def testSumToOne(self):
for _ in range(self.N_REPEATS):
len_vector = randint(10, self.MAX_LEN_VECTOR)
x = repeat(len_vector, random)
x = normalize_logspace(x)
s = np.sum(x)
self.assertAlmostEqual(s, 1.0, 12)
def init_d2_weighting(data, num_comp):
num_obs = data.shape[0]
cov_inv = np.linalg.inv(np.cov(data, rowvar=0))
select_prob = np.ones(num_obs) / num_obs
shortest_dist = np.inf * np.ones(num_obs)
centroid = np.ones(num_comp)
for k in range(num_comp):
# Select a random data point as centroid
centroid[k] = np.nonzero(multinomial(1, select_prob))[0]
# Recompute distances
for i, d in enumerate(shortest_dist):
d_new = mahalanobis(data[centroid[k], :], data[i, :], cov_inv)
if d_new < d:
shortest_dist[i] = d_new
select_prob = normalize_logspace(pow(shortest_dist.reshape(1, len(shortest_dist)), 2, 1))
select_prob = select_prob.flatten()
return centroid