java.lang.Object
- org.esa.snap.core.util.math.Approximator

```
public class Approximator
extends Object
```
A utility class which can be used to find approximation functions for a given dataset.

Constructor Summary

Constructors
Constructor Description

Approximator()

Method Summary

All Methods Static Methods Concrete Methods
Modifier and Type	Method	Description
`static void`	`approximateFX(double[][] data, int[] indices, FX[] f, double[] c)`	Solves a linear equation system with each term having the form c * f(x).
`static void`	`approximateFXY(double[][] data, int[] indices, FXY[] f, double[] c)`	Solves a linear equation system with each term having the form c * f(x,y).
`static double[]`	`computeErrorStatistics(double[][] data, int[] indices, FXY[] f, double[] c)`	Returns the root mean square error (RMSE) for the approximation of the given data with a function given by z(x,y) = `c[0]f[0](x,y) + c[1]f[1](x,y) + c[2]*f[2](x,y) + ...`
`static double`	`computeRMSE(double[][] data, int[] indices, FXY[] f, double[] c)`	Returns the root mean square error (RMSE) for the approximation of the given data with a function given by z(x,y) = `c[0]f[0](x,y) + c[1]f[1](x,y) + c[2]*f[2](x,y) + ...`
`static double`	`computeY(FX[] f, double[] c, double x)`	Computes y(x) = sum(c[i] f[i](x), i = 0, n - 1)*.
`static double`	`computeZ(FXY[] f, double[] c, double x, double y)`	Computes z(x,y) = sum(c[i] f[i](x,y), i = 0, n - 1)*.
`static double`	`getRMSE(double[][] data, int[] indices, FX[] f, double[] c)`	Returns the root mean square error (RMSE) for the approximation of the given data with a function given by y'(x) = `c[0]f[0](x) + c[1]f[1](x) + c[2]*f[2](x) + ...`
`static void`	`solve1(double[][] a, double[] b, double[] c)`	Solves the matrix equation A * C = B for given A,B by performing the operation C = A.solve(B).

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - Approximator
```
public Approximator()
```
- Method Detail
  - approximateFX
```
public static void approximateFX(double[][] data,
                                 int[] indices,
                                 FX[] f,
                                 double[] c)
```
    Solves a linear equation system with each term having the form c * f(x). The method finds the coefficients c[0] to c[n-1] with n = f.length for an approximation function y'(x) = c[0]*f[0](x) + c[1]*f[1](x) + c[2]*f[2](x) + ... + c[n-1]*f[n-1](x) which approximates the given data vector x_i=data[i][0], y_i=data[i][1] with i = 0 to data.length-1.
    
    Parameters:
    
    data - an array of values of the form {{x1,y1}, {x2,y2}, {x3,y3}, ...}
    
    indices - the co-ordinate indices vector, determining the indices of x,y within data. If null then indices defaults to {0, 1}.
    
    f - the function vector, each function has the form y=f(x)
    
    c - the resulting coefficient vector, must have the same size as the function vector
  - approximateFXY
```
public static void approximateFXY(double[][] data,
                                  int[] indices,
                                  FXY[] f,
                                  double[] c)
```
    Solves a linear equation system with each term having the form c * f(x,y). The method finds the coefficients c[0] to c[n-1] with n = f.length for an approximation function z'(x,y) = c[0]*f[0](x,y) + c[1]*f[1](x,y) + c[2]*f[2](x,y) + ... + c[n-1]*f[n-1](x,y) which approximates the given data vector x_i=data[i][0], y_i=data[i][1], z_i=data[i][2] with i = 0 to data.length-1.
    
    Parameters:
    
    data - an array of values of the form {{x1,y1,z1}, {x2,y2,z2}, {x3,y3,z3}, ...}
    
    indices - the co-ordinate indices vector, determining the indices of x,y,z within data. If null then indices defaults to {0, 1, 2}.
    
    f - the function vector, each function has the form z=f(x,y)
    
    c - the resulting coefficient vector, must have the same size as the function vector
  - getRMSE
```
public static double getRMSE(double[][] data,
                             int[] indices,
                             FX[] f,
                             double[] c)
```
    Returns the root mean square error (RMSE) for the approximation of the given data with a function given by y'(x) = c[0]*f[0](x) + c[1]*f[1](x) + c[2]*f[2](x) + ... + c[n-1]*f[n-1](x).
    
    Parameters:
    
    data - an array of values of the form {{x1,y1}, {x2,y2}, {x3,y3}, ...}
    
    indices - the co-ordinate indices vector, determining the indices of x,y within data. If null then indices defaults to {0, 1}.
    
    f - the function vector, each function has the form y=f(x)
    
    c - the coefficient vector, must have the same size as the function vector
  - computeRMSE
```
public static double computeRMSE(double[][] data,
                                 int[] indices,
                                 FXY[] f,
                                 double[] c)
```
    Returns the root mean square error (RMSE) for the approximation of the given data with a function given by z(x,y) = c[0]*f[0](x,y) + c[1]*f[1](x,y) + c[2]*f[2](x,y) + ... + c[n-1]*f[n-1](x,y).
    
    Parameters:
    
    data - an array of values of the form {{x1,y1,z1}, {x2,y2,z1}, {x3,y3,z1}, ...}
    
    indices - the co-ordinate indices vector, determining the indices of x,y,z within data. If null then indices defaults to {0, 1, 2}.
    
    f - the function vector, each function has the form z=f(x,y)
    
    c - the coefficient vector, must have the same size as the function vector
  - computeErrorStatistics
```
public static double[] computeErrorStatistics(double[][] data,
                                              int[] indices,
                                              FXY[] f,
                                              double[] c)
```
    Returns the root mean square error (RMSE) for the approximation of the given data with a function given by z(x,y) = c[0]*f[0](x,y) + c[1]*f[1](x,y) + c[2]*f[2](x,y) + ... + c[n-1]*f[n-1](x,y).
    
    Parameters:
    
    data - an array of values of the form {{x1,y1,z1}, {x2,y2,z1}, {x3,y3,z1}, ...}
    
    indices - the co-ordinate indices vector, determining the indices of x,y,z within data. If null then indices defaults to {0, 1, 2}.
    
    f - the function vector, each function has the form z=f(x,y)
    
    c - the coefficient vector, must have the same size as the function vector
  - computeY
```
public static double computeY(FX[] f,
                              double[] c,
                              double x)
```
    Computes y(x) = sum(c[i] * f[i](x), i = 0, n - 1).
    
    Parameters:
    
    f - the function vector
    
    c - the coeffcient vector
    
    x - the x value
    
    Returns:
    
    the y value
  - computeZ
```
public static double computeZ(FXY[] f,
                              double[] c,
                              double x,
                              double y)
```
    Computes z(x,y) = sum(c[i] * f[i](x,y), i = 0, n - 1).
    
    Parameters:
    
    f - the function vector
    
    c - the coeffcient vector
    
    x - the x value
    
    y - the y value
    
    Returns:
    
    the z value
  - solve1
```
public static void solve1(double[][] a,
                          double[] b,
                          double[] c)
```
    Solves the matrix equation A * C = B for given A,B by performing the operation C = A.solve(B).
    
    Parameters:
    
    a - matrix A
    
    b - matrix B
    
    c - result matrix C

Class Approximator

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

Approximator

Method Detail

approximateFX

approximateFXY

getRMSE

computeRMSE

computeErrorStatistics

computeY

computeZ

solve1