Created createW with the normPDF function. Needs to be modified to us…

…e the multivariateNormPDF
cudahpcproj · Apr 25, 2017 · d270e979710443a0611de52f033950f400767ee0 · d270e97
1 parent be06d94
commit d270e979710443a0611de52f033950f400767ee0
Unified Split

Showing with 23 additions and 2 deletions.

+23 −2 parallel/para_gibbs.cu
diff --git a/parallel/para_gibbs.cu b/parallel/para_gibbs.cu
@@ -91,7 +91,11 @@ __global__ void mergePosterior(int trials, float *dev_a_out,float *dev_b_out,int
 
 __global__ void sampleTdot(int trials, int *tDot,curandState *state);
 
-__device__ float posteriorMean(*tDot, float *dev_x_out, int M, int nThreads);
+__device__ float posteriorMean(int *tDot, float *dev_x_out, int M, int nThreads);
+
+__device__ float computeW(int *tDot, float *dev_x_out, int M, int nThreads, float h);
+
+__device__ float normPDF(float x, float mean, float sigma);
 
 int main(int argc, char **argv){
 
@@ -205,7 +209,7 @@ __global__ void mergePosterior(int trials, float *dev_a_out,float *dev_b_out,int
 }
 
 __device__ float posteriorMean(int *tDot, float *dev_x_out, int M, int nThreads) {
-  float sum;
+  float sum = 0;
   for (int i=0; i < M; i++) {
     int index =  tDot[i] + i * nThreads;  // trial m of posterior m (note: i = blockId)
     sum += dev_x_out[index];  // posterior_m_tm
@@ -214,7 +218,24 @@ __device__ float posteriorMean(int *tDot, float *dev_x_out, int M, int nThreads)
   return mean;
 }
 
+__device__ float computeW(int *tDot, float *dev_x_out, int M, int nThreads, float h) {
+  float posterior_mean = posteriorMean(tDot, dev_x_out, M, nThreads);
+  float product = 1;
+  float posterior_m_tm;
+
+  for (int i=0; i < M; i++) {
+    int index =  tDot[i] + i * nThreads;  // trial m of posterior m (note: i = blockId)
+    posterior_m_tm = dev_x_out[index];  // posterior_m_tm
+    product *= normPDF(posterior_m_tm, posterior_mean, h^2);
+  }
+  return product;
+}
 
+__device__ float normPDF(float x, float mean, float sigma) {
+  float denominator = sqrtf(2*PI*(sigma^2));
+  float numerator = expf( -1 * (x-mean)^2 / (2*sigma^2) );
+  return numerator/denominator;
+}
 
 /*
  *  Sample each theta from the appropriate gamma distribution