Class LinearDouble<T extends VectorDouble<T>>

java.lang.Object
gaiasky.util.math.LinearDouble<T>
Type Parameters:
T - The type of the control points.
All Implemented Interfaces:
PathDouble<T>
public class LinearDouble<T extends VectorDouble<T>> extends Object implements PathDouble<T>
Implementation of linear interpolation between control points using double precision.

  • Field Details

  • Constructor Details

    • LinearDouble

      public LinearDouble(T[] controlPoints)

  • Method Details

    • set

      public LinearDouble<T> set(T[] controlPoints)

    • derivativeAt

      public T derivativeAt(T out, double t)
      Specified by:
      derivativeAt in interface PathDouble<T extends VectorDouble<T>>

    • valueAt

      public T valueAt(T out, double t)
      Specified by:
      valueAt in interface PathDouble<T extends VectorDouble<T>>
      Returns:
      The value of the path at t where 0≤t≤1

    • approximate

      public double approximate(T v)
      Specified by:
      approximate in interface PathDouble<T extends VectorDouble<T>>
      Returns:
      The approximated value (between 0 and 1) on the path which is closest to the specified value. Note that the implementation of this method might be optimized for speed against precision, see PathDouble.locate(Object) for a more precise (but more intensive) method.

    • locate

      public double locate(T v)
      Specified by:
      locate in interface PathDouble<T extends VectorDouble<T>>
      Returns:
      The precise location (between 0 and 1) on the path which is closest to the specified value. Note that the implementation of this method might be CPU intensive, see PathDouble.approximate(Object) for a faster (but less precise) method.

    • approxLength

      public double approxLength(int samples)
      Specified by:
      approxLength in interface PathDouble<T extends VectorDouble<T>>
      Parameters:
      samples - The amount of divisions used to approximate length. Higher values will produce more precise results, but will be more CPU intensive.
      Returns:
      An approximated length of the spline through sampling the curve and accumulating the Euclidean distances between the sample points.