def process(self, image, out=None):
# 0.25 is the default value used in Ng's paper
alpha = self.specs.get('alpha', 0.25)
# check if we would like to do two-side thresholding. Default yes.
if self.specs.get('twoside', True):
# concatenate, and make sure the output is C_CONTIGUOUS
# for the temporary product, we check if we can utilize the
# buffer to save allocation time
product = mathutil.dot_image(image, self.dictionary.T)
imshape = product.shape[:-1]
N = product.shape[-1]
product.resize((np.prod(imshape), N))
if out is None:
out = np.empty((np.prod(imshape), N*2))
else:
out.resize((np.prod(imshape), N*2))
out[:,:N] = product
out[:,N:] = -product
out.resize(imshape + (N*2,))
elif self.specs['twoside'] == 'abs':
out = mathutil.dot_image(image, self.dictionary.T, out=out)
np.abs(out, out=out)
else:
out = mathutil.dot_image(image, self.dictionary.T, out=out)
# do threshold
out -= alpha
np.clip(out, 0., np.inf, out=out)
return out
def testdot_image(self):
A = np.random.rand(2,3,4)
B = np.random.rand(4,5)
result = mathutil.dot_image(A, B)
self.assertTrue(result.flags['C_CONTIGUOUS'])
self.assertEqual(result.shape[:-1], A.shape[:-1])
self.assertEqual(result.shape[-1], B.shape[-1])
B = np.array(B, order='f')
result = mathutil.dot_image(A, B)
self.assertTrue(result.flags['C_CONTIGUOUS'])
self.assertEqual(result.shape[:-1], A.shape[:-1])
self.assertEqual(result.shape[-1], B.shape[-1])
def testdot_image(self):
A = np.random.rand(2, 3, 4)
B = np.random.rand(4, 5)
result = mathutil.dot_image(A, B)
self.assertTrue(result.flags['C_CONTIGUOUS'])
self.assertEqual(result.shape[:-1], A.shape[:-1])
self.assertEqual(result.shape[-1], B.shape[-1])
B = np.array(B, order='f')
result = mathutil.dot_image(A, B)
self.assertTrue(result.flags['C_CONTIGUOUS'])
self.assertEqual(result.shape[:-1], A.shape[:-1])
self.assertEqual(result.shape[-1], B.shape[-1])
def process(self, image, out = None):
W, b = self.dictionary
# we create the offset in-place: this might introduce some numerical
# differences but should be fine most of the time
image += b
out = mathutil.dot_image(image, W, out=out)
image -= b
return out
def process(self, image):
# 0.25 is the default value used in Ng's paper
alpha = self.specs.get('alpha', 0.25)
output = mathutil.dot_image(image, self.dictionary.T)
# check if we would like to do two-side thresholding. Default yes.
if self.specs.get('twoside', True):
# concatenate, and make sure to be C_CONTIGUOUS
imshape = output.shape[:-1]
N = output.shape[-1]
output.resize((np.prod(imshape), N))
temp = np.empty((np.prod(imshape), N*2))
temp[:,:N] = output
temp[:,N:] = -output
output = temp.reshape(imshape + (N*2,))
else:
# otherwise, we will take the absolute value
output = np.abs(output)
output -= alpha
np.clip(output, 0., np.inf, out=output)
return output
def process(self, image, out = None):
return mathutil.dot_image(image, self.dictionary.T, out=out)
def process(self, image, out=None):
W, b = self.dictionary
out = mathutil.dot_image(image, W, out=out)
out += b
return out
def process(self, image):
return mathutil.dot_image(image, self.dictionary.T)
def process(self, image):
W, b = self.dictionary
output = mathutil.dot_image(image, W)
output += b
return output