/* * Encog(tm) Core v3.4 - Java Version * http://www.heatonresearch.com/encog/ * https://github.com/encog/encog-java-core * Copyright 2008-2016 Heaton Research, Inc. * * Licensed 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. * * For more information on Heaton Research copyrights, licenses * and trademarks visit: * http://www.heatonresearch.com/copyright */ package org.encog.neural.error; import org.encog.engine.network.activation.ActivationFunction; /** * An error function. This is used to calculate the errors for the * output layer during propagation training. * */ public interface ErrorFunction { /** * Calculate the error. * @param af The activation function used at the output layer. * @param b * The number to calculate the derivative of, the number "before" the * activation function was applied. * @param a * The number "after" an activation function has been applied. * @param ideal The ideal values. * @param actual The actual values. * @param error The resulting error values. * @param derivShift The amount to shift af derivativeFunction by * @param significance Weighting to apply to ideal[i] - actual[i] */ public void calculateError(ActivationFunction af, double[] b, double[] a, double[] ideal, double[] actual, double[] error, double derivShift, double significance); }