def validateClassifier():
cl = Classifier(dataset="binData/classificationTrainingPalmahim100.npz",regression=False)
path_list = ["../data/training_classification/positive", "../data/training_classification/negative"]
kmeans_path = 'binData/KmeansBlobsPalmahim100.pkl'
#cl.classificationValidation(path_list, kmeans,kernel='linear',gamma=None,C=1)
Cs = [0.001,0.002,0.003,0.004]
gammas = [0.1]
kernels = ["rbf","linear"]
for kernel in kernels:
if kernel == 'linear':
gamma = None
for C in Cs:
cl.classificationValidation(path_list, kmeans,kernel=kernel,gamma=gamma,C=C)
else:
for gamma in gammas:
for C in Cs:
cl.classificationValidation(path_list, kmeans,kernel=kernel,gamma=gamma,C=C)
cv.waitKey()
