protected internal void accumulate(int num, TrainerScore[] array, TrainerScore[] array2)
{
TrainerScore trainerScore = array[num];
this.currentLogLikelihood = 0f;
this.logLikelihood -= array[0].getScalingFactor();
SenoneHMMState senoneHMMState = (SenoneHMMState)trainerScore.getState();
if (senoneHMMState == null)
{
int num2 = trainerScore.getSenoneID();
if (num2 == -1)
{
this.accumulateMean(num2, array[num]);
this.accumulateVariance(num2, array[num]);
this.accumulateMixture(num2, array[num]);
this.accumulateTransition(num2, num, array, array2);
}
}
else if (senoneHMMState.isEmitting())
{
int num2 = this.senonePool.indexOf(senoneHMMState.getSenone());
this.accumulateMixture(num2, array[num]);
this.accumulateTransition(num2, num, array, array2);
}
}
private float calculateScores(int i)
{
SenoneHMMState senoneHMMState = (SenoneHMMState)this.graph.getNode(i).getObject();
float result;
if (senoneHMMState != null && senoneHMMState.isEmitting())
{
this.componentScores = senoneHMMState.calculateComponentScore(this.curFeature);
result = senoneHMMState.getScore(this.curFeature);
if (!BaumWelchLearner.assertionsDisabled && this.componentScores.Length != 1)
{
throw new AssertionError();
}
}
else
{
this.componentScores = null;
result = 0f;
}
return(result);
}
private void forwardPass(TrainerScore[] array)
{
for (int i = 0; i < this.graph.size(); i++)
{
this.outputProbs[i] = this.calculateScores(i);
array[i] = new TrainerScore(this.curFeature, this.outputProbs[i], (HMMState)this.graph.getNode(i).getObject(), this.componentScores);
array[i].setAlpha(this.probCurrentFrame[i]);
}
float[] array2 = this.probCurrentFrame;
this.probCurrentFrame = new float[this.graph.size()];
int j;
for (j = 0; j < this.graph.size(); j++)
{
Node node = this.graph.getNode(j);
if (node.isType("STATE"))
{
SenoneHMMState senoneHMMState = (SenoneHMMState)node.getObject();
SenoneHMM senoneHMM = (SenoneHMM)senoneHMMState.getHMM();
if (senoneHMMState.isEmitting())
{
this.probCurrentFrame[j] = float.MinValue;
node.startIncomingEdgeIterator();
while (node.hasMoreIncomingEdges())
{
Node source = node.nextIncomingEdge().getSource();
int num = this.graph.indexOf(source);
HMMState hmmstate = (HMMState)source.getObject();
float num2;
if (hmmstate != null)
{
if (!BaumWelchLearner.assertionsDisabled && hmmstate.isEmitting() && hmmstate.getHMM() != senoneHMM)
{
throw new AssertionError();
}
if (!hmmstate.isEmitting())
{
num2 = 0f;
}
else
{
num2 = senoneHMM.getTransitionProbability(hmmstate.getState(), senoneHMMState.getState());
}
}
else
{
num2 = 0f;
}
this.probCurrentFrame[j] = this.logMath.addAsLinear(this.probCurrentFrame[j], array2[num] + num2);
}
float[] array3 = this.probCurrentFrame;
int num3 = j;
float[] array4 = array3;
array4[num3] += this.outputProbs[j];
array[j].setAlpha(this.probCurrentFrame[j]);
}
}
}
j = 0;
while (j < this.graph.size())
{
Node node = this.graph.getNode(j);
HMMState hmmstate2 = null;
SenoneHMM senoneHMM = null;
if (node.isType("STATE"))
{
hmmstate2 = (HMMState)node.getObject();
senoneHMM = (SenoneHMM)hmmstate2.getHMM();
if (!hmmstate2.isEmitting())
{
goto IL_26F;
}
}
else
{
if (!this.graph.isInitialNode(node))
{
goto IL_26F;
}
array[j].setAlpha(float.MinValue);
this.probCurrentFrame[j] = float.MinValue;
}
IL_35D:
j++;
continue;
IL_26F:
this.probCurrentFrame[j] = float.MinValue;
node.startIncomingEdgeIterator();
while (node.hasMoreIncomingEdges())
{
Node source2 = node.nextIncomingEdge().getSource();
int num4 = this.graph.indexOf(source2);
float num5;
if (source2.isType("STATE"))
{
HMMState hmmstate3 = (HMMState)source2.getObject();
if (!BaumWelchLearner.assertionsDisabled && hmmstate3.isEmitting() && hmmstate3.getHMM() != senoneHMM)
{
throw new AssertionError();
}
if (!hmmstate3.isEmitting())
{
num5 = 0f;
}
else
{
num5 = senoneHMM.getTransitionProbability(hmmstate3.getState(), hmmstate2.getState());
}
}
else
{
num5 = 0f;
}
this.probCurrentFrame[j] = this.logMath.addAsLinear(this.probCurrentFrame[j], this.probCurrentFrame[num4] + num5);
}
array[j].setAlpha(this.probCurrentFrame[j]);
goto IL_35D;
}
}
