public function testLogRatioNormalization()
{
// Verified with Ralf Herbrich's F# implementation
$m1s2 = new GaussianDistribution(1, 2);
$m3s4 = new GaussianDistribution(3, 4);
$lrn = GaussianDistribution::logRatioNormalization($m1s2, $m3s4);
$this->assertEquals(2.6157405972171204, $lrn, '', GaussianDistributionTest::ERROR_TOLERANCE);
}
public function getLogNormalization()
{
$vars = $this->getVariables();
$messages = $this->getMessages();
return GaussianDistribution::logRatioNormalization($vars[0]->getValue(), $messages[0]->getValue());
}
public function getLogNormalization()
{
$vars = $this->getVariables();
$messages = $this->getMessages();
$result = 0.0;
// We start at 1 since offset 0 has the sum
$varCount = count($vars);
for ($i = 1; $i < $varCount; $i++) {
$result += GaussianDistribution::logRatioNormalization($vars[$i]->getValue(), $messages[$i]->getValue());
}
return $result;
}
