public void TestKNN(double[][] inputs, int[] outputs, int kValue)
{
var crossValidation = new CrossValidation(inputs.Length, KNNCVFolds);
crossValidation.Fitting = delegate(int k, int[] indicesTrain, int[] indicesValidation)
{
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);
var sw = Stopwatch.StartNew();
var knn = new KNN();
knn.TrainKNN(trainingInputs, trainingOutputs, kValue);
sw.Stop();
//addToTestResultBox("Training for: " + sw.ElapsedMilliseconds + "ms");
var error = knn.ComputeError(validationInputs, validationOutputs);
return(new CrossValidationValues(knn, 0, error));
};
// Compute the cross-validation
var result = crossValidation.Compute();
// Finally, access the measured performance.
var trainingErrors = result.Training.Mean;
var validationErrors = result.Validation.Mean;
textBoxTestResults.AppendText("Finished with " + trainingErrors + " training errors and " + validationErrors + " validation errors" + Environment.NewLine);
}
private void buttonClassify_Click(object sender, EventArgs e)
{
if (selectedAlgorithm.Equals(Algorithms.KNN))
{
textBoxResults.AppendText("Starting classification with kNN where k is " + classK + Environment.NewLine);
if (img == null)
{
textBoxResults.AppendText("ERROR: Please choose an image first" + Environment.NewLine);
}
else
{
var knn = new KNN();
string result;
textBoxResults.AppendText("Running training function..." + Environment.NewLine);
knn.TrainKNN(inputs, outputs, classK);
result = knn.Classify(imageProcessor.ProcessImages(img));
textBoxResults.AppendText("Result: " + result + Environment.NewLine);
}
}
else if (selectedAlgorithm.Equals(Algorithms.SVM))
{
textBoxResults.AppendText("Starting classification with SVM" + Environment.NewLine);
if (img == null)
{
textBoxResults.AppendText("ERROR: Please choose an image first" + Environment.NewLine);
}
else
{
var svm = new SVM();
string result;
textBoxResults.AppendText("Running training function..." + Environment.NewLine);
svm.TrainSVM(new RationalQuadratic(1), 3, inputs, outputs);
result = svm.Classify(imageProcessor.ProcessImages(img));
textBoxResults.AppendText("Result: " + result + Environment.NewLine);
}
}
}