Notebook 4-HMM num components.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook 4- HMM Num Componentes\n",
    "\n",
    "Here I evaluate the number of components each HMM should have.\n",
    "This is done by training multiple models with more and more components until the accuracy stops increasing.\n",
    "This took a significant amount of time to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from hmmlearn import hmm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data_lab = np.load('np_data_lab.npy')\n",
    "data_turk = np.load('np_data_turk.npy')\n",
    "lab_lengths = np.load('np_lab_lengths.npy')\n",
    "turk_lengths = np.load('np_turk_lengths.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -58505.5091             +nan\n",
      "         2      -49841.9411       +8663.5681\n",
      "         3      -49841.9411          +0.0000\n",
      "         1      -50797.6319             +nan\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 1 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         2      -48886.2750       +1911.3569\n",
      "         3      -47351.3780       +1534.8970\n",
      "         4      -43551.2104       +3800.1676\n",
      "         5      -37237.4848       +6313.7256\n",
      "         6      -34729.9941       +2507.4907\n",
      "         7      -34560.9666        +169.0276\n",
      "         8      -34549.1695         +11.7971\n",
      "         9      -34547.2222          +1.9473\n",
      "        10      -34546.6315          +0.5907\n",
      "        11      -34546.4191          +0.2124\n",
      "        12      -34546.3343          +0.0848\n",
      "        13      -34546.2971          +0.0372\n",
      "        14      -34546.2794          +0.0177\n",
      "        15      -34546.2703          +0.0091\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 2 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -50607.8407             +nan\n",
      "         2      -48751.7012       +1856.1395\n",
      "         3      -46403.6632       +2348.0380\n",
      "         4      -40939.5639       +5464.0993\n",
      "         5      -32497.8830       +8441.6809\n",
      "         6      -27934.9154       +4562.9676\n",
      "         7      -27331.8209        +603.0944\n",
      "         8      -27290.2026         +41.6183\n",
      "         9      -27283.6426          +6.5600\n",
      "        10      -27282.6484          +0.9942\n",
      "        11      -27282.4684          +0.1801\n",
      "        12      -27282.4217          +0.0467\n",
      "        13      -27282.4041          +0.0176\n",
      "        14      -27282.3956          +0.0084\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 3 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -52231.4615             +nan\n",
      "         2      -48474.4104       +3757.0510\n",
      "         3      -46175.2486       +2299.1618\n",
      "         4      -40589.5738       +5585.6748\n",
      "         5      -28746.1775      +11843.3964\n",
      "         6      -21132.1954       +7613.9820\n",
      "         7      -18949.9607       +2182.2348\n",
      "         8      -18552.1426        +397.8181\n",
      "         9      -18517.5323         +34.6103\n",
      "        10      -18514.0767          +3.4556\n",
      "        11      -18513.7010          +0.3757\n",
      "        12      -18513.6371          +0.0639\n",
      "        13      -18513.6235          +0.0136\n",
      "        14      -18513.6203          +0.0032\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 4 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -52795.5760             +nan\n",
      "         2      -47522.2751       +5273.3009\n",
      "         3      -43674.3454       +3847.9297\n",
      "         4      -37112.8568       +6561.4886\n",
      "         5      -30688.4412       +6424.4156\n",
      "         6      -27927.7664       +2760.6748\n",
      "         7      -26295.0628       +1632.7036\n",
      "         8      -24113.2862       +2181.7766\n",
      "         9      -20123.1891       +3990.0971\n",
      "        10      -19691.1768        +432.0122\n",
      "        11      -19642.9323         +48.2445\n",
      "        12      -19632.0401         +10.8922\n",
      "        13      -19627.9022          +4.1379\n",
      "        14      -19625.7346          +2.1676\n",
      "        15      -19624.4199          +1.3147\n",
      "        16      -19623.5421          +0.8778\n",
      "        17      -19622.9103          +0.6317\n",
      "        18      -19622.4298          +0.4805\n",
      "        19      -19622.0489          +0.3809\n",
      "        20      -19621.7369          +0.3120\n",
      "        21      -19621.4739          +0.2630\n",
      "        22      -19621.2465          +0.2274\n",
      "        23      -19621.0452          +0.2013\n",
      "        24      -19620.8631          +0.1821\n",
      "        25      -19620.6954          +0.1677\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 5 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -51376.8423             +nan\n",
      "         2      -48817.3205       +2559.5218\n",
      "         3      -46473.4022       +2343.9183\n",
      "         4      -38952.8173       +7520.5849\n",
      "         5      -26343.9406      +12608.8768\n",
      "         6      -20990.2697       +5353.6708\n",
      "         7      -19365.7324       +1624.5373\n",
      "         8      -19098.9602        +266.7722\n",
      "         9      -18966.6622        +132.2980\n",
      "        10      -18554.3391        +412.3231\n",
      "        11      -16957.2244       +1597.1147\n",
      "        12      -14039.2388       +2917.9856\n",
      "        13      -13239.4700        +799.7688\n",
      "        14      -13200.4977         +38.9722\n",
      "        15      -13195.7777          +4.7200\n",
      "        16      -13194.0265          +1.7512\n",
      "        17      -13192.9648          +1.0617\n",
      "        18      -13192.2414          +0.7233\n",
      "        19      -13191.7111          +0.5303\n",
      "        20      -13191.2957          +0.4154\n",
      "        21      -13190.9523          +0.3434\n",
      "        22      -13190.6571          +0.2952\n",
      "        23      -13190.3964          +0.2608\n",
      "        24      -13190.1617          +0.2347\n",
      "        25      -13189.9476          +0.2141\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 6 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -49938.7984             +nan\n",
      "         2      -48722.6716       +1216.1268\n",
      "         3      -46195.0545       +2527.6171\n",
      "         4      -39508.4317       +6686.6228\n",
      "         5      -31867.4838       +7640.9479\n",
      "         6      -28736.2454       +3131.2384\n",
      "         7      -24817.4501       +3918.7953\n",
      "         8      -19539.3494       +5278.1007\n",
      "         9      -14537.8761       +5001.4733\n",
      "        10      -12554.4927       +1983.3834\n",
      "        11       -9641.1117       +2913.3810\n",
      "        12       -8447.2361       +1193.8756\n",
      "        13       -8383.8863         +63.3499\n",
      "        14       -8379.3592          +4.5271\n",
      "        15       -8378.8755          +0.4836\n",
      "        16       -8378.7586          +0.1169\n",
      "        17       -8378.6999          +0.0587\n",
      "        18       -8378.6584          +0.0415\n",
      "        19       -8378.6243          +0.0341\n",
      "        20       -8378.5944          +0.0299\n",
      "        21       -8378.5673          +0.0271\n",
      "        22       -8378.5423          +0.0250\n",
      "        23       -8378.5190          +0.0232\n",
      "        24       -8378.4973          +0.0217\n",
      "        25       -8378.4770          +0.0204\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 7 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -50718.5324             +nan\n",
      "         2      -49037.5675       +1680.9649\n",
      "         3      -47401.3371       +1636.2304\n",
      "         4      -42368.3917       +5032.9454\n",
      "         5      -29933.7012      +12434.6905\n",
      "         6      -15401.0521      +14532.6491\n",
      "         7       -9769.3565       +5631.6956\n",
      "         8       -8545.5606       +1223.7960\n",
      "         9       -8459.8733         +85.6873\n",
      "        10       -8419.7635         +40.1098\n",
      "        11       -8403.3153         +16.4483\n",
      "        12       -8393.3383          +9.9769\n",
      "        13       -8387.2090          +6.1293\n",
      "        14       -8383.5659          +3.6432\n",
      "        15       -8381.3731          +2.1928\n",
      "        16       -8379.9819          +1.3912\n",
      "        17       -8379.0398          +0.9420\n",
      "        18       -8378.3622          +0.6776\n",
      "        19       -8377.8491          +0.5132\n",
      "        20       -8377.4433          +0.4058\n",
      "        21       -8377.1106          +0.3327\n",
      "        22       -8376.8297          +0.2808\n",
      "        23       -8376.5869          +0.2428\n",
      "        24       -8376.3728          +0.2142\n",
      "        25       -8376.1806          +0.1921\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 8 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -50534.6610             +nan\n",
      "         2      -49537.3891        +997.2719\n",
      "         3      -48969.8017        +567.5874\n",
      "         4      -47095.4059       +1874.3958\n",
      "         5      -41060.4131       +6034.9928\n",
      "         6      -31701.4406       +9358.9725\n",
      "         7      -23561.1107       +8140.3298\n",
      "         8      -19633.9308       +3927.1800\n",
      "         9      -16250.0328       +3383.8979\n",
      "        10      -14580.9753       +1669.0575\n",
      "        11      -14264.8781        +316.0971\n",
      "        12      -14221.7495         +43.1286\n",
      "        13      -14212.3568          +9.3927\n",
      "        14      -14207.1283          +5.2286\n",
      "        15      -14203.0263          +4.1020\n",
      "        16      -14199.3491          +3.6772\n",
      "        17      -14195.7384          +3.6107\n",
      "        18      -14191.9906          +3.7478\n",
      "        19      -14188.0121          +3.9785\n",
      "        20      -14183.7941          +4.2180\n",
      "        21      -14179.3968          +4.3973\n",
      "        22      -14174.9296          +4.4672\n",
      "        23      -14170.5285          +4.4012\n",
      "        24      -14166.3268          +4.2017\n",
      "        25      -14162.3606          +3.9662\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 9 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -50483.1842             +nan\n",
      "         2      -49442.4182       +1040.7660\n",
      "         3      -48835.9847        +606.4335\n",
      "         4      -47438.9482       +1397.0365\n",
      "         5      -43406.1593       +4032.7889\n",
      "         6      -33999.4745       +9406.6849\n",
      "         7      -21141.6466      +12857.8278\n",
      "         8      -12762.3671       +8379.2795\n",
      "         9       -9596.8834       +3165.4837\n",
      "        10       -8503.5570       +1093.3264\n",
      "        11       -8409.1431         +94.4139\n",
      "        12       -8395.0339         +14.1092\n",
      "        13       -8388.0430          +6.9909\n",
      "        14       -8382.9623          +5.0806\n",
      "        15       -8378.0497          +4.9126\n",
      "        16       -8371.7858          +6.2639\n",
      "        17       -8362.5831          +9.2028\n",
      "        18       -8355.4896          +7.0935\n",
      "        19       -8351.3421          +4.1475\n",
      "        20       -8348.1159          +3.2262\n",
      "        21       -8345.1928          +2.9231\n",
      "        22       -8342.3886          +2.8042\n",
      "        23       -8339.6828          +2.7058\n",
      "        24       -8337.1148          +2.5680\n",
      "        25       -8334.7106          +2.4041\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 10 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -50192.7080             +nan\n",
      "         2      -48820.1816       +1372.5265\n",
      "         3      -46493.0261       +2327.1554\n",
      "         4      -39521.8906       +6971.1356\n",
      "         5      -29478.1915      +10043.6991\n",
      "         6      -20944.7432       +8533.4483\n",
      "         7      -15565.7472       +5378.9959\n",
      "         8      -13305.0373       +2260.7100\n",
      "         9      -10422.5905       +2882.4468\n",
      "        10       -8648.4392       +1774.1512\n",
      "        11       -8402.6415        +245.7977\n",
      "        12       -8384.8345         +17.8070\n",
      "        13       -8381.3849          +3.4497\n",
      "        14       -8379.7188          +1.6661\n",
      "        15       -8378.4306          +1.2881\n",
      "        16       -8377.3039          +1.1267\n",
      "        17       -8376.2838          +1.0201\n",
      "        18       -8375.3460          +0.9378\n",
      "        19       -8374.4754          +0.8706\n",
      "        20       -8373.6609          +0.8145\n",
      "        21       -8372.8941          +0.7668\n",
      "        22       -8372.1683          +0.7258\n",
      "        23       -8371.4783          +0.6900\n",
      "        24       -8370.8198          +0.6585\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 11 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "        25       -8370.1893          +0.6305\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,12):\n",
    "    lab_model = hmm.MultinomialHMM(n_components=i, n_iter=25, verbose=True).fit(data_lab, lab_lengths)\n",
    "    print(f\"Model evaluation with {i} components\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "labEval = [-50797, -34546, -27282, -18513, -19620, -13189, -8378, -8376, -14162,  -8334, -8370] \n",
    "# TODO: Divide numbers by number of data samples to get a per sample evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1383472.8843             +nan\n",
      "         2    -1302381.7676      +81091.1167\n",
      "         3    -1302381.7676          +0.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 1 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1465197.7036             +nan\n",
      "         2    -1300826.8002     +164370.9034\n",
      "         3    -1298537.7497       +2289.0505\n",
      "         4    -1293757.0251       +4780.7246\n",
      "         5    -1281756.7911      +12000.2340\n",
      "         6    -1248957.5861      +32799.2050\n",
      "         7    -1161332.8329      +87624.7532\n",
      "         8    -1000201.9277     +161130.9052\n",
      "         9     -910884.4528      +89317.4749\n",
      "        10     -904772.8867       +6111.5661\n",
      "        11     -904173.9144        +598.9724\n",
      "        12     -904100.2196         +73.6948\n",
      "        13     -904082.1091         +18.1104\n",
      "        14     -904074.8031          +7.3061\n",
      "        15     -904071.0602          +3.7429\n",
      "        16     -904068.9298          +2.1303\n",
      "        17     -904067.6432          +1.2866\n",
      "        18     -904066.8289          +0.8144\n",
      "        19     -904066.2905          +0.5383\n",
      "        20     -904065.9203          +0.3703\n",
      "        21     -904065.6567          +0.2636\n",
      "        22     -904065.4637          +0.1930\n",
      "        23     -904065.3193          +0.1444\n",
      "        24     -904065.2093          +0.1100\n",
      "        25     -904065.1245          +0.0848\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 2 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1475547.5880             +nan\n",
      "         2    -1278509.7724     +197037.8156\n",
      "         3    -1231740.1444      +46769.6280\n",
      "         4    -1126566.3658     +105173.7787\n",
      "         5     -970031.0043     +156535.3615\n",
      "         6     -830441.6826     +139589.3217\n",
      "         7     -771434.2941      +59007.3885\n",
      "         8     -756575.4044      +14858.8897\n",
      "         9     -751492.6798       +5082.7246\n",
      "        10     -749937.9083       +1554.7715\n",
      "        11     -749614.2647        +323.6437\n",
      "        12     -749549.8628         +64.4018\n",
      "        13     -749537.9608         +11.9020\n",
      "        14     -749534.7313          +3.2295\n",
      "        15     -749533.4624          +1.2690\n",
      "        16     -749532.8746          +0.5878\n",
      "        17     -749532.5757          +0.2989\n",
      "        18     -749532.4107          +0.1650\n",
      "        19     -749532.3113          +0.0994\n",
      "        20     -749532.2458          +0.0655\n",
      "        21     -749532.1991          +0.0467\n",
      "        22     -749532.1635          +0.0356\n",
      "        23     -749532.1351          +0.0284\n",
      "        24     -749532.1116          +0.0235\n",
      "        25     -749532.0918          +0.0199\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 3 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1347930.9878             +nan\n",
      "         2    -1262051.6248      +85879.3630\n",
      "         3    -1181943.0106      +80108.6141\n",
      "         4     -993560.3403     +188382.6704\n",
      "         5     -708131.0937     +285429.2466\n",
      "         6     -614153.8899      +93977.2038\n",
      "         7     -606911.8702       +7242.0197\n",
      "         8     -606052.5363        +859.3339\n",
      "         9     -605878.8984        +173.6379\n",
      "        10     -605782.4141         +96.4843\n",
      "        11     -605691.8350         +90.5791\n",
      "        12     -605594.2852         +97.5498\n",
      "        13     -605485.7303        +108.5548\n",
      "        14     -605363.7667        +121.9637\n",
      "        15     -605226.2134        +137.5533\n",
      "        16     -605070.8069        +155.4065\n",
      "        17     -604895.1502        +175.6567\n",
      "        18     -604696.7303        +198.4199\n",
      "        19     -604472.9471        +223.7833\n",
      "        20     -604221.1205        +251.8266\n",
      "        21     -603938.4467        +282.6738\n",
      "        22     -603621.8752        +316.5715\n",
      "        23     -603267.8969        +353.9783\n",
      "        24     -602872.2723        +395.6245\n",
      "        25     -602429.7976        +442.4748\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 4 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1372107.4362             +nan\n",
      "         2    -1294888.7050      +77218.7312\n",
      "         3    -1283255.7551      +11632.9499\n",
      "         4    -1257767.3201      +25488.4350\n",
      "         5    -1188683.5528      +69083.7673\n",
      "         6     -987536.6036     +201146.9492\n",
      "         7     -708022.1428     +279514.4608\n",
      "         8     -625503.8267      +82518.3161\n",
      "         9     -595077.5967      +30426.2300\n",
      "        10     -530247.5207      +64830.0760\n",
      "        11     -453562.9874      +76684.5332\n",
      "        12     -429212.0221      +24350.9653\n",
      "        13     -365999.9579      +63212.0642\n",
      "        14     -332102.4847      +33897.4732\n",
      "        15     -329283.4777       +2819.0070\n",
      "        16     -328910.6423        +372.8354\n",
      "        17     -328805.5958        +105.0465\n",
      "        18     -328766.0532         +39.5425\n",
      "        19     -328749.8941         +16.1592\n",
      "        20     -328743.1422          +6.7519\n",
      "        21     -328740.3097          +2.8325\n",
      "        22     -328739.1207          +1.1890\n",
      "        23     -328738.6209          +0.4998\n",
      "        24     -328738.4100          +0.2108\n",
      "        25     -328738.3206          +0.0894\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 5 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1389813.6303             +nan\n",
      "         2    -1282192.8431     +107620.7872\n",
      "         3    -1232303.2307      +49889.6123\n",
      "         4    -1051099.8181     +181203.4127\n",
      "         5     -733118.0401     +317981.7780\n",
      "         6     -582172.6234     +150945.4167\n",
      "         7     -496489.5740      +85683.0494\n",
      "         8     -456554.0990      +39935.4750\n",
      "         9     -454566.8938       +1987.2052\n",
      "        10     -454052.3408        +514.5530\n",
      "        11     -453570.6464        +481.6944\n",
      "        12     -453044.7597        +525.8867\n",
      "        13     -452450.7311        +594.0286\n",
      "        14     -451776.0063        +674.7248\n",
      "        15     -451019.3542        +756.6522\n",
      "        16     -450187.3877        +831.9664\n",
      "        17     -449290.3075        +897.0802\n",
      "        18     -448346.8696        +943.4379\n",
      "        19     -447396.3414        +950.5282\n",
      "        20     -446503.0275        +893.3139\n",
      "        21     -445743.6098        +759.4177\n",
      "        22     -445175.1138        +568.4960\n",
      "        23     -444806.0544        +369.0594\n",
      "        24     -444598.4860        +207.5684\n",
      "        25     -444495.6809        +102.8051\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 6 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1446661.6833             +nan\n",
      "         2    -1258049.5924     +188612.0909\n",
      "         3    -1160158.4979      +97891.0945\n",
      "         4    -1006942.7955     +153215.7025\n",
      "         5     -809782.5683     +197160.2271\n",
      "         6     -600774.2465     +209008.3218\n",
      "         7     -446292.2941     +154481.9524\n",
      "         8     -351924.1124      +94368.1817\n",
      "         9     -329720.1474      +22203.9650\n",
      "        10     -326799.8974       +2920.2500\n",
      "        11     -325239.5355       +1560.3619\n",
      "        12     -323812.2873       +1427.2482\n",
      "        13     -322480.5581       +1331.7292\n",
      "        14     -321303.2145       +1177.3435\n",
      "        15     -320370.0529        +933.1617\n",
      "        16     -319730.6514        +639.4014\n",
      "        17     -319354.5446        +376.1068\n",
      "        18     -319164.0226        +190.5221\n",
      "        19     -319079.3259         +84.6967\n",
      "        20     -319044.4596         +34.8664\n",
      "        21     -319029.8629         +14.5966\n",
      "        22     -319022.7551          +7.1078\n",
      "        23     -319018.2283          +4.5268\n",
      "        24     -319014.5546          +3.6737\n",
      "        25     -319011.2395          +3.3151\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 7 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1366276.4108             +nan\n",
      "         2    -1263232.3740     +103044.0367\n",
      "         3    -1168263.6230      +94968.7510\n",
      "         4     -979130.7882     +189132.8348\n",
      "         5     -850572.6949     +128558.0933\n",
      "         6     -754661.5100      +95911.1849\n",
      "         7     -662791.3320      +91870.1781\n",
      "         8     -606957.6499      +55833.6820\n",
      "         9     -496968.4504     +109989.1996\n",
      "        10     -387221.4756     +109746.9747\n",
      "        11     -322493.3838      +64728.0918\n",
      "        12     -316554.1737       +5939.2101\n",
      "        13     -315545.2626       +1008.9111\n",
      "        14     -314396.1995       +1149.0631\n",
      "        15     -312382.9262       +2013.2733\n",
      "        16     -306377.6194       +6005.3068\n",
      "        17     -272465.8877      +33911.7317\n",
      "        18     -209453.2114      +63012.6763\n",
      "        19     -198850.5757      +10602.6357\n",
      "        20     -197642.6175       +1207.9582\n",
      "        21     -196721.0187        +921.5988\n",
      "        22     -196051.8024        +669.2163\n",
      "        23     -195639.1773        +412.6251\n",
      "        24     -195422.9713        +216.2061\n",
      "        25     -195323.6537         +99.3176\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 8 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1415458.2823             +nan\n",
      "         2    -1283398.1396     +132060.1427\n",
      "         3    -1234835.5273      +48562.6124\n",
      "         4    -1087632.8082     +147202.7190\n",
      "         5     -865402.7082     +222230.1000\n",
      "         6     -695055.4324     +170347.2758\n",
      "         7     -615188.1693      +79867.2631\n",
      "         8     -582783.5862      +32404.5831\n",
      "         9     -505982.8065      +76800.7797\n",
      "        10     -453547.6621      +52435.1443\n",
      "        11     -449648.3611       +3899.3011\n",
      "        12     -449064.7434        +583.6176\n",
      "        13     -448629.6015        +435.1420\n",
      "        14     -447880.8651        +748.7364\n",
      "        15     -445893.5458       +1987.3193\n",
      "        16     -437166.1204       +8727.4254\n",
      "        17     -393507.0473      +43659.0731\n",
      "        18     -333034.0238      +60473.0235\n",
      "        19     -324776.9504       +8257.0735\n",
      "        20     -324137.3704        +639.5800\n",
      "        21     -323824.0154        +313.3549\n",
      "        22     -323527.7245        +296.2909\n",
      "        23     -323215.7696        +311.9549\n",
      "        24     -322878.6271        +337.1425\n",
      "        25     -322508.6271        +370.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 9 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1373358.9473             +nan\n",
      "         2    -1294793.6282      +78565.3190\n",
      "         3    -1282678.5031      +12115.1252\n",
      "         4    -1251708.5550      +30969.9480\n",
      "         5    -1157056.5334      +94652.0216\n",
      "         6     -958792.0480     +198264.4854\n",
      "         7     -658354.0925     +300437.9556\n",
      "         8     -455689.5311     +202664.5613\n",
      "         9     -403943.9403      +51745.5908\n",
      "        10     -396611.7740       +7332.1663\n",
      "        11     -395026.3995       +1585.3744\n",
      "        12     -394221.6707        +804.7289\n",
      "        13     -393540.9680        +680.7027\n",
      "        14     -392745.1079        +795.8601\n",
      "        15     -391524.5239       +1220.5840\n",
      "        16     -389133.6613       +2390.8626\n",
      "        17     -383397.2077       +5736.4536\n",
      "        18     -367452.6256      +15944.5822\n",
      "        19     -323434.2554      +44018.3702\n",
      "        20     -248452.3606      +74981.8948\n",
      "        21     -208714.1826      +39738.1780\n",
      "        22     -203293.6885       +5420.4940\n",
      "        23     -201937.0552       +1356.6333\n",
      "        24     -200544.0120       +1393.0432\n",
      "        25     -199044.7221       +1499.2899\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 10 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1370984.2189             +nan\n",
      "         2    -1281799.3192      +89184.8997\n",
      "         3    -1238974.0846      +42825.2347\n",
      "         4    -1110981.9987     +127992.0858\n",
      "         5     -867881.5122     +243100.4866\n",
      "         6     -617526.4916     +250355.0206\n",
      "         7     -429327.8754     +188198.6162\n",
      "         8     -367055.9354      +62271.9400\n",
      "         9     -323159.8224      +43896.1130\n",
      "        10     -316870.9705       +6288.8519\n",
      "        11     -315784.9671       +1086.0034\n",
      "        12     -314794.9878        +989.9792\n",
      "        13     -313808.1156        +986.8723\n",
      "        14     -312903.3110        +904.8046\n",
      "        15     -312112.8232        +790.4878\n",
      "        16     -311387.2281        +725.5951\n",
      "        17     -310661.2564        +725.9717\n",
      "        18     -309902.8461        +758.4103\n",
      "        19     -309123.7095        +779.1366\n",
      "        20     -308370.1511        +753.5584\n",
      "        21     -307703.6488        +666.5023\n",
      "        22     -307176.2825        +527.3663\n",
      "        23     -306808.5003        +367.7823\n",
      "        24     -306582.1137        +226.3866\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 11 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "        25     -306455.5320        +126.5816\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,12):\n",
    "    turk_model = hmm.MultinomialHMM(n_components=i, n_iter=25, verbose=True).fit(data_turk, turk_lengths)\n",
    "    print(f\"Model evaluation with {i} components\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "turkEval = [-1302381, -904065, -749532, -602429, -328738, -444495, -319011, -195323, -322508, -199044, -306582]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Model Evaluation')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,len(labEval)+1), labEval)\n",
    "plt.title('Lab HMM model with differnt number of hidden states')\n",
    "plt.xlabel('Number of hidden states')\n",
    "plt.ylabel('Model Evaluation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Model Evaluation')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,len(turkEval)+1), turkEval)\n",
    "plt.title('Turk HMM model with differnt number of hidden states')\n",
    "plt.xlabel('Number of hidden states')\n",
    "plt.ylabel('Model Evaluation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# See if model improves with 12+ states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -50882.4007             +nan\n",
      "         2      -49040.9901       +1841.4106\n",
      "         3      -47484.0436       +1556.9465\n",
      "         4      -43567.3415       +3916.7021\n",
      "         5      -36687.3462       +6879.9953\n",
      "         6      -27106.0959       +9581.2503\n",
      "         7      -17022.0040      +10084.0919\n",
      "         8      -12423.2915       +4598.7126\n",
      "         9       -9318.0266       +3105.2649\n",
      "        10       -8440.8788        +877.1478\n",
      "        11       -8387.0318         +53.8470\n",
      "        12       -8374.6681         +12.3637\n",
      "        13       -8367.6831          +6.9849\n",
      "        14       -8362.0269          +5.6562\n",
      "        15       -8356.5672          +5.4597\n",
      "        16       -8350.7949          +5.7723\n",
      "        17       -8344.3673          +6.4276\n",
      "        18       -8337.0941          +7.2732\n",
      "        19       -8329.1035          +7.9906\n",
      "        20       -8320.7723          +8.3313\n",
      "        21       -8312.4905          +8.2817\n",
      "        22       -8304.6565          +7.8340\n",
      "        23       -8297.6652          +6.9913\n",
      "        24       -8291.7202          +5.9450\n",
      "        25       -8286.5108          +5.2094\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 12 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -50691.9668             +nan\n",
      "         2      -49430.0738       +1261.8930\n",
      "         3      -48646.1505        +783.9232\n",
      "         4      -46099.8459       +2546.3047\n",
      "         5      -37738.0545       +8361.7914\n",
      "         6      -26343.1059      +11394.9486\n",
      "         7      -20282.3450       +6060.7609\n",
      "         8      -15758.7271       +4523.6179\n",
      "         9      -12956.6901       +2802.0370\n",
      "        10      -10536.2786       +2420.4115\n",
      "        11       -8530.0110       +2006.2676\n",
      "        12       -8399.8628        +130.1482\n",
      "        13       -8382.3111         +17.5517\n",
      "        14       -8375.3073          +7.0038\n",
      "        15       -8371.3519          +3.9553\n",
      "        16       -8368.3663          +2.9857\n",
      "        17       -8365.8505          +2.5158\n",
      "        18       -8363.6363          +2.2141\n",
      "        19       -8361.6441          +1.9922\n",
      "        20       -8359.8232          +1.8209\n",
      "        21       -8358.1354          +1.6879\n",
      "        22       -8356.5495          +1.5859\n",
      "        23       -8355.0398          +1.5097\n",
      "        24       -8353.5849          +1.4550\n",
      "        25       -8352.1665          +1.4183\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 13 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -50599.2843             +nan\n",
      "         2      -48801.9817       +1797.3026\n",
      "         3      -46411.3816       +2390.6001\n",
      "         4      -39529.4972       +6881.8844\n",
      "         5      -30277.4792       +9252.0180\n",
      "         6      -23744.7486       +6532.7306\n",
      "         7      -21074.7737       +2669.9749\n",
      "         8      -20428.1685        +646.6052\n",
      "         9      -20305.5181        +122.6504\n",
      "        10      -20262.2972         +43.2208\n",
      "        11      -20228.9615         +33.3357\n",
      "        12      -20186.8540         +42.1075\n",
      "        13      -20142.4120         +44.4420\n",
      "        14      -20087.4644         +54.9476\n",
      "        15      -19914.4979        +172.9665\n",
      "        16      -17566.0764       +2348.4215\n",
      "        17      -15038.5172       +2527.5593\n",
      "        18      -14828.3614        +210.1557\n",
      "        19      -14797.9934         +30.3681\n",
      "        20      -14788.0888          +9.9045\n",
      "        21      -14780.2617          +7.8271\n",
      "        22      -14770.8936          +9.3681\n",
      "        23      -14760.8399         +10.0537\n",
      "        24      -14753.6572          +7.1827\n",
      "        25      -14748.4774          +5.1799\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 14 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1      -50196.8018             +nan\n",
      "         2      -49357.0061        +839.7957\n",
      "         3      -48405.5553        +951.4508\n",
      "         4      -44965.3565       +3440.1988\n",
      "         5      -32093.7405      +12871.6160\n",
      "         6      -17181.6853      +14912.0552\n",
      "         7      -14193.2511       +2988.4342\n",
      "         8      -13894.3242        +298.9269\n",
      "         9      -13835.0436         +59.2807\n",
      "        10      -13801.8717         +33.1719\n",
      "        11      -13774.3371         +27.5346\n",
      "        12      -13746.0028         +28.3343\n",
      "        13      -13710.5582         +35.4446\n",
      "        14      -13649.0523         +61.5059\n",
      "        15      -13299.4743        +349.5780\n",
      "        16      -10169.4967       +3129.9776\n",
      "        17       -8433.2737       +1736.2230\n",
      "        18       -8309.8298        +123.4438\n",
      "        19       -8290.0197         +19.8102\n",
      "        20       -8277.3976         +12.6221\n",
      "        21       -8267.9381          +9.4595\n",
      "        22       -8260.9695          +6.9686\n",
      "        23       -8255.4529          +5.5166\n",
      "        24       -8250.9258          +4.5271\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 15 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "        25       -8247.0699          +3.8559\n"
     ]
    }
   ],
   "source": [
    "for i in range(12,16):\n",
    "    lab_model = hmm.MultinomialHMM(n_components=i, n_iter=25, verbose=True).fit(data_lab, lab_lengths)\n",
    "    print(f\"Model evaluation with {i} components\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "labEval.extend([-8286, -8352, -14748, -8250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1434878.9513             +nan\n",
      "         2    -1283870.0926     +151008.8587\n",
      "         3    -1250701.9658      +33168.1268\n",
      "         4    -1162330.6635      +88371.3024\n",
      "         5     -966531.2544     +195799.4091\n",
      "         6     -767722.8034     +198808.4510\n",
      "         7     -576182.1786     +191540.6248\n",
      "         8     -441926.8142     +134255.3644\n",
      "         9     -337758.9159     +104167.8983\n",
      "        10     -228775.3607     +108983.5552\n",
      "        11     -206262.3478      +22513.0129\n",
      "        12     -205186.0732       +1076.2746\n",
      "        13     -204524.4524        +661.6208\n",
      "        14     -203720.4230        +804.0293\n",
      "        15     -202714.3139       +1006.1092\n",
      "        16     -201530.9134       +1183.4005\n",
      "        17     -200244.2376       +1286.6758\n",
      "        18     -198937.5020       +1306.7356\n",
      "        19     -197710.9262       +1226.5758\n",
      "        20     -196688.4902       +1022.4360\n",
      "        21     -195952.3311        +736.1590\n",
      "        22     -195494.1305        +458.2006\n",
      "        23     -195240.9934        +253.1371\n",
      "        24     -195106.2664        +134.7270\n",
      "        25     -195027.0630         +79.2034\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 12 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1366399.9169             +nan\n",
      "         2    -1285181.8077      +81218.1092\n",
      "         3    -1247944.4050      +37237.4027\n",
      "         4    -1121308.5725     +126635.8325\n",
      "         5     -826043.3711     +295265.2014\n",
      "         6     -528780.4398     +297262.9312\n",
      "         7     -353917.9700     +174862.4698\n",
      "         8     -325347.6146      +28570.3554\n",
      "         9     -323416.9489       +1930.6657\n",
      "        10     -321426.2135       +1990.7354\n",
      "        11     -315942.5623       +5483.6512\n",
      "        12     -295670.2664      +20272.2959\n",
      "        13     -242634.3203      +53035.9460\n",
      "        14     -206946.2181      +35688.1023\n",
      "        15     -204017.0649       +2929.1531\n",
      "        16     -203454.7266        +562.3384\n",
      "        17     -202999.8031        +454.9235\n",
      "        18     -202537.5596        +462.2435\n",
      "        19     -202053.7365        +483.8231\n",
      "        20     -201541.9032        +511.8333\n",
      "        21     -200996.9892        +544.9140\n",
      "        22     -200414.7732        +582.2159\n",
      "        23     -199792.4049        +622.3684\n",
      "        24     -199129.9328        +662.4721\n",
      "        25     -198432.6441        +697.2887\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 13 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1354051.3966             +nan\n",
      "         2    -1286910.8064      +67140.5902\n",
      "         3    -1247290.4077      +39620.3987\n",
      "         4    -1104965.1644     +142325.2433\n",
      "         5     -821083.5401     +283881.6243\n",
      "         6     -601671.4937     +219412.0464\n",
      "         7     -477513.5812     +124157.9125\n",
      "         8     -344551.3725     +132962.2087\n",
      "         9     -225072.2320     +119479.1405\n",
      "        10     -206869.5649      +18202.6671\n",
      "        11     -205666.6057       +1202.9592\n",
      "        12     -205131.5688        +535.0370\n",
      "        13     -204668.7092        +462.8596\n",
      "        14     -204218.1509        +450.5583\n",
      "        15     -203761.1690        +456.9818\n",
      "        16     -203278.6096        +482.5594\n",
      "        17     -202751.7730        +526.8367\n",
      "        18     -202166.9635        +584.8095\n",
      "        19     -201517.8789        +649.0846\n",
      "        20     -200804.8243        +713.0546\n",
      "        21     -200031.2254        +773.5989\n",
      "        22     -199201.9981        +829.2273\n",
      "        23     -198328.0485        +873.9496\n",
      "        24     -197436.2263        +891.8222\n",
      "        25     -196577.0138        +859.2126\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 14 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "         1    -1411952.8443             +nan\n",
      "         2    -1283507.5820     +128445.2623\n",
      "         3    -1244416.2941      +39091.2878\n",
      "         4    -1108587.6414     +135828.6527\n",
      "         5     -771540.7995     +337046.8419\n",
      "         6     -451267.2659     +320273.5336\n",
      "         7     -264610.8124     +186656.4535\n",
      "         8     -208424.3379      +56186.4746\n",
      "         9     -204904.5703       +3519.7676\n",
      "        10     -204129.2935        +775.2768\n",
      "        11     -203422.1825        +707.1110\n",
      "        12     -202576.8914        +845.2912\n",
      "        13     -201592.0120        +984.8794\n",
      "        14     -200540.7933       +1051.2187\n",
      "        15     -199498.8336       +1041.9597\n",
      "        16     -198527.0349        +971.7987\n",
      "        17     -197668.0158        +859.0191\n",
      "        18     -196936.8389        +731.1770\n",
      "        19     -196330.8250        +606.0138\n",
      "        20     -195849.1378        +481.6872\n",
      "        21     -195487.3426        +361.7952\n",
      "        22     -195228.2610        +259.0816\n",
      "        23     -195049.0880        +179.1730\n",
      "        24     -194926.6024        +122.4856\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation with 15 components\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "        25     -194840.3199         +86.2825\n"
     ]
    }
   ],
   "source": [
    "for i in range(12,16):\n",
    "    turk_model = hmm.MultinomialHMM(n_components=i, n_iter=25, verbose=True).fit(data_turk, turk_lengths)\n",
    "    print(f\"Model evaluation with {i} components\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "turkEval.extend([-195027, -198432, -196577])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-1302381,\n",
       " -904065,\n",
       " -749532,\n",
       " -602429,\n",
       " -328738,\n",
       " -444495,\n",
       " -319011,\n",
       " -195323,\n",
       " -322508,\n",
       " -199044,\n",
       " -306582,\n",
       " -195027,\n",
       " -198432,\n",
       " -196577]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turkEval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Model Evaluation')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,len(labEval)+1), labEval)\n",
    "plt.title('Lab HMM model with differnt number of hidden states')\n",
    "plt.xlabel('Number of hidden states')\n",
    "plt.ylabel('Model Evaluation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Model Evaluation')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,len(turkEval)+1), turkEval)\n",
    "plt.title('Turk HMM model with differnt number of hidden states')\n",
    "plt.xlabel('Number of hidden states')\n",
    "plt.ylabel('Model Evaluation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data from google colab, each model is trained with 50 iterations\n",
    "\n",
    "LabEval  = [-49841, -33731, -25826, -20937, -13682, -8378, -13179, -8359, -8316, -8307, -8224]\n",
    "TurkEval = [-1302381, -878660, -678653, -496042, -461424, -322624, -317170, -449264, -195265, -193896,  -194017]\n",
    "\n",
    "plt.plot(range(1,len(LabEval)+1), LabEval)\n",
    "plt.title('Lab HMM model with differnt number of hidden states')\n",
    "plt.xlabel('Number of hidden states')\n",
    "plt.ylabel('Model Evaluation')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(range(1,len(TurkEval)+1), TurkEval)\n",
    "plt.title('Turk HMM model with differnt number of hidden states')\n",
    "plt.xlabel('Number of hidden states')\n",
    "plt.ylabel('Model Evaluation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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   ],
   "source": [
    "# Do Normalised plot\n",
    "\n",
    "LabEval  = [-49841, -33731, -25826, -20937, -13682, -8378, -13179, -8359, -8316, -8307, -8224]\n",
    "TurkEval = [-1302381, -878660, -678653, -496042, -461424, -322624, -317170, -449264, -195265, -193896,  -194017]\n",
    "\n",
    "# Num of lab data is 14\n",
    "# Num of turk data is 461\n",
    "\n",
    "NormLabEval = [x / 14 for x in LabEval]\n",
    "NormTurkEval = [x / 461 for x in TurkEval]\n",
    "\n",
    "plt.plot(range(1,len(NormLabEval)+1), NormLabEval, label='Lab Model')\n",
    "plt.plot(range(1,len(NormTurkEval)+1), NormTurkEval, label='Turk Model')\n",
    "plt.title('HMM models with varying number of hidden states')\n",
    "plt.xlabel('Number of hidden states')\n",
    "plt.ylabel('Model Evaluation')\n",
    "plt.legend()\n",
    "plt.grid(linestyle='dotted')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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