private List <ImageData> GetPredictions(MultilabelSupportVectorMachine <Linear> machine1, MultilabelSupportVectorMachine <Linear> machine2)
{
var firstDigitInputs = _inputCreator.ConvertToArrays(_bitmaps[0]);
var secondDigitInputs = _inputCreator.ConvertToArrays(_bitmaps[1]);
var firstDigitPredict = machine1.Decide(firstDigitInputs);
var secondDigitPredict = machine2.Decide(secondDigitInputs);
return(_predictionHandler.HandlePredictions(firstDigitPredict, secondDigitPredict, _targetImgPaths));
}
public void RunTest()
{
Accord.Math.Random.Generator.Seed = 0;
// Sample data
// The following is a simple auto association function
// in which each input correspond to its own class. This
// problem should be easily solved using a Linear kernel.
// Sample input data
double[][] inputs =
{
new double[] { 0, 0 },
new double[] { 0, 1 },
new double[] { 1, 0 },
new double[] { 1, 1 },
};
// Outputs for each of the inputs
int[][] outputs =
{
// and or nand xor
new[] { -1, -1, +1, +1 },
new[] { -1, +1, +1, -1 },
new[] { -1, +1, +1, -1 },
new[] { +1, +1, -1, +1 },
};
// Create a new Linear kernel
IKernel linear = new Linear();
// Create a new Multi-class Support Vector Machine for one input,
// using the linear kernel and four disjoint classes.
var machine = new MultilabelSupportVectorMachine(inputs: 2, kernel: linear, classes: 4);
// Create the Multi-class learning algorithm for the machine
var teacher = new MultilabelSupportVectorLearning(machine, inputs, outputs);
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
teacher.Algorithm = (svm, classInputs, classOutputs, i, j) =>
new SequentialMinimalOptimization(svm, classInputs, classOutputs)
{
// Create a hard SVM
Complexity = 10000.0
};
// Run the learning algorithm
double error = teacher.Run();
// only xor is not learnable by
// a hard-margin linear machine
bool[][] pred = machine.Decide(inputs);
bool[][] train = Classes.Decide(outputs);
var and = pred.GetColumn(0);
Assert.IsTrue(and.IsEqual(train.GetColumn(0)));
var or = pred.GetColumn(1);
Assert.IsTrue(or.IsEqual(train.GetColumn(1)));
var nand = pred.GetColumn(2);
Assert.IsTrue(nand.IsEqual(train.GetColumn(2)));
var xor = pred.GetColumn(3);
Assert.IsFalse(xor.IsEqual(train.GetColumn(3)));
Assert.AreEqual(2 / 16.0, error);
}
