#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