org.ddogleg.optimization.functions

Interface FunctionNtoM

All Known Implementing Classes:

CalibPoseAndPointResiduals, CallCounterNtoM, CylinderToPointSignedDistance_F32, CylinderToPointSignedDistance_F64, EvalFuncBadlyScaledBrown.Func, EvalFuncCurveFit.Func, EvalFuncHelicalValley.Func, EvalFuncPowell.Func, EvalFuncRosenbrock.Func, EvalFuncRosenbrockMod.Func, EvalFuncTrigonometric.Func, EvalFuncVariablyDimensioned.Func, FunctionLineDistanceEuclidean, RefineEllipseEuclideanLeastSquares.Error, ResidualsCodecToMatrix, ResidualsEpipolarMatrix, ResidualsEpipolarMatrixN, ResidualsTriangulateSampson, ResidualsTriangulateSimple, SphereToPointSignedDistance_F32, SphereToPointSignedDistance_F64, TestLsToNonLinearDeriv.FuncLS, TrivialLeastSquaresResidual, Zhang99OptimizationFunction

public interface FunctionNtoM

Function which takes in N parameters as input and outputs M elements. The number N is typically determined by the number of parameters in a model, say 2 for a line in 2D (slope and intercept). The number M is determined by the number of observations, for M 2D points the output would be M variables representing the distance of each point from the line.

Method Summary

Modifier and Type	Method and Description
int	getNumOfInputsN() Number of input elements.
int	getNumOfOutputsM() Number of output elements.
void	process(double[] input, double[] output) Processes the input to compute the values found in the output array.

Method Detail

getNumOfInputsN

int getNumOfInputsN()

Number of input elements. Typically the parameters you are optimizing.

Returns:

number of input elements

getNumOfOutputsM

int getNumOfOutputsM()

Number of output elements. Typically the functions that are being optimized.

Returns:

number of output elements

process

void process(double[] input,
             double[] output)

Processes the input to compute the values found in the output array. The output's meaning depends on the application. For least-squares it is the residual error. See UnconstrainedLeastSquares.

The user can modify the input parameters here and the optimizer must use those changes.

Parameters:

input - Parameters for input model.

output - Storage for the output give the model.