Prediction with FanChenLinSupportVectorRegression - machine-learning

Question

I want to use FanChenLinSupportVectorRegression in accord.net. The predictions are correct for the learning inputs but the model doesn't work for other inputs. I don't understand my mistake?
In the below example, the first prediction is good, however if we want to predict a configuration not learned, the prediction is always the same regardless of the inputs:
// Declare a very simple regression problem
// with only 2 input variables (x and y):
double[][] inputs =
{
new[] { 3.0, 1.0 },
new[] { 7.0, 1.0 },
new[] { 3.0, 1.0 },
new[] { 3.0, 2.0 },
new[] { 6.0, 1.0 },
};
// The task is to output a weighted sum of those numbers
// plus an independent constant term: 7.4x + 1.1y + 42
double[] outputs =
{
7.4*3.0 + 1.1*1.0 + 42.0,
7.4*7.0 + 1.1*1.0 + 42.0,
7.4*3.0 + 1.1*1.0 + 42.0,
7.4*3.0 + 1.1*2.0 + 42.0,
7.4*6.0 + 1.1*1.0 + 42.0,
};
// Create a LibSVM-based support vector regression algorithm
var teacher = new FanChenLinSupportVectorRegression<Gaussian>()
{
Tolerance = 1e-5,
// UseKernelEstimation = true,
// UseComplexityHeuristic = true
Complexity = 10000,
Kernel = new Gaussian(0.1)
};
// Use the algorithm to learn the machine
var svm = teacher.Learn(inputs, outputs);
// Get machine's predictions for inputs
double[] prediction = svm.Score(inputs);
// It's OK the predictions are correct
double[][] inputs1 =
{
new[] { 2.0, 2.0 },
new[] { 5.0, 1.0 },
};
prediction = svm.Score(inputs1);
// predictions are wrong! what is my mistake?