public void Learn_HMMWithRandomDistributedProbs_LearnsAConsistentModelBasedOnData()
{
// Arrange
int[] symbols = Enumerable.Range(1, 42).ToArray();
List<int[]> obs = new List<int[]>();
Random rnd = new Random();
for (int i = 0; i < 10; i++)
{
obs.Add(symbols.OrderBy(x => rnd.Next()).ToArray());
}
HMMGraph hmm = new HMMGraph(symbols.Length);
hmm.AddNode(new Node());
hmm.AddNode(new Node());
hmm.AddNode(new Node());
hmm.AddNode(new Node());
foreach (Node n in hmm.Nodes)
{
foreach (Node m in hmm.Nodes)
{
n.SetTransition(m, 0.5);
}
foreach (int i in symbols)
{
n.SetEmission(i, 0.5);
}
}
hmm.Normalize();
BaumWelch bw = new BaumWelch(obs.Count, hmm);
// Act
for (int i = 0; i < 5; i++)
{
bw.Learn(hmm, obs.ToArray());
}
// Assert
const double PRECISION = .00000000001;
foreach (Node n in hmm.Nodes)
{
//check transitions
double sum = 0;
foreach (Node nb in n.Transitions.Keys)
{
sum += n.Transitions[nb];
}
Assert.IsTrue(1.0 - PRECISION < sum && sum < 1.0 + PRECISION);
sum = 0;
foreach (int o in n.Emissions.Keys)
{
sum += n.Emissions[o];
}
Assert.IsTrue(1.0 - PRECISION < sum && sum < 1.0 + PRECISION);
}
}
public void LearnTest_validInput_ModelDescribingTheData ()
{
// Arrange
HMMGraph hmm = new HMMGraph(NUMBER_OF_SYMBOLS_IN_HMMGRAPH);
//int[] t = {2,3,5,6,2,12,4,6,3,36,62,2,144,3,531,44,23,234,21};
List<int[]> obs = new List<int[]>();
Random rnd = new Random();
for(int i=0;i<4;i++) {
obs.Add(Enumerable.Range(1,49).OrderBy(x => rnd.Next()).ToArray());
}
hmm.AddNode(new Node());
hmm.AddNode(new Node());
hmm.AddNode(new Node());
hmm.AddNode(new Node());
BaumWelch BW = new BaumWelch();
// Act
HMMGraph result = BW.Learn(hmm, obs.ToArray());
// Assert
Assert.IsNotNull(result);
Assert.Inconclusive();
}
private static Node FindQPrime(HMMGraph graph, int[] combinedTrainData) {
BaumWelch bw = new BaumWelch(combinedTrainData.Length, graph);
bw.PreCompute(graph, combinedTrainData);
Node qPrime = graph.Nodes[0];
double best = 0.0;
double score = 0.0;
foreach (Node n in graph.Nodes) {
score = bw.ComputeGamma(n, graph, combinedTrainData); // relative (unscaled)
if (score > best) {
qPrime = n;
best = score;
}
}
return qPrime;
}
