def map_to_vectors(self, x, d=None, theta_max=np.pi / 2):
if d is None:
d = self.d
x *= theta_max
y = np.zeros((self.d, ) + x.shape)
for nd in range(1, self.d + 1):
y[nd - 1, :, :] = (np.sqrt(tm.combination(d - 1, nd - 1)) *
(np.cos(x)**(d - nd)) * (np.sin(x)**(nd - 1)))
return y
def multiple_images2vecs(self, theta_max=np.pi/2):
# Put the data of images in self.tmp!!!
# The pixels should have been normalized to [0, 1)
s = self.tmp.shape
self.tmp *= theta_max
self.vecsImages = np.zeros((self.d, ) + s)
for nd in range(1, self.d+1):
self.vecsImages[nd-1, :, :] = (np.sqrt(tm.combination(self.d-1, nd-1)) * (
np.cos(self.tmp)**(self.d-nd)) * (np.sin(self.tmp)**(nd-1)))
def images2vecs(self, theta_max=np.pi / 2):
# The pixels should have been normalized to [0, 1)
s = self.images.shape
self.numVecSample = s[1]
self.images *= theta_max
self.vecsImages = np.zeros((self.d, ) + s)
for nd in range(1, self.d + 1):
self.vecsImages[nd - 1, :, :] = (
np.sqrt(tm.combination(self.d - 1, nd - 1)) *
(np.cos(self.images)**(self.d - nd)) *
(np.sin(self.images)**(nd - 1)))
