Function sample_from_X

bayesian_ml/4/npbgmm.py:62–72

(X, m0, c0, a0, B0)

# samples mu, sigma from P(mu, sigma | X)
def sample_from_X(X, m0, c0, a0, B0):
  N = len(X)
  s = float(N)
  m = (c0 / (s + c0))*m0 + (1 / (s + c0))*X.sum(axis=0)
  c = s + c0
  a = s + a0
  meanX = X.mean(axis=0)
  B = (s / (a0*s + 1)) * np.outer(meanX - m0, meanX - m0) + B0
  for i in xrange(N):
    B += np.outer(X[i] - meanX, X[i] - meanX)
  return sample_from_prior(c, m, a, B)


def gmm(X, T=500):