/**
* Train the HMM with the given input sequence
*
* @param slHiddenMarkovModel $hmm
* @param array $sequence
* @return void
*/
public function train(slHiddenMarkovModel $hmm, array $sequence)
{
$sequence = $hmm->mapLabels($sequence);
$sequenceLength = count($sequence);
$oldHmm = clone $hmm;
$states = count($hmm);
$labels = $hmm->countLabels();
// Calculate forward and backwards variables, first
$forward = $this->calcForward($oldHmm, $sequence);
$backward = $this->calcBackwards($oldHmm, $sequence);
// Reevaluate the start probabilities
for ($i = 0; $i < $states; ++$i) {
$hmm->setStart($i, $this->calcGamma($oldHmm, $i, 0, $sequence, $forward, $backward));
}
// Reevaluate the transistion probabilities
for ($i = 0; $i < $states; ++$i) {
for ($j = 0; $j < $states; ++$j) {
$numerator = 0;
$denominator = 0;
for ($t = 0; $t <= $sequenceLength - 1; ++$t) {
$numerator += $this->calcP($oldHmm, $t, $i, $j, $sequence, $forward, $backward);
$denominator += $this->calcGamma($oldHmm, $i, $t, $sequence, $forward, $backward);
}
$hmm->setTransition($i, $j, $this->divide($numerator, $denominator));
}
}
// Reevaluate the emission probabilities
for ($i = 0; $i < $states; ++$i) {
for ($k = 0; $k < $labels; ++$k) {
for ($t = 0; $t <= $sequenceLength - 1; ++$t) {
$gamma = $this->calcGamma($oldHmm, $i, $t, $sequence, $forward, $backward);
$numerator += $gamma * ($k == $sequence[$t] ? 1 : 0);
$denominator += $gamma;
}
$hmm->setEmission($i, $k, $this->divide($numerator, $denominator));
}
}
}
public function testMapLabels()
{
$hmm = new slHiddenMarkovModel(4, array(1, 2, 3));
$this->assertSame(array(0, 1, 0, 2), $hmm->mapLabels(array(1, 2, 1, 3)));
}
