/**
* Computes the sufficient statistic \f$ t(x)\f$.
*
* @param x a point
* @return \f$ t(x) = x \f$
*/
public PVector t(PVector x) {
PVector t = (PVector) x.clone();
t.type = Parameter.TYPE.EXPECTATION_PARAMETER;
return t;
}
/**
* Computes \f$ \nabla G (\mathbf{H})\f$
*
* @param H expectation parameters \f$ \mathbf{H} = \eta \f$
* @return \f$ \nabla G(\mathbf{H}) = \eta \f$
*/
public PVector gradG(PVector H) {
PVector gradient = (PVector) H.clone();
gradient.type = Parameter.TYPE.NATURAL_PARAMETER;
return gradient;
}
/**
* Computes \f$ \nabla F ( \mathbf{\Theta} )\f$.
*
* @param T natural \f$ \mathbf{\Theta} = \theta \f$
* @return \f$ \nabla F( \mathbf{\Theta} ) = \theta \f$
*/
public PVector gradF(PVector T) {
PVector gradient = (PVector) T.clone();
gradient.type = Parameter.TYPE.EXPECTATION_PARAMETER;
return gradient;
}
/**
* Converts expectation parameters to source parameters.
*
* @param H expectation parameters \f$ \mathbf{H} = \eta \f$
* @return source parameters \f$ \mathbf{\Lambda} = \eta \f$
*/
public PVector Eta2Lambda(PVector H) {
PVector L = (PVector) H.clone();
L.type = Parameter.TYPE.SOURCE_PARAMETER;
return L;
}
/**
* Converts source parameters to expectation parameters.
*
* @param L source parameters \f$ \mathbf{\Lambda} = \mu \f$
* @return expectation parameters \f$ \mathbf{H} = \mu \f$
*/
public PVector Lambda2Eta(PVector L) {
PVector H = (PVector) L.clone();
H.type = Parameter.TYPE.EXPECTATION_PARAMETER;
return H;
}
/**
* Converts source parameters to natural parameters.
*
* @param L source parameters \f$ \mathbf{\Lambda} = \mu \f$
* @return natural parameters \f$ \mathbf{\Theta} = \mu \f$
*/
public PVector Lambda2Theta(PVector L) {
PVector T = (PVector) L.clone();
T.type = Parameter.TYPE.NATURAL_PARAMETER;
return T;
}
