/*
* 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);
}