public void learn_test()
{
#region doc_learn
double[][] inputs = // Example XOR problem
{
new double[] { 0, 0 }, // 0 xor 0: 1 (label +1)
new double[] { 0, 1 }, // 0 xor 1: 0 (label -1)
new double[] { 1, 0 }, // 1 xor 0: 0 (label -1)
new double[] { 1, 1 } // 1 xor 1: 1 (label +1)
};
int[] outputs = // XOR outputs
{
1, 0, 0, 1
};
// Instantiate a new SMO learning algorithm for SVMs
var smo = new SequentialMinimalOptimization<Gaussian>()
{
Kernel = new Gaussian(0.1),
Complexity = 1.0
};
// Learn a SVM using the algorithm
var svm = smo.Learn(inputs, outputs);
// Predict labels for each input sample
bool[] predicted = svm.Decide(inputs);
// Compute classification error
double error = new ZeroOneLoss(outputs).Loss(predicted);
// Instantiate the probabilistic calibration (using Platt's scaling)
var calibration = new ProbabilisticOutputCalibration<Gaussian>(svm);
// Run the calibration algorithm
calibration.Learn(inputs, outputs); // returns the same machine
// Predict probabilities of each input sample
double[] probabilities = svm.Probability(inputs);
// Compute the error based on a hard decision
double loss = new BinaryCrossEntropyLoss(outputs).Loss(probabilities);
// Compute the decision output for one of the input vectors,
// while also retrieving the probability of the answer
bool decision;
double probability = svm.Probability(inputs[0], out decision);
#endregion
// At this point, decision is +1 with a probability of 75%
Assert.AreEqual(true, decision);
Assert.AreEqual(0, error);
Assert.AreEqual(5.5451735748925355, loss);
Assert.AreEqual(0.74999975815069375, probability, 1e-10);
Assert.IsTrue(svm.IsProbabilistic);
Assert.AreEqual(-1.0986109988055595, svm.Weights[0]);
Assert.AreEqual(1.0986109988055595, svm.Weights[1]);
Assert.AreEqual(-1.0986109988055595, svm.Weights[2]);
Assert.AreEqual(1.0986109988055595, svm.Weights[3]);
}
