public NaiveBayesClassifier(Pair <TClass, TFeatures[]>[] training)
{
ExpandingCollator <TFeatures> tfeats = new ExpandingCollator <TFeatures>();
ExpandingCollator <TClass> tclass = new ExpandingCollator <TClass>();
ClassFeatureList[] cfl = Array.ConvertAll(training, x =>
new ClassFeatureList()
{
cls = tclass[x.a], features =
Array.ConvertAll(x.b, feat => tfeats[feat])
}
);
ffeats = new FixedCollator <TFeatures>(tfeats);
fclasses = new FixedCollator <TClass>(tclass);
classifier = new NaiveBayesClassifier(cfl, fclasses.Count, ffeats.Count);
}
public HiddenMarkovModel(Expectation <TInput, TOutput>[] training, HiddenMarkovModel.GenerationMethod generationMethod)
{
ExpandingCollator <TInput> tinputs = new ExpandingCollator <TInput>();
ExpandingCollator <TOutput> toutputs = new ExpandingCollator <TOutput>();
// collect the types
Expectation <int, int>[] intExpectation =
Array.ConvertAll(training, x =>
new Expectation <int, int>(
Array.ConvertAll(x.expectation, y =>
new Pair <int, int>()
{
a = tinputs[y.a],
b = toutputs[y.b]
}
)));
inputs = new FixedCollator <TInput>(tinputs);
outputs = new FixedCollator <TOutput>(toutputs);
hmm = new HiddenMarkovModel(intExpectation, tinputs.Count, toutputs.Count, generationMethod);
}