def _shape_extraction(self, X, k):
sz = X.shape[1]
Xp, mean_shift = y_shifted_sbd_vec(
self.cluster_centers_[k],
X[self.labels_ == k],
norm_ref=-1,
norms_dataset=self._norms[self.labels_ == k])
S = numpy.dot(Xp[:, :, 0].T, Xp[:, :, 0])
Q = numpy.eye(sz) - numpy.ones((sz, sz)) / sz
M = numpy.dot(Q.T, numpy.dot(S, Q))
_, vec = numpy.linalg.eigh(M)
mu_k = numpy.zeros((sz, 1))
mean_shift = -mean_shift
if mean_shift > 0:
mu_k[mean_shift:] = vec[:-mean_shift, -1].reshape(
(sz - mean_shift, 1))
elif mean_shift < 0:
mu_k[:mean_shift] = vec[-mean_shift:, -1].reshape(
(sz + mean_shift, 1))
else:
mu_k = vec[:, -1].reshape((sz, 1))
# The way the optimization problem is (ill-)formulated, both mu_k and -mu_k are candidates for barycenters
# In the following, we check which one is best candidate
dist_plus_mu = numpy.sum(numpy.linalg.norm(Xp - mu_k, axis=(1, 2)))
dist_minus_mu = numpy.sum(numpy.linalg.norm(Xp + mu_k, axis=(1, 2)))
if dist_minus_mu < dist_plus_mu:
mu_k *= -1
return mu_k
def _shape_extraction(self, X, k):
sz = X.shape[1]
Xp = y_shifted_sbd_vec(self.cluster_centers_[k],
X[self.labels_ == k],
norm_ref=-1,
norms_dataset=numpy.array(
[-1.])) # TODO: provide norms
S = numpy.dot(Xp[:, :, 0].T, Xp[:, :, 0])
Q = numpy.eye(sz) - numpy.ones((sz, sz)) / sz
M = numpy.dot(Q.T, numpy.dot(S, Q))
_, vec = numpy.linalg.eigh(M)
mu_k = vec[:, -1].reshape((sz, 1))
# The way the optimization problem is (ill-)formulated, both mu_k and -mu_k are candidates for barycenters
# In the following, we check which one is best candidate
dist_plus_mu = numpy.sum(numpy.linalg.norm(Xp - mu_k, axis=(1, 2)))
dist_minus_mu = numpy.sum(numpy.linalg.norm(Xp + mu_k, axis=(1, 2)))
if dist_minus_mu < dist_plus_mu:
mu_k *= -1
return mu_k
