void test_constructor_vfunc ()
{
Function_vector vfunc;
vfunc.push_back(cube_function_1);
vfunc.push_back(cube_function_2);
Labeling_function lfunc(vfunc);
Point_3 p1(0.5, 0.5, 0.5);
Point_3 p2(2.5, 2.5, 2.5);
Point_3 p3(1.5, 1.5, 1.5);
Point_3 p_out(4., 4., 4.);
Labeling_function::return_type rp1 = lfunc(p1);
Labeling_function::return_type rp2 = lfunc(p2);
Labeling_function::return_type rp3 = lfunc(p3);
Labeling_function::return_type rp_out = lfunc(p_out);
assert(rp1 != 0);
assert(rp2 != 0);
assert(rp3 != 0);
assert(rp_out == 0);
assert(rp1 != rp2);
assert(rp1 != rp3);
assert(rp2 != rp3);
}
loliObj* eval_list(loliObj* lst, loliObj* env){
loliObj* car = lcons(lst)->head()->eval(env);
loliObj* cdr = lcons(lst)->tail();
std::cout<<"HEAD: "<<car->toString()<<"\tTAIL: "<<cdr->toString()<<std::endl;
// std::cout<<cdr->type->toString()<<std::endl;
if(lfunc(car)->rtype){
if(lcons(lcons(lcons(cdr)->tail())->head())->hd == NULL){
std::cout<<lcons(lcons(lcons(cdr)->tail())->head())->toString()<<std::endl;
return c_apply(car, cdr->eval(env), env);
}
return c_apply(car, cdr, env);
}
else{
loli_err(car->toString() + " is not a function!");
}
return nil;
}
/*ARGSUSED1*/
void key (unsigned char key, int x, int y) {
switch (key) {
case 'b': bfunc(); break;
case 'c': cfunc(); break;
case 'l': lfunc(); break;
case 't': tfunc(); break;
case 'f': ffunc(); break;
case 'n': nfunc(); break;
case 'u': ufunc(); break;
case 'U': Ufunc(); break;
case 'p': pfunc(); break;
case 'P': Pfunc(); break;
case 'w': wfunc(); break;
case 'x': xfunc(); break;
case 'X': Xfunc(); break;
case 'y': yfunc(); break;
case '\033': exit(EXIT_SUCCESS); break;
default: break;
}
}
GDens *GDens2InitNew(double fac, double mean, double stdev, int n, GDensFunc_tp *lfunc, char *lfunc_arg)
{
/*
arguments are:
limits are mean +- fac*stdev, and n-regions.
lfunc: the function returning log(f(x,lfunc_arg)), unormalized
*/
GDens *ptr;
int i;
double dx, df, f, *cpoints;
fac = ABS(fac);
ptr = Calloc(1, GDens);
ptr->xmin = mean -fac*stdev;
ptr->xmax = mean +fac*stdev;
ptr->n = n;
ptr->elm = Calloc(n+1, GDensElm);
cpoints = cut_points(fac, n);
for(i=0;i<ptr->n;i++)
{
dx = stdev*(cpoints[i+1]-cpoints[i]);
ptr->elm[i].xl = mean + cpoints[i]*stdev;
ptr->elm[i].xr = ptr->elm[i].xl + dx;
ptr->elm[i].lfl = (i==0 ? lfunc(ptr->elm[i].xl, lfunc_arg) : ptr->elm[i-1].lfr);
ptr->elm[i].lfr = lfunc(ptr->elm[i].xr, lfunc_arg);
/*
now, try to decide a nice place to place the midpoint. currently, i try to split the
region into (approximately) equal integrals
*/
df = ptr->elm[i].lfr - ptr->elm[i].lfl;
f = (ISZERO(df) ? 0.5 : log(0.5*(exp(df)+1.0))/df);
/*
need also a more safe option than ISZERO
*/
if (ISZERO(f) || ISZERO(1.-f)) f = 0.5;
if (f < FLT_EPSILON || 1.-f < FLT_EPSILON) f = 0.5;
if (f == 0.5)
{
/*
this will emulate a log-linear spline
*/
ptr->elm[i].xm = ptr->elm[i].xl + dx*f;
ptr->elm[i].lfm = 0.5*(ptr->elm[i].lfl + ptr->elm[i].lfr);
}
else
{
ptr->elm[i].xm = ptr->elm[i].xl + dx*f;
ptr->elm[i].lfm = lfunc(ptr->elm[i].xm, lfunc_arg);
}
}
for(i=0;i<ptr->n-1;i++) ptr->elm[i].lfr = ptr->elm[i+1].lfl;
ptr->elm[ptr->n-1].lfr = lfunc(ptr->elm[ptr->n-1].xr, lfunc_arg);
GDens2Update(ptr);
return ptr;
}
GDens *GDens2Init(double xmin, double xmax, int n, int n_min, GDensFunc_tp *lfunc, char *lfunc_arg)
{
/*
arguments are:
xmin : left limit
xmax : right limit
n : total number of interior points, which makes n+1 cells
n_min: make first n_min interior points by equal division of (xmax-xmin), then
n-n_min divisions by dividing the cell with the largest probability in half.
lfunc: the function returning log(f(x,lfunc_arg)), unormalized
*/
GDens *ptr;
int i, nn;
double dx, df, f;
ptr = Calloc(1, GDens);
ptr->order= 2;
ptr->xmin = MIN(xmin, xmax);
ptr->xmax = MAX(xmin, xmax);
ptr->n = MAX(1, n_min);
nn = MAX(ptr->n, n);
ptr->elm = Calloc(nn+1, GDensElm);
/*
make first equal spaced points
*/
dx = (ptr->xmax -ptr->xmin)/((double)n_min);
for(i=0;i<ptr->n;i++)
{
ptr->elm[i].xl = ptr->xmin + i*dx;
ptr->elm[i].xr = ptr->elm[i].xl + dx;
ptr->elm[i].lfl = (i==0 ? lfunc(ptr->elm[i].xl, lfunc_arg) : ptr->elm[i-1].lfr);
ptr->elm[i].lfr = lfunc(ptr->elm[i].xr, lfunc_arg);
/*
now, try to decide a nice place to place the midpoint. currently, i try to split the
region into (approximately) equal integrals
*/
df = ptr->elm[i].lfr - ptr->elm[i].lfl;
f = (ISZERO(df) ? 0.5 : log(0.5*(exp(df)+1.0))/df);
/*
need also a more safe option than ISZERO
*/
if (ISZERO(f) || ISZERO(1.-f)) f = 0.5;
if (f < FLT_EPSILON || 1.-f < FLT_EPSILON) f = 0.5;
if (f == 0.5)
{
/*
this will emulate a log-linear spline
*/
ptr->elm[i].xm = ptr->elm[i].xl + dx*f;
ptr->elm[i].lfm = 0.5*(ptr->elm[i].lfl + ptr->elm[i].lfr);
}
else
{
ptr->elm[i].xm = ptr->elm[i].xl + dx*f;
ptr->elm[i].lfm = lfunc(ptr->elm[i].xm, lfunc_arg);
}
}
for(i=0;i<ptr->n-1;i++) ptr->elm[i].lfr = ptr->elm[i+1].lfl;
ptr->elm[ptr->n-1].lfr = lfunc(ptr->elm[ptr->n-1].xr, lfunc_arg);
GDens2Update(ptr);
/*
decide now the rest of the points by dividing the cell with largest probability in half. try
to split the region into (approximately) equal integrals
*/
for(i=n_min;i<nn;i++)
{
/*
split the first elm, and add one at the end
*/
ptr->elm[ptr->n].lfl = ptr->elm[0].lfm;
ptr->elm[ptr->n].xl = ptr->elm[0].xm;
ptr->elm[ptr->n].lfr = ptr->elm[0].lfr;
ptr->elm[ptr->n].xr = ptr->elm[0].xr;
df = ptr->elm[ptr->n].lfr - ptr->elm[ptr->n].lfl;
f = (ISZERO(df) ? 0.5 : log(0.5*(exp(df)+1.0))/df);
if (ISZERO(f) || ISZERO(1.-f)) f = 0.5;
if (f < FLT_EPSILON || 1.-f < FLT_EPSILON) f = 0.5;
if (f == 0.5)
{
/*
this will emulate a log-linear spline
*/
ptr->elm[ptr->n].xm = ptr->elm[ptr->n].xl + f*(ptr->elm[ptr->n].xr -ptr->elm[ptr->n].xl);
ptr->elm[ptr->n].lfm = 0.5*(ptr->elm[ptr->n].lfl + ptr->elm[ptr->n].lfr);
}
else
{
ptr->elm[ptr->n].xm = ptr->elm[ptr->n].xl + f*(ptr->elm[ptr->n].xr -ptr->elm[ptr->n].xl);
ptr->elm[ptr->n].lfm = lfunc(ptr->elm[ptr->n].xm, lfunc_arg);
}
ptr->elm[0].lfr = ptr->elm[0].lfm;
ptr->elm[0].xr = ptr->elm[0].xm;
df = ptr->elm[0].lfr - ptr->elm[0].lfl;
f = (ISZERO(df) ? 0.5 : log(0.5*(exp(df)+1.0))/df);
if (ISZERO(f) || ISZERO(1.-f)) f = 0.5;
if (f < FLT_EPSILON || 1.-f < FLT_EPSILON) f = 0.5;
if (f == 0.5)
{
/*
this will emulate a log-linear spline
*/
ptr->elm[0].xm = ptr->elm[0].xl + f*(ptr->elm[0].xr -ptr->elm[0].xl);
ptr->elm[0].lfm = 0.5*(ptr->elm[0].lfl + ptr->elm[0].lfr);
}
else
{
ptr->elm[0].xm = ptr->elm[0].xl + f*(ptr->elm[0].xr -ptr->elm[0].xl);
ptr->elm[0].lfm = lfunc(ptr->elm[0].xm, lfunc_arg);
}
ptr->n++;
GDens2Update(ptr);
}
return ptr;
}
GDens *GDens1Init(double xmin, double xmax, int n, int n_min, GDensFunc_tp *lfunc, char *lfunc_arg)
{
/*
arguments are:
xmin : left limit
xmax : right limit
n : total number of interior points, which makes n+1 cells
n_min: make first n_min interior points by equal division of (xmax-xmin), then
n-n_min divisions by dividing the cell with the largest probability in half.
lfunc: the function returning log(f(x)), unormalized
*/
GDens *ptr;
int i, nn;
double xnew, f, df, dx;
ptr = Calloc(1, GDens);
ptr->order= 1;
ptr->xmin = MIN(xmin, xmax);
ptr->xmax = MAX(xmin, xmax);
ptr->n = MAX(2, n_min);
nn = MAX(ptr->n, n);
ptr->elm = Calloc(nn+1, GDensElm);
/*
make first equal spaced points
*/
dx = (ptr->xmax -ptr->xmin)/((double)n_min);
for(i=0;i<ptr->n;i++)
{
ptr->elm[i].x = ptr->xmin + i*dx;
ptr->elm[i].dx = dx;
ptr->elm[i].lfl = lfunc(ptr->elm[i].x, lfunc_arg);
}
for(i=0;i<ptr->n-1;i++) ptr->elm[i].lfr = ptr->elm[i+1].lfl;
ptr->elm[ptr->n-1].lfr = lfunc(ptr->elm[ptr->n-1].x + ptr->elm[ptr->n-1].dx, lfunc_arg);
GDens1Update(ptr);
/*
decide now the rest of the points by dividing the cell with largest probability in half
*/
for(i=n_min;i<n;i++)
{
/*
split the first elm, and add one at the end
*/
if (ABS(ptr->elm[0].b) < FLT_EPSILON)
f = 0.5;
else
{
df = ptr->elm[0].b*ptr->elm[0].dx;
f = log(0.5*(exp(df)+1.0))/df;
}
xnew = ptr->elm[0].x + ptr->elm[0].dx*f;
ptr->elm[ptr->n].lfr = ptr->elm[0].lfr;
ptr->elm[ptr->n].x = xnew;
ptr->elm[ptr->n].dx = ptr->elm[0].dx*(1.-f);
ptr->elm[0].lfr = ptr->elm[ptr->n].lfl = lfunc(xnew, lfunc_arg);
ptr->elm[0].dx *= f;
ptr->n++;
GDens1Update(ptr);
}
return ptr;
}
