protected void UpdateInterpolation()
{
int count = _keyFrames.Count;
var xs = new double[count];
var ys = new double[count];
for (int i = 0; i < count; ++i)
{
xs[i] = (double)_keyFrames[i].time;
ys[i] = (double)_keyFrames[i].value;
}
if (count <= 1)
{
_interpolation = StepInterpolation.Interpolate(xs, ys);
}
else if (count <= 2)
{
_interpolation = LinearSpline.Interpolate(xs, ys);
}
else if (count <= 3)
{
_interpolation = MathNet.Numerics.Interpolate.Polynomial(xs, ys);
}
else if (count <= 4)
{
_interpolation = CubicSpline.InterpolateNatural(xs, ys);
}
else
{
_interpolation = CubicSpline.InterpolateAkima(xs, ys);
}
}
public void FitsAtSamplePoints()
{
IInterpolation ip = new StepInterpolation(_t, _y);
for (int i = 0; i < _y.Length; i++)
{
Assert.AreEqual(_y[i], ip.Interpolate(_t[i]), "A Exact Point " + i);
}
}
/// <summary>
/// Create a step-interpolation based on arbitrary points.
/// </summary>
/// <param name="points">The sample points t.</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.StepInterpolation.InterpolateSorted
/// instead, which is more efficient.
/// </remarks>
public static IInterpolation Step(IEnumerable <double> points, IEnumerable <double> values)
{
return(StepInterpolation.Interpolate(points, values));
}
public virtual double Interpolate_Step(double[] x, double[] y, double xValue)
{
return(StepInterpolation.Interpolate(x, y).Interpolate(xValue));
}
public void FewSamples()
{
Assert.That(() => StepInterpolation.Interpolate(new double[0], new double[0]), Throws.ArgumentException);
Assert.That(StepInterpolation.Interpolate(new[] { 1.0 }, new[] { 2.0 }).Interpolate(1.0), Is.EqualTo(2.0));
}