source: branches/OKBJavaConnector/ECJClient/src/ec/vector/ByteVectorIndividual.java @ 13067

/*
  Copyright 2006 by Sean Luke and George Mason University
  Licensed under the Academic Free License version 3.0
  See the file "LICENSE" for more information
*/


package ec.vector;

import ec.*;
import ec.util.*;
import java.io.*;

/*
 * ByteVectorIndividual.java
 * Created: Tue Mar 13 15:03:12 EST 2001
 */

/**
 * ByteVectorIndividual is a VectorIndividual whose genome is an array of bytes.
 * Gene values may range from species.mingene(x) to species.maxgene(x), inclusive.
 * The default mutation method randomizes genes to new values in this range,
 * with <tt>species.mutationProbability</tt>.

 * <P><b>From ec.Individual:</b>  
 *
 * <p>In addition to serialization for checkpointing, Individuals may read and write themselves to streams in three ways.
 *
 * <ul>
 * <li><b>writeIndividual(...,DataOutput)/readIndividual(...,DataInput)</b>&nbsp;&nbsp;&nbsp;This method
 * transmits or receives an individual in binary.  It is the most efficient approach to sending
 * individuals over networks, etc.  These methods write the evaluated flag and the fitness, then
 * call <b>readGenotype/writeGenotype</b>, which you must implement to write those parts of your 
 * Individual special to your functions-- the default versions of readGenotype/writeGenotype throw errors.
 * You don't need to implement them if you don't plan on using read/writeIndividual.
 *
 * <li><b>printIndividual(...,PrintWriter)/readIndividual(...,LineNumberReader)</b>&nbsp;&nbsp;&nbsp;This
 * approach transmits or receives an indivdual in text encoded such that the individual is largely readable
 * by humans but can be read back in 100% by ECJ as well.  To do this, these methods will encode numbers
 * using the <tt>ec.util.Code</tt> class.  These methods are mostly used to write out populations to
 * files for inspection, slight modification, then reading back in later on.  <b>readIndividual</b> reads
 * in the fitness and the evaluation flag, then calls <b>parseGenotype</b> to read in the remaining individual.
 * You are responsible for implementing parseGenotype: the Code class is there to help you.
 * <b>printIndividual</b> writes out the fitness and evaluation flag, then calls <b>genotypeToString</b> 
 * and printlns the resultant string. You are responsible for implementing the genotypeToString method in such
 * a way that parseGenotype can read back in the individual println'd with genotypeToString.  The default form
 * of genotypeToString simply calls <b>toString</b>, which you may override instead if you like.  The default
 * form of <b>parseGenotype</b> throws an error.  You are not required to implement these methods, but without
 * them you will not be able to write individuals to files in a simultaneously computer- and human-readable fashion.
 *
 * <li><b>printIndividualForHumans(...,PrintWriter)</b>&nbsp;&nbsp;&nbsp;This
 * approach prints an individual in a fashion intended for human consumption only.
 * <b>printIndividualForHumans</b> writes out the fitness and evaluation flag, then calls <b>genotypeToStringForHumans</b> 
 * and printlns the resultant string. You are responsible for implementing the genotypeToStringForHumans method.
 * The default form of genotypeToStringForHumans simply calls <b>toString</b>, which you may override instead if you like
 * (though note that genotypeToString's default also calls toString).  You should handle one of these methods properly
 * to ensure individuals can be printed by ECJ.
 * </ul>

 * <p>In general, the various readers and writers do three things: they tell the Fitness to read/write itself,
 * they read/write the evaluated flag, and they read/write the gene array.  If you add instance variables to
 * a VectorIndividual or subclass, you'll need to read/write those variables as well.
 <p><b>Default Base</b><br>
 vector.byte-vect-ind

 * @author Liviu Panait
 * @author Sean Luke
 * @version 1.0
 */

public class ByteVectorIndividual extends VectorIndividual
    {
    public static final String P_BYTEVECTORINDIVIDUAL = "byte-vect-ind";
    public byte[] genome;
    
    public Parameter defaultBase()
        {
        return VectorDefaults.base().push(P_BYTEVECTORINDIVIDUAL);
        }

    public Object clone()
        {
        ByteVectorIndividual myobj = (ByteVectorIndividual) (super.clone());

        // must clone the genome
        myobj.genome = (byte[])(genome.clone());
        
        return myobj;
        } 

    public void setup(final EvolutionState state, final Parameter base)
        {
        super.setup(state,base);  // actually unnecessary (Individual.setup() is empty)

        // since IntegerVectorSpecies set its constraint values BEFORE it called
        // super.setup(...) [which in turn called our setup(...)], we know that
        // stuff like genomeSize has already been set...
        
        Parameter def = defaultBase();
        
        if (!(species instanceof IntegerVectorSpecies)) 
            state.output.fatal("ByteVectorIndividual requires an IntegerVectorSpecies", base, def);
        IntegerVectorSpecies s = (IntegerVectorSpecies) species;
        
        genome = new byte[s.genomeSize];
        }
        
    public void defaultCrossover(EvolutionState state, int thread, VectorIndividual ind)
        {
        IntegerVectorSpecies s = (IntegerVectorSpecies) species;
        ByteVectorIndividual i = (ByteVectorIndividual) ind;
        byte tmp;
        int point;

        if (genome.length != i.genome.length)
            state.output.fatal("Genome lengths are not the same for fixed-length vector crossover");
        switch(s.crossoverType)
            {
            case VectorSpecies.C_ONE_POINT:
                point = state.random[thread].nextInt((genome.length / s.chunksize)+1);
                for(int x=0;x<point*s.chunksize;x++)
                    { 
                    tmp = i.genome[x];
                    i.genome[x] = genome[x]; 
                    genome[x] = tmp; 
                    }
                break;
            case VectorSpecies.C_TWO_POINT: 
                int point0 = state.random[thread].nextInt((genome.length / s.chunksize)+1);
                point = state.random[thread].nextInt((genome.length / s.chunksize)+1);
                if (point0 > point) { int p = point0; point0 = point; point = p; }
                for(int x=point0*s.chunksize;x<point*s.chunksize;x++)
                    {
                    tmp = i.genome[x];
                    i.genome[x] = genome[x];
                    genome[x] = tmp;
                    }
                break;
            case VectorSpecies.C_ANY_POINT:
                for(int x=0;x<genome.length/s.chunksize;x++) 
                    if (state.random[thread].nextBoolean(s.crossoverProbability))
                        for(int y=x*s.chunksize;y<(x+1)*s.chunksize;y++)
                            {
                            tmp = i.genome[y];
                            i.genome[y] = genome[y];
                            genome[y] = tmp;
                            }
                break;
            case VectorSpecies.C_LINE_RECOMB:
            {
            double alpha = state.random[thread].nextDouble() * (1 + 2*s.lineDistance) - s.lineDistance;
            double beta = state.random[thread].nextDouble() * (1 + 2*s.lineDistance) - s.lineDistance;
            long t,u;
            long min, max;
            for (int x = 0; x < genome.length; x++)
                {
                min = s.minGene(x);
                max = s.maxGene(x);
                t = (long) Math.floor(alpha * genome[x] + (1 - alpha) * i.genome[x] + 0.5);
                u = (long) Math.floor(beta * i.genome[x] + (1 - beta) * genome[x] + 0.5);
                if (!(t < min || t > max || u < min || u > max))
                    {
                    genome[x] = (byte) t;
                    i.genome[x] = (byte) u; 
                    }
                }
            }
            break;
            case VectorSpecies.C_INTERMED_RECOMB:
            {
            long t,u;
            long min, max;
            for (int x = 0; x < genome.length; x++)
                {
                do
                    {
                    double alpha = state.random[thread].nextDouble() * (1 + 2*s.lineDistance) - s.lineDistance;
                    double beta = state.random[thread].nextDouble() * (1 + 2*s.lineDistance) - s.lineDistance;
                    min = s.minGene(x);
                    max = s.maxGene(x);
                    t = (long) Math.floor(alpha * genome[x] + (1 - alpha) * i.genome[x] + 0.5);
                    u = (long) Math.floor(beta * i.genome[x] + (1 - beta) * genome[x] + 0.5);
                    } while (t < min || t > max || u < min || u > max);
                genome[x] = (byte) t;
                i.genome[x] = (byte) u; 
                }
            }
            break;
            }
        }

    /** Splits the genome into n pieces, according to points, which *must* be sorted. 
        pieces.length must be 1 + points.length */
    public void split(int[] points, Object[] pieces)
        {
        int point0, point1;
        point0 = 0; point1 = points[0];
        for(int x=0;x<pieces.length;x++)
            {
            pieces[x] = new byte[point1-point0];
            System.arraycopy(genome,point0,pieces[x],0,point1-point0);
            point0 = point1;
            if (x >=pieces.length-2)
                point1 = genome.length;
            else point1 = points[x+1];
            }
        }
    
    /** Joins the n pieces and sets the genome to their concatenation.*/
    public void join(Object[] pieces)
        {
        int sum=0;
        for(int x=0;x<pieces.length;x++)
            sum += ((byte[])(pieces[x])).length;
        
        int runningsum = 0;
        byte[] newgenome = new byte[sum];
        for(int x=0;x<pieces.length;x++)
            {
            System.arraycopy(pieces[x], 0, newgenome, runningsum, ((byte[])(pieces[x])).length);
            runningsum += ((byte[])(pieces[x])).length;
            }
        // set genome
        genome = newgenome;
        }

    /** Destructively mutates the individual in some default manner.  The default form
        simply randomizes genes to a uniform distribution from the min and max of the gene values. */
    public void defaultMutate(EvolutionState state, int thread)
        {
        IntegerVectorSpecies s = (IntegerVectorSpecies) species;
        if (s.mutationProbability>0.0)
            for(int x=0;x<genome.length;x++)
                if (state.random[thread].nextBoolean(s.mutationProbability))
                    genome[x] = (byte)((int)s.minGene(x) + state.random[thread].nextInt((int)s.maxGene(x)-(int)s.minGene(x)+1));
        }
        
    

    /** Initializes the individual by randomly choosing Bytes uniformly from mingene to maxgene. */
    public void reset(EvolutionState state, int thread)
        {
        IntegerVectorSpecies s = (IntegerVectorSpecies) species;
        for(int x=0;x<genome.length;x++)
            genome[x] = (byte)((int)s.minGene(x) + state.random[thread].nextInt((int)s.maxGene(x)-(int)s.minGene(x)+1));
        }

    public int hashCode()
        {
        // stolen from GPIndividual.  It's a decent algorithm.
        int hash = this.getClass().hashCode();

        hash = ( hash << 1 | hash >>> 31 );
        for(int x=0;x<genome.length;x++)
            hash = ( hash << 1 | hash >>> 31 ) ^ genome[x];

        return hash;
        }

    public String genotypeToStringForHumans()
        {
        String s = "";
        for( int i = 0 ; i < genome.length ; i++ )
            s = s + " " + genome[i];
        return s;
        }
        
    public String genotypeToString()
        {
        StringBuffer s = new StringBuffer();
        s.append( Code.encode( genome.length ) );
        for( int i = 0 ; i < genome.length ; i++ )
            s.append( Code.encode( genome[i] ) );
        return s.toString();
        }
                
    protected void parseGenotype(final EvolutionState state,
        final LineNumberReader reader) throws IOException
        {
        // read in the next line.  The first item is the number of genes
        String s = reader.readLine();
        DecodeReturn d = new DecodeReturn(s);
        Code.decode( d );
        int lll = (int)(d.l);

        genome = new byte[ lll ];

        // read in the genes
        for( int i = 0 ; i < genome.length ; i++ )
            {
            Code.decode( d );
            genome[i] = (byte)(d.l);
            }
        }

    public boolean equals(Object ind)
        {
        if (!(this.getClass().equals(ind.getClass()))) return false; // SimpleRuleIndividuals are special.
        ByteVectorIndividual i = (ByteVectorIndividual)ind;
        if( genome.length != i.genome.length )
            return false;
        for( int j = 0 ; j < genome.length ; j++ )
            if( genome[j] != i.genome[j] )
                return false;
        return true;
        }

    public Object getGenome()
        { return genome; }
    public void setGenome(Object gen)
        { genome = (byte[]) gen; }
    public int genomeLength()
        { return genome.length; }

    public void writeGenotype(final EvolutionState state,
        final DataOutput dataOutput) throws IOException
        {
        dataOutput.writeInt(genome.length);
        for(int x=0;x<genome.length;x++)
            dataOutput.writeByte(genome[x]);
        }

    public void readGenotype(final EvolutionState state,
        final DataInput dataInput) throws IOException
        {
        int len = dataInput.readInt();
        if (genome==null || genome.length != len)
            genome = new byte[len];
        for(int x=0;x<genome.length;x++)
            genome[x] = dataInput.readByte();
        }

    /** Clips each gene value to be within its specified [min,max] range. */
    public void clamp() 
        {
        IntegerVectorSpecies _species = (IntegerVectorSpecies)species;
        for (int i = 0; i < genomeLength(); i++)
            {
            byte minGene = (byte)_species.minGene(i);
            if (genome[i] < minGene)
                genome[i] = minGene;
            else 
                {
                byte maxGene = (byte)_species.maxGene(i);
                if (genome[i] > maxGene)
                    genome[i] = maxGene;
                }
            }
        }
                
    public void setGenomeLength(int len)
        {
        byte[] newGenome = new byte[len];
        System.arraycopy(genome, 0, newGenome, 0, 
            genome.length < newGenome.length ? genome.length : newGenome.length);
        genome = newGenome;
        }

    /** Returns true if each gene value is within is specified [min,max] range. */
    public boolean isInRange() 
        {
        IntegerVectorSpecies _species = (IntegerVectorSpecies)species;
        for (int i = 0; i < genomeLength(); i++)
            if (genome[i] < _species.minGene(i) ||
                genome[i] > _species.maxGene(i)) return false;
        return true;
        }

    public double distanceTo(Individual otherInd)
        {               
        if (!(otherInd instanceof ByteVectorIndividual)) 
            return super.distanceTo(otherInd);  // will return infinity!
                
        ByteVectorIndividual other = (ByteVectorIndividual) otherInd;
        byte[] otherGenome = other.genome;
        double sumSquaredDistance =0.0;
        for(int i=0; i < other.genomeLength(); i++)
            {
            long dist = this.genome[i] - (long)otherGenome[i];
            sumSquaredDistance += dist*dist;
            }
        return StrictMath.sqrt(sumSquaredDistance);
        }
    }