def get_S(self, dimensions=2, pub=None):
similarity = self.get_similarity(self.statistic)
if pub is not None:
pub.array_as_image('similarity', similarity,
caption='Similarity statistic')
plot_spectrum(pub, 'similarity', similarity)
if self.scale_score:
R = scale_score(similarity).astype('float32')
R = R / R.max()
if pub is not None:
pub.array_as_image('scale_score', R)
else:
R = similarity
D = 1 - R
D = D * np.pi / D.max()
np.fill_diagonal(D, 0)
if pub is not None:
# pub.array_as_image('D', D)
P = D * D
B = double_center(P)
# plot_spectrum(pub, 'D', D)
# plot_spectrum(pub, 'P', P)
plot_spectrum(pub, 'B', B)
S = mds(D, ndim=dimensions)
# S = inner_product_embedding(similarity, 3)
# S = S[1:3, :]
return S
def rank_test():
''' Check that the double-centered matrix has small rank. '''
for n in range(5, 50, 5):
for k in range(1, 5):
P = np.random.rand(k, n)
D = euclidean_distances(P)
B = double_center(D * D)
w, v = eigh(B) #@UnusedVariable
w = w[::-1] # descending
# normalize
wn = w / w[0]
small = np.abs(wn[k])
assert_allclose(0, small, atol=1e-7)
def rank_test():
''' Check that the double-centered matrix has small rank. '''
for n in range(5, 50, 5):
for k in range(1, 5):
P = np.random.rand(k, n)
D = euclidean_distances(P)
B = double_center(D * D)
w, v = eigh(B) #@UnusedVariable
w = w[::-1] # descending
# normalize
wn = w / w[0]
small = np.abs(wn[k])
assert_allclose(0, small, atol=1e-7)