def compute_moments_and_cgf(self, phi, mask=True):
r"""
Compute the moments and :math:`g(\phi)`.
.. math::
\overline{\mathbf{u}}
= \mathrm{E}[x]
= N \cdot \begin{bmatrix}
\frac{e^{\phi_1}}{\sum_i e^{\phi_i}}
& \cdots &
\frac{e^{\phi_D}}{\sum_i e^{\phi_i}} \end{bmatrix}
"""
# Compute the normalized probabilities in a numerically stable way
(p, logsum_p) = misc.normalized_exp(phi[0])
N = np.expand_dims(self.N, -1)
u0 = N * p
u = [u0]
g = -np.squeeze(N * logsum_p, axis=-1)
return (u, g)
def compute_moments_and_cgf(self, phi, mask=True):
r"""
Compute the moments and :math:`g(\phi)`.
.. math::
\overline{\mathbf{u}}
= \mathrm{E}[x]
= N \cdot \begin{bmatrix}
\frac{e^{\phi_1}}{\sum_i e^{\phi_i}}
& \cdots &
\frac{e^{\phi_D}}{\sum_i e^{\phi_i}} \end{bmatrix}
"""
# Compute the normalized probabilities in a numerically stable way
(p, logsum_p) = misc.normalized_exp(phi[0])
N = np.expand_dims(self.N, -1)
u0 = N * p
u = [u0]
g = -np.squeeze(N * logsum_p, axis=-1)
return (u, g)
def random(self, *phi, plates=None):
r"""
Draw a random sample from the distribution.
"""
(p, _) = misc.normalized_exp(phi[0])
return random.multinomial(self.N, p, size=plates)
def random(self, *phi, plates=None):
r"""
Draw a random sample from the distribution.
"""
(p, _) = misc.normalized_exp(phi[0])
return random.multinomial(self.N, p, size=plates)
