Package org.ddogleg.optimization.wrap
Class Individual_to_CoupledJacobian<S extends DMatrix>
java.lang.Object
org.ddogleg.optimization.wrap.Individual_to_CoupledJacobian<S>
- All Implemented Interfaces:
CoupledJacobian<S>,FunctionInOut
public class Individual_to_CoupledJacobian<S extends DMatrix>
extends Object
implements CoupledJacobian<S>
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Constructor Summary
ConstructorsConstructorDescriptionIndividual_to_CoupledJacobian(FunctionNtoM func, FunctionNtoMxN<S> jacobian) -
Method Summary
Modifier and TypeMethodDescriptionvoidcomputeFunctions(double[] output) voidcomputeJacobian(S jacobian) Processes the input parameters into the 2D Jacobian matrix.intNumber of input elements.intNumber of output elements.voidsetInput(double[] x) Specifies the input parameters.
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Constructor Details
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Individual_to_CoupledJacobian
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Method Details
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getNumOfInputsN
public int getNumOfInputsN()Description copied from interface:FunctionInOutNumber of input elements. Typically, the parameters you are optimizing.- Specified by:
getNumOfInputsNin interfaceFunctionInOut- Returns:
- number of input elements
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getNumOfOutputsM
public int getNumOfOutputsM()Description copied from interface:FunctionInOutNumber of output elements. Typically, the functions that are being optimized.- Specified by:
getNumOfOutputsMin interfaceFunctionInOut- Returns:
- number of output elements
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setInput
public void setInput(double[] x) Description copied from interface:CoupledJacobianSpecifies the input parameters. The user can modify these values and they will be modified inside the optimization function too.- Specified by:
setInputin interfaceCoupledJacobian<S extends DMatrix>- Parameters:
x- Optimization parameters.
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computeFunctions
public void computeFunctions(double[] output) - Specified by:
computeFunctionsin interfaceCoupledJacobian<S extends DMatrix>
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computeJacobian
Description copied from interface:CoupledJacobianProcesses the input parameters into the 2D Jacobian matrix. The matrix has a dimension of M rows and N columns and is formatted as a row major 1D-array. EJML can be used to provide a matrix wrapper around the output array: DenseMatrix J = DenseMatrix.wrap(m,n,output);
The user can modify the input parameters here and the optimizer must use those changes.
- Specified by:
computeJacobianin interfaceCoupledJacobian<S extends DMatrix>- Parameters:
jacobian- matrix with M rows and N columns.
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