main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get text file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "swed_file = open(os.getcwd()+\"\\\\dat410_europarl\\\\europarl-v7.sv-en.lc.sv\", mode='rt', encoding='utf-8')\n",
    "en_file = open(os.getcwd()+\"\\\\dat410_europarl\\\\europarl-v7.sv-en.lc.en\", mode='rt', encoding='utf-8')\n",
    "\n",
    "# read all texts\n",
    "swed_text = swed_file.read()\n",
    "en_text = en_file.read()\n",
    "# close the file\n",
    "swed_file.close()\n",
    "en_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "swed_sentences = swed_text.strip().replace(\"&quot;\",\"\").split('\\n')\n",
    "en_sentences = en_text.strip().replace(\"&quot;\",\"\").split('\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'skulle det vara möjligt för er , fru talman , att skriva ett brev till den srilankesiska presidenten i vilket parlamentets beklagande uttrycks över hans och de övriga brutala dödsfallen i sri lanka och uppmanar henne att göra allt som står i hennes makt för att få en fredlig lösning på en mycket komplicerad situation ?'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swed_sentences[7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Warmup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter, defaultdict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 10 most frequent words in Swedish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('att', 8896),\n",
       " ('och', 6110),\n",
       " ('i', 5715),\n",
       " ('det', 5347),\n",
       " ('för', 4632),\n",
       " ('som', 4425),\n",
       " ('av', 3961),\n",
       " ('är', 3680),\n",
       " ('en', 3570),\n",
       " ('vi', 3096)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swed_clean_text = swed_text.replace(\".\",\"\").replace(\",\",\"\").replace(\"&quot;\",\"\").replace(\"\\n\",\"\").replace(\"  \",\"\").strip()\n",
    "swed_words = swed_clean_text.split(' ')\n",
    "swed_counter_Words = Counter(swed_words)\n",
    "swed_counter_Words.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 10 most frequent words in english"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 18235),\n",
       " ('of', 9119),\n",
       " ('to', 8514),\n",
       " ('and', 5910),\n",
       " ('in', 5497),\n",
       " ('is', 4139),\n",
       " ('a', 4083),\n",
       " ('that', 3920),\n",
       " ('this', 2924),\n",
       " ('for', 2657)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_swed_text = en_text.replace(\".\",\"\").replace(\",\",\"\").replace(\"&quot;\",\"\").replace(\"\\n\",\"\").replace(\"  \",\"\").strip()\n",
    "en_words = en_swed_text.split(' ')\n",
    "en_counter_Words = Counter(en_words)\n",
    "en_counter_Words.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_probability(word,counter_Words):\n",
    "    value_word = counter_Words.get(word)\n",
    "    value_word= value_word if value_word is not None else 0\n",
    "    total_Count=sum(counter_Words.values())\n",
    "    return value_word/float(total_Count) * 100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what is the probability that it is speaker?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0032880405744206884"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_probability('speaker',en_counter_Words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the probability that it is zebra?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_probability('zebra',en_counter_Words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'i declare resumed the session of the european parliament adjourned on friday 17 december 1999 , and i would like once again to wish you a happy new year in the hope that you enjoyed a pleasant festive period .'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_sentences[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'i declare resumed the session of the european parliament adjourned on friday 17 december 1999 , and i would like once again to wish you a happy new year in the hope that you enjoyed a pleasant festive period .'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_sentences[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Language modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## bigram\n",
    "1. select pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigram_model = defaultdict(lambda: defaultdict(lambda: 0))\n",
    "for sentance in en_sentences:\n",
    "    words=sentance.split(' ')\n",
    "    len_words=len(words)\n",
    "    for i in range(0,len_words-3):\n",
    "        w1,w2 = words[i:i+2]\n",
    "        bigram_model[w1][w2] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. calculate probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for w1 in bigram_model:\n",
    "    total = sum(bigram_model[w1].values())\n",
    "    for w2 in bigram_model[w1]:\n",
    "        bigram_model[w1][w2] /= total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "predict the next word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'resumed': 0.5,\n",
       " 'an': 0.16666666666666666,\n",
       " 'that': 0.16666666666666666,\n",
       " 'our': 0.16666666666666666}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(bigram_model['declare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'arm': 0.5, 'millennium': 0.5}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(bigram_model['secular'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find long sentaance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "long_sentances = list(filter(lambda sentance: len(sentance.split(' ')) > 100, en_sentences))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "probability of the whole sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def probability_sentances(sentances):\n",
    "    probability_sentances_array=[]\n",
    "    for sentance in sentances:\n",
    "        words=sentance.split(' ')\n",
    "        len_words=len(words)\n",
    "        multiple_probability = 1\n",
    "        for i in range(0,len_words-3):\n",
    "            w1,w2 = words[i:i+2]\n",
    "            multiple_probability *= bigram_model[w1][w2]\n",
    "        probability_sentances_array.append((len_words,multiple_probability))\n",
    "    return probability_sentances_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(103, 3.9804137166732987e-169),\n",
       " (115, 1.8380791576565445e-185),\n",
       " (110, 1.893893136951211e-169),\n",
       " (118, 5.055905815628088e-188),\n",
       " (128, 9.559051777437025e-196),\n",
       " (105, 9.234294300990775e-166),\n",
       " (122, 1.8243097991719043e-187),\n",
       " (147, 2.0724393803976357e-232),\n",
       " (112, 3.1153328115092984e-185),\n",
       " (107, 2.5353977942196483e-154),\n",
       " (113, 1.3521808972251636e-182),\n",
       " (125, 7.378233577445764e-200),\n",
       " (141, 6.112009881848214e-242),\n",
       " (102, 8.591268609363214e-157),\n",
       " (113, 6.271327686822779e-182),\n",
       " (118, 3.5595415445903497e-187),\n",
       " (118, 8.539575838803183e-191),\n",
       " (124, 1.2238288748765904e-174),\n",
       " (131, 2.953075712105567e-202),\n",
       " (121, 4.7905508686311034e-178),\n",
       " (185, 3.383482851728785e-288),\n",
       " (129, 3.728036288794875e-202),\n",
       " (110, 2.567260330987985e-173),\n",
       " (113, 6.252598270242032e-177),\n",
       " (113, 2.0045496101195e-183)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability_sentances(long_sentances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "short_sentances = list(filter(lambda sentance: len(sentance.split(' ')) < 20, en_sentences))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(12, 1.9363885817087248e-13),\n",
       " (14, 1.1146276632180963e-18),\n",
       " (11, 1.7194854567000422e-12),\n",
       " (19, 1.6134127480583728e-28),\n",
       " (14, 3.9383828016894384e-18),\n",
       " (14, 5.827948176960894e-17),\n",
       " (12, 5.737137943398884e-13),\n",
       " (16, 1.0576779909809933e-21),\n",
       " (8, 3.3958111799210166e-09),\n",
       " (13, 3.543786540880243e-15),\n",
       " (18, 9.064330208095826e-23),\n",
       " (13, 1.8206208866225058e-17),\n",
       " (19, 1.7918476303140873e-25),\n",
       " (14, 2.4922038750757835e-16),\n",
       " (10, 1.7653209207513511e-09),\n",
       " (15, 7.515408990319437e-21),\n",
       " (17, 9.601028727703009e-26),\n",
       " (11, 2.5768000388569592e-14),\n",
       " (4, 0.975609756097561),\n",
       " (17, 2.6615735964889454e-25),\n",
       " (14, 3.493186837832068e-16),\n",
       " (7, 1.0748263681127036e-07),\n",
       " (14, 6.822180195683467e-18),\n",
       " (17, 5.057446716398626e-25),\n",
       " (12, 6.103978765279483e-18),\n",
       " (15, 4.6900923449167446e-18),\n",
       " (6, 0.0004678894319587283),\n",
       " (11, 7.536204869630549e-13),\n",
       " (18, 2.7109100084655502e-17),\n",
       " (18, 3.0315067133344304e-28),\n",
       " (15, 2.65507438378622e-13),\n",
       " (15, 7.14348148458201e-18),\n",
       " (8, 6.13292373008249e-11),\n",
       " (5, 0.00028641571194762687),\n",
       " (7, 6.676409319155583e-08),\n",
       " (19, 2.4610395205926726e-28),\n",
       " (10, 1.4291641431873751e-12),\n",
       " (18, 4.791825561052265e-24),\n",
       " (17, 7.039124870634353e-23),\n",
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       " (19, 4.504259319865148e-26),\n",
       " (10, 6.093727429668214e-16),\n",
       " (8, 4.362059633252934e-06),\n",
       " (19, 1.8232035983700052e-25),\n",
       " (18, 9.622829728496474e-25),\n",
       " (8, 3.199719925174564e-09),\n",
       " (14, 1.5115258921241883e-16),\n",
       " (18, 3.0987861140149173e-25),\n",
       " (19, 6.095193950704425e-29),\n",
       " (8, 4.657168325682851e-09),\n",
       " (17, 1.0611587746572548e-25),\n",
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       " (9, 6.016359632615516e-10),\n",
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       " (16, 3.401944175939423e-17),\n",
       " (6, 0.0003893789232122882),\n",
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       " (16, 2.848245865066135e-23),\n",
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       " (15, 7.09956449583192e-19),\n",
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       " (13, 2.3608870720765e-16),\n",
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       " (13, 1.0110933121464323e-16),\n",
       " (18, 1.4412901730505156e-26),\n",
       " (15, 1.5239814582657432e-19),\n",
       " (8, 2.1530743538395073e-09),\n",
       " (8, 4.344852606512131e-08),\n",
       " (10, 6.334404381380328e-11),\n",
       " (12, 8.643049350268128e-15),\n",
       " (14, 5.434573773886314e-19),\n",
       " (17, 7.913833471451568e-24),\n",
       " (14, 2.3776170515675626e-17),\n",
       " (14, 8.798677565156926e-17),\n",
       " (17, 2.273521531430615e-22),\n",
       " (13, 3.246693443472208e-14),\n",
       " (13, 4.593103161050128e-17),\n",
       " (11, 4.474414996489569e-13),\n",
       " (6, 1.2513069970337149e-05),\n",
       " (15, 5.274205682196681e-21),\n",
       " (11, 1.698536382167334e-14),\n",
       " (15, 1.1922889977874715e-21),\n",
       " (14, 1.3276059519472583e-16),\n",
       " (16, 1.2102651216913855e-18),\n",
       " (13, 1.2191051688264258e-16),\n",
       " (19, 2.46099877006012e-24),\n",
       " (12, 1.309183796612895e-16),\n",
       " (19, 5.62317556976043e-26),\n",
       " (7, 5.826060253792372e-09),\n",
       " (19, 1.0866084741463491e-26),\n",
       " (19, 1.3917230120913988e-22),\n",
       " (11, 5.063733190940641e-11),\n",
       " (17, 5.94663863373068e-25),\n",
       " (12, 3.6122670282162634e-14),\n",
       " (19, 1.196760501206877e-22),\n",
       " (13, 4.797898018504494e-17),\n",
       " (19, 3.324919804050458e-26),\n",
       " (11, 3.560819160307038e-14),\n",
       " (14, 1.1609286298268603e-20),\n",
       " (14, 1.4083270164911367e-15),\n",
       " (17, 3.855531563475693e-26),\n",
       " (14, 1.193033932271832e-20),\n",
       " (17, 1.093063851309132e-15),\n",
       " (18, 5.193073954222967e-27),\n",
       " (9, 2.100691240359277e-09),\n",
       " (15, 1.1866331673228659e-18),\n",
       " (14, 5.394119872771261e-23),\n",
       " (16, 1.1952916711078538e-19),\n",
       " (18, 9.985663569550242e-27),\n",
       " (19, 2.150938099400823e-27),\n",
       " (11, 2.094075709983142e-15),\n",
       " (15, 1.3074173756938392e-20),\n",
       " (18, 5.922694035700706e-26),\n",
       " (17, 2.4801232565849037e-23),\n",
       " (12, 1.5952570286259386e-14),\n",
       " (19, 6.577108189050757e-24),\n",
       " (15, 7.957385933452635e-19),\n",
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       " (8, 1.3936239972722931e-12),\n",
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       " (9, 4.2299376945438315e-11),\n",
       " (12, 9.870421099361609e-14),\n",
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       " (18, 5.524067956628983e-22),\n",
       " (11, 4.790997025881663e-12),\n",
       " (9, 2.5205382781234277e-11),\n",
       " (12, 1.9919310973817983e-13),\n",
       " (12, 5.4364725118799785e-14),\n",
       " (17, 4.3831926753528876e-23),\n",
       " (14, 2.5295130051109616e-20),\n",
       " (13, 5.442344798980734e-16),\n",
       " (14, 9.777475037267377e-20),\n",
       " (9, 2.319535905415261e-13),\n",
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       " (13, 2.2941890139003696e-17),\n",
       " (12, 1.866735508697888e-14),\n",
       " (18, 1.1095022839745147e-24),\n",
       " (10, 2.2690207376537256e-11),\n",
       " (11, 1.4928529266367772e-13),\n",
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       " (9, 1.734041240443517e-10),\n",
       " (16, 1.7463530921762358e-17),\n",
       " (9, 1.7726056189536478e-09),\n",
       " (7, 1.9400661767734438e-08),\n",
       " (19, 1.5811173193812847e-25),\n",
       " (19, 2.543083700248888e-25),\n",
       " (7, 1.1342467302547189e-06),\n",
       " (10, 5.726674023455476e-10),\n",
       " (6, 1.2674147045746505e-07),\n",
       " (11, 1.612226775372433e-10),\n",
       " (10, 1.7220553740701918e-10),\n",
       " (19, 5.473339934926228e-27),\n",
       " (12, 4.770414161989033e-19),\n",
       " (13, 1.3037069784704614e-17),\n",
       " (14, 2.0535944733407394e-19),\n",
       " (18, 4.479988412139132e-28),\n",
       " (13, 8.936371733988089e-17),\n",
       " (11, 3.275164753680008e-15),\n",
       " (19, 2.0924822297183403e-30),\n",
       " (18, 1.7211201460201893e-25),\n",
       " (13, 2.663278688872075e-18),\n",
       " (10, 1.258425081051294e-10),\n",
       " (8, 2.6808589201716913e-07),\n",
       " (16, 3.23531213567722e-23),\n",
       " (17, 6.7611495893997e-27),\n",
       " (15, 2.5333021790886348e-23),\n",
       " (18, 3.751726346447613e-24),\n",
       " (14, 1.7485198544343436e-19),\n",
       " (17, 1.1677297073151963e-26),\n",
       " (6, 2.9351794960101123e-06),\n",
       " (16, 6.088561496894794e-24),\n",
       " (10, 2.6724478776125556e-10),\n",
       " (16, 5.609910676118799e-20),\n",
       " (11, 1.5436881965936315e-12),\n",
       " (11, 3.996810096400671e-11),\n",
       " (13, 4.365477643990365e-19),\n",
       " (14, 1.2280870683149554e-21),\n",
       " (9, 3.1276851671815344e-10),\n",
       " (11, 2.4564171683160027e-13),\n",
       " (13, 1.2514609576170403e-16),\n",
       " (17, 8.387221072767165e-23),\n",
       " (15, 2.296145874580785e-22),\n",
       " (15, 1.3344378858387583e-20),\n",
       " (14, 8.891300992020963e-20),\n",
       " (10, 1.27702493184924e-14),\n",
       " (15, 2.7871169859578286e-21),\n",
       " (19, 1.1247511067192258e-28),\n",
       " (10, 5.726674023455476e-10),\n",
       " (5, 4.912131786601276e-06),\n",
       " (19, 1.7457792259079846e-25),\n",
       " (11, 4.616278239503657e-15),\n",
       " (11, 1.954923249373451e-06),\n",
       " (6, 2.7940869668882064e-06),\n",
       " (14, 9.772142830179584e-17),\n",
       " (14, 1.621122852268711e-17),\n",
       " (16, 2.1006447041012775e-20),\n",
       " (16, 3.816772834179279e-21),\n",
       " (12, 1.1110918677971203e-18),\n",
       " (12, 1.0586806590277868e-17),\n",
       " (14, 2.4051867040001423e-19),\n",
       " (14, 1.0248161536335851e-16),\n",
       " (7, 1.9224889565129483e-08),\n",
       " (11, 5.875656809998253e-14),\n",
       " (15, 1.6784596121128515e-23),\n",
       " (13, 3.226541675405187e-20),\n",
       " (10, 5.726674023455476e-10),\n",
       " (15, 1.0904295744543003e-19),\n",
       " (9, 4.54906925922341e-10),\n",
       " (17, 6.947613110299286e-27),\n",
       " (11, 1.2067274763329443e-13),\n",
       " (17, 4.110338595258013e-26),\n",
       " (16, 2.9135966634195026e-21),\n",
       " (16, 6.287115992015414e-22),\n",
       " (19, 9.377032701313136e-29),\n",
       " (11, 1.357864245035766e-14),\n",
       " (19, 1.4224766745004531e-31),\n",
       " (15, 3.452767422719547e-21),\n",
       " (15, 5.819713842388874e-23),\n",
       " (10, 8.05767191944986e-13),\n",
       " (13, 2.469664002639125e-17),\n",
       " (15, 2.5740079665586205e-22),\n",
       " (19, 7.564220951224659e-31),\n",
       " (14, 6.507145697380616e-18),\n",
       " ...]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability_sentances(short_sentances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "en_sentences = en_sentences[0:50]\n",
    "swed_sentences = swed_sentences[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "eng_word_matrix = [sentance.split(' ') for sentance in en_sentences ]\n",
    "\n",
    "swed_word_matrix = [sentance.split(' ') for sentance in swed_sentences]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "numberofsentances =len(swed_sentences)\n",
    "\n",
    "parallel = [ (swed_word_matrix[i],eng_word_matrix[i]) for i in range(numberofsentances)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "getwords= lambda l: [word for row in l for word in row]\n",
    "swed_words= getwords(swed_word_matrix)\n",
    "eng_words =  getwords(eng_word_matrix)\n",
    "probability={}\n",
    "for swed_word in swed_words:\n",
    "    for eng_word in eng_words:\n",
    "        probability[(swed_word,eng_word)] = 1/(len(eng_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0007178750897343862"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability[('jag','declare')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Translation modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prob_all_alignments(source_sentence, target_sentence):#source = swed\n",
    "    alignment_prob_for_t = defaultdict(lambda: 0.0)\n",
    "    for target in target_sentence:\n",
    "        for source in source_sentence:\n",
    "            alignment_prob_for_t[target] += probability[(target,source)]\n",
    "    return alignment_prob_for_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'declare',\n",
       " 'resumed',\n",
       " 'the',\n",
       " 'session',\n",
       " 'of',\n",
       " 'the',\n",
       " 'european',\n",
       " 'parliament',\n",
       " 'adjourned',\n",
       " 'on',\n",
       " 'friday',\n",
       " '17',\n",
       " 'december',\n",
       " '1999',\n",
       " ',',\n",
       " 'and',\n",
       " 'i',\n",
       " 'would',\n",
       " 'like',\n",
       " 'once',\n",
       " 'again',\n",
       " 'to',\n",
       " 'wish',\n",
       " 'you',\n",
       " 'a',\n",
       " 'happy',\n",
       " 'new',\n",
       " 'year',\n",
       " 'in',\n",
       " 'the',\n",
       " 'hope',\n",
       " 'that',\n",
       " 'you',\n",
       " 'enjoyed',\n",
       " 'a',\n",
       " 'pleasant',\n",
       " 'festive',\n",
       " 'period',\n",
       " '.']"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parallel[0][1]\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['jag',\n",
       " 'skulle',\n",
       " 'vilja',\n",
       " 'att',\n",
       " 'barón',\n",
       " 'crespo',\n",
       " ',',\n",
       " 'som',\n",
       " 'lämnat',\n",
       " 'begäran',\n",
       " ',',\n",
       " 'uttalar',\n",
       " 'sig',\n",
       " 'för',\n",
       " 'att',\n",
       " 'motivera',\n",
       " 'den',\n",
       " ',',\n",
       " 'om',\n",
       " 'han',\n",
       " 'vill',\n",
       " ',',\n",
       " 'naturligtvis',\n",
       " '.']"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " aligned_sentence[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = {}\n",
    "target_total={}\n",
    "for swed_word in swed_words:\n",
    "    for eng_word in eng_words:\n",
    "        count[(swed_word,eng_word)] = 0\n",
    "        target_total[eng_word]=0\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "for aligned_sentence in parallel:\n",
    "    src_sentence = aligned_sentence[1]\n",
    "    trg_sentence = aligned_sentence[0] \n",
    "    total_count = prob_all_alignments(src_sentence, trg_sentence)\n",
    "    for t in trg_sentence:\n",
    "        for s in src_sentence:\n",
    "            counts = probability[(t,s)]\n",
    "            normalized_count = counts / total_count[t]\n",
    "            count[(t,s)]=0\n",
    "            target_total[s]=0\n",
    "            count[(t,s)] += normalized_count\n",
    "            target_total[s] += normalized_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recalculating probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "for swed_word in swed_words:\n",
    "    for eng_word in eng_words:\n",
    "        probability[(swed_word,eng_word)] = count[(swed_word,eng_word)]/target_total[eng_word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alignment for sentence 1:\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "('j', 'i')",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-164-968e486d44d5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0malign\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mswed_words\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mprobability\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mew\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mew\u001b[0m \u001b[1;32min\u001b[0m \u001b[0meng_words\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m         \u001b[0mind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscores\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0malign\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mswed_words\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-164-968e486d44d5>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0malign\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mswed_words\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mprobability\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mew\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mew\u001b[0m \u001b[1;32min\u001b[0m \u001b[0meng_words\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m         \u001b[0mind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscores\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0malign\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mswed_words\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: ('j', 'i')"
     ]
    }
   ],
   "source": [
    "alignments = []\n",
    "for i in range(len(swed_words)):\n",
    "    print(\"Alignment for sentence {0}:\".format(i+1))\n",
    "    align = []\n",
    "    for word in swed_words[i]:\n",
    "        scores = [probability[(word, ew)] for ew in eng_words[i]]\n",
    "        ind = scores.index(max(scores))\n",
    "        align.append((swed_words[i].index(word), ind))\n",
    "    alignments.append(align)\n",
    "    print(align)"
   ]
  }
 ],
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