void IpoptData::SetTrialEqMultipliersFromStep(Number alpha,
const Vector& delta_y_c,
const Vector& delta_y_d)
{
DBG_ASSERT(have_prototypes_);
SmartPtr<IteratesVector> newvec = trial()->MakeNewContainer();
newvec->create_new_y_c();
newvec->y_c_NonConst()->AddTwoVectors(1., *curr()->y_c(), alpha, delta_y_c, 0.);
newvec->create_new_y_d();
newvec->y_d_NonConst()->AddTwoVectors(1., *curr()->y_d(), alpha, delta_y_d, 0.);
set_trial(newvec);
}
U_NAMESPACE_BEGIN
/*
These are the rolled-up versions of the uniform binary search.
Someday, if we need more performance, we can un-roll them.
Note: I put these in the base class, so they only have to
be written once. Since the base class doesn't define the
segment table, these routines assume that it's right after
the binary search header.
Another way to do this is to put each of these routines in one
of the derived classes, and implement it in the others by casting
the "this" pointer to the type that has the implementation.
*/
const LookupSegment *BinarySearchLookupTable::lookupSegment(const LETableReference &base, const LookupSegment *segments, LEGlyphID glyph, LEErrorCode &success) const
{
le_int16 unity = SWAPW(unitSize);
le_int16 probe = SWAPW(searchRange);
le_int16 extra = SWAPW(rangeShift);
TTGlyphID ttGlyph = (TTGlyphID) LE_GET_GLYPH(glyph);
LEReferenceTo<LookupSegment> entry(base, success, segments);
LEReferenceTo<LookupSegment> trial(entry, success, extra);
if(LE_FAILURE(success)) return NULL;
if (SWAPW(trial->lastGlyph) <= ttGlyph) {
entry = trial;
}
while (probe > unity && LE_SUCCESS(success)) {
probe >>= 1;
trial = entry; // copy
trial.addOffset(probe, success);
if (SWAPW(trial->lastGlyph) <= ttGlyph) {
entry = trial;
}
}
if (SWAPW(entry->firstGlyph) <= ttGlyph) {
return entry.getAlias();
}
return NULL;
}
void
rename_if_collision(SymbolTableObject *sto, SymbolTable *new_st)
{
LString orig = sto->get_name();
if (orig == emptyLString || !new_st->has_lookup_table_member(orig))
return;
for (IInteger serial = 0; ; ++serial) {
String suffix;
serial.write(suffix, 10);
LString trial(orig + suffix);
if (!new_st->has_lookup_table_member(trial)) {
sto->set_name(trial);
return;
}
}
}
void PDP::average(Function* target, int attempts, double rate)
{
int successes = 0;
int adjustments;
double total = 0;
for(int i = 0; i < attempts; i++) {
adjustments = trial(target, 1, rate);
if(adjustments != FAILURE) {
successes++;
total += adjustments;
}
}
if(successes > 0)
cout << "The average is " << total / successes << " for "
<< successes << " successes out of " << attempts
<< " attempts." << endl;
else
cout << "No successes out of " << attempts << " attempts." << endl;
}
void IpoptData::SetTrialBoundMultipliersFromStep(Number alpha,
const Vector& delta_z_L,
const Vector& delta_z_U,
const Vector& delta_v_L,
const Vector& delta_v_U)
{
DBG_ASSERT(have_prototypes_);
SmartPtr<IteratesVector> newvec = trial()->MakeNewContainer();
newvec->create_new_z_L();
newvec->z_L_NonConst()->AddTwoVectors(1., *curr()->z_L(), alpha, delta_z_L, 0.);
newvec->create_new_z_U();
newvec->z_U_NonConst()->AddTwoVectors(1., *curr()->z_U(), alpha, delta_z_U, 0.);
newvec->create_new_v_L();
newvec->v_L_NonConst()->AddTwoVectors(1., *curr()->v_L(), alpha, delta_v_L, 0.);
newvec->create_new_v_U();
newvec->v_U_NonConst()->AddTwoVectors(1., *curr()->v_U(), alpha, delta_v_U, 0.);
set_trial(newvec);
}
UIntT num_replications(RealT eps, UIntT r0, ::dcs::iterator::iterator_range< ::dcs::iterator::any_forward_iterator<RealT> > const& s0_range, RealT level=0.95, UIntT max_trials=::std::numeric_limits<UIntT>::max())
{
if (r0 < 2)
{
return r0;
}
UIntT r_max(0);
for (
::dcs::iterator::any_forward_iterator<RealT> it = s0_range.begin();
it != s0_range.end();
++it
) {
RealT r(0);
UIntT trial(0);
do
{
++r0;
::dcs::math::stats::students_t_distribution<RealT> dist(r0-1);
RealT t = dist.quantile((1+level)/RealT(2));
r = t*(*it)/eps;
r *= r;
++trial;
}
while (r0 < r && trial < max_trials);
r_max = ::std::max(r0, r_max);
}
return r_max;
}
UIntT num_replications(RealT eps, UIntT r0, RealT s0, RealT level=0.95, UIntT max_trials=::std::numeric_limits<UIntT>::max())
{
if (r0 < 2)
{
return r0;
}
RealT r(0);
UIntT trial(0);
while (r0 < r && trial < max_trials)
{
::dcs::math::stats::students_t_distribution<RealT> dist(r0-1);
RealT t = dist.quantile((1+level)/RealT(2));
r = t*s0/eps;
r *= r;
++trial;
++r0;
}
return r0;
}
/* stress test the realloc routine */
static int stressTestRealloc( void )
{
const size_t nmax = 256;
int keep = lalDebugLevel;
v = NULL;
XLALClobberDebugLevel(lalDebugLevel | LALMEMDBGBIT | LALMEMPADBIT | LALMEMTRKBIT);
XLALClobberDebugLevel(lalDebugLevel & ~LALMEMINFOBIT);
/* ascending */
for ( n = 1; n <= nmax; ++n )
{
size_t *u;
trial( v = LALRealloc( v, n * sizeof( *v ) ), 0, "" );
trial( u = v[n - 1] = LALRealloc( NULL, n * sizeof( **v ) ), 0, "" );
for ( i = 0; i < n; ++i ) u[i] = n - 1;
for ( i = 0; i < n; ++i )
{
trial( u = v[i] = LALRealloc( v[i], n * sizeof( *u ) ), 0, "" );
for ( j = 0; j < n - 1; ++j )
if ( u[j] != n - 1 ) die( wrong contents );
for ( j = 0; j < n; ++j )
u[j] = n;
}
}
for ( n = 0; n < nmax; ++n )
{
trial( v[n] = LALRealloc( v[n], 0 ), 0, "" );
}
trial( v = LALRealloc( v, 0 ), 0, "" );
trial( LALCheckMemoryLeaks(), 0, "" );
XLALClobberDebugLevel(keep);
return 0;
}
/* does end of month actions */
void passmonth(char &clearformess,char canseethings)
{
short oldlaw[LAWNUM];
memmove(oldlaw,law,sizeof(short)*LAWNUM);
int l, v, p;
//TIME ADVANCE
day=1;
month++;
if(month==13)
{
month=1;
year++;
}
switch(endgamestate)
{
case ENDGAME_NONE:
if(publicmood(-1)>60)
{
endgamestate=ENDGAME_CCS_APPEARANCE;
attitude[VIEW_CONSERVATIVECRIMESQUAD]=0;
}
break;
case ENDGAME_CCS_APPEARANCE:
if(publicmood(-1)>80)
endgamestate=ENDGAME_CCS_ATTACKS;
break;
case ENDGAME_CCS_ATTACKS:
if(publicmood(-1)>90)
endgamestate=ENDGAME_CCS_SIEGES;
break;
case ENDGAME_CCS_SIEGES:
case ENDGAME_CCS_DEFEATED:
//if(publicmood(-1)>85&&presparty!=LIBERAL_PARTY)
// endgamestate=ENDGAME_MARTIALLAW;
break;
}
//CLEAR RENT EXEMPTIONS
for(l=0;l<len(location);l++) location[l]->newrental=0;
//YOUR PAPER AND PUBLIC OPINION AND STUFF
vector<int> nploc;
for(l=0;l<len(location);l++)
{
if((location[l]->compound_walls & COMPOUND_PRINTINGPRESS)&&
!location[l]->siege.siege&&
location[l]->renting!=RENTING_CCS) nploc.push_back(l);
}
// Check for game over
endcheck(END_DEAD);
dispersalcheck(clearformess);
int guardianpower=0;
if(len(nploc)&&!disbanding)
{
//DO SPECIAL EDITIONS
int loottypeindex=choosespecialedition(clearformess);
if(loottypeindex!=-1)
{
guardianpower+=10*len(nploc);
printnews(loottypeindex,len(nploc));
if(loottype[loottypeindex]->get_idname()=="LOOT_INTHQDISK"|| //For special edition xml file? -XML
loottype[loottypeindex]->get_idname()=="LOOT_SECRETDOCUMENTS")
{
for(int l=0;l<len(nploc);l++)
criminalizepool(LAWFLAG_TREASON,-1,nploc[l]);
}
}
}
int libpower[VIEWNUM]={0};
//STORIES STALE EVEN IF NOT PRINTED
for(v=0;v<VIEWNUM;v++)public_interest[v]/=2;
int conspower=200-attitude[VIEW_AMRADIO]-attitude[VIEW_CABLENEWS];
//HAVING SLEEPERS
for(int pl=len(pool)-1;pl>0;pl--)
if(pool[pl]->alive&&(pool[pl]->flag & CREATUREFLAG_SLEEPER))
sleepereffect(*pool[pl],clearformess,canseethings,libpower);
//Manage graffiti
for(int l=0;l<len(location);l++) // Check each location
{
for(int c=len(location[l]->changes)-1;c>=0;c--) // Each change to the map
{
if(location[l]->changes[c].flag==SITEBLOCK_GRAFFITI||
location[l]->changes[c].flag==SITEBLOCK_GRAFFITI_CCS||
location[l]->changes[c].flag==SITEBLOCK_GRAFFITI_OTHER) // Find changes that refer specifically to graffiti
{
int power=0,align=0;
if(location[l]->changes[c].flag==SITEBLOCK_GRAFFITI) align=1;
if(location[l]->changes[c].flag==SITEBLOCK_GRAFFITI_CCS) align=-1;
//Purge graffiti from more secure sites (or from non-secure
//sites about once every five years), but these will
//influence people more for the current month
if(securityable(location[l]->type))
{
location[l]->changes.erase(location[l]->changes.begin()+c);
power=5;
}
else
{
if(location[l]->renting==RENTING_CCS)
location[l]->changes[c].flag=SITEBLOCK_GRAFFITI_CCS; // Convert to CCS tags
else if(location[l]->renting==RENTING_PERMANENT)
location[l]->changes[c].flag=SITEBLOCK_GRAFFITI; // Convert to LCS tags
else
{
power=1;
if(!LCSrandom(10))
location[l]->changes[c].flag=SITEBLOCK_GRAFFITI_OTHER; // Convert to other tags
if(!LCSrandom(10)&&endgamestate<ENDGAME_CCS_DEFEATED&&endgamestate>0)
location[l]->changes[c].flag=SITEBLOCK_GRAFFITI_CCS; // Convert to CCS tags
if(!LCSrandom(30))
location[l]->changes.erase(location[l]->changes.begin()+c); // Clean up
}
}
if(align==1)
{
background_liberal_influence[VIEW_LIBERALCRIMESQUAD]+=power;
background_liberal_influence[VIEW_CONSERVATIVECRIMESQUAD]+=power;
}
else if(align==-1)
{
background_liberal_influence[VIEW_LIBERALCRIMESQUAD]-=power;
background_liberal_influence[VIEW_CONSERVATIVECRIMESQUAD]-=power;
}
}
}
}
int mediabalance=0;
int issuebalance[VIEWNUM-5];
//int stimulus=0;
//double cost=0;
//double tax=0;
//PUBLIC OPINION NATURAL MOVES
for(v=0;v<VIEWNUM;v++)
{
// Liberal essays add their power to the effect of sleepers
libpower[v]+=background_liberal_influence[v];
background_liberal_influence[v]=static_cast<short>(background_liberal_influence[v]*0.66);
if(v==VIEW_LIBERALCRIMESQUADPOS) continue;
if(v==VIEW_LIBERALCRIMESQUAD) continue;
//if(v==VIEW_POLITICALVIOLENCE)
//{
// change_public_opinion(VIEW_POLITICALVIOLENCE,-1,0);
// continue;
//}
if(v==VIEW_CONSERVATIVECRIMESQUAD) continue;
if(v!=VIEW_AMRADIO&&v!=VIEW_CABLENEWS)
{
issuebalance[v] = libpower[v] - conspower;
mediabalance += issuebalance[v];
// Heavy randomization -- balance of power just biases the roll
int roll = issuebalance[v] + LCSrandom(400)-200;
// If +/-50 to either side, that side wins the tug-of-war
if(roll < -50)
change_public_opinion(v,-1,0);
else if(roll > 50)
change_public_opinion(v,1,0);
else // Else random movement
change_public_opinion(v,LCSrandom(2)*2-1,0);
}
// AM Radio and Cable News popularity slowly shift to reflect public
// opinion over time -- if left unchecked, their subtle influence
// on society will become a self-perpetuating Conservative nightmare!
else if(v==VIEW_AMRADIO||v==VIEW_CABLENEWS)
{
if(publicmood(-1)<attitude[v])change_public_opinion(v,-1);
else change_public_opinion(v,1);
}
}
// Temporary Stalinizing Code (TODO: Implement the Stalinist Comrade Squad for changing public opinion, then remove this)
if(stalinmode) for(int v=0;v<VIEWNUM-3;v++)
{
if(stalinview(v,false)) { if((attitude[v]+=3)>100) attitude[v]=100; }
else { if(--attitude[v]<0) attitude[v]=0; }
}
// End Temporary Stalinizing Code (TODO: Implement the Stalinist Comrade Squad for changing public opinion, then remove this)
// Seduction monthly experience stipends for those liberals
// who have been getting it on with their love slaves/masters
// in the background
for(int s=0;s<len(pool);s++)
{
pool[s]->train(SKILL_SEDUCTION,loveslaves(*pool[s])*5);
if(pool[s]->flag & CREATUREFLAG_LOVESLAVE)
pool[s]->train(SKILL_SEDUCTION,5);
}
/*******************************************************
* INTELLIGENCE REPORT *
* ONLY IF SHOWMECHANICS OR SHOWWAIT IS DEFINED *
* EYES ONLY - LCS PROPERTY - TOP SECRET *
*******************************************************/
#if defined(SHOWMECHANICS) || defined(SHOWWAIT)
if(canseethings)
{
music.play(MUSIC_ELECTIONS);
erase();
set_color(COLOR_WHITE,COLOR_BLACK,1);
mvaddstr(0,23,"LCS MONTHLY INTELLIGENCE REPORT");
mvaddstr(2,27,"CURRENT POLITICAL TRENDS");
int numviews=(endgamestate>=ENDGAME_CCS_DEFEATED||newscherrybusted<2)?VIEWNUM-1:VIEWNUM;
for(int v=-1-stalinmode,y=4,x=0,pip;v<numviews;v++)
{
if((y-4)*2>=numviews+1+stalinmode) y=4,x=40;
for(pip=2;pip>=-2;pip--)
{
set_alignment_color(pip,true);
if(pip==2) mvaddchar(y,x+22,'\x11');
addstr("ÄÄÄ");
if(pip==-2) addchar('\x10');
}
if(v>=0) pip=14-(attitude[v]*14)/100;
else pip=14-(publicmood(v)*14)/100;
set_alignment_color((14-pip)/3-2,true);
mvaddstr(y,x,getview(v,false));
mvaddchar(y++,x+23+pip,'O');
}
set_color(COLOR_GREEN,COLOR_BLACK,1);
mvaddstr(23,0,"Elite Liberal ");
set_color(COLOR_WHITE,COLOR_BLACK,0);
addstr("- ");
set_color(COLOR_CYAN,COLOR_BLACK,1);
addstr("Liberal ");
set_color(COLOR_WHITE,COLOR_BLACK,0);
addstr("- ");
set_color(COLOR_YELLOW,COLOR_BLACK,1);
addstr("moderate ");
set_color(COLOR_WHITE,COLOR_BLACK,0);
addstr("- ");
set_color(COLOR_MAGENTA,COLOR_BLACK,1);
addstr("Conservative ");
set_color(COLOR_WHITE,COLOR_BLACK,0);
addstr("- ");
set_color(COLOR_RED,COLOR_BLACK,1);
addstr("Arch-Conservative");
set_color(COLOR_WHITE,COLOR_BLACK,0);
mvaddstr(24,0,"Press any key to reflect on these poll numbers.");
clearformess=1;
getkey();
}
#endif
/*******************************************************
* *
* END INTELLIGENCE REPORT *
* *
*******************************************************/
//ELECTIONS
if(month==11){elections(clearformess,canseethings);clearformess=1;}
//SUPREME COURT
if(month==6){supremecourt(clearformess,canseethings);clearformess=1;}
//CONGRESS
congress(clearformess,canseethings);clearformess=1;
//DID YOU WIN?
if(wincheck())
{
liberalagenda(1);
savehighscore(END_WON);
reset(savefile_name);
viewhighscores();
end_game();
}
//CONTROL LONG DISBANDS
if(disbanding&&year-disbandtime>=50)
{
music.play(MUSIC_DEFEAT);
set_color(COLOR_WHITE,COLOR_BLACK,1);
erase();
move(12,10);
addstr("The Liberal Crime Squad is now just a memory.", gamelog);
gamelog.newline();
getkey();
set_color(COLOR_WHITE,COLOR_BLACK,0);
erase();
move(12,12);
addstr("The last LCS members have all been hunted down.", gamelog);
gamelog.newline();
getkey();
set_color(COLOR_BLACK,COLOR_BLACK,1);
erase();
move(12,14);
addstr("They will never see the utopia they dreamed of...", gamelog);
gamelog.newline();
gamelog.nextMessage();
getkey();
savehighscore(END_DISBANDLOSS);
reset(savefile_name);
viewhighscores();
end_game();
}
//UPDATE THE WORLD IN CASE THE LAWS HAVE CHANGED
updateworld_laws(law,oldlaw);
//THE SYSTEM!
for(p=len(pool)-1;p>=0;p--)
{
if(disbanding) break;
if(!pool[p]->alive) continue;
if(pool[p]->flag & CREATUREFLAG_SLEEPER) continue;
if(pool[p]->location==-1) continue;
if(location[pool[p]->location]->type==SITE_GOVERNMENT_POLICESTATION)
{
if(clearformess) erase();
else makedelimiter();
if(pool[p]->flag & CREATUREFLAG_MISSING)
{
set_color(COLOR_MAGENTA,COLOR_BLACK,1);
move(8,1);
addstr("Cops re-polluted ", gamelog);
addstr(pool[p]->name, gamelog);
addstr("'s mind with Conservatism!", gamelog);
gamelog.nextMessage();
getkey();
removesquadinfo(*pool[p]);
delete_and_remove(pool,p);
continue;
}
else if(pool[p]->flag & CREATUREFLAG_ILLEGALALIEN && law[LAW_IMMIGRATION]!=2)
{
set_color(COLOR_MAGENTA,COLOR_BLACK,1);
move(8,1);
addstr(pool[p]->name, gamelog);
addstr(" has been shipped out to the INS to face ", gamelog);
if(law[LAW_IMMIGRATION]==-2 && law[LAW_DEATHPENALTY]==-2)
addstr("execution.", gamelog);
else addstr("deportation.", gamelog);
gamelog.newline();
getkey();
removesquadinfo(*pool[p]);
delete_and_remove(pool,p);
continue;
}
else
{
//TRY TO GET RACKETEERING CHARGE
int copstrength=100;
if(law[LAW_POLICEBEHAVIOR]==-2) copstrength=200;
if(law[LAW_POLICEBEHAVIOR]==-1) copstrength=150;
if(law[LAW_POLICEBEHAVIOR]==1) copstrength=75;
if(law[LAW_POLICEBEHAVIOR]==2) copstrength=50;
copstrength=(copstrength*pool[p]->heat)/4;
if(copstrength>200)copstrength=200;
//Confession check
if(LCSrandom(copstrength)>pool[p]->juice + pool[p]->get_attribute(ATTRIBUTE_HEART,true)*5 -
pool[p]->get_attribute(ATTRIBUTE_WISDOM,true)*5 + pool[p]->get_skill(SKILL_PSYCHOLOGY)*5
/*+ pool[p]->get_skill(SKILL_SURVIVAL)*5*/ && pool[p]->hireid!=-1)
{
int nullify=0;
int p2=getpoolcreature(pool[p]->hireid);
if(pool[p2]->alive && (pool[p2]->location==-1 || location[pool[p2]->location]->type!=SITE_GOVERNMENT_PRISON))
{ //Charge the boss with racketeering!
criminalize(*pool[p2],LAWFLAG_RACKETEERING);
//Rack up testimonies against the boss in court!
pool[p2]->confessions++;
}
if(!nullify)
{ //Issue a raid on this guy's base!
if(pool[p]->base>=0)location[pool[p]->base]->heat+=300;
set_color(COLOR_WHITE,COLOR_BLACK,1);
move(8,1);
addstr(pool[p]->name, gamelog);
addstr(" has broken under the pressure and ratted you out!", gamelog);
gamelog.newline();
getkey();
set_color(COLOR_WHITE,COLOR_BLACK,1);
move(9,1);
addstr("The traitor will testify in court, and safehouses may be compromised.", gamelog);
gamelog.nextMessage();
getkey();
removesquadinfo(*pool[p]);
delete_and_remove(pool,p);
continue; //no trial for this person; skip to next person
}
//else continue to trial
}
set_color(COLOR_WHITE,COLOR_BLACK,1);
move(8,1);
addstr(pool[p]->name, gamelog);
addstr(" is moved to the courthouse for trial.", gamelog);
gamelog.nextMessage();
getkey();
pool[p]->location=find_courthouse(*pool[p]);
Armor prisoner(*armortype[getarmortype("ARMOR_PRISONER")]);
pool[p]->give_armor(prisoner,NULL);
}
}
else if(location[pool[p]->location]->type==SITE_GOVERNMENT_COURTHOUSE)
{ trial(*pool[p]); clearformess=1; }
else if(location[pool[p]->location]->type==SITE_GOVERNMENT_PRISON)
if(prison(*pool[p])) clearformess=1;
}
//NUKE EXECUTION VICTIMS
for(p=len(pool)-1;p>=0;p--)
{
if(pool[p]->location==-1) continue;
if(location[pool[p]->location]->type==SITE_GOVERNMENT_PRISON&&!pool[p]->alive)
{
removesquadinfo(*pool[p]);
pool[p]->die();
pool[p]->location=-1;
}
}
//MUST DO AN END OF GAME CHECK HERE BECAUSE OF EXECUTIONS
endcheck(END_EXECUTED);
//DISPERSAL CHECK
dispersalcheck(clearformess);
//FUND REPORTS
if(canseethings)fundreport(clearformess);
ledger.resetMonthlyAmounts();
if(clearformess) erase();
//HEAL CLINIC PEOPLE
for(p=0;p<len(pool);p++)
{
if(disbanding) break;
if(!(pool[p]->alive)) continue;
if(pool[p]->clinic>0)
{
pool[p]->clinic--;
for(int w=0;w<BODYPARTNUM;w++)
{
if((pool[p]->wound[w]&WOUND_NASTYOFF)||(pool[p]->wound[w]&WOUND_CLEANOFF))
pool[p]->wound[w]=(char)WOUND_CLEANOFF;
else pool[p]->wound[w]=0;
}
int healthdamage = 0;
if(pool[p]->special[SPECIALWOUND_RIGHTLUNG]!=1)
{
pool[p]->special[SPECIALWOUND_RIGHTLUNG]=1;
if(LCSrandom(2)) healthdamage++;
}
if(pool[p]->special[SPECIALWOUND_LEFTLUNG]!=1)
{
pool[p]->special[SPECIALWOUND_LEFTLUNG]=1;
if(LCSrandom(2)) healthdamage++;
}
if(pool[p]->special[SPECIALWOUND_HEART]!=1)
{
pool[p]->special[SPECIALWOUND_HEART]=1;
if(LCSrandom(3)) healthdamage++;
}
pool[p]->special[SPECIALWOUND_LIVER]=1;
pool[p]->special[SPECIALWOUND_STOMACH]=1;
pool[p]->special[SPECIALWOUND_RIGHTKIDNEY]=1;
pool[p]->special[SPECIALWOUND_LEFTKIDNEY]=1;
pool[p]->special[SPECIALWOUND_SPLEEN]=1;
pool[p]->special[SPECIALWOUND_RIBS]=RIBNUM;
if(!pool[p]->special[SPECIALWOUND_NECK])
pool[p]->special[SPECIALWOUND_NECK]=2;
if(!pool[p]->special[SPECIALWOUND_UPPERSPINE])
pool[p]->special[SPECIALWOUND_UPPERSPINE]=2;
if(!pool[p]->special[SPECIALWOUND_LOWERSPINE])
pool[p]->special[SPECIALWOUND_LOWERSPINE]=2;
// Inflict permanent health damage
pool[p]->set_attribute(ATTRIBUTE_HEALTH,pool[p]->get_attribute(ATTRIBUTE_HEALTH,0)-healthdamage);
if(pool[p]->get_attribute(ATTRIBUTE_HEALTH,0)<=0)
pool[p]->set_attribute(ATTRIBUTE_HEALTH,1);
if(pool[p]->blood<=20&&pool[p]->clinic<=2)pool[p]->blood=50;
if(pool[p]->blood<=50&&pool[p]->clinic<=1)pool[p]->blood=75;
// If at clinic and in critical condition, transfer to university hospital
if(pool[p]->clinic > 2 &&
pool[p]->location > -1 &&
location[pool[p]->location]->type==SITE_HOSPITAL_CLINIC)
{
int hospital=find_hospital(*pool[p]);
if(hospital!=-1)
{
pool[p]->location=hospital;
set_color(COLOR_WHITE,COLOR_BLACK,1);
move(8,1);
addstr(pool[p]->name, gamelog);
addstr(" has been transferred to ", gamelog);
addstr(location[hospital]->name, gamelog);
addstr(".", gamelog);
gamelog.nextMessage();
getkey();
}
}
// End treatment
if(pool[p]->clinic==0)
{
pool[p]->blood=100;
if(clearformess) erase();
else makedelimiter();
set_color(COLOR_WHITE,COLOR_BLACK,1);
move(8,1);
addstr(pool[p]->name, gamelog);
addstr(" has left ", gamelog);
addstr(location[pool[p]->location]->name, gamelog);
addstr(".", gamelog);
gamelog.nextMessage();
int hs=find_homeless_shelter(*pool[p]);
if(hs==-1) hs=0; //TODO: Error unable to find location
if(location[pool[p]->base]->siege.siege||
location[pool[p]->base]->renting==RENTING_NOCONTROL)
pool[p]->base=hs;
pool[p]->location=pool[p]->base;
getkey();
}
}
}
}
void GetP3Responses(ap::template_2d_array<float,true>& signal,
ap::template_1d_array<short int,true>& trialnr,
ap::template_1d_array<double,true>& windowlen,
ap::template_1d_array<unsigned short int, true>& stimulusCode,
ap::template_1d_array<unsigned short int, true>& stimulusType,
const ap::template_1d_array<unsigned char, true>& Flashing,
const ap::template_1d_array<double, true>& channels,
const int MAfilter,
const int DecFact)
{
////////////////////////////////////////////////////////////////////////////
// Section: Define variables
int bound_min, bound_max, row, col, row_windowlen,
row_channels, numchannels, lenflash, sig_ds_len, siglen;
double val_temp;
ap::template_1d_array<float, true> vect;
ap::template_1d_array<float, true> vect_r;
ap::template_1d_array<float, true> a; //variable to store filter coefficients
ap::template_1d_array<float, true> b; //Variable to store filter coefficients
ap::template_2d_array<float, true> sig; //filtered and downsampled signal
ap::template_1d_array<float, true> y_downsampled;
ap::template_1d_array<unsigned short int, true> Code;
ap::template_1d_array<unsigned short int, true> Type;
ap::template_1d_array<short int, true> trial;
vector<int> tmp;
////////////////////////////////////////////////////////////////////////////
// Section: Get dimensions
row = signal.gethighbound(1)+1;
col = signal.gethighbound(0)+1;
row_windowlen = windowlen.gethighbound(1)+1;
row_channels = channels.gethighbound(1)+1;
numchannels = col;
lenflash = row;
////////////////////////////////////////////////////////////////////////////
// Section: Check if windowlen has only one value (e.g. windlen = 800)
if (row_windowlen == 1)
{
val_temp = windowlen(0);
windowlen.setbounds(0, row_windowlen);
windowlen(0) = 0;
windowlen(1) = val_temp;
}
////////////////////////////////////////////////////////////////////////////
// Section: Identify changes in Flashing
for (int i=1; i<lenflash; i++)
{
if (Flashing(i-1) == 0 && Flashing(i) ==1)
{
if((i+windowlen(1)-2)<lenflash)
tmp.push_back(i);
}
}
Code.setbounds(0,static_cast<int>(tmp.size())-1);
Type.setbounds(0,static_cast<int>(tmp.size())-1);
trial.setbounds(0, static_cast<int>(tmp.size())-1);
////////////////////////////////////////////////////////////////////////////
// Section: Filter and downsample the signal
sig_ds_len = ap::iceil((windowlen(1)-windowlen(0))/DecFact);
siglen = sig_ds_len * numchannels;
sig.setbounds(0,static_cast<int>(tmp.size())-1,0, siglen-1);
y_downsampled.setbounds(0, sig_ds_len-1);
// Define filter coefficients (Moving Average Filter)
a.setbounds(0, MAfilter-1);
b.setbounds(0, MAfilter-1);
for (int i=0; i<MAfilter; i++)
{
a(i) = (float) 1;
b(i) = (float) 1/MAfilter;
}
// Extract the signal according to the specified window
// then downsample and filtering it.
for(int j=0; j<static_cast<int>(tmp.size()); j++)
{
bound_min = static_cast<int>( tmp[j] + windowlen(0) - 1 );
bound_max = static_cast<int>( tmp[j] + windowlen(1) - 2 );
vect.setbounds(0, bound_max-bound_min);
vect_r.setbounds(0, bound_max-bound_min);
for (int i=0; i<col; i++)
{
ap::vmove(vect.getvector(0, bound_max-bound_min), signal.getcolumn(i, bound_min, bound_max));
filter(MAfilter-1, a, b, bound_max-bound_min, vect, vect_r);
downsampling(vect_r, DecFact, y_downsampled);
ap::vmove(sig.getrow(j, i*sig_ds_len, ((i+1)*sig_ds_len)-1), y_downsampled.getvector(0, sig_ds_len-1));
}
Code(j) = stimulusCode(tmp[j]);
Type(j) = stimulusType(tmp[j]);
trial(j) = trialnr(tmp[j]);
}
// Overwrite signal and states
signal.setbounds(0,static_cast<int>(tmp.size())-1,0, siglen-1);
stimulusCode.setbounds(0, static_cast<int>(tmp.size())-1);
stimulusType.setbounds(0, static_cast<int>(tmp.size())-1);
trialnr.setbounds(0, static_cast<int>(tmp.size())-1);
signal = sig;
stimulusCode = Code;
stimulusType = Type;
trialnr = trial;
}
void run ()
{
mask.allowed_data_types = INT_MASK;
// Main program =========================================================
params.V1.read(fn1);
params.V1().setXmippOrigin();
params.V2.read(fn2);
params.V2().setXmippOrigin();
// Initialize best_fit
double best_fit = 1e38;
Matrix1D<double> best_align(8);
bool first = true;
// Generate mask
if (mask_enabled)
{
mask.generate_mask(params.V1());
params.mask_ptr = &(mask.get_binary_mask());
}
else
params.mask_ptr = NULL;
// Exhaustive search
if (!usePowell && !useFRM)
{
// Count number of iterations
int times = 1;
if (!tell)
{
if (grey_scale0 != grey_scaleF)
times *= FLOOR(1 + (grey_scaleF - grey_scale0) / step_grey);
if (grey_shift0 != grey_shiftF)
times *= FLOOR(1 + (grey_shiftF - grey_shift0) / step_grey_shift);
if (rot0 != rotF)
times *= FLOOR(1 + (rotF - rot0) / step_rot);
if (tilt0 != tiltF)
times *= FLOOR(1 + (tiltF - tilt0) / step_tilt);
if (psi0 != psiF)
times *= FLOOR(1 + (psiF - psi0) / step_psi);
if (scale0 != scaleF)
times *= FLOOR(1 + (scaleF - scale0) / step_scale);
if (z0 != zF)
times *= FLOOR(1 + (zF - z0) / step_z);
if (y0 != yF)
times *= FLOOR(1 + (yF - y0) / step_y);
if (x0 != xF)
times *= FLOOR(1 + (xF - x0) / step_x);
init_progress_bar(times);
}
else
std::cout << "#grey_factor rot tilt psi scale z y x fitness\n";
// Iterate
int itime = 0;
int step_time = CEIL((double)times / 60.0);
Matrix1D<double> r(3);
Matrix1D<double> trial(9);
for (double grey_scale = grey_scale0; grey_scale <= grey_scaleF ; grey_scale += step_grey)
for (double grey_shift = grey_shift0; grey_shift <= grey_shiftF ; grey_shift += step_grey_shift)
for (double rot = rot0; rot <= rotF ; rot += step_rot)
for (double tilt = tilt0; tilt <= tiltF ; tilt += step_tilt)
for (double psi = psi0; psi <= psiF ; psi += step_psi)
for (double scale = scale0; scale <= scaleF ; scale += step_scale)
for (ZZ(r) = z0; ZZ(r) <= zF ; ZZ(r) += step_z)
for (YY(r) = y0; YY(r) <= yF ; YY(r) += step_y)
for (XX(r) = x0; XX(r) <= xF ; XX(r) += step_x)
{
// Form trial vector
trial(0) = grey_scale;
trial(1) = grey_shift;
trial(2) = rot;
trial(3) = tilt;
trial(4) = psi;
trial(5) = scale;
trial(6) = ZZ(r);
trial(7) = YY(r);
trial(8) = XX(r);
// Evaluate
double fit = fitness(MATRIX1D_ARRAY(trial));
// The best?
if (fit < best_fit || first)
{
best_fit = fit;
best_align = trial;
first = false;
if (tell)
std::cout << "Best so far\n";
}
// Show fit
if (tell)
std::cout << trial << " " << fit << std::endl;
else
if (++itime % step_time == 0)
progress_bar(itime);
}
if (!tell)
progress_bar(times);
}
else if (usePowell)
{
// Use Powell optimization
Matrix1D<double> x(9), steps(9);
double fitness;
int iter;
steps.initConstant(1);
if (onlyShift)
steps(0)=steps(1)=steps(2)=steps(3)=steps(4)=steps(5)=0;
if (params.alignment_method == COVARIANCE)
steps(0)=steps(1)=0;
x(0)=grey_scale0;
x(1)=grey_shift0;
x(2)=rot0;
x(3)=tilt0;
x(4)=psi0;
x(5)=scale0;
x(6)=z0;
x(7)=y0;
x(8)=x0;
powellOptimizer(x,1,9,&wrapperFitness,NULL,0.01,fitness,iter,steps,true);
best_align=x;
best_fit=fitness;
first=false;
}
else if (useFRM)
{
String scipionPython;
initializeScipionPython(scipionPython);
PyObject * pFunc = getPointerToPythonFRMFunction();
double rot,tilt,psi,x,y,z,score;
Matrix2D<double> A;
alignVolumesFRM(pFunc, params.V1(), params.V2(), Py_None, rot,tilt,psi,x,y,z,score,A,maxShift,maxFreq,params.mask_ptr);
best_align.initZeros(9);
best_align(0)=1; // Gray scale
best_align(1)=0; // Gray shift
best_align(2)=rot;
best_align(3)=tilt;
best_align(4)=psi;
best_align(5)=1; // Scale
best_align(6)=z;
best_align(7)=y;
best_align(8)=x;
best_fit=-score;
}
if (!first)
std::cout << "The best correlation is for\n"
<< "Scale : " << best_align(5) << std::endl
<< "Translation (X,Y,Z) : " << best_align(8)
<< " " << best_align(7) << " " << best_align(6)
<< std::endl
<< "Rotation (rot,tilt,psi): "
<< best_align(2) << " " << best_align(3) << " "
<< best_align(4) << std::endl
<< "Best grey scale : " << best_align(0) << std::endl
<< "Best grey shift : " << best_align(1) << std::endl
<< "Fitness value : " << best_fit << std::endl;
Matrix1D<double> r(3);
XX(r) = best_align(8);
YY(r) = best_align(7);
ZZ(r) = best_align(6);
Matrix2D<double> A,Aaux;
Euler_angles2matrix(best_align(2), best_align(3), best_align(4),
A, true);
translation3DMatrix(r,Aaux);
A = A * Aaux;
scale3DMatrix(vectorR3(best_align(5), best_align(5), best_align(5)),Aaux);
A = A * Aaux;
if (verbose!=0)
std::cout << "xmipp_transform_geometry will require the following values"
<< "\n Angles: " << best_align(2) << " "
<< best_align(3) << " " << best_align(4)
<< "\n Shifts: " << A(0,3) << " " << A(1,3) << " " << A(2,3)
<< std::endl;
if (apply)
{
applyTransformation(params.V2(),params.Vaux(),MATRIX1D_ARRAY(best_align));
params.V2()=params.Vaux();
params.V2.write(fnOut);
}
}
/* test to make sure padding does what it's supposed to do */
static int testPadding( void )
{
int keep = lalDebugLevel;
XLALClobberDebugLevel(lalDebugLevel | LALMEMDBGBIT | LALMEMPADBIT);
XLALClobberDebugLevel(lalDebugLevel & ~LALMEMTRKBIT);
/* try to free NULL pointer */
/* changed behaviour: LALFree is a no-op when passed NULL */
// trial( LALFree( NULL ), SIGSEGV, "error: tried to free NULL pointer" );
/* double free */
/* actually, this cannot be done robustly -- system can change values
* in unallocated space at will */
//trial( p = LALMalloc( 2 * sizeof( *p ) ), 0, "" );
//trial( LALFree( p ), 0, "" );
//trial( LALFree( p ), SIGSEGV, "error: tried to free a freed pointer" );
//trial( LALCheckMemoryLeaks(), 0, "" );
/* wrong magic */
trial( p = LALMalloc( 2 * sizeof( *p ) ), 0, "" );
p[-1] = 4;
trial( LALFree( p ), SIGSEGV, "error: wrong magic" );
p[-1] = 0xABadCafe;
trial( LALFree( p ), 0, "" );
trial( LALCheckMemoryLeaks(), 0, "" );
/* corrupt size */
trial( p = LALMalloc( 4 * sizeof( *p ) ), 0, "");
n = p[-2];
p[-2] = -2;
trial( LALFree( p ), SIGSEGV, "error: corrupt size descriptor" );
p[-2] = n;
trial( LALFree( p ), 0, "" );
trial( LALCheckMemoryLeaks(), 0, "" );
/* overwritten array bounds */
trial( p = LALMalloc( 8 * sizeof( *p ) ), 0, "" );
n = p[8];
p[8] = 0;
trial( LALFree( p ), SIGSEGV, "error: array bounds overwritten" );
p[8] = n;
trial( LALFree( p ), 0, "" );
trial( LALCheckMemoryLeaks(), 0, "" );
/* free too much memory */
q = malloc( 4 * sizeof( *p ) );
trial( p = LALMalloc( sizeof( *p ) ), 0, "" );
memcpy( q, p - 2, 4 * sizeof( *p ) );
trial( LALFree( p ), 0, "" );
trial( LALFree( q + 2 ), SIGSEGV, "error: lalMallocTotal too small" );
free( q );
trial( LALCheckMemoryLeaks(), 0, "" );
XLALClobberDebugLevel(keep);
return 0;
}
void Network::test_from_file (
char *dataname , // Input file name
char *outname, // Output file name
int netmod
)
{
int i, maxlin, did_any, best,name=0,win1,win2,win3 ;
float *inputs, *iptr, maxi1,maxi2,maxi3 ;
char msg[81], *line, *lptr, *tab[20];
FILE *fp_in, *fp_out, *fname;
/*
Open the file which contains the data to be classified
*/
if ((fp_in = fopen ( dataname , "rt" )) == NULL) {
strcpy ( msg , "Cannot open input data file " ) ;
strcat ( msg , dataname ) ;
error_message ( msg ) ;
return ;
}
/*
Open the file to which outputs will be written.
If it already exists, write a newline at its end.
*/
fp_out = fopen ( outname , "rt" ) ;
if (fp_out != NULL) {
did_any = 1 ;
fclose ( fp_out ) ;
}
else
did_any = 0 ;
if ((fp_out = fopen ( outname , "at" )) == NULL) {
strcpy ( msg , "Cannot open output file " ) ;
strcat ( msg , outname ) ;
error_message ( msg ) ;
fclose ( fp_in ) ;
return ;
}
if (did_any)
{
fprintf ( fp_out , "\n" ) ;
}
/*
Allocate for the file lines as read. Conservatively guess length.
Also allocate for network input vector.
*/
maxlin = nin * 20 + 100 ;
if (maxlin < 1024)
maxlin = 1024 ;
MEMTEXT ( "EXECUTE:line, inputs" ) ;
line = (char *) MALLOC ( maxlin ) ;
inputs = (float *) MALLOC ( nin * sizeof(float) ) ;
if ((line == NULL) || (inputs == NULL)) {
memory_message ( "to execute" ) ;
fclose ( fp_in ) ;
fclose ( fp_out ) ;
if (line != NULL)
FREE ( line ) ;
if (inputs != NULL)
FREE ( inputs ) ;
return ;
}
/*
Read and process the file.
*/
did_any = 0 ; /* If file runs out on first try, ERROR! */
int e=0;
for (;;) { // Endless loop reads until file exhausted
if ((fgets ( line , maxlin , fp_in ) == NULL) || (strlen ( line ) < 2)) {
if ((! did_any) || ferror ( fp_in )) {
strcpy ( msg , "Problem reading file " ) ;
strcat ( msg , dataname ) ;
error_message ( msg ) ;
}
break ;
}
lptr = line ; // Parse the data from this line
iptr = inputs ; // This will be the network inputs
for (i=0 ; i<nin ; i++)
*iptr++ = ParseDouble ( &lptr ) ;
if (did_any) // Start each new case on new line
fprintf ( fp_out , "\n" ) ;
did_any = 1 ; // Flag that at least one found
trial ( inputs ) ; // Compute network's outputs
maxi1=maxi2=maxi3=0.0;
win1=win2=win3=0;
// Saving maximun activation and the winner of the output vector
if (netmod==NETMOD_KOH)
{
// First maximum
for (i=0 ; i<nout ; i++)
{
if (out[i]>maxi1)
{
maxi1=out[i];
win1=i;
}
}
// 2nd Maximum
for (i=0 ; i<nout ; i++)
{
if ( (out[i]>maxi2) && (out[i]<maxi1) )
{
maxi2=out[i];
win2=i;
}
}
// 3rd Maximum
for (i=0 ; i<nout ; i++)
{
if ( (out[i]>maxi3) && (out[i]<maxi2) )
{
maxi3=out[i];
win3=i;
}
}
}
if (netmod==NETMOD_KOH)
{
fprintf (fp_out,"%d %3.2lf\n",win1,maxi1*100.0);
fprintf (fp_out,"%d %3.2lf\n",win2,maxi2*100.0);
fprintf (fp_out,"%d %3.2lf",win3,maxi3*100.0);
}
else
for (i=0 ; i<nout ; i++)
{
fprintf ( fp_out , "%.4lf ",out[i]);
}
e++;
while ((! feof ( fp_in )) && (line[strlen(line)-1] != '\n'))
fgets ( line , maxlin , fp_in ) ; // Line length may exceed maxlin
if (feof ( fp_in ))
break ;
} /* Endless loop until a file runs out */
MEMTEXT ( "EXECUTE:line, inputs" ) ;
fclose ( fp_in ) ;
fclose ( fp_out ) ;
FREE ( line ) ;
FREE ( inputs ) ;
}
void bee_colony::evolve(population &pop) const
{
// Let's store some useful variables.
const problem::base &prob = pop.problem();
const problem::base::size_type prob_i_dimension = prob.get_i_dimension(), D = prob.get_dimension(), Dc = D - prob_i_dimension, prob_c_dimension = prob.get_c_dimension();
const decision_vector &lb = prob.get_lb(), &ub = prob.get_ub();
const population::size_type NP = (int) pop.size();
//We perform some checks to determine wether the problem/population are suitable for ABC
if ( Dc == 0 ) {
pagmo_throw(value_error,"There is no continuous part in the problem decision vector for ABC to optimise");
}
if ( prob.get_f_dimension() != 1 ) {
pagmo_throw(value_error,"The problem is not single objective and ABC is not suitable to solve it");
}
if ( prob_c_dimension != 0 ) {
pagmo_throw(value_error,"The problem is not box constrained and ABC is not suitable to solve it");
}
if (NP < 2) {
pagmo_throw(value_error,"for ABC at least 2 individuals in the population are needed");
}
// Get out if there is nothing to do.
if (m_iter == 0) {
return;
}
// Some vectors used during evolution are allocated here.
fitness_vector fnew(prob.get_f_dimension());
decision_vector dummy(D,0); //used for initialisation purposes
std::vector<decision_vector > X(NP,dummy); //set of food sources
std::vector<fitness_vector> fit(NP); //food sources fitness
decision_vector temp_solution(D,0);
std::vector<int> trial(NP,0);
std::vector<double> probability(NP);
population::size_type neighbour = 0;
decision_vector::size_type param2change = 0;
std::vector<double> selectionfitness(NP), cumsum(NP), cumsumTemp(NP);
std::vector <population::size_type> selection(NP);
double r = 0;
// Copy the food sources position and their fitness
for ( population::size_type i = 0; i<NP; i++ ) {
X[i] = pop.get_individual(i).cur_x;
fit[i] = pop.get_individual(i).cur_f;
}
// Main ABC loop
for (int j = 0; j < m_iter; ++j) {
//1- Send employed bees
for (population::size_type ii = 0; ii< NP; ++ii) {
//selects a random component (only of the continuous part) of the decision vector
param2change = boost::uniform_int<decision_vector::size_type>(0,Dc-1)(m_urng);
//randomly chose a solution to be used to produce a mutant solution of solution ii
//randomly selected solution must be different from ii
do{
neighbour = boost::uniform_int<population::size_type>(0,NP-1)(m_urng);
}
while(neighbour == ii);
//copy local solution into temp_solution (the whole decision_vector, also the integer part)
for(population::size_type i=0; i<D; ++i) {
temp_solution[i] = X[ii][i];
}
//mutate temp_solution
temp_solution[param2change] = X[ii][param2change] + boost::uniform_real<double>(-1,1)(m_drng) * (X[ii][param2change] - X[neighbour][param2change]);
//if generated parameter value is out of boundaries, it is shifted onto the boundaries*/
if (temp_solution[param2change]<lb[param2change]) {
temp_solution[param2change] = lb[param2change];
}
if (temp_solution[param2change]>ub[param2change]) {
temp_solution[param2change] = ub[param2change];
}
//Calling void prob.objfun(fitness_vector,decision_vector) is more efficient as no memory allocation occur
//A call to fitness_vector prob.objfun(decision_vector) allocates memory for the return value.
prob.objfun(fnew,temp_solution);
//If the new solution is better than the old one replace it with the mutant one and reset its trial counter
if(prob.compare_fitness(fnew, fit[ii])) {
X[ii][param2change] = temp_solution[param2change];
pop.set_x(ii,X[ii]);
prob.objfun(fit[ii], X[ii]); //update the fitness vector
trial[ii] = 0;
}
else {
trial[ii]++; //if the solution can't be improved incrase its trial counter
}
} //End of loop on the population members
//2 - Send onlooker bees
//We scale all fitness values from 0 (worst) to absolute value of the best fitness
fitness_vector worstfit=fit[0];
for (pagmo::population::size_type i = 1; i < NP;i++) {
if (prob.compare_fitness(worstfit,fit[i])) worstfit=fit[i];
}
for (pagmo::population::size_type i = 0; i < NP; i++) {
selectionfitness[i] = fabs(worstfit[0] - fit[i][0]) + 1.;
}
// We build and normalise the cumulative sum
cumsumTemp[0] = selectionfitness[0];
for (pagmo::population::size_type i = 1; i< NP; i++) {
cumsumTemp[i] = cumsumTemp[i - 1] + selectionfitness[i];
}
for (pagmo::population::size_type i = 0; i < NP; i++) {
cumsum[i] = cumsumTemp[i]/cumsumTemp[NP-1];
}
for (pagmo::population::size_type i = 0; i < NP; i++) {
r = m_drng();
for (pagmo::population::size_type j = 0; j < NP; j++) {
if (cumsum[j] > r) {
selection[i]=j;
break;
}
}
}
for(pagmo::population::size_type t = 0; t < NP; ++t) {
r = m_drng();
pagmo::population::size_type ii = selection[t];
//selects a random component (only of the continuous part) of the decision vector
param2change = boost::uniform_int<decision_vector::size_type>(0,Dc-1)(m_urng);
//randomly chose a solution to be used to produce a mutant solution of solution ii
//randomly selected solution must be different from ii
do{
neighbour = boost::uniform_int<population::size_type>(0,NP-1)(m_urng);
}
while(neighbour == ii);
//copy local solution into temp_solution (also integer part)
for(population::size_type i=0; i<D; ++i) {
temp_solution[i] = X[ii][i];
}
//mutate temp_solution
temp_solution[param2change] = X[ii][param2change] + boost::uniform_real<double>(-1,1)(m_drng) * (X[ii][param2change] - X[neighbour][param2change]);
/*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/
if (temp_solution[param2change]<lb[param2change]) {
temp_solution[param2change] = lb[param2change];
}
if (temp_solution[param2change]>ub[param2change]) {
temp_solution[param2change] = ub[param2change];
}
//Calling void prob.objfun(fitness_vector,decision_vector) is more efficient as no memory allocation occur
//A call to fitness_vector prob.objfun(decision_vector) allocates memory for the return value.
prob.objfun(fnew,temp_solution);
//If the new solution is better than the old one replace it with the mutant one and reset its trial counter
if(prob.compare_fitness(fnew, fit[ii])) {
X[ii][param2change] = temp_solution[param2change];
pop.set_x(ii,X[ii]);
prob.objfun(fit[ii], X[ii]); //update the fitness vector
trial[ii] = 0;
}
else {
trial[ii]++; //if the solution can't be improved incrase its trial counter
}
}
//3 - Send scout bees
int maxtrialindex = 0;
for (population::size_type ii=1; ii<NP; ++ii)
{
if (trial[ii] > trial[maxtrialindex]) {
maxtrialindex = ii;
}
}
if(trial[maxtrialindex] >= m_limit)
{
//select a new random solution
for(problem::base::size_type jj = 0; jj < Dc; ++jj) {
X[maxtrialindex][jj] = boost::uniform_real<double>(lb[jj],ub[jj])(m_drng);
}
trial[maxtrialindex] = 0;
pop.set_x(maxtrialindex,X[maxtrialindex]);
}
} // end of main ABC loop
}
float LayerNet::find_grad (
TrainingSet *tptr ,
float *hid2delta ,
float *outdelta ,
float *grad
)
{
int i, j, size, tset, tclass, n, nprev, nnext ;
float error, *dptr, diff, delta, *hid1grad, *hid2grad, *outgrad ;
float *outprev, *prevact, *nextcoefs, *nextdelta, *gradptr ;
char msg[80];
/*
Compute size of each training sample
*/
if (outmod == OUTMOD_CLASSIFY)
size = nin + 1 ;
else if (outmod == OUTMOD_AUTO)
size = nin ;
else if (outmod == OUTMOD_GENERAL)
size = nin + nout ;
/*
Compute length of grad vector and gradient positions in it.
Also point to layer previous to output and its size.
Ditto for layer after hid1.
*/
if (nhid1 == 0) { // No hidden layer
n = nout * (nin+1) ;
outgrad = grad ;
nprev = nin ;
}
else if (nhid2 == 0) { // One hidden layer
n = nhid1 * (nin+1) + nout * (nhid1+1) ;
hid1grad = grad ;
outgrad = grad + nhid1 * (nin+1) ;
outprev = hid1 ;
nprev = nhid1 ;
nnext = nout ;
nextcoefs = out_coefs ;
nextdelta = outdelta ;
}
else { // Two hidden layers
n = nhid1 * (nin+1) + nhid2 * (nhid1+1) + nout * (nhid2+1) ;
hid1grad = grad ;
hid2grad = grad + nhid1 * (nin+1) ;
outgrad = hid2grad + nhid2 * (nhid1+1) ;
outprev = hid2 ;
nprev = nhid2 ;
nnext = nhid2 ;
nextcoefs = hid2_coefs ;
nextdelta = hid2delta ;
}
for (i=0 ; i<n ; i++) // Zero gradient for summing
grad[i] = 0.0 ;
error = 0.0 ; // Will cumulate total error here
for (tset=0 ; tset<tptr->ntrain ; tset++) { // Do all samples
sprintf ( msg , "Learning Pattern Nø %d ", tset) ;
normal_message ( msg ) ;
dptr = tptr->data + size * tset ; // Point to this sample
trial ( dptr ) ; // Evaluate network for it
if (outmod == OUTMOD_AUTO) { // If this is AUTOASSOCIATIVE
for (i=0 ; i<nout ; i++) { // then the expected outputs
diff = *dptr++ - out[i] ; // are just the inputs
error += diff * diff ;
outdelta[i] = diff * actderiv ( out[i] ) ;
}
}
else if (outmod == OUTMOD_CLASSIFY) { // If this is Classification
tclass = (int) dptr[nin] - 1 ; // class is stored after inputs
for (i=0 ; i<nout ; i++) { // Recall that train added a
if (tclass == i) // fraction so that the above
diff = NEURON_ON - out[i] ; // truncation to get tclass is
else // always safe in any radix
diff = NEURON_OFF - out[i] ;
error += diff * diff ;
outdelta[i] = diff * actderiv ( out[i] ) ;
}
}
else if (outmod == OUTMOD_GENERAL) { // If this is GENERAL output
dptr += nin ; // outputs stored after inputs
for (i=0 ; i<nout ; i++) {
diff = *dptr++ - out[i] ;
error += diff * diff ;
outdelta[i] = diff * actderiv ( out[i] ) ;
}
}
/*
Cumulate output gradient
*/
if (nhid1 == 0) // No hidden layer
prevact = tptr->data + size * tset ;
else
prevact = outprev ; // Point to previous layer
gradptr = outgrad ;
for (i=0 ; i<nout ; i++) {
delta = outdelta[i] ;
for (j=0 ; j<nprev ; j++)
*gradptr++ += delta * prevact[j] ;
*gradptr++ += delta ; // Bias activation is always 1
}
/*
Cumulate hid2 gradient (if it exists)
*/
if (nhid2) {
gradptr = hid2grad ;
for (i=0 ; i<nhid2 ; i++) {
delta = 0.0 ;
for (j=0 ; j<nout ; j++)
delta += outdelta[j] * out_coefs[j*(nhid2+1)+i] ;
delta *= actderiv ( hid2[i] ) ;
hid2delta[i] = delta ;
for (j=0 ; j<nhid1 ; j++)
*gradptr++ += delta * hid1[j] ;
*gradptr++ += delta ; // Bias activation is always 1
}
}
/*
Cumulate hid1 gradient (if it exists)
*/
if (nhid1) {
prevact = tptr->data + size * tset ;
gradptr = hid1grad ;
for (i=0 ; i<nhid1 ; i++) {
delta = 0.0 ;
for (j=0 ; j<nnext ; j++)
delta += nextdelta[j] * nextcoefs[j*(nhid1+1)+i] ;
delta *= actderiv ( hid1[i] ) ;
for (j=0 ; j<nin ; j++)
*gradptr++ += delta * prevact[j] ;
*gradptr++ += delta ; // Bias activation is always 1
}
}
} // for all tsets
return error / ((float) tptr->ntrain * (float) nout) ;
}
void firm::learn(macro *world)
{
if (type == "foreign")
{
price = 5 * world->price;
sold = 0;
sales = 0;
}
else
{
money += profit;
/* if (profit/(price * sold) > margin)
salary *= 1.01;
/*if (profit > 0)
price *= 1.01;//*/
margin = (0.6 * profit/(price * sold) + 0.4 * margin) * 1.01 >= 0? (0.6 * profit/(price * sold) + 0.4 * margin) * 1.01: 0.01;
share = (0.6 * sold/world->sold + 0.4 * share);
expected = world->sales * share;
plan = expected/price - storage;
if (plan > 0)
{
if (plan <= labor_capacity * labor_productivity)
{
if (trial(expected, salary, labor_capacity) >= margin)
{
while (trial(expected, 1.01 * salary, labor_capacity) >= margin)
salary *= 1.01;
}
else
{
while (labor_capacity - 1 >= 0 && plan <= (labor_capacity - 1) * labor_productivity)
{
labor_capacity--;
if (trial(expected, salary, labor_capacity) >= margin)
break;
}
while (trial(expected, 0.99 * salary, labor_capacity) < margin)
salary *= 0.99;
}
}
else
{
while (plan > (labor_capacity + 1)* labor_productivity)
{
labor_capacity++;
if (trial(expected, 0.99* salary, labor_capacity) < margin)
salary *= 0.99;
}
}
}
else
{
labor_capacity = 1;
}
/*
if (trial(expected, salary, labor_capacity) > 2 * margin)
{
while (true)
{
if (trial(expected, salary, labor_capacity+1) <= 2* margin)
{
labor_capacity++;
break;
}
else
labor_capacity++;
}
}
else
if (trial(expected, salary, labor_capacity) < margin)
{
while (true)
{
if (trial(expected, salary, labor_capacity - 1) >= margin && labor_capacity - 1 > 0)
{
labor_capacity--;
break;
}
else
if (labor_capacity - 1 > 0)
labor_capacity--;
else
break;
}
}
else
{
while (true)
{
if (trial(expected, salary, labor_capacity+1) < 2 * margin &&trial(expected, salary, labor_capacity+1) >= margin)
labor_capacity++;
else
break;
}
}//*/
}
time++;
sold = 0;
sales = 0;
}
/* test to make sure alloc list does what it's supposed to do */
static int testAllocList( void )
{
int keep = lalDebugLevel;
s = malloc( sizeof( *s ) );
XLALClobberDebugLevel(lalDebugLevel | LALMEMDBGBIT | LALMEMTRKBIT);
XLALClobberDebugLevel(lalDebugLevel & ~LALMEMPADBIT);
/* empty allocation list */
trial( LALCheckMemoryLeaks(), 0, "" );
trial( LALFree( s ), SIGSEGV, "not found" );
/* can't find allocation in PopAlloc */
trial( p = LALMalloc( 2 * sizeof( *p ) ), 0, "" );
trial( q = LALMalloc( 4 * sizeof( *q ) ), 0, "" );
trial( r = LALMalloc( 8 * sizeof( *r ) ), 0, "" );
trial( LALFree( s ), SIGSEGV, "not found" );
trial( LALFree( p ), 0, "" );
trial( LALFree( r ), 0, "" );
trial( LALCheckMemoryLeaks(), SIGSEGV, "memory leak" );
trial( LALFree( q ), 0, "" );
trial( LALCheckMemoryLeaks(), 0, "" );
/* can't find allocation in ModAlloc */
/* For some reason this next test fails on Snow Leopard... */
/* trial( s = LALRealloc( s, 1024 ), SIGSEGV, "not found" ); */
trial( p = LALRealloc( NULL, 2 * sizeof( *p ) ), 0, "" );
/* trial( s = LALRealloc( s, 1024 ), SIGSEGV, "not found" ); */
trial( LALFree( p ), 0, "" );
trial( LALCheckMemoryLeaks(), 0, "" );
free( s );
XLALClobberDebugLevel(keep);
return 0;
}
void Network::classify_from_file ( char *name , double thresh )
{
int i, maxlin, did_any, best ;
double *inputs, *iptr, maxact ;
char msg[81], *line, *lptr ;
FILE *fp ;
/*
Open the file which contains the data to be classified
*/
if ((fp = fopen ( name , "rt" )) == NULL) {
strcpy ( msg , "Cannot open " ) ;
strcat ( msg , name ) ;
error_message ( msg ) ;
return ;
}
/*
Allocate for the file lines as read. Conservatively guess length.
Also allocate for network input vector.
*/
maxlin = nin * 20 + 100 ;
if (maxlin < 1024)
maxlin = 1024 ;
MEMTEXT ( "CONFUSE:line, inputs" ) ;
line = (char *) MALLOC ( maxlin ) ;
inputs = (double *) MALLOC ( nin * sizeof(double) ) ;
if ((line == NULL) || (inputs == NULL)) {
memory_message ( "to classify" ) ;
fclose ( fp ) ;
if (line != NULL)
FREE ( line ) ;
if (inputs != NULL)
FREE ( inputs ) ;
return ;
}
/*
Read the file.
*/
did_any = 0 ; /* If file runs out on first try, ERROR! */
for (;;) { // Endless loop reads until file exhausted
if ((fgets ( line , maxlin , fp ) == NULL) || (strlen ( line ) < 2)) {
if ((! did_any) || ferror ( fp )) {
strcpy ( msg , "Problem reading file " ) ;
strcat ( msg , name ) ;
error_message ( msg ) ;
}
break ;
}
lptr = line ; // Parse the data from this line
iptr = inputs ; // This will be the network inputs
for (i=0 ; i<nin ; i++)
*iptr++ = ParseDouble ( &lptr ) ;
did_any = 1 ; // Flag that at least one found
trial ( inputs ) ; // Compute network's outputs
maxact = -1.e30 ; // Will keep highest activity here
best = 0 ; // Insurance only (good habit)
for (i=0 ; i<nout ; i++) { // Find winning output
if (out[i] > maxact) {
maxact = out[i] ;
best = i ;
}
}
if (maxact >= thresh) // If winner has enough activation
++confusion[best] ; // count it in confusion
else // If too little, throw it
++confusion[nout] ; // in the reject category
while ((! feof ( fp )) && (line[strlen(line)-1] != '\n'))
fgets ( line , maxlin , fp ) ; // Line length may exceed maxlin
if (feof ( fp ))
break ;
} /* Endless loop until a file runs out */
fclose ( fp ) ;
MEMTEXT ( "CONFUSE:line, inputs" ) ;
FREE ( line ) ;
FREE ( inputs ) ;
}
/* do a bunch of allocations/deallocations that are OK */
static int testOK( void )
{
int keep = lalDebugLevel;
XLALClobberDebugLevel(lalDebugLevel | LALMEMDBGBIT | LALMEMPADBIT | LALMEMTRKBIT);
trial( p = LALMalloc( 1024 * sizeof( *p ) ), 0, "" );
for ( i = 0; i < 1024; ++i ) p[i] = i;
trial( q = LALCalloc( 1024, sizeof( *q ) ), 0, "" );
for ( i = 0; i < 1024; ++i ) if ( q[i] ) die( memory not blanked );
trial( p = LALRealloc( p, 4096 * sizeof( *p ) ), 0, "" );
for ( i = 0; i < 1024; ++i ) if ( p[i] != i ) die( memory not copied );
trial( q = LALRealloc( q, 0 ), 0, "" );
if ( q ) die( memory not freed );
trial( LALCheckMemoryLeaks(), SIGSEGV, "LALCheckMemoryLeaks: memory leak\n" );
if ( *( p - 1 ) != (size_t)0xABadCafe ) die( wrong magic );
if ( *( p - 2 ) != 4096 * sizeof( *p ) ) die( wrong size );
trial( LALFree( p ), 0, "" );
trial( LALCheckMemoryLeaks(), 0, "" );
XLALClobberDebugLevel(lalDebugLevel & ~LALMEMPADBIT);
trial( p = LALMalloc( 1024 * sizeof( *p ) ), 0, "" );
for ( i = 0; i < 1024; ++i ) p[i] = i;
trial( q = LALCalloc( 1024, sizeof( *q ) ), 0, "" );
for ( i = 0; i < 1024; ++i ) if ( q[i] ) die( memory not blanked );
trial( p = LALRealloc( p, 4096 * sizeof( *p ) ), 0, "" );
for ( i = 0; i < 1024; ++i ) if ( p[i] != i ) die( memory not copied );
trial( q = LALRealloc( q, 0 ), 0, "" );
if ( q ) die( memory not freed );
trial( LALCheckMemoryLeaks(), SIGSEGV, "LALCheckMemoryLeaks: memory leak\n" );
trial( LALFree( p ), 0, "" );
trial( LALCheckMemoryLeaks(), 0, "" );
XLALClobberDebugLevel(lalDebugLevel | LALMEMPADBIT);
XLALClobberDebugLevel(lalDebugLevel & ~LALMEMTRKBIT);
trial( p = LALMalloc( 1024 * sizeof( *p ) ), 0, "" );
for ( i = 0; i < 1024; ++i ) p[i] = i;
trial( q = LALCalloc( 1024, sizeof( *q ) ), 0, "" );
for ( i = 0; i < 1024; ++i ) if ( q[i] ) die( memory not blanked );
trial( p = LALRealloc( p, 4096 * sizeof( *p ) ), 0, "" );
for ( i = 0; i < 1024; ++i ) if ( p[i] != i ) die( memory not copied );
trial( q = LALRealloc( q, 0 ), 0, "" );
if ( q ) die( memory not freed );
trial( LALCheckMemoryLeaks(), SIGSEGV, "LALCheckMemoryLeaks: memory leak\n" );
if ( *( p - 1 ) != (size_t)0xABadCafe ) die( wrong magic );
if ( *( p - 2 ) != 4096 * sizeof( *p ) ) die( wrong size );
trial( LALFree( p ), 0, "" );
trial( LALCheckMemoryLeaks(), 0, "" );
XLALClobberDebugLevel(lalDebugLevel & ~LALMEMDBGBIT);
trial( p = LALMalloc( 1024 * sizeof( *p ) ), 0, "" );
for ( i = 0; i < 1024; ++i ) p[i] = i;
trial( q = LALCalloc( 1024, sizeof( *q ) ), 0, "" );
for ( i = 0; i < 1024; ++i ) if ( q[i] ) die( memory not blanked );
trial( p = LALRealloc( p, 4096 * sizeof( *p ) ), 0, "" );
for ( i = 0; i < 1024; ++i ) if ( p[i] != i ) die( memory not copied );
trial( q = LALRealloc( q, 0 ), 0, "" );
/* if ( q ) die( memory not freed ); */
trial( LALCheckMemoryLeaks(), 0, "" );
trial( LALFree( p ), 0, "" );
trial( LALCheckMemoryLeaks(), 0, "" );
XLALClobberDebugLevel(keep);
return 0;
}