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 java.util.LinkedList;
033
034import org.openimaj.ml.timeseries.TimeSeries;
035import org.openimaj.ml.timeseries.TimeSeriesArithmaticOperator;
036import org.openimaj.ml.timeseries.collection.TimeSeriesCollectionAssignable;
037
038/**
039 * Given time step calculate each timestep such that 
040 * value[timeStep(x)] = sum from x-1 to x as n [ timeStep(n) ]
041 * 
042 * The exact meaning of "sum" for any given timestep data must be defined. This processor works on any time series, 
043 * a function must be implemented to explain how TimeSeries data is to be added.
044 * 
045 * This processor implicity assumes that the first time step is "the beggining of the time series"
046 * 
047 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
048 * 
049 * @param <ALLDATA>
050 * @param <DATA>
051 * @param <TS>
052 */
053public class IntervalSummationProcessor
054        <
055                ALLDATA,
056                DATA,
057                TS extends 
058                        TimeSeries<ALLDATA,DATA,TS> 
059                        & TimeSeriesArithmaticOperator<DATA,TS> 
060                        & TimeSeriesCollectionAssignable<DATA,TS>
061        > 
062        implements TimeSeriesProcessor<ALLDATA,DATA, TS>{
063        
064        private long[] times;
065
066        /**
067         * A processor which maps across given time steps
068         * @param times
069         */
070        public IntervalSummationProcessor(long[] times) {
071                this.times = times;
072        }
073        
074        @Override
075        public void process(TS series) {
076                LinkedList<Long> times = new LinkedList<Long>();
077                LinkedList<DATA> data  = new LinkedList<DATA>();
078                times.addLast(this.times[0]);           
079                long firstTime = series.getTimes()[0];
080                
081                TS interval = series.get(firstTime,this.times[0]);
082                
083                long previousTime = -1;
084                if(interval.size() > 0){
085                        long[] intervalTimes = interval.getTimes();
086                        previousTime = intervalTimes[intervalTimes.length - 1] + 1;
087                }
088                else{
089                        previousTime = this.times[0] + 1;
090                }
091                
092                data.addLast(interval.sum());
093                for (int i = 1; i < this.times.length; i++) {
094                        long currentTime = this.times[i];
095                        interval = series.get(previousTime,currentTime);
096                        if(interval.size() > 0){
097                                long[] intervalTimes = interval.getTimes();
098                                previousTime = intervalTimes[intervalTimes.length - 1] + 1;
099                        }
100                        else{
101                                previousTime = currentTime + 1;
102                        }
103                        times.add(currentTime);
104                        data.add(interval.sum());
105                }
106                series.internalAssign(times,data);
107        }
108}