "len(Topics)  391\n",
      "iter:  2  ,docID  30000\n",
      "len(Topics)  393\n",
      "alpha  0.9 Topics  393  iter  2   0.8520084038679665\n",
      "iter:  3  ,docID  0\n",
      "len(Topics)  393\n",
      "iter:  3  ,docID  1000\n",
      "len(Topics)  400\n",
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      "len(Topics)  400\n",
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      "len(Topics)  397\n",
      "iter:  3  ,docID  4000\n",
      "len(Topics)  397\n",
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      "len(Topics)  397\n",
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      "len(Topics)  401\n",
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      "len(Topics)  399\n",
      "iter:  3  ,docID  8000\n",
      "len(Topics)  402\n",
      "iter:  3  ,docID  9000\n",
      "len(Topics)  404\n",
      "iter:  3  ,docID  10000\n",
      "len(Topics)  407\n",
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      "len(Topics)  406\n",
      "iter:  3  ,docID  12000\n",
      "len(Topics)  408\n",
      "iter:  3  ,docID  13000\n",
      "len(Topics)  408\n",
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      "len(Topics)  406\n",
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      "len(Topics)  405\n",
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      "len(Topics)  405\n",
      "iter:  3  ,docID  17000\n",
      "len(Topics)  409\n",
      "iter:  3  ,docID  18000\n",
      "len(Topics)  408\n",
      "iter:  3  ,docID  19000\n",
      "len(Topics)  410\n",
      "iter:  3  ,docID  20000\n",
      "len(Topics)  409\n",
      "iter:  3  ,docID  21000\n",
      "len(Topics)  408\n",
      "iter:  3  ,docID  22000\n",
      "len(Topics)  411\n",
      "iter:  3  ,docID  23000\n",
      "len(Topics)  411\n",
      "iter:  3  ,docID  24000\n",
      "len(Topics)  411\n",
      "iter:  3  ,docID  25000\n",
      "len(Topics)  413\n",
      "iter:  3  ,docID  26000\n",
      "len(Topics)  413\n",
      "iter:  3  ,docID  27000\n",
      "len(Topics)  415\n",
      "iter:  3  ,docID  28000\n",
      "len(Topics)  414\n",
      "iter:  3  ,docID  29000\n",
      "len(Topics)  415\n",
      "iter:  3  ,docID  30000\n",
      "len(Topics)  414\n",
      "alpha  0.9 Topics  414  iter  3   0.8585887559955881\n",
      "iter:  4  ,docID  0\n",
      "len(Topics)  414\n",
      "iter:  4  ,docID  1000\n",
      "len(Topics)  416\n",
      "iter:  4  ,docID  2000\n",
      "len(Topics)  418\n",
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      "len(Topics)  419\n",
      "iter:  4  ,docID  4000\n",
      "len(Topics)  423\n",
      "iter:  4  ,docID  5000\n",
      "len(Topics)  422\n",
      "iter:  4  ,docID  6000\n",
      "len(Topics)  423\n",
      "iter:  4  ,docID  7000\n",
      "len(Topics)  419\n",
      "iter:  4  ,docID  8000\n",
      "len(Topics)  420\n",
      "iter:  4  ,docID  9000\n",
      "len(Topics)  419\n",
      "iter:  4  ,docID  10000\n",
      "len(Topics)  423\n",
      "iter:  4  ,docID  11000\n",
      "len(Topics)  422\n",
      "iter:  4  ,docID  12000\n",
      "len(Topics)  420\n",
      "iter:  4  ,docID  13000\n",
      "len(Topics)  419\n",
      "iter:  4  ,docID  14000\n",
      "len(Topics)  418\n",
      "iter:  4  ,docID  15000\n",
      "len(Topics)  419\n",
      "iter:  4  ,docID  16000\n",
      "len(Topics)  416\n",
      "iter:  4  ,docID  17000\n",
      "len(Topics)  415\n",
      "iter:  4  ,docID  18000\n",
      "len(Topics)  415\n",
      "iter:  4  ,docID  19000\n",
      "len(Topics)  412\n",
      "iter:  4  ,docID  20000\n",
      "len(Topics)  412\n",
      "iter:  4  ,docID  21000\n",
      "len(Topics)  415\n",
      "iter:  4  ,docID  22000\n",
      "len(Topics)  412\n",
      "iter:  4  ,docID  23000\n",
      "len(Topics)  411\n",
      "iter:  4  ,docID  24000\n",
      "len(Topics)  412\n",
      "iter:  4  ,docID  25000\n",
      "len(Topics)  411\n",
      "iter:  4  ,docID  26000\n",
      "len(Topics)  411\n",
      "iter:  4  ,docID  27000\n",
      "len(Topics)  412\n",
      "iter:  4  ,docID  28000\n",
      "len(Topics)  411\n",
      "iter:  4  ,docID  29000\n",
      "len(Topics)  409\n",
      "iter:  4  ,docID  30000\n",
      "len(Topics)  408\n",
      "alpha  0.9 Topics  408  iter  4   0.8616546663773301\n",
      "iter:  5  ,docID  0\n",
      "len(Topics)  408\n",
      "iter:  5  ,docID  1000\n",
      "len(Topics)  405\n",
      "iter:  5  ,docID  2000\n",
      "len(Topics)  403\n",
      "iter:  5  ,docID  3000\n",
      "len(Topics)  400\n",
      "iter:  5  ,docID  4000\n",
      "len(Topics)  404\n",
      "iter:  5  ,docID  5000\n",
      "len(Topics)  403\n",
      "iter:  5  ,docID  6000\n",
      "len(Topics)  400\n",
      "iter:  5  ,docID  7000\n",
      "len(Topics)  401\n",
      "iter:  5  ,docID  8000\n",
      "len(Topics)  399\n",
      "iter:  5  ,docID  9000\n",
      "len(Topics)  400\n",
      "iter:  5  ,docID  10000\n",
      "len(Topics)  398\n",
      "iter:  5  ,docID  11000\n",
      "len(Topics)  399\n",
      "iter:  5  ,docID  12000\n",
      "len(Topics)  400\n",
      "iter:  5  ,docID  13000\n",
      "len(Topics)  402\n",
      "iter:  5  ,docID  14000\n",
      "len(Topics)  402\n",
      "iter:  5  ,docID  15000\n",
      "len(Topics)  400\n",
      "iter:  5  ,docID  16000\n",
      "len(Topics)  401\n",
      "iter:  5  ,docID  17000\n",
      "len(Topics)  400\n",
      "iter:  5  ,docID  18000\n",
      "len(Topics)  401\n",
      "iter:  5  ,docID  19000\n",
      "len(Topics)  401\n",
      "iter:  5  ,docID  20000\n",
      "len(Topics)  402\n",
      "iter:  5  ,docID  21000\n",
      "len(Topics)  400\n",
      "iter:  5  ,docID  22000\n",
      "len(Topics)  401\n",
      "iter:  5  ,docID  23000\n",
      "len(Topics)  401\n",
      "iter:  5  ,docID  24000\n",
      "len(Topics)  400\n",
      "iter:  5  ,docID  25000\n",
      "len(Topics)  403\n",
      "iter:  5  ,docID  26000\n",
      "len(Topics)  403\n",
      "iter:  5  ,docID  27000\n",
      "len(Topics)  403\n",
      "iter:  5  ,docID  28000\n",
      "len(Topics)  404\n",
      "iter:  5  ,docID  29000\n",
      "len(Topics)  404\n",
      "iter:  5  ,docID  30000\n",
      "len(Topics)  405\n",
      "alpha  0.9 Topics  404  iter  5   0.8636121650006485\n",
      "iter:  6  ,docID  0\n",
      "len(Topics)  404\n",
      "iter:  6  ,docID  1000\n",
      "len(Topics)  407\n",
      "iter:  6  ,docID  2000\n",
      "len(Topics)  408\n",
      "iter:  6  ,docID  3000\n",
      "len(Topics)  410\n",
      "iter:  6  ,docID  4000\n",
      "len(Topics)  410\n",
      "iter:  6  ,docID  5000\n",
      "len(Topics)  411\n",
      "iter:  6  ,docID  6000\n",
      "len(Topics)  413\n",
      "iter:  6  ,docID  7000\n",
      "len(Topics)  414\n",
      "iter:  6  ,docID  8000\n",
      "len(Topics)  413\n",
      "iter:  6  ,docID  9000\n",
      "len(Topics)  413\n",
      "iter:  6  ,docID  10000\n",
      "len(Topics)  414\n",
      "iter:  6  ,docID  11000\n",
      "len(Topics)  413\n",
      "iter:  6  ,docID  12000\n",
      "len(Topics)  412\n",
      "iter:  6  ,docID  13000\n",
      "len(Topics)  412\n",
      "iter:  6  ,docID  14000\n",
      "len(Topics)  410\n",
      "iter:  6  ,docID  15000\n",
      "len(Topics)  411\n",
      "iter:  6  ,docID  16000\n",
      "len(Topics)  410\n",
      "iter:  6  ,docID  17000\n",
      "len(Topics)  409\n",
      "iter:  6  ,docID  18000\n",
      "len(Topics)  408\n",
      "iter:  6  ,docID  19000\n",
      "len(Topics)  408\n",
      "iter:  6  ,docID  20000\n",
      "len(Topics)  409\n",
      "iter:  6  ,docID  21000\n",
      "len(Topics)  410\n",
      "iter:  6  ,docID  22000\n",
      "len(Topics)  410\n",
      "iter:  6  ,docID  23000\n",
      "len(Topics)  411\n",
      "iter:  6  ,docID  24000\n",
      "len(Topics)  414\n",
      "iter:  6  ,docID  25000\n",
      "len(Topics)  413\n",
      "iter:  6  ,docID  26000\n",
      "len(Topics)  413\n",
      "iter:  6  ,docID  27000\n",
      "len(Topics)  414\n",
      "iter:  6  ,docID  28000\n",
      "len(Topics)  415\n",
      "iter:  6  ,docID  29000\n",
      "len(Topics)  415\n",
      "iter:  6  ,docID  30000\n",
      "len(Topics)  416\n",
      "alpha  0.9 Topics  415  iter  6   0.8647667540918188\n",
      "iter:  7  ,docID  0\n",
      "len(Topics)  415\n",
      "iter:  7  ,docID  1000\n",
      "len(Topics)  414\n",
      "iter:  7  ,docID  2000\n",
      "len(Topics)  413\n",
      "iter:  7  ,docID  3000\n",
      "len(Topics)  412\n",
      "iter:  7  ,docID  4000\n",
      "len(Topics)  410\n",
      "iter:  7  ,docID  5000\n",
      "len(Topics)  407\n",
      "iter:  7  ,docID  6000\n",
      "len(Topics)  406\n",
      "iter:  7  ,docID  7000\n",
      "len(Topics)  409\n",
      "iter:  7  ,docID  8000\n",
      "len(Topics)  409\n",
      "iter:  7  ,docID  9000\n",
      "len(Topics)  409\n",
      "iter:  7  ,docID  10000\n",
      "len(Topics)  410\n",
      "iter:  7  ,docID  11000\n",
      "len(Topics)  409\n",
      "iter:  7  ,docID  12000\n",
      "len(Topics)  409\n",
      "iter:  7  ,docID  13000\n",
      "len(Topics)  409\n",
      "iter:  7  ,docID  14000\n",
      "len(Topics)  409\n",
      "iter:  7  ,docID  15000\n",
      "len(Topics)  411\n",
      "iter:  7  ,docID  16000\n",
      "len(Topics)  409\n",
      "iter:  7  ,docID  17000\n",
      "len(Topics)  408\n",
      "iter:  7  ,docID  18000\n",
      "len(Topics)  408\n",
      "iter:  7  ,docID  19000\n",
      "len(Topics)  408\n",
      "iter:  7  ,docID  20000\n",
      "len(Topics)  407\n",
      "iter:  7  ,docID  21000\n",
      "len(Topics)  406\n",
      "iter:  7  ,docID  22000\n",
      "len(Topics)  405\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  7  ,docID  23000\n",
      "len(Topics)  406\n",
      "iter:  7  ,docID  24000\n",
      "len(Topics)  406\n",
      "iter:  7  ,docID  25000\n",
      "len(Topics)  404\n",
      "iter:  7  ,docID  26000\n",
      "len(Topics)  409\n",
      "iter:  7  ,docID  27000\n",
      "len(Topics)  411\n",
      "iter:  7  ,docID  28000\n",
      "len(Topics)  410\n",
      "iter:  7  ,docID  29000\n",
      "len(Topics)  409\n",
      "iter:  7  ,docID  30000\n",
      "len(Topics)  410\n",
      "alpha  0.9 Topics  410  iter  7   0.8659703330417178\n",
      "iter:  8  ,docID  0\n",
      "len(Topics)  410\n",
      "iter:  8  ,docID  1000\n",
      "len(Topics)  409\n",
      "iter:  8  ,docID  2000\n",
      "len(Topics)  408\n",
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      "len(Topics)  408\n",
      "iter:  8  ,docID  4000\n",
      "len(Topics)  408\n",
      "iter:  8  ,docID  5000\n",
      "len(Topics)  409\n",
      "iter:  8  ,docID  6000\n",
      "len(Topics)  411\n",
      "iter:  8  ,docID  7000\n",
      "len(Topics)  410\n",
      "iter:  8  ,docID  8000\n",
      "len(Topics)  408\n",
      "iter:  8  ,docID  9000\n",
      "len(Topics)  408\n",
      "iter:  8  ,docID  10000\n",
      "len(Topics)  406\n",
      "iter:  8  ,docID  11000\n",
      "len(Topics)  405\n",
      "iter:  8  ,docID  12000\n",
      "len(Topics)  405\n",
      "iter:  8  ,docID  13000\n",
      "len(Topics)  405\n",
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      "len(Topics)  405\n",
      "iter:  8  ,docID  15000\n",
      "len(Topics)  404\n",
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      "len(Topics)  405\n",
      "iter:  8  ,docID  17000\n",
      "len(Topics)  403\n",
      "iter:  8  ,docID  18000\n",
      "len(Topics)  404\n",
      "iter:  8  ,docID  19000\n",
      "len(Topics)  404\n",
      "iter:  8  ,docID  20000\n",
      "len(Topics)  404\n",
      "iter:  8  ,docID  21000\n",
      "len(Topics)  405\n",
      "iter:  8  ,docID  22000\n",
      "len(Topics)  407\n",
      "iter:  8  ,docID  23000\n",
      "len(Topics)  407\n",
      "iter:  8  ,docID  24000\n",
      "len(Topics)  408\n",
      "iter:  8  ,docID  25000\n",
      "len(Topics)  408\n",
      "iter:  8  ,docID  26000\n",
      "len(Topics)  406\n",
      "iter:  8  ,docID  27000\n",
      "len(Topics)  406\n",
      "iter:  8  ,docID  28000\n",
      "len(Topics)  408\n",
      "iter:  8  ,docID  29000\n",
      "len(Topics)  409\n",
      "iter:  8  ,docID  30000\n",
      "len(Topics)  409\n",
      "alpha  0.9 Topics  409  iter  8   0.8661819671243521\n",
      "iter:  9  ,docID  0\n",
      "len(Topics)  409\n",
      "iter:  9  ,docID  1000\n",
      "len(Topics)  412\n",
      "iter:  9  ,docID  2000\n",
      "len(Topics)  411\n",
      "iter:  9  ,docID  3000\n",
      "len(Topics)  414\n",
      "iter:  9  ,docID  4000\n",
      "len(Topics)  418\n",
      "iter:  9  ,docID  5000\n",
      "len(Topics)  414\n",
      "iter:  9  ,docID  6000\n",
      "len(Topics)  410\n",
      "iter:  9  ,docID  7000\n",
      "len(Topics)  410\n",
      "iter:  9  ,docID  8000\n",
      "len(Topics)  411\n",
      "iter:  9  ,docID  9000\n",
      "len(Topics)  410\n",
      "iter:  9  ,docID  10000\n",
      "len(Topics)  408\n",
      "iter:  9  ,docID  11000\n",
      "len(Topics)  408\n",
      "iter:  9  ,docID  12000\n",
      "len(Topics)  406\n",
      "iter:  9  ,docID  13000\n",
      "len(Topics)  406\n",
      "iter:  9  ,docID  14000\n",
      "len(Topics)  405\n",
      "iter:  9  ,docID  15000\n",
      "len(Topics)  404\n",
      "iter:  9  ,docID  16000\n",
      "len(Topics)  406\n",
      "iter:  9  ,docID  17000\n",
      "len(Topics)  406\n",
      "iter:  9  ,docID  18000\n",
      "len(Topics)  405\n",
      "iter:  9  ,docID  19000\n",
      "len(Topics)  406\n",
      "iter:  9  ,docID  20000\n",
      "len(Topics)  407\n",
      "iter:  9  ,docID  21000\n",
      "len(Topics)  404\n",
      "iter:  9  ,docID  22000\n",
      "len(Topics)  401\n",
      "iter:  9  ,docID  23000\n",
      "len(Topics)  401\n",
      "iter:  9  ,docID  24000\n",
      "len(Topics)  400\n",
      "iter:  9  ,docID  25000\n",
      "len(Topics)  400\n",
      "iter:  9  ,docID  26000\n",
      "len(Topics)  401\n",
      "iter:  9  ,docID  27000\n",
      "len(Topics)  399\n",
      "iter:  9  ,docID  28000\n",
      "len(Topics)  397\n",
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      "len(Topics)  394\n",
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      "alpha  1.0 Topics  347  iter  1   0.8442618261984854\n",
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      "alpha  1.0 Topics  355  iter  2   0.8537297916179764\n",
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      "alpha  1.0 Topics  383  iter  3   0.8563090346087737\n",
      "iter:  4  ,docID  0\n",
      "len(Topics)  383\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter:  4  ,docID  1000\n",
      "len(Topics)  383\n",
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      "alpha  1.0 Topics  381  iter  4   0.858915167047042\n",
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      "alpha  1.0 Topics  399  iter  5   0.8605771605767868\n",
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      "alpha  1.0 Topics  403  iter  6   0.8620725629103934\n",
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      "iter:  7  ,docID  29000\n",
      "len(Topics)  404\n",
      "iter:  7  ,docID  30000\n",
      "len(Topics)  403\n",
      "alpha  1.0 Topics  403  iter  7   0.8634088728138789\n",
      "iter:  8  ,docID  0\n",
      "len(Topics)  403\n",
      "iter:  8  ,docID  1000\n",
      "len(Topics)  404\n",
      "iter:  8  ,docID  2000\n",
      "len(Topics)  404\n",
      "iter:  8  ,docID  3000\n",
      "len(Topics)  403\n",
      "iter:  8  ,docID  4000\n",
      "len(Topics)  402\n",
      "iter:  8  ,docID  5000\n",
      "len(Topics)  401\n",
      "iter:  8  ,docID  6000\n",
      "len(Topics)  400\n",
      "iter:  8  ,docID  7000\n",
      "len(Topics)  400\n",
      "iter:  8  ,docID  8000\n",
      "len(Topics)  399\n",
      "iter:  8  ,docID  9000\n",
      "len(Topics)  399\n",
      "iter:  8  ,docID  10000\n",
      "len(Topics)  400\n",
      "iter:  8  ,docID  11000\n",
      "len(Topics)  401\n",
      "iter:  8  ,docID  12000\n",
      "len(Topics)  399\n",
      "iter:  8  ,docID  13000\n",
      "len(Topics)  399\n",
      "iter:  8  ,docID  14000\n",
      "len(Topics)  398\n",
      "iter:  8  ,docID  15000\n",
      "len(Topics)  398\n",
      "iter:  8  ,docID  16000\n",
      "len(Topics)  400\n",
      "iter:  8  ,docID  17000\n",
      "len(Topics)  401\n",
      "iter:  8  ,docID  18000\n",
      "len(Topics)  401\n",
      "iter:  8  ,docID  19000\n",
      "len(Topics)  401\n",
      "iter:  8  ,docID  20000\n",
      "len(Topics)  400\n",
      "iter:  8  ,docID  21000\n",
      "len(Topics)  400\n",
      "iter:  8  ,docID  22000\n",
      "len(Topics)  399\n",
      "iter:  8  ,docID  23000\n",
      "len(Topics)  401\n",
      "iter:  8  ,docID  24000\n",
      "len(Topics)  403\n",
      "iter:  8  ,docID  25000\n",
      "len(Topics)  405\n",
      "iter:  8  ,docID  26000\n",
      "len(Topics)  408\n",
      "iter:  8  ,docID  27000\n",
      "len(Topics)  409\n",
      "iter:  8  ,docID  28000\n",
      "len(Topics)  409\n",
      "iter:  8  ,docID  29000\n",
      "len(Topics)  411\n",
      "iter:  8  ,docID  30000\n",
      "len(Topics)  412\n",
      "alpha  1.0 Topics  412  iter  8   0.8641283646042093\n",
      "iter:  9  ,docID  0\n",
      "len(Topics)  412\n",
      "iter:  9  ,docID  1000\n",
      "len(Topics)  416\n",
      "iter:  9  ,docID  2000\n",
      "len(Topics)  413\n",
      "iter:  9  ,docID  3000\n",
      "len(Topics)  415\n",
      "iter:  9  ,docID  4000\n",
      "len(Topics)  416\n",
      "iter:  9  ,docID  5000\n",
      "len(Topics)  419\n",
      "iter:  9  ,docID  6000\n",
      "len(Topics)  420\n",
      "iter:  9  ,docID  7000\n",
      "len(Topics)  417\n",
      "iter:  9  ,docID  8000\n",
      "len(Topics)  415\n",
      "iter:  9  ,docID  9000\n",
      "len(Topics)  414\n",
      "iter:  9  ,docID  10000\n",
      "len(Topics)  413\n",
      "iter:  9  ,docID  11000\n",
      "len(Topics)  411\n",
      "iter:  9  ,docID  12000\n",
      "len(Topics)  411\n",
      "iter:  9  ,docID  13000\n",
      "len(Topics)  411\n",
      "iter:  9  ,docID  14000\n",
      "len(Topics)  411\n",
      "iter:  9  ,docID  15000\n",
      "len(Topics)  411\n",
      "iter:  9  ,docID  16000\n",
      "len(Topics)  410\n",
      "iter:  9  ,docID  17000\n",
      "len(Topics)  406\n",
      "iter:  9  ,docID  18000\n",
      "len(Topics)  406\n",
      "iter:  9  ,docID  19000\n",
      "len(Topics)  405\n",
      "iter:  9  ,docID  20000\n",
      "len(Topics)  407\n",
      "iter:  9  ,docID  21000\n",
      "len(Topics)  406\n",
      "iter:  9  ,docID  22000\n",
      "len(Topics)  404\n",
      "iter:  9  ,docID  23000\n",
      "len(Topics)  403\n",
      "iter:  9  ,docID  24000\n",
      "len(Topics)  403\n",
      "iter:  9  ,docID  25000\n",
      "len(Topics)  399\n",
      "iter:  9  ,docID  26000\n",
      "len(Topics)  396\n",
      "iter:  9  ,docID  27000\n",
      "len(Topics)  394\n",
      "iter:  9  ,docID  28000\n",
      "len(Topics)  395\n",
      "iter:  9  ,docID  29000\n",
      "len(Topics)  393\n",
      "iter:  9  ,docID  30000\n",
      "len(Topics)  393\n",
      "alpha  1.0 Topics  393  iter  9   0.8686112631279349\n"
     ]
    }
   ],
   "source": [
    "alpha_NMI = {}\n",
    "alpha_topics = {}\n",
    "for alpha in np.arange(0.1,1.1,0.1):\n",
    "    alpha = np.around(alpha,decimals=1)\n",
    "    docID_assign_z = {}\n",
    "    m_z = {}\n",
    "    n_z = {}\n",
    "    n_w = {}\n",
    "    n_b = {}\n",
    "    Topics = []\n",
    "    V = set()\n",
    "    D = set()\n",
    "    #alpha = 1\n",
    "    beta = 0.02\n",
    "    total_iter = 10\n",
    "    for iter in range(total_iter):\n",
    "        for docID in range(len(corpus)):\n",
    "            if docID % 1000 == 0:\n",
    "                print(\"iter: \",iter,\" ,docID \",docID)\n",
    "                print(\"len(Topics) \",len(Topics))\n",
    "            words = corpus[docID]\n",
    "            D.discard(docID)\n",
    "            if docID in docID_assign_z.keys():\n",
    "                before_k = docID_assign_z[docID]\n",
    "                m_z[before_k].discard(docID)\n",
    "                for biterm in words:\n",
    "                    for word in biterm.split(','):\n",
    "                        n_z[before_k][word] -= 1\n",
    "                        n_w[before_k] -=1\n",
    "            else:\n",
    "                before_k = -1\n",
    "            if len(D) == 0 and len(V) == 0:\n",
    "                choose_k = 0\n",
    "                D.add(docID)\n",
    "                docID_assign_z[docID] = choose_k\n",
    "                if choose_k not in m_z.keys():\n",
    "                    m_z[choose_k] = set()\n",
    "                m_z[choose_k].add(docID)\n",
    "                for biterm in words:\n",
    "                    for word in biterm.split(','):\n",
    "                        if choose_k not in n_w.keys():\n",
    "                            n_w[choose_k] = 0\n",
    "                        if choose_k not in n_z.keys():\n",
    "                            n_z[choose_k] = {}\n",
    "                        if word not in n_z[choose_k].keys():\n",
    "                            n_z[choose_k][word] = 0\n",
    "                        n_z[choose_k][word] += 1\n",
    "                        n_w[choose_k] += 1\n",
    "                        V.add(word)\n",
    "                if choose_k not in Topics:\n",
    "                    Topics.append(choose_k)\n",
    "            else:\n",
    "                log_pro = []\n",
    "                for k in Topics:\n",
    "                    pro_k = len(m_z[k])\n",
    "                    if pro_k != 0:\n",
    "                        i = 0\n",
    "                        for biterm in words:\n",
    "                            each_word = biterm.strip().split(',')\n",
    "                            for word in each_word:\n",
    "                                if word not in n_z[k].keys():\n",
    "                                    n_z[k][word] = 0\n",
    "                            for j in range(doc_biterm[docID][biterm]):\n",
    "                                pro_k *= (n_z[k][each_word[0]] + n_z[k][each_word[-1]] + beta + j) / ( n_w[k] + len(V)*beta + i) \n",
    "                                i += 1\n",
    "\n",
    "                    if pro_k == 0:\n",
    "                        pro_k = sys.float_info.min\n",
    "                    log_pro.append(pro_k)\n",
    "\n",
    "                pro_new_k = alpha*(len(D))\n",
    "                i = 0\n",
    "                for biterm in words:\n",
    "                    for j in range(doc_biterm[docID][biterm]):\n",
    "                        pro_new_k *= ( beta + j) / ( len(V)*beta + i) \n",
    "                        i += 1\n",
    "\n",
    "                if pro_new_k == 0:\n",
    "                    pro_new_k = sys.float_info.min        \n",
    "                log_pro.append(pro_new_k)\n",
    "\n",
    "                sum_pro=sum(log_pro)\n",
    "\n",
    "                normalized_posterior = [i/sum_pro for i in log_pro]    \n",
    "                select_k = None\n",
    "                if iter == (total_iter - 1):\n",
    "                    select_k = normalized_posterior.index(max(normalized_posterior))\n",
    "\n",
    "                else:\n",
    "                    select_k = np.random.choice( (len(Topics)+1) , 1, p=normalized_posterior)[0]  \n",
    "\n",
    "                if select_k == len(Topics):\n",
    "                    choose_k = np.max(Topics) + 1\n",
    "                else:\n",
    "                    choose_k = Topics[select_k]\n",
    "\n",
    "                D.add(docID)\n",
    "                docID_assign_z[docID] = choose_k\n",
    "                if choose_k not in m_z.keys():\n",
    "                    m_z[choose_k] = set()\n",
    "                m_z[choose_k].add(docID)\n",
    "                for biterm in words:\n",
    "                    for word in biterm.split(','):\n",
    "                        if choose_k not in n_w.keys():\n",
    "                            n_w[choose_k] = 0\n",
    "                        if choose_k not in n_z.keys():\n",
    "                            n_z[choose_k] = {}\n",
    "                        if word not in n_z[choose_k].keys():\n",
    "                            n_z[choose_k][word] = 0\n",
    "                        n_z[choose_k][word] += 1\n",
    "                        n_w[choose_k] += 1\n",
    "                        V.add(word)\n",
    "                if choose_k not in Topics:\n",
    "                    Topics.append(choose_k)\n",
    "\n",
    "            count_k = []\n",
    "            for k in Topics:\n",
    "                if k in m_z.keys() and len(m_z[k]) == 0:\n",
    "                    m_z.pop(k, None)\n",
    "                    n_z.pop(k, None)\n",
    "                    n_w.pop(k, None)\n",
    "                    count_k.append(k)\n",
    "            for k in count_k:\n",
    "                Topics.remove(k)   \n",
    "\n",
    "        from sklearn.metrics.cluster import normalized_mutual_info_score\n",
    "        nmi_sample = []\n",
    "        nmi_result = []\n",
    "        for key, value in news_labels.items():\n",
    "                nmi_sample.append(value)\n",
    "                nmi_result.append(docID_assign_z[key])\n",
    "        print(\"alpha \", alpha, \"Topics \",len(Topics), \" iter \",iter,\" \",normalized_mutual_info_score(np.array(nmi_sample), np.array(nmi_result)))\n",
    "        alpha_NMI[alpha] = normalized_mutual_info_score(np.array(nmi_sample), np.array(nmi_result))\n",
    "        alpha_topics[alpha] = len(Topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values([0.8667865634503141, 0.8656853471524877, 0.8665974423500961, 0.8709520495334826, 0.8640038818629493, 0.8655290262237872, 0.870667958105711, 0.8614436874163404, 0.8694058094984684, 0.8686112631279349])\n",
      "dict_keys([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])\n"
     ]
    }
   ],
   "source": [
    "print(alpha_NMI.values())\n",
    "print(alpha_NMI.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values([266, 281, 321, 341, 337, 336, 374, 374, 395, 393])\n",
      "dict_keys([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])\n"
     ]
    }
   ],
   "source": [
    "print(alpha_topics.values())\n",
    "print(alpha_topics.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values([0.8630841853405375, 0.8590541089822986, 0.8687674321235832, 0.8644798941318023, 0.861674271985366, 0.867613577256499, 0.8694736783205159, 0.8718119277938999, 0.8706175047674019, 0.8701634637462661])\n",
      "dict_keys([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])\n"
     ]
    }
   ],
   "source": [
    "print(alpha_NMI.values())\n",
    "print(alpha_NMI.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values([231, 268, 277, 303, 330, 302, 334, 364, 381, 363])\n",
      "dict_keys([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])\n"
     ]
    }
   ],
   "source": [
    "print(alpha_topics.values())\n",
    "print(alpha_topics.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start  0  end  1895 topics:  125 truth:  89  NMI:  0.8410839966127501\n",
      "[226.21218378253968]\n",
      "start  0  end  3790 topics:  150 truth:  98  NMI:  0.834194791775418\n",
      "[226.21218378253968, 578.0604549883734]\n",
      "start  0  end  5685 topics:  166 truth:  104  NMI:  0.8357588712834243\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396]\n",
      "start  0  end  7580 topics:  178 truth:  108  NMI:  0.8317386819103495\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453]\n",
      "start  0  end  9475 topics:  202 truth:  175  NMI:  0.8343894508861612\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453, 1405.792722277292]\n",
      "start  0  end  11370 topics:  227 truth:  194  NMI:  0.847137619069315\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453, 1405.792722277292, 1749.0887730012873]\n",
      "start  0  end  13265 topics:  225 truth:  202  NMI:  0.8591345644310551\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453, 1405.792722277292, 1749.0887730012873, 1678.5135972794324]\n",
      "start  0  end  15160 topics:  240 truth:  211  NMI:  0.8567648808179479\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453, 1405.792722277292, 1749.0887730012873, 1678.5135972794324, 1998.0087691913395]\n",
      "start  0  end  17055 topics:  245 truth:  216  NMI:  0.8616216004468453\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453, 1405.792722277292, 1749.0887730012873, 1678.5135972794324, 1998.0087691913395, 2358.7405467155877]\n",
      "start  0  end  18950 topics:  263 truth:  217  NMI:  0.8741302729956864\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453, 1405.792722277292, 1749.0887730012873, 1678.5135972794324, 1998.0087691913395, 2358.7405467155877, 2723.069167090578]\n",
      "start  0  end  20845 topics:  266 truth:  219  NMI:  0.8729821034743214\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453, 1405.792722277292, 1749.0887730012873, 1678.5135972794324, 1998.0087691913395, 2358.7405467155877, 2723.069167090578, 3056.4731802763654]\n",
      "start  0  end  22740 topics:  279 truth:  222  NMI:  0.8701692773410632\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453, 1405.792722277292, 1749.0887730012873, 1678.5135972794324, 1998.0087691913395, 2358.7405467155877, 2723.069167090578, 3056.4731802763654, 3567.895796106459]\n",
      "start  0  end  24635 topics:  289 truth:  262  NMI:  0.8710001030734816\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453, 1405.792722277292, 1749.0887730012873, 1678.5135972794324, 1998.0087691913395, 2358.7405467155877, 2723.069167090578, 3056.4731802763654, 3567.895796106459, 3901.5783781217615]\n",
      "start  0  end  26530 topics:  295 truth:  269  NMI:  0.8626024689585353\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453, 1405.792722277292, 1749.0887730012873, 1678.5135972794324, 1998.0087691913395, 2358.7405467155877, 2723.069167090578, 3056.4731802763654, 3567.895796106459, 3901.5783781217615, 4212.452981540078]\n",
      "start  0  end  28425 topics:  304 truth:  269  NMI:  0.8678857295916514\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453, 1405.792722277292, 1749.0887730012873, 1678.5135972794324, 1998.0087691913395, 2358.7405467155877, 2723.069167090578, 3056.4731802763654, 3567.895796106459, 3901.5783781217615, 4212.452981540078, 4675.964403122598]\n",
      "start  0  end  30322 topics:  344 truth:  269  NMI:  0.8627665917368702\n",
      "[226.21218378253968, 578.0604549883734, 859.6052154228396, 1087.1243275125453, 1405.792722277292, 1749.0887730012873, 1678.5135972794324, 1998.0087691913395, 2358.7405467155877, 2723.069167090578, 3056.4731802763654, 3567.895796106459, 3901.5783781217615, 4212.452981540078, 4675.964403122598, 5580.871709900668]\n"
     ]
    }
   ],
   "source": [
    "import timeit\n",
    "total_batch = 16\n",
    "NMI_batch = {}\n",
    "start = 0\n",
    "end = 0\n",
    "topic_batch = {}\n",
    "time_iteration_list = []\n",
    "for batch_i in range(1,17):\n",
    "    if batch_i == 16:\n",
    "        end = int(len(corpus))\n",
    "    else:\n",
    "        end = int(len(corpus) / total_batch) * batch_i\n",
    "    \n",
    "    docID_assign_z = {}\n",
    "    m_z = {}\n",
    "    n_z = {}\n",
    "    n_w = {}\n",
    "    n_b = {}\n",
    "    Topics = []\n",
    "    V = set()\n",
    "    D = set()\n",
    "    alpha = 0.3\n",
    "    beta = 0.02\n",
    "    NMI_batch[batch_i] = 0\n",
    "    topic_batch[batch_i] = 0\n",
    "    \n",
    "    time_start = timeit.default_timer()\n",
    "    total_iter = 10\n",
    "    for iter in range(total_iter):\n",
    "        for docID in range(start,end):\n",
    "            words = corpus[docID]\n",
    "            D.discard(docID)\n",
    "            if docID in docID_assign_z.keys():\n",
    "                before_k = docID_assign_z[docID]\n",
    "                m_z[before_k].discard(docID)\n",
    "                for biterm in words:\n",
    "                    for word in biterm.split(','):\n",
    "                        n_z[before_k][word] -= 1\n",
    "                        n_w[before_k] -=1\n",
    "            else:\n",
    "                before_k = -1\n",
    "            if len(D) == 0 and len(V) == 0:\n",
    "                choose_k = 0\n",
    "                D.add(docID)\n",
    "                docID_assign_z[docID] = choose_k\n",
    "                if choose_k not in m_z.keys():\n",
    "                    m_z[choose_k] = set()\n",
    "                m_z[choose_k].add(docID)\n",
    "                for biterm in words:\n",
    "                    for word in biterm.split(','):\n",
    "                        if choose_k not in n_w.keys():\n",
    "                            n_w[choose_k] = 0\n",
    "                        if choose_k not in n_z.keys():\n",
    "                            n_z[choose_k] = {}\n",
    "                        if word not in n_z[choose_k].keys():\n",
    "                            n_z[choose_k][word] = 0\n",
    "                        n_z[choose_k][word] += 1\n",
    "                        n_w[choose_k] += 1\n",
    "                        V.add(word)\n",
    "                if choose_k not in Topics:\n",
    "                    Topics.append(choose_k)\n",
    "            else:\n",
    "                log_pro = []\n",
    "                for k in Topics:\n",
    "                    pro_k = len(m_z[k])\n",
    "                    if pro_k != 0:\n",
    "                        i = 0\n",
    "                        for biterm in words:\n",
    "                            each_word = biterm.strip().split(',')\n",
    "                            for word in each_word:\n",
    "                                if word not in n_z[k].keys():\n",
    "                                    n_z[k][word] = 0\n",
    "                            for j in range(doc_biterm[docID][biterm]):\n",
    "                                pro_k *= (n_z[k][each_word[0]] + n_z[k][each_word[-1]] + beta + j) / ( n_w[k] + len(V)*beta + i) \n",
    "                                i += 1\n",
    "\n",
    "                    if pro_k == 0:\n",
    "                        pro_k = sys.float_info.min\n",
    "                    log_pro.append(pro_k)\n",
    "\n",
    "                pro_new_k = alpha*(len(D))\n",
    "                i = 0\n",
    "                for biterm in words:\n",
    "                    for j in range(doc_biterm[docID][biterm]):\n",
    "                        pro_new_k *= ( beta + j) / ( len(V)*beta + i) \n",
    "                        i += 1\n",
    "\n",
    "                if pro_new_k == 0:\n",
    "                    pro_new_k = sys.float_info.min        \n",
    "                log_pro.append(pro_new_k)\n",
    "\n",
    "                sum_pro=sum(log_pro)\n",
    "\n",
    "                normalized_posterior = [i/sum_pro for i in log_pro]    \n",
    "                select_k = None\n",
    "                if iter == (total_iter - 1):\n",
    "                    select_k = normalized_posterior.index(max(normalized_posterior))\n",
    "\n",
    "                else:\n",
    "                    select_k = np.random.choice( (len(Topics)+1) , 1, p=normalized_posterior)[0]  \n",
    "\n",
    "                if select_k == len(Topics):\n",
    "                    choose_k = np.max(Topics) + 1\n",
    "                else:\n",
    "                    choose_k = Topics[select_k]\n",
    "\n",
    "                D.add(docID)\n",
    "                docID_assign_z[docID] = choose_k\n",
    "                if choose_k not in m_z.keys():\n",
    "                    m_z[choose_k] = set()\n",
    "                m_z[choose_k].add(docID)\n",
    "                for biterm in words:\n",
    "                    for word in biterm.split(','):\n",
    "                        if choose_k not in n_w.keys():\n",
    "                            n_w[choose_k] = 0\n",
    "                        if choose_k not in n_z.keys():\n",
    "                            n_z[choose_k] = {}\n",
    "                        if word not in n_z[choose_k].keys():\n",
    "                            n_z[choose_k][word] = 0\n",
    "                        n_z[choose_k][word] += 1\n",
    "                        n_w[choose_k] += 1\n",
    "                        V.add(word)\n",
    "                if choose_k not in Topics:\n",
    "                    Topics.append(choose_k)\n",
    "\n",
    "            count_k = []\n",
    "            for k in Topics:\n",
    "                if k in m_z.keys() and len(m_z[k]) == 0:\n",
    "                    m_z.pop(k, None)\n",
    "                    n_z.pop(k, None)\n",
    "                    n_w.pop(k, None)\n",
    "                    count_k.append(k)\n",
    "            for k in count_k:\n",
    "                Topics.remove(k)   \n",
    "        if iter == 9:\n",
    "            from sklearn.metrics.cluster import normalized_mutual_info_score\n",
    "            nmi_sample = []\n",
    "            nmi_result = []\n",
    "            for key, value in news_labels.items():\n",
    "                if key < end and key >= start:\n",
    "                    nmi_sample.append(value)\n",
    "                    nmi_result.append(docID_assign_z[key])\n",
    "            NMI_batch[batch_i] = normalized_mutual_info_score(np.array(nmi_sample), np.array(nmi_result))\n",
    "            topic_batch[batch_i] = len(Topics)\n",
    "            print(\"start \",start, \" end \",end, \"topics: \",len(Topics),\"truth: \",len(np.unique(nmi_sample)), \" NMI: \",normalized_mutual_info_score(np.array(nmi_sample), np.array(nmi_result)))\n",
    "#     if batch_i != 1:\n",
    "#         start = int(len(corpus) / total_batch) * (batch_i -1) + 1\n",
    "    time_stop = timeit.default_timer()\n",
    "    time_iteration_list.append(time_stop-time_start)\n",
    "    print(time_iteration_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])\n",
      "dict_values([125, 150, 166, 178, 202, 227, 225, 240, 245, 263, 266, 279, 289, 295, 304, 344])\n"
     ]
    }
   ],
   "source": [
    "print(topic_batch.keys())\n",
    "print(topic_batch.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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