SlicerCollapse.java

/*
    BeepBeep, an event stream processor
    Copyright (C) 2008-2017 Sylvain Hallé

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU Lesser General Public License as published
    by the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU Lesser General Public License for more details.

    You should have received a copy of the GNU Lesser General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */
package basic;

import ca.uqac.lif.cep.Connector;
import static ca.uqac.lif.cep.Connector.INPUT;
import static ca.uqac.lif.cep.Connector.OUTPUT;
import ca.uqac.lif.cep.GroupProcessor;
import ca.uqac.lif.cep.Pullable;
import ca.uqac.lif.cep.functions.Constant;
import ca.uqac.lif.cep.functions.Cumulate;
import ca.uqac.lif.cep.functions.CumulativeFunction;
import ca.uqac.lif.cep.functions.Function;
import ca.uqac.lif.cep.functions.ApplyFunction;
import ca.uqac.lif.cep.functions.IdentityFunction;
import ca.uqac.lif.cep.functions.TurnInto;
import ca.uqac.lif.cep.util.Maps.Values;
import ca.uqac.lif.cep.util.Numbers;
import ca.uqac.lif.cep.util.Bags.RunOn;
import ca.uqac.lif.cep.tmf.QueueSource;
import ca.uqac.lif.cep.tmf.Slice;

/**
 * Apply an aggregation function on the output of a slicer.
 * <p>
 * You should first read {@link SlicerSimple}. This example starts in the
 * same way; however, it shows how the values of each slice can be aggregated.
 * This is done by first extracting the image (i.e. set of values in each
 * key-value pair) of the map, and then applying a
 * {@link ca.uqac.lif.cep.sets.CumulateOnSet CumulateOnSet} function on the
 * resulting set.
 * <p>
 * In this program, the slicing function is the identity, and the processor
 * given to the slicer is a simple counter that increments every time an event
 * is received. Since there is one such counter for each different input
 * event, the slicer effectively maintains the count of how many times each
 * value has been seen in its input stream. Graphically, this can be
 * represented as: 
 * <p>
 * <img src="./doc-files/basic/SlicerCollapse.png" alt="Processor chain">
 * <p>
 * The expected output of this program is:
 * <pre>
 * 1.0
 * 1.0
 * 1.0
 * 1.0
 * 1.0
 * 2.0
 * 2.0
 * 3.0
 * 3.0
 * &hellip;
 * </pre>
 * @author Sylvain Hallé
 * @difficulty Medium
 */
public class SlicerCollapse 
{
    public static void main(String[] args)
    {
        /* We first setup a stream of numbers to be used as a source */
        QueueSource source = new QueueSource();
        source.setEvents(1, 6, 4, 3, 2, 1, 9);
        
        /* The function we'll use to create slices is the identity.
         * This will create one distinct subtrace per number .*/
        Function slice_fct = new IdentityFunction(1);
        
        /* The processor chain to apply to each subtrace is a simple
         * counter of events. */
        GroupProcessor counter = new GroupProcessor(1, 1);
        {
            TurnInto to_one = new TurnInto(new Constant(1));
            Cumulate sum = new Cumulate(new CumulativeFunction<Number>(Numbers.addition));
            Connector.connect(to_one, sum);
            counter.addProcessors(to_one, sum);
            counter.associateInput(INPUT, to_one, INPUT);
            counter.associateOutput(OUTPUT, sum, OUTPUT);
        }
        
        /* Create the slicer processor, by giving it the slicing function and
         * the processor to apply on each slide. */
        Slice slicer = new Slice(slice_fct, counter);
        Connector.connect(source, slicer);
        
        /* Extract the image of the resulting map, by applying the
         * MapValues function. The result is a Multiset of all the objects
         * that occur as values in the input map. */
        ApplyFunction map_values = new ApplyFunction(Values.instance);
        Connector.connect(slicer, map_values);
        
        /* Apply the CumulateOnSet processor. This processor applies a
         * cumulative function successively on every value of the input
         * set. Here the function is Maximum, meaning that the resulting
         * event is the maximum of all values in the input set. */
        RunOn max = new RunOn(new Cumulate(new CumulativeFunction<Number>(Numbers.maximum)));
        Connector.connect(map_values, max);
        
        /* Let us now pull and print 10 events from the output. */
        Pullable p = max.getPullableOutput();
        for (int i = 0; i < 10; i++)
        {
            Object o = p.pull();
            System.out.println(o);
        }       
    }
}