## Class LeastSquareWithPenaltyResults

• ### Constructor Summary

Constructors
Constructor Description
LeastSquareWithPenaltyResults​(double chiSqr, double penalty, DoubleArray parameters, DoubleMatrix covariance)
Holder for the results of minimising $\sum_{i=1}^N (y_i - f_i(\mathbf{x}))^2 + \mathbf{x}^T\mathbf{P}\mathbf{x}$ WRT $\mathbf{x}$ (the vector of model parameters).
LeastSquareWithPenaltyResults​(double chiSqr, double penalty, DoubleArray parameters, DoubleMatrix covariance, DoubleMatrix inverseJacobian)
Holder for the results of minimising $\sum_{i=1}^N (y_i - f_i(\mathbf{x}))^2 + \mathbf{x}^T\mathbf{P}\mathbf{x}$ WRT $\mathbf{x}$ (the vector of model parameters).
• ### Method Summary

All Methods
Modifier and Type Method Description
double getPenalty()
Gets the value of the penalty.
• ### Methods inherited from class com.opengamma.strata.math.impl.statistics.leastsquare.LeastSquareResults

equals, getChiSq, getCovariance, getFitParameters, getFittingParameterSensitivityToData, hashCode, toString
• ### Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait
• ### Constructor Detail

• #### LeastSquareWithPenaltyResults

public LeastSquareWithPenaltyResults​(double chiSqr,
double penalty,
DoubleArray parameters,
DoubleMatrix covariance)
Holder for the results of minimising $\sum_{i=1}^N (y_i - f_i(\mathbf{x}))^2 + \mathbf{x}^T\mathbf{P}\mathbf{x}$ WRT $\mathbf{x}$ (the vector of model parameters).
Parameters:
chiSqr - The value of the first term (the chi-squared)- the sum of squares between the 'observed' values $y_i$ and the model values $f_i(\mathbf{x})$
penalty - The value of the second term (the penalty)
parameters - The value of $\mathbf{x}$
covariance - The covariance matrix for $\mathbf{x}$
• #### LeastSquareWithPenaltyResults

public LeastSquareWithPenaltyResults​(double chiSqr,
double penalty,
DoubleArray parameters,
DoubleMatrix covariance,
DoubleMatrix inverseJacobian)
Holder for the results of minimising $\sum_{i=1}^N (y_i - f_i(\mathbf{x}))^2 + \mathbf{x}^T\mathbf{P}\mathbf{x}$ WRT $\mathbf{x}$ (the vector of model parameters).
Parameters:
chiSqr - The value of the first term (the chi-squared)- the sum of squares between the 'observed' values $y_i$ and the model values $f_i(\mathbf{x})$
penalty - The value of the second term (the penalty)
parameters - The value of $\mathbf{x}$
covariance - The covariance matrix for $\mathbf{x}$
inverseJacobian - The inverse Jacobian - this is the sensitivities of the model parameters to the 'observed' values
• ### Method Detail

• #### getPenalty

public double getPenalty()
Gets the value of the penalty.
Returns:
the penalty