help='Directory containing the MNIST files')
parser.add_argument('-d', '--dataset', dest='dataset', action='store', required=True,
choices = ("TEST","TRAIN"),
help='Dataset to use (testing or training)')
parser.add_argument('-i', '--index', dest='index', action='store', type=int, required=True,
help="Image index")
args = parser.parse_args()
return args
args = parse_command_line_arguments()
datadir = args.datadir
dataset = args.dataset
idx = args.index
if dataset == "TRAIN":
data = MNISTReader("%s/train-images-idx3-ubyte" % datadir, "%s/train-labels-idx1-ubyte" % datadir)
elif dataset == "TEST":
data = MNISTReader("%s/t10k-images-idx3-ubyte" % datadir, "%s/t10k-labels-idx1-ubyte" % datadir)
img = data.get_image(idx)
print img.as_asciiart()
print "Labelled as %d" % img.label
img.as_image().show()
help="Image index")
args = parser.parse_args()
return args
args = parse_command_line_arguments()
datadir = args.datadir
k = args.k
idx = args.index
if idx == None:
train = MNISTReader("%s/train-images-idx3-ubyte" % datadir,
"%s/train-labels-idx1-ubyte" % datadir,
preload=True)
test = MNISTReader("%s/t10k-images-idx3-ubyte" % datadir,
"%s/t10k-labels-idx1-ubyte" % datadir,
preload=True)
else:
train = MNISTReader("%s/train-images-idx3-ubyte" % datadir,
"%s/train-labels-idx1-ubyte" % datadir)
test = MNISTReader("%s/t10k-images-idx3-ubyte" % datadir,
"%s/t10k-labels-idx1-ubyte" % datadir)
if idx == None:
i = 0
for p, known_label in test.images(as_array=True):
label, nearest = knn(p, train.images(as_array=True), k)
dest='index',
action='store',
type=int,
required=True,
help="Image index")
args = parser.parse_args()
return args
args = parse_command_line_arguments()
datadir = args.datadir
dataset = args.dataset
idx = args.index
if dataset == "TRAIN":
data = MNISTReader("%s/train-images-idx3-ubyte" % datadir,
"%s/train-labels-idx1-ubyte" % datadir)
elif dataset == "TEST":
data = MNISTReader("%s/t10k-images-idx3-ubyte" % datadir,
"%s/t10k-labels-idx1-ubyte" % datadir)
img = data.get_image(idx)
print img.as_asciiart()
print "Labelled as %d" % img.label
img.as_image().show()
help='Number of neighbors to use')
parser.add_argument('-i', '--index', dest='index', action='store', type=int,
help="Image index")
args = parser.parse_args()
return args
args = parse_command_line_arguments()
datadir = args.datadir
k = args.k
idx = args.index
if idx == None:
train = MNISTReader("%s/train-images-idx3-ubyte" % datadir, "%s/train-labels-idx1-ubyte" % datadir, preload=True)
test = MNISTReader("%s/t10k-images-idx3-ubyte" % datadir, "%s/t10k-labels-idx1-ubyte" % datadir, preload=True)
else:
train = MNISTReader("%s/train-images-idx3-ubyte" % datadir, "%s/train-labels-idx1-ubyte" % datadir)
test = MNISTReader("%s/t10k-images-idx3-ubyte" % datadir, "%s/t10k-labels-idx1-ubyte" % datadir)
if idx == None:
i = 0
for p, known_label in test.images(as_array=True):
label, nearest = knn(p, train.images(as_array=True), k)
if label == known_label:
x=""
else:
x="XXX"
parser.add_argument('-o', '--outfile', metavar='FILE', dest='outfile', action='store',
help='The file to save k-mean images to. If none is specified, the image is displayed interactively.')
parser.add_argument('-t', '--threshold', dest='cutoff', action='store', type=float, default=100.0,
help="Converge once the centroids move less than this threshold.")
args = parser.parse_args()
return args
args = parse_command_line_arguments()
k = args.k
cutoff = args.cutoff
print "Loading data..."
test = MNISTReader("%s/t10k-images-idx3-ubyte" % args.datadir, "%s/t10k-labels-idx1-ubyte" % args.datadir)
points = None
for img in test.images():
if points is None:
points = numpy.array([img.imgdata])
else:
points = numpy.append(points, [img.imgdata], axis=0)
km = KMeans(points, k)
centroids = km.select_random_centroids()
iteration=1
while True:
print "Iteration #%i" % iteration