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In [1]:

    
import matplotlib

matplotlib.use('TkAgg')
%matplotlib inline
import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [0, 3, 7])
plt.show()



In [2]:

    
import pandas as pd
import csv
import numpy as np
from math import sqrt
from sklearn.feature_extraction.text import CountVectorizer
countVector = CountVectorizer(min_df=1)
import re



In [3]:

    
# Gautam's video input
with open('./data/RAW_abhyudaya/final_gb1.csv') as f:
    reader = csv.reader(f)
    affectivavideooutput = list(reader)

with open('./data/RAW_abhyudaya/final_gb4.csv') as f:
    reader = csv.reader(f)
    affectivavideooutput2 = list(reader)
    
with open('./data/RAW_abhyudaya/final_gb6.csv') as f:
    reader = csv.reader(f)
    affectivavideooutput3 = list(reader)
    
with open('./data/RAW_abhyudaya/final_gb7.csv') as f:
    reader = csv.reader(f)
    affectivavideooutput4 = list(reader)

affectivavideooutput2.pop(0)
affectivavideooutput3.pop(0)
affectivavideooutput4.pop(0)

print len(affectivavideooutput)
print len(affectivavideooutput2)
print len(affectivavideooutput3)
print len(affectivavideooutput4)

affectivavideooutput.extend(affectivavideooutput2)
print len(affectivavideooutput)
#affectivavideooutput.extend(affectivavideooutput3)
print len(affectivavideooutput)
affectivavideooutput.extend(affectivavideooutput4)

print len(affectivavideooutput)
print affectivavideooutput[0]









    



9467
1952
1553
1676
11419
11419
13095
['', 'TimeStamp', 'interocularDistance', 'glasses', 'pitch', 'yaw', 'roll', 'joy', 'fear', 'disgust', 'sadness', 'anger', 'surprise', 'contempt', 'valence', 'engagement', 'smile', 'innerBrowRaise', 'browRaise', 'browFurrow', 'noseWrinkle', 'upperLipRaise', 'lipCornerDepressor', 'chinRaise', 'lipPucker', 'lipPress', 'lipSuck', 'mouthOpen', 'smirk', 'eyeClosure', 'attention', 'eyeWiden', 'cheekRaise', 'lidTighten', 'dimpler', 'lipStretch', 'jawDrop', 'relaxed', 'smiley', 'laughing', 'kissing', 'disappointed', 'rage', 'smirk.1', 'wink', 'stuckOutTongueWinkingEye', 'stuckOutTongue', 'flushed', 'scream', 'FRUSTRATED', 'TIRED', 'ENGAGED', 'CLASS']



In [4]:

    
print len(affectivavideooutput3)



In [5]:

    
affectivavideooutput.pop(0)
affectivadataframe = pd.DataFrame(affectivavideooutput, 
                                columns = ['', 'TimeStamp', 'interocularDistance', 'glasses', 'pitch', 'yaw', 'roll', 'joy', 
                                           'fear', 'disgust', 'sadness', 'anger', 'surprise', 'contempt', 'valence', 
                                           'engagement', 'smile', 'innerBrowRaise', 'browRaise', 'browFurrow', 'noseWrinkle', 
                                           'upperLipRaise', 'lipCornerDepressor', 'chinRaise', 'lipPucker', 'lipPress', 
                                           'lipSuck', 'mouthOpen', 'smirk', 'eyeClosure', 'attention', 'eyeWiden',
                                           'cheekRaise', 'lidTighten', 'dimpler', 'lipStretch', 'jawDrop', 'relaxed',
                                           'smiley', 'laughing', 'kissing', 'disappointed', 'rage', 'smirk.1', 'wink', 
                                           'stuckOutTongueWinkingEye', 'stuckOutTongue', 'flushed',
                                           'scream', 'frustrated', 'tired', 'engaged', 'class'])
affectivadataframe[0:10]
#drop the columns that are outright irrelevant for data analysis
affectivadataframe = affectivadataframe.drop(['','TimeStamp'],axis=1)
#affectivadataframe[-10:]
print len(affectivadataframe)



In [6]:

    
import statsmodels.api as sm
X= affectivadataframe[['smile','innerBrowRaise','browRaise', 'browFurrow', 'noseWrinkle', 'upperLipRaise', 
                        'lipCornerDepressor', 'chinRaise', 'lipPucker', 'lipPress', 'lipSuck', 
                        'mouthOpen', 'smirk', 'eyeClosure', 'attention', 'eyeWiden', 'cheekRaise',
                        'lidTighten', 'dimpler', 'lipStretch', 'jawDrop','engaged','frustrated','tired']].copy()

for column in X.columns:
     X[column]=X[column].apply(lambda x:float(x.replace('"','').replace("\n",'').replace('nan','')))

X['engaged'] = X['engaged'].apply(lambda x:int(x))
X['frustrated'] = X['frustrated'].apply(lambda x:int(x))
X['tired'] = X['tired'].apply(lambda x:int(x))



In [7]:

    
import sklearn.utils
df = sklearn.utils.shuffle(X)
df = df.reset_index(drop=True)
df[0:10]









    Out[7]:






  
    
      
      smile
      innerBrowRaise
      browRaise
      browFurrow
      noseWrinkle
      upperLipRaise
      lipCornerDepressor
      chinRaise
      lipPucker
      lipPress
      ...
      attention
      eyeWiden
      cheekRaise
      lidTighten
      dimpler
      lipStretch
      jawDrop
      engaged
      frustrated
      tired
    
  
  
    
      0
      0.0008
      0.2388
      0.0057
      0.2553
      0.1219
      0.0034
      0.0001
      0.0067
      0.0380
      0.0946
      ...
      83.0258
      0.0026
      0.0020
      0.0209
      0.0112
      0.0098
      6.4668
      1
      0
      0
    
    
      1
      0.0000
      0.0001
      0.0005
      0.0022
      0.0000
      0.0000
      0.0109
      0.0749
      0.0017
      0.0016
      ...
      89.0099
      0.0118
      0.4017
      0.0544
      0.0006
      0.0022
      0.3176
      1
      1
      1
    
    
      2
      0.0000
      0.0004
      0.0575
      0.0007
      0.0000
      0.0021
      0.0000
      0.0000
      0.5980
      0.0002
      ...
      63.9603
      0.0156
      0.0108
      0.0110
      0.0001
      0.0001
      79.5952
      1
      0
      1
    
    
      3
      0.0003
      1.2592
      0.0050
      0.1682
      0.0743
      2.2715
      4.5193
      0.0445
      0.0531
      3.1848
      ...
      96.6968
      0.0034
      0.0370
      0.0103
      89.4438
      0.1338
      0.0243
      1
      0
      0
    
    
      4
      0.0000
      0.7415
      0.0310
      0.0092
      0.0246
      0.0473
      0.0031
      0.0220
      0.0129
      0.0048
      ...
      94.8980
      0.0354
      0.0000
      0.0057
      0.0216
      0.0051
      0.3838
      1
      0
      0
    
    
      5
      0.0016
      2.0825
      1.3171
      0.0081
      0.3119
      0.0001
      0.0012
      99.7759
      0.4525
      7.3817
      ...
      96.9536
      0.0012
      70.5788
      0.1526
      73.1294
      0.5042
      61.3236
      1
      0
      0
    
    
      6
      0.0000
      0.0002
      0.0249
      0.0174
      0.0018
      2.4737
      0.0791
      0.0008
      0.1355
      0.0091
      ...
      51.7223
      3.8399
      0.0016
      0.0184
      0.0081
      0.0002
      4.2949
      1
      0
      0
    
    
      7
      0.0060
      0.0681
      0.0143
      0.0009
      0.0810
      30.4586
      0.0012
      0.0004
      5.5293
      0.2761
      ...
      98.5840
      0.0174
      0.0008
      0.0074
      0.0190
      0.1081
      94.5897
      1
      0
      0
    
    
      8
      0.0000
      0.0000
      98.1941
      0.0000
      0.0000
      0.0000
      0.0001
      0.0411
      0.0246
      0.0001
      ...
      82.3883
      0.0298
      0.0002
      0.0000
      0.0000
      0.0001
      1.6839
      1
      0
      0
    
    
      9
      0.0000
      0.0072
      0.8344
      0.0266
      0.0014
      0.0007
      2.5742
      0.0154
      1.3654
      0.1056
      ...
      48.9700
      1.8493
      0.0028
      0.0398
      0.1563
      0.0011
      1.1260
      1
      0
      0
    
  

10 rows × 24 columns



In [12]:

    
dftestVideo = pd.DataFrame(affectivavideooutput3, 
                                columns = ['', 'TimeStamp', 'interocularDistance', 'glasses', 'pitch', 'yaw', 'roll', 'joy', 
                                           'fear', 'disgust', 'sadness', 'anger', 'surprise', 'contempt', 'valence', 
                                           'engagement', 'smile', 'innerBrowRaise', 'browRaise', 'browFurrow', 'noseWrinkle', 
                                           'upperLipRaise', 'lipCornerDepressor', 'chinRaise', 'lipPucker', 'lipPress', 
                                           'lipSuck', 'mouthOpen', 'smirk', 'eyeClosure', 'attention', 'eyeWiden',
                                           'cheekRaise', 'lidTighten', 'dimpler', 'lipStretch', 'jawDrop', 'relaxed',
                                           'smiley', 'laughing', 'kissing', 'disappointed', 'rage', 'smirk.1', 'wink', 
                                           'stuckOutTongueWinkingEye', 'stuckOutTongue', 'flushed',
                                           'scream', 'frustrated', 'tired', 'engaged', 'class'])
affectivadataframe[0:10]
#drop the columns that are outright irrelevant for data analysis
dftestVideo = dftestVideo.drop(['','TimeStamp'],axis=1)
#affectivadataframe[-10:]
print len(dftestVideo)
for column in dftestVideo.columns:
     dftestVideo[column]=dftestVideo[column].apply(lambda x:float(x.replace('"','').replace("\n",'').replace('nan','')))

dftestVideo['engaged'] = dftestVideo['engaged'].apply(lambda x:int(x))
dftestVideo['frustrated'] = dftestVideo['frustrated'].apply(lambda x:int(x))
dftestVideo['tired'] = dftestVideo['tired'].apply(lambda x:int(x))



In [9]:

    
import statsmodels.api as sm
X= affectivadataframe[['smile','innerBrowRaise','browRaise', 'browFurrow', 'noseWrinkle', 'upperLipRaise', 
                        'lipCornerDepressor', 'chinRaise', 'lipPucker', 'lipPress', 'lipSuck', 
                        'mouthOpen', 'smirk', 'eyeClosure', 'attention', 'eyeWiden', 'cheekRaise',
                        'lidTighten', 'dimpler', 'lipStretch', 'jawDrop','engaged','frustrated','tired']].copy()

for column in X.columns:
     X[column]=X[column].apply(lambda x:float(x.replace('"','').replace("\n",'').replace('nan','')))

X['engaged'] = X['engaged'].apply(lambda x:int(x))
X['frustrated'] = X['frustrated'].apply(lambda x:int(x))
X['tired'] = X['tired'].apply(lambda x:int(x))
dftrain = X
print len(dftrain)



In [10]:

    
from sklearn.preprocessing import StandardScaler
explanatoryStd = X[['smile','innerBrowRaise', 'browRaise', 'browFurrow', 'noseWrinkle', 'upperLipRaise', 
                        'lipCornerDepressor', 'chinRaise', 'lipPucker', 'lipPress', 'lipSuck', 
                        'mouthOpen', 'smirk', 'eyeClosure', 'eyeWiden', 'cheekRaise',
                        'lidTighten', 'dimpler', 'lipStretch', 'jawDrop']].copy() 
X_std = X
#X_std = StandardScaler().fit_transform(explanatoryStd)
backtodf = pd.DataFrame(X_std, index=explanatoryStd.index, columns=explanatoryStd.columns)
Dependent = X['frustrated']
logit = sm.Logit(Dependent,backtodf)
result = logit.fit()
print(result.summary())









    



Optimization terminated successfully.
         Current function value: 0.371130
         Iterations 13
                           Logit Regression Results                           
==============================================================================
Dep. Variable:             frustrated   No. Observations:                13094
Model:                          Logit   Df Residuals:                    13074
Method:                           MLE   Df Model:                           19
Date:                Thu, 08 Dec 2016   Pseudo R-squ.:                -0.06581
Time:                        08:59:02   Log-Likelihood:                -4859.6
converged:                       True   LL-Null:                       -4559.5
                                        LLR p-value:                     1.000
======================================================================================
                         coef    std err          z      P>|z|      [95.0% Conf. Int.]
--------------------------------------------------------------------------------------
smile                 -0.9840      0.441     -2.233      0.026        -1.848    -0.120
innerBrowRaise        -0.1664      0.019     -8.605      0.000        -0.204    -0.129
browRaise             -0.0143      0.004     -3.961      0.000        -0.021    -0.007
browFurrow            -0.0331      0.007     -4.736      0.000        -0.047    -0.019
noseWrinkle           -0.0441      0.011     -3.906      0.000        -0.066    -0.022
upperLipRaise         -2.0208      0.144    -14.044      0.000        -2.303    -1.739
lipCornerDepressor    -0.0627      0.005    -11.889      0.000        -0.073    -0.052
chinRaise             -0.0066      0.003     -2.224      0.026        -0.012    -0.001
lipPucker             -0.0286      0.005     -6.249      0.000        -0.038    -0.020
lipPress               0.0049      0.002      2.315      0.021         0.001     0.009
lipSuck                0.0417      0.003     14.747      0.000         0.036     0.047
mouthOpen             -0.0229      0.004     -5.824      0.000        -0.031    -0.015
smirk                 -0.0002      0.001     -0.149      0.882        -0.002     0.002
eyeClosure             0.0011      0.002      0.672      0.502        -0.002     0.004
eyeWiden              -0.0493      0.010     -5.058      0.000        -0.068    -0.030
cheekRaise             0.0109      0.003      4.120      0.000         0.006     0.016
lidTighten             0.4283      0.070      6.155      0.000         0.292     0.565
dimpler               -0.0128      0.002     -6.429      0.000        -0.017    -0.009
lipStretch             0.0087      0.005      1.850      0.064        -0.001     0.018
jawDrop               -0.1493      0.008    -19.271      0.000        -0.164    -0.134
======================================================================================



In [14]:

    
from sklearn import metrics
from math import sqrt 
from ggplot import *
count1 = 0
count0 = 0
count = 0
sum1 = 0.0

per = result.predict(dftestVideo[['smile','innerBrowRaise', 'browRaise', 'browFurrow', 'noseWrinkle', 'upperLipRaise', 
                        'lipCornerDepressor', 'chinRaise', 'lipPucker', 'lipPress', 'lipSuck', 
                        'mouthOpen', 'smirk', 'eyeClosure', 'eyeWiden', 'cheekRaise',
                        'lidTighten', 'dimpler', 'lipStretch', 'jawDrop']] )

fpr, tpr, pr = metrics.roc_curve(dftestVideo['frustrated'], per)
df = pd.DataFrame(dict(fpr=fpr, tpr=tpr))
ggplot(df, aes(x='fpr', y='tpr')) +\
    geom_line() +\
    geom_abline(linetype='dashed')









    












    Out[14]:





<ggplot: (297658193)>



In [15]:

    
from numpy import *
import math
import matplotlib.pyplot as plt
#print 1-fpr
plt.plot(pr,1-fpr,'r',label= 'specificity')
plt.plot(pr,tpr,'b',label= 'sensivity')
plt.legend()









    Out[15]:





<matplotlib.legend.Legend at 0x11b17e1d0>



In [16]:

    
auc = metrics.auc(fpr,tpr)
print auc









    



0.487599253857



In [102]:

    
for i in pr:
    if i > 1:
        print i









    



1.99999996883



In [103]:

    
from sklearn.preprocessing import StandardScaler
explanatoryStd = dftrain[['smile','innerBrowRaise', 'browRaise', 'browFurrow', 'noseWrinkle', 'upperLipRaise', 
                        'lipCornerDepressor', 'chinRaise', 'lipPucker', 'lipPress', 'lipSuck', 
                        'mouthOpen', 'smirk', 'eyeClosure', 'eyeWiden', 'cheekRaise',
                        'lidTighten', 'dimpler', 'lipStretch', 'jawDrop']].copy() 
X_std = StandardScaler().fit_transform(explanatoryStd)
backtodf = pd.DataFrame(X_std, index=explanatoryStd.index, columns=explanatoryStd.columns)
Dependent = dftrain['engaged']
logit = sm.Logit(Dependent,backtodf)
result = logit.fit()
print(result.summary())









    



Optimization terminated successfully.
         Current function value: 0.667587
         Iterations 5
                           Logit Regression Results                           
==============================================================================
Dep. Variable:                engaged   No. Observations:                13182
Model:                          Logit   Df Residuals:                    13162
Method:                           MLE   Df Model:                           19
Date:                Thu, 08 Dec 2016   Pseudo R-squ.:                 -0.4559
Time:                        08:34:29   Log-Likelihood:                -8800.1
converged:                       True   LL-Null:                       -6044.3
                                        LLR p-value:                     1.000
======================================================================================
                         coef    std err          z      P>|z|      [95.0% Conf. Int.]
--------------------------------------------------------------------------------------
smile                  0.0164      0.022      0.744      0.457        -0.027     0.060
innerBrowRaise        -0.2026      0.023     -8.970      0.000        -0.247    -0.158
browRaise              0.0189      0.020      0.946      0.344        -0.020     0.058
browFurrow             0.2619      0.025     10.426      0.000         0.213     0.311
noseWrinkle           -0.0106      0.022     -0.489      0.625        -0.053     0.032
upperLipRaise         -0.0514      0.021     -2.401      0.016        -0.093    -0.009
lipCornerDepressor    -0.0209      0.020     -1.027      0.305        -0.061     0.019
chinRaise              0.1086      0.028      3.818      0.000         0.053     0.164
lipPucker             -0.2284      0.023    -10.064      0.000        -0.273    -0.184
lipPress               0.0400      0.029      1.390      0.165        -0.016     0.096
lipSuck               -0.2469      0.028     -8.775      0.000        -0.302    -0.192
mouthOpen              0.1871      0.023      8.289      0.000         0.143     0.231
smirk                 -0.1721      0.023     -7.373      0.000        -0.218    -0.126
eyeClosure            -0.1083      0.020     -5.514      0.000        -0.147    -0.070
eyeWiden               0.0364      0.019      1.939      0.052        -0.000     0.073
cheekRaise             0.0636      0.020      3.245      0.001         0.025     0.102
lidTighten            -0.1222      0.024     -5.191      0.000        -0.168    -0.076
dimpler                0.2822      0.030      9.536      0.000         0.224     0.340
lipStretch            -0.1231      0.028     -4.331      0.000        -0.179    -0.067
jawDrop                0.1014      0.022      4.613      0.000         0.058     0.144
======================================================================================



In [104]:

    
from sklearn import metrics
from math import sqrt 
from ggplot import *
count1 = 0
count0 = 0
count = 0
sum1 = 0.0

per = result.predict(dftest[['smile','innerBrowRaise', 'browRaise', 'browFurrow', 'noseWrinkle', 'upperLipRaise', 
                        'lipCornerDepressor', 'chinRaise', 'lipPucker', 'lipPress', 'lipSuck', 
                        'mouthOpen', 'smirk', 'eyeClosure', 'eyeWiden', 'cheekRaise',
                        'lidTighten', 'dimpler', 'lipStretch', 'jawDrop']] )

fpr, tpr, pr = metrics.roc_curve(dftest['engaged'], per)
df = pd.DataFrame(dict(fpr=fpr, tpr=tpr))
ggplot(df, aes(x='fpr', y='tpr')) +\
    geom_line() +\
    geom_abline(linetype='dashed')









    












    Out[104]:





<ggplot: (322371465)>



In [105]:

    
from numpy import *
import math
import matplotlib.pyplot as plt
#print 1-fpr
plt.plot(pr,1-fpr,'r',label= 'specificity')
plt.plot(pr,tpr,'b',label= 'sensitivity')
plt.legend()









    Out[105]:





<matplotlib.legend.Legend at 0x12e42e690>



In [77]:

    
from sklearn.preprocessing import StandardScaler
explanatoryStd = dftrain[['innerBrowRaise', 'browRaise', 'browFurrow', 'noseWrinkle', 'upperLipRaise', 
                        'lipCornerDepressor', 'chinRaise', 'lipPucker', 'lipPress', 'lipSuck', 
                        'mouthOpen', 'smirk', 'eyeClosure', 'eyeWiden', 'cheekRaise',
                        'lidTighten', 'dimpler', 'lipStretch', 'jawDrop']].copy() 
X_std = StandardScaler().fit_transform(explanatoryStd)
backtodf = pd.DataFrame(X_std, index=explanatoryStd.index, columns=explanatoryStd.columns)
Dependent = dftrain['tired']
logit = sm.Logit(Dependent,backtodf)
result = logit.fit()
print(result.summary())









    



Optimization terminated successfully.
         Current function value: 0.684854
         Iterations 4
                           Logit Regression Results                           
==============================================================================
Dep. Variable:                  tired   No. Observations:                13182
Model:                          Logit   Df Residuals:                    13163
Method:                           MLE   Df Model:                           18
Date:                Thu, 08 Dec 2016   Pseudo R-squ.:                  -2.530
Time:                        08:29:11   Log-Likelihood:                -9027.7
converged:                       True   LL-Null:                       -2557.3
                                        LLR p-value:                     1.000
======================================================================================
                         coef    std err          z      P>|z|      [95.0% Conf. Int.]
--------------------------------------------------------------------------------------
innerBrowRaise         0.0983      0.020      4.957      0.000         0.059     0.137
browRaise             -0.0406      0.020     -2.059      0.039        -0.079    -0.002
browFurrow            -0.0085      0.023     -0.377      0.707        -0.053     0.036
noseWrinkle            0.0004      0.021      0.018      0.986        -0.041     0.042
upperLipRaise          0.0109      0.021      0.524      0.600        -0.030     0.051
lipCornerDepressor    -0.0854      0.019     -4.382      0.000        -0.124    -0.047
chinRaise              0.0106      0.026      0.403      0.687        -0.041     0.062
lipPucker             -0.0430      0.018     -2.328      0.020        -0.079    -0.007
lipPress              -0.0858      0.027     -3.123      0.002        -0.140    -0.032
lipSuck                0.0334      0.025      1.359      0.174        -0.015     0.082
mouthOpen              0.1954      0.022      8.946      0.000         0.153     0.238
smirk                  0.0954      0.022      4.251      0.000         0.051     0.139
eyeClosure             0.1082      0.019      5.691      0.000         0.071     0.145
eyeWiden              -0.0059      0.018     -0.329      0.742        -0.041     0.029
cheekRaise             0.0536      0.019      2.772      0.006         0.016     0.091
lidTighten             0.0109      0.023      0.479      0.632        -0.034     0.056
dimpler               -0.0101      0.027     -0.370      0.711        -0.064     0.043
lipStretch            -0.0048      0.026     -0.183      0.855        -0.056     0.046
jawDrop               -0.0801      0.021     -3.754      0.000        -0.122    -0.038
======================================================================================



In [106]:

    
auc = metrics.auc(fpr,tpr)
print auc









    



0.58232405892



In [107]:

    
from sklearn import metrics
from math import sqrt 
from ggplot import *
count1 = 0
count0 = 0
count = 0
sum1 = 0.0

per = result.predict(dftest[['smile','innerBrowRaise', 'browRaise', 'browFurrow', 'noseWrinkle', 'upperLipRaise', 
                        'lipCornerDepressor', 'chinRaise', 'lipPucker', 'lipPress', 'lipSuck', 
                        'mouthOpen', 'smirk', 'eyeClosure', 'eyeWiden', 'cheekRaise',
                        'lidTighten', 'dimpler', 'lipStretch', 'jawDrop']] )

fpr, tpr, pr = metrics.roc_curve(dftest['tired'], per)
df = pd.DataFrame(dict(fpr=fpr, tpr=tpr))
ggplot(df, aes(x='fpr', y='tpr')) +\
    geom_line() +\
    geom_abline(linetype='dashed')









    












    Out[107]:





<ggplot: (319607045)>



In [108]:

    
auc = metrics.auc(fpr,tpr)
print auc









    



0.472359607446



In [ ]:

	smile	innerBrowRaise	browRaise	browFurrow	noseWrinkle	upperLipRaise	lipCornerDepressor	chinRaise	lipPucker	lipPress	...	attention	eyeWiden	cheekRaise	lidTighten	dimpler	lipStretch	jawDrop	engaged	frustrated	tired
0	0.0008	0.2388	0.0057	0.2553	0.1219	0.0034	0.0001	0.0067	0.0380	0.0946	...	83.0258	0.0026	0.0020	0.0209	0.0112	0.0098	6.4668	1	0	0
1	0.0000	0.0001	0.0005	0.0022	0.0000	0.0000	0.0109	0.0749	0.0017	0.0016	...	89.0099	0.0118	0.4017	0.0544	0.0006	0.0022	0.3176	1	1	1
2	0.0000	0.0004	0.0575	0.0007	0.0000	0.0021	0.0000	0.0000	0.5980	0.0002	...	63.9603	0.0156	0.0108	0.0110	0.0001	0.0001	79.5952	1	0	1
3	0.0003	1.2592	0.0050	0.1682	0.0743	2.2715	4.5193	0.0445	0.0531	3.1848	...	96.6968	0.0034	0.0370	0.0103	89.4438	0.1338	0.0243	1	0	0
4	0.0000	0.7415	0.0310	0.0092	0.0246	0.0473	0.0031	0.0220	0.0129	0.0048	...	94.8980	0.0354	0.0000	0.0057	0.0216	0.0051	0.3838	1	0	0
5	0.0016	2.0825	1.3171	0.0081	0.3119	0.0001	0.0012	99.7759	0.4525	7.3817	...	96.9536	0.0012	70.5788	0.1526	73.1294	0.5042	61.3236	1	0	0
6	0.0000	0.0002	0.0249	0.0174	0.0018	2.4737	0.0791	0.0008	0.1355	0.0091	...	51.7223	3.8399	0.0016	0.0184	0.0081	0.0002	4.2949	1	0	0
7	0.0060	0.0681	0.0143	0.0009	0.0810	30.4586	0.0012	0.0004	5.5293	0.2761	...	98.5840	0.0174	0.0008	0.0074	0.0190	0.1081	94.5897	1	0	0
8	0.0000	0.0000	98.1941	0.0000	0.0000	0.0000	0.0001	0.0411	0.0246	0.0001	...	82.3883	0.0298	0.0002	0.0000	0.0000	0.0001	1.6839	1	0	0
9	0.0000	0.0072	0.8344	0.0266	0.0014	0.0007	2.5742	0.0154	1.3654	0.1056	...	48.9700	1.8493	0.0028	0.0398	0.1563	0.0011	1.1260	1	0	0