    001    /*
    002     * Licensed to the Apache Software Foundation (ASF) under one or more
    003     * contributor license agreements.  See the NOTICE file distributed with
    004     * this work for additional information regarding copyright ownership.
    005     * The ASF licenses this file to You under the Apache License, Version 2.0
    006     * (the "License"); you may not use this file except in compliance with
    007     * the License.  You may obtain a copy of the License at
    008     *
    009     *      http://www.apache.org/licenses/LICENSE-2.0
    010     *
    011     * Unless required by applicable law or agreed to in writing, software
    012     * distributed under the License is distributed on an "AS IS" BASIS,
    013     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    014     * See the License for the specific language governing permissions and
    015     * limitations under the License.
    016     */
    017
    018    package org.apache.commons.math.optimization.fitting;
    019
    020    import org.apache.commons.math.FunctionEvaluationException;
    021
    022    /**
    023     * An interface representing a real function that depends on one independent
    024     * variable plus some extra parameters.
    025     *
    026     * @version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 f??vr. 2011) $
    027     */
    028    public interface ParametricRealFunction {
    029
    030        /**
    031         * Compute the value of the function.
    032         * @param x the point for which the function value should be computed
    033         * @param parameters function parameters
    034         * @return the value
    035         * @throws FunctionEvaluationException if the function evaluation fails
    036         */
    037        double value(double x, double[] parameters)
    038            throws FunctionEvaluationException;
    039
    040        /**
    041         * Compute the gradient of the function with respect to its parameters.
    042         * @param x the point for which the function value should be computed
    043         * @param parameters function parameters
    044         * @return the value
    045         * @throws FunctionEvaluationException if the function evaluation fails
    046         */
    047        double[] gradient(double x, double[] parameters)
    048            throws FunctionEvaluationException;
    049
    050    }