source: cpp/frams/genetics/oper_fx.cpp @ 152

// This file is a part of the Framsticks GDK.
// Copyright (C) 2002-2014  Maciej Komosinski and Szymon Ulatowski.  See LICENSE.txt for details.
// Refer to http://www.framsticks.com/ for further information.

#include <ctype.h>  //isupper()
#include "oper_fx.h"
#include <common/framsg.h>
#include <common/nonstd_math.h>
#include <frams/util/rndutil.h>

static double distrib_force[]=   // for '!'
{
3,                 // distribution 0 -__/ +1
0.001, 0.2,        // "slow" neurons
0.001, 1,
    1, 1,          // "fast" neurons
};
static double distrib_inertia[]=  // for '='
{
2,                 // distribution 0 |..- +1
  0, 0,            // "fast" neurons
0.7, 0.98,
};
static double distrib_sigmo[]=  // for '/'
{
5,                 // distribution -999 -..-^-..- +999
-999, -999,        //"perceptron"
 999, 999,
  -5, -1,          // nonlinear
   1, 5,
  -1, 1,           // ~linear
};


int GenoOperators::roulette(const double *probtab,const int count)
{
   double sum=0;
   int i;
   for (i=0;i<count;i++) sum+=probtab[i];
   double sel=rnd01*sum;
   for (sum=0,i=0;i<count;i++) {sum+=probtab[i]; if (sel<sum) return i;}
   return -1;
}

bool GenoOperators::getMinMaxDef(ParamInterface *p,int i,double &mn,double &mx,double &def)
{
   mn=mx=def=0;
   int defined=0;
   if (p->type(i)[0]=='f')
   {
      double _mn=0,_mx=1,_def=0.5;
      defined=p->getMinMax(i,_mn,_mx,_def);
      if (defined==1) _mx=_mn+1.0;
      if (_mx<_mn && defined==3) _mn=_mx=_def; //only default was defined, let's assume min=max=default
      if (defined<3) _def=(_mn+_mx)/2.0;
      mn=_mn; mx=_mx; def=_def;
   }
   if (p->type(i)[0]=='d')
   {
      long _mn=0,_mx=1,_def=0;
      defined=p->getMinMax(i,_mn,_mx,_def);
      if (defined==1) _mx=_mn+1;
      if (_mx<_mn && defined==3) _mn=_mx=_def; //only default was defined, let's assume min=max=default
      if (defined<3) _def=(_mn+_mx)/2;
      mn=_mn; mx=_mx; def=_def;
   }
   return defined==3;
}

int GenoOperators::selectRandomProperty(Neuro* n)
{
   int neuext=n->extraProperties().getPropCount(),
       neucls=n->getClass()==NULL?0:n->getClass()->getProperties().getPropCount();
   if (neuext+neucls==0) return -1; //no properties in this neuron
   int index=randomN(neuext+neucls);
   if (index>=neuext) index=index-neuext+100;
   return index;
}

double GenoOperators::mutateNeuProperty(double current,Neuro *n,int i)
{
   if (i==-1) return mutateCreepNoLimit('f',current,-10,10); //i==-1: mutating weight of neural connection
   Param p;
   if (i>=100) {i-=100; p=n->getClass()->getProperties();}
      else p=n->extraProperties();
   double newval=current;
   /*bool ok=*/getMutatedProperty(p,i,current,newval);
   return newval;
}

bool GenoOperators::mutatePropertyNaive(ParamInterface &p,int i)
{
   double mn,mx,df;
   if (p.type(i)[0]!='f' && p.type(i)[0]!='d') return false; //don't know how to mutate
   getMinMaxDef(&p,i,mn,mx,df);

   ExtValue ev;
   p.get(i,ev);
   ev.setDouble(mutateCreep(p.type(i)[0],ev.getDouble(),mn,mx));
   p.set(i,ev);
   return true;
}

bool GenoOperators::mutateProperty(ParamInterface &p,int i)
{
   double newval;
   ExtValue ev;
   p.get(i,ev);
   bool ok=getMutatedProperty(p,i,ev.getDouble(),newval);
   if (ok) {ev.setDouble(newval); p.set(i,ev);}
   return ok;
}

bool GenoOperators::getMutatedProperty(ParamInterface &p,int i,double oldval,double &newval)
{
   newval=0;
   if (p.type(i)[0]!='f' && p.type(i)[0]!='d') return false; //don't know how to mutate
   const char *n=p.id(i),*na=p.name(i);
     if (strcmp(n,"si")==0 && strcmp(na,"Sigmoid")==0) newval=CustomRnd(distrib_sigmo); else
     if (strcmp(n,"in")==0 && strcmp(na,"Inertia")==0) newval=CustomRnd(distrib_inertia); else
     if (strcmp(n,"fo")==0 && strcmp(na,"Force")==0) newval=CustomRnd(distrib_force); else
     {
       double mn,mx,df;
       getMinMaxDef(&p,i,mn,mx,df);
       newval=mutateCreep(p.type(i)[0],oldval,mn,mx);
     }
   return true;
}

double GenoOperators::mutateCreepNoLimit(char type,double current,double mn,double mx)
{
   double result=RndGen.Gauss(current,(mx-mn)/2/5); // /halfinterval, 5 times narrower
   if (type=='d') {result=int(result+0.5); if (result==current) result+=randomN(2)*2-1;}
    else result=floor(result*1000+0.5)/1000.0; //round
   return result;
}

double GenoOperators::mutateCreep(char type, double current, double mn, double mx)
{
        double result = mutateCreepNoLimit(type, current, mn, mx); //TODO consider that when boundary is touched (reflect/absorb below), the default precision (3 digits) may change. Is it good or bad?
        //reflect:
        if (result > mx) result = mx - (result - mx); else
                if (result<mn) result = mn + (mn - result);
        //absorb (just in case 'result' exceeded the allowed range so much):
        if (result>mx) result = mx; else
                if (result < mn) result = mn;
        return result;
}

void GenoOperators::setIntFromDoubleWithProbabilisticDithering(ParamInterface &p, int index, double value) //TODO
{
        p.setInt(index, value); //TODO value=2.5 will result in 2 but we want it to be 2 or 3 with equal probability. value=2.1 would be mostly 2, rarely 3. Careful with negative values (test it!)
}

void GenoOperators::linearMix(ParamInterface &p1, int i1, ParamInterface &p2, int i2, double proportion)
{
        if (p1.type(i1)[0] == 'f' && p2.type(i2)[0] == 'f')
        {
                double v1 = p1.getDouble(i1);
                double v2 = p2.getDouble(i2);
                p1.setDouble(i1, v1*proportion + v2*(1 - proportion));
                p2.setDouble(i2, v2*proportion + v1*(1 - proportion));
        }
        if (p1.type(i1)[0] == 'd' && p2.type(i2)[0] == 'd')
        {
                int v1 = p1.getInt(i1);
                int v2 = p2.getInt(i2);
                setIntFromDoubleWithProbabilisticDithering(p1, i1, v1*proportion + v2*(1 - proportion));
                setIntFromDoubleWithProbabilisticDithering(p2, i2, v2*proportion + v1*(1 - proportion));
        }
}


NeuroClass* GenoOperators::getRandomNeuroClass()
{
        SListTempl<NeuroClass*> active;
   for(int i=0;i<Neuro::getClassCount();i++)
      if (Neuro::getClass(i)->genactive) active+=Neuro::getClass(i);
   if (!active==0) return NULL; else return active(randomN(!active));
}

NeuroClass* GenoOperators::parseNeuroClass(char*& s)
{
   int len=strlen(s);
   int Len=0;
   NeuroClass *I=NULL;
   for(int i=0;i<Neuro::getClassCount();i++)
   {
      const char *n=Neuro::getClass(i)->name;
      int l=strlen(n);
      if (len>=l && l>Len && (strncmp(s,n,l)==0)) {I=Neuro::getClass(i); Len=l;}
   }
   s+=Len;
   return I;
}

Neuro* GenoOperators::findNeuro(const Model *m,const NeuroClass *nc)
{
  if (!m) return NULL;
  for(int i=0;i<m->getNeuroCount();i++)
     if (m->getNeuro(i)->getClass()==nc) return m->getNeuro(i);
  return NULL; //neuron of class 'nc' was not found
}

int GenoOperators::neuroClassProp(char*& s,NeuroClass *nc,bool also_v1_N_props)
{
   int len=strlen(s);
   int Len=0,I=-1;
   if (nc)
   {
      Param p=nc->getProperties();
      for(int i=0;i<p.getPropCount();i++)
      {
         const char *n=p.id(i);
         int l=strlen(n);
         if (len>=l && l>Len && (strncmp(s,n,l)==0)) {I=100+i; Len=l;}
         if (also_v1_N_props) //recognize old properties symbols /=!
         {
            if (strcmp(n,"si")==0) n="/"; else
            if (strcmp(n,"in")==0) n="="; else
            if (strcmp(n,"fo")==0) n="!";
            l=strlen(n);
            if (len>=l && l>Len && (strncmp(s,n,l)==0)) {I=100+i; Len=l;}
         }
      }
   }
   Neuro n;
   Param p=n.extraProperties();
   for(int i=0;i<p.getPropCount();i++)
   {
      const char *n=p.id(i);
      int l=strlen(n);
      if (len>=l && l>Len && (strncmp(s,n,l)==0)) {I=i; Len=l;}
   }
   s+=Len;
   return I;
}

bool GenoOperators::isWS(const char c)
{return c==' ' || c=='\n' || c=='\t' || c=='\r';}

void GenoOperators::skipWS(char *&s)
{ if (!s) FramMessage("GenoOperators","skipWS","NULL reference!",1); else
     while (isWS(*s)) s++;
}

bool GenoOperators::areAlike(char *g1,char *g2)
{
        while (*g1 || *g2)
        {
                skipWS(g1);
                skipWS(g2);
                if (*g1 != *g2) return false; //when difference
      if (!*g1 && !*g2) break; //both end
      g1++;
      g2++;
        }
        return true; //equal
}

char* GenoOperators::strchrn0(const char *str,char ch)
{ return ch==0?NULL:strchr((char*)str,ch); }

bool GenoOperators::isNeuroClassName(const char firstchar)
{
   return isupper(firstchar) || firstchar=='|' || firstchar=='@' || firstchar=='*';
}