def log_normalization(self):
result = 0.0
for i in range(1, len(self.variables)):
result += Gaussian.log_ratio_normalization(self.variables[i].value,
self.messages[i].value)
return result
def testLogRatioNormalization(self):
m1s2 = Gaussian(1.0, 2.0)
m3s4 = Gaussian(3.0, 4.0)
lrn = Gaussian.log_ratio_normalization(m1s2, m3s4)
answer = 2.6157405972171204
self.assertAlmostEqual(answer, lrn, None,
"testLogRatioNormalization lrn expected %.15f, got %.15f" % (answer, lrn),
GaussianDistributionTest.ERROR_TOLERANCE)
def testLogRatioNormalization(self):
m1s2 = Gaussian(1.0, 2.0)
m3s4 = Gaussian(3.0, 4.0)
lrn = Gaussian.log_ratio_normalization(m1s2, m3s4)
answer = 2.6157405972171204
self.assertAlmostEqual(
answer, lrn, None,
"testLogRatioNormalization lrn expected %.15f, got %.15f" %
(answer, lrn), GaussianDistributionTest.ERROR_TOLERANCE)
def log_normalization(self):
result = 0.0
for i in range(1, len(self.variables)):
result += Gaussian.log_ratio_normalization(self.variables[i].value, self.messages[i].value)
return result
def log_normalization(self):
return Gaussian.log_ratio_normalization(
self.variables[0].value,
self.messages[0].value
)
def log_normalization(self):
return Gaussian.log_ratio_normalization(self.variables[0].value,
self.messages[0].value)
