Accord.Math.Random.Generator.Seed = 0;
int[][] inputs = new int[][]
{
new int[] { 0,1,1,0 },
new int[] { 0,0,1,0 },
new int[] { 0,1,1,1,0 },
new int[] { 0,1,1,1,0 },
new int[] { 0,1,1,0 },
new int[] { 0,0,0,0,0 },
new int[] { 0,0,0,1,0 },
new int[] { 0,0,0,0,0 },
new int[] { 0,0,0 },
new int[] { 0,0,0,0 },
new int[] { 1,0,0,1 },
new int[] { 1,1,0,1 },
new int[] { 1,0,0,0,1 },
new int[] { 1,0,1 },
new int[] { 1,1,0,1 },
};
int[] outputs = new int[]
{
0,0,0,0,0,
1,1,1,1,1,
2,2,2,2,2,
};
var crossvalidation = new CrossValidation<HiddenMarkovClassifier, int[]>()
{
K = 3,
Learner = (s) => new HiddenMarkovClassifierLearning()
{
Learner = (p) => new BaumWelchLearning()
{
NumberOfStates = 3
}
},
Loss = (expected, actual, p) =>
{
var cm = new GeneralConfusionMatrix(classes: p.Model.NumberOfClasses, expected: expected, predicted: actual);
p.Variance = cm.Variance;
return p.Value = cm.Kappa;
},
Stratify = false,
};
crossvalidation.ParallelOptions.MaxDegreeOfParallelism = 1;
var result = crossvalidation.Learn(inputs, outputs);
GeneralConfusionMatrix gcm = result.ToConfusionMatrix(inputs, outputs);
double trainingErrors = result.Training.Mean;
double validationErrors = result.Validation.Mean;
double trainingErrorVar = result.Training.Variance;
double validationErrorVar = result.Validation.Variance;
double trainingErrorPooledVar = result.Training.PooledVariance;
double validationErrorPooledVar = result.Validation.PooledVariance;
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