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Clean up LeastSquaresFactory 

* There are now 3 factory methods: one using the previous interfaces, one using
  the new interfaces with weights, and one using the new interfaces without
  weights.

* Make model(...) method public.

* Fix javadoc typo

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1569355 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe 2014-02-18 14:33:00 +00:00
parent 0079828734
commit 4dcae270e6
1 changed files with 31 additions and 54 deletions
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85
src/main/java/org/apache/commons/math3/fitting/leastsquares/LeastSquaresFactory.java
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@ -56,65 +56,44 @@ public class LeastSquaresFactory {
/**
* Create a {@link org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem}
* from the given elements. There will be no weights applied (Identity weights).
* from the given elements.
*
* @param model the model function. Produces the computed values.
* @param observed the observed (target) values
* @param start the initial guess.
* @param weight the weight matrix
* @param checker convergence checker
* @param maxEvaluations the maximum number of times to evaluate the model
* @param maxIterations the maximum number to times to iterate in the algorithm
* @return the specified General Least Squares problem.
*/
public static LeastSquaresProblem create(final MultivariateJacobianFunction model,
final double[] observed,
final double[] start,
final RealVector observed,
final RealVector start,
final RealMatrix weight,
final ConvergenceChecker<Evaluation> checker,
final int maxEvaluations,
final int maxIterations) {
return create(
model,
new ArrayRealVector(observed, false),
new ArrayRealVector(start, false),
checker,
maxEvaluations,
maxIterations
);
}
/**
* Create a {@link org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem}
* from the given elements. There will be no weights applied (Identity weights).
*
* @param model the model function. Produces the computed values.
* @param jacobian the jacobian of the model with respect to the parameters
* @param observed the observed (target) values
* @param start the initial guess.
* @param checker convergence checker
* @param maxEvaluations the maximum number of times to evaluate the model
* @param maxIterations the maximum number to times to iterate in the algorithm
* @return the specified General Least Squares problem.
*/
public static LeastSquaresProblem create(final MultivariateVectorFunction model,
final MultivariateMatrixFunction jacobian,
final double[] observed,
final double[] start,
final ConvergenceChecker<Evaluation> checker,
final int maxEvaluations,
final int maxIterations) {
return create(
combine(model, jacobian),
observed,
start,
checker,
maxEvaluations,
maxIterations
);
return weightMatrix(
create(
model,
observed,
start,
checker,
maxEvaluations,
maxIterations
),
weight);
}
/**
* Create a {@link org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem}
* from the given elements.
* <p/>
* This factory method is provided for continuity with previous interfaces. Newer
* applications should use {@link #create(MultivariateJacobianFunction, RealVector,
* RealVector, ConvergenceChecker, int, int)}, or {@link #create(MultivariateJacobianFunction,
* RealVector, RealVector, RealMatrix, ConvergenceChecker, int, int)}.
*
* @param model the model function. Produces the computed values.
* @param jacobian the jacobian of the model with respect to the parameters
@ -134,17 +113,15 @@ public class LeastSquaresFactory {
final ConvergenceChecker<Evaluation> checker,
final int maxEvaluations,
final int maxIterations) {
return weightMatrix(
create(
model,
jacobian,
observed,
start,
checker,
maxEvaluations,
maxIterations
),
weight);
return create(
model(model, jacobian),
new ArrayRealVector(observed, false),
new ArrayRealVector(start, false),
weight,
checker,
maxEvaluations,
maxIterations
);
}
/**
@ -167,7 +144,7 @@ public class LeastSquaresFactory {
}
/**
* Apply a diagon weight matrix to the {@link LeastSquaresProblem}.
* Apply a diagonal weight matrix to the {@link LeastSquaresProblem}.
*
* @param problem the unweighted problem
* @param weights the diagonal of the weight matrix
@ -260,7 +237,7 @@ public class LeastSquaresFactory {
* @param jacobian the Jacobian function
* @return a function that computes both at the same time
*/
private static MultivariateJacobianFunction combine(
public static MultivariateJacobianFunction model(
final MultivariateVectorFunction value,
final MultivariateMatrixFunction jacobian
) {