@Deprecated public abstract class AbstractLeastSquaresOptimizer extends BaseAbstractMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction> implements DifferentiableMultivariateVectorOptimizer
optimize and assumes that the rows of that matrix iterate on the model
 functions while the columns iterate on the parameters; thus, the numbers
 of rows is equal to the dimension of the
 Target while
 the number of columns is equal to the dimension of the
 InitialGuess.| Modifier and Type | Field and Description | 
|---|---|
| protected int | colsDeprecated. 
 As of 3.1. | 
| protected double | costDeprecated. 
 As of 3.1. Field to become "private" in 4.0.
 Please use  setCost(double). | 
| protected double[] | objectiveDeprecated. 
 As of 3.1. | 
| protected double[] | pointDeprecated. 
 As of 3.1. | 
| protected int | rowsDeprecated. 
 As of 3.1. | 
| protected double[][] | weightedResidualJacobianDeprecated. 
 As of 3.1. To be removed in 4.0. Please use
  computeWeightedJacobian(double[])instead. | 
| protected double[] | weightedResidualsDeprecated. 
 As of 3.1. | 
evaluations| Modifier | Constructor and Description | 
|---|---|
| protected  | AbstractLeastSquaresOptimizer()Deprecated. 
 | 
| protected  | AbstractLeastSquaresOptimizer(ConvergenceChecker<PointVectorValuePair> checker)Deprecated.  | 
| Modifier and Type | Method and Description | 
|---|---|
| protected double | computeCost(double[] residuals)Deprecated.  Computes the cost. | 
| double[][] | computeCovariances(double[] params,
                  double threshold)Deprecated.  Get the covariance matrix of the optimized parameters. | 
| protected double[] | computeResiduals(double[] objectiveValue)Deprecated.  Computes the residuals. | 
| double[] | computeSigma(double[] params,
            double covarianceSingularityThreshold)Deprecated.  Computes an estimate of the standard deviation of the parameters. | 
| protected RealMatrix | computeWeightedJacobian(double[] params)Deprecated.  Computes the Jacobian matrix. | 
| double | getChiSquare()Deprecated.  Get a Chi-Square-like value assuming the N residuals follow N
 distinct normal distributions centered on 0 and whose variances are
 the reciprocal of the weights. | 
| double[][] | getCovariances()Deprecated. 
 As of 3.1. Please use  computeCovariances(double[],double)instead. | 
| double[][] | getCovariances(double threshold)Deprecated. 
 As of 3.1. Please use  computeCovariances(double[],double)instead. | 
| int | getJacobianEvaluations()Deprecated.  | 
| double | getRMS()Deprecated.  Get the Root Mean Square value. | 
| RealMatrix | getWeightSquareRoot()Deprecated.  Gets the square-root of the weight matrix. | 
| double[] | guessParametersErrors()Deprecated. 
 as of version 3.1,  computeSigma(double[],double)should be used
 instead. It should be emphasized thatguessParametersErrorsandcomputeSigmaare not strictly equivalent. | 
| PointVectorValuePair | optimize(int maxEval,
        DifferentiableMultivariateVectorFunction f,
        double[] target,
        double[] weights,
        double[] startPoint)Deprecated. 
 As of 3.1. Please use
  optimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)instead. | 
| PointVectorValuePair | optimize(int maxEval,
        MultivariateDifferentiableVectorFunction f,
        double[] target,
        double[] weights,
        double[] startPoint)Deprecated. 
 As of 3.1. Please use
  optimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)instead. | 
| protected PointVectorValuePair | optimizeInternal(int maxEval,
                MultivariateDifferentiableVectorFunction f,
                OptimizationData... optData)Deprecated. 
 As of 3.1. Override is necessary only until this class's generic
 argument is changed to  MultivariateDifferentiableVectorFunction. | 
| protected void | setCost(double cost)Deprecated.  Sets the cost. | 
| protected void | setUp()Deprecated.  Method which a subclass must override whenever its internal
 state depend on the  inputparsed by this base
 class. | 
| protected void | updateJacobian()Deprecated. 
 As of 3.1. Please use  computeWeightedJacobian(double[])instead. | 
| protected void | updateResidualsAndCost()Deprecated. 
 As of 3.1. Please use  computeResiduals(double[]),BaseAbstractMultivariateVectorOptimizer.computeObjectiveValue(double[]),computeCost(double[])andsetCost(double)instead. | 
computeObjectiveValue, doOptimize, getConvergenceChecker, getEvaluations, getMaxEvaluations, getObjectiveFunction, getStartPoint, getTarget, getTargetRef, getWeight, getWeightRef, optimize, optimizeInternal, optimizeInternalclone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetConvergenceChecker, getEvaluations, getMaxEvaluations@Deprecated protected double[][] weightedResidualJacobian
computeWeightedJacobian(double[]) instead.updateJacobian(), but may be modified by the solver
 in the derived class (the Levenberg-Marquardt optimizer does this).@Deprecated protected int cols
@Deprecated protected int rows
@Deprecated protected double[] point
@Deprecated protected double[] objective
@Deprecated protected double[] weightedResiduals
@Deprecated protected double cost
setCost(double).@Deprecated protected AbstractLeastSquaresOptimizer()
SimpleValueChecker.SimpleValueChecker()SimpleVectorValueChecker.protected AbstractLeastSquaresOptimizer(ConvergenceChecker<PointVectorValuePair> checker)
checker - Convergence checker.public int getJacobianEvaluations()
@Deprecated protected void updateJacobian()
computeWeightedJacobian(double[])
 instead.DimensionMismatchException - if the Jacobian dimension does not
 match problem dimension.protected RealMatrix computeWeightedJacobian(double[] params)
params - Model parameters at which to compute the Jacobian.DimensionMismatchException - if the Jacobian dimension does not
 match problem dimension.@Deprecated protected void updateResidualsAndCost()
computeResiduals(double[]),
 BaseAbstractMultivariateVectorOptimizer.computeObjectiveValue(double[]), computeCost(double[])
 and setCost(double) instead.DimensionMismatchException - if the dimension does not match the
 problem dimension.TooManyEvaluationsException - if the maximal number of evaluations is exceeded.protected double computeCost(double[] residuals)
residuals - Residuals.computeResiduals(double[])public double getRMS()
public double getChiSquare()
public RealMatrix getWeightSquareRoot()
protected void setCost(double cost)
cost - Cost value.@Deprecated public double[][] getCovariances()
computeCovariances(double[],double)
 instead.SingularMatrixException - if the covariance matrix cannot be computed (singular problem).getCovariances(double)@Deprecated public double[][] getCovariances(double threshold)
computeCovariances(double[],double)
 instead.JTJ matrix, where J is the
 Jacobian matrix.
 The threshold parameter is a way for the caller to specify
 that the result of this computation should be considered meaningless,
 and thus trigger an exception.threshold - Singularity threshold.SingularMatrixException - if the covariance matrix cannot be computed (singular problem).public double[][] computeCovariances(double[] params,
                            double threshold)
JTJ matrix, where J is the
 Jacobian matrix.
 The threshold parameter is a way for the caller to specify
 that the result of this computation should be considered meaningless,
 and thus trigger an exception.params - Model parameters.threshold - Singularity threshold.SingularMatrixException - if the covariance matrix cannot be computed (singular problem).@Deprecated public double[] guessParametersErrors()
computeSigma(double[],double) should be used
 instead. It should be emphasized that guessParametersErrors and
 computeSigma are not strictly equivalent.
 Returns an estimate of the standard deviation of each parameter. The
 returned values are the so-called (asymptotic) standard errors on the
 parameters, defined as sd(a[i]) = sqrt(S / (n - m) * C[i][i]),
 where a[i] is the optimized value of the i-th parameter,
 S is the minimized value of the sum of squares objective function
 (as returned by getChiSquare()), n is the number of
 observations, m is the number of parameters and C is the
 covariance matrix.
 
See also Wikipedia, or MathWorld, equations (34) and (35) for a particular case.
SingularMatrixException - if the covariance matrix cannot be computed.NumberIsTooSmallException - if the number of degrees of freedom is not
 positive, i.e. the number of measurements is less or equal to the number of
 parameters.public double[] computeSigma(double[] params,
                    double covarianceSingularityThreshold)
sd(a[i]) ~= sqrt(C[i][i]), where a[i]
 is the optimized value of the i-th parameter, and C is
 the covariance matrix.params - Model parameters.covarianceSingularityThreshold - Singularity threshold (see
 computeCovariances).SingularMatrixException - if the covariance matrix cannot be computed.@Deprecated public PointVectorValuePair optimize(int maxEval, DifferentiableMultivariateVectorFunction f, double[] target, double[] weights, double[] startPoint)
optimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)
 instead.∑weighti(objectivei - targeti)2optimize in interface BaseMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction>optimize in class BaseAbstractMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction>maxEval - Maximum number of function evaluations.f - Objective function.target - Target value for the objective functions at optimum.weights - Weights for the least squares cost computation.startPoint - Start point for optimization.@Deprecated public PointVectorValuePair optimize(int maxEval, MultivariateDifferentiableVectorFunction f, double[] target, double[] weights, double[] startPoint)
optimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)
 instead.∑weighti(objectivei - targeti)2f - Objective function.target - Target value for the objective functions at optimum.weights - Weights for the least squares cost computation.startPoint - Start point for optimization.maxEval - Maximum number of function evaluations.DimensionMismatchException - if the start point dimension is wrong.TooManyEvaluationsException - if the maximal number of evaluations is exceeded.NullArgumentException - if
 any argument is null.@Deprecated protected PointVectorValuePair optimizeInternal(int maxEval, MultivariateDifferentiableVectorFunction f, OptimizationData... optData)
MultivariateDifferentiableVectorFunction.∑weighti(objectivei - targeti)2maxEval - Allowed number of evaluations of the objective function.f - Objective function.optData - Optimization data. The following data will be looked for:
 TooManyEvaluationsException - if
 the maximal number of evaluations is exceeded.DimensionMismatchException - if the target, and weight arguments
 have inconsistent dimensions.BaseAbstractMultivariateVectorOptimizer.optimizeInternal(int,
 org.apache.commons.math3.analysis.MultivariateVectorFunction,OptimizationData[])protected void setUp()
input parsed by this base
 class.
 It will be called after the parsing step performed in the
 optimize method and just before BaseAbstractMultivariateVectorOptimizer.doOptimize().setUp in class BaseAbstractMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction>protected double[] computeResiduals(double[] objectiveValue)
objectiveValue - Value of the the objective function. This is
 the value returned from a call to
 computeObjectiveValue
 (whose array argument contains the model parameters).DimensionMismatchException - if params has a wrong
 length.Copyright © 2003–2016 The Apache Software Foundation. All rights reserved.