public void SplitSetConstructorTest1()
{
Accord.Math.Random.Generator.Seed = 0;
// This is a sample code on how to use two split sets
// to assess the performance of Support Vector Machines.
// Consider the example binary data. We will be trying
// to learn a XOR problem and see how well does SVMs
// perform on this data.
double[][] data =
{
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
new double[] { -1, -1 }, new double[] { 1, -1 },
new double[] { -1, 1 }, new double[] { 1, 1 },
};
int[] xor = // result of xor for the sample input data
{
-1, 1,
1, -1,
-1, 1,
1, -1,
-1, 1,
1, -1,
-1, 1,
1, -1,
};
// Create a new split set validation algorithm passing the set size and the split set proportion
var splitset = new SplitSetValidation<KernelSupportVectorMachine>(size: data.Length, proportion: 0.4);
// 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 the split sets.
splitset.Fitting = delegate(int[] indicesTrain)
{
// 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 = data.Submatrix(indicesTrain);
var trainingOutputs = xor.Submatrix(indicesTrain);
// Create a Kernel Support Vector Machine to operate on the set
var svm = new KernelSupportVectorMachine(new Polynomial(2), 2);
// Create a training algorithm and learn the training data
var smo = new SequentialMinimalOptimization(svm, trainingInputs, trainingOutputs);
double trainingError = smo.Run();
// Compute results for the training set
int[] computedOutputs = trainingInputs.Apply(svm.Compute).Apply(Math.Sign);
// Compute the absolute error
int[] errors = (computedOutputs.Subtract(trainingOutputs)).Abs();
// Retrieve error statistics
double mean = errors.Mean();
double variance = errors.Variance();
// Return a new information structure containing the model and the errors.
return SplitSetStatistics.Create(svm, trainingInputs.Length, mean, variance);
};
splitset.Evaluation = delegate(int[] indicesValidation, KernelSupportVectorMachine svm)
{
// Lets now grab the training data:
var validationInputs = data.Submatrix(indicesValidation);
var validationOutputs = xor.Submatrix(indicesValidation);
// Compute results for the validation set
int[] computedOutputs = validationInputs.Apply(svm.Compute).Apply(Math.Sign);
// Compute the absolute error
int[] errors = (computedOutputs.Subtract(validationOutputs)).Abs();
// Retrieve error statistics
double mean = errors.Mean();
double variance = errors.Variance();
// Return a new information structure containing the model and the errors.
return SplitSetStatistics.Create(svm, validationInputs.Length, mean, variance);
};
// Compute the bootstrap estimate
var result = splitset.Compute();
// Finally, access the measured performance.
double trainingErrors = result.Training.Value;
double validationErrors = result.Validation.Value;
Assert.AreEqual(0, trainingErrors);
Assert.AreEqual(0, validationErrors);
}
