public void CrossvalidationConstructorTest2()
{
Accord.Math.Tools.SetupGenerator(0);
// This is a sample code on how to use Cross-Validation
// to assess the performance of Hidden Markov Models.
// Declare some testing data
int[][] inputs = new int[][]
{
new int[] { 0,1,1,0 }, // Class 0
new int[] { 0,0,1,0 }, // Class 0
new int[] { 0,1,1,1,0 }, // Class 0
new int[] { 0,1,1,1,0 }, // Class 0
new int[] { 0,1,1,0 }, // Class 0
new int[] { 0,1,1,1,0 }, // Class 0
new int[] { 0,1,1,1,0 }, // Class 0
new int[] { 0,1,0,1,0 }, // Class 0
new int[] { 0,1,0 }, // Class 0
new int[] { 0,1,1,0 }, // Class 0
new int[] { 1,0,0,1 }, // Class 1
new int[] { 1,1,0,1 }, // Class 1
new int[] { 1,0,0,0,1 }, // Class 1
new int[] { 1,0,1 }, // Class 1
new int[] { 1,1,0,1 }, // Class 1
new int[] { 1,0,1 }, // Class 1
new int[] { 1,0,0,1 }, // Class 1
new int[] { 1,0,0,0,1 }, // Class 1
new int[] { 1,0,1 }, // Class 1
new int[] { 1,0,0,0,1 }, // Class 1
};
int[] outputs = new int[]
{
0,0,0,0,0,0,0,0,0,0, // First 10 sequences are of class 0
1,1,1,1,1,1,1,1,1,1, // Last 10 sequences are of class 1
};
// Create a new Cross-validation algorithm passing the data set size and the number of folds
var crossvalidation = new CrossValidation<HiddenMarkovClassifier>(size: inputs.Length, folds: 3);
// Define a fitting function using Support Vector Machines. The objective of this
// function is to learn a SVM in the subset of the data indicated by cross-validation.
crossvalidation.Fitting = delegate(int k, int[] indicesTrain, int[] indicesValidation)
{
// The fitting function is passing the indices of the original set which
// should be considered training data and the indices of the original set
// which should be considered validation data.
// Lets now grab the training data:
var trainingInputs = inputs.Submatrix(indicesTrain);
var trainingOutputs = outputs.Submatrix(indicesTrain);
// And now the validation data:
var validationInputs = inputs.Submatrix(indicesValidation);
var validationOutputs = outputs.Submatrix(indicesValidation);
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to two symbols (0 or 1)
int symbols = 2;
// Nested models will have two states each
int[] states = new int[] { 2, 2 };
// Creates a new Hidden Markov Model Classifier with the given parameters
HiddenMarkovClassifier classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(trainingInputs, trainingOutputs);
double trainingError = teacher.ComputeError(trainingInputs, trainingOutputs);
// Now we can compute the validation error on the validation data:
double validationError = teacher.ComputeError(validationInputs, validationOutputs);
// Return a new information structure containing the model and the errors achieved.
return new CrossValidationValues<HiddenMarkovClassifier>(classifier, trainingError, validationError);
};
// Compute the cross-validation
var result = crossvalidation.Compute();
// Finally, access the measured performance.
double trainingErrors = result.Training.Mean;
double validationErrors = result.Validation.Mean;
Assert.AreEqual(3, crossvalidation.K);
Assert.AreEqual(0, result.Training.Mean);
Assert.AreEqual(0.055555555555555552, result.Validation.Mean);
Assert.AreEqual(3, crossvalidation.Folds.Length);
Assert.AreEqual(3, result.Models.Length);
}
