/** {@inheritDoc} */
@Override
public Evaluation evaluate(final RealVector point) {
// Copy so optimizer can change point without changing our instance.
final RealVector p =
paramValidator == null ? point.copy() : paramValidator.validate(point.copy());
if (lazyEvaluation) {
return new LazyUnweightedEvaluation((ValueAndJacobianFunction) model, target, p);
} else {
// Evaluate value and jacobian in one function call.
final Pair<RealVector, RealMatrix> value = model.value(p);
return new UnweightedEvaluation(value.getFirst(), value.getSecond(), target, p);
}
}
/**
* Create a {@link LeastSquaresProblem} from the given data.
*
* @param model the model function
* @param target the observed data
* @param start the initial guess
* @param checker the convergence checker
* @param maxEvaluations the allowed evaluations
* @param maxIterations the allowed iterations
* @param lazyEvaluation Whether the call to {@link Evaluation#evaluate(RealVector)} will defer
* the evaluation until access to the value is requested.
* @param paramValidator Model parameters validator.
*/
LocalLeastSquaresProblem(
final MultivariateJacobianFunction model,
final RealVector target,
final RealVector start,
final ConvergenceChecker<Evaluation> checker,
final int maxEvaluations,
final int maxIterations,
final boolean lazyEvaluation,
final ParameterValidator paramValidator) {
super(maxEvaluations, maxIterations, checker);
this.target = target;
this.model = model;
this.start = start;
this.lazyEvaluation = lazyEvaluation;
this.paramValidator = paramValidator;
if (lazyEvaluation && !(model instanceof ValueAndJacobianFunction)) {
// Lazy evaluation requires that value and Jacobian
// can be computed separately.
throw new MathIllegalStateException(
LocalizedFormats.INVALID_IMPLEMENTATION, model.getClass().getName());
}
}