/*
 * 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);
}