wordpair.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy imports\n",
    "import numpy as np\n",
    "from numpy import dot\n",
    "from numpy.linalg import norm\n",
    "\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.test.utils import common_texts, get_tmpfile\n",
    "import gensim.downloader as api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = api.load(\"text8\")\n",
    "model = Word2Vec(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vectors(word1, word2):\n",
    "    try:\n",
    "        vec1 = model[word1]/norm(model[word1])\n",
    "    except:\n",
    "        print('Word1 is not present in the model')\n",
    "        \n",
    "    try:\n",
    "        vec2 = model[word2]/norm(model[word2])\n",
    "    except:\n",
    "        print('Word2 is not present in the model')\n",
    "        \n",
    "    return vec1, vec2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cosine_similarity(word1, word2):\n",
    "    try:\n",
    "        v1, v2 = get_vectors(word1, word2)\n",
    "    except:\n",
    "        return\n",
    "    cos_sim = dot(v1, v2)/(norm(v1)*norm(v2))\n",
    "    return abs(cos_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def euclidean_distance(word1, word2):\n",
    "    try:\n",
    "        v1, v2 = get_vectors(word1, word2)\n",
    "    except:\n",
    "        return\n",
    "    dist = 1/(1 + norm(v1 - v2))\n",
    "    return abs(dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word1 is not present in the model\n",
      "Word2 is not present in the model\n",
      "None\n",
      "Word1 is not present in the model\n",
      "Word2 is not present in the model\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "word1 = 'ass'\n",
    "word2 = 'donkey'\n",
    "\n",
    "print(cosine_similarity(word1, word2))\n",
    "print(euclidean_distance(word1, word2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "word1     word2     Cosine       Euclidean\n",
    "\n",
    "War       Spear     0.24926732   0.25380212\n",
    "Cat       Dog       0.9429585    0.06486539"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    " arr = [[\"restricted\", \"nature\", \"aspect\"],\n",
    "[\"character\", \"necessary\", \"mandatory\"],\n",
    "[\"requisite\", \"negate\", \"contradict\"],\n",
    "[\"refute\", \"negligent\", \"careless\"],\n",
    "[\"remiss\", \"negotiate\", \"bargain\"],\n",
    "[\"deal\", \"nice\", \"affable\"],\n",
    "[\"benign\", \"noble\", \"aristocratic\"],\n",
    "[\"distinguished\", \"novice\", \"beginner\"],\n",
    "[\"nonprofessional\", \"nuisance\", \"annoyance\"],\n",
    "[\"loyal\", \"objection\", \"disapproval\"],\n",
    "[\"protest\", \"obligatory\", \"compulsory\"],\n",
    "[\"required\", \"observe\", \"notice\"],\n",
    "[\"watch\", \"obvious\", \"conspicuous\"],\n",
    "[\"definite\", \"offend\", \"anger\"],\n",
    "[\"irritate\", \"offer\", \"bid\"],\n",
    "[\"proposal\", \"omen\", \"premonition\"],\n",
    "[\"sign\", \"omit\", \"exclude\"],\n",
    "[\"remove\", \"opportune\", \"advantageous\"],\n",
    "[\"placate\", \"pain\", \"ache\"],\n",
    "[\"discomfort\", \"paramount\", \"chief\"],\n",
    "[\"leading\", \"partisan\", \"biased\"],\n",
    "[\"dogmatic\", \"passive\", \"inactive\"],\n",
    "[\"lethargic\", \"pause\", \"break\"],\n",
    "[\"cease\", \"permeate\", \"diffuse\"],\n",
    "[\"disseminate\", \"perpetuate\", \"endure\"],\n",
    "[\"preserve\", \"perplex\", \"astonish\"],\n",
    "[\"baffle\", \"persecute\", \"afflict\"],\n",
    "[\"emanate\", \"radical\", \"basic\"],\n",
    "[\"fundamental\", \"range\", \"anger\"],\n",
    "[\"furor\", \"rank\", \"arrange\"],\n",
    "[\"classify\", \"realize\", \"accomplish\"],\n",
    "[\"fulfill\", \"recalcitrant\", \"obstinate\"],\n",
    "[\"stubborn\", \"receptacle\", \"container\"],\n",
    "[\"repository\", \"reconcile\", \"atone\"],\n",
    "[\"conciliate\", \"regret\", \"deplore\"],\n",
    "[\"grieve\", \"reliable\", \"dependable\"],\n",
    "[\"permit\", \"scope\", \"aim\"],\n",
    "[\"extent\", \"section\", \"division\"],\n",
    "[\"portion\", \"settle\", \"adjust\"],\n",
    "[\"compromise\", \"shallow\", \"superficial\"],\n",
    "[\"trivial\", \"shrewd\", \"careful\"],\n",
    "[\"calculating\", \"significant\", \"distinctive\"],\n",
    "[\"important\", \"slight\", \"delicate\"],\n",
    "[\"slender\", \"spontaneous\", \"impromptu\"],\n",
    "[\"unplanned\", \"spread\", \"announce\"],\n",
    "[\"broadcast\", \"stabilize\", \"balance\"],\n",
    "[\"subdue\", \"tangle\", \"intertwine\"],\n",
    "[\"twist\", \"temper\", \"mood\"],\n",
    "[\"nature\", \"tendency\", \"inclination\"],\n",
    "[\"trend\", \"term\", \"cycle\"],\n",
    "[\"duration\", \"thrift\", \"conservation\"],\n",
    "[\"prudence\", \"tough\", \"aggressive\"],\n",
    "[\"unyielding\", \"transfer\", \"convey\"],\n",
    "[\"exchange\", \"tumult\", \"agitation\"],\n",
    "[\"commotion\", \"turbulent\", \"disordered\"],\n",
    "[\"inflated\", \"valid\", \"authorized\"],\n",
    "[\"legitimate\", \"variety\", \"assortment\"],\n",
    "[\"diversify\", \"verify\", \"authenticate\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "restricted\t\tnature\t\tCosine Similarity = 0.23243318\n",
      "requisite\t\tcontradict\t\tCosine Similarity = 0.20010321\n",
      "refute\t\tcareless\t\tCosine Similarity = 0.28112528\n",
      "remiss\t\tbargain\t\tCosine Similarity = 0.18323237\n",
      "nice\t\taffable\t\tCosine Similarity = -0.038948134\n",
      "deal\t\taffable\t\tCosine Similarity = -0.07305119\n",
      "benign\t\tnoble\t\tCosine Similarity = 0.23531143\n",
      "noble\t\taristocratic\t\tCosine Similarity = 0.14214388\n",
      "Word1 is not present in the model\n",
      "Word1 is not present in the model\n",
      "Word1 is not present in the model\n",
      "Word1 is not present in the model\n",
      "loyal\t\tobjection\t\tCosine Similarity = 0.09456172\n",
      "loyal\t\tdisapproval\t\tCosine Similarity = 0.0967596\n",
      "required\t\tobserve\t\tCosine Similarity = 0.24575338\n",
      "obvious\t\tconspicuous\t\tCosine Similarity = 0.29732957\n",
      "watch\t\tconspicuous\t\tCosine Similarity = -0.11826063\n",
      "irritate\t\toffer\t\tCosine Similarity = 0.19192529\n",
      "irritate\t\tbid\t\tCosine Similarity = -0.024354054\n",
      "proposal\t\tomen\t\tCosine Similarity = 0.22228998\n",
      "proposal\t\tpremonition\t\tCosine Similarity = 0.20883064\n",
      "sign\t\tomit\t\tCosine Similarity = 0.2118386\n",
      "remove\t\topportune\t\tCosine Similarity = 0.22529714\n",
      "remove\t\tadvantageous\t\tCosine Similarity = 0.21130224\n",
      "placate\t\tpain\t\tCosine Similarity = -0.17071822\n",
      "placate\t\tache\t\tCosine Similarity = -0.08164575\n",
      "discomfort\t\tparamount\t\tCosine Similarity = 0.19592606\n",
      "paramount\t\tchief\t\tCosine Similarity = 0.26240364\n",
      "discomfort\t\tchief\t\tCosine Similarity = 0.19680752\n",
      "dogmatic\t\tinactive\t\tCosine Similarity = 0.17213023\n",
      "lethargic\t\tpause\t\tCosine Similarity = 0.022498488\n",
      "lethargic\t\tbreak\t\tCosine Similarity = 0.0043416508\n",
      "cease\t\tdiffuse\t\tCosine Similarity = 0.29998383\n",
      "disseminate\t\tendure\t\tCosine Similarity = 0.27417386\n",
      "emanate\t\tradical\t\tCosine Similarity = 0.042910915\n",
      "emanate\t\tbasic\t\tCosine Similarity = -0.0403357\n",
      "furor\t\trank\t\tCosine Similarity = 0.012861526\n",
      "rank\t\tarrange\t\tCosine Similarity = 0.2913366\n",
      "furor\t\tarrange\t\tCosine Similarity = -0.08442728\n",
      "fulfill\t\trecalcitrant\t\tCosine Similarity = 0.2650922\n",
      "stubborn\t\treceptacle\t\tCosine Similarity = 0.2367615\n",
      "stubborn\t\tcontainer\t\tCosine Similarity = 0.025072066\n",
      "Word1 is not present in the model\n",
      "Word1 is not present in the model\n",
      "Word1 is not present in the model\n",
      "Word1 is not present in the model\n",
      "slender\t\tspontaneous\t\tCosine Similarity = 0.29780093\n",
      "slender\t\timpromptu\t\tCosine Similarity = 0.19116259\n",
      "unplanned\t\tspread\t\tCosine Similarity = 0.029203715\n",
      "unplanned\t\tannounce\t\tCosine Similarity = 0.10606081\n",
      "broadcast\t\tstabilize\t\tCosine Similarity = 0.08177403\n",
      "stabilize\t\tbalance\t\tCosine Similarity = 0.21746513\n",
      "broadcast\t\tbalance\t\tCosine Similarity = 0.2924176\n",
      "duration\t\tthrift\t\tCosine Similarity = -0.07241049\n",
      "thrift\t\tconservation\t\tCosine Similarity = 0.10855056\n",
      "duration\t\tconservation\t\tCosine Similarity = 0.09186341\n",
      "prudence\t\ttough\t\tCosine Similarity = 0.15020898\n",
      "prudence\t\taggressive\t\tCosine Similarity = 0.21382353\n",
      "unyielding\t\ttransfer\t\tCosine Similarity = 0.08073719\n",
      "transfer\t\tconvey\t\tCosine Similarity = 0.021368764\n",
      "exchange\t\ttumult\t\tCosine Similarity = 0.12235361\n",
      "exchange\t\tagitation\t\tCosine Similarity = 0.23340946\n",
      "inflated\t\tvalid\t\tCosine Similarity = 0.22695522\n",
      "inflated\t\tauthorized\t\tCosine Similarity = 0.25759545\n",
      "restricted\t\tnature\t\tEuclidean Similarity = 0.44662692003577653\n",
      "restricted\t\taspect\t\tEuclidean Similarity = 0.4921003323841671\n",
      "character\t\tmandatory\t\tEuclidean Similarity = 0.460377048256344\n",
      "requisite\t\tcontradict\t\tEuclidean Similarity = 0.4415343607053216\n",
      "refute\t\tnegligent\t\tEuclidean Similarity = 0.4686911786455013\n",
      "negligent\t\tcareless\t\tEuclidean Similarity = 0.4809705313949279\n",
      "refute\t\tcareless\t\tEuclidean Similarity = 0.45473936562710127\n",
      "negotiate\t\tbargain\t\tEuclidean Similarity = 0.498356842668896\n",
      "remiss\t\tbargain\t\tEuclidean Similarity = 0.43896261207570414\n",
      "nice\t\taffable\t\tEuclidean Similarity = 0.4095858004922681\n",
      "deal\t\taffable\t\tEuclidean Similarity = 0.40568639406723267\n",
      "benign\t\tnoble\t\tEuclidean Similarity = 0.4470912421610807\n",
      "noble\t\taristocratic\t\tEuclidean Similarity = 0.43292816953039315\n",
      "benign\t\taristocratic\t\tEuclidean Similarity = 0.4767737691128348\n",
      "distinguished\t\tbeginner\t\tEuclidean Similarity = 0.4925951659176099\n",
      "Word1 is not present in the model\n",
      "Word1 is not present in the model\n",
      "Word1 is not present in the model\n",
      "Word1 is not present in the model\n",
      "loyal\t\tobjection\t\tEuclidean Similarity = 0.42631413700157816\n",
      "loyal\t\tdisapproval\t\tEuclidean Similarity = 0.42661135699722363\n",
      "protest\t\tobligatory\t\tEuclidean Similarity = 0.4656747167003842\n",
      "obligatory\t\tcompulsory\t\tEuclidean Similarity = 0.49000647336451747\n",
      "protest\t\tcompulsory\t\tEuclidean Similarity = 0.4921326955940479\n",
      "required\t\tobserve\t\tEuclidean Similarity = 0.44879129116959066\n",
      "observe\t\tnotice\t\tEuclidean Similarity = 0.4919755097468662\n",
      "obvious\t\tconspicuous\t\tEuclidean Similarity = 0.45756733315125786\n",
      "watch\t\tconspicuous\t\tEuclidean Similarity = 0.4007212459635802\n",
      "definite\t\toffend\t\tEuclidean Similarity = 0.47774114298061304\n",
      "definite\t\tanger\t\tEuclidean Similarity = 0.4782736780337375\n",
      "irritate\t\toffer\t\tEuclidean Similarity = 0.4402806149856607\n",
      "irritate\t\tbid\t\tEuclidean Similarity = 0.41129736477912116\n",
      "proposal\t\tomen\t\tEuclidean Similarity = 0.4450051815368064\n",
      "proposal\t\tpremonition\t\tEuclidean Similarity = 0.4428873528923865\n",
      "sign\t\tomit\t\tEuclidean Similarity = 0.4433573545344406\n",
      "sign\t\texclude\t\tEuclidean Similarity = 0.475158833609262\n",
      "remove\t\topportune\t\tEuclidean Similarity = 0.4454836657738167\n",
      "remove\t\tadvantageous\t\tEuclidean Similarity = 0.44327341191689174\n",
      "placate\t\tpain\t\tEuclidean Similarity = 0.39522954632745855\n",
      "placate\t\tache\t\tEuclidean Similarity = 0.40472505899762157\n",
      "discomfort\t\tparamount\t\tEuclidean Similarity = 0.4408922870150024\n",
      "paramount\t\tchief\t\tEuclidean Similarity = 0.451553899236279\n",
      "discomfort\t\tchief\t\tEuclidean Similarity = 0.4410274479731268\n",
      "leading\t\tpartisan\t\tEuclidean Similarity = 0.4936400780798675\n",
      "partisan\t\tbiased\t\tEuclidean Similarity = 0.49881382724929224\n",
      "leading\t\tbiased\t\tEuclidean Similarity = 0.47794436374194427\n",
      "dogmatic\t\tpassive\t\tEuclidean Similarity = 0.4933053704111974\n",
      "passive\t\tinactive\t\tEuclidean Similarity = 0.4674691417776059\n",
      "dogmatic\t\tinactive\t\tEuclidean Similarity = 0.4373008067948742\n",
      "lethargic\t\tpause\t\tEuclidean Similarity = 0.4169769286009584\n",
      "lethargic\t\tbreak\t\tEuclidean Similarity = 0.4147415395249012\n",
      "cease\t\tpermeate\t\tEuclidean Similarity = 0.4835784889348221\n",
      "permeate\t\tdiffuse\t\tEuclidean Similarity = 0.47397868753468203\n",
      "cease\t\tdiffuse\t\tEuclidean Similarity = 0.45803701138206093\n",
      "disseminate\t\tendure\t\tEuclidean Similarity = 0.45354655757539514\n",
      "preserve\t\tperplex\t\tEuclidean Similarity = 0.4603612827782645\n",
      "perplex\t\tastonish\t\tEuclidean Similarity = 0.4992473445483648\n",
      "preserve\t\tastonish\t\tEuclidean Similarity = 0.4700717989915147\n",
      "baffle\t\tpersecute\t\tEuclidean Similarity = 0.47353846803457506\n",
      "persecute\t\tafflict\t\tEuclidean Similarity = 0.48360087509964034\n",
      "baffle\t\tafflict\t\tEuclidean Similarity = 0.4892832112197084\n",
      "emanate\t\tradical\t\tEuclidean Similarity = 0.4195443482449833\n",
      "radical\t\tbasic\t\tEuclidean Similarity = 0.47604597676101545\n",
      "emanate\t\tbasic\t\tEuclidean Similarity = 0.4094244153008014\n",
      "fundamental\t\trange\t\tEuclidean Similarity = 0.46553152099235157\n",
      "range\t\tanger\t\tEuclidean Similarity = 0.4645098499904729\n",
      "fundamental\t\tanger\t\tEuclidean Similarity = 0.4616367899395568\n",
      "furor\t\trank\t\tEuclidean Similarity = 0.4157849019261371\n",
      "rank\t\tarrange\t\tEuclidean Similarity = 0.45651356775031804\n",
      "furor\t\tarrange\t\tEuclidean Similarity = 0.40441571828942624\n",
      "classify\t\trealize\t\tEuclidean Similarity = 0.4701218002454454\n",
      "classify\t\taccomplish\t\tEuclidean Similarity = 0.47038301755526335\n",
      "fulfill\t\trecalcitrant\t\tEuclidean Similarity = 0.45200609506624334\n",
      "fulfill\t\tobstinate\t\tEuclidean Similarity = 0.49153018581107705\n",
      "stubborn\t\treceptacle\t\tEuclidean Similarity = 0.4473258645780175\n",
      "receptacle\t\tcontainer\t\tEuclidean Similarity = 0.4674828968441786\n",
      "stubborn\t\tcontainer\t\tEuclidean Similarity = 0.4172974051783014\n",
      "repository\t\treconcile\t\tEuclidean Similarity = 0.46291018841253345\n",
      "repository\t\tatone\t\tEuclidean Similarity = 0.4892362414029728\n",
      "Word1 is not present in the model\n",
      "Word1 is not present in the model\n",
      "Word1 is not present in the model\n",
      "Word1 is not present in the model\n",
      "grieve\t\treliable\t\tEuclidean Similarity = 0.4625196052084711\n",
      "grieve\t\tdependable\t\tEuclidean Similarity = 0.4810834305552664\n",
      "permit\t\tscope\t\tEuclidean Similarity = 0.4879623163104532\n",
      "extent\t\tsection\t\tEuclidean Similarity = 0.4664486838820909\n",
      "extent\t\tdivision\t\tEuclidean Similarity = 0.478956585229965\n",
      "portion\t\tsettle\t\tEuclidean Similarity = 0.48063160086450124\n",
      "portion\t\tadjust\t\tEuclidean Similarity = 0.48380478772844393\n",
      "compromise\t\tshallow\t\tEuclidean Similarity = 0.4812482541816312\n",
      "compromise\t\tsuperficial\t\tEuclidean Similarity = 0.4620519424852021\n",
      "trivial\t\tshrewd\t\tEuclidean Similarity = 0.4778676116685354\n",
      "shrewd\t\tcareful\t\tEuclidean Similarity = 0.47236299580090274\n",
      "trivial\t\tcareful\t\tEuclidean Similarity = 0.4766572226660334\n",
      "calculating\t\tsignificant\t\tEuclidean Similarity = 0.4924602335482476\n",
      "significant\t\tdistinctive\t\tEuclidean Similarity = 0.480352663702607\n",
      "calculating\t\tdistinctive\t\tEuclidean Similarity = 0.4665335129257067\n",
      "important\t\tslight\t\tEuclidean Similarity = 0.49829050274152775\n",
      "important\t\tdelicate\t\tEuclidean Similarity = 0.4951277308036666\n",
      "slender\t\tspontaneous\t\tEuclidean Similarity = 0.4576506101756813\n",
      "slender\t\timpromptu\t\tEuclidean Similarity = 0.4401644105852813\n",
      "unplanned\t\tspread\t\tEuclidean Similarity = 0.4178138364852549\n",
      "spread\t\tannounce\t\tEuclidean Similarity = 0.49406479071117143\n",
      "unplanned\t\tannounce\t\tEuclidean Similarity = 0.42787784248697547\n",
      "broadcast\t\tstabilize\t\tEuclidean Similarity = 0.4246000327790953\n",
      "stabilize\t\tbalance\t\tEuclidean Similarity = 0.44424158105918304\n",
      "broadcast\t\tbalance\t\tEuclidean Similarity = 0.4567029558048874\n",
      "temper\t\tmood\t\tEuclidean Similarity = 0.49884924510217044\n",
      "twist\t\tmood\t\tEuclidean Similarity = 0.48718403355541734\n",
      "nature\t\tinclination\t\tEuclidean Similarity = 0.4714045685952111\n",
      "trend\t\tcycle\t\tEuclidean Similarity = 0.4818127989680718\n",
      "duration\t\tthrift\t\tEuclidean Similarity = 0.40575841091989523\n",
      "thrift\t\tconservation\t\tEuclidean Similarity = 0.4282192576886535\n",
      "duration\t\tconservation\t\tEuclidean Similarity = 0.4259502933553016\n",
      "prudence\t\ttough\t\tEuclidean Similarity = 0.4340880335860845\n",
      "prudence\t\taggressive\t\tEuclidean Similarity = 0.4436685215511385\n",
      "unyielding\t\ttransfer\t\tEuclidean Similarity = 0.42446220847345906\n",
      "transfer\t\tconvey\t\tEuclidean Similarity = 0.41683653047995584\n",
      "unyielding\t\tconvey\t\tEuclidean Similarity = 0.476125279528795\n",
      "exchange\t\ttumult\t\tEuclidean Similarity = 0.4301307188805188\n",
      "exchange\t\tagitation\t\tEuclidean Similarity = 0.4467842282324584\n",
      "commotion\t\tturbulent\t\tEuclidean Similarity = 0.47992087486186397\n",
      "turbulent\t\tdisordered\t\tEuclidean Similarity = 0.49534385136415476\n",
      "commotion\t\tdisordered\t\tEuclidean Similarity = 0.4783805403335156\n",
      "inflated\t\tvalid\t\tEuclidean Similarity = 0.44574831668877\n",
      "valid\t\tauthorized\t\tEuclidean Similarity = 0.4993716041263154\n",
      "inflated\t\tauthorized\t\tEuclidean Similarity = 0.4507494402176165\n",
      "legitimate\t\tvariety\t\tEuclidean Similarity = 0.4720787195550851\n",
      "variety\t\tassortment\t\tEuclidean Similarity = 0.48596416720103597\n",
      "legitimate\t\tassortment\t\tEuclidean Similarity = 0.4675322967107853\n",
      "diversify\t\tverify\t\tEuclidean Similarity = 0.48272320863139534\n",
      "verify\t\tauthenticate\t\tEuclidean Similarity = 0.484164277476438\n"
     ]
    }
   ],
   "source": [
    "cs = [1, 1, 1]\n",
    "ed = [1, 1, 1]\n",
    "for i in range(len(arr)):\n",
    "    cs[0] = [arr[i][0], arr[i][1], cosine_similarity(arr[i][0], arr[i][1])]\n",
    "    ed[0] = [arr[i][0], arr[i][1], euclidean_distance(arr[i][0], arr[i][1])]\n",
    "    cs[1] = [arr[i][1], arr[i][2], cosine_similarity(arr[i][1], arr[i][2])]\n",
    "    ed[1] = [arr[i][1], arr[i][2], euclidean_distance(arr[i][1], arr[i][2])]\n",
    "    cs[2] = [arr[i][0], arr[i][2], cosine_similarity(arr[i][0], arr[i][2])]\n",
    "    ed[2] = [arr[i][0], arr[i][2], euclidean_distance(arr[i][0], arr[i][2])]\n",
    "    try:\n",
    "        for j in range(3):\n",
    "            if cs[j][2] < 0.3:\n",
    "                print(cs[j][0], '\\t\\t', cs[j][1], '\\t\\tCosine Similarity = ', cs[j][2], sep = '')\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "for i in range(len(arr)):\n",
    "    cs[0] = [arr[i][0], arr[i][1], cosine_similarity(arr[i][0], arr[i][1])]\n",
    "    ed[0] = [arr[i][0], arr[i][1], euclidean_distance(arr[i][0], arr[i][1])]\n",
    "    cs[1] = [arr[i][1], arr[i][2], cosine_similarity(arr[i][1], arr[i][2])]\n",
    "    ed[1] = [arr[i][1], arr[i][2], euclidean_distance(arr[i][1], arr[i][2])]\n",
    "    cs[2] = [arr[i][0], arr[i][2], cosine_similarity(arr[i][0], arr[i][2])]\n",
    "    ed[2] = [arr[i][0], arr[i][2], euclidean_distance(arr[i][0], arr[i][2])]\n",
    "    try:\n",
    "        for j in range(3):\n",
    "            if ed[j][2] < 0.5:\n",
    "                print(ed[j][0], '\\t\\t', ed[j][1], '\\t\\tEuclidean Similarity = ', ed[j][2], sep = '')\n",
    "\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['diversify', 'verify', 0.48272320863139534], ['verify', 'authenticate', 0.484164277476438], ['diversify', 'authenticate', 0.5730800341035787]]\n"
     ]
    }
   ],
   "source": [
    "print(ed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------\n",
      "Similar Words : \n",
      "Cosine Similarity - red  <->  blue : 0.8107394\n",
      "Euclidean Similarity - red  <->  blue : 0.6191027321647792\n",
      "\n",
      "Cosine Similarity - he  <->  she : 0.8571601\n",
      "Euclidean Similarity - he  <->  she : 0.6516822212021112\n",
      "\n",
      "Cosine Similarity - king  <->  queen : 0.6977704\n",
      "Euclidean Similarity - king  <->  queen : 0.56259749633421\n",
      "\n",
      "Cosine Similarity - dog  <->  cat : 0.8301628\n",
      "Euclidean Similarity - dog  <->  cat : 0.6317853730294948\n",
      "\n",
      "Cosine Similarity - writer  <->  author : 0.81893647\n",
      "Euclidean Similarity - writer  <->  author : 0.6243093873310401\n",
      "\n",
      "Cosine Similarity - warm  <->  hot : 0.81814516\n",
      "Euclidean Similarity - warm  <->  hot : 0.6237977816181873\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Non Similar Words : \n",
      "Cosine Similarity - yellow  <->  yell : 0.19158947\n",
      "Euclidean Similarity - yellow  <->  yell : 0.4402294359058649\n",
      "\n",
      "Cosine Similarity - word  <->  dirt : 0.0070551326\n",
      "Euclidean Similarity - word  <->  dirt : 0.415072779181403\n",
      "\n",
      "Cosine Similarity - short  <->  are : 0.07330289\n",
      "Euclidean Similarity - short  <->  are : 0.4056581241393144\n",
      "\n",
      "Cosine Similarity - we  <->  mobile : 0.024959996\n",
      "Euclidean Similarity - we  <->  mobile : 0.4172834349985888\n",
      "\n",
      "Cosine Similarity - sent  <->  box : 0.043245055\n",
      "Euclidean Similarity - sent  <->  box : 0.4195868639415638\n",
      "\n",
      "Cosine Similarity - load  <->  wet : 0.28132597\n",
      "Euclidean Similarity - load  <->  wet : 0.4547739783181565\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vabha\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\vabha\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "similar_words = [[\"red\",\"blue\"],[\"he\",\"she\"],[\"king\",\"queen\"],[\"dog\",\"cat\"],[\"writer\",\"author\"],[\"warm\",\"hot\"]]\n",
    "\n",
    "non_similar_words = [[\"yellow\",\"yell\"],[\"word\",\"dirt\"],[\"short\",\"are\"],[\"we\",\"mobile\"],[\"sent\",\"box\"],[\"load\",\"wet\"]]\n",
    "\n",
    "otp_logs = [\"Similar Words : \",\"Non Similar Words : \"]\n",
    "\n",
    "words = [similar_words,non_similar_words]\n",
    "\n",
    "for i in range(2):\n",
    "    print(\"---------------------------------------------------------------------\")\n",
    "    print(otp_logs[i])\n",
    "    for word in words[i]:\n",
    "        print(\"Cosine Similarity -\",word[0],\" <-> \",word[1],\":\",cosine_similarity(word[0],word[1]))\n",
    "        print(\"Euclidean Similarity -\",word[0],\" <-> \",word[1],\":\",euclidean_distance(word[0],word[1]))\n",
    "        print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}