Class AbstractLeastSquaresOptimizer
- java.lang.Object
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- org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction>
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- org.apache.commons.math3.optimization.general.AbstractLeastSquaresOptimizer
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- All Implemented Interfaces:
BaseMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction>
,BaseOptimizer<PointVectorValuePair>
,DifferentiableMultivariateVectorOptimizer
- Direct Known Subclasses:
GaussNewtonOptimizer
,LevenbergMarquardtOptimizer
@Deprecated public abstract class AbstractLeastSquaresOptimizer extends BaseAbstractMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction> implements DifferentiableMultivariateVectorOptimizer
Deprecated.As of 3.1 (to be removed in 4.0).Base class for implementing least squares optimizers. It handles the boilerplate methods associated to thresholds settings, Jacobian and error estimation.
This class constructs the Jacobian matrix of the function argument in methodoptimize
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 theTarget
while the number of columns is equal to the dimension of theInitialGuess
.- Since:
- 1.2
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Field Summary
Fields Modifier and Type Field Description protected int
cols
Deprecated.As of 3.1.protected double
cost
Deprecated.As of 3.1.protected double[]
objective
Deprecated.As of 3.1.protected double[]
point
Deprecated.As of 3.1.protected int
rows
Deprecated.As of 3.1.protected double[][]
weightedResidualJacobian
Deprecated.As of 3.1.protected double[]
weightedResiduals
Deprecated.As of 3.1.-
Fields inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer
evaluations
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Constructor Summary
Constructors Modifier Constructor Description protected
AbstractLeastSquaresOptimizer()
Deprecated.protected
AbstractLeastSquaresOptimizer(ConvergenceChecker<PointVectorValuePair> checker)
Deprecated.
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Method Summary
All Methods Instance Methods Concrete Methods Deprecated Methods Modifier and Type Method 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.double[][]
getCovariances(double threshold)
Deprecated.As of 3.1.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.PointVectorValuePair
optimize(int maxEval, DifferentiableMultivariateVectorFunction f, double[] target, double[] weights, double[] startPoint)
Deprecated.As of 3.1.PointVectorValuePair
optimize(int maxEval, MultivariateDifferentiableVectorFunction f, double[] target, double[] weights, double[] startPoint)
Deprecated.As of 3.1.protected PointVectorValuePair
optimizeInternal(int maxEval, MultivariateDifferentiableVectorFunction f, OptimizationData... optData)
Deprecated.As of 3.1.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 theinput
parsed by this base class.protected void
updateJacobian()
Deprecated.As of 3.1.protected void
updateResidualsAndCost()
Deprecated.As of 3.1.-
Methods inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer
computeObjectiveValue, doOptimize, getConvergenceChecker, getEvaluations, getMaxEvaluations, getObjectiveFunction, getStartPoint, getTarget, getTargetRef, getWeight, getWeightRef, optimize, optimizeInternal, optimizeInternal
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Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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Methods inherited from interface org.apache.commons.math3.optimization.BaseOptimizer
getConvergenceChecker, getEvaluations, getMaxEvaluations
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Field Detail
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weightedResidualJacobian
@Deprecated protected double[][] weightedResidualJacobian
Deprecated.As of 3.1. To be removed in 4.0. Please usecomputeWeightedJacobian(double[])
instead.Jacobian matrix of the weighted residuals. This matrix is in canonical form just after the calls toupdateJacobian()
, but may be modified by the solver in the derived class (theLevenberg-Marquardt optimizer
does this).
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cols
@Deprecated protected int cols
Deprecated.As of 3.1.Number of columns of the jacobian matrix.
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rows
@Deprecated protected int rows
Deprecated.As of 3.1.Number of rows of the jacobian matrix.
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point
@Deprecated protected double[] point
Deprecated.As of 3.1.Current point.
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objective
@Deprecated protected double[] objective
Deprecated.As of 3.1.Current objective function value.
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weightedResiduals
@Deprecated protected double[] weightedResiduals
Deprecated.As of 3.1.Weighted residuals
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cost
@Deprecated protected double cost
Deprecated.As of 3.1. Field to become "private" in 4.0. Please usesetCost(double)
.Cost value (square root of the sum of the residuals).
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Constructor Detail
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AbstractLeastSquaresOptimizer
@Deprecated protected AbstractLeastSquaresOptimizer()
Deprecated.Simple constructor with default settings. The convergence check is set to aSimpleVectorValueChecker
.
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AbstractLeastSquaresOptimizer
protected AbstractLeastSquaresOptimizer(ConvergenceChecker<PointVectorValuePair> checker)
Deprecated.- Parameters:
checker
- Convergence checker.
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Method Detail
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getJacobianEvaluations
public int getJacobianEvaluations()
Deprecated.- Returns:
- the number of evaluations of the Jacobian function.
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updateJacobian
@Deprecated protected void updateJacobian()
Deprecated.As of 3.1. Please usecomputeWeightedJacobian(double[])
instead.Update the jacobian matrix.- Throws:
DimensionMismatchException
- if the Jacobian dimension does not match problem dimension.
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computeWeightedJacobian
protected RealMatrix computeWeightedJacobian(double[] params)
Deprecated.Computes the Jacobian matrix.- Parameters:
params
- Model parameters at which to compute the Jacobian.- Returns:
- the weighted Jacobian: W1/2 J.
- Throws:
DimensionMismatchException
- if the Jacobian dimension does not match problem dimension.- Since:
- 3.1
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updateResidualsAndCost
@Deprecated protected void updateResidualsAndCost()
Deprecated.As of 3.1. Please usecomputeResiduals(double[])
,BaseAbstractMultivariateVectorOptimizer.computeObjectiveValue(double[])
,computeCost(double[])
andsetCost(double)
instead.Update the residuals array and cost function value.- Throws:
DimensionMismatchException
- if the dimension does not match the problem dimension.TooManyEvaluationsException
- if the maximal number of evaluations is exceeded.
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computeCost
protected double computeCost(double[] residuals)
Deprecated.Computes the cost.- Parameters:
residuals
- Residuals.- Returns:
- the cost.
- Since:
- 3.1
- See Also:
computeResiduals(double[])
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getRMS
public double getRMS()
Deprecated.Get the Root Mean Square value. Get the Root Mean Square value, i.e. the root of the arithmetic mean of the square of all weighted residuals. This is related to the criterion that is minimized by the optimizer as follows: if c if the criterion, and n is the number of measurements, then the RMS is sqrt (c/n).- Returns:
- RMS value
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getChiSquare
public 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.- Returns:
- chi-square value
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getWeightSquareRoot
public RealMatrix getWeightSquareRoot()
Deprecated.Gets the square-root of the weight matrix.- Returns:
- the square-root of the weight matrix.
- Since:
- 3.1
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setCost
protected void setCost(double cost)
Deprecated.Sets the cost.- Parameters:
cost
- Cost value.- Since:
- 3.1
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getCovariances
@Deprecated public double[][] getCovariances()
Deprecated.As of 3.1. Please usecomputeCovariances(double[],double)
instead.Get the covariance matrix of the optimized parameters.- Returns:
- the covariance matrix.
- Throws:
SingularMatrixException
- if the covariance matrix cannot be computed (singular problem).- See Also:
getCovariances(double)
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getCovariances
@Deprecated public double[][] getCovariances(double threshold)
Deprecated.As of 3.1. Please usecomputeCovariances(double[],double)
instead.Get the covariance matrix of the optimized parameters.
Note that this operation involves the inversion of theJTJ
matrix, whereJ
is the Jacobian matrix. Thethreshold
parameter is a way for the caller to specify that the result of this computation should be considered meaningless, and thus trigger an exception.- Parameters:
threshold
- Singularity threshold.- Returns:
- the covariance matrix.
- Throws:
SingularMatrixException
- if the covariance matrix cannot be computed (singular problem).
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computeCovariances
public double[][] computeCovariances(double[] params, double threshold)
Deprecated.Get the covariance matrix of the optimized parameters.
Note that this operation involves the inversion of theJTJ
matrix, whereJ
is the Jacobian matrix. Thethreshold
parameter is a way for the caller to specify that the result of this computation should be considered meaningless, and thus trigger an exception.- Parameters:
params
- Model parameters.threshold
- Singularity threshold.- Returns:
- the covariance matrix.
- Throws:
SingularMatrixException
- if the covariance matrix cannot be computed (singular problem).- Since:
- 3.1
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guessParametersErrors
@Deprecated public double[] guessParametersErrors()
Deprecated.as of version 3.1,computeSigma(double[],double)
should be used instead. It should be emphasized thatguessParametersErrors
andcomputeSigma
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])
, wherea[i]
is the optimized value of thei
-th parameter,S
is the minimized value of the sum of squares objective function (as returned bygetChiSquare()
),n
is the number of observations,m
is the number of parameters andC
is the covariance matrix.See also Wikipedia, or MathWorld, equations (34) and (35) for a particular case.
- Returns:
- an estimate of the standard deviation of the optimized parameters
- Throws:
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.
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computeSigma
public double[] computeSigma(double[] params, double covarianceSingularityThreshold)
Deprecated.Computes an estimate of the standard deviation of the parameters. The returned values are the square root of the diagonal coefficients of the covariance matrix,sd(a[i]) ~= sqrt(C[i][i])
, wherea[i]
is the optimized value of thei
-th parameter, andC
is the covariance matrix.- Parameters:
params
- Model parameters.covarianceSingularityThreshold
- Singularity threshold (seecomputeCovariances
).- Returns:
- an estimate of the standard deviation of the optimized parameters
- Throws:
SingularMatrixException
- if the covariance matrix cannot be computed.- Since:
- 3.1
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optimize
@Deprecated public PointVectorValuePair optimize(int maxEval, DifferentiableMultivariateVectorFunction f, double[] target, double[] weights, double[] startPoint)
Deprecated.As of 3.1. Please useoptimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)
instead.Optimize an objective function. Optimization is considered to be a weighted least-squares minimization. The cost function to be minimized is∑weighti(objectivei - targeti)2
- Specified by:
optimize
in interfaceBaseMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction>
- Overrides:
optimize
in classBaseAbstractMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction>
- Parameters:
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.- Returns:
- the point/value pair giving the optimal value for objective function.
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optimize
@Deprecated public PointVectorValuePair optimize(int maxEval, MultivariateDifferentiableVectorFunction f, double[] target, double[] weights, double[] startPoint)
Deprecated.As of 3.1. Please useoptimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)
instead.Optimize an objective function. Optimization is considered to be a weighted least-squares minimization. The cost function to be minimized is∑weighti(objectivei - targeti)2
- Parameters:
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.maxEval
- Maximum number of function evaluations.- Returns:
- the point/value pair giving the optimal value for objective function.
- Throws:
DimensionMismatchException
- if the start point dimension is wrong.TooManyEvaluationsException
- if the maximal number of evaluations is exceeded.NullArgumentException
- if any argument isnull
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optimizeInternal
@Deprecated 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 toMultivariateDifferentiableVectorFunction
.Optimize an objective function. Optimization is considered to be a weighted least-squares minimization. The cost function to be minimized is∑weighti(objectivei - targeti)2
- Parameters:
maxEval
- Allowed number of evaluations of the objective function.f
- Objective function.optData
- Optimization data. The following data will be looked for:- Returns:
- the point/value pair giving the optimal value of the objective function.
- Throws:
TooManyEvaluationsException
- if the maximal number of evaluations is exceeded.DimensionMismatchException
- if the target, and weight arguments have inconsistent dimensions.- Since:
- 3.1
- See Also:
BaseAbstractMultivariateVectorOptimizer.optimizeInternal(int, org.apache.commons.math3.analysis.MultivariateVectorFunction,OptimizationData[])
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setUp
protected void setUp()
Deprecated.Method which a subclass must override whenever its internal state depend on theinput
parsed by this base class. It will be called after the parsing step performed in theoptimize
method and just beforeBaseAbstractMultivariateVectorOptimizer.doOptimize()
.- Overrides:
setUp
in classBaseAbstractMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction>
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computeResiduals
protected double[] computeResiduals(double[] objectiveValue)
Deprecated.Computes the residuals. The residual is the difference between the observed (target) values and the model (objective function) value. There is one residual for each element of the vector-valued function.- Parameters:
objectiveValue
- Value of the the objective function. This is the value returned from a call tocomputeObjectiveValue
(whose array argument contains the model parameters).- Returns:
- the residuals.
- Throws:
DimensionMismatchException
- ifparams
has a wrong length.- Since:
- 3.1
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