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':
# Generate 50 random ECOC vectors
codes = np.random.randint(0,2,size=(20,10))
train_codes, test_codes = codes.copy(), codes.copy().astype(bool)
train_codes[train_codes==0] = -1.0
# Iterate through each ECOC
pool = Pool(processes=11)
c = np.sum(Yi.ravel()==H)
print "r=%f:" % 4.6, float(c)/float(len(Yi))
elif args.d == "d":
# 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, 10000)
test_images, test_labels = sample(test_dataset, 1000)
# 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)
# Run knn
H = wnn(train_data, test_data, train_labels, d=cosine_distance, r=0.325)
c = np.sum(test_labels.ravel()==H)
print "r=%f:" % 0.325, float(c)/float(len(test_labels))
else:
pass
