JNIEXPORT jint JNICALL Java_edu_berkeley_bid_CUMACH_multinomial
(JNIEnv *env, jobject obj, jint nrows, jint ncols, jobject jA, jobject jB, jobject jNorm, jint nvals)
{
float *A = (float*)getPointer(env, jA);
int *B = (int*)getPointer(env, jB);
float *Norm = (float*)getPointer(env, jNorm);
return multinomial(nrows, ncols, A, B, Norm, nvals);
}
void number_partition::multinomial_ordered(base &res, INT f_v)
{
number_partition q;
base a;
type(q);
multinomial(res, f_v);
q.multinomial(a, f_v);
res *= a;
if (f_v) {
cout << "multinomial_ordered(" << *this << ") = " << res << endl;
}
}
/* # code for computing a hierarchical model, with normally distributed
# level 1 errors (variance known) and level 2 follows a DP
# y[i]: observed datum for obs i
# theta[i]: level 1 mean for obs i
# phi: vector of unique values of theta (i.e., clusters)
# config[i]: cluster label / configuration indicator
####################################################################
*/
HHRESULT CGaussianMDP::sample_config
(
int *&config,
int obs,
double *sigma2,
int n,
double *y,
double *phi,
double alpha
)
{
/*
# config: vector of configuration indicators
# obs: index of observation under study
# sigma2: (known) level 1 variances(
# n: sample size
*/
int i,j,nclus,oldconfig,ind;
int sumconfig = 0;
double sumprob;
double tempphi = 0.0;
HHRESULT hr = HH_OK;
/* get number of configurations/clusters
also set up other things to check */
sumconfig = 0;
nclus = 0; /* number of configurations */
for(i=0; i<n; i++)
{
if(config[i]==config[obs]) sumconfig++;
nclus = imax2(config[i], nclus);
}
/* ## STEP 1: nothing changes if obs under study (obs) has its own
cluster w/prob */
if( (sumconfig == 1) && (runif(0.0,1.0) < (nclus-1.0)/nclus))
{
goto Cleanup;
}
// nconfig counts obs in clusters, current obs not included
for(i=0; i<nclus; i++)
{
nconfig[i] = 0;
}
for(j=0; j<n; j++)
{
nconfig[config[j]-1]++;
}
nconfig[config[obs]-1]--; /* #nclus-star */
/* STEP 2: if there are more than 1 obs in case i's cluster, then: */
if(sumconfig > 1)
{
sumprob = 0;
for(j=0; j<nclus; j++)
{
prob[j] = nconfig[j] *
dnorm(y[obs], phi[j], sqrt(sigma2[obs]), 0);
sumprob += prob[j];
}
prob[nclus] = (alpha/(nclus+1)) *
dnorm(y[obs], phi[nclus], sqrt(sigma2[obs]), 0);
sumprob+=prob[nclus];
if(sumprob==0)
{
for(j=0; j<=nclus; j++) prob[j]=1.0;
}
/* need to add in a sample-type function */
config[obs] = multinomial(nclus+1,prob);
goto Cleanup;
}
/* STEP 3: if there is just one obs in cluster but need to sample new clustr:*/
/* else s(i)=1 and need to sample new cluster */
if(sumconfig==1) /* # s/b unnec line */
{
oldconfig=config[obs];
for(i=0; i<n; i++)
{
if(config[i] > oldconfig) config[i]--;
}
config[i]=nclus;
for(i=1; i<nclus; i++)
{
if(i>=oldconfig)
{
nconfig[i-1]=nconfig[i];/* last elt of nconfig now useless */
}
}
// shifting the phis down by one, move phi[oldconfig-1] to the end
if((oldconfig < nclus) && (nclus>1))
{
tempphi = phi[oldconfig-1];
for(i=oldconfig; i<nclus; i++)
{
phi[i-1] = phi[i];
}
phi[nclus-1] = tempphi;
}
nclus--;
sumprob = 0.0;
for(j=0; j<nclus; j++)
{
prob[j] = nconfig[j] *
dnorm(y[obs], phi[j], sqrt(sigma2[obs]), 0);
sumprob += prob[j];
}
prob[nclus] = (alpha/(nclus+1)) *
dnorm(y[obs], phi[nclus], sqrt(sigma2[obs]), 0);
sumprob += prob[nclus];
if(sumprob == 0)
{
for(i=0; i<=nclus; i++) prob[i] = 1.0;
}
config[obs] = multinomial(nclus+1,prob);
}
Cleanup:
return hr;
Error:
goto Cleanup;
}
