#testing.params_lbp['pproc__feature_extractor__method'] = ['uniform']
testing.params_lbp['pproc__feature_extractor__n_tiles'] = [[1,1],[3,3],[5,5],[7,7]]
#testing.params_lbp['pproc__feature_extractor__n_tiles'] = [[1,1],[7,7]]
testing.params_auto['pca__n_components'] = [10, 30, 50, 100, 300, 500]
#testing.params_auto['pca__n_components'] = [100]
testing.params_svm['pred__C'] = [0.0001, 0.01, 0.01, 1, 100, 5000]
#testing.params_svm['pred__C'] = [1000]
testing.params_svm['pred__gamma'] = [0.0001, 0.001, 0.01, 0.1]
#testing.params_svm['pred__gamma'] = [0.001]
testing.var_sensor('LBP',datasettrain='all',sensortrain ='all',datasettest='all',sensortest ='all')
#testing.var_sensor('LBP','all','')
"""
testing = Testing()
testing.size_percentage = 1.0
testing.divide_by = 80
testing.cross_validation = False
testing.augmentation = False
testing.aug_rotate = False
testing.multi_column = False
testing.roi = False
testing.low_pass = False
testing.high_pass = False
testing.n_processes_pproc = 3
testing.n_processes_cv = 1
testing.lbp__method = 'uniform'
testing.lbp__n_tiles = [1, 1]
testing.predict = 'SVM'
testing.svm__kernel = 'rbf'
testing.svm__gamma = 0.01
testing.svm__C = 1000
#ConvNets+PCA
pproc = PreProcess('ConvNet',1,False,False,False,False,1.0,[(9, 9), (9, 9), (7, 7), (5, 5), (5, 5)],\
[(9, 9), (9, 9), (7, 7), (5, 5), (5, 5)],[(9, 9), (9, 9), (7, 7), (5, 5), (5, 5)],\
[64, 128, 256, 512, 1024],[(3,3),(2,2),(2,2),(2,2),(2,2)],False,False,None,None,False)
X = pproc.transform(lstFilesX)
pca = RandomizedPCA(n_components=2)
X = pca.fit_transform(X)
fig = pyplot.figure()
pyplot.plot(X[y==False,0],X[y==False,1],'ro')
pyplot.plot(X[y==True,0],X[y==True,1],'bo')
pyplot.title('2D Visualization, Crossmatch LivDet 2013 Testing, ConvNet 5 Layers+PCA')
pyplot.show()
"""
testing = Testing()
testing.divide_by = 5
testing.n_processes_pproc = 3
lstFilesX, y = testing.load_dataset('Training', 'LivDet2011', 'digital')
#PCA only
pproc = PreProcess('',1,False,False,False,False,1.0,None,\
None,None,None,None,None,None,None,None,None)
X = pproc.transform(lstFilesX)
pca = RandomizedPCA(n_components=2)
X = pca.fit_transform(X)
fig = pyplot.figure()
pyplot.plot(X[y == False, 0], X[y == False, 1], 'ro')
pyplot.plot(X[y == True, 0], X[y == True, 1], 'bo')
pyplot.title('2D Visualization, Digital LivDet 2011 Training, PCA only')
pyplot.show()
#ConvNets+PCA
pproc = PreProcess('ConvNet',1,False,False,False,False,1.0,[(9, 9), (9, 9), (7, 7), (5, 5), (5, 5)],\
[(9, 9), (9, 9), (7, 7), (5, 5), (5, 5)],[(9, 9), (9, 9), (7, 7), (5, 5), (5, 5)],\
[64, 128, 256, 512, 1024],[(3,3),(2,2),(2,2),(2,2),(2,2)],False,False,None,None,False)
X = pproc.transform(lstFilesX)
pca = RandomizedPCA(n_components=2)
X = pca.fit_transform(X)
fig = pyplot.figure()
pyplot.plot(X[y==False,0],X[y==False,1],'ro')
pyplot.plot(X[y==True,0],X[y==True,1],'bo')
pyplot.title('2D Visualization, Crossmatch LivDet 2013 Testing, ConvNet 5 Layers+PCA')
pyplot.show()
"""
testing = Testing()
testing.divide_by = 5
testing.n_processes_pproc =3
lstFilesX,y = testing.load_dataset('Training', 'LivDet2011', 'digital')
#PCA only
pproc = PreProcess('',1,False,False,False,False,1.0,None,\
None,None,None,None,None,None,None,None,None)
X = pproc.transform(lstFilesX)
pca = RandomizedPCA(n_components=2)
X = pca.fit_transform(X)
fig = pyplot.figure()
pyplot.plot(X[y==False,0],X[y==False,1],'ro')
pyplot.plot(X[y==True,0],X[y==True,1],'bo')
pyplot.title('2D Visualization, Digital LivDet 2011 Training, PCA only')
pyplot.show()
#testing.params_lbp['pproc__feature_extractor__method'] = ['uniform']
testing.params_lbp['pproc__feature_extractor__n_tiles'] = [[1,1],[3,3],[5,5],[7,7]]
#testing.params_lbp['pproc__feature_extractor__n_tiles'] = [[1,1],[7,7]]
testing.params_auto['pca__n_components'] = [10, 30, 50, 100, 300, 500]
#testing.params_auto['pca__n_components'] = [100]
testing.params_svm['pred__C'] = [0.0001, 0.01, 0.01, 1, 100, 5000]
#testing.params_svm['pred__C'] = [1000]
testing.params_svm['pred__gamma'] = [0.0001, 0.001, 0.01, 0.1]
#testing.params_svm['pred__gamma'] = [0.001]
testing.var_sensor('LBP',datasettrain='all',sensortrain ='all',datasettest='all',sensortest ='all')
#testing.var_sensor('LBP','all','')
"""
testing = Testing()
testing.size_percentage = 1.0
testing.divide_by = 80
testing.cross_validation = False
testing.augmentation = False
testing.aug_rotate = False
testing.multi_column = False
testing.roi = False
testing.low_pass = False
testing.high_pass = False
testing.n_processes_pproc = 3
testing.n_processes_cv =1
testing.lbp__method = 'uniform'
testing.lbp__n_tiles = [1,1]
testing.predict = 'SVM'
testing.svm__kernel='rbf'
testing.svm__gamma = 0.01
testing.svm__C = 1000
