001/**
002 * Copyright (c) 2011, The University of Southampton and the individual contributors.
003 * All rights reserved.
004 *
005 * Redistribution and use in source and binary forms, with or without modification,
006 * are permitted provided that the following conditions are met:
007 *
008 *   *  Redistributions of source code must retain the above copyright notice,
009 *      this list of conditions and the following disclaimer.
010 *
011 *   *  Redistributions in binary form must reproduce the above copyright notice,
012 *      this list of conditions and the following disclaimer in the documentation
013 *      and/or other materials provided with the distribution.
014 *
015 *   *  Neither the name of the University of Southampton nor the names of its
016 *      contributors may be used to endorse or promote products derived from this
017 *      software without specific prior written permission.
018 *
019 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
020 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
021 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
022 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
023 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
024 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
025 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
026 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
027 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
028 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
029 */
030package org.openimaj.ml.timeseries.processor;
031
032import org.apache.commons.math.stat.StatUtils;
033import org.openimaj.ml.timeseries.processor.interpolation.LinearInterpolationProcessor;
034import org.openimaj.ml.timeseries.processor.interpolation.TimeSeriesInterpolation;
035import org.openimaj.ml.timeseries.series.DoubleTimeSeries;
036
037/**
038 * Calculates a moving average over a specified window in the past such that
039 *
040 * data[t_n] = sum^{m}_{i=1}{data[t_{n-i}}
041 *
042 * This processor returns a value for each time in the underlying time series.
043 * For sensible results, consider interpolating a consistent time span using an
044 * {@link LinearInterpolationProcessor} followed by this processor.
045 *
046 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
047 *
048 */
049public class MovingAverageProcessor implements TimeSeriesProcessor<double[], Double, DoubleTimeSeries>
050{
051
052        private long length;
053
054        /**
055         * @see TimeSeriesInterpolation#TimeSeriesInterpolation(long[])
056         * @param length
057         *            the length of the window placed ending at t_n
058         */
059        public MovingAverageProcessor(long length) {
060                this.length = length;
061        }
062
063        @Override
064        public void process(DoubleTimeSeries series) {
065                final long[] times = series.getTimes();
066                final double[] data = series.getData();
067                final int size = series.size();
068                for (int i = size - 1; i >= 0; i--) {
069                        final long latest = times[i];
070                        final long earliest = latest - length;
071                        final DoubleTimeSeries spanoftime = series.get(earliest, latest);
072                        data[i] = StatUtils.mean(spanoftime.getData());
073                }
074        }
075}