void main_comp(double *A1, double *B1, int N, int n, int K, int ngrid, double data[], double data1[], int delta[], int **freq, double *grid, double **F, double **Fsmooth, double **hazard, const char *appdir)
{
int i,j,k,**ind,iterations,n_Iterations=1000;
int *njumps;
double phi,**fdens;
double step,**p,**jumploc;
double A,B,c,h;
SampleTime *obs;
delta[0]=0;
data[0]=data1[0]=0;
obs = new SampleTime[N];
for (i=0; i<N; i++)
{
obs[i].t = data[i+1];
obs[i].delta = delta[i+1];
}
qsort(obs,N,sizeof(SampleTime),CompareTime);
for (i=0; i<N; i++)
{
data[i+1]= obs[i].t ;
delta[i+1] = obs[i].delta;
}
ind = new int *[K+2];
for (i=0;i<K+2;i++)
ind[i]= new int[N+1];
for (k=1;k<=K+1;k++)
F[k][0]=0;
njumps = new int[K+2];
fdens= new double *[K+2];
p= new double *[K+2];
jumploc= new double *[K+2];
for (i=0;i<K+2;i++)
{
fdens[i]= new double[ngrid+1];
p[i]= new double[n+1];
jumploc[i]= new double[n+1];
}
ICM(N,n,freq,K,ind,F,n_Iterations,&phi,&iterations);
ofstream file2_("MLE.txt");
if (file2_.is_open())
{
for (i=1;i<=n;i++)
{
if (F[K+1][i]>F[K+1][i-1])
{
file2_ << setprecision(11) << setw(20) << data1[i];
for (k=1;k<=K+1;k++)
file2_ << setprecision(11) << setw(20) << F[k][i];
file2_ << "\n";
}
}
file2_.close();
}
for (k=1;k<=K+1;k++)
{
j=0;
for (i=1;i<=n;i++)
{
if (F[k][i]>F[k][i-1])
{
p[k][j]=F[k][i]-F[k][i-1];
jumploc[k][j]=data1[i];
j++;
}
}
njumps[k]=j;
}
A=min(N,data);
B=max(N,data);
*A1=A;
*B1=B;
step=(B-A)/ngrid;
c=B;
for (i=0;i<=ngrid;i++)
grid[i]=A+i*step;
h = fmin(c*pow(N,-1.0/7),9.9);
for (i=0;i<=ngrid;i++)
{
for (k=1;k<K+1;k++)
fdens[k][i]=dens_estimate(A,B,k,njumps,jumploc,p,h,grid[i]);
}
h = B*pow(N,-1.0/5);
for (i=0;i<=ngrid;i++)
{
for (k=1;k<=K;k++)
Fsmooth[k][i]=bdf(A,B,k,njumps,jumploc,p,h,grid[i]);
}
for (i=0;i<=ngrid;i++)
{
for (k=1;k<=K;k++)
hazard[k][i]=fdens[k][i]/(1-Fsmooth[k][i]);
}
ofstream file_("SMLE.txt");
if (file_.is_open())
{
for (i=1;i<=ngrid;i++)
{
file_ << setprecision(10) << setw(20) << grid[i];
for (k=1;k<K+1;k++)
file_ << setprecision(10) << setw(20) << Fsmooth[k][i];
file_ << "\n";
}
file_.close();
}
ofstream file1_("data.txt");
if (file1_.is_open())
{
for (i=1;i<=N;i++)
{
file1_ << setprecision(11) << setw(20) << data[i];
file1_ << setw(10) << delta[i];
file1_ << "\n";
}
file1_.close();
}
ofstream file3_("hazard.txt");
if (file3_.is_open())
{
for (i=1;i<=ngrid;i++)
{
file3_ << setprecision(10) << setw(20) << grid[i];
for (k=1;k<K+1;k++)
file3_ << setprecision(10) << setw(20) << hazard[k][i];
file3_ << "\n";
}
file3_.close();
}
// free memory
for (i = 0; i < K+2; i++)
delete[] ind[i], delete[] p[i], delete[] jumploc[i], delete[] fdens[i];
delete[] ind, delete[] p, delete[] jumploc, delete[] njumps, delete[] fdens;
delete[] obs;
}
/* perform adaptive MAP on given src and initial segmentation label */
void Amap(double *src, unsigned char *label, unsigned char *prob, double *mean, int n_classes, int niters, int sub, int *dims, int pve, double weight_MRF, double *voxelsize, int niters_ICM, double offset)
{
int i, nix, niy, niz;
int area, nvol, vol;
int histo[65536];
int n[MAX_NC], j;
double var[MAX_NC];
double thresh[2], beta[1];
double min_src = HUGE, max_src = -HUGE;
int cumsum[65536];
struct point *r;
area = dims[0]*dims[1];
vol = area*dims[2];
for(i = 0; i < vol; i++) {
min_src = MIN(src[i], min_src);
max_src = MAX(src[i], max_src);
}
/* build histogram */
for(i = 0; i < 65536; i++) histo[i] = 0;
for(i = 0; i < vol; i++) {
if (label[i] == 0) continue;
histo[(int)ROUND(65535.0*(src[i]-min_src)/(max_src-min_src))]++;
}
/* find values between 1% and 99% quartile */
cumsum[0] = histo[0];
for(i = 1; i < 65536; i++) cumsum[i] = cumsum[i-1] + histo[i];
for(i = 0; i < 65536; i++) cumsum[i] = (int) ROUND(1000.0*(double)cumsum[i]/(double)cumsum[65535]);
for(i = 0; i < 65536; i++) if (cumsum[i] >= 10) break;
thresh[0] = (double)i/65535.0*(max_src-min_src);
for(i = 65535; i > 0; i--) if (cumsum[i] <= 990) break;
thresh[1] = (double)i/65535.0*(max_src-min_src);
/* define grid dimensions */
nix = (int) ceil((dims[0]-1)/((double) sub))+1;
niy = (int) ceil((dims[1]-1)/((double) sub))+1;
niz = (int) ceil((dims[2]-1)/((double) sub))+1;
nvol = nix*niy*niz;
r = (struct point*)malloc(sizeof(struct point)*MAX_NC*nvol);
if(r == NULL) {
printf("Memory allocation error\n");
exit(EXIT_FAILURE);
}
/* estimate 3 classes before PVE */
EstimateSegmentation(src, label, prob, r, mean, var, n_classes, niters, sub, dims, thresh, beta, offset);
/* Use marginalized likelihood to estimate initial 5 or 6 classes */
if (pve) {
ComputeInitialPveLabel(src, label, prob, r, n_classes, sub, dims, pve);
n_classes = pve;
/* recalculate means for pure and mixed classes */
for(j = 0; j < n_classes; j++) {
n[j] = 0;
mean[j] = 0.0;
}
for(i = 0; i < vol; i++) {
if(label[i] == 0) continue;
n[label[i]-1]++;
mean[label[i]-1] += src[i];
}
for(j = 0; j < n_classes; j++) mean[j] /= n[j];
}
/* use much smaller beta for if no pve is selected */
if(!pve) beta[0] /= 20.0;
/* Iterative Conditional Mode */
if(niters_ICM > 0) {
if(weight_MRF != 1.0) {
beta[0] *= weight_MRF;
printf("Weighted MRF beta %3.3f\n",beta[0]);
}
ICM(prob, label, n_classes, dims, beta[0], niters_ICM, voxelsize);
}
free(r);
return;
}
