else:
return self.j
if __name__=="__main__":
# Argparse
parser = argparse.ArgumentParser()
parser.add_argument('-m', help='ECOC or VOTE')
args = parser.parse_args(sys.argv[1:])
# Load data using specialized script
train_dataset = load_mnist(path="../data/mnist/", dataset="training")
test_dataset = load_mnist(path="../data/mnist/", dataset="testing")
# Take a fraction of the data to speed computation
train_images, train_labels = sample(train_dataset, 5000)
test_images, test_labels = sample(test_dataset, 100)
# Get the bounds of the haar rectangles
bounds = genbounds(28, 28, 100)
# Create data, using same rectangles for training and testing
train_data = genfeatures(train_images, bounds).astype(float)
test_data = genfeatures(test_images, bounds).astype(float)
# Normalize the data
zmscaler = preprocessing.StandardScaler()
train_data = zmscaler.fit_transform(train_data)
test_data = zmscaler.transform(test_data)
if args.m == 'ECOC':
else:
return self.j
if __name__=="__main__":
# Argparse
parser = argparse.ArgumentParser()
parser.add_argument('-m', help='ECOC or VOTE')
args = parser.parse_args(sys.argv[1:])
# Load data using specialized script
train_dataset = load_mnist(path="../data/mnist/", dataset="training")
test_dataset = load_mnist(path="../data/mnist/", dataset="testing")
# Take a fraction of the data to speed computation
train_images, train_labels = sample(train_dataset, 1000)
test_images, test_labels = test_dataset
# Get the bounds of the haar rectangles
bounds = genbounds(28, 28, 100)
# Create data, using same rectangles for training and testing
train_data = genfeatures(train_images, bounds)
#test_data = genfeatures(test_images, bounds)
# Normalize the data
zmscaler = preprocessing.StandardScaler()
train_data = zmscaler.fit_transform(train_data)
#test_data = zmscaler.transform(test_data)
if args.m == 'ECOC':