public override void Learn(SequenceData trainingData, SequenceData validationData, SequenceData testData)
{
//HMMGraph graph = RandomGraph(trainingData.NumSymbols);
HMMGraph graph;
//bestHMM = ModelConverter.Graph2HMM(graph);
bestHMM = SparseHiddenMarkovModel.FromCompleteGraph(trainingData.NumSymbols, trainingData.NumSymbols);
bestHMM.Learn(trainingData.GetNonempty(), baumwelchThreshold);
//while (bestHMM.States < maxStates)
while (bestHMM.NumberOfStates < maxStates)
{
WriteLine("Taking one more iteration");
//graph = ModelConverter.HMM2Graph(bestHMM);
graph = bestHMM.ToGraph();
Dictionary<int, double> nodePerformance = new Dictionary<int, double>();
Dictionary<int, int> nodeOccurence = new Dictionary<int, int>();
double hiddenStateSequenceProbability;
foreach (int[] signal in validationData.GetNonempty())
{
//int[] hiddenStateSequence = bestHMM.Decode(signal, out hiddenStateSequenceProbability);
int[] hiddenStateSequence = bestHMM.Viterby(signal, out hiddenStateSequenceProbability);
for (int j = 0; j < hiddenStateSequence.Length; j++)
{
if (nodePerformance.ContainsKey(hiddenStateSequence[j]))
{
//nodePerformance[hiddenStateSequence[j]] += (Math.Log(hiddenStateSequenceProbability) + Math.Log(bestHMM.Emissions[hiddenStateSequence[j], signal[j]]));
nodePerformance[hiddenStateSequence[j]] += (Math.Log(hiddenStateSequenceProbability) + Math.Log(bestHMM.EmissionProbability(hiddenStateSequence[j], signal[j])));
nodeOccurence[hiddenStateSequence[j]]++;
}
else
{
nodePerformance.Add(hiddenStateSequence[j], (Math.Log(hiddenStateSequenceProbability) + Math.Log(bestHMM.EmissionProbability(hiddenStateSequence[j], signal[j]))));
nodeOccurence.Add(hiddenStateSequence[j], 1);
}
}
}
foreach (int node in nodeOccurence.Keys)
{
nodePerformance[node] /= nodeOccurence[node];
}
int weakPoint = nodePerformance.Keys.Aggregate((a, b) => ((nodePerformance[b] < nodePerformance[a]) ? b : a));
SplitWorstPerformingNode(graph, weakPoint);
//bestHMM = ModelConverter.Graph2HMM(graph);
bestHMM = SparseHiddenMarkovModel.FromGraph(graph);
WriteLine("Running BaumWelch");
bestHMM.Learn(trainingData.GetNonempty(), baumwelchThreshold); //Run the BaumWelch algorithm
WriteLine("");
WriteLine("Log Likelihood: " + LogLikelihood(bestHMM, trainingData));
}
}
public override void Learn(SequenceData trainingData, SequenceData validationData, SequenceData testData) {
HMMGraph graph = new HMMGraph(trainingData.NumSymbols);
//Add nodes and set initial and emission probabilities
for (int i = 0; i < states; i++) {
Node new_node = new Node();
for (int s = 0; s < trainingData.NumSymbols; s++)
new_node.SetEmission(s, ran.NextDouble());
new_node.InitialProbability = ran.NextDouble();
graph.AddNode(new_node);
}
//Add random transmissions. Each node will have at most Log(n) edges in both directions
//for (int i = 0; i < graph.Nodes.Count; i++)
//{
// List<Node> shuffled = graph.Nodes.Select(e => e).ToList();
// Utilities.Shuffle(shuffled);
// int upperBound = (int)Math.Ceiling(Math.Log(graph.Nodes.Count));
// if (upperBound >= graph.Nodes.Count)
// upperBound = graph.Nodes.Count - 1;
// for (int p = 0; p <= upperBound; p++)
// {
// Node from = graph.Nodes[i];
// Node to = graph.Nodes[p];
// from.SetTransition(to, ran.NextDouble());
// }
//}
int numberOfTransitions = (int)Math.Ceiling(Math.Log(states));
foreach (Node node in graph.Nodes)
{
for (int i = 0; i < numberOfTransitions; i++)
{
Node target;
while (node.Transitions.ContainsKey(target = graph.Nodes[ran.Next(states)]));
node.SetTransition(target, ran.NextDouble());
}
}
graph.Normalize();
hmm = SparseHiddenMarkovModel.FromGraph(graph);
hmm.Learn(trainingData.GetNonempty(), tolerance);
}
