private TrainerScore[][] prepareScore()
{
ArrayList arrayList = new ArrayList();
int num = this.graph.size();
TrainerScore[] array = new TrainerScore[num];
this.betas = new float[num];
this.outputProbs = new float[num];
this.probCurrentFrame = new float[num];
Node initialNode = this.graph.getInitialNode();
int num2 = this.graph.indexOf(initialNode);
for (int i = 0; i < num; i++)
{
this.probCurrentFrame[i] = float.MinValue;
}
this.probCurrentFrame[num2] = 0f;
initialNode.startOutgoingEdgeIterator();
while (initialNode.hasMoreOutgoingEdges())
{
Edge edge = initialNode.nextOutgoingEdge();
Node destination = edge.getDestination();
int num3 = this.graph.indexOf(destination);
if (!destination.isType("STATE"))
{
this.probCurrentFrame[num3] = 0f;
}
else
{
HMMState hmmstate = (HMMState)destination.getObject();
if (!hmmstate.isEmitting())
{
this.probCurrentFrame[num3] = 0f;
}
if (!BaumWelchLearner.assertionsDisabled)
{
throw new AssertionError();
}
}
}
this.lastFeatureIndex = 0;
while (this.getFeature())
{
this.forwardPass(array);
arrayList.add(array);
this.lastFeatureIndex++;
}
BaumWelchLearner.logger.info(new StringBuilder().append("Feature frames read: ").append(this.lastFeatureIndex).toString());
for (int i = 0; i < this.probCurrentFrame.Length; i++)
{
this.probCurrentFrame[i] = float.MinValue;
}
Node finalNode = this.graph.getFinalNode();
int num4 = this.graph.indexOf(finalNode);
this.probCurrentFrame[num4] = 0f;
finalNode.startIncomingEdgeIterator();
while (finalNode.hasMoreIncomingEdges())
{
Edge edge2 = finalNode.nextIncomingEdge();
Node source = edge2.getSource();
int num5 = this.graph.indexOf(source);
if (!source.isType("STATE"))
{
this.probCurrentFrame[num5] = 0f;
if (!BaumWelchLearner.assertionsDisabled)
{
throw new AssertionError();
}
}
else
{
HMMState hmmstate2 = (HMMState)source.getObject();
if (!hmmstate2.isEmitting())
{
this.probCurrentFrame[num5] = 0f;
}
}
}
return((TrainerScore[][])arrayList.toArray(new TrainerScore[arrayList.size()][]));
}