Python load_learning_set Examples

Example #1

File: create_classifier.py Project: chenjh19/licenseplates

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

Example #2

File: find_svm_params.py Project: chenjh19/licenseplates

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)

Example #3

File: find_svm_params.py Project: imargon/scrapy_demo

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)