def testLogProductNormalization(self):
standard_normal = Gaussian(0, 1)
lpn = Gaussian.log_product_normalization(standard_normal, standard_normal)
answer = -1.2655121234846454
self.assertAlmostEqual(answer, lpn, None,
"testLogProductNormalization lpn expected %.15f, got %.15f" % (answer, lpn),
GaussianDistributionTest.ERROR_TOLERANCE)
m1s2 = Gaussian(1.0, 2.0)
m3s4 = Gaussian(3.0, 4.0)
lpn2 = Gaussian.log_product_normalization(m1s2, m3s4)
answer = -2.5168046699816684
self.assertAlmostEqual(answer, lpn2, None,
"testLogProductNormalization lpn2 expected %.15f, got %.15f" % (answer, lpn2),
GaussianDistributionTest.ERROR_TOLERANCE)
def log_normalization(self):
marginal = self.variables[0].value
message = self.messages[0].value
message_from_variable = marginal / message
return (-Gaussian.log_product_normalization(message_from_variable, message) +
log(Gaussian.cumulative_to((message_from_variable.mean - self.epsilon) / message_from_variable.stdev)))
def testLogProductNormalization(self):
standard_normal = Gaussian(0, 1)
lpn = Gaussian.log_product_normalization(standard_normal,
standard_normal)
answer = -1.2655121234846454
self.assertAlmostEqual(
answer, lpn, None,
"testLogProductNormalization lpn expected %.15f, got %.15f" %
(answer, lpn), GaussianDistributionTest.ERROR_TOLERANCE)
m1s2 = Gaussian(1.0, 2.0)
m3s4 = Gaussian(3.0, 4.0)
lpn2 = Gaussian.log_product_normalization(m1s2, m3s4)
answer = -2.5168046699816684
self.assertAlmostEqual(
answer, lpn2, None,
"testLogProductNormalization lpn2 expected %.15f, got %.15f" %
(answer, lpn2), GaussianDistributionTest.ERROR_TOLERANCE)
def log_normalization(self):
marginal = self.variables[0].value
message = self.messages[0].value
message_from_variable = marginal / message
mean = message_from_variable.mean
std = message_from_variable.stdev
z = (Gaussian.cumulative_to((self.epsilon - mean) / std) -
Gaussian.cumulative_to((-self.epsilon - mean) / std))
return -Gaussian.log_product_normalization(message_from_variable, message) + log(z)
def log_normalization(self):
marginal = self.variables[0].value
message = self.messages[0].value
message_from_variable = marginal / message
return (
-Gaussian.log_product_normalization(message_from_variable, message)
+ log(
Gaussian.cumulative_to(
(message_from_variable.mean - self.epsilon) /
message_from_variable.stdev)))
def log_normalization(self):
marginal = self.variables[0].value
message = self.messages[0].value
message_from_variable = marginal / message
mean = message_from_variable.mean
std = message_from_variable.stdev
z = (Gaussian.cumulative_to(
(self.epsilon - mean) / std) - Gaussian.cumulative_to(
(-self.epsilon - mean) / std))
return -Gaussian.log_product_normalization(message_from_variable,
message) + log(z)
def send_message_variable(self, message, variable):
marginal = variable.value
message_value = message.value
log_z = Gaussian.log_product_normalization(marginal, message_value)
variable.value = marginal * message_value
return log_z
def send_message_variable(self, message, variable):
marginal = variable.value
message_value = message.value
log_z = Gaussian.log_product_normalization(marginal, message_value)
variable.value = marginal * message_value
return log_z
