public void GaussProcessRegression_test()
{
var X_train = np.arange(0, 100, 1).reshape(-1, 1);
var y_train = np.sin(X_train);
var option = new GaussProcessRegressor.Options();
var GP = new GaussProcessRegressor(option);
var res = GP.Fit(X_train, y_train).Transform(np.array <double>(new double[] { 10 }).reshape(-1, 1)).Item1.reshape(-1, 1);
((double)res[0, 0]).Should().BeApproximately(Math.Sin(10), 0.1);
}
public void RBF_Test()
{
var x1 = np.random.standard_normal(3, 2);
var x2 = np.random.standard_normal(5, 2);
var x1_1 = x1["1,:"];
var x2_1 = x2["1,:"];
var res = GaussProcessRegressor.RBF(x1, x2, 1, 1);
res.shape.Dimensions.Should().BeEquivalentTo(new int[] { 3, 5 });
((double)res[1, 1]).Should().BeApproximately((double)np.exp(-0.5 * np.dot(np.subtract(x1_1, x2_1), np.subtract(x1_1, x2_1).T)), 0.01);
}