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    }