ActivationLinear.java

/*
 * Artificial Intelligence for Humans
 * Volume 3: Deep Learning and Neural Networks
 * Java Version
 * http://www.aifh.org
 * http://www.jeffheaton.com
 *
 * Code repository:
 * https://github.com/jeffheaton/aifh
 *
 * Copyright 2014-2015 by Jeff Heaton
 *
 * 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 com.heatonresearch.aifh.ann.activation;

/**
 * The Linear layer is really not an activation function at all. The input is
 * simply passed on, unmodified, to the output. The linear activation function
 * is usually used on the output layer of a regression network.
 */
public class ActivationLinear implements ActivationFunction {

	/**
	 * Default empty parameters.
	 */
	public static final double P[] = new double[0];
	
	/**
	 * Default empty parameters.
	 */
	public static final String N[] = new String[0];
	
	/**
	 * Serial id for this class.
	 */
	private static final long serialVersionUID = -5356580554235104944L;

	/**
	 * The parameters.
	 */
	private final double[] params;

	/**
	 * Construct a linear activation function, with a slope of 1.
	 */
	public ActivationLinear() {
		this.params = new double[0];
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public final void activationFunction(final double[] x, final int start,
			final int size) {
	}

	/**
	 * @return The object cloned.
	 */
	@Override
	public final ActivationFunction clone() {
		return new ActivationLinear();
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public final double derivativeFunction(final double b, final double a) {
		return 1;
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public final String[] getParamNames() {
		final String[] result = {};
		return result;
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public final double[] getParams() {
		return this.params;
	}

	/**
	 * @return Return true, linear has a 1 derivative.
	 */
	@Override
	public final boolean hasDerivative() {
		return true;
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public final void setParam(final int index, final double value) {
		this.params[index] = value;
	}

}