public void FitsAtSamplePoints()
{
IInterpolation it = Barycentric.InterpolatePolynomialEquidistant(Tmin, Tmax, _y);
for (int i = 0; i < _y.Length; i++)
{
Assert.AreEqual(_y[i], it.Interpolate(i), "A Exact Point " + i);
}
}
public void SupportsLinearCase(int samples)
{
double[] x, y, xtest, ytest;
LinearInterpolationCase.Build(out x, out y, out xtest, out ytest, samples);
IInterpolation it = Barycentric.InterpolatePolynomialEquidistant(x, y);
for (int i = 0; i < xtest.Length; i++)
{
Assert.AreEqual(ytest[i], it.Interpolate(xtest[i]), 1e-12, "Linear with {0} samples, sample {1}", samples, i);
}
}
/// <summary>
/// Create a barycentric polynomial interpolation where the given sample points are equidistant.
/// </summary>
/// <param name="points">The sample points t, must be equidistant.</param>
/// <param name="values">The sample point values x(t).</param>
/// <returns>
/// An interpolation scheme optimized for the given sample points and values,
/// which can then be used to compute interpolations and extrapolations
/// on arbitrary points.
/// </returns>
/// <remarks>
/// if your data is already sorted in arrays, consider to use
/// MathNet.Numerics.Interpolation.Barycentric.InterpolatePolynomialEquidistantSorted
/// instead, which is more efficient.
/// </remarks>
public static IInterpolation PolynomialEquidistant(IEnumerable <double> points, IEnumerable <double> values)
{
return(Barycentric.InterpolatePolynomialEquidistant(points, values));
}
public void FitsAtArbitraryPoints(double t, double x, double maxAbsoluteError)
{
IInterpolation it = Barycentric.InterpolatePolynomialEquidistant(Tmin, Tmax, _y);
Assert.AreEqual(x, it.Interpolate(t), maxAbsoluteError, "Interpolation at {0}", t);
}
public void FewSamples()
{
Assert.That(() => Barycentric.InterpolatePolynomialEquidistant(new double[0], new double[0]), Throws.ArgumentException);
Assert.That(Barycentric.InterpolatePolynomialEquidistant(new[] { 1.0 }, new[] { 2.0 }).Interpolate(1.0), Is.EqualTo(2.0));
}