def get_joint_parameters(self):
'''Return the vector of means
and the covariance matrix
for the full joint distribution.
For now, by definition, all
the variables in a Gaussian
Bayesian Network are either
univariate gaussian or
conditional guassians.
'''
ordered = self.get_topological_sort()
mu_x = Matrix([[ordered[0].func.mean]])
sigma_x = Matrix([[ordered[0].func.variance]])
# Iteratively build up the mu and sigma matrices
for node in ordered[1:]:
beta_0 = node.func.mean
beta = zeros((node.index, 1))
total = 0
for parent in node.parents:
#beta_0 -= node.func.betas[parent.variable_name] * \
# parent.func.mean
beta[parent.index, 0] = node.func.betas[parent.variable_name]
sigma_c = node.func.variance
mu_x, sigma_x = conditional_to_joint(mu_x, sigma_x, beta_0, beta,
sigma_c)
# Now set the names on the covariance matrix to
# the graphs variabe names
names = [n.variable_name for n in ordered]
mu_x.set_names(names)
sigma_x.set_names(names)
return mu_x, sigma_x
def get_joint_parameters(self):
'''Return the vector of means
and the covariance matrix
for the full joint distribution.
For now, by definition, all
the variables in a Gaussian
Bayesian Network are either
univariate gaussian or
conditional guassians.
'''
ordered = self.get_topological_sort()
mu_x = Matrix([[ordered[0].func.mean]])
sigma_x = Matrix([[ordered[0].func.variance]])
# Iteratively build up the mu and sigma matrices
for node in ordered[1:]:
beta_0 = node.func.mean
beta = zeros((node.index, 1))
total = 0
for parent in node.parents:
#beta_0 -= node.func.betas[parent.variable_name] * \
# parent.func.mean
beta[parent.index, 0] = node.func.betas[parent.variable_name]
sigma_c = node.func.variance
mu_x, sigma_x = conditional_to_joint(
mu_x, sigma_x, beta_0, beta, sigma_c)
# Now set the names on the covariance matrix to
# the graphs variabe names
names = [n.variable_name for n in ordered]
mu_x.set_names(names)
sigma_x.set_names(names)
return mu_x, sigma_x
def conditional_gaussianized(*args, **kwds):
'''Since this function will never
be called directly we dont need anything here.
'''
# First we need to construct a vector
# out of the args...
x = zeros((len(args), 1))
for i, a in enumerate(args):
x[i, 0] = a
sigma = conditional_gaussianized.covariance_matrix
mu = conditional_gaussianized.joint_mu
return 1 / (2 * math.pi * sigma.det()) ** 0.5 \
* math.exp(-0.5 * ((x - mu).T * sigma.I * (x - mu))[0, 0])
def conditional_gaussianized(*args, **kwds):
'''Since this function will never
be called directly we dont need anything here.
'''
# First we need to construct a vector
# out of the args...
x = zeros((len(args), 1))
for i, a in enumerate(args):
x[i, 0] = a
sigma = conditional_gaussianized.covariance_matrix
mu = conditional_gaussianized.joint_mu
return 1 / (2 * math.pi * sigma.det()) ** 0.5 \
* math.exp(-0.5 * ((x - mu).T * sigma.I * (x - mu))[0, 0])
