Modifier and Type	Class and Description
public static interface	LeastSquaresProblem.Evaluation An evaluation of a `LeastSquaresProblem` at a particular point.

Modifier and Type	Method and Description
public LeastSquaresProblem.Evaluation	Returns: the model's value and derivative at the given point. evaluate(RealVector the parameter values. point) Evaluate the model at the specified point.
public int	Returns: the number of scalar observations getObservationSize() Get the number of observations (rows in the Jacobian) in this problem.
public int	Returns: the number of scalar parameters getParameterSize() Get the number of parameters (columns in the Jacobian) in this problem.
public RealVector	Returns: the initial guess values. getStart() Gets the initial guess.

Method Detail

evaluate	back to summary
public LeastSquaresProblem.Evaluation evaluate(RealVector point) Evaluate the model at the specified point. Parameters point:RealVector the parameter values. Returns:LeastSquaresProblem.Evaluation the model's value and derivative at the given point. Exceptions TooManyEvaluationsException: if the maximal number of evaluations (of the model vector function) is exceeded.

evaluate

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public LeastSquaresProblem.Evaluation evaluate(RealVector point)

Evaluate the model at the specified point.

Parameters

point:RealVector: the parameter values.

Returns:LeastSquaresProblem.Evaluation

the model's value and derivative at the given point.

Exceptions

TooManyEvaluationsException:: if the maximal number of evaluations (of the model vector function) is exceeded.

getObservationSize	back to summary
public int getObservationSize() Get the number of observations (rows in the Jacobian) in this problem. Returns:int the number of scalar observations

getObservationSize

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public int getObservationSize()

Get the number of observations (rows in the Jacobian) in this problem.

Returns:int: the number of scalar observations

getParameterSize	back to summary
public int getParameterSize() Get the number of parameters (columns in the Jacobian) in this problem. Returns:int the number of scalar parameters

getParameterSize

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public int getParameterSize()

Get the number of parameters (columns in the Jacobian) in this problem.

Returns:int: the number of scalar parameters

getStart	back to summary
public RealVector getStart() Gets the initial guess. Returns:RealVector the initial guess values.

getStart

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public RealVector getStart()

Gets the initial guess.

Returns:RealVector: the initial guess values.

org.apache.commons.math3.fitting.leastsquares back to summary

public Interface LeastSquaresProblem.Evaluation

Known Direct Subinterfaces: org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer.Optimum
Known Direct Implementers: org.apache.commons.math3.fitting.leastsquares.AbstractEvaluation

An evaluation of a LeastSquaresProblem at a particular point. This class also computes several quantities derived from the value and its Jacobian.

Method Summary

Modifier and Type	Method and Description
public double	Returns: the cost. getCost() Get the cost.
public RealMatrix	Returns: the covariance matrix. getCovariances(double Singularity threshold. threshold) Get the covariance matrix of the optimized parameters.
public RealMatrix	Returns: the weighted Jacobian: W^1/2 J. getJacobian() Get the weighted Jacobian matrix.
public RealVector	Returns: the point provided to `evaluate(RealVector)`. getPoint() Get the abscissa (independent variables) of this evaluation.
public RealVector	Returns: the weighted residuals: W^1/2 K. getResiduals() Get the weighted residuals.
public double	Returns: the cost. getRMS() Get the normalized cost.
public RealVector	Returns: an estimate of the standard deviation of the optimized parameters getSigma(double Singularity threshold (see `computeCovariances`). covarianceSingularityThreshold) Get an estimate of the standard deviation of the parameters.

Method Detail

getCost back to summary

getCost	back to summary
public double getCost() Get the cost. Returns:double the cost. See Also `getResiduals()`

public double getCost()

Get the cost.

Returns:double: the cost.
See Also: getResiduals()

getCovariances back to summary

getCovariances	back to summary
public RealMatrix getCovariances(double threshold) Get the covariance matrix of the optimized parameters. Note that this operation involves the inversion of the `J^TJ` 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. Parameters threshold:double Singularity threshold. Returns:RealMatrix the covariance matrix. Exceptions SingularMatrixException: if the covariance matrix cannot be computed (singular problem).

public RealMatrix getCovariances(double threshold)

Get the covariance matrix of the optimized parameters.
Note that this operation involves the inversion of the J^TJ 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.

Parameters

threshold:double: Singularity threshold.

Returns:RealMatrix

the covariance matrix.

Exceptions

SingularMatrixException:: if the covariance matrix cannot be computed (singular problem).

getJacobian	back to summary
public RealMatrix getJacobian() Get the weighted Jacobian matrix. Returns:RealMatrix the weighted Jacobian: W^1/2 J. Exceptions DimensionMismatchException: if the Jacobian dimension does not match problem dimension.

getJacobian

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public RealMatrix getJacobian()

Get the weighted Jacobian matrix.

Returns:RealMatrix

the weighted Jacobian: W^1/2 J.

Exceptions

DimensionMismatchException:: if the Jacobian dimension does not match problem dimension.

getPoint back to summary

getPoint	back to summary
public RealVector getPoint() Get the abscissa (independent variables) of this evaluation. Returns:RealVector the point provided to `evaluate(RealVector)`.

public RealVector getPoint()

Get the abscissa (independent variables) of this evaluation.

Returns:RealVector: the point provided to evaluate(RealVector).

getResiduals	back to summary
public RealVector getResiduals() Get the weighted 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. The raw residuals are then multiplied by the square root of the weight matrix. Returns:RealVector the weighted residuals: W^1/2 K. Exceptions DimensionMismatchException: if the residuals have the wrong length.

getResiduals

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public RealVector getResiduals()

Get the weighted 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. The raw residuals are then multiplied by the square root of the weight matrix.

Returns:RealVector

the weighted residuals: W^1/2 K.

Exceptions

DimensionMismatchException:: if the residuals have the wrong length.

getRMS	back to summary
public double getRMS() Get the normalized cost. It is the square-root of the sum of squared of the residuals, divided by the number of measurements. Returns:double the cost.

getRMS

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public double getRMS()

Get the normalized cost. It is the square-root of the sum of squared of the residuals, divided by the number of measurements.

Returns:double: the cost.

getSigma back to summary

getSigma	back to summary
public RealVector getSigma(double covarianceSingularityThreshold) Get 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])`, where `a[i]` is the optimized value of the `i`-th parameter, and `C` is the covariance matrix. Parameters covarianceSingularityThreshold:double Singularity threshold (see `computeCovariances`). Returns:RealVector an estimate of the standard deviation of the optimized parameters Exceptions SingularMatrixException: if the covariance matrix cannot be computed.

public RealVector getSigma(double covarianceSingularityThreshold)

Get 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]), where a[i] is the optimized value of the i-th parameter, and C is the covariance matrix.

Parameters

covarianceSingularityThreshold:double: Singularity threshold (see computeCovariances).

Returns:RealVector

an estimate of the standard deviation of the optimized parameters

Exceptions

SingularMatrixException:: if the covariance matrix cannot be computed.

public Interface LeastSquaresProblem

Nested and Inner Type Summary

Method Summary

Method Detail

public Interface LeastSquaresProblem.Evaluation

Method Summary

Method Detail