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results_for_speaker_recognition.ipynb
3526 lines (3525 with data), 437.0 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import os\n",
"import subprocess\n",
"import csv\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"def unit_gaussian(x,mu,sigma):\n",
" inv_cov = np.linalg.inv(sigma)\n",
" D = mu.shape[0]\n",
" exponent = np.exp((-0.5)*np.dot(np.dot((x - mu),inv_cov),(x - mu).T))\n",
" z = 1/(((2*np.pi)**(D/2))*(np.linalg.det(sigma)**0.5))\n",
" return z*exponent\n",
"\n",
"def calculate_likelihood(N,K,data,mu_k,cov_k,pi_k):\n",
" log_likelihood = 0\n",
" for n in range(N):\n",
" temp = 0\n",
" for k in range(K):\n",
" temp += pi_k[k] * (unit_gaussian(data[n],mu_k[k,:],cov_k[k,:,:]))\n",
" log_likelihood += np.log(temp)\n",
" return log_likelihood\n",
"\n",
"def testing_model(MAP_ADAPTED_MODEL,test_array,index1,index2,D = 13,K = 32):\n",
" mu_map,cov_map,pi_map = MAP_ADAPTED_MODEL[\"mean\"],MAP_ADAPTED_MODEL[\"cov\"],MAP_ADAPTED_MODEL[\"pi\"]\n",
" if len(test_array[index1:index2]) == 1500:\n",
" return calculate_likelihood(1500,K,np.array(test_array[index1:index2]),mu_map,cov_map,pi_map)\n",
" else:\n",
" print(\"not enough data points\")\n",
" \n",
"def load_csv_file(file_path):\n",
" array = []\n",
" with open(file_path) as csv_file:\n",
" csv_reader = csv.reader(csv_file)\n",
" for e in csv_reader:\n",
" array.append(e)\n",
" return np.array(array,dtype = np.float64),len(array)\n",
"\n",
"def load_map_model(map_file_path):\n",
" return np.load(map_file_path).item()\n",
"\n",
"def check_name(list_of_strings,file_name_string):\n",
" for e in list_of_strings:\n",
" if e in file_name_string:\n",
" return True\n",
" return False"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"ROOT_TESTING_FOLDER = \"/Users/siva/Documents/speaker_recognition/DHI_audio_files/converted_16k_voice_samples_with_mfcc\"\n",
"ROOT_MODELS_FOLDER = \"/Users/siva/Documents/speaker_recognition/numpy_files/new_models_for_results\"\n",
"NO_OF_DATAPOINTS = 1500"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"all_test_files = []\n",
"for root,dirs,files in os.walk(ROOT_TESTING_FOLDER):\n",
" if files != []:\n",
" for file in files:\n",
" if \".csv\" in file:\n",
" all_test_files.append(root+\"/\"+file)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"all_models = []\n",
"for root,dirs,files in os.walk(ROOT_MODELS_FOLDER):\n",
" if files != []:\n",
" for file in files:\n",
" all_models.append(root+\"/\"+file)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"*****************************\n",
"TESTING MODEL: /Users/siva/Documents/speaker_recognition/numpy_files/new_models_for_results/lin_new_10000.npy\n",
"testing file: Jourdon_training_baseline_close.csv index 0-1500 likelihood: [-129329.2563718]\n",
"testing file: Jourdon_training_baseline_close.csv index 1500-3000 likelihood: [-135378.86811802]\n",
"testing file: Jourdon_training_baseline_close.csv index 3000-4500 likelihood: [-133352.17381548]\n",
"testing file: Jourdon_training_baseline_close.csv index 4500-6000 likelihood: [-135945.50166592]\n",
"testing file: Jourdon_training_baseline_close.csv index 6000-7500 likelihood: [-142442.74105524]\n",
"testing file: Jourdon_training_baseline_close.csv index 7500-9000 likelihood: [-151997.41810179]\n",
"testing file: Jourdon_training_baseline_close.csv index 9000-10500 likelihood: [-151447.01597465]\n",
"testing file: Jourdon_training_baseline_close.csv index 10500-12000 likelihood: [-151719.85946069]\n",
"testing file: rapid_speech_jourdan.csv index 0-1500 likelihood: [-131274.23715224]\n",
"testing file: rapid_speech_jourdan.csv index 1500-3000 likelihood: [-134667.36620407]\n",
"testing file: rapid_speech_jourdan.csv index 3000-4500 likelihood: [-132032.83092133]\n",
"testing file: distant_speech_jourdan.csv index 0-1500 likelihood: [-128715.68661464]\n",
"testing file: distant_speech_jourdan.csv index 1500-3000 likelihood: [-128684.89435243]\n",
"testing file: distant_speech_jourdan.csv index 3000-4500 likelihood: [-127268.60636873]\n",
"testing file: distant_speech_jourdan.csv index 4500-6000 likelihood: [-128740.51009579]\n",
"testing file: soft_speech_jourdan.csv index 0-1500 likelihood: [-127755.98031207]\n",
"testing file: soft_speech_jourdan.csv index 1500-3000 likelihood: [-126134.27901894]\n",
"testing file: soft_speech_jourdan.csv index 3000-4500 likelihood: [-124691.25915858]\n",
"testing file: soft_speech_jourdan.csv index 4500-6000 likelihood: [-124086.64352547]\n",
"testing file: high_pitched_speech_jourdan.csv index 0-1500 likelihood: [-135362.62206744]\n",
"testing file: high_pitched_speech_jourdan.csv index 1500-3000 likelihood: [-136596.90881855]\n",
"testing file: high_pitched_speech_jourdan.csv index 3000-4500 likelihood: [-138585.80678991]\n",
"testing file: high_pitched_speech_jourdan.csv index 4500-6000 likelihood: [-137595.37786231]\n",
"testing file: baseline_jourdan copy.csv index 0-1500 likelihood: [-129329.25637094]\n",
"testing file: baseline_jourdan copy.csv index 1500-3000 likelihood: [-135378.86811786]\n",
"testing file: baseline_jourdan copy.csv index 3000-4500 likelihood: [-133352.17381545]\n",
"testing file: baseline_jourdan copy.csv index 4500-6000 likelihood: [-135945.50166586]\n",
"testing file: loud_speech_jourdan.csv index 0-1500 likelihood: [-141064.01773757]\n",
"testing file: loud_speech_jourdan.csv index 1500-3000 likelihood: [-144779.80137065]\n",
"testing file: loud_speech_jourdan.csv index 3000-4500 likelihood: [-143309.95486706]\n",
"testing file: loud_speech_jourdan.csv index 4500-6000 likelihood: [-144068.55259307]\n",
"testing file: close_speech_jourdan.csv index 0-1500 likelihood: [-149224.56788413]\n",
"testing file: close_speech_jourdan.csv index 1500-3000 likelihood: [-152014.66837167]\n",
"testing file: close_speech_jourdan.csv index 3000-4500 likelihood: [-151068.4132818]\n",
"testing file: close_speech_jourdan.csv index 4500-6000 likelihood: [-151671.91081967]\n",
"testing file: slow_speech_jourdan.csv index 0-1500 likelihood: [-126028.02123455]\n",
"testing file: slow_speech_jourdan.csv index 1500-3000 likelihood: [-124915.64148843]\n",
"testing file: slow_speech_jourdan.csv index 3000-4500 likelihood: [-129223.75072356]\n",
"testing file: slow_speech_jourdan.csv index 4500-6000 likelihood: [-124986.22182111]\n",
"testing file: slow_speech_jourdan.csv index 6000-7500 likelihood: [-124886.85828078]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Distant_Speech_(Cindy).csv index 0-1500 likelihood: [-76695.372029]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Distant_Speech_(Cindy).csv index 1500-3000 likelihood: [-76470.21039228]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Distant_Speech_(Cindy).csv index 3000-4500 likelihood: [-75090.75455301]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Distant_Speech_(Cindy).csv index 4500-6000 likelihood: [-75380.51632619]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Rapid_(Cindy).csv index 0-1500 likelihood: [-114867.6405495]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Rapid_(Cindy).csv index 1500-3000 likelihood: [-117005.82094256]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Rapid_(Cindy).csv index 3000-4500 likelihood: [-116654.83075626]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Slow_(Cindy)_.csv index 0-1500 likelihood: [-120561.75077736]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Slow_(Cindy)_.csv index 1500-3000 likelihood: [-117932.92356644]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Slow_(Cindy)_.csv index 3000-4500 likelihood: [-119845.70671776]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Slow_(Cindy)_.csv index 4500-6000 likelihood: [-117580.76834749]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Slow_(Cindy)_.csv index 6000-7500 likelihood: [-117689.66325089]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Slow_(Cindy)_.csv index 7500-9000 likelihood: [-118326.60854191]\n",
"testing file: Cindy_training_baseline_close.csv index 0-1500 likelihood: [-86677.36071049]\n",
"testing file: Cindy_training_baseline_close.csv index 1500-3000 likelihood: [-86399.80330546]\n",
"testing file: Cindy_training_baseline_close.csv index 3000-4500 likelihood: [-86229.68512347]\n",
"testing file: Cindy_training_baseline_close.csv index 4500-6000 likelihood: [-86726.15791624]\n",
"testing file: Cindy_training_baseline_close.csv index 6000-7500 likelihood: [-80659.20956012]\n",
"testing file: Cindy_training_baseline_close.csv index 7500-9000 likelihood: [-79696.08556841]\n",
"testing file: Cindy_training_baseline_close.csv index 9000-10500 likelihood: [-78846.09851842]\n",
"testing file: Cindy_training_baseline_close.csv index 10500-12000 likelihood: [-79446.87093434]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Close_Speech_(Cindy).csv index 0-1500 likelihood: [-79250.68627092]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Close_Speech_(Cindy).csv index 1500-3000 likelihood: [-79480.54961842]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Close_Speech_(Cindy).csv index 3000-4500 likelihood: [-78302.98049184]\n",
"testing file: AMP_Lab_Voice_Characteristic_Audio_Samples-Close_Speech_(Cindy).csv index 4500-6000 likelihood: [-79922.54008377]\n",
"testing file: Baseline_(Cindy).csv index 0-1500 likelihood: [-86677.36071038]\n",
"testing file: Baseline_(Cindy).csv index 1500-3000 likelihood: [-86399.80330542]\n",
"testing file: Baseline_(Cindy).csv index 3000-4500 likelihood: [-86229.68512356]\n",
"testing file: Baseline_(Cindy).csv index 4500-6000 likelihood: [-86726.15791621]\n",
"testing file: Samarth_loud.csv index 0-1500 likelihood: [-140114.47508715]\n",
"testing file: Samarth_loud.csv index 1500-3000 likelihood: [-139612.28827676]\n",
"testing file: Samarth_loud.csv index 3000-4500 likelihood: [-136847.99045289]\n",
"testing file: Samarth_loud.csv index 4500-6000 likelihood: [-139927.89537999]\n",
"testing file: Samarth_close.csv index 0-1500 likelihood: [-145127.44618988]\n",
"testing file: Samarth_close.csv index 1500-3000 likelihood: [-142371.65864098]\n",
"testing file: Samarth_close.csv index 3000-4500 likelihood: [-140537.26457818]\n",
"testing file: Samarth_close.csv index 4500-6000 likelihood: [-142719.55623537]\n",
"testing file: Samarth_training_soft_close.csv index 0-1500 likelihood: [-130616.59717904]\n",
"testing file: Samarth_training_soft_close.csv index 1500-3000 likelihood: [-130462.06616411]\n",
"testing file: Samarth_training_soft_close.csv index 3000-4500 likelihood: [-129582.11215316]\n",
"testing file: Samarth_training_soft_close.csv index 4500-6000 likelihood: [-129439.0160399]\n",
"testing file: Samarth_training_soft_close.csv index 6000-7500 likelihood: [-140028.94347108]\n",
"testing file: Samarth_training_soft_close.csv index 7500-9000 likelihood: [-144088.06026937]\n",
"testing file: Samarth_training_soft_close.csv index 9000-10500 likelihood: [-139479.54031771]\n",
"testing file: Samarth_training_soft_close.csv index 10500-12000 likelihood: [-142348.79956405]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"testing file: Samarth_slow.csv index 0-1500 likelihood: [-132574.99455214]\n",
"testing file: Samarth_slow.csv index 1500-3000 likelihood: [-131879.27556795]\n",
"testing file: Samarth_slow.csv index 3000-4500 likelihood: [-130373.77125809]\n",
"testing file: Samarth_slow.csv index 4500-6000 likelihood: [-130993.78611114]\n",
"testing file: Samarth_slow.csv index 6000-7500 likelihood: [-129594.12619138]\n",
"testing file: Samarth_soft.csv index 0-1500 likelihood: [-130616.59717912]\n",
"testing file: Samarth_soft.csv index 1500-3000 likelihood: [-130462.06616392]\n",
"testing file: Samarth_soft.csv index 3000-4500 likelihood: [-129582.11215304]\n",
"testing file: Samarth_soft.csv index 4500-6000 likelihood: [-129439.0160398]\n",
"testing file: Samarth_rapid.csv index 0-1500 likelihood: [-134678.31474404]\n",
"testing file: Samarth_rapid.csv index 1500-3000 likelihood: [-136440.85637217]\n",
"testing file: Samarth_rapid.csv index 3000-4500 likelihood: [-134503.91912748]\n",
"testing file: Samarth_distance.csv index 0-1500 likelihood: [-127095.07877196]\n",
"testing file: Samarth_distance.csv index 1500-3000 likelihood: [-128493.91258371]\n",
"testing file: Samarth_distance.csv index 3000-4500 likelihood: [-126165.53016845]\n",
"testing file: Samarth_distance.csv index 4500-6000 likelihood: [-126420.14593693]\n",
"testing file: Anne_crying.csv index 0-1500 likelihood: [-83914.38686509]\n",
"testing file: Anne_crying.csv index 1500-3000 likelihood: [-84930.77516069]\n",
"testing file: Anne_crying.csv index 3000-4500 likelihood: [-84179.71107426]\n",
"testing file: Anne_crying.csv index 4500-6000 likelihood: [-83659.85373566]\n",
"testing file: Anne_distant_.csv index 0-1500 likelihood: [-84898.20301283]\n",
"testing file: Anne_distant_.csv index 1500-3000 likelihood: [-85601.25401424]\n",
"testing file: Anne_distant_.csv index 3000-4500 likelihood: [-86384.65337556]\n",
"testing file: Anne_high_pitched.csv index 0-1500 likelihood: [-88041.33136462]\n",
"testing file: Anne_high_pitched.csv index 1500-3000 likelihood: [-89028.22185154]\n",
"testing file: Anne_high_pitched.csv index 3000-4500 likelihood: [-88598.58487157]\n",
"testing file: Anne_close.csv index 0-1500 likelihood: [-84019.3948547]\n",
"testing file: Anne_close.csv index 1500-3000 likelihood: [-85287.10196284]\n",
"testing file: Anne_close.csv index 3000-4500 likelihood: [-84380.06539965]\n",
"testing file: Anne_soft.csv index 0-1500 likelihood: [-82882.75230928]\n",
"testing file: Anne_soft.csv index 1500-3000 likelihood: [-84758.17173328]\n",
"testing file: Anne_soft.csv index 3000-4500 likelihood: [-83118.43846749]\n",
"testing file: Anne_baseline.csv index 0-1500 likelihood: [-84544.39817022]\n",
"testing file: Anne_baseline.csv index 1500-3000 likelihood: [-86653.99837206]\n",
"testing file: Anne_baseline.csv index 3000-4500 likelihood: [-85500.84035968]\n",
"testing file: Anne_slow.csv index 0-1500 likelihood: [-83246.23363959]\n",
"testing file: Anne_slow.csv index 1500-3000 likelihood: [-86208.61344029]\n",
"testing file: Anne_slow.csv index 3000-4500 likelihood: [-86640.16076416]\n",
"testing file: Anne_slow.csv index 4500-6000 likelihood: [-85828.2704403]\n",
"testing file: Anne_slow.csv index 6000-7500 likelihood: [-85481.27842444]\n",
"testing file: Anne_training_baseline_close.csv index 0-1500 likelihood: [-84544.39817002]\n",
"testing file: Anne_training_baseline_close.csv index 1500-3000 likelihood: [-86653.99837215]\n",
"testing file: Anne_training_baseline_close.csv index 3000-4500 likelihood: [-85500.84035966]\n",
"testing file: Anne_training_baseline_close.csv index 4500-6000 likelihood: [-84236.08445445]\n",
"testing file: Anne_training_baseline_close.csv index 6000-7500 likelihood: [-85159.25375265]\n",
"testing file: Anne_training_baseline_close.csv index 7500-9000 likelihood: [-84314.82863777]\n",
"testing file: Anne_training_baseline_close.csv index 9000-10500 likelihood: [-85074.1056864]\n",
"testing file: Anne_low_pitched.csv index 0-1500 likelihood: [-83691.23014846]\n",
"testing file: Anne_low_pitched.csv index 1500-3000 likelihood: [-85054.26491605]\n",
"testing file: Anne_low_pitched.csv index 3000-4500 likelihood: [-84497.76219642]\n",
"testing file: Anne_low_pitched.csv index 4500-6000 likelihood: [-87202.45003717]\n",
"testing file: Anne_loud.csv index 0-1500 likelihood: [-85202.24173575]\n",
"testing file: Anne_loud.csv index 1500-3000 likelihood: [-86453.75048707]\n",
"testing file: Anne_loud.csv index 3000-4500 likelihood: [-85417.64051526]\n",
"testing file: Anne_rapid.csv index 0-1500 likelihood: [-83623.37334066]\n",
"testing file: Anne_rapid.csv index 1500-3000 likelihood: [-85517.07530667]\n",
"testing file: Anne_laughing.csv index 0-1500 likelihood: [-83843.05738547]\n",
"testing file: Anne_laughing.csv index 1500-3000 likelihood: [-83675.48003256]\n",
"testing file: Anne_laughing.csv index 3000-4500 likelihood: [-82977.72281049]\n",
"testing file: EW_angry.csv index 0-1500 likelihood: [-83753.20688678]\n",
"testing file: EW_quietly_concerned.csv index 0-1500 likelihood: [-86315.01390725]\n",
"testing file: EW_exclamatory.csv index 0-1500 likelihood: [-84751.33069311]\n",
"testing file: EW_nervous.csv index 0-1500 likelihood: [-86518.52111941]\n",
"testing file: EW_sad.csv index 0-1500 likelihood: [-86457.7711404]\n",
"testing file: EW_happy.csv index 0-1500 likelihood: [-85869.35514058]\n",
"testing file: EW_2_min_sample_1.csv index 0-1500 likelihood: [-88106.61855067]\n",
"testing file: EW_2_min_sample_1.csv index 1500-3000 likelihood: [-85062.6334048]\n",
"testing file: EW_2_min_sample_1.csv index 3000-4500 likelihood: [-85013.03845551]\n",
"testing file: EW_2_min_sample_1.csv index 4500-6000 likelihood: [-86268.80190319]\n",
"testing file: EW_2_min_sample_1.csv index 6000-7500 likelihood: [-84561.89187157]\n",
"testing file: EW_2_min_sample_1.csv index 7500-9000 likelihood: [-85358.3269124]\n",
"testing file: EW_2_min_sample_1.csv index 9000-10500 likelihood: [-85795.90597152]\n",
"testing file: EW_2_min_sample_1.csv index 10500-12000 likelihood: [-87494.72755107]\n",
"testing file: EW_2_min_sample_2.csv index 0-1500 likelihood: [-85320.59992363]\n",
"testing file: EW_2_min_sample_2.csv index 1500-3000 likelihood: [-86588.09856377]\n",
"testing file: EW_2_min_sample_2.csv index 3000-4500 likelihood: [-86942.18212465]\n",
"testing file: EW_2_min_sample_2.csv index 4500-6000 likelihood: [-86208.08702225]\n",
"testing file: EW_2_min_sample_2.csv index 6000-7500 likelihood: [-84873.52850155]\n",
"testing file: EW_2_min_sample_2.csv index 7500-9000 likelihood: [-85518.80619451]\n",
"testing file: EW_2_min_sample_2.csv index 9000-10500 likelihood: [-86599.97460921]\n",
"testing file: EW_2_min_sample_2.csv index 10500-12000 likelihood: [-86330.1387579]\n",
"testing file: EW_2_min_sample_2.csv index 12000-13500 likelihood: [-85494.89881734]\n",
"testing file: Rapid_speech_(Kara).csv index 0-1500 likelihood: [-134109.95563754]\n",
"testing file: Rapid_speech_(Kara).csv index 1500-3000 likelihood: [-137511.03127007]\n",
"testing file: Rapid_speech_(Kara).csv index 3000-4500 likelihood: [-134690.7997985]\n",
"testing file: Soft_speech_(Kara).csv index 0-1500 likelihood: [-121202.10542826]\n",
"testing file: Soft_speech_(Kara).csv index 1500-3000 likelihood: [-124013.42280593]\n",
"testing file: Soft_speech_(Kara).csv index 3000-4500 likelihood: [-120260.10950018]\n",
"testing file: Soft_speech_(Kara).csv index 4500-6000 likelihood: [-121896.40688826]\n",
"testing file: Distant_speech_(Kara).csv index 0-1500 likelihood: [-127477.66980829]\n",
"testing file: Distant_speech_(Kara).csv index 1500-3000 likelihood: [-128320.19591302]\n",
"testing file: Distant_speech_(Kara).csv index 3000-4500 likelihood: [-126916.27465508]\n",
"testing file: Baseline_(Kara).csv index 0-1500 likelihood: [-132996.52445665]\n",
"testing file: Baseline_(Kara).csv index 1500-3000 likelihood: [-138082.2688659]\n",
"testing file: Baseline_(Kara).csv index 3000-4500 likelihood: [-138710.6886532]\n",
"testing file: Kara_training_baseline_close.csv index 0-1500 likelihood: [-132996.52445736]\n",
"testing file: Kara_training_baseline_close.csv index 1500-3000 likelihood: [-138082.26886657]\n",
"testing file: Kara_training_baseline_close.csv index 3000-4500 likelihood: [-138710.68865336]\n",
"testing file: Kara_training_baseline_close.csv index 4500-6000 likelihood: [-135006.05948741]\n",
"testing file: Kara_training_baseline_close.csv index 6000-7500 likelihood: [-146637.54608762]\n",
"testing file: Kara_training_baseline_close.csv index 7500-9000 likelihood: [-147416.7577028]\n",
"testing file: Kara_training_baseline_close.csv index 9000-10500 likelihood: [-146742.41598817]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"testing file: Close_speech_(Kara).csv index 0-1500 likelihood: [-141330.950432]\n",
"testing file: Close_speech_(Kara).csv index 1500-3000 likelihood: [-147326.11046992]\n",
"testing file: Close_speech_(Kara).csv index 3000-4500 likelihood: [-145676.91393768]\n",
"testing file: Slow_speech_(Kara).csv index 0-1500 likelihood: [-122896.75104654]\n",
"testing file: Slow_speech_(Kara).csv index 1500-3000 likelihood: [-122761.44267291]\n",
"testing file: Slow_speech_(Kara).csv index 3000-4500 likelihood: [-124380.29446873]\n",
"testing file: Slow_speech_(Kara).csv index 4500-6000 likelihood: [-124995.78981026]\n",
"testing file: Slow_speech_(Kara).csv index 6000-7500 likelihood: [-122505.57967736]\n",
"testing file: Slow_speech_(Kara).csv index 7500-9000 likelihood: [-126603.00269709]\n",
"testing file: rapid_LB.csv index 0-1500 likelihood: [-73172.10325706]\n",
"testing file: rapid_LB.csv index 1500-3000 likelihood: [-73281.35396522]\n",
"testing file: low_pitch_LB.csv index 0-1500 likelihood: [-76154.66776181]\n",
"testing file: low_pitch_LB.csv index 1500-3000 likelihood: [-76012.15867228]\n",
"testing file: low_pitch_LB.csv index 3000-4500 likelihood: [-74967.46343285]\n",
"testing file: loud_LB.csv index 0-1500 likelihood: [-76656.29725553]\n",
"testing file: loud_LB.csv index 1500-3000 likelihood: [-77309.8657286]\n",
"testing file: loud_LB.csv index 3000-4500 likelihood: [-76342.2971899]\n",
"testing file: laughing_LB.csv index 0-1500 likelihood: [-77992.38100223]\n",
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"text": [
"testing file: Crying_Mariah.csv index 7500-9000 likelihood: [-87257.53362443]\n",
"testing file: Soft_AG_Mar_01,_196842.csv index 0-1500 likelihood: [-75622.29984451]\n",
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"*****************************\n",
"TESTING MODEL: /Users/siva/Documents/speaker_recognition/numpy_files/new_models_for_results/mariah_new_10000.npy\n",
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"testing file: CloseSpeech_Mariah.csv index 4500-6000 likelihood: [-75286.74616795]\n",
"testing file: LowPitch_Vocalfry_Mariah.csv index 0-1500 likelihood: [-72845.76938129]\n",
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"*****************************\n",
"TESTING MODEL: /Users/siva/Documents/speaker_recognition/numpy_files/new_models_for_results/ew_new_10000.npy\n",
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"*****************************\n",
"TESTING MODEL: /Users/siva/Documents/speaker_recognition/numpy_files/new_models_for_results/kara_new_10000.npy\n",
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"*****************************\n",
"TESTING MODEL: /Users/siva/Documents/speaker_recognition/numpy_files/new_models_for_results/cindy_new_10000.npy\n",
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"*****************************\n",
"TESTING MODEL: /Users/siva/Documents/speaker_recognition/numpy_files/new_models_for_results/samarth_new_10000.npy\n",
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"*****************************\n",
"TESTING MODEL: /Users/siva/Documents/speaker_recognition/numpy_files/new_models_for_results/jourdon_new_10000.npy\n",
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"*****************************\n",
"TESTING MODEL: /Users/siva/Documents/speaker_recognition/numpy_files/new_models_for_results/arti_new_10000.npy\n",
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"testing file: Mariah_training_baseline_close.csv index 9000-10500 likelihood: [-86555.3285883]\n",
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"*****************************\n",
"TESTING MODEL: /Users/siva/Documents/speaker_recognition/numpy_files/new_models_for_results/anne_new_10000.npy\n",
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"testing file: Anne_rapid.csv index 0-1500 likelihood: [-71663.96037657]\n",
"testing file: Anne_rapid.csv index 1500-3000 likelihood: [-72414.77353638]\n",
"testing file: Anne_laughing.csv index 0-1500 likelihood: [-75751.99042488]\n",
"testing file: Anne_laughing.csv index 1500-3000 likelihood: [-77297.30669436]\n",
"testing file: Anne_laughing.csv index 3000-4500 likelihood: [-77068.3745852]\n",
"testing file: EW_angry.csv index 0-1500 likelihood: [-81666.29925851]\n",
"testing file: EW_quietly_concerned.csv index 0-1500 likelihood: [-77768.05894019]\n",
"testing file: EW_exclamatory.csv index 0-1500 likelihood: [-79973.25096728]\n",
"testing file: EW_nervous.csv index 0-1500 likelihood: [-80855.94035902]\n",
"testing file: EW_sad.csv index 0-1500 likelihood: [-77272.33515303]\n",
"testing file: EW_happy.csv index 0-1500 likelihood: [-83002.20927398]\n",
"testing file: EW_2_min_sample_1.csv index 0-1500 likelihood: [-79479.90210673]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"testing file: EW_2_min_sample_1.csv index 1500-3000 likelihood: [-80649.19987832]\n",
"testing file: EW_2_min_sample_1.csv index 3000-4500 likelihood: [-78518.73732367]\n",
"testing file: EW_2_min_sample_1.csv index 4500-6000 likelihood: [-79931.22088625]\n",
"testing file: EW_2_min_sample_1.csv index 6000-7500 likelihood: [-78980.47207128]\n",
"testing file: EW_2_min_sample_1.csv index 7500-9000 likelihood: [-79042.91307302]\n",
"testing file: EW_2_min_sample_1.csv index 9000-10500 likelihood: [-78747.56793844]\n",
"testing file: EW_2_min_sample_1.csv index 10500-12000 likelihood: [-79879.22399497]\n",
"testing file: EW_2_min_sample_2.csv index 0-1500 likelihood: [-80133.58866574]\n",
"testing file: EW_2_min_sample_2.csv index 1500-3000 likelihood: [-80121.09981488]\n",
"testing file: EW_2_min_sample_2.csv index 3000-4500 likelihood: [-81042.71287343]\n",
"testing file: EW_2_min_sample_2.csv index 4500-6000 likelihood: [-79598.19877141]\n",
"testing file: EW_2_min_sample_2.csv index 6000-7500 likelihood: [-80055.46954599]\n",
"testing file: EW_2_min_sample_2.csv index 7500-9000 likelihood: [-79234.70876891]\n",
"testing file: EW_2_min_sample_2.csv index 9000-10500 likelihood: [-78919.564817]\n",
"testing file: EW_2_min_sample_2.csv index 10500-12000 likelihood: [-79423.67616814]\n",
"testing file: EW_2_min_sample_2.csv index 12000-13500 likelihood: [-81661.15332561]\n",
"testing file: Rapid_speech_(Kara).csv index 0-1500 likelihood: [-150764.14498691]\n",
"testing file: Rapid_speech_(Kara).csv index 1500-3000 likelihood: [-156569.27711795]\n",
"testing file: Rapid_speech_(Kara).csv index 3000-4500 likelihood: [-153248.20873946]\n",
"testing file: Soft_speech_(Kara).csv index 0-1500 likelihood: [-135064.74666545]\n",
"testing file: Soft_speech_(Kara).csv index 1500-3000 likelihood: [-136521.25881057]\n",
"testing file: Soft_speech_(Kara).csv index 3000-4500 likelihood: [-132583.79731488]\n",
"testing file: Soft_speech_(Kara).csv index 4500-6000 likelihood: [-137572.78167122]\n",
"testing file: Distant_speech_(Kara).csv index 0-1500 likelihood: [-142544.61783617]\n",
"testing file: Distant_speech_(Kara).csv index 1500-3000 likelihood: [-145126.21250637]\n",
"testing file: Distant_speech_(Kara).csv index 3000-4500 likelihood: [-144224.03097638]\n",
"testing file: Baseline_(Kara).csv index 0-1500 likelihood: [-150132.2980651]\n",
"testing file: Baseline_(Kara).csv index 1500-3000 likelihood: [-156756.71796522]\n",
"testing file: Baseline_(Kara).csv index 3000-4500 likelihood: [-158761.92266613]\n",
"testing file: Kara_training_baseline_close.csv index 0-1500 likelihood: [-150132.29806591]\n",
"testing file: Kara_training_baseline_close.csv index 1500-3000 likelihood: [-156756.71796621]\n",
"testing file: Kara_training_baseline_close.csv index 3000-4500 likelihood: [-158761.92266642]\n",
"testing file: Kara_training_baseline_close.csv index 4500-6000 likelihood: [-149774.4712909]\n",
"testing file: Kara_training_baseline_close.csv index 6000-7500 likelihood: [-166717.23441114]\n",
"testing file: Kara_training_baseline_close.csv index 7500-9000 likelihood: [-166160.97378897]\n",
"testing file: Kara_training_baseline_close.csv index 9000-10500 likelihood: [-165379.07165688]\n",
"testing file: Close_speech_(Kara).csv index 0-1500 likelihood: [-157706.98750554]\n",
"testing file: Close_speech_(Kara).csv index 1500-3000 likelihood: [-166131.88272222]\n",
"testing file: Close_speech_(Kara).csv index 3000-4500 likelihood: [-164662.44313391]\n",
"testing file: Slow_speech_(Kara).csv index 0-1500 likelihood: [-134398.81371693]\n",
"testing file: Slow_speech_(Kara).csv index 1500-3000 likelihood: [-133406.17832103]\n",
"testing file: Slow_speech_(Kara).csv index 3000-4500 likelihood: [-134572.33798118]\n",
"testing file: Slow_speech_(Kara).csv index 4500-6000 likelihood: [-137373.92741842]\n",
"testing file: Slow_speech_(Kara).csv index 6000-7500 likelihood: [-134013.98508954]\n",
"testing file: Slow_speech_(Kara).csv index 7500-9000 likelihood: [-139901.63346621]\n",
"testing file: rapid_LB.csv index 0-1500 likelihood: [-83381.38787459]\n",
"testing file: rapid_LB.csv index 1500-3000 likelihood: [-79883.4905319]\n",
"testing file: low_pitch_LB.csv index 0-1500 likelihood: [-82147.9496014]\n",
"testing file: low_pitch_LB.csv index 1500-3000 likelihood: [-80957.5957614]\n",
"testing file: low_pitch_LB.csv index 3000-4500 likelihood: [-79721.09445384]\n",
"testing file: loud_LB.csv index 0-1500 likelihood: [-87832.22338268]\n",
"testing file: loud_LB.csv index 1500-3000 likelihood: [-88903.2723898]\n",
"testing file: loud_LB.csv index 3000-4500 likelihood: [-88746.06625863]\n",
"testing file: laughing_LB.csv index 0-1500 likelihood: [-87854.60276911]\n",
"testing file: laughing_LB.csv index 1500-3000 likelihood: [-86278.96650207]\n",
"testing file: laughing_LB.csv index 3000-4500 likelihood: [-86640.44251383]\n",
"testing file: close_LB.csv index 0-1500 likelihood: [-83029.79234366]\n",
"testing file: close_LB.csv index 1500-3000 likelihood: [-83210.08127001]\n",
"testing file: close_LB.csv index 3000-4500 likelihood: [-82942.94055081]\n",
"testing file: close_LB.csv index 4500-6000 likelihood: [-82598.9450319]\n",
"testing file: distant_LB.csv index 0-1500 likelihood: [-78763.47920163]\n",
"testing file: distant_LB.csv index 1500-3000 likelihood: [-77145.86430039]\n",
"testing file: distant_LB.csv index 3000-4500 likelihood: [-77144.835821]\n",
"testing file: distant_LB.csv index 4500-6000 likelihood: [-77600.22208533]\n",
"testing file: baseline_LB.csv index 0-1500 likelihood: [-83229.51954909]\n",
"testing file: baseline_LB.csv index 1500-3000 likelihood: [-81777.74302624]\n",
"testing file: baseline_LB.csv index 3000-4500 likelihood: [-82199.03040593]\n",
"testing file: Lin_training_baseline_close.csv index 0-1500 likelihood: [-83229.51954924]\n",
"testing file: Lin_training_baseline_close.csv index 1500-3000 likelihood: [-81777.74302633]\n",
"testing file: Lin_training_baseline_close.csv index 3000-4500 likelihood: [-82199.03040615]\n",
"testing file: Lin_training_baseline_close.csv index 4500-6000 likelihood: [-81484.42630089]\n",
"testing file: Lin_training_baseline_close.csv index 6000-7500 likelihood: [-83935.45723853]\n",
"testing file: Lin_training_baseline_close.csv index 7500-9000 likelihood: [-83332.78775298]\n",
"testing file: Lin_training_baseline_close.csv index 9000-10500 likelihood: [-82198.64069138]\n",
"testing file: soft_LB.csv index 0-1500 likelihood: [-79241.60012672]\n",
"testing file: soft_LB.csv index 1500-3000 likelihood: [-78085.66054934]\n",
"testing file: soft_LB.csv index 3000-4500 likelihood: [-78301.50250875]\n",
"testing file: soft_LB.csv index 4500-6000 likelihood: [-78625.40668538]\n",
"testing file: slow_LB.csv index 0-1500 likelihood: [-80209.34518086]\n",
"testing file: slow_LB.csv index 1500-3000 likelihood: [-78912.60065247]\n",
"testing file: slow_LB.csv index 3000-4500 likelihood: [-77511.40984703]\n",
"testing file: slow_LB.csv index 4500-6000 likelihood: [-77922.76396932]\n",
"testing file: slow_LB.csv index 6000-7500 likelihood: [-78280.54430116]\n",
"testing file: Laughing_Mariah.csv index 0-1500 likelihood: [-78407.93151535]\n",
"testing file: Laughing_Mariah.csv index 1500-3000 likelihood: [-77800.08506162]\n",
"testing file: Laughing_Mariah.csv index 3000-4500 likelihood: [-77188.59105944]\n",
"testing file: Laughing_Mariah.csv index 4500-6000 likelihood: [-77245.39836181]\n",
"testing file: Laughing_Mariah.csv index 6000-7500 likelihood: [-77428.92568191]\n",
"testing file: HighPitch_Mariah.csv index 0-1500 likelihood: [-78709.55205388]\n",
"testing file: HighPitch_Mariah.csv index 1500-3000 likelihood: [-79283.43039051]\n",
"testing file: HighPitch_Mariah.csv index 3000-4500 likelihood: [-78231.42856192]\n",
"testing file: HighPitch_Mariah.csv index 4500-6000 likelihood: [-78830.10324993]\n",
"testing file: Rapid_Mariah.csv index 0-1500 likelihood: [-77586.7383227]\n",
"testing file: Rapid_Mariah.csv index 1500-3000 likelihood: [-76339.45255464]\n",
"testing file: Soft_Mariah.csv index 0-1500 likelihood: [-76473.56694685]\n",
"testing file: Soft_Mariah.csv index 1500-3000 likelihood: [-76988.22359147]\n",
"testing file: Soft_Mariah.csv index 3000-4500 likelihood: [-76241.47055147]\n",
"testing file: Soft_Mariah.csv index 4500-6000 likelihood: [-76465.90411096]\n",
"testing file: Slow_Mariah.csv index 0-1500 likelihood: [-77409.43550795]\n",
"testing file: Slow_Mariah.csv index 1500-3000 likelihood: [-74827.25161181]\n",
"testing file: Slow_Mariah.csv index 3000-4500 likelihood: [-75468.63545158]\n",
"testing file: Slow_Mariah.csv index 4500-6000 likelihood: [-75488.11651754]\n",
"testing file: Slow_Mariah.csv index 6000-7500 likelihood: [-74984.24461286]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"testing file: Slow_Mariah.csv index 7500-9000 likelihood: [-74625.939675]\n",
"testing file: Slow_Mariah.csv index 9000-10500 likelihood: [-73899.92291916]\n",
"testing file: Slow_Mariah.csv index 10500-12000 likelihood: [-74217.90200664]\n",
"testing file: CloseSpeech_Mariah.csv index 0-1500 likelihood: [-76703.35366339]\n",
"testing file: CloseSpeech_Mariah.csv index 1500-3000 likelihood: [-76736.13302837]\n",
"testing file: CloseSpeech_Mariah.csv index 3000-4500 likelihood: [-76851.42832318]\n",
"testing file: CloseSpeech_Mariah.csv index 4500-6000 likelihood: [-76901.31634103]\n",
"testing file: LowPitch_Vocalfry_Mariah.csv index 0-1500 likelihood: [-75422.29245945]\n",
"testing file: LowPitch_Vocalfry_Mariah.csv index 1500-3000 likelihood: [-75026.21420273]\n",
"testing file: LowPitch_Vocalfry_Mariah.csv index 3000-4500 likelihood: [-74911.18698838]\n",
"testing file: LowPitch_Vocalfry_Mariah.csv index 4500-6000 likelihood: [-74960.04769876]\n",
"testing file: DistanceSpeech_Mariah.csv index 0-1500 likelihood: [-78726.47156179]\n",
"testing file: DistanceSpeech_Mariah.csv index 1500-3000 likelihood: [-78666.9674299]\n",
"testing file: DistanceSpeech_Mariah.csv index 3000-4500 likelihood: [-78109.06483664]\n",
"testing file: DistanceSpeech_Mariah.csv index 4500-6000 likelihood: [-78182.05133368]\n",
"testing file: Baseline_Mariah.csv index 0-1500 likelihood: [-76276.23611846]\n",
"testing file: Baseline_Mariah.csv index 1500-3000 likelihood: [-75649.42879153]\n",
"testing file: Baseline_Mariah.csv index 3000-4500 likelihood: [-76090.54233329]\n",
"testing file: Baseline_Mariah.csv index 4500-6000 likelihood: [-74906.94050997]\n",
"testing file: Loud_Mariah.csv index 0-1500 likelihood: [-79965.42963228]\n",
"testing file: Loud_Mariah.csv index 1500-3000 likelihood: [-79397.83795042]\n",
"testing file: Loud_Mariah.csv index 3000-4500 likelihood: [-79582.30285576]\n",
"testing file: Loud_Mariah.csv index 4500-6000 likelihood: [-78903.38123288]\n",
"testing file: Mariah_training_baseline_close.csv index 0-1500 likelihood: [-76276.23611847]\n",
"testing file: Mariah_training_baseline_close.csv index 1500-3000 likelihood: [-75649.42879153]\n",
"testing file: Mariah_training_baseline_close.csv index 3000-4500 likelihood: [-76090.54233354]\n",
"testing file: Mariah_training_baseline_close.csv index 4500-6000 likelihood: [-74906.94050993]\n",
"testing file: Mariah_training_baseline_close.csv index 6000-7500 likelihood: [-75548.09463004]\n",
"testing file: Mariah_training_baseline_close.csv index 7500-9000 likelihood: [-76771.61954148]\n",
"testing file: Mariah_training_baseline_close.csv index 9000-10500 likelihood: [-77063.36644014]\n",
"testing file: Mariah_training_baseline_close.csv index 10500-12000 likelihood: [-76935.82023911]\n",
"testing file: Crying_Mariah.csv index 0-1500 likelihood: [-78331.56550611]\n",
"testing file: Crying_Mariah.csv index 1500-3000 likelihood: [-80019.84346687]\n",
"testing file: Crying_Mariah.csv index 3000-4500 likelihood: [-79173.96891957]\n",
"testing file: Crying_Mariah.csv index 4500-6000 likelihood: [-79431.02623039]\n",
"testing file: Crying_Mariah.csv index 6000-7500 likelihood: [-80049.94474471]\n",
"testing file: Crying_Mariah.csv index 7500-9000 likelihood: [-79980.25393662]\n",
"testing file: Soft_AG_Mar_01,_196842.csv index 0-1500 likelihood: [-78119.1320131]\n",
"testing file: Soft_AG_Mar_01,_196842.csv index 1500-3000 likelihood: [-77478.07336397]\n",
"testing file: Soft_AG_Mar_01,_196842.csv index 3000-4500 likelihood: [-77461.48375582]\n",
"testing file: Arti_training_baseline_close.csv index 0-1500 likelihood: [-82382.98550995]\n",
"testing file: Arti_training_baseline_close.csv index 1500-3000 likelihood: [-80855.58445509]\n",
"testing file: Arti_training_baseline_close.csv index 3000-4500 likelihood: [-80450.67008897]\n",
"testing file: Arti_training_baseline_close.csv index 4500-6000 likelihood: [-80090.09504808]\n",
"testing file: Arti_training_baseline_close.csv index 6000-7500 likelihood: [-81272.23636189]\n",
"testing file: Arti_training_baseline_close.csv index 7500-9000 likelihood: [-82760.8170807]\n",
"testing file: Arti_training_baseline_close.csv index 9000-10500 likelihood: [-82161.24648926]\n",
"testing file: distant_AG.csv index 0-1500 likelihood: [-79571.84110828]\n",
"testing file: distant_AG.csv index 1500-3000 likelihood: [-76838.47837473]\n",
"testing file: distant_AG.csv index 3000-4500 likelihood: [-75972.14972839]\n",
"testing file: close_AG.csv index 0-1500 likelihood: [-81018.02104511]\n",
"testing file: close_AG.csv index 1500-3000 likelihood: [-81983.92835054]\n",
"testing file: close_AG.csv index 3000-4500 likelihood: [-82090.97584138]\n",
"testing file: Loud_AG_Mar_01,_845111.csv index 0-1500 likelihood: [-85880.20183383]\n",
"testing file: Loud_AG_Mar_01,_845111.csv index 1500-3000 likelihood: [-83838.31096511]\n",
"testing file: Loud_AG_Mar_01,_845111.csv index 3000-4500 likelihood: [-84030.69814239]\n",
"testing file: slow_AG_Mar_01,_621548.csv index 0-1500 likelihood: [-78434.95333526]\n",
"testing file: slow_AG_Mar_01,_621548.csv index 1500-3000 likelihood: [-79663.24665646]\n",
"testing file: slow_AG_Mar_01,_621548.csv index 3000-4500 likelihood: [-78870.5338413]\n",
"testing file: slow_AG_Mar_01,_621548.csv index 4500-6000 likelihood: [-80253.93009268]\n",
"testing file: slow_AG_Mar_01,_621548.csv index 6000-7500 likelihood: [-80870.7528392]\n",
"testing file: Baseline_AG_Mar_01,_689201.csv index 0-1500 likelihood: [-82382.98550964]\n",
"testing file: Baseline_AG_Mar_01,_689201.csv index 1500-3000 likelihood: [-80855.58445497]\n",
"testing file: Baseline_AG_Mar_01,_689201.csv index 3000-4500 likelihood: [-80450.67008876]\n",
"testing file: rapid_AG_Mar_01,_559134.csv index 0-1500 likelihood: [-82726.86355803]\n",
"testing file: rapid_AG_Mar_01,_559134.csv index 1500-3000 likelihood: [-81576.60284441]\n"
]
}
],
"source": [
"testing_results = {\"model\":[],\"testing_file\":[],\"index1\":[],\"index2\":[],\"likelihood\":[]}\n",
"for each_model in all_models:\n",
" map_model = load_map_model(each_model)\n",
" print(\"*****************************\")\n",
" print(\"TESTING MODEL:\",each_model)\n",
" for each_test_file in all_test_files:\n",
" test_file,len_ = load_csv_file(each_test_file)\n",
" no_of_splits = len_//NO_OF_DATAPOINTS\n",
" for i in range(0,no_of_splits):\n",
" likelihood = testing_model(map_model,test_file,i*NO_OF_DATAPOINTS,(i+1)*NO_OF_DATAPOINTS)\n",
" print(\"testing file:\",each_test_file.split(\"/\")[-1],\"index {0}-{1}\".format(i*NO_OF_DATAPOINTS,(i+1)*NO_OF_DATAPOINTS),\\\n",
" \"likelihood:\",likelihood)\n",
" testing_results[\"model\"].append(each_model)\n",
" testing_results[\"testing_file\"].append(each_test_file)\n",
" testing_results[\"index1\"].append(i*NO_OF_DATAPOINTS)\n",
" testing_results[\"index2\"].append((i+1)*NO_OF_DATAPOINTS)\n",
" testing_results[\"likelihood\"].append(likelihood)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"np.save(\"DHI_testing_results.npy\",testing_results)"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [],
"source": [
"def generate_confusion_matrix(results,model_name,file_name_strings,file_name_exceptions):\n",
" positive_likelihood_values = {}\n",
" negative_likelihood_values = {}\n",
" for model,file,likelihood in zip(results[\"model\"],results[\"testing_file\"],\\\n",
" results[\"likelihood\"]):\n",
" file_ = file.split(\"/\")[-1]\n",
" model_ = model.split(\"/\")[-1]\n",
" if model_ == model_name:\n",
" if check_name(file_name_strings,file_):\n",
" if not check_name(file_name_exceptions,file_):\n",
" #print(file_)\n",
" positive_likelihood_values[abs(likelihood[0])] = file_\n",
" #else:\n",
" #print(file_)\n",
" else:\n",
" negative_likelihood_values[abs(likelihood[0])] = file_\n",
" mean = np.mean(list(positive_likelihood_values.keys()))\n",
" TP = sum(list(positive_likelihood_values.keys())<mean)\n",
" TN = sum(list(negative_likelihood_values.keys())>mean)\n",
" FP = sum(list(negative_likelihood_values.keys())<mean)\n",
" FN = sum(list(positive_likelihood_values.keys())>mean)\n",
" arr = [[TP,FP],[FN,TN]]\n",
" s = [[\"TP\",\"FP\"],[\"FN\",\"TN\"]]\n",
" plt.imshow(arr, interpolation='nearest', cmap=plt.cm.Wistia)\n",
" plt.colorbar()\n",
" for i in range(2):\n",
" for j in range(2):\n",
" plt.text(j,i, str(s[i][j])+\" = \"+str(arr[i][j]))\n",
" print(\"mean:\",mean,\"TP:\",TP,\"TN:\",TN,\"FP:\",FP,\"FN:\",FN,\"Accuracy:\",np.sum(np.diag(arr))/np.sum(arr))"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean: 74168.7048373 TP: 13 TN: 282 FP: 0 FN: 11 Accuracy: 0.964052287582\n"
]
},
{
"data": {
"image/png": 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1r3L7+Nd5+fnZfLpgCdPmfIfuNXWfifjrXz3HQ/dO55UX59C5c3umfnRC2dpj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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111176e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"generate_confusion_matrix(testing_results,\"lin_new_10000.npy\",[\"LB\",\"Lin\"],[\"training\",\"baseline\",\"close\"])"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean: 75031.3006428 TP: 21 TN: 259 FP: 4 FN: 20 Accuracy: 0.921052631579\n"
]
},
{
"data": {
"image/png": 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b39uavv2K+60z8pCHGf9MbZP7+2+4Jk9MOrzodsz9eAlr1nSgXbvkP9gLz8+i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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1096292e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"generate_confusion_matrix(testing_results,\"mariah_new_10000.npy\",[\"Mariah\"],[\"training\",\"Baseline\",\"Close\"])"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean: 74610.5095023 TP: 9 TN: 297 FP: 0 FN: 6 Accuracy: 0.980769230769\n"
]
},
{
"data": {
"image/png": 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tesMvh8O+m66qs3wFXPIE/PlF+HAhDOoFF4+A/bKmCd7s1/BeIwMMT90Zfn1g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\nXuF2H1fJAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1109d4588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"generate_confusion_matrix(testing_results,\"ew_new_10000.npy\",[\"EW\"],[\"sample_1\"])"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean: 76969.941595 TP: 9 TN: 262 FP: 29 FN: 7 Accuracy: 0.882736156352\n"
]
},
{
"data": {
"image/png": 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l1cPYrFcymGnyC+8x6qKX+fCDT9lmuw244uq9+NLwPs3V7OKq8EEmKvSv87xPnHRRfgQM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\n/+gAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ff947f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"generate_confusion_matrix(testing_results,\"kara_new_10000.npy\",[\"Kara\"],[\"Baseline\",\"Close\",\"training\"])"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean: 116992.612935 TP: 4 TN: 101 FP: 190 FN: 9 Accuracy: 0.345394736842\n"
]
},
{
"data": {
"image/png": 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qjPv1k8yfV/95GBV8c7rlFi36hFEjbqVTJ3HLvV/l9ocOY+mSZRx1cFHvQVsb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\ngU0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1085180b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"generate_confusion_matrix(testing_results,\"cindy_new_10000.npy\",[\"Cindy\"],[\"Baseline\",\"Close\",\"training\"])"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean: 75047.9095684 TP: 8 TN: 288 FP: 0 FN: 8 Accuracy: 0.973684210526\n"
]
},
{
"data": {
"image/png": 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A0cBtAEpSgdHARY0c9hiwZ17Zbml5k047bwc236pvyxtsZtYG9h+3KfuP27Re2XMz57Dn\n0D+0UYtWjqSfAmOAjYFPgUeBkyLipZw6Y4AJJOM0egFbRMSzeeepAc4HvgHUkIzHODoi3i20LeWe\nyeR84EhJ4yVtDPwO6AZcBSDpbEm5z7j9Dlhf0jmSBks6GjgwPY+ZmZVSeWYyGQn8BhhO8qhXZ+Bu\nSavk1OkOPAycCDTWjXghyaj7A4AdgAHAzUW8u/Leg4uIGyT1Bk4H+gFPA7tHxHtplf7AOjn1X5e0\nN8moyeOAN4EjIiJ/ZKWZmbVDEbFX7uv0NtO7JNnatLTOn9J969JAyJRUS/I42diIeDAtOwyYJWlY\nREwvpC1lH2QSEZNIHtxuaN9hDZQ9RPJBmJlZ5etJkqV9WMQxQ0ni09S6goh4UdJsksfM2keAMzOz\ndqrMoyjTcRcXAtMi4p/N1c/RH1gcEfPzyuem+wriAGdmZg2a/BhMfrx+2bxPijrFJODLQJs84+MA\nZ2ZmDRo3ItlyzXwdhv6y+WMlXQzsBYyMiGLX1poDdJFUm5fF9Uv3FcTrwZmZZVWZ1oNLg9t+wE4R\nMbuZ6g2NopwBLCV5rKzunIOBgRTw2FgdZ3BmZlYykiYB44B9gYWS6tbVmhcRn6V1VicJVmuRhMyN\n0/t1cyJibkTMl3QFcL6kj4AFJM9PP1LoCEpwBmdmlm2lX817AlALPAC8nbMdlFNnX+Ap4G8kGdxk\nYCZwVE6diSTLp92Uc64DinlrzuDMzKxkIqLZxCldxLrJhazTyfWPTbcWcQZnZmZVyRmcmVlWVflq\nAs7gzMysKjmDMzPLKmdwZmZmlccBzszMqpIDnJmZVSXfgzMzyyrfgzMzM6s8zuDMzLLKGZyZmVnl\ncQZnZpZlFZSRFcsZnJmZVSUHODMzq0ruojQzyyoPMjEzM6s8zuDMzLLKGZyZmVnlcYAzM7Oq5ABn\nZmZVyffgzMyyyvfgzMzMCiNppKTbJL0labmkffP2d5d0saQ3JH0i6R+SjsqrUyPpEknvS1og6SZJ\nfYttiwOcmVlWqYVb07oDTwNHA9HA/guA3YBvAhunry+WtE9OnQuBvYEDgB2AAcDNRb47d1GamVnp\nRMQUYAqApIbC4Qjg6oh4OH19uaQJwDDgdkm1wOHA2Ih4MD3PYcAsScMiYnqhbXEGZ2ZmrelRYF9J\nAwAk7QRsBNyV7h9KknxNrTsgIl4EZpMEx4I5gzMzy7LWHzRyLPB74E1JS4FlwJER8Ui6vz+wOCLm\n5x03N91XMAc4MzNrTccBw4F9SLKyHYBJkt6OiPtKeSEHODOzrGpm0Mjkh5Mt17yFK3E5qStwJrB/\nRNyZFj8vaUvgR8B9wBygi6TavCyuX7qvYA5wZmbWoHEjky3XzFdg6I9afMrO6bYsr3wZK8aEzACW\nAqOBvwJIGgwMBB4r5mIOcGZmVjKSugMbsiI3XF/SEODDiHhD0oPAryQdC/wbGAWMB04AiIj5kq4A\nzpf0EbAAuAh4pJgRlOAAZ2aWXeWZyWRr4H6SZ+ACOC8tv5pk+P83gLOBPwG9SILcTyPi9znnmEiS\n1d0E1JA8dnBMkS11gDMzs9JJn11r9BG0iHgXOKKZcywiGW157Mq0xc/BmZlZVXIGZ2aWVZ5s2czM\nrPI4wJmZWVVyF6WZWVa5i9LMzKzyOIMzM8uyCsrIiuUMzszMqpIDnJmZVSV3UZqZZZUHmZiZmVUe\nZ3BmZlnlDM7MzKzyOMCZmVlVcoAzM7Oq5HtwZmZZ5XtwZmZmlccZnJlZVjmDMzMzqzzO4MzMsqyC\nMrJiOYMzM7Oq5ABnZmZVyQHOzCyr1MKtqVNKIyXdJuktScsl7Zu3/8q0PHe7I69OjaRLJL0vaYGk\nmyT1LfbtOcCZmVkpdQeeBo4GopE6dwL9gP7pNi5v/4XA3sABwA7AAODmYhviQSZmZllVhscEImIK\nMAVAUmO1F0XEew2eXqoFDgfGRsSDadlhwCxJwyJieqFNdQZnZmatbZSkuZJekDRJUq+cfUNJkq+p\ndQUR8SIwGxhRzEWcwZmZWWu6k6S78TVgA+Bs4A5JIyIiSLosF0fE/Lzj5qb7CuYAZ2ZmDZp8f7Ll\nmrdw5c4ZETfkvPyHpOeAV4BRwP0NHtRCZQ9wko4BfkQSeZ8Bjo2IvzdSd0e++AYDWDMi3i1rQ83M\nsqaZe3Djdk62XDNfhqFHl64JEfGapPeBDUl+/88Bukiqzcvi+qX7ClbWe3CSvgGcB5wCbEkS4O6S\n1LuJwwLYiBWjaxzczMyqlKS1gTWAd9KiGcBSYHROncHAQOCxYs5d7gxuInBpRFwDIGkCydDPw4Fz\nmzjuvQb6X83MrJTKMIpSUneSbKyu5vqShgAfptspJPfg5qT1zgFeAu4CiIj5kq4Azpf0EbAAuAh4\npJgRlFDGDE5SZ5LRMLkjYQK4l6ZHwgh4WtLbku6WtG252mhmZiW3NfAUSSYWJL14M4HTgGXA5sCt\nwIvAZcDfgR0iYknOOSYCtwM3AQ8Ab5M8E1eUcmZwvYGOJCNfcs0FBjdyzDvAUcCTQA1wJPBA+uzD\n0+VqqJmZlUb67FpTydMeBZxjEXBsurVYuxpFGREvkaSqdR6XtAFJNP92U8ee8sOHqO3RpV7Z/mMH\nM2ZsY7HUrHj9Tz2rrZtgVea65+C65+uXzfusFRtQxasJlDPAvU+SjvbLKy92JMx0YLvmKp123g5s\nvlXRU5WZmbWpsV9Jtlwz34Fhv2+b9lSTst2DS/tTZ1B/JIzS148WcaotWDG6xszMSkWAVOTW1o0u\nXLm7KM8HrpI0gyQTmwh0A64CkHQ2MCAivp2+Pp7k6fZ/AF1J7sHtBOxa5naamWVPGUZRtidlDXAR\ncUP6zNvpJF2TTwO750yy2R9YJ+eQLiQjbgYAnwDPAqMj4qFyttPMzKpP2QeZRMQkYFIj+w7Le/2/\nwP+Wu01mZlb92tUoSjMza0V199WKOgYaX+atfXGAMzPLKt+DMzOz6tSSCAfO4MzMrP2roIysWF7R\n28zMqpIzODOzrPI9ODMzq04tvQdXGRzgzMyyyhmcmZlVpRY/B1cZPMjEzMyqkgOcmZlVJXdRmpll\nVd1yOUUdUxkPeYMDnJlZdlX5IBN3UZqZWVVyBmdmllktGEVZQSmcMzgzMysZSSMl3SbpLUnLJe2b\ns6+TpHMkPSvp47TO1ZLWzDtHjaRLJL0vaYGkmyT1LbYtDnBmZlmlFm5N6w48DRzNF5cd6AZsAZwG\nbAmMAQYDt+bVuxDYGzgA2AEYANxc3JtzF6WZWYaVfpRJREwBpgBI9fs/I2I+sHu9s0nfB56QtHZE\nvCmpFjgcGBsRD6Z1DgNmSRoWEdMLbakzODOzrCpPBlesniSZ3n/S10NJkq+pdRUi4kVgNjCimBM7\ngzMzy6o2fkxAUg3wP8C1EfFxWtwfWJxme7nmpvsK5gBnZpZVzcxFOXnKUibftaxe2byPG6lc9KXV\nCbiRJHs7ujRnrc8BzszMGjRuj06M26N+mJj5wnKGHrJopc6bE9zWAXbOyd4A5gBdJNXmZXH90n0F\n8z04MzNrNTnBbX1gdER8lFdlBrAUGJ1zzGBgIPBYMddyBmdmllmlf9BbUndgw5yK60saAnwIvEMy\n3H8LYB+gs6R+ab0PI2JJRMyXdAVwvqSPgAXARcAjxYygBAc4M7PsKs8gk62B+0nurQVwXlp+Ncnz\nb19Ly5/OOWMAOwEPpWUTgWXATUANyWMHxxTZUgc4MzMrnfTZtaZufzV7aywiFgHHpluLOcCZmWVV\ni5bLKUtLysKDTMzMrCo5gzMzy6oqXw/OAc7MLLOqO8I5wJmZZVV1xzcHODOzTKuggFUsBzgzs8yq\n7hTOoyjNzKwqOYMzM8uq6k7gHODMzDKrmeVyGj2mQriL0szMqpIzODOzrKryLkpncGZmVpWcwZmZ\nZZXvwZmZmVUeZ3BmZllWQRlZsRzgzMyyqsoHmTjAmZllVnVHON+DMzOzquQMzswsq6o7gXOAMzPL\nrCoPcO6iNDPLLLVwa+KM0qqSLpT0uqRPJE2TtHVendMlvZ3uv0fShqV+Z+AAZ2aWXaWPbwBXAKOB\ng4HNgHuAeyWtCSDpJOD7wHeBYcBC4C5JXUr3xhIOcGZmmaUVs5kUujUR4SR1Bb4O/DgiHomIVyPi\nNOBfwPfSascDZ0TE7RHxPDAeGADsX+p35wBnZmal0gnoCCzKK/8U2F7SekB/YGrdjoiYDzwBjCh1\nYxzgzMysJCLiY+Ax4GRJa0rqIOkQkuC1JklwC2Bu3qFz030l5QBnZpZV5bkHd0ha6y3gM5L7bdcC\ny0vd/Ob4MQEzs6xqZjWBybctYvLf6vc2zlvQdJyKiNeAnSStAtRGxFxJ1wGvAnNIgl8/6mdx/YCn\nWvIWmuIAZ2ZmDRq3bw3j9q2pVzbz+aUM3fc/zR4bEZ8Cn0paHdgd+FFEvCZpDskoy2cBJNUCw4FL\nStx8Bzgzs8wSLVgPrpnd0m5prReBjYBzgX8CV6VVLgR+IelfwOvAGcCbwK3FNaR5DnBmZlZKPYCz\ngbWAD4GbgF9ExDKAiDhXUjfgUqAn8DCwZ0QsLnVDHODMzLKqDFN1RcSNwI3N1DkVOLXIKxfNoyjN\nzKwqOYMzM8uspkdRNnpMhXCAMzPLKq8mYGZmVnmcwZmZZVZ1p3AOcBl3whF3c+M1s5BERAAgiUde\n+Dbrrt+DEw6/mxv/OIufnbUdx/x4xZJOU257hSMOuJ23lhxftra9N3chp584jYenzubjBUvYYPDq\nHP/TbdhrTFmWjrIy6nxacqsn/YrVI8HJO8L4IbDhr6Fvd3jpOOies3jK1pfCfhsn9crhllnwuyfh\nmTmwaBl8uQ/8chTstkH9er9+HH7/JMyeB727wde/DGeNhpr0N+nygFPvh8nPwZyPYcBqMH4L+PkO\n5Wn3Sqvu+OYAZ7DzHoO48A+71vvls0afVZIfBF1X6cSk/53Bt777FWp7rJjVQEXfnC7Osd++m4/n\nL+aaW/dl9TVW4S/XvsBRY+9gyvRxbDqkT1mvbaX11o9W/Hz983DaAzDr+8msuwCrdoH3FiY/L1gM\n5z2aBJjW8tC/YdcN4MzR0LMrXPkU7D8ZHvsODEmnAJ78HPx8KvxhP/jqOvDSB3DELdBB8L+7JXXO\nmQaXzYArxyRBcsbbcPgtyTmPGdZ676dgVR7gfA/O6FLTkTX6dKN33xVbbvAaOXogffp346Kz/96q\n7Zrx+Dsc/v0hbD60H+sMquX4nw2jtmcNz858t1XbYSuvb/cVW4+a5Hdkn5yybp1X1D1mGFz4OLz/\nSeu17/w94IfbwtABsEEv+O/RsFEvuP2lFXUefwO2GwgHbQYDe8Au6yc///2tnDpvwr6DYY8Nkzpj\nNkkCZ26d9qU8sy23F87grFkdO4qf/Pe2HH3wFL5z3Bb0H7BqQccdss8tPDHt7Ub3rzOolvuePqTR\n/dtsO4Bbb3iJnfccRI+eNdx6w0ssXrSMbXdcu+j3YJVBgrGbwdRX4YwH4Nd7FXbctNmwz5+bOC/w\n231g7FcKO19Ekkn2WmVF2Yh14NrnkmC1zVrw6kcw5WX41pCcOmvDFTPh5Q9gozWSLs9H34Dzdi/s\nuq2uyjO4sgY4SSOBHwNDSdYC2j8ibmvmmFHAecCmwGzgzIi4upztzLp7bn+NjXpO+vz1znsO4tLJ\n9X+z7LHvBmw6pA+/Ou1xfnXpLgWd97zLduWzT5c2ur9T56Y7EH43eU8mjLuTTfteSqdOHejWvTNX\n3LQP667fo6DrW+WJSILcf49OughPGAHrrd78cdsMgJkTmq7Tr3vh7fjVo7BwMfzXpivKxn4lySp3\nvDJp57KAo4bCiduvqHPS9jB/EWx6CXRUck/ujJ3hG5sVfm0rnXJncN2Bp4ErgL80V1nSIOB2YBLw\nTWAX4HJJb0fEPeVrZrZtt9PanDNp58/vwXXr3vDX4hdnb8dBu/2FCT8YWtB5+61ZxG+UBpzzy8dY\nMG8RN95zAKuv0ZUpt77CUWPv4JYH/4vBm66xUue29m23DZLuwFPuh2u+3nz9mk6wfgGBsBCTn4Mz\nH4K/jk0GktR54HX4n2kwae8kg3vlQzhhCvR/aMUgkhv+Adc9D9ceAJv0STK4iVOSwSaHDGnwcm3M\nD3q3WES4Ez2iAAAOnElEQVRMAaYAqLARCd8DXo2IE9PXL0raHpgIOMCVSbfunRm4XvNZ0fCRa7Hj\nbuty1s8e4aBvb9Js/ZXpovz3q/O4atIzPPDst9hok14AbPKV3jz+8Ftc9dtnOPvinZu9vlW2s0bD\nyD/AD7Ztvm6puiivfx4m/A2uPwh2Wq/+vlPvh4M3h0O3TF5v2hc+XgwTbl8R4H5yT5LFHbjpijqv\n/ycZfNI+A1x1a2/34L4K3JtXdhdwQRu0xRrwszO3Zdeh17LBl3o2W3dluig//WQJkujQsf7fRR07\ndmD58gbGmltVyP0zeJu1kkEaP7u3+ZyhFF2U1z0H3/0bXHtgMkgk36dLoFPeV7aDkrbVda1+sgQ6\nNlCn3X5lfQ+uVfWn/iqvpK9rJdVExKIGjrFWtPFmvfn6NzfmioufabbuynRRbrhxLwZt0IMTJ0zl\n5HO2Z/U1VuHOW17h4amz+eNt+7X4vNa+5T8nd/pOsPkk6Nyx6eNWtoty8nPJcP4L90yC5dyPk/JV\nOkNt+mTM3l9KnoMb0g+GrZ0MJDn1Afja4BWBeZ/BcNZDsFYtbNoHnnonOebwrVretrIqw3pw7Ul7\nC3AtdsoPH6K2R5d6ZfuPHcyYsYPbqEXV68enfpXbbnip+K77InTq1IE/3b4fZ/3sEQ4d8zcWfryE\nQRv25NdX7cao3dct34WtTeV/pzZaAw7bEi6fWd7rXj4zGTRy7B3JVmf8ELg8/XvqFzsm2dgp98Nb\nC6BPtyS4nZ7TW37RnvDL++G4O+Ddhcm9t6O2To5tzHXPJfftcs37rHTvrWnVncIpGppaoBwXkpbT\nzChKSQ8CMyLiBzllhwIXRESDf59J2gqYMWX6ODbfqm+JW21WX98zft3WTbAMmPkODPs9AEMjouTh\nve735oy7+7DV5l2arV+vbc8uZuhu75WtbaXU3jK4x4A988p2S8vNzKyEQh0IFTffR7H121JZWyqp\nu6QhkrZIi9ZPX6+T7j9bUu4zbr9L65wjabCko4EDgfPL2U4zs2zyTCYrY2vgfpIp54LkAW6Aq4HD\nSQaVrFNXOSJel7Q3yajJ44A3gSMiIn9kpZmZraRARJF5TjjAJSLiQZrIEiPisAbKHiKZ+cTMzMpJ\nLXjQu8yTrJdSe7sHZ2ZmraYDxd+p8j04MzPLKEkDJP1R0vuSPpH0TDpyM7fO6ZLeTvffI6nkCz06\nwJmZZVQyOEJFbk2T1BN4BFgE7A5sAvwQ+CinzknA94HvAsOAhcBdkop7ZqEZ7qI0M8usDlD0sP9m\n6/8EmB0R38kp+3deneOBMyLidgBJ40lmrdofuKHIBjXKGZyZWUYVn72pkFGUXwOelHSDpLmSZkr6\nPNhJWo9kBP3Uz9sRMR94AhhRyvfnAGdmllVSksEVtTUb4NYnWRnmRZKJOn4LXCTpW+n+/iS9ow3N\nO9y/dG/OXZRmZlZaHYDpEXFy+voZSZsBE4A/tmZDHODMzDKr6S7H626ex/V/mVevbP785c2d9B1g\nVl7ZLKBu6do5JNOh9KN+FtcPeKrZJhfBAc7MLLOafg5u7AGrM/aA+vPcP/XMpwwf/UpTJ30EyF/G\nZTDpQJOIeE3SHGA08CyApFpgOHBJce1vmgOcmVlWlWcmkwuARyT9lGRE5HDgO8CROXUuBH4h6V/A\n68AZJFMz3lpcY5rmAGdmllHlmIsyIp6UNAb4H+Bk4DXg+Ii4LqfOuZK6AZcCPYGHgT0jYnFx76Bp\nDnBmZplVngVPI+IO4I5m6pwKnFrkxYvixwTMzKwqOYMzM8uoal/w1AHOzCyzytNF2V44wJmZZZQX\nPDUzs+rkBU/NzKw6ecFTMzOziuMMzswso+oWPC32mErhAGdmllllWfC03XCAMzPLqAIXMP3CMZXC\nAc7MLKvqFjwt9pgKUTm5ppmZWRGcwZmZZZZnMjEzs6rUoeiZTCqp488BzswsqzyTiZmZVSPPRWlm\nZlWquu/BVU5nqpmZWRGcwZmZZZQXPDUzsypWOV2OxXKAMzPLrOpeLscBzswso0Iiihz2X2z9tlQ5\nodjMzEpMrMjiCt2aDnCSJkh6RtK8dHtU0h55dU6X9LakTyTdI2nDUr8zcIAzM7PSegM4CdgKGArc\nB9wqaRMASScB3we+CwwDFgJ3SepS6oa4i9LMLKOCFnRRNpPBRcT/5RX9QtL3gK8Cs4DjgTMi4nYA\nSeOBucD+wA1FNaYZzuDMzDKr2O7J4galSOogaSzQDXhU0npAf2BqXZ2ImA88AYxY+fdTnzM4M7PM\nKn7B00IeK5C0GfAY0BVYAIyJiBcljQCCJGPLNZck8JWUA5yZWVY1s+DpTTe8wc03vFGvbP68JYWc\n+QVgCNADOBC4RtIOLW9oyzjAmZllVtNzUR540EAOPGhgvbJnnvqIUdtNbeSIREQsBV5NXz4laRjJ\nvbdz0wv2o34W1w94qsjGN8v34MzMrNw6ADUR8RowBxhdt0NSLTAceLTUF3UGZ2aWUdGCBU+bqy/p\nLOBOYDawGnAwsCOwW1rlQpKRlf8CXgfOAN4Ebi2qIQVwgDMzyyrRggVPm63RF7gaWBOYBzwL7BYR\n9wFExLmSugGXAj2Bh4E9I2JxcQ1pngOcmVlGlSODi4jvNHuOiFOBU4u6cAs4wJmZZZYXPDUzM6s4\nzuDMzDIqWU2g2AVPKyeDc4AzM8us6u6idIAzM8sqdWhyJpNGj6kQDnBmZhkVLZiLsvi5K9uOA5yZ\nWWbVLXha7DGVoXJyTTMzsyI4gzMzyyoVv+Bp0TOftCEHODOzzCpuAdMVx1QGBzgzs8zyYwJmZlaF\nghY86O0AZ2Zm7V91Z3CV05lqZmZWBGdwZmYZVY7lctoTBzgzs6wqz4Kn7YYDnJlZRjmDMzOzKlXd\ng0wc4MzMskpqwWoClRPgKifXNDMzK4IDXIb99boX27oJFee659q6BZXJn1v7VLdcTrFbpShrgJM0\nUtJtkt6StFzSvs3U3zGtl7stk9S3nO3Mqlsc4Ip23fNt3YLK5M+tnapb8LTYrbnTSsdIek3Sp5Ie\nl7RNK7ybLyh3BtcdeBo4GogCjwlgI6B/uq0ZEe+Wp3lmZlnWkuyt6QxO0jeA84BTgC2BZ4C7JPUu\n+9vJU9ZBJhExBZgCIBV1Z/K9iJhfnlaZmVmiLAueTgQujYhrACRNAPYGDgfOLbaFK6M93oMT8LSk\ntyXdLWnbtm6QmZk1T1JnYCgwta4sIgK4FxjR2u1pb48JvAMcBTwJ1ABHAg9IGhYRTzdyTFeAl1/4\nsHVaWEXmz1vMszPd+1uMeZ/BzHfauhWVx59bcV54//Mfu5bzOi+98GHRC54287u2N9ARmJtXPhcY\nXNSFSkBJcG2FC0nLgf0j4rYij3sA+HdEfLuR/d8E/rzyLTQza3cOjohrS31SSQOBWUC3Fp5iEfCl\niJidd941gbeAERHxRE75OcAOEdGqWVx7y+AaMh3Yron9dwEHA68Dn7VGg8zMyqwrMIjk91vJRcRs\nSZuQZFwt8X5+cKsrB5YB/fLK+wFzWnitFquEALcFSddlgyLiA6Dkf+GYmbWxR8t58jRANRSkVuac\nSyTNAEYDt8HnAwxHAxeV8lqFKGuAk9Qd2JAVw27WlzQE+DAi3pB0NjCgrvtR0vHAa8A/SP6CORLY\nCdi1nO00M7OSOR+4Kg1000lGVXYDrmrthpQ7g9sauJ/k2bYgeTYC4GqSIaP9gXVy6ndJ6wwAPgGe\nBUZHxENlbqeZmZVARNyQPvN2OknX5NPA7hHxXmu3pdUGmZiZmbWm9vgcnJmZ2UpzgDMzs6pUkQFO\n0uqS/ixpnqSPJF2eDmhp6pgrG5jI+Y7WanNbKHbCU0mjJM2Q9JmklyQ1+OxhNSvmM/Pk4MVPqJ4e\n4++ZJ6JvFRUZ4EgeC9iEZOjp3sAOwKUFHHcnyU3Puomcx5WrgW2t2AlPJQ0CbieZYmcI8GvgckmZ\nGcHawklisz45eFETqvt79jlPRN8KKm6QiaSNgX8CQyPiqbRsd+D/gLUjosGHCSVdCfSIiK+3WmPb\nkKTHgSci4vj0tYA3gIsi4gsTnqYzDewZEZvnlE0m+cz2aqVmt6kWfGY7AvcBq3ty8MJmK/L37IsK\n/Nz8XWuBSszgRgAf1QW31L0kf90Mb+bYUZLmSnpB0iRJvcrWyjbUwglPv5ruz3VXE/WrykpMEuvJ\nwYuT6e/ZSvJ3rUiVGOD6A/XS8ohYBnyY7mvMncB4YGfgRGBH4I4il/GpFE1NeNrYZ9S/kfq1kmpK\n27x2qSWfWd3k4AcAXyfJ9h6QtEW5GlkFsv49ayl/11qg3UzVlc5qclITVYLkvluLRMQNOS//Iek5\n4BVgFMnD6GZFiYiXgJdyih6XtAHJzA2ZGzhh5ePvWsu0mwAH/Aq4spk6r5JM2Flv5JCkjkAvipjM\nMyJek/Q+yVRi1RbgWjLh6ZxG6s+PiEWlbV67VKpJYpubHDzrsv49KyV/15rRbrooI+KDiHipmW0p\n8BjQU9KWOYePJumffqLBkzdA0trAGjQxkXOlioglQN2Ep0C9CU8bm8D1sdz6qd3S8qrXws+sIU1O\nDm7Z/p6VmL9rzYmIituAO0gWRd2G5C+YF4E/5tV5Adgv/bk7yVLpw4F1Sf6DPUmyHlLntn4/ZfqM\nDiKZz3M8sDHJYxQfAH3S/WcDV+fUHwQsAM4hWZjwaGAxsEtbv5d2/JkdD+wLbABsClwILAFGtfV7\nacXPrDvJcP8tgOXACenrdfw9K+nnlvnvWos+57ZuQAu/HD2BPwHzgI+Ay4BueXWWAePTn7sCU0i6\nRz4j6er8bd0vrmrd0l8erwOfkvyFvHXOviuB+/Lq70CSxXwKvAx8q63fQ3v+zIAfp5/TQuA9khGY\nO7T1e2jlz2vH9Bf0srztD/6ele5z83etZVvFPQdnZmZWiHZzD87MzKyUHODMzKwqOcCZmVlVcoAz\nM7Oq5ABnZmZVyQHOzMyqkgOcmZlVJQc4MzOrSg5wZmZWlRzgzMysKjnAmZlZVfp/9KFQ5Cqpb6oA\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1111722b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"generate_confusion_matrix(testing_results,\"samarth_new_10000.npy\",[\"Samarth\"],[\"soft\",\"close\",\"training\"])"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean: 78381.3612369 TP: 12 TN: 190 FP: 90 FN: 12 Accuracy: 0.664473684211\n"
]
},
{
"data": {
"image/png": 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XLWbk7p2cwZUBj6K0Zqkd1ubZc5trl173lcnTuPOW11h7vVV5+t1jAbjpqsksWrSEow/8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\nZmYVyQHOzMwq0v8HObtJ4nFhmUEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110e4bc88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"generate_confusion_matrix(testing_results,\"jourdon_new_10000.npy\",[\"Jourdon\",\"jourdan\"],[\"baseline\",\"close\",\"training\"])"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean: 73216.6479664 TP: 10 TN: 291 FP: 0 FN: 6 Accuracy: 0.980456026059\n"
]
},
{
"data": {
"image/png": 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tnvQrVocEv9oNjtoGNv099OoKr5wCXXPmlt/hSjh4QFKvHP41Ff70FDw7AxYt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\nxGlJPgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110a359b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"generate_confusion_matrix(testing_results,\"arti_new_10000.npy\",[\"AG\",\"Arti\"],[\"Baseline\",\"close\",\"training\"])"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean: 73593.0159238 TP: 16 TN: 277 FP: 0 FN: 14 Accuracy: 0.954397394137\n"
]
},
{
"data": {
"image/png": 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N4/MFS3l97gjWqm74qfZLl67gwG/cxSvTP2HiP49m2x3WK1m7rHCdfpXc6okG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\nXgRqIuKJErfTzMyKICJuz8x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"text/plain": [
"<matplotlib.figure.Figure at 0x10ffbdf98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"generate_confusion_matrix(testing_results,\"anne_new_10000.npy\",[\"Anne\"],[\"baseline\",\"close\",\"training\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}