class first_smci:
def __init__(self, dbm, sample_size=500, initial_update=1000, update_time=1):
self.sampler = None
self.dbm = dbm
self.initial_update = initial_update
self.update_time = update_time
self.sample_size = sample_size
self.mariginalize = self.dbm.propagation.first_smci_marginalize
def expectation(self):
if self.sampler is None:
self.sampler = Sampler(self.dbm, self.sample_size, self.initial_update, self.update_time)
values = self.sampler.sampling()
signals = [None for i in self.dbm.layers]
signals[0] = self.dbm.signal(values[1], -1)
for i in range(1, len(self.dbm.layers)-1):
signals[i] = self.dbm.signal(values[i-1], i) + self.dbm.signal(values[i+1], -(i+1))
signals[-1] = self.dbm.signal(values[-2], len(self.dbm.weights))
multiply_up = [None for i in self.dbm.weights]
multiply_down = [None for i in self.dbm.weights]
for i,_ in enumerate(self.dbm.weights):
multiply_up[i] = values[i][:, :, tf.newaxis] * self.dbm.weights[i]
multiply_down[i] = values[i+1][:, tf.newaxis, :] * self.dbm.weights[i]
expectations = [None for i in self.dbm.weights]
for i,_ in enumerate(self.dbm.weights):
expectations[i] = self.mariginalize( signals[i][:, :, tf.newaxis]-multiply_down[i], signals[i+1][:, tf.newaxis, :]-multiply_up[i], self.dbm.weights[i])
return expectations
class four_layer_second_smci:
def __init__(self, dbm, sample_size=500, initial_update=1000, update_time=1):
if len(dbm.layers) != 4:
raise ValueError("2-SMCI supports only 4-layered DBM.")
self.sampler = None
self.dbm = dbm
self.initial_update = initial_update
self.update_time = update_time
self.sample_size = sample_size
self.mariginalize = self.dbm.propagation.first_smci_marginalize
self.mariginalize2 = self.dbm.propagation.second_smci_marginalize
def expectation(self):
if self.sampler is None:
self.sampler = Sampler(self.dbm, self.sample_size, self.initial_update, self.update_time)
values = self.sampler.sampling()
expectations = [None for i in self.dbm.weights]
signal_up = [None for i in self.dbm.weights]
signal_down = [None for i in self.dbm.weights]
multiply_up = [None for i in self.dbm.weights]
multiply_down = [None for i in self.dbm.weights]
for i,_ in enumerate(self.dbm.weights):
signal_up[i] = self.dbm.signal(values[i], i+1)
signal_down[i] = self.dbm.signal(values[i+1], -(i+1))
multiply_up[i] = signal_up[i][:, tf.newaxis, :] - values[i][:, :, tf.newaxis] * self.dbm.weights[i]
multiply_down[i] = signal_down[i][:, :, tf.newaxis] - values[i+1][:, tf.newaxis, :] * self.dbm.weights[i]
# expectation[0]
x = multiply_up[0] + self.mariginalize2( signal_down[2][:, tf.newaxis, :] + multiply_up[1], self.dbm.weights[1], axis=2)[:, tf.newaxis, :]
y = multiply_down[0]
z = self.dbm.weights[0][tf.newaxis, :, :]
expectations[0] = self.mariginalize(x, y, z)
# expectation[1]
x = multiply_down[1] + self.mariginalize2( multiply_down[0], self.dbm.weights[0], axis=1)[:, :, tf.newaxis]
y = multiply_up[1] + self.mariginalize2( multiply_up[2], self.dbm.weights[2], axis=2)[:, tf.newaxis, :]
z = self.dbm.weights[1][tf.newaxis, :, :]
expectations[1] = self.mariginalize(x, y, z)
# expectation[2]
x = multiply_up[2]
y = multiply_down[2] + self.mariginalize2( signal_up[0][:, :, tf.newaxis] + multiply_down[1], self.dbm.weights[1], axis=1)[:, :, tf.newaxis]
z = self.dbm.weights[2][tf.newaxis, :, :]
expectations[2] = self.mariginalize(x, y, z)
return expectations
class montecarlo:
def __init__(self, dbm, sample_size=500, initial_update=1000, update_time=1):
self.sampler = None
self.dbm = dbm
self.initial_update = initial_update
self.update_time = update_time
self.sample_size = sample_size
def expectation(self):
if self.sampler is None:
self.sampler = Sampler(self.dbm, self.sample_size, self.initial_update, self.update_time)
values = self.sampler.sampling()
weight = self.dbm.weight_matrix(values)
return weight