public void FittingTest()
{
int[] folds = CrossValidation.Splittings(100, 10);
int[] samples = Matrix.Indices(0, 100);
CrossValidation val = new CrossValidation(folds, 10);
val.RunInParallel = false;
int current = 0;
val.Fitting = (k, trainingSamples, validationSamples) =>
{
Assert.AreEqual(current, k);
Assert.AreEqual(90, trainingSamples.Length);
Assert.AreEqual(10, validationSamples.Length);
int[] trainingSet = samples.Submatrix(trainingSamples);
int[] validationSet = samples.Submatrix(validationSamples);
for (int i = 0; i < trainingSet.Length; i++)
{
Assert.AreEqual(samples[trainingSamples[i]], trainingSet[i]);
}
for (int i = 0; i < validationSet.Length; i++)
{
Assert.AreEqual(samples[validationSamples[i]], validationSet[i]);
}
current++;
return(new CrossValidationValues(k, 2 * k));
};
var result = val.Compute();
Assert.AreEqual(10, current);
Assert.AreEqual(4.5, result.Training.Mean);
Assert.AreEqual(9.0, result.Validation.Mean);
Assert.AreEqual(
2 * result.Training.StandardDeviation,
result.Validation.StandardDeviation);
Assert.AreEqual(val.Folds.Length, result.Training.Sizes.Length);
Assert.AreEqual(val.Folds.Length, result.Validation.Sizes.Length);
for (int i = 0; i < result.Training.Sizes.Length; i++)
{
Assert.AreEqual(90, result.Training.Sizes[i]);
}
for (int i = 0; i < result.Validation.Sizes.Length; i++)
{
Assert.AreEqual(10, result.Validation.Sizes[i]);
}
}
public void SplittingsTest()
{
int[] folds = CrossValidation.Splittings(100, 10);
for (int i = 0; i < 10; i++)
{
int actual = folds.Count(x => x == i);
int expected = 10;
Assert.AreEqual(expected, actual);
}
}
public override Task <GeneralConfusionMatrix> ComputeConfusionMatrixAsync(ClassificationModel classificationModel)
{
return(Task.Factory.StartNew(() =>
{
int numFeatures = classificationModel.FeatureVectors.Count;
double[][] input = new double[numFeatures][];
int[] responses = new int[numFeatures];
for (int featureIndex = 0; featureIndex < classificationModel.FeatureVectors.Count; ++featureIndex)
{
var featureVector = classificationModel.FeatureVectors[featureIndex];
input[featureIndex] = Array.ConvertAll(featureVector.FeatureVector.BandIntensities, s => (double)s / ushort.MaxValue);
responses[featureIndex] = featureVector.FeatureClass;
}
var folds = new int[input.Length][];
var splittings = CrossValidation.Splittings(input.Length, 2);
for (int i = 0; i < 2; ++i)
{
folds[i] = splittings.Find(x => x == i);
}
int[] indicesTrain = folds[0];
int[] indicesValidation = folds[1];
// Lets now grab the training data:
var trainingInputs = input.Get(indicesTrain);
var trainingOutputs = responses.Get(indicesTrain);
// And now the validation data:
var validationInputs = input.Get(indicesValidation);
var validationOutputs = responses.Get(indicesValidation);
// Predict
int[] prediction;
switch (Kernel)
{
case Kernel.Gaussian:
var gaussianLearningKfold = new MulticlassSupportVectorLearning <Gaussian>
{
Kernel = Gaussian.FromGamma(Gamma),
Learner = p => new SequentialMinimalOptimization <Gaussian>
{
UseKernelEstimation = false,
UseComplexityHeuristic = false,
Complexity = Complexity,
Token = CancellationTokenSource.Token,
Tolerance = 0.01
}
};
var svmGaussian = gaussianLearningKfold.Learn(trainingInputs, trainingOutputs);
prediction = svmGaussian.Decide(validationInputs);
break;
case Kernel.Linear:
var linearLearning = new MulticlassSupportVectorLearning <Linear>
{
Learner = p => new LinearDualCoordinateDescent <Linear>
{
Complexity = Complexity,
UseComplexityHeuristic = false,
Token = CancellationTokenSource.Token
}
};
var svmLinear = linearLearning.Learn(trainingInputs, trainingOutputs);
prediction = svmLinear.Decide(validationInputs);
break;
default:
throw new NotImplementedException();
}
GeneralConfusionMatrix confusionMatrix = new GeneralConfusionMatrix(classificationModel.LandCoverTypes.Count, prediction, validationOutputs);
return confusionMatrix;
}));
}