void perturb(Polygon_with_holes_2& p, Number_type epsilon) { perturb(p.outer_boundary(), epsilon); // Polygon_with_holes_2 q(p.outer_boundary()); for (Polygon_with_holes_2::Hole_iterator it = p.holes_begin(); it != p.holes_end(); ++it) { perturb(*it, epsilon); // q.add_hole(); } // p = q; }
static void recursive_keep(double x, double y, double width, double length, double height, int level) { int i; if (irandomn(1000) < 300) { xlate(x, y, 0); keep_block(width, length, height); endxlate(); xlate(0, 0, height); recursive_keep(x, y, width, length, perturb(height, 0.2), level); endxlate(); return; } if (level == 0 || irandomn(1000) < 300) { xlate(x, y, 0); xkeep_topper(width, length, height, 1); endxlate(); return; } i = irandomn(100); xlate(x, y, 0); keep_block(width, length, height); endxlate(); xlate(0, 0, height); if ((level % 2) == 0) { double offset, nw1, nw2, x1, x2; offset = randomn(width * 0.2) - (0.1 * width); offset = 0.0; nw1 = (width / 2.0) + offset; nw2 = (width - nw1); x1 = x - offset / 2.0 -0.25 * width; x2 = x + 0.25 * width - offset / 2.0; printf("/* x/y = %lf,%lf, o=%lf nw1 = %lf, nw2 = %lf, x1 = %lf, x2 = %lf */\n", x, y, offset, nw1, nw2, x1, x2); recursive_keep(x1, y, nw1, length, perturb(height, 0.2), level - 1); recursive_keep(x2, y, nw2, length, perturb(height, 0.2), level - 1); } else { double offset, nl1, nl2, y1, y2; offset = randomn(length * 0.2) - (0.1 * length); offset = 0.0; nl1 = (length / 2.0) + offset; nl2 = (length - nl1); y1 = y - offset / 2.0 - 0.25 * length; y2 = y + 0.25 * length - offset / 2.0; printf("/* x/y = %lf,%lf, o=%lf nl1 = %lf, nl2 = %lf, y1 = %lf, y2 = %lf */\n", x, y, offset, nl1, nl2, y1, y2); recursive_keep(x, y1, width, nl1, perturb(height, 0.2), level - 1); recursive_keep(x, y2, width, nl2, perturb(height, 0.2), level - 1); } endxlate(); }
void perturb(Polygon_2& p, Number_type epsilon) { Polygon_2::Vertex_iterator vit; for (vit = p.vertices_begin(); vit != p.vertices_end(); ++vit) { Point_2 pnt = *vit; // Make sure we get a consistent perturbation with each point across runs const static Number_type prime = 827; srand((int)(pnt.x() + prime*(pnt.y() + prime*pnt.z()))); pnt = Point_25_<Kernel>(perturb(pnt.x(), epsilon), perturb(pnt.y(), epsilon), pnt.z(), pnt.id()); p.set(vit, pnt); } p = Polygon_2(p.vertices_begin(), unique(p.vertices_begin(), p.vertices_end())); }
void Userwork_in_loop(MeshS *pM) { GridS *pGrid; int nl,nd; Real newtime; for (nl=0; nl<(pM->NLevels); nl++){ for (nd=0; nd<(pM->DomainsPerLevel[nl]); nd++){ if (pM->Domain[nl][nd].Grid != NULL){ pGrid = pM->Domain[nl][nd].Grid; if (isnan(pGrid->dt)) ath_error("Time step is NaN!"); if (idrive == 0) { /* driven turbulence */ /* Integration has already been done, but time not yet updated */ newtime = pGrid->time + pGrid->dt; #ifndef IMPULSIVE_DRIVING /* Drive on every time step */ perturb(pGrid, pGrid->dt); #endif /* IMPULSIVE_DRIVING */ if (newtime >= (tdrive+dtdrive)) { /* If we start with large time steps so that tdrive would get way * behind newtime, this makes sure we don't keep generating after * dropping down to smaller time steps */ while ((tdrive+dtdrive) <= newtime) tdrive += dtdrive; #ifdef IMPULSIVE_DRIVING /* Only drive at intervals of dtdrive */ perturb(pGrid, dtdrive); #endif /* IMPULSIVE_DRIVING */ /* Compute new spectrum after dtdrive. Putting this after perturb() * means we won't be applying perturbations from a new power spectrum * just before writing outputs. At the very beginning, we'll go a * little longer before regenerating, but the energy injection rate * was off on the very first timestep anyway. When studying driven * turbulence, all we care about is the saturated state. */ generate(); } } } } } return; }
static void xkeep_topper(double width, double length, double height, int rot) { if (rot) rotate(90, 0, 0, 1); if (irandomn(100) < 30) { english_house(width, length, height, min(width, length) * 0.75); if (rot) endrotate(); return; } if (irandomn(100) < 30) { gothic_hall(length, width, height, 3, 2, length * 0.1, 1); if (rot) endrotate(); return; } if (rot) endrotate(); rotate(90, 0, 0, 1); crenelated_rectangle(length, width, height * 0.2, length * 0.1, length * 0.05); endrotate(); if (irandomn(1000) < 500) generic_tower(0, 0, min(length, width) / 3.0, perturb(height * 2.0, 0.2), 0); }
static void wall(double x1, double y1, double x2, double y2, double thickness, double height) { double angle; double dist; double clen, ch, c; height = perturb(height, 0.1); angle = atan2(y2 - y1, x2 - x1); printf("/* %lf, %lf, angle = %d */\n", (y2 - y1), (x2 - x1), (int) (angle * 180.0 / M_PI)); dist = hypot(x2 - x1, y2 - y1); xlate(x1, y1, 0); rotate(angle * 180 / M_PI, 0, 0, 1); /*diff(); */ cube(dist, thickness, height, 0); c = 0.0; ch = thickness; clen = thickness *1.5; while (c < dist) { xlate(c, 0, height - thickness); cube(clen, ch, ch * 2, 0); endxlate(); c += clen + thickness; } /* enddiff(); */ endrotate(); endxlate(); }
static void generic_tower(double x, double y, double r, double h, int flying_allowed) { double origh = h; double origr = r; int flying = (irandomn(100) < 30) && flying_allowed; h = perturbup(h, TOWER_HEIGHT_RANDOMNESS); r = perturb(r, TOWER_RADIUS_RANDOMNESS); switch (irandomn(20)) { case 0: angular_tower(x, y, origr, origh, flying, 90.0); break; case 1: angular_tower(x, y, r, h, flying, 90.0); break; case 2: pointy_tower(x, y, r, h, flying, 5.0); break; case 3: angular_tower(x, y, r, h, flying, 72.0); break; case 4: angular_tower(x, y, r, h, flying, 90.0); break; case 5: angular_tower(x, y, r, h, flying, 60.0); break; case 6: angular_tower(x, y, r, h, flying, 45.0); break; case 7: pointy_tower(x, y, r, h, flying, 72.0); break; case 8: pointy_tower(x, y, r, h, flying, 90.0); break; case 9: pointy_tower(x, y, r, h, flying, 60.0); break; case 10: pointy_tower(x, y, r, h, flying, 45.0); break; default: round_tower(x, y, r, h, flying ); break; } }
int main(int argc, char *argv[]) { double x,y; int i; int xyz=0; startgraphics(); randinit(); D2=4*R*R; for (i=0; n<N; i++) { tryInsert(); perturb(); if (counter>5500) { drawObjects(); check4event(); counter=0; printf("."); } } while(1) { drawObjects(); check4event(); sleep(1); } }
void SrTerrain::calHeight(float* height) { memset(height, 0, sizeof(float) * mParam.mNumCols * mParam.mNumRows); addPerlinNoise(height, mParam.mYSize); perturb(height, mParam.mF, mParam.mD); for (int i = 0; i < 10; i++ ) erode(height, mParam.mErode); smoothen(height); }
bool GMWMI::get_seed (Point<float>& p) { Interp interp (interp_template); do { init_seeder.get_seed (p); if (find_interface (p, interp)) { if (perturb (p, interp)) return true; } } while (1); return false; }
void Compute_dQdBeta_CTSE(Real* dQdB, SolutionSpace<Real>& space, Int beta) { RCmplx perturb(0.0, 1.0e-11); Mesh<Real>* rm = space.m; SolutionSpace<RCmplx> cspace(space); Mesh<RCmplx>* cm = cspace.m; PObj<RCmplx>* cp = cspace.p; EqnSet<RCmplx>* ceqnset = cspace.eqnset; std::vector<SolutionSpaceBase<RCmplx>*> cSolSpaces; cSolSpaces.push_back(&cspace); SolutionOrdering<RCmplx> operations; std::string name = cspace.name; operations.Insert("Iterate " + name); operations.Finalize(cSolSpaces); Int cnnode = cm->GetNumNodes(); Real* dxdb = new Real[cnnode*3]; Get_dXdB(space.param->path+space.param->spacename, dxdb, rm, beta); //perturb complex part by dxdb*perturb for(Int i = 0; i < cnnode; i++){ for(Int j = 0; j < 3; j++){ cm->xyz[i*3 + j] += dxdb[i*3 + j]*perturb; } } //update xyz coords cp->UpdateXYZ(cm->xyz); //calculate metrics with new grid positions cm->CalcMetrics(); //create a temporary operations vector with just a single space iterator on it //run solver Solve(cSolSpaces, operations); for(Int i = 0; i < cnnode; i++){ for(Int j = 0; j < ceqnset->neqn; j++){ dQdB[i*ceqnset->neqn + j] = imag(cspace.q[i*(ceqnset->neqn+ceqnset->nauxvars) + j]) / imag(perturb); } } delete [] dxdb; return; }
void perturb(Slice& slice, Number_type epsilon) { list<string> components; slice.components(back_inserter(components)); for (list<string>::const_iterator it = components.begin(); it != components.end(); ++it) { for (Slice::Contour_iterator cit = slice.begin(*it); cit != slice.end(*it); ++cit) { perturb((*cit)->polygon(), epsilon); } } }
/* * return the optimal solution for n items (first is e) and * capacity c. Value so far is v. */ int knapsack(struct item *e, int c, int n, int v) { int with, without, best; double ub; /* base case: full knapsack or no items */ if (c < 0) return INT_MIN; if (n == 0 || c == 0) return v; /* feasible solution, with value v */ ub = (double) v + c * e->value / e->weight; if (ub < best_so_far) { /* prune ! */ return INT_MIN; } /* * compute the best solution without the current item in the knapsack */ perturb(prob, n, length); without = cilk_spawn knapsack(e + 1, c, n - 1, v); /* compute the best solution with the current item in the knapsack */ with = cilk_spawn knapsack(e + 1, c - e->weight, n - 1, v + e->value); cilk_sync; best = with > without ? with : without; /* * notice the race condition here. The program is still * correct, in the sense that the best solution so far * is at least best_so_far. Moreover best_so_far gets updated * when returning, so eventually it should get the right * value. The program is highly non-deterministic. */ if (best > best_so_far) best_so_far = best; return best; }
inline void step_dt(const base::Time& dt){ if(mode == base::JointState::POSITION) { j_state.position = j_setpoint.position; j_state.speed = j_setpoint.speed; j_state.effort = j_setpoint.effort; trunc_to_limit(base::JointState::POSITION); trunc_to_limit(base::JointState::SPEED); trunc_to_limit(base::JointState::EFFORT); } else if(mode == base::JointState::SPEED) { j_state.speed = j_setpoint.speed; j_state.effort = j_setpoint.effort; trunc_to_limit(base::JointState::SPEED); trunc_to_limit(base::JointState::EFFORT); j_state.position += j_setpoint.speed*dt.toSeconds(); trunc_to_limit(base::JointState::POSITION); } else if(mode == base::JointState::EFFORT) { j_state.effort = j_setpoint.effort; trunc_to_limit(base::JointState::EFFORT); j_state.speed += j_setpoint.effort*dt.toSeconds(); j_state.position += j_setpoint.speed*dt.toSeconds(); trunc_to_limit(base::JointState::POSITION); trunc_to_limit(base::JointState::SPEED); } perturb(dt.toSeconds()); }
void moremap() { float rx,ry,nrx,nry,px,py; int f,i,j,k,c,x,y,ix,iy,displayloop; // Generate some more of the map for (maploop=1; maploop<scrwid*scrhei/20; maploop++) { rx=(float)mmx/scrwid*2-1; ry=(float)(mmy-scrhei/2)/scrwid*2; /* From QB later SUB move (x, y) IF fon%(1) THEN x = x * var(1): y = y * var(1) END IF IF fon%(6) THEN x = var(1) * SGN(x) * ABS(x) ^ var(6) y = var(1) * SGN(y) * ABS(y) ^ var(6) END IF IF fon%(2) THEN nx = x * COS(var(2)) + y * SIN(var(2)) ny = -x * SIN(var(2)) + y * COS(var(2)) x = nx y = ny END IF IF fon%(3) THEN 'y = y - .01 * (y - 1) 'y = 1 + .99 * (y - 1) 'y = (y + 1) * .7 - 1 y = y * var(3) END IF IF fon%(4) THEN y = (y - 1) * var(4) + 1 END IF IF fon%(5) THEN x = x + var(5) * x END IF IF fon%(7) THEN IF fon%(10) THEN IF fon%(9) THEN x = x + var(7) * (-1 + 2 * (p% MOD 2)) ELSE x = x + var(7) * (-1 + 2 * (p% MOD 50) / 49) END IF ELSE x = x + var(7) END IF END IF IF fon%(8) THEN IF fon%(10) THEN IF fon%(9) THEN y = y + var(8) * (-1 + 2 * (p% MOD 2)) ELSE y = y + var(8) * (-1 + 2 * (p% MOD 50) / 49) END IF ELSE y = y + var(8) END IF END IF END SUB */ if (fon[0]) { rx = mysgn(rx)/var[7]*mypow(myabs(rx),1/var[0]); ry = mysgn(ry)/var[7]*mypow(myabs(ry),1/var[0]); } if (fon[1]) { rx = rx / var[1]; ry = ry / var[1]; } if (fon[2]) { nrx = rx * cos(var[2]) + ry * sin(var[2]); nry = -rx * sin(var[2]) + ry * cos(var[2]); rx = nrx; ry=nry; } if (fon[3]) { ry = ry - mysgn(ry) * sin(var[6]*pi*myabs(ry)) * var[3]; } if (fon[4]) { ry = ((myabs(ry) - 1) / var[4] + 1) * mysgn(ry); } if (fon[5]) { rx = rx - mysgn(rx) * sin(var[6]*pi*myabs(rx)) * var[5]; } px=(rx+1)/2*scrwid; py=scrhei/2+(ry)/2*scrwid; ix=(int)px; iy=(int)py; if (ix<0 || ix>=scrwid-1 || iy<0 || iy>=scrhei-1) { ix=px; iy=py; } amount[mmx][mmy][0][0][makingmap]=((float)ix+1-(float)px)*((float)(iy+1)-(float)py); amount[mmx][mmy][1][0][makingmap]=((float)px-(float)ix)*((float)(iy+1)-(float)py); amount[mmx][mmy][0][1][makingmap]=((float)ix+1-(float)px)*((float)py-(float)iy); amount[mmx][mmy][1][1][makingmap]=((float)px-(float)ix)*((float)py-(float)iy); pix[mmx][mmy][makingmap]=ix; piy[mmx][mmy][makingmap]=iy; if (ix<0 || ix>=scrwid-1 || iy<0 || iy>=scrhei-1) { pix[mmx][mmy][makingmap]=scrwid/2; piy[mmx][mmy][makingmap]=scrhei/2; for (i=0; i<=1; i++) { for (j=0; j<=1; j++) { amount[mmx][mmy][i][j][makingmap]=0; } } } mmx++; if (mmx>=scrwid) { mmx=0; mmy++; if (mmy>=scrhei) { mmy=0; tmpmap=usingmap; usingmap=makingmap; makingmap=tmpmap; for (f=0; f<fs; f++) { perturb(f); } } } } }
void Compute_dRdQ_Product_MatrixFree(SolutionSpace<RCmplx>& cspace, Real* vector, Real* prod) { RCmplx perturb(0.0, 1.0e-11); Mesh<RCmplx>* cm = cspace.m; Param<RCmplx>* param = cspace.param; EqnSet<RCmplx>* ceqnset = cspace.eqnset; RCmplx* q = cspace.q; PObj<RCmplx>* p = cspace.p; Int cnnode = cm->GetNumNodes(); Int cgnode = cm->GetNumParallelNodes(); Int cnbnode = cm->GetNumBoundaryNodes(); Int neqn = ceqnset->neqn; Int nvars = ceqnset->nauxvars + neqn; for(Int i = 0; i < cnnode; i++){ RCmplx* iq = &q[i*nvars + 0]; for(Int j = 0; j < neqn; j++){ iq[j] += vector[i*neqn + j]*perturb; } ceqnset->ComputeAuxiliaryVariables(iq); } p->UpdateGeneralVectors(q, nvars); //the gaussian source is sometimes used to modify boundary velocities, etc. //pre-compute it for bc call if(cspace.param->gaussianSource){ cspace.gaussian->ApplyToResidual(); } UpdateBCs(&cspace); p->UpdateGeneralVectors(q, nvars); //now, compute gradients and limiters if(param->sorder > 1 || param->viscous){ for(Int i = 0; i < (cnnode+cnbnode+cgnode); i++){ ceqnset->NativeToExtrapolated(&q[i*nvars]); } cspace.grad->Compute(); cspace.limiter->Compute(&cspace); for(Int i = 0; i < (cnnode+cnbnode+cgnode); i++){ ceqnset->ExtrapolatedToNative(&q[i*nvars]); } } //it is assumed that this routine is going to be called iteratively, //therefore, calling the turbulence model here, will slowly converge the //turbulence model towards the correct sensitivity //This should theoretically be converged here at every call to this routine, //but that is horribly costly.... so we cheat //WARNING: ---- for now, we assume frozen turbulence models if(param->viscous && false){ cspace.turb->Compute(); if(param->gcl){ std::cout << "MATRIX FREE: COMPUTE GCL FOR TURB MODEL!" << std::endl; } } //Now compute residual, this only works for spatial residual right now SpatialResidual(&cspace); ExtraSourceResidual(&cspace); //Now do the CTSE derivative part for(Int i = 0; i < cnnode; i++){ for(Int j = 0; j < neqn; j++){ prod[i*neqn + j] = imag(cspace.crs->b[i*neqn + j])/imag(perturb); } } //re-zero the complex part of the q vector in case we reuse the cspace for(Int i = 0; i < cnnode; i++){ RCmplx* iq = &q[i*nvars + 0]; for(Int j = 0; j < neqn; j++){ iq[j] = real(iq[j]); } ceqnset->ComputeAuxiliaryVariables(iq); } p->UpdateGeneralVectors(q, nvars); return; }
void LayerNet::anneal ( TrainingSet *tptr , // Training set to use struct LearnParams *lptr , // User's general learning parameters LayerNet *bestnet , // Work area used to keep best network int init // Use zero suffix (initialization) anneal parms? ) { int ntemps, niters, setback, reg, nvars, key, user_quit ; int i, iter, improved, ever_improved, itemp ; long seed, bestseed ; char msg[80] ; double tempmult, temp, fval, bestfval, starttemp, stoptemp, fquit ; SingularValueDecomp *sptr ; struct AnnealParams *aptr ; // User's annealing parameters aptr = lptr->ap ; /* The parameter 'init' is nonzero if we are initializing weights for learning. If zero we are attempting to break out of a local minimum. The main effect of this parameter is whether or not we use the zero suffix variables in the anneal parameters. A second effect is that regression is used only for initialization, not for escape. */ if (init) { ntemps = aptr->temps0 ; niters = aptr->iters0 ; setback = aptr->setback0 ; starttemp = aptr->start0 ; stoptemp = aptr->stop0 ; } else { ntemps = aptr->temps ; niters = aptr->iters ; setback = aptr->setback ; starttemp = aptr->start ; stoptemp = aptr->stop ; } /* Initialize other local parameters. Note that there is no sense using regression if there are no hidden layers. Also, regression is almost always counterproductive for local minimum escape. */ fquit = lptr->quit_err ; reg = init && nhid1 && (lptr->init != 1) ; /* Allocate the singular value decomposition object for REGRESS. Also allocate a work area for REGRESS to preserve matrix. */ if (reg) { if (nhid1 == 0) // No hidden layer nvars = nin + 1 ; else if (nhid2 == 0) // One hidden layer nvars = nhid1 + 1 ; else // Two hidden layers nvars = nhid2 + 1 ; MEMTEXT ( "ANNEAL: new SingularValueDecomp" ) ; sptr = new SingularValueDecomp ( tptr->ntrain , nvars , 1 ) ; if ((sptr == NULL) || ! sptr->ok) { memory_message ( "for annealing with regression. Try ANNEAL NOREGRESS."); if (sptr != NULL) delete sptr ; neterr = 1.0 ; // Flag failure to LayerNet::learn which called us return ; } } /* For every temperature, the center around which we will perturb is the best point so far. This is kept in 'bestnet', so initialize it to the user's starting estimate. Also, initialize 'bestfval', the best function value so far, to be the function value at that starting point. */ copy_weights ( bestnet , this ) ; // Current weights are best so far if (init) bestfval = 1.e30 ; // Force it to accept SOMETHING else bestfval = trial_error ( tptr ) ; /* This is the temperature reduction loop and the iteration within temperature loop. We use a slick trick to keep track of the best point at a given temperature. We certainly don't want to replace the best every time an improvement is had, as then we would be moving our center about, compromising the global nature of the algorithm. We could, of course, have a second work area in which we save the 'best so far for this temperature' point. But if there are a lot of variables, the usual case, this wastes memory. What we do is to save the seed of the random number generator which created the improvement. Then later, when we need to retrieve the best, simply set the random seed and regenerate it. This technique also saves a lot of copying time if many improvements are made for a single temperature. */ temp = starttemp ; tempmult = exp( log( stoptemp / starttemp ) / (ntemps-1)) ; ever_improved = 0 ; // Flags if improved at all user_quit = 0 ; // Flags user pressed ESCape for (itemp=0 ; itemp<ntemps ; itemp++) { // Temp reduction loop improved = 0 ; // Flags if this temp improved if (init) { sprintf ( msg , "\nANNEAL temp=%.2lf ", temp ) ; progress_message ( msg ) ; } for (iter=0 ; iter<niters ; iter++) { // Iters per temp loop seed = longrand () ; // Get a random seed slongrand ( seed ) ; // Brute force set it perturb (bestnet, this, temp, reg) ;// Randomly perturb about best if (reg) // If using regression, estimate fval = regress ( tptr , sptr ) ; // out weights now else // Otherwise just evaluate fval = trial_error ( tptr ) ; if (fval < bestfval) { // If this iteration improved bestfval = fval ; // then update the best so far bestseed = seed ; // and save seed to recreate it ever_improved = improved = 1 ; // Flag that we improved if (bestfval <= fquit) // If we reached the user's break ; // limit, we can quit iter -= setback ; // It often pays to keep going if (iter < 0) // at this temperature if we iter = 0 ; // are still improving } } // Loop: for all iters at a temp if (improved) { // If this temp saw improvement slongrand ( bestseed ) ; // set seed to what caused it perturb (bestnet, this, temp, reg) ;// and recreate that point copy_weights ( bestnet , this ) ; // which will become next center slongrand ( bestseed / 2 + 999 ) ; // Jog seed away from best if (init) { sprintf ( msg , " err=%.3lf%% ", 100.0 * bestfval ) ; progress_message ( msg ) ; } } if (bestfval <= fquit) // If we reached the user's break ; // limit, we can quit /*********************************************************************** if (kbhit()) { // Was a key pressed? key = getch () ; // Read it if so while (kbhit()) // Flush key buffer in case function key getch () ; // or key was held down if (key == 27) { // ESCape user_quit = 1 ; // Flags user that ESCape was pressed break ; } } ***********************************************************************/ if (user_quit) break ; temp *= tempmult ; // Reduce temp for next pass } // through this temperature loop /* The trials left this weight set and neterr in random condition. Make them equal to the best, which will be the original if we never improved. Also, if we improved and are using regression, recall that bestnet only contains the best hidden weights, as we did not bother to run regress when we updated bestnet. Do that now before returning. */ copy_weights ( this , bestnet ) ; // Return best weights in this net neterr = bestfval ; // Trials destroyed weights, err if (ever_improved && reg) neterr = regress ( tptr , sptr ) ; // regressed output weights if (reg) { MEMTEXT ( "ANNEAL: delete SingularValueDecomp" ) ; delete sptr ; } }
void problem(Grid *pGrid, Domain *pD) { int i, is=pGrid->is, ie = pGrid->ie; int j, js=pGrid->js, je = pGrid->je; int k, ks=pGrid->ks, ke = pGrid->ke; rseed = (pGrid->my_id+1); initialize(pGrid, pD); tdrive = 0.0; /* Initialize uniform density and momenta */ for (k=ks-nghost; k<=ke+nghost; k++) { for (j=js-nghost; j<=je+nghost; j++) { for (i=is-nghost; i<=ie+nghost; i++) { pGrid->U[k][j][i].d = rhobar; pGrid->U[k][j][i].M1 = 0.0; pGrid->U[k][j][i].M2 = 0.0; pGrid->U[k][j][i].M3 = 0.0; } } } #ifdef MHD /* Initialize uniform magnetic field */ for (k=ks-nghost; k<=ke+nghost; k++) { for (j=js-nghost; j<=je+nghost; j++) { for (i=is-nghost; i<=ie+nghost; i++) { pGrid->U[k][j][i].B1c = B0; pGrid->U[k][j][i].B2c = 0.0; pGrid->U[k][j][i].B3c = 0.0; pGrid->B1i[k][j][i] = B0; pGrid->B2i[k][j][i] = 0.0; pGrid->B3i[k][j][i] = 0.0; } } } #endif /* MHD */ /* Set the initial perturbations. Note that we're putting in too much * energy this time. This is okay since we're only interested in the * saturated state. */ generate(); perturb(pGrid, dtdrive); /* If decaying turbulence, no longer need the driving memory */ if (idrive == 1) { ath_pout(0,"De-allocating driving memory.\n"); /* Free Athena-style arrays */ free_3d_array(dv1); free_3d_array(dv2); free_3d_array(dv3); /* Free FFTW-style arrays */ ath_3d_fft_free(fv1); ath_3d_fft_free(fv2); ath_3d_fft_free(fv3); } return; }
void foo() { printf("-------- before ---------\n"); /*cilk_sleep(500000);*/ perturb(1, 1, 500000); printf("-------- after ---------\n"); }
void compute_kernels(const struct ecalib_conf* conf, long nskerns_dims[5], complex float** nskerns_ptr, unsigned int SN, float svals[SN], const long caldims[DIMS], const complex float* caldata) { assert(1 == md_calc_size(DIMS - 5, caldims + 5)); nskerns_dims[0] = conf->kdims[0]; nskerns_dims[1] = conf->kdims[1]; nskerns_dims[2] = conf->kdims[2]; nskerns_dims[3] = caldims[3]; long N = md_calc_size(4, nskerns_dims); assert(N > 0); nskerns_dims[4] = N; complex float* nskerns = md_alloc(5, nskerns_dims, CFL_SIZE); *nskerns_ptr = nskerns; complex float (*vec)[N] = xmalloc(N * N * sizeof(complex float)); assert((NULL == svals) || (SN == N)); float* val = (NULL != svals) ? svals : xmalloc(N * FL_SIZE); debug_printf(DP_DEBUG1, "Build calibration matrix and SVD...\n"); #ifdef CALMAT_SVD calmat_svd(conf->kdims, N, vec, val, caldims, caldata); for (int i = 0; i < N; i++) for (int j = 0; j < N; j++) #ifndef FLIP nskerns[i * N + j] = (vec[j][i]) * (conf->weighting ? val[i] : 1.); #else nskerns[i * N + j] = (vec[j][N - 1 - i]) * (conf->weighting ? val[N - 1 - i] : 1.); #endif #else covariance_function(conf->kdims, N, vec, caldims, caldata); debug_printf(DP_DEBUG1, "Eigen decomposition... (size: %ld)\n", N); // we could apply Nystroem method here to speed it up float tmp_val[N]; lapack_eig(N, tmp_val, vec); for (int i = 0; i < N; i++) val[i] = sqrtf(tmp_val[N - 1 - i]); for (int i = 0; i < N; i++) for (int j = 0; j < N; j++) #ifndef FLIP nskerns[i * N + j] = vec[N - 1 - i][j] * (conf->weighting ? val[i] : 1.); // flip #else nskerns[i * N + j] = vec[i][j] * (conf->weighting ? val[N - 1 - i] : 1.); // flip #endif #endif if (conf->perturb > 0.) { long dims[2] = { N, N }; perturb(dims, nskerns, conf->perturb); } #ifndef FLIP nskerns_dims[4] = number_of_kernels(conf, N, val); #else nskerns_dims[4] = N - number_of_kernels(conf, N, val); #endif if (NULL == svals) free(val); free(vec); }
int main(void) { int f,i,j,k,c,x,y,ix,iy,displayloop; int usingmap,makingmap,mmx,mmy,tmpmap,maploop; float rx,ry,nrx,nry,px,py; RGB rgb; FILE *fp; img=(uchar **)calloc(scrhei,sizeof(uchar *)); for (y=0; y<scrhei; y++) { img[y]=(uchar *)calloc(scrwid,sizeof(uchar)); for (x=0; x<scrwid; x++) { img[y][x]=myrnd()*255; } } img2=(uchar **)calloc(scrhei,sizeof(uchar *)); for (y=0; y<scrhei; y++) { img2[y]=(uchar *)calloc(scrwid,sizeof(uchar)); for (x=0; x<scrwid; x++) { img2[y][x]=myrnd()*255; } } srand((int)time(NULL)); usingmap=0; makingmap=1; mmx=0; mmy=0; /* Originals from QB op[0] = 1; damp[0] = .999; force[0] = .005; op[1] = 1.02; damp[1] = .999; force[1] = .002; op[2] = 0; damp[2] = .999; force[2] = .002; op[3] = 1; damp[3] = .999; force[3] = .005; op[4] = 1; damp[4] = .999; force[4] = .005; op[5] = 0; damp[5] = .999; force[5] = .002; */ // 0 Accelerate op[0] = 1; damp[0] = .999; force[0] = .005; // 1 Velocity op[1] = 1.02; damp[1] = .999; force[1] = .01; // 2 Rotation op[2] = 0; damp[2] = .999; force[2] = .05; // 3 Drip op[3] = 1; damp[3] = .999; force[3] = .03; // 4 Dribble op[4] = 1; damp[4] = .999; force[4] = .01; // 5 Slide op[5] = 0; damp[5] = .999; force[5] = .01; for (f=0; f<fs; f++) { var[f] = op[f]; fon[f]=1; } allegro_init (); install_keyboard (); install_timer (); set_gfx_mode (GFX_AUTODETECT, scrwid, scrhei, 0, 0); set_pallete (desktop_palette); _farsetsel(screen->seg); for (c=0; c<=255; c++) { rgb.r=saw(0,c); rgb.g=saw(256/3,c); rgb.b=saw(2*256/3,c); set_color(c,&rgb); } while(!key[KEY_ESC]) { // Generate some more of the map for (maploop=1; maploop<scrwid*scrhei/15; maploop++) { rx=(float)mmx/scrwid*2-1; ry=(float)(mmy-scrhei/2)/scrwid*2; if (fon[1]) { rx = rx / var[1]; ry = ry / var[1]; } if (fon[0]) { rx = mysgn(rx)/var[1]*mypow(myabs(rx),1/var[6]); ry = mysgn(ry)/var[1]*mypow(myabs(ry),1/var[6]); } if (fon[2]) { nrx = rx * cos(var[2]) + ry * sin(var[2]); nry = -rx * sin(var[2]) + ry * cos(var[2]); rx = nrx; ry=nry; } if (fon[3]) { ry = ry / var[3]; } if (fon[4]) { ry = ((myabs(ry) - 1) / var[4] + 1) * mysgn(ry); } if (fon[5]) { rx = rx + var[5] * mysgn(rx); } px=(rx+1)/2*scrwid; py=scrhei/2+(ry)/2*scrwid; ix=(int)px; iy=(int)py; amount[mmx][mmy][0][0][makingmap]=((float)ix+1-(float)px)*((float)(iy+1)-(float)py); amount[mmx][mmy][1][0][makingmap]=((float)px-(float)ix)*((float)(iy+1)-(float)py); amount[mmx][mmy][0][1][makingmap]=((float)ix+1-(float)px)*((float)py-(float)iy); amount[mmx][mmy][1][1][makingmap]=((float)px-(float)ix)*((float)py-(float)iy); pix[mmx][mmy][makingmap]=ix; piy[mmx][mmy][makingmap]=iy; if (ix<0 || ix>=scrwid-1 || iy<0 || iy>=scrhei-1) { pix[mmx][mmy][makingmap]=scrwid/2; piy[mmx][mmy][makingmap]=scrhei/2; for (i=0; i<=1; i++) { for (j=0; j<=1; j++) { amount[mmx][mmy][i][j][makingmap]=0; } } } mmx++; if (mmx>=scrwid) { mmx=0; mmy++; if (mmy>=scrhei) { mmy=0; tmpmap=usingmap; usingmap=makingmap; makingmap=tmpmap; for (f=0; f<fs; f++) { perturb(f); } for (i=0; i<4; i++) { f = myrnd() * fs; if (myrnd()<.8) { if (myrnd()<.5) fon[f] = 1; else fon[f]=0; } } } } } // Animate for (x=0; x<scrwid; x++) { for (y=0; y<scrhei; y++) { c=0; for (i=0; i<=1; i++) { for (j=0; j<=1; j++) { c=c+amount[x][y][i][j][usingmap]*img[piy[x][y][usingmap]+j][pix[x][y][usingmap]+i]; } } c--; img2[y][x]=c; } } /* for (y=0;y<scrhei;y++) { for (x=0;x<scrwid;x++) { _farpokeb(screen->seg, (unsigned long)screen->line[y]+x, img2[y][x]); } }*/ for (y=0; y<scrhei; y++) { movedata(_my_ds(), img2[y], screen->seg, bmp_write_line(screen,y), scrwid); } for (f=0; f<fs; f++) { if (fon[f]) { hline(screen, scrwid/2, f*2, scrwid/2+(var[f] - op[f]) * scrwid * 4, 0); } } imgtmp=img; img=img2; img2=imgtmp; for (i=1; i<=5; i++) { mycircle(myrnd()*scrwid,myrnd()*scrhei,2+myrnd()*8,myrnd()*255); } } }
void moremap() { float rx,ry,nrx,nry,px,py; int f,i,j,k,c,x,y,ix,iy,displayloop; // Generate some more of the map for (maploop=1;maploop<scrwid*scrhei/framespermap;maploop++) { rx=2.0*(float)mmx/(float)scrwid-1.0; ry=2.0*(float)(mmy-scrhei/2)/(float)scrwid; if (fon[8]) { ry=(ry-1)/var[8]+1; } if (fon[9]) { rx=rx+var[9]*rx; } if (fon[10]) { rx=rx+var[10]; } if (fon[11]) { ry=ry+var[11]; } if (fon[0]) { rx = rx + (var[7]-1.0)*0.2 * sin((20.0+30.0*(var[8]-1.0))*ry); ry = ry - (var[7]-1.0)*0.2 * sin((20.0+30.0*(var[8]-1.0))*rx); } if (fon[1]) { rx = rx / var[1]; ry = ry / var[1]; } if (fon[2]) { nrx = rx * cos(var[2]) + ry * sin(var[2]); nry = -rx * sin(var[2]) + ry * cos(var[2]); rx = nrx; ry=nry; } if (fon[3]) { // ry = ry + mysgn(ry) * sin(var[6]*pi*myabs(ry)) * var[3]; rx = rx + (var[4]-1.0)*0.3 * sin((20.0+30.0*(var[3]-1.0))*ry); } if (fon[5]) { // rx = rx + mysgn(rx) * sin(var[6]*pi*myabs(rx)) * var[5]; ry = ry + (var[3]-1.0)*0.2 * sin((20.0+30.0*(var[4]-1.0))*rx); } /* if (fon[4]) { ry = ((myabs(ry) - 1) / var[4] + 1) * mysgn(ry); }*/ px=(rx+1)/2*scrwid; py=scrhei/2+(ry)/2*scrwid; ix=(int)px; iy=(int)py; if (ix<0 || ix>=scrwid-1 || iy<0 || iy>=scrhei-1) { ix=px; iy=py; } amount[mmx][mmy][0][0][makingmap]=amountmax*((float)ix+1-(float)px)*((float)(iy+1)-(float)py); amount[mmx][mmy][1][0][makingmap]=amountmax*((float)px-(float)ix)*((float)(iy+1)-(float)py); amount[mmx][mmy][0][1][makingmap]=amountmax*((float)ix+1-(float)px)*((float)py-(float)iy); amount[mmx][mmy][1][1][makingmap]=amountmax*((float)px-(float)ix)*((float)py-(float)iy); pix[mmx][mmy][makingmap]=ix; piy[mmx][mmy][makingmap]=iy; if (ix<0 || ix>=scrwid-1 || iy<0 || iy>=scrhei-1) { /* pix[mmx][mmy][makingmap]=scrwid/2; piy[mmx][mmy][makingmap]=scrhei/2; for (i=0;i<=1;i++) { for (j=0;j<=1;j++) { amount[mmx][mmy][i][j][makingmap]=0; } }*/ /* pix[mmx][mmy][makingmap]=myrnd()*(scrwid-1); piy[mmx][mmy][makingmap]=myrnd()*(scrhei-1); for (i=0;i<=1;i++) { for (j=0;j<=1;j++) { amount[mmx][mmy][i][j][makingmap]=myrnd()/4; } }*/ pix[mmx][mmy][makingmap]=( mmx<scrwid-1 ? mmx : mmx-1 ); piy[mmx][mmy][makingmap]=( mmy<scrhei-1 ? mmy : mmy-1 ); float splitall=myrnd(); float splitleft=myrnd(); float splitright=myrnd(); amount[mmx][mmy][0][0][makingmap]=amountmax/4; amount[mmx][mmy][1][0][makingmap]=amountmax/4; amount[mmx][mmy][0][1][makingmap]=amountmax/4; amount[mmx][mmy][1][1][makingmap]=amountmax/4; } mmx++; if (mmx>=scrwid) { mmx=0; mmy++; if (mmy>=scrhei) { mmy=0; tmpmap=usingmap; usingmap=makingmap; makingmap=tmpmap; int rep=4; if (myrnd()<0.2) rep=200; for (int r=1;r<=rep;r++) { for (f=0;f<fs;f++) { perturb(f); /* if (myabs(var[f]-op[f])<force[f]) fon[f]=( myrnd()<0.6 ? 1 : 0 );*/ } } } } } }
int init_candy(void) { frameno=0; register int i,j,x,y; srand((int)time(NULL)); usingmap=0; makingmap=1; mmx=0; mmy=0; img=JBmp(scrwid,scrhei); img2=JBmp(scrwid,scrhei); for (y=0;y<scrhei;y++) { for (x=0;x<scrwid;x++) { // img.bmp[y][x]=256*y/scrhei; // img.bmp[y][x]=0; img.bmp[y][x]=255; if (x<scrwid-1 && y<scrhei-1) { pix[x][y][usingmap]=x; piy[x][y][usingmap]=y; for (i=0;i<=1;i++) for (j=0;j<=1;j++) amount[x][y][i][j][usingmap]=amountmax/4; } } } /* Originals from QB op[0] = 1; damp[0] = .999; force[0] = .005; op[1] = 1.02; damp[1] = .999; force[1] = .002; op[2] = 0; damp[2] = .999; force[2] = .002; op[3] = 1; damp[3] = .999; force[3] = .005; op[4] = 1; damp[4] = .999; force[4] = .005; op[5] = 0; damp[5] = .999; force[5] = .002; */ /* From QB later name$(1) = "Velocity" op(1) = 1: damp(1) = .999: force(1) = .002 name$(2) = "Rotation" op(2) = 0: damp(2) = .999: force(2) = .002 name$(3) = "Drip" op(3) = 1: damp(3) = .999: force(3) = .005 name$(4) = "Dribble" op(4) = 1: damp(4) = .999: force(4) = .005 name$(5) = "Slide" op(5) = 0: damp(5) = .999: force(5) = .002 name$(6) = "Accelerate" op(6) = 1: damp(6) = .999: force(6) = .005 name$(7) = "xDisplace" op(7) = 0: damp(7) = .999: force(7) = .005 name$(8) = "yDisplace" op(8) = 0: damp(8) = .999: force(8) = .005 REM 9 and 10 are options for splitting displacements (no var) name$(9) = "2d/3d split" name$(10) = "Split" */ // 0 Accelerate op[0] = 1; damp[0] = .9; force[0] = .01; // 1 Velocity op[1] = 1; damp[1] = .9; force[1] = .01; // 2 Rotation op[2] = 0; damp[2] = .9; force[2] = .02; // 5 x splurge op[5] = 0; damp[5] = .9; force[5] = .01; op[6]=2;damp[6]=.99;force[6]=.01; op[3] = 1; damp[3] = .9; force[3] = .01; op[4] = 1; damp[4] = .9; force[4] = .01; op[7]=1;damp[7]=.99;force[7]=.01; // Dribble op[8] = 1; damp[8] = .9; force[8] = .01; // Slide op[9] = 0; damp[9] = .9; force[9] = .01; // xDisplace op[10] = 0; damp[10] = .9; force[10] = .01; // yDisplace op[11] = 0; damp[11] = .9; force[11] = .01; for (f=0;f<fs;f++) { var[f] = op[f]; fon[f]=1; } for (j=1;j<=10000;j++) for (i=0;i<fs;i++) perturb(i); for (i=0;i<=framespermap;i++) { moremap(); } #ifdef ALLEGRO allegrosetup(scrwid,scrhei); // _farsetsel(screen->seg); #endif redocolors(); }
double VRP::RTR_solve(int heuristics, int intensity, int max_stuck, int max_perturbs, double dev, int nlist_size, int perturb_type, int accept_type, bool verbose) { /// /// Uses the given parameters to generate a /// VRP solution via record-to-record travel. /// Assumes that data has already been imported into V and that we have /// some existing solution. /// Returns the objective function value of the best solution found /// // Make sure accept_type is either VRPH_BEST_ACCEPT or VRPH_FIRST_ACCEPT - matters only // for the downhill phase as we use VRPH_LI_ACCEPT in the diversification phase if(accept_type!=VRPH_BEST_ACCEPT && accept_type!=VRPH_FIRST_ACCEPT) report_error("%s: accept_type must be VRPH_BEST_ACCEPT or VRPH_FIRST_ACCEPT\n"); int ctr, n, j, i, R, random, fixed, neighbor_list, objective, tabu; random=fixed=neighbor_list=0; if(heuristics & VRPH_RANDOMIZED) random=VRPH_RANDOMIZED; if(heuristics & VRPH_FIXED_EDGES) fixed=VRPH_FIXED_EDGES; if(heuristics & VRPH_USE_NEIGHBOR_LIST) neighbor_list=VRPH_USE_NEIGHBOR_LIST; objective=VRPH_SAVINGS_ONLY; // default strategy if(heuristics & VRPH_MINIMIZE_NUM_ROUTES) objective=VRPH_MINIMIZE_NUM_ROUTES; if(heuristics & VRPH_TABU) { tabu=VRPH_TABU; // We will use a primitive Tabu Search in the uphill phase // Clear the tabu list this->tabu_list->empty(); } else tabu=0; n=num_nodes; // Define the heuristics we will use OnePointMove OPM; TwoPointMove TPM; TwoOpt TO; OrOpt OR; ThreeOpt ThreeO; CrossExchange CE; ThreePointMove ThreePM; double start_val; int *perm; perm=new int[this->num_nodes]; j=VRPH_ABS(this->next_array[VRPH_DEPOT]); for(i=0;i<this->num_nodes;i++) { perm[i]=j; if(!routed[j]) report_error("%s: Unrouted node in solution!!\n"); j=VRPH_ABS(this->next_array[j]); } if(j!=VRPH_DEPOT) report_error("%s: VRPH_DEPOT is not last node in solution!!\n"); int rules; // Set the neighbor list size used in the improvement search neighbor_list_size=VRPH_MIN(nlist_size, this->num_nodes); // Set the deviation deviation=dev; int num_perturbs=0; record=this->total_route_length; this->best_total_route_length=this->total_route_length; this->export_solution_buff(this->current_sol_buff); this->export_solution_buff(this->best_sol_buff); normalize_route_numbers(); ctr=0; uphill: // Start an uphill phase using the following "rules": double beginning_best=this->best_total_route_length; rules=VRPH_LI_ACCEPT+VRPH_RECORD_TO_RECORD+objective+random+fixed+neighbor_list+tabu; if(verbose) printf("Uphill starting at %5.2f\n",this->total_route_length); for(int k=1;k<intensity;k++) { start_val=total_route_length; if(heuristics & ONE_POINT_MOVE) { if(random) random_permutation(perm, this->num_nodes); for(i=1;i<=n;i++) { #if FIXED_DEBUG if(fixed && !check_fixed_edges("Before 1PM\n")) fprintf(stderr,"Error before OPM search(%d)\n",perm[i-1]); #endif OPM.search(this,perm[i-1],rules); #if FIXED_DEBUG if(fixed && !check_fixed_edges("After 1PM\n")) { fprintf(stderr,"Error after OPM search(%d)\n",perm[i-1]); this->show_route(this->route_num[perm[i-1]]); } #endif } } if(heuristics & TWO_POINT_MOVE) { if(random) random_permutation(perm, this->num_nodes); for(i=1;i<=n;i++) TPM.search(this,perm[i-1],rules + VRPH_INTER_ROUTE_ONLY); //check_fixed_edges("After 2PM\n"); } if(heuristics & THREE_POINT_MOVE) { if(random) random_permutation(perm, this->num_nodes); for(i=1;i<=n;i++) ThreePM.search(this,perm[i-1],rules + VRPH_INTER_ROUTE_ONLY); //check_fixed_edges("After 3PM\n"); } if(heuristics & TWO_OPT) { if(random) random_permutation(perm, this->num_nodes); for(i=1;i<=n;i++) TO.search(this,perm[i-1],rules); //check_fixed_edges("After TO\n"); } if(heuristics & OR_OPT) { if(random) random_permutation(perm, this->num_nodes); for(i=1;i<=n;i++) OR.search(this,perm[i-1],4,rules); for(i=1;i<=n;i++) OR.search(this,perm[i-1],3,rules); for(i=1;i<=n;i++) OR.search(this,perm[i-1],2,rules); //check_fixed_edges("After OR\n"); } if(heuristics & THREE_OPT) { normalize_route_numbers(); R=total_number_of_routes; for(i=1; i<=R; i++) ThreeO.route_search(this,i,rules-neighbor_list); //check_fixed_edges("After 3O\n"); } if(heuristics & CROSS_EXCHANGE) { normalize_route_numbers(); this->find_neighboring_routes(); R=total_number_of_routes; for(i=1; i<=R-1; i++) { for(j=0;j<1;j++) CE.route_search(this,i, route[i].neighboring_routes[j],rules-neighbor_list); } //check_fixed_edges("After CE\n"); } } if(total_route_length<record) record = total_route_length; if(verbose) { printf("Uphill complete\t(%d,%5.2f,%5.2f)\n",count_num_routes(),total_route_length, record); printf("# of recorded routes: %d[%d]\n",total_number_of_routes,count_num_routes()); } if(this->best_total_route_length<beginning_best-VRPH_EPSILON) { if(verbose) printf("New best found in uphill!\n"); // We found a new best solution during the uphill phase that might // now be "forgotten"!! I have seen this happen where it is never recovered // again, so we just import it and start the downhill phase with this solution... //this->import_solution_buff(this->best_sol_buff); } downhill: // Now enter a downhill phase double orig_val=total_route_length; if(verbose) printf("Downhill starting at %f (best=%f)\n",orig_val,this->best_total_route_length); if((heuristics & ONE_POINT_MOVE)|| (heuristics & KITCHEN_SINK) ) { rules=VRPH_DOWNHILL+objective+random+fixed+neighbor_list+accept_type; for(;;) { // One Point Move start_val=total_route_length; if(random) random_permutation(perm, this->num_nodes); for(i=1;i<=n;i++) OPM.search(this,perm[i-1],rules ); if(VRPH_ABS(total_route_length-start_val)<VRPH_EPSILON) break; } } if((heuristics & TWO_POINT_MOVE) || (heuristics & KITCHEN_SINK) ) { rules=VRPH_DOWNHILL+VRPH_INTER_ROUTE_ONLY+objective+random+fixed+neighbor_list+accept_type; for(;;) { // Two Point Move start_val=total_route_length; if(random) random_permutation(perm, this->num_nodes); for(i=1;i<=n;i++) TPM.search(this,perm[i-1],rules); if(VRPH_ABS(total_route_length-start_val)<VRPH_EPSILON) break; } } if((heuristics & TWO_OPT)|| (heuristics & KITCHEN_SINK) ) { // Do inter-route first a la Li rules=VRPH_DOWNHILL+VRPH_INTER_ROUTE_ONLY+objective+random+fixed+neighbor_list+accept_type; for(;;) { start_val=total_route_length; if(random) random_permutation(perm, this->num_nodes); for(i=1;i<=n;i++) TO.search(this,perm[i-1],rules); if(VRPH_ABS(total_route_length-start_val)<VRPH_EPSILON) break; } // Now do both intra and inter rules=VRPH_DOWNHILL+objective+random+fixed+neighbor_list+accept_type; for(;;) { start_val=total_route_length; if(random) random_permutation(perm, this->num_nodes); for(i=1;i<=n;i++) TO.search(this,perm[i-1],rules); if(VRPH_ABS(total_route_length-start_val)<VRPH_EPSILON) break; } } if((heuristics & THREE_POINT_MOVE) || (heuristics & KITCHEN_SINK) ) { rules=VRPH_DOWNHILL+VRPH_INTER_ROUTE_ONLY+objective+random+fixed+accept_type+neighbor_list; for(;;) { // Three Point Move start_val=total_route_length; if(random) random_permutation(perm, this->num_nodes); for(i=1;i<=n;i++) ThreePM.search(this,perm[i-1],rules); if(VRPH_ABS(total_route_length-start_val)<VRPH_EPSILON) break; } } if((heuristics & OR_OPT) || (heuristics & KITCHEN_SINK)) { rules=VRPH_DOWNHILL+ objective +random +fixed + accept_type + neighbor_list; for(;;) { // OrOpt start_val=total_route_length; if(random) random_permutation(perm, this->num_nodes); for(i=1;i<=n;i++) OR.search(this,perm[i-1],4,rules); for(i=1;i<=n;i++) OR.search(this,perm[i-1],3,rules); for(i=1;i<=n;i++) OR.search(this,perm[i-1],2,rules); if(VRPH_ABS(total_route_length-start_val)<VRPH_EPSILON) break; } } if((heuristics & THREE_OPT) || (heuristics & KITCHEN_SINK) ) { normalize_route_numbers(); R= total_number_of_routes; rules=VRPH_DOWNHILL+objective+VRPH_INTRA_ROUTE_ONLY+ random +fixed + accept_type; for(;;) { // 3OPT start_val=total_route_length; for(i=1;i<=R;i++) ThreeO.route_search(this,i,rules); if(VRPH_ABS(total_route_length-start_val)<VRPH_EPSILON) break; } } if( (heuristics & CROSS_EXCHANGE) ) { normalize_route_numbers(); this->find_neighboring_routes(); R=total_number_of_routes; rules=VRPH_DOWNHILL+objective+VRPH_INTRA_ROUTE_ONLY+ random +fixed + accept_type; for(i=1; i<=R-1; i++) { for(j=0;j<=1;j++) CE.route_search(this,i, route[i].neighboring_routes[j], rules); } } // Repeat the downhill phase until we find no more improvements if(total_route_length<orig_val-VRPH_EPSILON) goto downhill; if(verbose) printf("Downhill complete: %5.2f[downhill started at %f] (%5.2f)\n",total_route_length,orig_val, this->best_total_route_length); if(total_route_length < record-VRPH_EPSILON) { // New record - reset ctr ctr=1; record=total_route_length; } else ctr++; if(ctr<max_stuck) goto uphill; if(ctr==max_stuck) { if(num_perturbs<max_perturbs) { if(verbose) printf("perturbing\n"); if(perturb_type==VRPH_LI_PERTURB) perturb(); else osman_perturb(VRPH_MAX(20,num_nodes/10),.5+lcgrand(20)); // Reset record this->record=this->total_route_length; if(tabu) this->tabu_list->empty(); ctr=1; num_perturbs++; goto uphill; } } if(verbose) { if(has_service_times==false) printf("BEST OBJ: %f\n",best_total_route_length); else printf("BEST OBJ: %f\n",best_total_route_length-total_service_time); } delete [] perm; // Import the best solution found this->import_solution_buff(best_sol_buff); if(has_service_times==false) return best_total_route_length; else return best_total_route_length-total_service_time; }
int main(void) { int f,i,j,k,c,x,y,ix,iy,displayloop; int usingmap,makingmap,mmx,mmy,tmpmap,maploop; float rx,ry,nrx,nry,px,py,thru,ctmp; RGB rgb; FILE *fp; srand((int)time(NULL)); usingmap=0; makingmap=1; mmx=0; mmy=0; img=(uchar **)calloc(scrhei,sizeof(uchar *)); img2=(uchar **)calloc(scrhei,sizeof(uchar *)); for (y=0;y<scrhei;y++) { img[y]=(uchar *)calloc(scrwid,sizeof(uchar)); img2[y]=(uchar *)calloc(scrwid,sizeof(uchar)); for (x=0;x<scrwid;x++) { img[y][x]=255*y/scrhei; img2[y][x]=myrnd()*255; if (x<scrwid-1 && y<scrhei-1) { pix[x][y][usingmap]=x; piy[x][y][usingmap]=y; for (i=0;i<=1;i++) for (j=0;j<=1;j++) amount[x][y][i][j][usingmap]=(float)1/4; } } } /* Originals from QB op[0] = 1; damp[0] = .999; force[0] = .005; op[1] = 1.02; damp[1] = .999; force[1] = .002; op[2] = 0; damp[2] = .999; force[2] = .002; op[3] = 1; damp[3] = .999; force[3] = .005; op[4] = 1; damp[4] = .999; force[4] = .005; op[5] = 0; damp[5] = .999; force[5] = .002; */ // 0 Accelerate op[0] = 1; damp[0] = .999; force[0] = .005; // 1 Velocity op[1] = 1.02; damp[1] = .999; force[1] = .01; // 2 Rotation op[2] = 0; damp[2] = .995; force[2] = .03; // 3 y splurge op[3] = 0; damp[3] = .999; force[3] = .01; // 4 Dribble op[4] = 1; damp[4] = 0; force[4] = .01; // 5 x splurge op[5] = 0; damp[5] = .999; force[5] = .01; op[6]=2;damp[6]=.9999;force[6]=.01; op[7]=1;damp[7]=.999;force[7]=.01; for (f=0;f<fs;f++) { var[f] = op[f]; fon[f]=1; } allegrosetup(scrwid,scrhei); _farsetsel(screen->seg); starttimer(); while(!key[KEY_ESC]) { // Generate some more of the map for (maploop=1;maploop<scrwid*scrhei/20;maploop++) { rx=(float)mmx/scrwid*2-1; ry=(float)(mmy-scrhei/2)/scrwid*2; if (fon[0]) { rx = mysgn(rx)/var[7]*mypow(myabs(rx),1/var[0]); ry = mysgn(ry)/var[7]*mypow(myabs(ry),1/var[0]); } if (fon[1]) { rx = rx / var[1]; ry = ry / var[1]; } if (fon[2]) { nrx = rx * cos(var[2]) + ry * sin(var[2]); nry = -rx * sin(var[2]) + ry * cos(var[2]); rx = nrx; ry=nry; } if (fon[3]) { ry = ry - mysgn(ry) * sin(var[6]*pi*myabs(ry)) * var[3]; } if (fon[4]) { ry = ((myabs(ry) - 1) / var[4] + 1) * mysgn(ry); } if (fon[5]) { rx = rx - mysgn(rx) * sin(var[6]*pi*myabs(rx)) * var[5]; } px=(rx+1)/2*scrwid; py=scrhei/2+(ry)/2*scrwid; ix=(int)px; iy=(int)py; if (ix<0 || ix>=scrwid-1 || iy<0 || iy>=scrhei-1) { ix=px; iy=py; } amount[mmx][mmy][0][0][makingmap]=((float)ix+1-(float)px)*((float)(iy+1)-(float)py); amount[mmx][mmy][1][0][makingmap]=((float)px-(float)ix)*((float)(iy+1)-(float)py); amount[mmx][mmy][0][1][makingmap]=((float)ix+1-(float)px)*((float)py-(float)iy); amount[mmx][mmy][1][1][makingmap]=((float)px-(float)ix)*((float)py-(float)iy); pix[mmx][mmy][makingmap]=ix; piy[mmx][mmy][makingmap]=iy; if (ix<0 || ix>=scrwid-1 || iy<0 || iy>=scrhei-1) { pix[mmx][mmy][makingmap]=scrwid/2; piy[mmx][mmy][makingmap]=scrhei/2; for (i=0;i<=1;i++) { for (j=0;j<=1;j++) { amount[mmx][mmy][i][j][makingmap]=0; } } } mmx++; if (mmx>=scrwid) { mmx=0; mmy++; if (mmy>=scrhei) { mmy=0; tmpmap=usingmap; usingmap=makingmap; makingmap=tmpmap; for (f=0;f<fs;f++) { perturb(f); } } } } // Animate for (x=0; x<scrwid; x++) { for (y=0; y<scrhei; y++) { c=0; for (i=0;i<=1;i++) { for (j=0;j<=1;j++) { c=c+amount[x][y][i][j][usingmap]*img[piy[x][y][usingmap]+j][pix[x][y][usingmap]+i]; } } c--; img2[y][x]=c; } } /* for (y=0;y<scrhei;y++) { for (x=0;x<scrwid;x++) { _farpokeb(screen->seg, (unsigned long)screen->line[y]+x, img2[y][x]); } }*/ for (y=0; y<scrhei; y++) { movedata(_my_ds(), img2[y], screen->seg, bmp_write_line(screen,y), scrwid); } for (f=0;f<fs;f++) { if (fon[f]) { hline(screen, scrwid/2, f*2, scrwid/2+(var[f] - op[f]) * scrwid * 4, 0); } } toff=toff-(float)1/128; for (c=0;c<=255;c++) { thru=saw((float)c/255-toff); rgb.r=huefor(thru,(float)0); rgb.g=huefor(thru,(float)1/3); rgb.b=huefor(thru,(float)2/3); set_color(c,&rgb); } imgtmp=img; img=img2; img2=imgtmp; for (i=1;i<=5;i++) { mycircle(myrnd()*scrwid,myrnd()*scrhei,2+myrnd()*8,myrnd()*255); } framedone(); } allegro_exit(); displayframespersecond(); }
int LayerNet::ssg_core ( TrainingSet *tptr , // Training set to use struct LearnParams *lptr , // User's general learning parameters LayerNet *avgnet , // Work area used to keep average weights LayerNet *bestnet , // And the best so far double *work1 , // Gradient work vector double *work2 , // Ditto double *grad , // Ditto double *avg_grad , // Ditto int n_grad // Length of above vectors ) { int ntemps, niters, setback, reg, nvars, user_quit ; int i, iter, itemp, n_good, n_bad, use_grad ; char msg[80] ; double tempmult, temp, fval, bestfval, starttemp, stoptemp, fquit ; double avg_func, new_fac, gradlen, grad_weight, weight_used ; enum RandomDensity density ; SingularValueDecomp *sptr ; struct AnnealParams *aptr ; // User's annealing parameters aptr = lptr->ap ; ntemps = aptr->temps0 ; niters = aptr->iters0 ; setback = aptr->setback0 ; starttemp = aptr->start0 ; stoptemp = aptr->stop0 ; if (aptr->random0 == ANNEAL_GAUSSIAN) density = NormalDensity ; else if (aptr->random0 == ANNEAL_CAUCHY) density = CauchyDensity ; if (! (ntemps * niters)) return 0 ; /* Initialize other local parameters. Note that there is no sense using regression if there are no hidden layers. */ use_grad = (grad != NULL) ; fquit = lptr->quit_err ; reg = nhid1 ; /* Allocate the singular value decomposition object for REGRESS. Also allocate a work area for REGRESS to preserve matrix. */ if (reg) { // False if no hidden layers if (nhid2 == 0) // One hidden layer nvars = nhid1_n ; else // Two hidden layers nvars = nhid2_n ; i = (model == NETMOD_COMPLEX) ? 2 * tptr->ntrain : tptr->ntrain ; if (i < nvars) { warning_message ( "Too few training sets for regression." ) ; reg = 0 ; } else { MEMTEXT ( "SSG: new SingularValueDecomp" ) ; sptr = new SingularValueDecomp ( i , nvars , 1 ) ; if ((sptr == NULL) || ! sptr->ok) { memory_message ( "for SS(G) with regression. Using total randomization."); if (sptr != NULL) delete sptr ; reg = 0 ; } } } /* For the basic algorithm, we will keep the current 'average' network weight set in avgnet. This will be the moving center about which the perturbation is done. Although not directly related to the algorithm itself, we keep track of the best network ever found in bestnet. That is what the user will get at the end. */ copy_weights ( bestnet , this ) ; // Current weights are best so far copy_weights ( avgnet , this ) ; // Center of perturbation bestfval = trial_error ( tptr ) ; /* If this is being used to initialize the weights, make sure that they are not identically zero. Do this by setting bestfval huge so that SOMETHING is accepted later. */ if (nhid1) { i = nhid1 * nin_n ; while (i--) { if (fabs(hid1_coefs[i]) > 1.e-10) break ; } if (i < 0) bestfval = 1.e30 ; } /* Initialize by cumulating a bunch of points */ normal_message ( "Initializing..." ) ; avg_func = 0.0 ; // Mean function around center if (use_grad) { for (i=0 ; i<n_grad ; i++) // Zero the mean gradient avg_grad[i] = 0.0 ; } for (iter=0 ; iter<niters ; iter++) { // Initializing iterations perturb ( avgnet , this , starttemp , reg , density ) ; // Move point if (reg) // If using regression, estimate fval = regress ( tptr , sptr ) ; // out weights now, ignore fval if (use_grad) // Also need gradient? fval = gradient ( tptr , work1 , work2 , grad ) ; // fval redundant else if (! reg) // If reg we got fval from regress fval = trial_error ( tptr ) ; avg_func += fval ; // Cumulate mean function if (use_grad) { // Also need gradient? for (i=0 ; i<n_grad ; i++) // Cumulate mean gradient avg_grad[i] += grad[i] ; } if (fval < bestfval) { // If this iteration improved bestfval = fval ; // then update the best so far copy_weights ( bestnet , this ) ; // Keep the network if (bestfval <= fquit) // If we reached the user's goto FINISH ; // limit, we can quit } if ((user_quit = user_pressed_escape ()) != 0) goto FINISH ; } // Loop: for all initial iters avg_func /= niters ; // Mean of all points around avgnet new_fac = 1.0 / niters ; // Weight of each point sprintf ( msg , " avg=%.6lf best=%.6lf", avg_func, bestfval ) ; progress_message ( msg ) ; if (use_grad) { // Also need gradient? gradlen = 0.0 ; // Will cumulate grad length for (i=0 ; i<n_grad ; i++) { // Find gradient mean and length avg_grad[i] /= niters ; gradlen += avg_grad[i] * avg_grad[i] ; } gradlen = sqrt ( gradlen ) ; grad_weight = 0.5 ; } /* This is the temperature reduction loop and the iteration within temperature loop. */ temp = starttemp ; tempmult = exp( log( stoptemp / starttemp ) / (ntemps-1)) ; user_quit = 0 ; // Flags user pressed ESCape for (itemp=0 ; itemp<ntemps ; itemp++) { // Temp reduction loop n_good = n_bad = 0 ; // Counts better and worse sprintf ( msg , "Temp=%.3lf ", temp ) ; normal_message ( msg ) ; for (iter=0 ; iter<niters ; iter++) { // Iters per temp loop if ((n_bad >= 10) && ((double) n_good / (double) (n_good+n_bad) < 0.15)) break ; perturb ( avgnet , this , temp , reg , density ) ; // Randomly perturb about center if (use_grad) // Bias per gradient? weight_used = shift ( grad , this , grad_weight , reg ) ; if (reg) { // If using regression, estimate fval = regress ( tptr , sptr ) ; // out weights now if ((user_quit = user_pressed_escape ()) != 0) break ; if (fval >= avg_func) { // If this would raise mean ++n_bad ; // Count this bad point for user continue ; // Skip it and try again } } if (use_grad) // Need gradient, fval redundant fval = gradient ( tptr , work1 , work2 , grad ) ; else if (! reg) // If reg we got fval from regress fval = trial_error ( tptr ) ; if ((user_quit = user_pressed_escape ()) != 0) break ; if (fval >= avg_func) { // If this would raise mean ++n_bad ; // Count this bad point for user continue ; // Skip it and try again } ++n_good ; if (fval < bestfval) { // If this iteration improved bestfval = fval ; // then update the best so far copy_weights ( bestnet , this ) ; // Keep the network if (bestfval <= fquit) // If we reached the user's break ; // limit, we can quit iter -= setback ; // It often pays to keep going if (iter < 0) // at this temperature if we iter = 0 ; // are still improving } adjust ( avgnet , this , reg , new_fac ) ; // Move center slightly avg_func = new_fac * fval + (1.0 - new_fac) * avg_func ; if (use_grad) { grad_weight = new_fac * weight_used + (1.0 - new_fac) * grad_weight ; for (i=0 ; i<n_grad ; i++) // Adjust mean gradient avg_grad[i] = new_fac * grad[i] + (1.0 - new_fac) * avg_grad[i] ; } } // Loop: for all iters at a temp /* Iters within temp loop now complete */ sprintf ( msg , " %.3lf%% improved avg=%.5lf best=%.5lf", 100.0 * n_good / (double) (n_good+n_bad), avg_func, bestfval ) ; progress_message ( msg ) ; if (use_grad) { gradlen = 0.0 ; // Will cumulate grad length for (i=0 ; i<n_grad ; i++) // Find gradient length gradlen += avg_grad[i] * avg_grad[i] ; gradlen = sqrt ( gradlen ) ; sprintf ( msg , " grad=%.5lf", gradlen ) ; progress_message ( msg ) ; } if (bestfval <= fquit) // If we reached the user's break ; // limit, we can quit if (user_quit) break ; temp *= tempmult ; // Reduce temp for next pass } // through this temperature loop /* The trials left this weight set and neterr in random condition. Make them equal to the best, which will be the original if we never improved. */ FINISH: copy_weights ( this , bestnet ) ; // Return best weights in this net neterr = bestfval ; // Trials destroyed weights, err if (reg) { MEMTEXT ( "SSG: delete SingularValueDecomp" ) ; delete sptr ; } if (user_quit) return 1 ; else return 0 ; }