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Java example source code file (MultivariateVectorOptimizer.java)

This example Java source code file (MultivariateVectorOptimizer.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

basemultivariateoptimizer, deprecated, dimensionmismatchexception, modelfunction, multivariatevectorfunction, multivariatevectoroptimizer, optimizationdata, override, pointvectorvaluepair, realmatrix, target, toomanyevaluationsexception, weight

The MultivariateVectorOptimizer.java Java example source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.commons.math3.optim.nonlinear.vector;

import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.optim.OptimizationData;
import org.apache.commons.math3.optim.BaseMultivariateOptimizer;
import org.apache.commons.math3.optim.ConvergenceChecker;
import org.apache.commons.math3.optim.PointVectorValuePair;
import org.apache.commons.math3.linear.RealMatrix;

/**
 * Base class for a multivariate vector function optimizer.
 *
 * @since 3.1
 */
@Deprecated
public abstract class MultivariateVectorOptimizer
    extends BaseMultivariateOptimizer<PointVectorValuePair> {
    /** Target values for the model function at optimum. */
    private double[] target;
    /** Weight matrix. */
    private RealMatrix weightMatrix;
    /** Model function. */
    private MultivariateVectorFunction model;

    /**
     * @param checker Convergence checker.
     */
    protected MultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
        super(checker);
    }

    /**
     * Computes the objective function value.
     * This method <em>must be called by subclasses to enforce the
     * evaluation counter limit.
     *
     * @param params Point at which the objective function must be evaluated.
     * @return the objective function value at the specified point.
     * @throws TooManyEvaluationsException if the maximal number of evaluations
     * (of the model vector function) is exceeded.
     */
    protected double[] computeObjectiveValue(double[] params) {
        super.incrementEvaluationCount();
        return model.value(params);
    }

    /**
     * {@inheritDoc}
     *
     * @param optData Optimization data. In addition to those documented in
     * {@link BaseMultivariateOptimizer#parseOptimizationData(OptimizationData[])
     * BaseMultivariateOptimizer}, this method will register the following data:
     * <ul>
     *  <li>{@link Target}
     *  <li>{@link Weight}
     *  <li>{@link ModelFunction}
     * </ul>
     * @return {@inheritDoc}
     * @throws TooManyEvaluationsException if the maximal number of
     * evaluations is exceeded.
     * @throws DimensionMismatchException if the initial guess, target, and weight
     * arguments have inconsistent dimensions.
     */
    @Override
    public PointVectorValuePair optimize(OptimizationData... optData)
        throws TooManyEvaluationsException,
               DimensionMismatchException {
        // Set up base class and perform computation.
        return super.optimize(optData);
    }

    /**
     * Gets the weight matrix of the observations.
     *
     * @return the weight matrix.
     */
    public RealMatrix getWeight() {
        return weightMatrix.copy();
    }
    /**
     * Gets the observed values to be matched by the objective vector
     * function.
     *
     * @return the target values.
     */
    public double[] getTarget() {
        return target.clone();
    }

    /**
     * Gets the number of observed values.
     *
     * @return the length of the target vector.
     */
    public int getTargetSize() {
        return target.length;
    }

    /**
     * Scans the list of (required and optional) optimization data that
     * characterize the problem.
     *
     * @param optData Optimization data. The following data will be looked for:
     * <ul>
     *  <li>{@link Target}
     *  <li>{@link Weight}
     *  <li>{@link ModelFunction}
     * </ul>
     */
    @Override
    protected void parseOptimizationData(OptimizationData... optData) {
        // Allow base class to register its own data.
        super.parseOptimizationData(optData);

        // The existing values (as set by the previous call) are reused if
        // not provided in the argument list.
        for (OptimizationData data : optData) {
            if (data instanceof ModelFunction) {
                model = ((ModelFunction) data).getModelFunction();
                continue;
            }
            if (data instanceof Target) {
                target = ((Target) data).getTarget();
                continue;
            }
            if (data instanceof Weight) {
                weightMatrix = ((Weight) data).getWeight();
                continue;
            }
        }

        // Check input consistency.
        checkParameters();
    }

    /**
     * Check parameters consistency.
     *
     * @throws DimensionMismatchException if {@link #target} and
     * {@link #weightMatrix} have inconsistent dimensions.
     */
    private void checkParameters() {
        if (target.length != weightMatrix.getColumnDimension()) {
            throw new DimensionMismatchException(target.length,
                                                 weightMatrix.getColumnDimension());
        }
    }
}

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