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Showing content from http://accord-framework.net/docs/html/T_Accord_MachineLearning_Performance_CrossValidation_3.htm below:

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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