def show(self):
"""
Print the distribution using standard parameterization.
"""
logsum_p = utils.logsumexp(self.phi[0], axis=-1, keepdims=True)
p = np.exp(self.phi[0] - logsum_p)
p /= np.sum(p, axis=-1, keepdims=True)
print("%s ~ Multinomial(p)" % self.name)
print(" p = ")
print(p)
return
def compute_moments_and_cgf(self, phi, mask=True):
"""
Compute the moments and :math:`g(\phi)`.
"""
# Compute the normalized probabilities in a numerically stable way
logsum_p = utils.logsumexp(phi[0], axis=-1, keepdims=True)
logp = phi[0] - logsum_p
p = np.exp(logp)
# Because of small numerical inaccuracy, normalize the probabilities
# again for more accurate results
N = np.expand_dims(self.N, -1)
u0 = N * p / np.sum(p, axis=-1, keepdims=True)
u = [u0]
g = -N * np.squeeze(logsum_p, axis=-1)
return (u, g)
