def sampler(self, n_particles, n_visible=None, rng=7823748):
"""Return an `HMC_sampler` that will draw samples from the distribution over visible
units specified by this RBM.
:param n_particles: this many parallel chains will be simulated.
:param rng: seed or numpy RandomState object to initialize particles, and to drive the simulation.
"""
#TODO: why not expose all HMC arguments somehow?
#TODO: Consider returning a sample kwargs for passing to HMC_sampler?
if not hasattr(rng, 'randn'):
rng = np.random.RandomState(rng)
if n_visible is None:
n_visible = self.n_visible_units()
rval = HMC_sampler.new_from_shared_positions(
shared_positions=sharedX(rng.randn(n_particles, n_visible),
name='particles'),
energy_fn=self.free_energy_given_v,
seed=int(rng.randint(2**30)))
return rval
def CD1_sampler(self, v, n_particles, n_visible=None, rng=8923984):
"""Return a symbolic negative-phase particle obtained by simulating the Hamiltonian
associated with the energy function.
"""
#TODO: why not expose all HMC arguments somehow?
if not hasattr(rng, 'randn'):
rng = np.random.RandomState(rng)
if n_visible is None:
n_visible = self.n_visible_units()
# create a dummy hmc object because we want to use *some* of it
hmc = HMC_sampler.new_from_shared_positions(
shared_positions=v, # v is not shared, so some functionality will not work
energy_fn=self.free_energy_given_v,
seed=int(rng.randint(2**30)),
shared_positions_shape=(n_particles,n_visible),
compile_simulate=False)
updates = dict(hmc.updates())
final_p = updates.pop(v)
return hmc, final_p, updates
def CD1_sampler(self, v, n_particles, n_visible=None, rng=8923984):
"""Return a symbolic negative-phase particle obtained by simulating the Hamiltonian
associated with the energy function.
"""
#TODO: why not expose all HMC arguments somehow?
if not hasattr(rng, 'randn'):
rng = np.random.RandomState(rng)
if n_visible is None:
n_visible = self.n_visible_units()
# create a dummy hmc object because we want to use *some* of it
hmc = HMC_sampler.new_from_shared_positions(
shared_positions=
v, # v is not shared, so some functionality will not work
energy_fn=self.free_energy_given_v,
seed=int(rng.randint(2**30)),
shared_positions_shape=(n_particles, n_visible),
compile_simulate=False)
updates = dict(hmc.updates())
final_p = updates.pop(v)
return hmc, final_p, updates
def sampler(self, n_particles, n_visible=None, rng=7823748):
"""Return an `HMC_sampler` that will draw samples from the distribution over visible
units specified by this RBM.
:param n_particles: this many parallel chains will be simulated.
:param rng: seed or numpy RandomState object to initialize particles, and to drive the simulation.
"""
#TODO: why not expose all HMC arguments somehow?
#TODO: Consider returning a sample kwargs for passing to HMC_sampler?
if not hasattr(rng, 'randn'):
rng = np.random.RandomState(rng)
if n_visible is None:
n_visible = self.n_visible_units()
rval = HMC_sampler.new_from_shared_positions(
shared_positions = sharedX(
rng.randn(
n_particles,
n_visible),
name='particles'),
energy_fn=self.free_energy_given_v,
seed=int(rng.randint(2**30)))
return rval
