public void RunTest1()
{
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] outputs =
{
-1,
1,
1,
-1
};
KernelSupportVectorMachine svm = new KernelSupportVectorMachine(new Gaussian(3.6), 2);
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(svm, inputs, outputs);
double error1 = smo.Run();
Assert.AreEqual(0, error1);
double[] distances = new double[outputs.Length];
for (int i = 0; i < outputs.Length; i++)
{
int y = svm.Compute(inputs[i], out distances[i]);
Assert.AreEqual(outputs[i], y);
}
ProbabilisticOutputLearning target = new ProbabilisticOutputLearning(svm, inputs, outputs);
double ll0 = target.LogLikelihood(inputs, outputs);
double ll1 = target.Run();
double ll2 = target.LogLikelihood(inputs, outputs);
Assert.AreEqual(3.4256203116918824, ll1);
Assert.AreEqual(ll1, ll2);
Assert.IsTrue(ll1 > ll0);
double[] probs = new double[outputs.Length];
for (int i = 0; i < outputs.Length; i++)
{
int y = svm.Compute(inputs[i], out probs[i]);
Assert.AreEqual(outputs[i], y);
}
Assert.AreEqual(0.25, probs[0], 1e-5);
Assert.AreEqual(0.75, probs[1], 1e-5);
Assert.AreEqual(0.75, probs[2], 1e-5);
Assert.AreEqual(0.25, probs[3], 1e-5);
foreach (var p in probs)
Assert.IsFalse(Double.IsNaN(p));
}
public void RunTest3()
{
// Example XOR problem
double[][] inputs =
{
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)
};
// Dichotomy SVM outputs should be given as [-1;+1]
int[] labels =
{
1, -1, -1, 1
};
// Create a Kernel Support Vector Machine for the given inputs
KernelSupportVectorMachine svm = new KernelSupportVectorMachine(new Gaussian(0.1), inputs[0].Length);
// Instantiate a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(svm, inputs, labels);
// Set up the learning algorithm
smo.Complexity = 1.0;
// Run the learning algorithm
double error = smo.Run();
// Instantiate the probabilistic learning calibration
ProbabilisticOutputLearning calibration = new ProbabilisticOutputLearning(svm, inputs, labels);
// Run the calibration algorithm
double loglikelihood = calibration.Run();
// Compute the decision output for one of the input vectors,
// while also retrieving the probability of the answer
double probability;
int decision = svm.Compute(inputs[0], out probability);
// At this point, decision is +1 with a probability of 75%
Assert.AreEqual(1, decision);
Assert.AreEqual(0.74999975815069375, probability);
}
