org.apache.commons.math3.optim.nonlinear.vector.jacobian

## Class AbstractLeastSquaresOptimizer

• ### Method Detail

• #### computeWeightedJacobian

protected RealMatrix computeWeightedJacobian(double[] params)
Deprecated.
Computes the weighted 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.
• #### computeCost

protected double computeCost(double[] residuals)
Deprecated.
Computes the cost.
Parameters:
residuals - Residuals.
Returns:
the cost.
computeResiduals(double[])
• #### getRMS

public double getRMS()
Deprecated.
Gets the root-mean-square (RMS) value. The RMS 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:
the RMS value.
• #### 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
• #### getWeightSquareRoot

public RealMatrix getWeightSquareRoot()
Deprecated.
Gets the square-root of the weight matrix.
Returns:
the square-root of the weight matrix.
• #### setCost

protected void setCost(double cost)
Deprecated.
Sets the cost.
Parameters:
cost - Cost value.
• #### 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 the 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.
Parameters:
params - Model parameters.
threshold - Singularity threshold.
Returns:
the covariance matrix.
Throws:
SingularMatrixException - if the covariance matrix cannot be computed (singular problem).
• #### 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]), where a[i] is the optimized value of the i-th parameter, and C is the covariance matrix.
Parameters:
params - Model parameters.
covarianceSingularityThreshold - Singularity threshold (see computeCovariances).
Returns:
an estimate of the standard deviation of the optimized parameters
Throws:
SingularMatrixException - if the covariance matrix cannot be computed.
• #### 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 to computeObjectiveValue (whose array argument contains the model parameters).
Returns:
the residuals.
Throws:
DimensionMismatchException - if params has a wrong length.