public void RunTest()
{
Accord.Math.Tools.SetupGenerator(0);
// Example regression problem. Suppose we are trying
// to model the following equation: f(x, y) = 2x + y
double[][] inputs = // (x, y)
{
new double[] { 0, 1 }, // 2*0 + 1 = 1
new double[] { 4, 3 }, // 2*4 + 3 = 11
new double[] { 8, -8 }, // 2*8 - 8 = 8
new double[] { 2, 2 }, // 2*2 + 2 = 6
new double[] { 6, 1 }, // 2*6 + 1 = 13
new double[] { 5, 4 }, // 2*5 + 4 = 14
new double[] { 9, 1 }, // 2*9 + 1 = 19
new double[] { 1, 6 }, // 2*1 + 6 = 8
};
double[] outputs = // f(x, y)
{
1, 11, 8, 6, 13, 14, 19, 8
};
// Create a new linear Support Vector Machine
var machine = new SupportVectorMachine(inputs: 2);
// Create the linear regression coordinate descent teacher
var learn = new LinearRegressionCoordinateDescent(machine, inputs, outputs)
{
Complexity = 10000000,
Epsilon = 1e-10
};
// Run the learning algorithm
double error = learn.Run();
// Compute the answer for one particular example
double fxy = machine.Compute(inputs[0]); // 1.000
// Check for correct answers
double[] answers = new double[inputs.Length];
for (int i = 0; i < answers.Length; i++)
{
answers[i] = machine.Compute(inputs[i]);
}
Assert.AreEqual(1.0, fxy, 1e-5);
for (int i = 0; i < outputs.Length; i++)
{
Assert.AreEqual(outputs[i], answers[i], 1e-2);
}
}
public void learn_test()
{
Accord.Math.Random.Generator.Seed = 0;
#region doc_learn
// Example regression problem. Suppose we are trying
// to model the following equation: f(x, y) = 2x + y
double[][] inputs = // (x, y)
{
new double[] { 0, 1 }, // 2*0 + 1 = 1
new double[] { 4, 3 }, // 2*4 + 3 = 11
new double[] { 8, -8 }, // 2*8 - 8 = 8
new double[] { 2, 2 }, // 2*2 + 2 = 6
new double[] { 6, 1 }, // 2*6 + 1 = 13
new double[] { 5, 4 }, // 2*5 + 4 = 14
new double[] { 9, 1 }, // 2*9 + 1 = 19
new double[] { 1, 6 }, // 2*1 + 6 = 8
};
double[] outputs = // f(x, y)
{
1, 11, 8, 6, 13, 14, 19, 8
};
// Create the linear regression coordinate descent teacher
var learn = new LinearRegressionCoordinateDescent()
{
Complexity = 10000000,
Epsilon = 1e-10
};
// Run the learning algorithm
var svm = learn.Learn(inputs, outputs);
// Compute the answer for one particular example
double fxy = svm.Score(inputs[0]); // 1.000
// Check for correct answers
double[] answers = svm.Score(inputs);
#endregion
Assert.AreEqual(1.0, fxy, 1e-5);
for (int i = 0; i < outputs.Length; i++)
{
Assert.AreEqual(outputs[i], answers[i], 1e-2);
}
}
