def load_classifier(neighbours, blur_scale, c=None, gamma=None, verbose=0):
classifier_file = 'classifier_%s_%s.dat' \
% (blur_scale, neighbours)
classifier_path = DATA_FOLDER + classifier_file
if exists(classifier_file):
if verbose:
print 'Loading classifier...'
classifier = Classifier(filename=classifier_path, \
neighbours=neighbours, verbose=verbose)
elif c != None and gamma != None:
if verbose:
print 'Training new classifier...'
classifier = Classifier(c=c, gamma=gamma, neighbours=neighbours, \
verbose=verbose)
learning_set = load_learning_set(neighbours, blur_scale, \
verbose=verbose)
classifier.train(learning_set)
classifier.save(classifier_path)
else:
raise Exception('No soft margin and gamma specified.')
return classifier
if len(argv) < 3:
print 'Usage: python %s NEIGHBOURS BLUR_SCALE' % argv[0]
exit(1)
neighbours = int(argv[1])
blur_scale = float(argv[2])
suffix = '_%s_%s' % (blur_scale, neighbours)
if not os.path.exists(RESULTS_FOLDER):
os.mkdir(RESULTS_FOLDER)
classifier_file = DATA_FOLDER + 'classifier%s.dat' % suffix
results_file = '%sresult%s.txt' % (RESULTS_FOLDER, suffix)
# Load learning set and test set
learning_set = load_learning_set(neighbours, blur_scale, verbose=1)
test_set = load_test_set(neighbours, blur_scale, verbose=1)
# Perform a grid-search to find the optimal values for C and gamma
C = [float(2**p) for p in xrange(-5, 16, 2)]
Y = [float(2**p) for p in xrange(-15, 4, 2)]
results = []
best = (0, )
i = 0
for c in C:
for y in Y:
classifier = Classifier(c=c, gamma=y, neighbours=neighbours, verbose=1)
classifier.train(learning_set)
result = classifier.test(test_set)
if len(argv) < 3:
print 'Usage: python %s NEIGHBOURS BLUR_SCALE' % argv[0]
exit(1)
neighbours = int(argv[1])
blur_scale = float(argv[2])
suffix = '_%s_%s' % (blur_scale, neighbours)
if not os.path.exists(RESULTS_FOLDER):
os.mkdir(RESULTS_FOLDER)
classifier_file = DATA_FOLDER + 'classifier%s.dat' % suffix
results_file = '%sresult%s.txt' % (RESULTS_FOLDER, suffix)
# Load learning set and test set
learning_set = load_learning_set(neighbours, blur_scale, verbose=1)
test_set = load_test_set(neighbours, blur_scale, verbose=1)
# Perform a grid-search to find the optimal values for C and gamma
C = [float(2 ** p) for p in xrange(-5, 16, 2)]
Y = [float(2 ** p) for p in xrange(-15, 4, 2)]
results = []
best = (0,)
i = 0
for c in C:
for y in Y:
classifier = Classifier(c=c, gamma=y, neighbours=neighbours, verbose=1)
classifier.train(learning_set)
result = classifier.test(test_set)