split M-step out. verify labels

Parallel_Algorithm/OpenMP/Kmean_seq.c

@@ -201,16 +201,19 @@ int main() {
  labels[i] = k_best;
  dist_sum_new += dist_min;
 
- // M-Step (half): set the cluster centers to the mean
- cluster_sizes[k_best]++; // add one more points to this cluster
- // As the total number of samples in each cluster is not known yet,
- // here we are just calculating the sum, not the mean.
- for (j=0; j<N_features; j++)
- new_cluster_centers[k_best][j] += X[i][j];
+ }
 
- } //end if E-Step and half M-Step
+ // M-Step first half: set the cluster centers to the mean
+ for (i = 0; i < N_samples; i++) {
+ k_best = labels[i];
+ cluster_sizes[k_best]++; // add one more points to this cluster
+ // As the total number of samples in each cluster is not known yet,
+ // here we are just calculating the sum, not the mean.
+ for (j=0; j<N_features; j++)
+ new_cluster_centers[k_best][j] += X[i][j]; 
+ }
 
- // M-Step-continued: convert the sum to the mean
+ // M-Step second half: convert the sum to the mean
  for (k=0; k<N_clusters; k++) {
  for (j=0; j<N_features; j++) {
 
@@ -229,10 +232,12 @@ int main() {
  //printf("Final inertia: %f, iteration: %d \n",dist_sum_new,i_iter);
 
  // record the best results
- if (dist_sum_new < inert_best) 
+ if (dist_sum_new < inert_best) {
  inert_best = dist_sum_new;
  for (i = 0; i < N_samples; i++)
  labels_best[i] = labels[i];
+ }
+
  } //end of one repeated run
  double iElaps2 = seconds() - iStart2;
 

Parallel_Algorithm/python_reference/check_results.py

@@ -0,0 +1,10 @@
+import xarray as xr
+
+dirname = "../test_data/"
+filename = "Blobs_smp20000_fea30_cls8.nc"
+
+with xr.open_dataset(dirname+filename) as ds:
+ mismatch = (ds["Y_Py"].values != ds["Y_C"].values) 
+
+print("total number of samples: ",mismatch.size)
+print("inconsistent labels: ",mismatch.sum())