IR Project-d.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",
    "\t\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*0.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Similirity {'pencil': {'pencil': 0, 'bus': 0.06666666666666667, 'signal': 0.04000000000000001, 'sail': 0.0625, 'pen': 0.08888888888888889, 'book': 0.05882352941176471, 'traffic': 0.03636363636363637, 'boat': 0.06666666666666667, 'table': 0.06666666666666667, 'river': 0.02857142857142857, 'fish': 0.047058823529411764, 'road': 0.0625}, 'bus': {'pencil': 0.06666666666666667, 'bus': 0, 'signal': 0.05, 'sail': 0.06666666666666667, 'pen': 0.06666666666666667, 'book': 0.05882352941176471, 'traffic': 0.03529411764705883, 'boat': 0.07500000000000001, 'table': 0.06666666666666667, 'river': 0.02666666666666667, 'fish': 0.047058823529411764, 'road': 0.0625}, 'signal': {'pencil': 0.04000000000000001, 'bus': 0.05, 'signal': 0, 'sail': 0.031578947368421054, 'pen': 0.022222222222222223, 'book': 0.06, 'traffic': 0.044444444444444446, 'boat': 0.022222222222222223, 'table': 0.04000000000000001, 'river': 0.02666666666666667, 'fish': 0.02857142857142857, 'road': 0.04000000000000001}, 'sail': {'pencil': 0.0625, 'bus': 0.06666666666666667, 'signal': 0.04000000000000001, 'sail': 0, 'pen': 0.07500000000000001, 'book': 0.06666666666666667, 'traffic': 0.05714285714285714, 'boat': 0.08888888888888889, 'table': 0.0625, 'river': 0.03076923076923077, 'fish': 0.05333333333333334, 'road': 0.07142857142857144}, 'pen': {'pencil': 0.08888888888888889, 'bus': 0.06666666666666667, 'signal': 0.03333333333333333, 'sail': 0.07500000000000001, 'pen': 0, 'book': 0.05882352941176471, 'traffic': 0.02857142857142857, 'boat': 0.06666666666666667, 'table': 0.06666666666666667, 'river': 0.02666666666666667, 'fish': 0.07500000000000001, 'road': 0.0625}, 'book': {'pencil': 0.05882352941176471, 'bus': 0.05882352941176471, 'signal': 0.06, 'sail': 0.06666666666666667, 'pen': 0.05882352941176471, 'book': 0, 'traffic': 0.08000000000000002, 'boat': 0.05882352941176471, 'table': 0.05882352941176471, 'river': 0.02857142857142857, 'fish': 0.05, 'road': 0.06666666666666667}, 'traffic': {'pencil': 0.03636363636363637, 'bus': 0.03529411764705883, 'signal': 0.044444444444444446, 'sail': 0.05714285714285714, 'pen': 0.02857142857142857, 'book': 0.08000000000000002, 'traffic': 0, 'boat': 0.025, 'table': 0.05454545454545454, 'river': 0.020000000000000004, 'fish': 0.03333333333333333, 'road': 0.06666666666666667}, 'boat': {'pencil': 0.06666666666666667, 'bus': 0.07500000000000001, 'signal': 0.02857142857142857, 'sail': 0.08888888888888889, 'pen': 0.06666666666666667, 'book': 0.05882352941176471, 'traffic': 0.025, 'boat': 0, 'table': 0.06666666666666667, 'river': 0.02666666666666667, 'fish': 0.047058823529411764, 'road': 0.0625}, 'table': {'pencil': 0.06666666666666667, 'bus': 0.06666666666666667, 'signal': 0.04000000000000001, 'sail': 0.0625, 'pen': 0.06666666666666667, 'book': 0.05882352941176471, 'traffic': 0.05454545454545454, 'boat': 0.06666666666666667, 'table': 0, 'river': 0.03076923076923077, 'fish': 0.047058823529411764, 'road': 0.0625}, 'river': {'pencil': 0.02857142857142857, 'bus': 0.02666666666666667, 'signal': 0.02666666666666667, 'sail': 0.03076923076923077, 'pen': 0.02666666666666667, 'book': 0.02857142857142857, 'traffic': 0.018181818181818184, 'boat': 0.02666666666666667, 'table': 0.03076923076923077, 'river': 0, 'fish': 0.03636363636363637, 'road': 0.03076923076923077}, 'fish': {'pencil': 0.047058823529411764, 'bus': 0.047058823529411764, 'signal': 0.04000000000000001, 'sail': 0.05333333333333334, 'pen': 0.07500000000000001, 'book': 0.05, 'traffic': 0.03333333333333333, 'boat': 0.047058823529411764, 'table': 0.047058823529411764, 'river': 0.03636363636363637, 'fish': 0, 'road': 0.05333333333333334}, 'road': {'pencil': 0.0625, 'bus': 0.0625, 'signal': 0.04000000000000001, 'sail': 0.07142857142857144, 'pen': 0.0625, 'book': 0.06666666666666667, 'traffic': 0.06666666666666667, 'boat': 0.0625, 'table': 0.0625, 'river': 0.03076923076923077, 'fish': 0.05333333333333334, '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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Term Vector sumation similarity\n",
      "{'boat': [1.1112525656287864,\n",
      "          0.884634851079533,\n",
      "          1.5828765270991407,\n",
      "          0.319395794188902],\n",
      " 'book': [1.485099946706822,\n",
      "          0.9432892166068212,\n",
      "          0.9579543409135542,\n",
      "          0.277906779596252],\n",
      " 'bus': [1.3100530842949614,\n",
      "         0.6889230593113135,\n",
      "         0.7941371420429792,\n",
      "         0.23821247388579497],\n",
      " 'fish': [1.0186640938630045,\n",
      "          0.9141781022987787,\n",
      "          1.5503528954771872,\n",
      "          0.31267323411388737],\n",
      " 'pen': [1.0016360468805563,\n",
      "         1.0429865569803831,\n",
      "         1.0000780483295366,\n",
      "         0.5505951637784396],\n",
      " 'pencil': [1.0222922769536482,\n",
      "            0.6521269407047414,\n",
      "            0.7348828151778977,\n",
      "            0.48985659852484],\n",
      " 'river': [0.5784650075975524,\n",
      "           0.508821649661671,\n",
      "           1.1587934843502528,\n",
      "           0.1643420151532734],\n",
      " 'road': [1.3233760846877052,\n",
      "          1.6647841410015376,\n",
      "          1.2885165246100705,\n",
      "          0.3776819424848444],\n",
      " 'sail': [0.9243640973883366,\n",
      "          0.8182092146327838,\n",
      "          1.4241451787158224,\n",
      "          0.27131394825022886],\n",
      " 'signal': [0.6026212484120077,\n",
      "            1.0465438108657963,\n",
      "            0.49864169749448256,\n",
      "            0.1441003059530558],\n",
      " 'table': [1.6627181723593676,\n",
      "           1.0082197775065538,\n",
      "           1.1194677265234365,\n",
      "           0.33767267261791045],\n",
      " 'traffic': [0.8508282194061488,\n",
      "             1.2937616236073204,\n",
      "             0.7535970500460133,\n",
      "             0.2087570780948992]}\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': 0.83329003041911132, 'signal': 0.36168794745293731, 'sail': 0.4402263383423719, 'pen': 0.77141184789925366, 'book': 0.83909202238759695, 'traffic': 0.40415342282693079, 'road': 0.49239580760665536, 'table': 0.84691869192319669, 'river': 0.36607962416302042, 'fish': 0.45412535813395305, 'boat': 0.47244925593769693}, 'bus': {'pencil': 0.83329003041911132, 'bus': 1.0, 'signal': 0.3381833600819516, 'sail': 0.41297809051944268, 'pen': 0.49169359262027013, 'book': 0.9973143723005643, 'traffic': 0.38330217721814974, 'road': 0.46563784887933668, 'table': 0.99705677395955172, 'river': 0.33828530318550643, 'fish': 0.42376037713319437, 'boat': 0.4504506914780595}, 'signal': {'pencil': 0.36168794745293731, 'bus': 0.3381833600819516, 'signal': 0.99999999999999989, 'sail': 0.32710537612106427, 'pen': 0.78344055775515131, 'book': 0.40407666054350577, 'traffic': 0.9979267640908317, 'road': 0.97971948048752588, 'table': 0.39408014305109129, 'river': 0.25267706446331151, 'fish': 0.34180917997087512, 'boat': 0.32346843659881763}, 'sail': {'pencil': 0.4402263383423719, 'bus': 0.41297809051944268, 'signal': 0.32710537612106427, 'sail': 1.0, 'pen': 0.51992828544194414, 'book': 0.4420225851525712, 'traffic': 0.37920808022629926, 'road': 0.48771982779743583, 'table': 0.46412825489909082, 'river': 0.99481066023210152, 'fish': 0.9998052286068001, 'boat': 0.999016113590941}, 'pen': {'pencil': 0.77141184789925366, 'bus': 0.49169359262027013, 'signal': 0.78344055775515131, 'sail': 0.51992828544194414, 'pen': 1.0000000000000002, 'book': 0.54110516131019315, 'traffic': 0.80498430951298772, 'road': 0.84841937916239873, 'table': 0.54623339605798604, 'river': 0.44647633952906707, 'fish': 0.53585506629187918, 'boat': 0.52765242765305365}, 'book': {'pencil': 0.83909202238759695, 'bus': 0.9973143723005643, 'signal': 0.40407666054350577, 'sail': 0.4420225851525712, 'pen': 0.54110516131019315, 'book': 1.0000000000000002, 'traffic': 0.44852390316913437, 'road': 0.5288949704003838, 'table': 0.99949773398289166, 'river': 0.36453741281265795, 'fish': 0.45347474949870026, 'boat': 0.47790589460954047}, 'traffic': {'pencil': 0.40415342282693079, 'bus': 0.38330217721814974, 'signal': 0.9979267640908317, 'sail': 0.37920808022629926, 'pen': 0.80498430951298772, 'book': 0.44852390316913437, 'traffic': 0.99999999999999978, 'road': 0.99051380065406058, 'table': 0.43991336763029021, 'river': 0.3039644046020179, 'fish': 0.3938964292438154, 'boat': 0.37700455856940662}, 'road': {'pencil': 0.49239580760665536, 'bus': 0.46563784887933668, 'signal': 0.97971948048752588, 'sail': 0.48771982779743583, 'pen': 0.84841937916239873, 'book': 0.5288949704003838, 'traffic': 0.99051380065406058, 'road': 1.0, 'table': 0.52371500327632037, 'river': 0.41214490603886589, 'fish': 0.50221930478910859, 'boat': 0.48826126608837928}, 'table': {'pencil': 0.84691869192319669, 'bus': 0.99705677395955172, 'signal': 0.39408014305109129, 'sail': 0.46412825489909082, 'pen': 0.54623339605798604, 'book': 0.99949773398289166, 'traffic': 0.43991336763029021, 'road': 0.52371500327632037, 'table': 0.99999999999999978, 'river': 0.38807450972610325, 'fish': 0.47534211971893359, 'boat': 0.49986842356410266}, 'river': {'pencil': 0.36607962416302042, 'bus': 0.33828530318550643, 'signal': 0.25267706446331151, 'sail': 0.99481066023210152, 'pen': 0.44647633952906707, 'book': 0.36453741281265795, 'traffic': 0.3039644046020179, 'road': 0.41214490603886589, 'table': 0.38807450972610325, 'river': 1.0000000000000002, 'fish': 0.99270131592722299, 'boat': 0.99119536407697495}, 'fish': {'pencil': 0.45412535813395305, 'bus': 0.42376037713319437, 'signal': 0.34180917997087512, 'sail': 0.9998052286068001, 'pen': 0.53585506629187918, 'book': 0.45347474949870026, 'traffic': 0.3938964292438154, 'road': 0.50221930478910859, 'table': 0.47534211971893359, 'river': 0.99270131592722299, 'fish': 1.0, 'boat': 0.99919681175483832}, 'boat': {'pencil': 0.47244925593769693, 'bus': 0.4504506914780595, 'signal': 0.32346843659881763, 'sail': 0.999016113590941, 'pen': 0.52765242765305365, 'book': 0.47790589460954047, 'traffic': 0.37700455856940662, 'road': 0.48826126608837928, 'table': 0.49986842356410266, 'river': 0.99119536407697495, 'fish': 0.99919681175483832, 'boat': 1.0}}, 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, 0.83329003041911132, 0, 0, 0.77141184789925366, 0.83909202238759695, 0, 0, 0.84691869192319669, 0, 0, 0], [0.83329003041911132, 1.0, 0, 0, 0, 0.9973143723005643, 0, 0, 0.99705677395955172, 0, 0, 0], [0, 0, 0.99999999999999989, 0, 0.78344055775515131, 0, 0.9979267640908317, 0, 0, 0, 0, 0.97971948048752588], [0, 0, 0, 1.0, 0.51992828544194414, 0, 0, 0.999016113590941, 0, 0.99481066023210152, 0.9998052286068001, 0], [0.77141184789925366, 0, 0.78344055775515131, 0.51992828544194414, 1.0000000000000002, 0.54110516131019315, 0.80498430951298772, 0.52765242765305365, 0.54623339605798604, 0, 0.53585506629187918, 0.84841937916239873], [0.83909202238759695, 0.9973143723005643, 0, 0, 0.54110516131019315, 1.0000000000000002, 0, 0, 0.99949773398289166, 0, 0, 0.5288949704003838], [0, 0, 0.9979267640908317, 0, 0.80498430951298772, 0, 0.99999999999999978, 0, 0, 0, 0, 0.99051380065406058], [0, 0, 0, 0.999016113590941, 0.52765242765305365, 0, 0, 1.0, 0, 0.99119536407697495, 0.99919681175483832, 0], [0.84691869192319669, 0.99705677395955172, 0, 0, 0.54623339605798604, 0.99949773398289166, 0, 0, 0.99999999999999978, 0, 0, 0.52371500327632037], [0, 0, 0, 0.99481066023210152, 0, 0, 0, 0.99119536407697495, 0, 1.0000000000000002, 0.99270131592722299, 0], [0, 0, 0, 0.9998052286068001, 0.53585506629187918, 0, 0, 0.99919681175483832, 0, 0.99270131592722299, 1.0, 0.50221930478910859], [0, 0, 0.97971948048752588, 0, 0.84841937916239873, 0.5288949704003838, 0.99051380065406058, 0, 0.52371500327632037, 0, 0.50221930478910859, 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, 4, 5, 8, 11], 1: [0, 1, 4, 5, 8], 2: [2, 4, 6, 11], 3: [3, 7, 9, 10], 4: [0, 2, 4, 5, 6, 8, 10, 11], 5: [0, 1, 4, 5, 8], 6: [2, 4, 6, 11], 7: [3, 7, 9, 10], 8: [0, 1, 4, 5, 8], 9: [3, 7, 9, 10], 10: [3, 4, 7, 9, 10], 11: [2, 4, 6, 11]}\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQQAAAECCAYAAAAYUakXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDIKk9n/jZ+UwYPVQ0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f82f5b43450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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t5wOc9hpCgiC01hHBPxLIP+ZzwS/Hfut6SZL2S5L0qSRJJx46coHyNiaRGPAU\nId7XUlS7jCOVf8fhKjz5iV3EqA8m1v9BSm2fUePYdcK0Zr2J33YnGSzexN5zKQPe+wt9Ft9J0CV9\nKXrvJ8q/2dMinabByu2HOZRXiknfhwYlr6NvRRAueF01zORrIE7TtH7AGmBZW4kkSbpTkqSdkiTt\nLC8v76KinVtkWSbQeyLdgp/HQx9BZtXfKKhdiqLaz3bRAPA2JhPpezsFNW9gd2YfN51e1mE8wfpA\nnlGBRM0aj85soiG35d+1h9PE+kNHWLxmG397P4fciqbgvz+nmIpaW8fciCBc4Dqiw7cQOLYmH8XR\njl0ANE2rPObjW8C/27qQpmlvAm9C02ifDijbeUsvexHlN4cgr0sprF1CesVDhHnfiMVz3Fmf9OTv\nORKXu4wc639ICngaoz6gzXRmgwfaL/sYK/UO0h77kMAJvfGMDkTS6ajemo7b5sB3UMuJcaNDuhMf\nEkB6UQWhlkCk2qbr78stIuWD1Xh6GIgO9CMxLJCekSEkhAai14uJYIJwKjoi+O8AkiVJiqcp6N8M\n3HJsAkmSwjVNK/7l41VAagfke0EwGSJJDPwb1oatFNe9T2XDD0T6zsHLmHBWyxVivppGpZTs6n+S\nHPhUm2k8ZAPoDMhIyAY93klhlH+3D2dZDUgSpqgAEh6+Cv+R3QCQf3kRddXBny67CFVV2ZyWy5p1\nTfsjG3Q6bh07kGA/L44UV3GkpJKfUrOpa3ASbjETFxpA94hgekaFYvHu+t3RBOF80iHj/CVJmgL8\nl6ahnks0TfuHJElPATs1TftKkqRnaQr6ClAF3K1p2uETXfP3Ps7/dKiqk1Lb51Q2rMHXYwgRvtPb\ntfZ+55VHJdv6LCARb3m0zTeS7Xk5PHDgHdy6ttdE+pVe0hHoYea/fefwyYaDVNbZmTtpGN0ig7E1\npnCk8mVyCv7MziMFVNba6R0TyuiecfSNCaO2wUlqQSlpReXklFZRbLXh42kkJsifxNAAekaHEBfs\nf9bfmAShK4hJXr9jTqWcwtql2F3pBJuvJcjzsrMW2BTVzpHKBXgZkom2zG3xXUFlDf/+33omDIrn\nO9c2UmrycanuFg8BvSSjaQp9/aL59+DZWIxeqKrKmn2ZfLUjhYt6xHLjiL6kV99FUuDTmPQRFFbV\nsPFQFruzinAqbvrHhTO2VwJJ4U1vCIqiklVaSWphGUdKKsmrsNLochMZ6Et8SADdwoPoGR2C2eTR\npb+VIHQFEfwvALWOPRTVvYeEjkjfWSfdg7ezOJUKMqvmE+R1GSHmpnWFympsPPPZj0zok8jVw3oD\nkFdfwX2rP8Tt50CV3NTUNXJdX+BzAAAgAElEQVRt0mB8azcQoPfguiF/b3Hd4upaFq/ZjsOpcOP4\nn4m0DCPEfMwuaqrKkdJKNh7KZn9uCR4GHYMSIhnXO4Fw/5ZrLVXU2kgtKCe9qJyc8mpKrTb8vT2J\nDbaQFB5Ez8gQIgN9xduBcN4Twf8CoaoK5faVlNevxGzsQ4TPjON2wHYmuzOLrOpniPK9A9QB/OPT\nHxiUEMEfxgxsTlNrd/DQslW8MvdqjHo99739Ff93zThUSvg560Eu7v4mwb4tRwmrqsrXO1M5XLyC\n/okNTO717zYDtKqq7MspZtPhHFLzywjy9WZIYiRjesXjb/Zqld6pKKQVVZBeWE5WaRX5FVbcqkZU\noB8JoQF0iwiie1QwXsZzZ7KdILSHCP4XGKdipbhuOXXO/QR7XU6w99Xt2oqxI9U07CDH+jorfx5D\nqG9v5lw8pEWg3pyaw5p96Sy4eTIAC1asZsrgHgxLjuHjHQ9g1Fm4ZlDbu4AdKU1hb+E8DqT/kbmT\nRxIZ4HfccjgVhW0Z+WxNy+NISSUxwRaGJ0czskfsCYN5cXUtKfmlZJZUkVtWTUVdPUE+3sSG+JMc\nHkjv6FBCLWevj0UQ2kMs6XyBMeotxPrfS70zjYKad6hu2ES473T8TINPfnIH8dANZNOBbvRL+pZL\nek5pVUM/XFhG4i/t8gCBvt4UV9cBMDhmDjvzHqXKVkaAOaTVtRNDe+HQItCcLv7xyY9cMaQnlw3s\n1uZbgFGvZ0zPeMb0jMfmaGRzai4/p+Xxyc8H6B4ZzMhusQxJjGo1PDTc35dwf18u7tf02e50klZQ\nTlpROdvS8/lsy0F0skR0kIWE0AB6RIaQHBGIUS/+GQnnH1Hz/x1SVZWqhrWU2j7D0xBHhO8sTPrw\nTs1TUVSe+3IDep3MtHFZ2JWDJAU+jV4+2uTy+Pvfce3wPgxJalqa+aNN+6htcDD3kuEAfLz9L5j0\n4Vw1qO3du3Kq/4uHLhxb/UTeWrMDP28Td14yjCDf9u1nXFFrY2NKDruOFGCtd9A3NoxRPePoHRXS\nrrZ+VVUprKwlpaCMzJIK8sqtVNc3EGoxExfsT7eIYHpGBbe7PILQGUSzj4Ci2iiu/ZCaxq0EeE4g\nzHwDstzx499VVeXFVZuxOZw8cu049LJMjvU/qJqDBP95yLIeu8PJ/e98zX9vv7K56WXDoSNsPpzL\nvOsnApBWvJM9BQuY0vtdfL38W+VTUb8Wa8NPJAUtxKkofPDTXnZmFnDDyL6M75N4SmXOL7eyMSWb\nXVkFaMDA+AjG9IwnPvTU+ktsjkZS88tIL6ogq6yKwsoaMQlNOKtE8BeaNThzKaxdglOtJNznZvw9\nR3fYtVVV5c012ymsquGx6yc0B3ZVdZJZOR+TIZoYy5/YkZHPVztTePoPlzafm1FczqLV2/jPzKMj\neD7adjdmj0SmDnioVV5OpYq0igfpHfJm86qnB/JKeOeHHUQG+HH7pGGnPLlLVVUOF5az+XAO+3NK\n8DYZGJIUxZie8afVvq+qKjnl1aTmNw0zza2wYnM4ifD3ITbEX0xCEzqdCP5CK1X2nyixrcCoCyHS\nZxaextgzvua7G3ZxMLeUx2+YiK9Xy4DmVKwcqXqCAM8JfLszFk2D2ROP/j9pdzi5b8lXLPrjdc3N\nLofyt3Cg5Fmu6vsBXqbWzSeHy/9KuM90/ExHRxHZnU6Wr9tNSn4pt4wZyIjuMad1L4qisie7kC1p\nuaQWlhPq583QpGjG9IpvdW+nwlrvEJPQhC4jgr/QJlV1UGL7hKqGdVhMFxHmc0uLdvlT8dmWA2w+\nnMO86ycct527wZVPVtVTrNnVmxGJ17UKzH9Z/CXzbpjQYlz+im1z8TP15fL+97a6Xr71TSRJT5Tf\nnFbf7cws4N0Nu+kWEcTsCUPwMp3+ME2HU2FbRi5b0vLILqsmPsSfEd1iGNEtFpPxzDp4xSQ0oTOJ\n4C+ckEMpprD2HRyuPELN1xPgefEp1Ty/23OYb3en8+h141tNqPqtCttu1qT8jYt7PkOIT8vtKp/8\ncDVXDe3V3AkMsC9nA6ll/49rBnyAydjywWRt2Eqp7X90D/5Xm3nV2h0s+XEHOWXVzBw/mIEJba0u\nfmpq7Q42peawPSOP0pp6ekYGM7J7LAPjIzusLV9MQhM6igj+QrvUNOygqO49dLI3kb6z8TYmn/Sc\nDYeO8MnPB/jrVWPb1UG6J6uQDYff55LBR0gMXNBi5NFLKzeREBbIFUNabue4Ytts/D2Hcmm/P7U4\nrqh2UsvupmfIKydc1+in1Gw+3rSf/nHhTB836Ixr678qtdbxU2o2OzMLqHe46BcXxqgecfSIDO7Q\nwHyySWg9ooJJjggSk9CEVkTwF9pNVRXK6r+gwv4dPsYBRPrOQK9ruza/IyOfpet28ZcpF9EjqvV4\n/La8v2EPDU4XU4ZnUOvYSVLQU82B+8Of9mBvdHH7pGEt88n6jiMVb3LdgBUYfxPgMioeJ9BrMgFe\n406Yb2WdnbfXbqe8tp7bLx7a7vK2V3ZpFT+lZrM7qxCdLDMoPpKxveKJDrZ0aD6/EpPQhPYQwV84\nZU6lgqK65dQ7Uwn2voogr6ktarMH8kp4/dstzL1k2Ck1pyz8aA0T+yUxqnsseTX/RXFbSQh4ElnW\n8+OBTLZn5PPodRNanbdi220EeU9gUp+W7ftFte/hcluJ9T/5ltCqqrJ2fyZfbk/hou6x3Diqb4dP\nylJVlUMFZWxOzeFAbgl+XiaGJEUxtldcp475P3YSWlZpFYWVtWISmiBm+AqnzqgPIs7/QeoaD1JU\nu5SqhvVE+s7Cx6MvGcXlvPHdVm4bP+iUAr9TUSisqqVvTDiyLBPj92eOVD1Ffs2rRPv9hQh/X6ps\nbe9SFhdwPTnV76EoM9AfE8B8PAZQULOoXfnLsszkAd3oHxfOm2u2M3/FGuZeMoyE0MCTn9xOsizT\nNyaMvjFhKIrKziMFbEnP5YkP0okI8GV4cgyjesZ2eAeul9HIwITI5r+P305C25aRh7Xe0WISWq/o\nUAJ9Tq+DX/h9ETV/oU2qqlJh/4by+i9xu2JZtiaOKYOHc3G/k/cJHOtAXgnvrd/Nv2ZMaT6mqHVk\nVDyBxTQSb/01PLRsFW/cdW2rNnNFUfhk122E+0xlfK/px5RNIaX8TpIDn8VDH3pK97RyZyrf7Uln\nYr9Erhvep1M7UO1OJ1sO57ItI5/ccitJYYGM6B7D8OToLquNi0loFx5R8xfOiCzLhJivwOUcyFcH\n/8nUUQfoGWZAVWObJ1i1R0peaatOYb3sQ3zAIxypXIjRHIJBp6Oizk6IX8smEr1eT5TlGgpqPkNR\nbm6u/cuyHk99AjWNuwjRT6G9ZFnmqmG9GRgfyZtrt3Egt4Q7Jw8/4SJxZ8LLaOTifslc3C+Zapud\nn1KyWbM3gw837qVndAije8TRPy68Ux9AZpMHQ5OjGZrctNPqbyehbUzJFpPQLlCi5i8cV7XNzj8+\n+5EhiVFcOcxMUe07KGodET7T8fMcdvILAH//9AdG94hrc/mFusaD5Fr/Hyu3DOfyAVfQPy6iVRpF\nUfhk9y1E+d3EmO43NB8vrfsf9a4MEgIePq17UxSVz7YeYMOhLKYO7sHlg7p32TDK4upaNhzKYndW\nIY0uN/1iwxjbO57E0MCzMpTzt5PQiqrr8PXyEJPQzlOiw1c4IzZHI//49EeSw4OYc/FQoKnWWN2w\nnlLbJ3gYooj0nY1J3zpg/0pRVP781hf849bLjtvOXGXfwKaMlzByL5f1b3v0zvqU9yixfcMNg5Y3\n1/4bnLlkVf+dnsGLzigoZRZX8tbabfh4mph7ybBWbx+dLbO4ko0pWezLKcao1zEoIYKxvRM67W2k\nPRRFJbOkgrSicjEJ7Twkgr9w2uxOJ//6fD0hfmbuvnRE67Z41U5J3Qqsjs34e44l3DytzQXjDheU\n8dYP21us3dOWz3c+h8m0j8k9X2tziKmiKHy6+2bi/GcyIrlppzBVVTlcfg+x/n/F25h0Bnfb1Cm9\nYtM+tqXnc/3IPkzse2bXOx2qqnIgr4TNqTkczCsl0NeLIYlRjD3OZjRdraLWxqH8UjKLK8UktHOc\nCP7CaXEqCv/5ciMGnY4Hrhhzwo5Ah6uQwtolNLqLCTNPw2Ia0+If/2dbDlBireOeyy86YZ6r96ZT\n0bCIIckeJAUsaLNPYe3BJVTY13PzsOXNx3Kqn8dDH024z02ncaetHcorZcmPOwiz+HDHpKFnLeg6\nFYUdGQVsTc8jo7iC6CALw5KjGdU99oyWrOhIv05CSyssI7u0mrxyK6omJqGdC0TwF06Zqqr8d+Um\n7E4XD18zrt0jUqobtlBS9z562Z9I3zl4GeMBePazdQxJiuKS/iceIXQgr4QPNu7g9st3o5e9ifF7\nsFUN0ul08vneaSQG3cXQhMsAqKhfjdWxhaTAtnf/Oh12p5N31+/hYG4Jt4wdwMjuZ7743RmVx+Fk\n0+EctmfmU1BRQ7eIIEZ2j2VwYuQ5NX5fVVVKa2yk5JeSUVxJXrlVTEI7S0TwF06JqqosWr2N4uo6\nHr1+/CnX2FTVSantMyob1uLnMZRQ8y3c//YPzJ826aT/4Kttdh577zteuuMSsqrn4+PRn0jfma3S\nfb//NaobdnDz8HeApklpaRX/R++QxR2+ZeXurAKWrdtNUnggsycOOSfatyvrmkYM7TxSQLWtgT4x\noYzqEUefmNBzsrnlt5PQCipq0OtkMQmtk4ngL5ySpet2crigjHnXt16a+VQ0KqUU1i6joi6V9fsS\neOiKx08amFRV5U9vfsGz0y/Dy1RHZuV8Qs3XEuR9aYt0DqedL/beQs+QB+gf19Q5fLjsASJ8Z+B7\nzBLPHcXmaGTJDzvJKq3ssEXiOkpBZQ0bDmWxJ7sQl6IyID6Ccb3jO3TyWkf77SS03PJqMQmtE4jg\nL7Tbp1v2syUtj3nXT+ywf3grd3+OW/qcHlGRRPrOxuzR44TpH3vvW24ZO5C+MWHUO9PIrn6OaL97\nWqzbD/DtvhepcRzk5uGLAcizvo4sexLlO6tDyt2Wzak5rNi0j35xYdw2bnCHLRLXEVRVJb2ogk2p\nOezPLcbTaGBQQiTj+yScF00sYhJaxxPBX2iXb3Yd5vu97Vua+VQ898UG+sYGMjApg3L7KnyMfQn3\nuQ2jvu1VQJ/7YgP948KZPKAb0NSPUFS7hHj/ec19CAA2h5WVB2bQN+wxekePpLphM2W2r+ke/M8O\nK3tbqm123lq7g9KaOuZMHEqv6PbPLO4qiqKyP6+ITam5pBaUEfLLZjSje8afN5O2xE5oZ04Ef+Gk\n1h88wmdbDvLQNWOJDW69Z+7pUlWVe9/+ikevm0BUoB9OxUpx3TLqnAcI9ppKsPeVrdrol67biU6W\nuG3c4OZjpbYvqLSvISng6RYPjZV7nqPemcW04a+jqDZSy+6hZ8ir6OXOH6P/w/4MPt96iBHdo5k2\nqv85217tcCrsyMxnS1ouR0qriAv2Z3i3aEZ0jznvRuBU2+yk5JeRUVJxTk1C01QrqNWAHnTBSNK5\n8UASwV84oa1peby7YTf3TR1Ft8jgDr12bnk1z3+5kf/OubLFP0hb42EKa5ei4STc57YWTTrf7TlM\nSn4ZD141tsW18q1vYHdlkxy4sHkuQa29mm8O3cagqKfpFj6Q9PLHCDZP7dC9iU+krMbGotXbsDc6\nO3yRuM5Qa3fwc1oO29LzKa6uo0dUMBd1j2VQfNR52Zzy6yS0w4XlZJV2zCS09evXM2HCBNatW8f4\n8eOPm07TFGj8Ac32JiiHQTIAGmgqeE5F8pqDZOh2Rve3dOlSZs+eTXZ2NnFxcad8vgj+wnHtzynm\nje+3ctelw9tcUuFMrdqZSlpReatADk1vBVUNayi1fY6nIYFI31l46EPZl1PEx5v3849bL2uVPtv6\nLBIycZZHmh8mX+7+B41KCTcNe5nC2mW41XpiLH9qlV9nUVWVb3an8c2uw0zom8i1w/qcF4G0rMbW\nPGKorqGRvrHhjO4RR8+ojt2Mpqv9Ogkto6iS3IpTn4RWW1tLSkoKvXr1wte37eZPzV2KVnUbqGWg\ntbUSrQ4wgNeNSD6PI0mn93t2VfA/N99ZhU6TXljOotXbmDF+cKcEfoD04gq6RbT9NiHLMkHel2Lx\nHEVx7QdkVM4jwHMi4ZbLqbY1tJk+1vIARyqfpKhuCVF+dwAwpttc1qTO5kjpAUL8BlBQu6RT7uV4\nZFnmiiE9GRAfwZurjy4SFxV49pZlaI8QPzPXj+zL9SP7kltezcaULN76YTsSMDAhgrG9Ejq0CbCr\nBPmaGdfbzLjeTWtIOZwKGSVNk9D2Zhfx9Y7UE05C8/X1ZcSIEce9vqZWoVXeAGoF4D5OKnfTH/sn\naJoTfJ9CkqSOvdEOdP4+6oVTlltezcvf/Mz1I/u02ki9I+WUVdP7JB2ietlMtOVOEvyfwO5Mo6Lx\nSYIsadTaHW2k9SLe/1FqG3dTZvsagABzCEZ5CLvz3sHb2BtFrcWplHfK/ZxIVKAfT944ib6xYTzz\n6Y+s3JmKqqpdXo7TERvsz23jBvP8zKncfvEw7I0Kz32xgcfe+5bPthygrMZ2tot42kxGPX1jwrhh\nZD8evnY8L95+JX8YGMOqV5/hjismMLhbHCFhkfQcNobXvt3MC0veR5Ik1q9f33wNt9vNE088QXh4\nON7mUCZdt5vDGQ3owjNY+J/K5nQL/1OJLjyDjCwnV0wvxDfxIPG9/8VT8+e0+H/B4XDwwAMP0KdP\nH8xmM2FhYVx55ZUcPny4K3+aZqLmf4Eotdbxwlc/cdnAbp26dk1hVQ2KWyW2nVsZehnjSQhYgNXx\nE30SXuRw2QL6Rt6DpyG6RTqjPog4y0NkVT+DUReMxXMEo5Pv5Ie0OymoPoKnIZ7axt0E6S89Tk6d\nR6+XufGifgxOjOTN1dvYm1PEnZcM7/JF4k6XLMv0jgmld0woiqKyO7uAn9NyefLD1YT7+zC8WzQX\ndY87o/kfZ5ssy8y+ZRr+/v4sfXsxQUFBZOXm8snnX+BrMrI7vxSAl1ZtYodVIiE0gNUfLGHxqy/y\n0F//xMVDVrJ7v41rZhUdN4/r5xQz62Zf7r/Tn5Wr61nw9FJi4scye/ZsABobG6mrq2t+oFRVVfHa\na68xcuRIUlNTCQsL65Lf4lci+F8AKuvs/PuLDYzuGcfU32yU3tEO5pYSG2w5pfZjWZYJ8BpHTkEj\nweb9HKmaj8U0ijCfP6CXj8478DImEO17F/m1izDoggj2TcIoDWR79hJGdx+KzXmg1cSwrpQQGshT\nf5jMR5v3sfCjtVw3ovcpb35ztun1MsOSYxiWHIPd6WRrWh7b0vP537YUEkMDGNk9lqFJ0efUXIf2\nqKioIDMzky+//JKrrroKgHHA7BkzAIiS6nkbuHnUAPxiw9mfkcU7i9+gx+jJXHadxOhYby4ZZ8Ro\nkHhoYUWbeTzwRwuzb25q9ps01ot1mxv48IN3moO/n58fb731VnN6t9vNpZdeSmhoKB9++CEPPPBA\n5/0AbRDNPr9ztXYHz32xnj4xYdx4Ub9Ozy+tqPy47f0nE+hjoaj8YpIC/06ju4S08gepqF/b4tXZ\nz3MoIebryK1+HqdSzkVJd6BoB2lsDKXemXnWm1yMej23jRvMPZeP5Ns9aTz3xQaqj7NN5bnOy2hk\nYt8kHrt+Av+6bQq9okNZuz+DB975mpdWbWZnZsFZ/73bKzAwkISEBB599FEWL15MRkZGm+lCLGYu\nHdiN4WFeuBod/OfxBxgcXYxedgFw/RXHf5ubOsm7xefePTzJy8tucezjjz9m+PDhWCwW9Ho93t7e\n2Gw20tLSzvAOT9359fgWTond6eQ/X24kJsjCzPGDuiTP7LIqLh90ekPdQi0+ZJVUYtJHkBjwODUN\n2ymqe4+qhh+J9J2Nt7GpFh3iPQWnUkJW9T9JCnwavdSHnTmrGBhlxVX7dwySCrIPkqEPeExEkgwd\neYvt0is6lKf+MJn3N+zhyQ/XcPPo/ozqGdfl5egoFm8TVwzpyRVDelJcXcvGQ9l88vN+lq/fRb/Y\ncEb3jKNbRNA5O2Kovr6BN5e/yX/+/R8e+r+HqK2pJTg8kMnXj2XM1AGk7UsFYOX2FzjiXM6ujekA\npBQ/z/DuJc2RMjRYd9w8Aiwtv/MwSjgcjc2fv/76a6ZNm8bMmTOZP38+QUFNv9eUKVNwOFr3dXU2\nEfx/p5yKwv/7ahN+Xib+2Maa/J2hrMaGw6mQeJrj3sMsPmzPyG/+7Oc5DB+PQZTWf0529T/xMQ4k\n0ncGep0vET6zyLE+R27184yNjsPD+Qremork2N18viZ5Azo0r9uQzHcjSV07ucnLaGTuJcPZnVXA\n8vW72ZVVyJyLz41F4s5EuL8v00b3Z9r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xz9tfcvbYGmbWVvxHrQJEUaTTHaC+y0mzw0O7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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f82f5dfde90>"
      ]
     },
     "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clusters {0: [0, 1, 4, 5, 8, 11], 1: [0, 1, 4, 5, 8], 2: [2, 4, 6, 11], 3: [3, 7, 9, 10], 4: [0, 2, 4, 5, 6, 8, 10, 11], 5: [0, 1, 4, 5, 8], 6: [2, 4, 6, 11], 7: [3, 7, 9, 10], 8: [0, 1, 4, 5, 8], 9: [3, 7, 9, 10], 10: [3, 4, 7, 9, 10], 11: [2, 4, 6, 11]}\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f82f59bb090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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PQVSmEW1I5wqniUWNM9Wpj2K2o16RRerhAxw8eJC3336b1q1bk56eTmRkJKmpqQQGBt6x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\n3neMMQ+24YlOjkuRqlQqHu3YmIg9B+g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9O3Mmt7jeQbBWq+GJqcPYcPScPQNbLfERwRxN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\nPyatBGtJ4/xCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f82ff96a690>"
      ]
     },
     "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, 4, 5, 8, 11], 1: [0, 1, 4, 5, 8], 2: [2, 4, 6, 11], 3: [3, 7, 9, 10], 4: [0, 2, 4, 5, 6, 8, 10, 11], 5: [0, 1, 4, 5, 8], 6: [2, 4, 6, 11], 7: [3, 7, 9, 10], 8: [0, 1, 4, 5, 8], 9: [3, 7, 9, 10], 10: [3, 4, 7, 9, 10], 11: [2, 4, 6, 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": [
      "[0.32308612440191387, 0.23063211298505415]\n",
      "['pencil', 'bus', 'pen', 'book', 'table', 'road'] Education\n",
      "[0.13700159489633174, 0.11463046757164404]\n",
      "['pencil', 'bus', 'pen', 'book', 'table'] Education\n",
      "[0.246978021978022, 0.18983957219251338]\n",
      "['signal', 'pen', 'traffic', 'road'] Education\n",
      "[0.08401116427432216, 0.14306878306878307]\n",
      "['sail', 'boat', 'river', 'fish'] Travel\n",
      "[0.4302914191072086, 0.312450294803236]\n",
      "['pencil', 'signal', 'pen', 'book', 'traffic', 'table', 'fish', 'road'] Education\n",
      "[0.13700159489633174, 0.11463046757164404]\n",
      "['pencil', 'bus', 'pen', 'book', 'table'] Education\n",
      "[0.246978021978022, 0.18983957219251338]\n",
      "['signal', 'pen', 'traffic', 'road'] Education\n",
      "[0.08401116427432216, 0.14306878306878307]\n",
      "['sail', 'boat', 'river', 'fish'] Travel\n",
      "[0.13700159489633174, 0.11463046757164404]\n",
      "['pencil', 'bus', 'pen', 'book', 'table'] Education\n",
      "[0.08401116427432216, 0.14306878306878307]\n",
      "['sail', 'boat', 'river', 'fish'] Travel\n",
      "[0.09512227538543327, 0.1652910052910053]\n",
      "['sail', 'pen', 'boat', 'river', 'fish'] Travel\n",
      "[0.246978021978022, 0.18983957219251338]\n",
      "['signal', 'pen', 'traffic', '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, 5, 8], 1: [0, 1, 5, 8], 2: [2, 6, 11], 3: [3, 7, 9, 10], 4: [4, 11], 5: [0, 1, 5, 8], 6: [2, 4, 6, 11], 7: [3, 7, 9, 10], 8: [0, 1, 5, 8], 9: [3, 7, 9, 10], 10: [3, 7, 9, 10], 11: [2, 4, 6, 11]}\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f82f598b9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+YN8/6wnan8jSZ9/ltz4fsqjjeAYEd6G+LQoPh5FkQxp/ObcxYtcUanRvjhCC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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f82ff96a5d0>"
      ]
     },
     "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;"
   ]
  }
 ],
 "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
}