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@saurabhghatnekar
Created April 8, 2018 18:25
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final training
/home/heimdall/anaconda3/bin/python /mnt/attic/Projects/normimages/newMethod/stdData.py
Extracting the top 150 eigenfaces from 1702 faces
/home/heimdall/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:58: DeprecationWarning: Class RandomizedPCA is deprecated; RandomizedPCA was deprecated in 0.18 and will be removed in 0.20. Use PCA(svd_solver='randomized') instead. The new implementation DOES NOT store whiten ``components_``. Apply transform to get them.
warnings.warn(msg, category=DeprecationWarning)
done in 2.670s
Projecting the input data on the eigenfaces orthonormal basis
done in 0.524s
Fitting the classifier to the training set
done in 73.977s
Best estimator found by grid search:
SVC(C=1000.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
samples in train data (1702, 16384)
precision recall f1-score support
akshada 1.00 1.00 1.00 184
ashvini 1.00 1.00 1.00 179
deepali 1.00 1.00 1.00 169
geetanjali 1.00 1.00 1.00 169
maam 1.00 1.00 1.00 93
prerana 1.00 1.00 1.00 180
radhika 1.00 1.00 1.00 38
ravina 1.00 1.00 1.00 175
shweta 1.00 1.00 1.00 170
snehal 1.00 1.00 1.00 170
sujata 1.00 1.00 1.00 175
avg / total 1.00 1.00 1.00 1702
[[184 0 0 0 0 0 0 0 0 0 0]
[ 0 179 0 0 0 0 0 0 0 0 0]
[ 0 0 169 0 0 0 0 0 0 0 0]
[ 0 0 0 169 0 0 0 0 0 0 0]
[ 0 0 0 0 93 0 0 0 0 0 0]
[ 0 0 0 0 0 180 0 0 0 0 0]
[ 0 0 0 0 0 0 38 0 0 0 0]
[ 0 0 0 0 0 0 0 175 0 0 0]
[ 0 0 0 0 0 0 0 0 170 0 0]
[ 0 0 0 0 0 0 0 0 0 170 0]
[ 0 0 0 0 0 0 0 0 0 0 175]]
samples in test data (568, 16384)
precision recall f1-score support
akshada 1.00 1.00 1.00 50
ashvini 1.00 1.00 1.00 55
deepali 1.00 1.00 1.00 65
geetanjali 1.00 1.00 1.00 65
maam 1.00 1.00 1.00 28
prerana 1.00 1.00 1.00 54
radhika 1.00 1.00 1.00 5
ravina 1.00 1.00 1.00 59
shweta 1.00 1.00 1.00 64
snehal 1.00 1.00 1.00 64
sujata 1.00 1.00 1.00 59
avg / total 1.00 1.00 1.00 568
[[50 0 0 0 0 0 0 0 0 0 0]
[ 0 55 0 0 0 0 0 0 0 0 0]
[ 0 0 65 0 0 0 0 0 0 0 0]
[ 0 0 0 65 0 0 0 0 0 0 0]
[ 0 0 0 0 28 0 0 0 0 0 0]
[ 0 0 0 0 0 54 0 0 0 0 0]
[ 0 0 0 0 0 0 5 0 0 0 0]
[ 0 0 0 0 0 0 0 59 0 0 0]
[ 0 0 0 0 0 0 0 0 64 0 0]
[ 0 0 0 0 0 0 0 0 0 64 0]
[ 0 0 0 0 0 0 0 0 0 0 59]]
Process finished with exit code 0
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