def prep_first_order_network_inference(population):
""" Initialize functions that compute the gradient and Hessian of
the log probability with respect to the differentiable GLM
parameters, e.g. the weight matrix if it exists.
"""
network = population.network
syms = population.get_variables()
# Compute gradients of the log prob wrt the GLM parameters
network_syms = differentiable(syms['net'])
print "Computing gradient of the network prior w.r.t. the differentiable GLM parameters"
g_network_logprior, _ = grad_wrt_list(network.log_p, _flatten(network_syms))
# TODO: Replace this with a function that just gets the shapes?
x0 = population.sample()
nvars = population.extract_vars(x0, 0)
dnvars = get_vars(network_syms, nvars['net'])
_,network_shapes = packdict(dnvars)
# Private function to compute the log probability and its gradient
# with respect to a set of parameters
def nlp(x_network_vec, x):
"""
Helper function to compute the negative log posterior for a given set
of GLM parameters. The parameters are passed in as a vector.
"""
x_network = unpackdict(x_network_vec, network_shapes)
set_vars(network_syms, x['net'], x_network)
lp = seval(network.log_prior,
syms,
x)
return -1.0 * lp
def grad_nlp(x_glm_vec, x):
"""
Helper function to compute the gradient of negative log posterior for
a given set of GLM parameters. The parameters are passed in as a vector.
"""
x_network = unpackdict(x_glm_vec, network_shapes)
set_vars(network_syms, x['net'], x_network)
glp = seval(g_network_logprior,
syms,
x)
return -1.0 * glp
return network_syms, nlp, grad_nlp
def prep_first_order_glm_inference(population):
""" Initialize functions that compute the gradient and Hessian of
the log probability with respect to the differentiable GLM
parameters, e.g. the weight matrix if it exists.
"""
glm = population.glm
syms = population.get_variables()
# Compute gradients of the log prob wrt the GLM parameters
glm_syms = differentiable(syms['glm'])
print "Computing gradient of the prior w.r.t. the differentiable GLM parameters"
g_glm_logprior, _ = grad_wrt_list(glm.log_prior, _flatten(glm_syms))
print "Computing gradient of the GLM likelihood w.r.t. the differentiable GLM parameters"
g_glm_ll, _ = grad_wrt_list(glm.ll, _flatten(glm_syms))
# TODO: Replace this with a function that just gets the shapes?
x0 = population.sample()
nvars = population.extract_vars(x0, 0)
dnvars = get_vars(glm_syms, nvars['glm'])
_,glm_shapes = packdict(dnvars)
# Private function to compute the log probability and its gradient
# with respect to a set of parameters
def nlp(x_glm_vec, x):
"""
Helper function to compute the negative log posterior for a given set
of GLM parameters. The parameters are passed in as a vector.
"""
x_glm = unpackdict(x_glm_vec, glm_shapes)
set_vars(glm_syms, x['glm'], x_glm)
lp = seval(glm.log_prior,
syms,
x)
# Add the likelihood of each data sequence
for data in population.data_sequences:
# Set the data
population.set_data(data)
lp += seval(glm.ll,
syms,
x)
return -1.0 * lp
def grad_nlp(x_glm_vec, x):
"""
Helper function to compute the gradient of negative log posterior for
a given set of GLM parameters. The parameters are passed in as a vector.
"""
x_glm = unpackdict(x_glm_vec, glm_shapes)
set_vars(glm_syms, x['glm'], x_glm)
glp = seval(g_glm_logprior,
syms,
x)
# Add the likelihood of each data sequence
for data in population.data_sequences:
# Set the data
population.set_data(data)
glp += seval(g_glm_ll,
syms,
x)
return -1.0 * glp
return glm_syms, nlp, grad_nlp