JNIEXPORT jint JNICALL Java_edu_berkeley_bid_CUMAT_gamrnd
(JNIEnv *env, jobject obj, jint nrows, jint ncols, jobject jA, jint atype, jobject jB, jint btype, jobject jOut)
{
float *A = (float*)getPointer(env, jA);
float *B = (float*)getPointer(env, jB);
float *Out = (float*)getPointer(env, jOut);
return gamrnd(nrows, ncols, A, atype, B, btype, Out);
}
__kernel void CalculateStatisticalMapsGLMBayesian(__global float* Statistical_Maps,
__global float* Beta_Volumes,
__global float* AR_Estimates,
__global const float* Volumes,
__global const float* Mask,
__global const int* Seeds,
__constant float* c_X_GLM,
__constant float* c_InvOmega0,
__constant float* c_S00,
__constant float* c_S01,
__constant float* c_S11,
__private int DATA_W,
__private int DATA_H,
__private int DATA_D,
__private int NUMBER_OF_VOLUMES,
__private int NUMBER_OF_REGRESSORS,
__private int NUMBER_OF_ITERATIONS,
__private int slice)
{
int x = get_global_id(0);
int y = get_global_id(1);
int z = get_global_id(2);
int3 tIdx = {get_local_id(0), get_local_id(1), get_local_id(2)};
if (x >= DATA_W || y >= DATA_H || z >= DATA_D)
return;
if ( Mask[Calculate3DIndex(x,y,slice,DATA_W,DATA_H)] != 1.0f )
{
Statistical_Maps[Calculate4DIndex(x,y,slice,0,DATA_W,DATA_H,DATA_D)] = 0.0f;
Statistical_Maps[Calculate4DIndex(x,y,slice,1,DATA_W,DATA_H,DATA_D)] = 0.0f;
Statistical_Maps[Calculate4DIndex(x,y,slice,2,DATA_W,DATA_H,DATA_D)] = 0.0f;
Statistical_Maps[Calculate4DIndex(x,y,slice,3,DATA_W,DATA_H,DATA_D)] = 0.0f;
Statistical_Maps[Calculate4DIndex(x,y,slice,4,DATA_W,DATA_H,DATA_D)] = 0.0f;
Statistical_Maps[Calculate4DIndex(x,y,slice,5,DATA_W,DATA_H,DATA_D)] = 0.0f;
Beta_Volumes[Calculate4DIndex(x,y,slice,0,DATA_W,DATA_H,DATA_D)] = 0.0f;
Beta_Volumes[Calculate4DIndex(x,y,slice,1,DATA_W,DATA_H,DATA_D)] = 0.0f;
AR_Estimates[Calculate3DIndex(x,y,slice,DATA_W,DATA_H)] = 0.0f;
return;
}
// Get seed from host
int seed = Seeds[Calculate3DIndex(x,y,slice,DATA_W,DATA_H)];
// Prior options
float iota = 1.0f; // Decay factor for lag length in prior for rho.
float r = 0.5f; // Prior mean on rho1
float c = 0.3f; // Prior standard deviation on first lag.
float a0 = 0.01f; // First parameter in IG prior for sigma^2
float b0 = 0.01f; // Second parameter in IG prior for sigma^2
float InvA0 = c * c;
// Algorithmic options
float prcBurnin = 10.0f; // Percentage of nIter used for burnin. Note: effective number of iter is nIter.
float beta[2];
float betaT[2];
int nBurnin = (int)round((float)NUMBER_OF_ITERATIONS*(prcBurnin/100.0f));
int probability1 = 0;
int probability2 = 0;
int probability3 = 0;
int probability4 = 0;
int probability5 = 0;
int probability6 = 0;
float m00[2];
float m01[2];
float m10[2];
float m11[2];
m00[0] = 0.0f;
m00[1] = 0.0f;
m01[0] = 0.0f;
m01[1] = 0.0f;
m10[0] = 0.0f;
m10[1] = 0.0f;
m11[0] = 0.0f;
m11[1] = 0.0f;
float g00 = 0.0f;
float g01 = 0.0f;
float g11 = 0.0f;
float old_value = Volumes[Calculate3DIndex(x,y,0,DATA_W,DATA_H)];
m00[0] += c_X_GLM[NUMBER_OF_VOLUMES * 0 + 0] * old_value;
m00[1] += c_X_GLM[NUMBER_OF_VOLUMES * 1 + 0] * old_value;
g00 += old_value * old_value;
for (int v = 1; v < NUMBER_OF_VOLUMES; v++)
{
float value = Volumes[Calculate3DIndex(x,y,v,DATA_W,DATA_H)];
m00[0] += c_X_GLM[NUMBER_OF_VOLUMES * 0 + v] * value;
m00[1] += c_X_GLM[NUMBER_OF_VOLUMES * 1 + v] * value;
m01[0] += c_X_GLM[NUMBER_OF_VOLUMES * 0 + v] * old_value;
m01[1] += c_X_GLM[NUMBER_OF_VOLUMES * 1 + v] * old_value;
m10[0] += c_X_GLM[NUMBER_OF_VOLUMES * 0 + (v - 1)] * value;
m10[1] += c_X_GLM[NUMBER_OF_VOLUMES * 1 + (v - 1)] * value;
m11[0] += c_X_GLM[NUMBER_OF_VOLUMES * 0 + (v - 1)] * old_value;
m11[1] += c_X_GLM[NUMBER_OF_VOLUMES * 1 + (v - 1)] * old_value;
g00 += value * value;
g01 += value * old_value;
g11 += old_value * old_value;
old_value = value;
}
float InvOmegaT[2][2];
float OmegaT[2][2];
float Xtildesquared[2][2];
float XtildeYtilde[2];
float Ytildesquared;
Xtildesquared[0][0] = c_S00[0 + 0*2];
Xtildesquared[0][1] = c_S00[0 + 1*2];
Xtildesquared[1][0] = c_S00[1 + 0*2];
Xtildesquared[1][1] = c_S00[1 + 1*2];
XtildeYtilde[0] = m00[0];
XtildeYtilde[1] = m00[1];
Ytildesquared = g00;
float sigma2;
float rho, rhoT, rhoProp, bT;
rho = 0.0f;
// Loop over iterations
for (int i = 0; i < (nBurnin + NUMBER_OF_ITERATIONS); i++)
{
InvOmegaT[0][0] = c_InvOmega0[0 + 0 * NUMBER_OF_REGRESSORS] + Xtildesquared[0][0];
InvOmegaT[0][1] = c_InvOmega0[0 + 1 * NUMBER_OF_REGRESSORS] + Xtildesquared[0][1];
InvOmegaT[1][0] = c_InvOmega0[1 + 0 * NUMBER_OF_REGRESSORS] + Xtildesquared[1][0];
InvOmegaT[1][1] = c_InvOmega0[1 + 1 * NUMBER_OF_REGRESSORS] + Xtildesquared[1][1];
Invert_2x2(InvOmegaT, OmegaT);
betaT[0] = OmegaT[0][0] * XtildeYtilde[0] + OmegaT[0][1] * XtildeYtilde[1];
betaT[1] = OmegaT[1][0] * XtildeYtilde[0] + OmegaT[1][1] * XtildeYtilde[1];
float aT = a0 + (float)NUMBER_OF_VOLUMES/2.0f;
float temp[2];
temp[0] = InvOmegaT[0][0] * betaT[0] + InvOmegaT[0][1] * betaT[1];
temp[1] = InvOmegaT[1][0] * betaT[0] + InvOmegaT[1][1] * betaT[1];
bT = b0 + 0.5f * (Ytildesquared - betaT[0] * temp[0] - betaT[1] * temp[1]);
// Block 1 - Step 1a. Update sigma2
sigma2 = gamrnd(aT,bT,&seed);
// Block 1 - Step 1b. Update beta | sigma2
MultivariateRandom2(beta,betaT,OmegaT,sigma2,&seed);
if (i > nBurnin)
{
if (beta[0] > 0.0f)
{
probability1++;
}
if (beta[1] > 0.0f)
{
probability2++;
}
if (beta[0] < 0.0f)
{
probability3++;
}
if (beta[1] < 0.0f)
{
probability4++;
}
if ((beta[0] - beta[1]) > 0.0f)
{
probability5++;
}
if ((beta[1] - beta[0]) > 0.0f)
{
probability6++;
}
}
// Block 2, update rho
float zsquared = 0.0f;
float zu = 0.0f;
float old_eps = 0.0f;
// Calculate residuals
for (int v = 1; v < NUMBER_OF_VOLUMES; v++)
{
float eps = Volumes[Calculate3DIndex(x,y,v,DATA_W,DATA_H)];
eps -= c_X_GLM[NUMBER_OF_VOLUMES * 0 + v] * beta[0];
eps -= c_X_GLM[NUMBER_OF_VOLUMES * 1 + v] * beta[1];
zsquared += eps * eps;
zu += eps * old_eps;
old_eps = eps;
}
// Generate rho
float InvAT = InvA0 + zsquared / sigma2;
float AT = 1.0f / InvAT;
rhoT = AT * zu / sigma2;
MultivariateRandom1(&rhoProp,rhoT,AT,sigma2,&seed);
if (myabs(rhoProp) < 1.0f)
{
rho = rhoProp;
}
// Prewhitening of regressors and data
Xtildesquared[0][0] = c_S00[0 + 0*2] - 2.0f * rho * c_S01[0 + 0*2] + rho * rho * c_S11[0 + 0*2];
Xtildesquared[0][1] = c_S00[0 + 1*2] - 2.0f * rho * c_S01[0 + 1*2] + rho * rho * c_S11[0 + 1*2];
Xtildesquared[1][0] = c_S00[1 + 0*2] - 2.0f * rho * c_S01[1 + 0*2] + rho * rho * c_S11[1 + 0*2];
Xtildesquared[1][1] = c_S00[1 + 1*2] - 2.0f * rho * c_S01[1 + 1*2] + rho * rho * c_S11[1 + 1*2];
XtildeYtilde[0] = m00[0] - rho * (m01[0] + m10[0]) + rho * rho * m11[0];
XtildeYtilde[1] = m00[1] - rho * (m01[1] + m10[1]) + rho * rho * m11[1];
Ytildesquared = g00 - 2.0f * rho * g01 + rho * rho * g11;
}
Statistical_Maps[Calculate4DIndex(x,y,slice,0,DATA_W,DATA_H,DATA_D)] = (float)probability1/(float)NUMBER_OF_ITERATIONS;
Statistical_Maps[Calculate4DIndex(x,y,slice,1,DATA_W,DATA_H,DATA_D)] = (float)probability2/(float)NUMBER_OF_ITERATIONS;
Statistical_Maps[Calculate4DIndex(x,y,slice,2,DATA_W,DATA_H,DATA_D)] = (float)probability3/(float)NUMBER_OF_ITERATIONS;
Statistical_Maps[Calculate4DIndex(x,y,slice,3,DATA_W,DATA_H,DATA_D)] = (float)probability4/(float)NUMBER_OF_ITERATIONS;
Statistical_Maps[Calculate4DIndex(x,y,slice,4,DATA_W,DATA_H,DATA_D)] = (float)probability5/(float)NUMBER_OF_ITERATIONS;
Statistical_Maps[Calculate4DIndex(x,y,slice,5,DATA_W,DATA_H,DATA_D)] = (float)probability6/(float)NUMBER_OF_ITERATIONS;
Beta_Volumes[Calculate4DIndex(x,y,slice,0,DATA_W,DATA_H,DATA_D)] = beta[0];
Beta_Volumes[Calculate4DIndex(x,y,slice,1,DATA_W,DATA_H,DATA_D)] = beta[1];
AR_Estimates[Calculate3DIndex(x,y,slice,DATA_W,DATA_H)] = rhoT;
}
