def extend_train_data(path, new_sample_count, sigma=2.0, alpha=8.0):
data = decode(join(path, 'train-images.idx3-ubyte'))
labels = decode(join(path, 'train-labels.idx1-ubyte'))
data_shape = data.shape[0] + new_sample_count, data.shape[1], data.shape[2]
new_data = np.zeros(data_shape, dtype=data.dtype)
new_data[:data.shape[0]] = data
new_label = np.zeros((labels.shape[0] + new_sample_count, ),
dtype=labels.dtype)
new_label[:labels.shape[0]] = labels
rnd = random.Random()
offset, cur = len(data), 0
for index in (rnd.randint(0, offset - 1) for _ in range(new_sample_count)):
img_vector = np.zeros((28*28), dtype=np.float32)
img_vector[:] = data[index].reshape(28*28)
ml.normalize(img_vector, (0.0, 255.0))
dx, dy = create_distortion_maps((28, 28), sigma, alpha)
displaced_img = displace(img_vector, dx, dy)
new_data[offset + cur, :, :] = np.round(displaced_img * 255).reshape((28, 28))
new_label[offset + cur] = labels[index]
cur += 1
encode(new_data, join(path, 'extended-images.idx3-ubyte'))
encode(new_label, join(path, 'extended-labels.idx1-ubyte'))
def test_normalize(self):
self.assertEqual(ml.normalize(unitArray),
[1.0 / len(unitArray)] * len(unitArray))
self.assertEqual(ml.normalize(emptyArray), [])
self.assertEqual(ml.normalize(rangeArray),
[(x * 2.0) / (len(unitArray) * (len(unitArray) - 1))
for x in rangeArray])
def load_test_data(path):
def classify(num):
result = np.zeros(10)
result[num] = 255.0
return result
data = decode(join(path, 't10k-images.idx3-ubyte'))
labels_ = decode(join(path, 't10k-labels.idx1-ubyte'))
labels = np.asarray([classify(n) for n in labels_])
available_data = np.zeros((10000, 28*28+10), dtype=np.float32)
available_data[:, :28*28] = data.reshape((10000, 28*28))
available_data[:, 28*28:] = labels
ml.normalize(available_data, (0.0, 255.0))
return available_data
def load_train_data(path, extended=True):
def classify(num):
result = np.zeros(10)
result[num] = 255.0
return result
prefix = 'extended' if extended else 'train'
data = decode(join(path, prefix + '-images.idx3-ubyte'))
labels_ = decode(join(path, prefix + '-labels.idx1-ubyte'))
labels = np.asarray([classify(n) for n in labels_])
available_data = np.zeros((len(data), 28*28+10), dtype=np.float32)
available_data[:, :28*28] = data.reshape((len(data), 28*28))
available_data[:, 28*28:] = labels
ml.normalize(available_data, (0.0, 255.0))
return available_data
def test_normalize(self):
self.assertEqual(ml.normalize(unitArray), [1.0 / len(unitArray)] * len(unitArray))
self.assertEqual(ml.normalize(emptyArray), [])
self.assertEqual(ml.normalize(rangeArray), [(x * 2.0) / (len(unitArray) * (len(unitArray) - 1)) for x in rangeArray])