IR Project-d-try.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.corpus import wordnet\n",
    "import os\n",
    "import re\n",
    "import math\n",
    "from scipy import spatial\n",
    "import numpy as np\n",
    "import pprint\n",
    "import sklearn.cluster\n",
    "from sklearn.cluster import spectral_clustering\n",
    "import pickle\n",
    "\n",
    "\n",
    "def SpecialCharacters(word):\n",
    "\tif len(word)<3:\n",
    "\t\treturn 1\n",
    "\treturn 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'about', 'above', 'across', 'after', 'again', 'against', 'all', 'almost', 'alone', 'along', 'already', 'also', 'although', 'always', 'among', 'an', 'and', 'another', 'any', 'anybody', 'anyone', 'anything', 'anywhere', 'are', 'area', 'areas', 'around', 'as', 'ask', 'asked', 'asking', 'asks', 'at', 'away', 'b', 'back', 'backed', 'backing', 'backs', 'be', 'became', 'because', 'become', 'becomes', 'been', 'before', 'began', 'behind', 'being', 'beings', 'best', 'better', 'between', 'big', 'both', 'but', 'by', 'c', 'came', 'can', 'cannot', 'case', 'cases', 'certain', 'certainly', 'clear', 'clearly', 'come', 'could', 'd', 'did', 'differ', 'different', 'differently', 'do', 'does', 'done', 'down', 'down', 'downed', 'downing', 'downs', 'during', 'e', 'each', 'early', 'either', 'end', 'ended', 'ending', 'ends', 'enough', 'even', 'evenly', 'ever', 'every', 'everybody', 'everyone', 'everything', 'everywhere', 'f', 'face', 'faces', 'fact', 'facts', 'far', 'felt', 'few', 'find', 'finds', 'first', 'for', 'four', 'from', 'full', 'fully', 'further', 'furthered', 'furthering', 'furthers', 'g', 'gave', 'general', 'generally', 'get', 'gets', 'give', 'given', 'gives', 'go', 'going', 'good', 'goods', 'got', 'great', 'greater', 'greatest', 'group', 'grouped', 'grouping', 'groups', 'h', 'had', 'has', 'have', 'having', 'he', 'her', 'here', 'herself', 'high', 'high', 'high', 'higher', 'highest', 'him', 'himself', 'his', 'how', 'however', 'i', 'if', 'important', 'in', 'interest', 'interested', 'interesting', 'interests', 'into', 'is', 'it', 'its', 'itself', 'j', 'just', 'k', 'keep', 'keeps', 'kind', 'knew', 'know', 'known', 'knows', 'l', 'large', 'largely', 'last', 'later', 'latest', 'least', 'less', 'let', 'lets', 'like', 'likely', 'long', 'longer', 'longest', 'm', 'made', 'make', 'making', 'man', 'many', 'may', 'me', 'member', 'members', 'men', 'might', 'more', 'most', 'mostly', 'mr', 'mrs', 'much', 'must', 'my', 'myself', 'n', 'necessary', 'need', 'needed', 'needing', 'needs', 'never', 'new', 'new', 'newer', 'newest', 'next', 'no', 'nobody', 'non', 'noone', 'not', 'nothing', 'now', 'nowhere', 'number', 'numbers', 'o', 'of', 'off', 'often', 'old', 'older', 'oldest', 'on', 'once', 'one', 'only', 'open', 'opened', 'opening', 'opens', 'or', 'order', 'ordered', 'ordering', 'orders', 'other', 'others', 'our', 'out', 'over', 'p', 'part', 'parted', 'parting', 'parts', 'per', 'perhaps', 'place', 'places', 'point', 'pointed', 'pointing', 'points', 'possible', 'present', 'presented', 'presenting', 'presents', 'problem', 'problems', 'put', 'puts', 'q', 'quite', 'r', 'rather', 'really', 'right', 'right', 'room', 'rooms', 's', 'said', 'same', 'saw', 'say', 'says', 'second', 'seconds', 'see', 'seem', 'seemed', 'seeming', 'seems', 'sees', 'several', 'shall', 'she', 'should', 'show', 'showed', 'showing', 'shows', 'side', 'sides', 'since', 'small', 'smaller', 'smallest', 'so', 'some', 'somebody', 'someone', 'something', 'somewhere', 'state', 'states', 'still', 'still', 'such', 'sure', 't', 'take', 'taken', 'than', 'that', 'the', 'their', 'them', 'then', 'there', 'therefore', 'these', 'they', 'thing', 'things', 'think', 'thinks', 'this', 'those', 'though', 'thought', 'thoughts', 'three', 'through', 'thus', 'to', 'today', 'together', 'too', 'took', 'toward', 'turn', 'turned', 'turning', 'turns', 'two', 'u', 'under', 'until', 'up', 'upon', 'us', 'use', 'used', 'uses', 'v', 'very', 'w', 'want', 'wanted', 'wanting', 'wants', 'was', 'way', 'ways', 'we', 'well', 'wells', 'went', 'were', 'what', 'when', 'where', 'whether', 'which', 'while', 'who', 'whole', 'whose', 'why', 'will', 'with', 'within', 'without', 'work', 'worked', 'working', 'works', 'would', 'x', 'y', 'year', 'years', 'yet', 'you', 'young', 'younger', 'youngest', 'your', 'yours', 'z', ';', ',', \"'\", '-', '(', ')', ':', '/', '.', '?']\n"
     ]
    }
   ],
   "source": [
    "inp_stop=open('data/stopwords.txt','r+') #read stopword from file\n",
    "stopwords=eval(inp_stop.read())\n",
    "print stopwords\n",
    "id=-1\n",
    "i=0\n",
    "directory=\"d\"\n",
    "II={}\n",
    "Document_TF_Count={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "book pencil bus table\n",
      "signal road pen traffic\n",
      "fish boat river sail\n",
      "pencil pen \n"
     ]
    }
   ],
   "source": [
    "Documents=os.listdir(directory)\n",
    "for Document in Documents:\n",
    "# \tif i<4:\n",
    "\ti=i+1\n",
    "# \telse:\n",
    "# \t\tbreak\n",
    "\tf=open(directory+\"/\"+Document,'r');\n",
    "\tc=f.read()\n",
    "# \tc=f.read().split('\\n')\n",
    "# \tprint c\n",
    "\tf.close();\n",
    "\t# id=c[4].replace('DOC ID:','')\n",
    "\tid=id+1\n",
    "\tcontent=c\n",
    "# \tcontent=c[5].replace('CONTENT:','')\n",
    "\tprint content\n",
    "\twords=word_tokenize(str(content))\n",
    "\tlower_words=[x.lower() for x in words if not SpecialCharacters(x)]\n",
    "\tupper_words=[x for x in words if x.upper()==x and not SpecialCharacters(x)]\n",
    "\twords=[]\n",
    "\twords=lower_words+upper_words\n",
    "\tWordCount1={}\n",
    "\t#\t\n",
    "\tfor w in words:\n",
    "\t\t# if word in stopwords:\n",
    "\t\t#Term Frequency \n",
    "\t\tif w in WordCount1:\n",
    "\t\t\tWordCount1[w]=WordCount1[w]+1\n",
    "\t\telse:\n",
    "\t\t\tWordCount1[w]=1\n",
    "\t\t#Inverted Index\t\n",
    "\t\tif w in II:\n",
    "\t\t\tif id not in II[w]:\n",
    "\t\t\t\tII[w].append(id)\n",
    "\t\telse:\n",
    "\t\t\tII[w]=[]\n",
    "\t\t\tII[w].append(id)\n",
    "\tDocument_TF_Count[id]=WordCount1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: {'pencil': 1, 'bus': 1, 'signal': 0, 'sail': 0, 'pen': 0, 'book': 1, 'traffic': 0, 'road': 0, 'table': 1, 'river': 0, 'fish': 0, 'boat': 0}, 1: {'pencil': 0, 'bus': 0, 'signal': 1, 'sail': 0, 'pen': 1, 'book': 0, 'traffic': 1, 'boat': 0, 'table': 0, 'river': 0, 'fish': 0, 'road': 1}, 2: {'pencil': 0, 'bus': 0, 'fish': 1, 'sail': 1, 'pen': 0, 'book': 0, 'traffic': 0, 'road': 0, 'table': 0, 'river': 1, 'signal': 0, 'boat': 1}, 3: {'pencil': 1, 'bus': 0, 'signal': 0, 'sail': 0, 'pen': 1, 'book': 0, 'traffic': 0, 'road': 0, 'table': 0, 'river': 0, 'fish': 0, 'boat': 0}}\n"
     ]
    }
   ],
   "source": [
    "#Assign Term Frequency as Zero\n",
    "vocab=II.keys()\n",
    "for v in vocab:\n",
    "\tfor key in Document_TF_Count:\n",
    "\t\tif v not in Document_TF_Count[key]:\n",
    "\t\t\tDocument_TF_Count[key][v]=0\n",
    "print Document_TF_Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: {'pencil': 1, 'bus': 1, 'signal': 0, 'sail': 0, 'pen': 0, 'book': 1, 'traffic': 0, 'road': 0, 'table': 1, 'river': 0, 'fish': 0, 'boat': 0}, 1: {'pencil': 0, 'bus': 0, 'signal': 1, 'sail': 0, 'pen': 1, 'book': 0, 'traffic': 1, 'boat': 0, 'table': 0, 'river': 0, 'fish': 0, 'road': 1}, 2: {'pencil': 0, 'bus': 0, 'fish': 1, 'sail': 1, 'pen': 0, 'book': 0, 'traffic': 0, 'road': 0, 'table': 0, 'river': 1, 'signal': 0, 'boat': 1}, 3: {'pencil': 1, 'bus': 0, 'signal': 0, 'sail': 0, 'pen': 1, 'book': 0, 'traffic': 0, 'road': 0, 'table': 0, 'river': 0, 'fish': 0, 'boat': 0}}\n",
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "##Stemming#\n",
    "stemmer=PorterStemmer()\n",
    "l=len(vocab)\n",
    "for key in Document_TF_Count:\n",
    "\tfor t1 in range(l):\n",
    "\t\tfor t2 in range(t1+1,l):\n",
    "\t\t\tv1=vocab[t1]\n",
    "\t\t\tv2=vocab[t2]\n",
    "\t\t\tstem1=stemmer.stem(v1)\n",
    "\t\t\tstem2=stemmer.stem(v2)\n",
    "\n",
    "\t\t\ttc1=Document_TF_Count[key][v1]\n",
    "\t\t\ttc2=Document_TF_Count[key][v2]\t\n",
    "\t\t\tif tc1>0 and tc2>0 and v1!=v2 and stem1==stem2 and v1.upper()!=v2 and v2.upper()!=v1:\n",
    "\t\t\t#terms should exists in the same document not be equal, \n",
    "\t\t\t#even upper or lower case should not be equal but stem should be\t\t\t\n",
    "\t\t\t\t# print stem1\n",
    "\t\t\t\t# print stem2\n",
    "\t\t\t\tDocument_TF_Count[key][v1]=tc1+1\n",
    "\t\t\t\tDocument_TF_Count[key][v2]=tc1+1\n",
    "print Document_TF_Count\n",
    "#Calculate TF-IDF\n",
    "print Document_TF_Count.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Document_TF_Count', 'wb') as fp:\n",
    "    pickle.dump(Document_TF_Count, fp)\n",
    "\n",
    "with open ('Document_TF_Count', 'rb') as fp:\n",
    "    Document_TF_Count = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Term Vector without sumation similarity\n",
      "{'boat': [0.0, 0.0, 0.6020599913279623, 0.0],\n",
      " 'book': [0.6020599913279623, 0.0, 0.0, 0.0],\n",
      " 'bus': [0.6020599913279623, 0.0, 0.0, 0.0],\n",
      " 'fish': [0.0, 0.0, 0.6020599913279623, 0.0],\n",
      " 'pen': [0.0, 0.30102999566398114, 0.0, 0.30102999566398114],\n",
      " 'pencil': [0.30102999566398114, 0.0, 0.0, 0.30102999566398114],\n",
      " 'river': [0.0, 0.0, 0.6020599913279623, 0.0],\n",
      " 'road': [0.0, 0.6020599913279623, 0.0, 0.0],\n",
      " 'sail': [0.0, 0.0, 0.6020599913279623, 0.0],\n",
      " 'signal': [0.0, 0.6020599913279623, 0.0, 0.0],\n",
      " 'table': [0.6020599913279623, 0.0, 0.0, 0.0],\n",
      " 'traffic': [0.0, 0.6020599913279623, 0.0, 0.0]}\n"
     ]
    }
   ],
   "source": [
    "term_vector={}\n",
    "for v in vocab:\n",
    "\tidf=math.log((float(id+1)/len(II[v])),10)\n",
    "\tarray=[]\n",
    "\tfor key in Document_TF_Count:\n",
    "\t\tk=int(key)\n",
    "\t\tarray+=[Document_TF_Count[key][v]*idf]\n",
    "\tterm_vector[v]=array\n",
    "print \"Term Vector without sumation similarity\"\n",
    "pprint.pprint(term_vector)\n",
    "#~ print \"++++++++++++++++++\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('termvector', 'wb') as fp:\n",
    "    pickle.dump(term_vector, fp)\n",
    "\n",
    "with open ('termvector', 'rb') as fp:\n",
    "    term_vector = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update TF-IDF using TMI\n",
    "def sim_f(word1, word2):\n",
    "\tif word1==word2:\n",
    "\t\treturn 0\n",
    "\tmax_value=0.0\n",
    "# \twordFromList1 = wordnet.synsets(word1)\n",
    "# \twordFromList2 = wordnet.synsets(word2)\n",
    "# \tfor wl1 in wordFromList1:\n",
    "# \t\tif wl1 in wordFromList2:\n",
    "# \t\t\treturn 1\n",
    "\treturn 0\t\n",
    "# \t\tfor wl2 in wordFromList2:\n",
    "# \t\t\tp=wl1.wup_similarity(wl2)\n",
    "# \t\t\tif max_value < p: \n",
    "# \t\t\t\tmax_value = p\n",
    "# \treturn max_value\n",
    "\n",
    "def sim_f1(word1, word2):\n",
    "\tif word1==word2:\n",
    "\t\treturn 0\n",
    "\tmax_value=0.0\n",
    "\twordFromList1 = wordnet.synsets(word1)\n",
    "\twordFromList2 = wordnet.synsets(word2)\n",
    "\tfor wl1 in wordFromList1:\n",
    "\t\tif wl1 in wordFromList2:\n",
    "\t\t\treturn 1\n",
    "\treturn 0\t\n",
    "# \t\tfor wl2 in wordFromList2:\n",
    "# \t\t\tp=wl1.wup_similarity(wl2)\n",
    "# \t\t\tif max_value < p: \n",
    "# \t\t\t\tmax_value = p\n",
    "# \treturn max_value\n",
    "\n",
    "def sim_f2(word1, word2):\n",
    "\tif word1==word2:\n",
    "\t\treturn 0\n",
    "\tmax_value=0.0\n",
    "\twordFromList1 = wordnet.synsets(word1)\n",
    "\twordFromList2 = wordnet.synsets(word2)\n",
    "\tfor wl1 in wordFromList1:\n",
    "# \t\tif wl1 in wordFromList2:\n",
    "# \t\t\treturn 1\n",
    "# \treturn 0\t\n",
    "\t\tfor wl2 in wordFromList2:\n",
    "\t\t\tp=wl1.wup_similarity(wl2)\n",
    "\t\t\tif max_value < p: \n",
    "\t\t\t\tmax_value = p\n",
    "\treturn max_value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Similirity {'pencil': {'pencil': 0, 'bus': 0.6666666666666666, 'signal': 0.4, 'sail': 0.625, 'pen': 0.8888888888888888, 'book': 0.5882352941176471, 'traffic': 0.36363636363636365, 'boat': 0.6666666666666666, 'table': 0.6666666666666666, 'river': 0.2857142857142857, 'fish': 0.47058823529411764, 'road': 0.625}, 'bus': {'pencil': 0.6666666666666666, 'bus': 0, 'signal': 0.5, 'sail': 0.6666666666666666, 'pen': 0.6666666666666666, 'book': 0.5882352941176471, 'traffic': 0.35294117647058826, 'boat': 0.75, 'table': 0.6666666666666666, 'river': 0.26666666666666666, 'fish': 0.47058823529411764, 'road': 0.625}, 'signal': {'pencil': 0.4, 'bus': 0.5, 'signal': 0, 'sail': 0.3157894736842105, 'pen': 0.2222222222222222, 'book': 0.6, 'traffic': 0.4444444444444444, 'boat': 0.2222222222222222, 'table': 0.4, 'river': 0.26666666666666666, 'fish': 0.2857142857142857, 'road': 0.4}, 'sail': {'pencil': 0.625, 'bus': 0.6666666666666666, 'signal': 0.4, 'sail': 0, 'pen': 0.75, 'book': 0.6666666666666666, 'traffic': 0.5714285714285714, 'boat': 0.8888888888888888, 'table': 0.625, 'river': 0.3076923076923077, 'fish': 0.5333333333333333, 'road': 0.7142857142857143}, 'pen': {'pencil': 0.8888888888888888, 'bus': 0.6666666666666666, 'signal': 0.3333333333333333, 'sail': 0.75, 'pen': 0, 'book': 0.5882352941176471, 'traffic': 0.2857142857142857, 'boat': 0.6666666666666666, 'table': 0.6666666666666666, 'river': 0.26666666666666666, 'fish': 0.75, 'road': 0.625}, 'book': {'pencil': 0.5882352941176471, 'bus': 0.5882352941176471, 'signal': 0.6, 'sail': 0.6666666666666666, 'pen': 0.5882352941176471, 'book': 0, 'traffic': 0.8, 'boat': 0.5882352941176471, 'table': 0.5882352941176471, 'river': 0.2857142857142857, 'fish': 0.5, 'road': 0.6666666666666666}, 'traffic': {'pencil': 0.36363636363636365, 'bus': 0.35294117647058826, 'signal': 0.4444444444444444, 'sail': 0.5714285714285714, 'pen': 0.2857142857142857, 'book': 0.8, 'traffic': 0, 'boat': 0.25, 'table': 0.5454545454545454, 'river': 0.2, 'fish': 0.3333333333333333, 'road': 0.6666666666666666}, 'boat': {'pencil': 0.6666666666666666, 'bus': 0.75, 'signal': 0.2857142857142857, 'sail': 0.8888888888888888, 'pen': 0.6666666666666666, 'book': 0.5882352941176471, 'traffic': 0.25, 'boat': 0, 'table': 0.6666666666666666, 'river': 0.26666666666666666, 'fish': 0.47058823529411764, 'road': 0.625}, 'table': {'pencil': 0.6666666666666666, 'bus': 0.6666666666666666, 'signal': 0.4, 'sail': 0.625, 'pen': 0.6666666666666666, 'book': 0.5882352941176471, 'traffic': 0.5454545454545454, 'boat': 0.6666666666666666, 'table': 0, 'river': 0.3076923076923077, 'fish': 0.47058823529411764, 'road': 0.625}, 'river': {'pencil': 0.2857142857142857, 'bus': 0.26666666666666666, 'signal': 0.26666666666666666, 'sail': 0.3076923076923077, 'pen': 0.26666666666666666, 'book': 0.2857142857142857, 'traffic': 0.18181818181818182, 'boat': 0.26666666666666666, 'table': 0.3076923076923077, 'river': 0, 'fish': 0.36363636363636365, 'road': 0.3076923076923077}, 'fish': {'pencil': 0.47058823529411764, 'bus': 0.47058823529411764, 'signal': 0.4, 'sail': 0.5333333333333333, 'pen': 0.75, 'book': 0.5, 'traffic': 0.3333333333333333, 'boat': 0.47058823529411764, 'table': 0.47058823529411764, 'river': 0.36363636363636365, 'fish': 0, 'road': 0.5333333333333333}, 'road': {'pencil': 0.625, 'bus': 0.625, 'signal': 0.4, 'sail': 0.7142857142857143, 'pen': 0.625, 'book': 0.6666666666666666, 'traffic': 0.6666666666666666, 'boat': 0.625, 'table': 0.625, 'river': 0.3076923076923077, 'fish': 0.5333333333333333, 'road': 0}}\n"
     ]
    }
   ],
   "source": [
    "sim={}\n",
    "for term1 in term_vector:\n",
    "\tsim[term1]={}\n",
    "\tfor term2 in term_vector:\n",
    "\t\tsim[term1][term2]=sim_f2(term1,term2)\n",
    "\n",
    "with open('sim', 'wb') as fp:\n",
    "    pickle.dump(sim, fp)\n",
    "\n",
    "with open ('sim', 'rb') as fp:\n",
    "    sim = pickle.load(fp)\n",
    "print \"Similirity\",sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Term Vector sumation similarity\n",
      "{'boat': [23.64902218741366,\n",
      "          21.565057521001062,\n",
      "          24.699713954069907,\n",
      "          6.578635640156678],\n",
      " 'book': [11.80809161241831,\n",
      "          10.757553501459538,\n",
      "          11.84753632921607,\n",
      "          3.1732560337709357],\n",
      " 'bus': [2.3295393128506774,\n",
      "         1.8262456592761653,\n",
      "         2.1187902085100117,\n",
      "         0.5797614731306303],\n",
      " 'fish': [64.97861267436362,\n",
      "          59.91019887371499,\n",
      "          67.42494576839523,\n",
      "          18.12288805364567],\n",
      " 'pen': [7.524243921822745,\n",
      "         6.831723960940127,\n",
      "         7.791120084259965,\n",
      "         2.288724553298774],\n",
      " 'pencil': [1.457929586843203,\n",
      "            1.1036246154165854,\n",
      "            1.2330003113715868,\n",
      "            0.5686122140319644],\n",
      " 'river': [31.06815277323863,\n",
      "           28.654002708510465,\n",
      "           32.60442347877912,\n",
      "           8.636492975915129],\n",
      " 'road': [116.20750077971213,\n",
      "          107.43497287000125,\n",
      "          119.68994081108065,\n",
      "          32.32977708761651],\n",
      " 'sail': [4.181893615472304,\n",
      "          3.9198858939243792,\n",
      "          4.710355125357251,\n",
      "          1.2013512499797558],\n",
      " 'signal': [2.350001482490582,\n",
      "            2.531934436623843,\n",
      "            2.2090770171756176,\n",
      "            0.5842211767700967],\n",
      " 'table': [43.523981800212894,\n",
      "           39.76367614662147,\n",
      "           44.457828720090475,\n",
      "           12.010158948015206],\n",
      " 'traffic': [16.711098783498734,\n",
      "             15.972511575806907,\n",
      "             17.045292599348294,\n",
      "             4.550056187090233]}\n"
     ]
    }
   ],
   "source": [
    "for term in term_vector:\n",
    " \tl=len(term_vector[term])\n",
    " \t#~ print term\n",
    " \tfor i in range(l):\n",
    "\t\tsumation=0\n",
    "\t\tfor v in vocab:\n",
    "\t\t\tsumation+=term_vector[v][i]*sim[term][v]\t\n",
    "\t\tterm_vector[term][i]+=sumation\t\t\n",
    "print \"Term Vector sumation similarity\"\n",
    "pprint.pprint(term_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance Matrix\n",
      "array({'pencil': {'pencil': 0.99999999999999978, 'bus': 0.99508199040277434, 'signal': 0.98339219025809876, 'sail': 0.98813508774975123, 'pen': 0.99289738536211469, 'book': 0.99198169697357963, 'traffic': 0.99041880925817793, 'road': 0.99142639504022345, 'table': 0.99182494182455561, 'river': 0.99088015220835424, 'fish': 0.99130270452450175, 'boat': 0.99128637925633301}, 'bus': {'pencil': 0.99508199040277434, 'bus': 0.99999999999999989, 'signal': 0.99021031652196401, 'sail': 0.99545267930515213, 'pen': 0.99765823200013659, 'book': 0.99811735993441864, 'traffic': 0.9968441837592058, 'road': 0.9975687432031024, 'table': 0.99789460031340149, 'river': 0.99735771827717512, 'fish': 0.99752833082228887, 'boat': 0.99762319097987484}, 'signal': {'pencil': 0.98339219025809876, 'bus': 0.99021031652196401, 'signal': 0.99999999999999978, 'sail': 0.99137611442633133, 'pen': 0.99347160176719085, 'book': 0.99519998454698844, 'traffic': 0.99653615479191371, 'road': 0.99486857552968588, 'table': 0.99466816436577798, 'river': 0.99406451297088516, 'fish': 0.99447535589976266, 'boat': 0.99373533358838717}, 'sail': {'pencil': 0.98813508774975123, 'bus': 0.99545267930515213, 'signal': 0.99137611442633133, 'sail': 1.0000000000000002, 'pen': 0.99923549326982353, 'book': 0.99873160402164507, 'traffic': 0.99862073926616446, 'road': 0.99919122877065114, 'table': 0.99910135737844619, 'river': 0.99951821134758534, 'fish': 0.99934100114714364, 'boat': 0.99948693395454558}, 'pen': {'pencil': 0.99289738536211469, 'bus': 0.99765823200013659, 'signal': 0.99347160176719085, 'sail': 0.99923549326982353, 'pen': 0.99999999999999978, 'book': 0.99970286712052303, 'traffic': 0.99943167627845608, 'road': 0.99983660125045726, 'table': 0.99984471771085515, 'river': 0.99984160832400126, 'fish': 0.99986308296973159, 'boat': 0.99987271093320806}, 'book': {'pencil': 0.99198169697357963, 'bus': 0.99811735993441864, 'signal': 0.99519998454698844, 'sail': 0.99873160402164507, 'pen': 0.99970286712052303, 'book': 1.0, 'traffic': 0.9998150749746807, 'road': 0.99993532071876434, 'table': 0.99996388391545787, 'river': 0.99981159684606924, 'fish': 0.99989405871202275, 'boat': 0.99981725272299171}, 'traffic': {'pencil': 0.99041880925817793, 'bus': 0.9968441837592058, 'signal': 0.99653615479191371, 'sail': 0.99862073926616446, 'pen': 0.99943167627845608, 'book': 0.9998150749746807, 'traffic': 1.0000000000000002, 'road': 0.9998320453588363, 'table': 0.99977601836396224, 'river': 0.99966610153687585, 'fish': 0.99975925002098498, 'boat': 0.99958621201862985}, 'road': {'pencil': 0.99142639504022345, 'bus': 0.9975687432031024, 'signal': 0.99486857552968588, 'sail': 0.99919122877065114, 'pen': 0.99983660125045726, 'book': 0.99993532071876434, 'traffic': 0.9998320453588363, 'road': 1.0, 'table': 0.99998819719314513, 'river': 0.99995432030378317, 'fish': 0.99999091194165513, 'boat': 0.99993912470332391}, 'table': {'pencil': 0.99182494182455561, 'bus': 0.99789460031340149, 'signal': 0.99466816436577798, 'sail': 0.99910135737844619, 'pen': 0.99984471771085515, 'book': 0.99996388391545787, 'traffic': 0.99977601836396224, 'road': 0.99998819719314513, 'table': 1.0000000000000002, 'river': 0.99993439626539982, 'fish': 0.99997846208494234, 'boat': 0.99993928639637286}, 'river': {'pencil': 0.99088015220835424, 'bus': 0.99735771827717512, 'signal': 0.99406451297088516, 'sail': 0.99951821134758534, 'pen': 0.99984160832400126, 'book': 0.99981159684606924, 'traffic': 0.99966610153687585, 'road': 0.99995432030378317, 'table': 0.99993439626539982, 'river': 0.99999999999999989, 'fish': 0.99998512104812687, 'boat': 0.99998979766965967}, 'fish': {'pencil': 0.99130270452450175, 'bus': 0.99752833082228887, 'signal': 0.99447535589976266, 'sail': 0.99934100114714364, 'pen': 0.99986308296973159, 'book': 0.99989405871202275, 'traffic': 0.99975925002098498, 'road': 0.99999091194165513, 'table': 0.99997846208494234, 'river': 0.99998512104812687, 'fish': 0.99999999999999989, 'boat': 0.99997552665804179}, 'boat': {'pencil': 0.99128637925633301, 'bus': 0.99762319097987484, 'signal': 0.99373533358838717, 'sail': 0.99948693395454558, 'pen': 0.99987271093320806, 'book': 0.99981725272299171, 'traffic': 0.99958621201862985, 'road': 0.99993912470332391, 'table': 0.99993928639637286, 'river': 0.99998979766965967, 'fish': 0.99997552665804179, 'boat': 0.99999999999999989}}, dtype=object)\n"
     ]
    }
   ],
   "source": [
    "Matrix={}\n",
    "for tv1 in term_vector:\n",
    "\tMatrix[tv1]={}\n",
    "\tfor tv2 in term_vector:\n",
    "\t\tMatrix[tv1][tv2] =1-spatial.distance.cosine(term_vector[tv1],term_vector[tv2])\n",
    "        \n",
    "Distance_Matrix=np.array(Matrix)\n",
    "print \"Distance Matrix\"\n",
    "pprint.pprint(Distance_Matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('matrix', 'wb') as fp:\n",
    "    pickle.dump(Matrix, fp)\n",
    "\n",
    "with open ('matrix', 'rb') as fp:\n",
    "    Matrix = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.99999999999999978, 0.99508199040277434, 0.98339219025809876, 0.98813508774975123, 0.99289738536211469, 0.99198169697357963, 0.99041880925817793, 0.99128637925633301, 0.99182494182455561, 0.99088015220835424, 0.99130270452450175, 0.99142639504022345], [0.99508199040277434, 0.99999999999999989, 0.99021031652196401, 0.99545267930515213, 0.99765823200013659, 0.99811735993441864, 0.9968441837592058, 0.99762319097987484, 0.99789460031340149, 0.99735771827717512, 0.99752833082228887, 0.9975687432031024], [0.98339219025809876, 0.99021031652196401, 0.99999999999999978, 0.99137611442633133, 0.99347160176719085, 0.99519998454698844, 0.99653615479191371, 0.99373533358838717, 0.99466816436577798, 0.99406451297088516, 0.99447535589976266, 0.99486857552968588], [0.98813508774975123, 0.99545267930515213, 0.99137611442633133, 1.0000000000000002, 0.99923549326982353, 0.99873160402164507, 0.99862073926616446, 0.99948693395454558, 0.99910135737844619, 0.99951821134758534, 0.99934100114714364, 0.99919122877065114], [0.99289738536211469, 0.99765823200013659, 0.99347160176719085, 0.99923549326982353, 0.99999999999999978, 0.99970286712052303, 0.99943167627845608, 0.99987271093320806, 0.99984471771085515, 0.99984160832400126, 0.99986308296973159, 0.99983660125045726], [0.99198169697357963, 0.99811735993441864, 0.99519998454698844, 0.99873160402164507, 0.99970286712052303, 1.0, 0.9998150749746807, 0.99981725272299171, 0.99996388391545787, 0.99981159684606924, 0.99989405871202275, 0.99993532071876434], [0.99041880925817793, 0.9968441837592058, 0.99653615479191371, 0.99862073926616446, 0.99943167627845608, 0.9998150749746807, 1.0000000000000002, 0.99958621201862985, 0.99977601836396224, 0.99966610153687585, 0.99975925002098498, 0.9998320453588363], [0.99128637925633301, 0.99762319097987484, 0.99373533358838717, 0.99948693395454558, 0.99987271093320806, 0.99981725272299171, 0.99958621201862985, 0.99999999999999989, 0.99993928639637286, 0.99998979766965967, 0.99997552665804179, 0.99993912470332391], [0.99182494182455561, 0.99789460031340149, 0.99466816436577798, 0.99910135737844619, 0.99984471771085515, 0.99996388391545787, 0.99977601836396224, 0.99993928639637286, 1.0000000000000002, 0.99993439626539982, 0.99997846208494234, 0.99998819719314513], [0.99088015220835424, 0.99735771827717512, 0.99406451297088516, 0.99951821134758534, 0.99984160832400126, 0.99981159684606924, 0.99966610153687585, 0.99998979766965967, 0.99993439626539982, 0.99999999999999989, 0.99998512104812687, 0.99995432030378317], [0.99130270452450175, 0.99752833082228887, 0.99447535589976266, 0.99934100114714364, 0.99986308296973159, 0.99989405871202275, 0.99975925002098498, 0.99997552665804179, 0.99997846208494234, 0.99998512104812687, 0.99999999999999989, 0.99999091194165513], [0.99142639504022345, 0.9975687432031024, 0.99486857552968588, 0.99919122877065114, 0.99983660125045726, 0.99993532071876434, 0.9998320453588363, 0.99993912470332391, 0.99998819719314513, 0.99995432030378317, 0.99999091194165513, 1.0]]\n",
      "Vocab ['pencil' 'bus' 'signal' 'sail' 'pen' 'book' 'traffic' 'boat' 'table'\n",
      " 'river' 'fish' 'road']\n",
      "Labels {0: 'pencil', 1: 'bus', 2: 'signal', 3: 'sail', 4: 'pen', 5: 'book', 6: 'traffic', 7: 'boat', 8: 'table', 9: 'river', 10: 'fish', 11: 'road'}\n"
     ]
    }
   ],
   "source": [
    "#~ from sklearn.cluster import AgglomerativeClustering\n",
    "#~ agg = AgglomerativeClustering(n_clusters=5, affinity='precomputed')\n",
    "#~ agg.fit_predict(Distance_Matrix)\n",
    "i=0\n",
    "l=len(vocab)\n",
    "w, h = 8, 5;\n",
    "DM = [[0 for x in range(l)] for y in range(l)]\n",
    "labels={}\n",
    "\n",
    "i=0\n",
    "for l1 in Matrix:\n",
    "\tlabels[i]=l1\n",
    "\tj=0\n",
    "\tfor l2 in Matrix[l1]:\n",
    "\t\tif Matrix[l1][l2]<0.5:\n",
    "\t\t\tDM[i][j]=0            \n",
    "\t\telse:\n",
    "\t\t\tDM[i][j]=Matrix[l1][l2]\n",
    "\t\tj=j+1\n",
    "\ti=i+1\t\n",
    "vocab=np.asarray(vocab)\t\n",
    "\n",
    "print DM\n",
    "print \"Vocab\",vocab\n",
    "print \"Labels\",labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MCL Start\t\n",
    "import mcl_c\n",
    "import networkx as nx\n",
    "DM=np.array(DM)\n",
    "G = nx.from_numpy_matrix(np.matrix(DM))\n",
    "#~ print \"============\"\n",
    "#~ print DM\n",
    "#~ print \"============\"\n",
    "#~ print G.nodes()\n",
    "#~ print \"============\"\n",
    "#~ print labels\n",
    "#~ #inflate > more finer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clusters {0: [0], 1: [1], 2: [2], 3: [3], 4: [4], 5: [5], 6: [6], 7: [7], 8: [8], 9: [9], 10: [10], 11: [11]}\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa64a6e4650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6OppRo0bx8ccfc+DAgSvW37lzJ2lpadx///0kJ6djMhkBCA1uWugxwYENXba9vWqQlpbg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa64a99de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M, clusters = mcl_c.mcl(DM, expand_factor = 4, inflate_factor = 3, max_loop = 10, mult_factor = 5)\n",
    "#5,2,10,5\n",
    "print \"Clusters\",clusters\n",
    "pos=mcl_c.draw(G,M,clusters,labels)\n",
    "#MCL END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clusters {0: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 1: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 2: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 3: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 4: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 5: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 6: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 7: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 8: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 9: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 10: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 11: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]}\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa64a559f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZtbvXML7Xc9SJWzrcMF++dzoTQgvoE/njmYu0U/0pMenbXZ19fGgC4/zLWJM2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa64ec6d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M, clusters = mcl_c.mcl(DM, expand_factor = 5, inflate_factor = 2, max_loop = 10, mult_factor = 5)\n",
    "#5,2,10,5\n",
    "print \"Clusters\",clusters\n",
    "pos=mcl_c.draw(G,M,clusters,labels)\n",
    "#MCL END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 1: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 2: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 3: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 4: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 5: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 6: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 7: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 8: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 9: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 10: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 11: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]}\n"
     ]
    }
   ],
   "source": [
    "final={}\n",
    "no=0\n",
    "for k in clusters:\n",
    "    t=[]\n",
    "    for v in clusters[k]:\n",
    "        t+=[labels[v]]\n",
    "#     if len(t)==len(clusters[k]):\n",
    "#         for i in t:\n",
    "    final[no]=t\n",
    "    no=no+1\n",
    "print clusters            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5960808927914192, 0.5317865141394553]\n",
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Education\n",
      "[0.5960808927914192, 0.5317865141394553]\n",
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Education\n",
      "[0.5960808927914192, 0.5317865141394553]\n",
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Education\n",
      "[0.5960808927914192, 0.5317865141394553]\n",
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Education\n",
      "[0.5960808927914192, 0.5317865141394553]\n",
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Education\n",
      "[0.5960808927914192, 0.5317865141394553]\n",
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Education\n",
      "[0.5960808927914192, 0.5317865141394553]\n",
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Education\n",
      "[0.5960808927914192, 0.5317865141394553]\n",
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Education\n",
      "[0.5960808927914192, 0.5317865141394553]\n",
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Education\n",
      "[0.5960808927914192, 0.5317865141394553]\n",
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Education\n",
      "[0.5960808927914192, 0.5317865141394553]\n",
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Education\n",
      "[0.5960808927914192, 0.5317865141394553]\n",
      "['pencil', 'bus', 'signal', 'sail', 'pen', 'book', 'traffic', 'boat', 'table', 'river', 'fish', 'road'] Education\n"
     ]
    }
   ],
   "source": [
    "keywords=['Education','Travel']\n",
    "\n",
    "for l in final:\n",
    "    keypoint=[]\n",
    "    for k in keywords:\n",
    "        point=0\n",
    "        wordFromList1 = wordnet.synsets(k)\n",
    "        for i in final[l]:\n",
    "            wordFromList2 = wordnet.synsets(i)\n",
    "            p=0\n",
    "            for wl1 in wordFromList1:\n",
    "                for wl2 in wordFromList2:\n",
    "                    sim_p=wl1.wup_similarity(wl2)\n",
    "                    if sim_p>p:\n",
    "                        p=sim_p\n",
    "            if len(wordFromList1)>0 or len(wordFromList1)>0: \n",
    "                point=point+p\n",
    "        keypoint.append(point/float(len(wordFromList1)+len(wordFromList2)))\n",
    "    print keypoint\n",
    "    max_value = max(keypoint)\n",
    "    max_index = keypoint.index(max_value)\n",
    "    print final[l],keywords[max_index]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clusters {0: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 1: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 2: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 3: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 4: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 5: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 6: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 7: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 8: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 9: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 10: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 11: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]}\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa64a44e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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MZ6hfPhqZhaRyDxR1rzKh35g2lyHxxyKJv8SfmoMZSezJfY/oDjkEO9TgrjBhvLihqsKD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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa65450bc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    }
   ],
   "source": [
    "M, clusters = mcl_c.mcl(DM, expand_factor = 4, inflate_factor = 2, max_loop = 10, mult_factor = 6)\n",
    "print \"Clusters\",clusters\n",
    "pos=mcl_c.draw(G,M,clusters,labels)\n",
    "print len(clusters)\n",
    "no=0;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pencil\n",
      "bus\n",
      "signal\n",
      "sail\n",
      "boat, book, fish, pen, river, road, table, traffic\n",
      "5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.59327577479981186"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "affprop = sklearn.cluster.AffinityPropagation(affinity=\"precomputed\", damping=0.5)\n",
    "# affprop = sklearn.cluster.AffinityPropagation(preference=-565,affinity=\"euclidean\",convergence_iter=5, damping=0.5)\n",
    "affprop.fit(DM)\n",
    "count=0\n",
    "for cluster_id in np.unique(affprop.labels_):\n",
    "    exemplar = vocab[affprop.cluster_centers_indices_[cluster_id]]\n",
    "    cluster = np.unique(vocab[np.nonzero(affprop.labels_==cluster_id)])\n",
    "    cluster_str = \", \".join(cluster)\n",
    "    print(cluster_str)\n",
    "    count=count+1\n",
    "print count\n",
    "metrics.silhouette_score(DM,affprop.labels_,metric='sqeuclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Attachments",
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}