import numpy as np

import numbers

from scipy.stats import multivariate_normal

def distance(point1, point2):

return np.sqrt(np.sum(np.square(point1 - point2), axis=1))

def check_random_state(seed):

if seed is None or seed is np.random:

return np.random.mtrand._rand

if isinstance(seed, (numbers.Integral, np.integer)):

return np.random.RandomState(seed)

if isinstance(seed, np.random.RandomState):

return seed

raise ValueError('%r cannot be used to seed a numpy.random.RandomState'

' instance' % seed)

def sortLabel(label):

label = np.array(label)

labelOld = []

labelNum = len(list(set(label)))

for i in label:

if i not in labelOld:

labelOld.append(i)

if len(labelOld) == labelNum:

break

labelNew = sorted(labelOld)

for i, old in enumerate(labelOld):

label[label == old] = labelNew[i] + 10000

return label - 10000

def prob(x, mu, cov):

norm = multivariate_normal(mean=mu, cov=cov)

return norm.pdf(x)

def log_prob(x, mu, cov):

norm = multivariate_normal(mean=mu, cov=cov)

return norm.logpdf(x)

def log_weight_prob(x, alpha, mu, cov):

N = x.shape[0]

return np.mat(np.log(alpha) + log_prob(x, mu, cov)).reshape([N, 1])