def delta_logp(logp, vars, shared):
[logp0], inarray0 = join_nonshared_inputs([logp], vars, shared)
tensor_type = inarray0.type
inarray1 = tensor_type('inarray1')
logp1 = CallableTensor(logp0)(inarray1)
f = theano.function([inarray1, inarray0], logp1 - logp0)
f.trust_input = True
return f
def logp_forw(out_vars, vars, shared):
"""Compile Theano function of the model and the input and output variables.
Parameters
----------
out_vars: List
containing :class:`pymc3.Distribution` for the output variables
vars: List
containing :class:`pymc3.Distribution` for the input variables
shared: List
containing :class:`theano.tensor.Tensor` for depended shared data
"""
out_list, inarray0 = join_nonshared_inputs(out_vars, vars, shared)
f = theano_function([inarray0], out_list[0])
f.trust_input = True
return f
def _theano_hamiltonian(model_vars, shared, logpt, potential):
"""Creates a Hamiltonian with shared inputs.
Parameters
----------
model_vars : array of variables to be sampled
shared : theano tensors that are already shared
logpt : model log probability
potential : hamiltonian potential
Returns
-------
Hamiltonian : namedtuple with log pdf, gradient of log pdf, and potential functions
q : Starting position variable.
"""
dlogp = gradient(logpt, model_vars)
(logp, dlogp), q = join_nonshared_inputs([logpt, dlogp], model_vars,
shared)
logp = CallableTensor(logp)
dlogp = CallableTensor(dlogp)
return Hamiltonian(logp, dlogp, potential), q
def _theano_hamiltonian(model_vars, shared, logpt, potential):
"""Creates a Hamiltonian with shared inputs.
Parameters
----------
model_vars : array of variables to be sampled
shared : theano tensors that are already shared
logpt : model log probability
potential : hamiltonian potential
Returns
-------
Hamiltonian : namedtuple with log pdf, gradient of log pdf, and potential functions
q : Starting position variable.
"""
dlogp = gradient(logpt, model_vars)
(logp, dlogp), q = join_nonshared_inputs([logpt, dlogp], model_vars, shared)
dlogp_func = theano.function(inputs=[q], outputs=dlogp)
dlogp_func.trust_input = True
logp = CallableTensor(logp)
dlogp = CallableTensor(dlogp)
return Hamiltonian(logp, dlogp, potential), q, dlogp_func
def _theano_hamiltonian(model_vars, shared, logpt, potential):
"""Create a Hamiltonian with shared inputs.
Parameters
----------
model_vars : array of variables to be sampled
shared : theano tensors that are already shared
logpt : model log probability
potential : hamiltonian potential
Returns
-------
Hamiltonian : namedtuple with log pdf, gradient of log pdf, and potential functions
q : Initial position for Hamiltonian Monte Carlo
dlogp_func: theano function that computes the gradient of a log pdf at a point
"""
dlogp = gradient(logpt, model_vars)
(logp, dlogp), q = join_nonshared_inputs([logpt, dlogp], model_vars, shared)
dlogp_func = theano.function(inputs=[q], outputs=dlogp)
dlogp_func.trust_input = True
logp = CallableTensor(logp)
dlogp = CallableTensor(dlogp)
return Hamiltonian(logp, dlogp, potential), q, dlogp_func
def logp_forw(logp, vars, shared):
[logp0], inarray0 = join_nonshared_inputs([logp], vars, shared)
f = theano.function([inarray0], logp0)
f.trust_input = True
return f
def logp_forw(logp, vars, shared):
[logp0], inarray0 = join_nonshared_inputs([logp], vars, shared)
f = theano.function([inarray0], logp0)
f.trust_input = True
return f
