def __init__(self, model, batch_size, num_chains=3, betas=None):
# Call constructor of superclass
super(PT, self).__init__(model=model, batch_size=batch_size)
self.sampler = RBM_SAMPLER.ParallelTemperingSampler(
self.rbm, num_chains, betas)
def __init__(self, model, betas=3, data=None):
""" The constructor initializes the IPT trainer with a given models anddata.
:param model: The models to sample from.
:type model: Valid models class.
:param betas: List of inverse temperatures to sample from. If a scalar is given, the temperatures will be set \
linearly from 0.0 to 1.0 in 'betas' steps.
:type betas: int, numpy array [num betas]
:param data: Data for initialization, only has effect if the centered gradient is used.
:type data: numpy array [num. samples x input dim]
"""
# Call super constructor of CD
super(PT, self).__init__(model, data)
if numx.isscalar(betas):
self.sampler = sampler.ParallelTemperingSampler(model, betas, None)
else:
self.sampler = sampler.ParallelTemperingSampler(
model, betas.shape[0], betas)
def test_Parallel_Tempering_sampler(self):
sys.stdout.write('RBM Sampler -> Performing ParallelTemperingSampler test ... ')
sys.stdout.flush()
numx.random.seed(42)
sampler = Sampler.ParallelTemperingSampler(self.bbrbm, 10)
probCD1, probCD2, probCS1, probCS2, probCS3, probCS4, sumProbs = self.execute_sampler(sampler, self.num_samples)
assert numx.all(numx.abs(1.0 / 4.0 - probCD1) < self.epsilon)
assert numx.all(numx.abs(1.0 / 4.0 - probCD2) < self.epsilon)
assert numx.all(numx.abs(1.0 / 8.0 - probCS1) < self.epsilon)
assert numx.all(numx.abs(1.0 / 8.0 - probCS2) < self.epsilon)
assert numx.all(numx.abs(1.0 / 8.0 - probCS3) < self.epsilon)
assert numx.all(numx.abs(1.0 / 8.0 - probCS4) < self.epsilon)
assert numx.all(numx.abs(1.0 - sumProbs) < self.epsilon)
print('successfully passed!')
sys.stdout.flush()