/// <summary>
/// Updates the underlying interpolation mode, if needed.
/// </summary>
private void UpdateModel()
{
switch (this.interpolationType)
{
// Right now only the Least Squares requires this operation to be done.
case EInterpolationType.LEAST_SQUARES:
this.quadraticModel = new Fairmat.Optimization.QuadraticModel();
// Unroll matrix and coordinate vectors in order to make it suitable
// for the Quadratic model implementation.
int n = this.values.R * this.values.C;
Matrix xy = new Matrix(n, 2);
Vector z = new Vector(n);
int count = 0;
for (int x = 0; x < this.coordinatesX.Length; x++)
{
for (int y = 0; y < this.coordinatesY.Length; y++)
{
xy[count, Range.All] = ((Matrix) new Vector()
{
this[x, -1], this[-1, y]
}).T;
z[count] = this[x, y];
count++;
}
}
this.quadraticModel.Estimate(xy.ToArray() as double[, ], z.ToArray() as double[]);
break;
default:
break;
}
}
/// <summary>
/// Updates the underlying interpolation mode, if needed.
/// </summary>
private void UpdateModel()
{
switch (this.interpolationType)
{
// Right now only the Least Squares requires this operation to be done.
case EInterpolationType.LEAST_SQUARES:
this.quadraticModel = new Fairmat.Optimization.QuadraticModel();
// Unroll matrix and coordinate vectors in order to make it suitable
// for the Quadratic model implementation.
int n = this.values.R * this.values.C;
Matrix xy = new Matrix(n, 2);
Vector z = new Vector(n);
int count = 0;
for (int x = 0; x < this.coordinatesX.Length; x++)
{
for (int y = 0; y < this.coordinatesY.Length; y++)
{
xy[count, Range.All] = ((Matrix)new Vector() { this[x, -1], this[-1, y] }).T;
z[count] = this[x, y];
count++;
}
}
this.quadraticModel.Estimate(xy.ToArray() as double[,], z.ToArray() as double[]);
break;
default:
break;
}
}
