version 1.0-11

cran · Apr 14, 2023 · 80c5df5 · 80c5df5
1 parent 30656e7
commit 80c5df5
Show file tree

Hide file tree

Showing 5 changed files with 16 additions and 15 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -2,8 +2,8 @@ Package: RGBM
 Type: Package
 Title: LS-TreeBoost and LAD-TreeBoost for Gene Regulatory Network
         Reconstruction
-Version: 1.0-10
-Date: 2022-09-25
+Version: 1.0-11
+Date: 2023-04-13
 Author: Raghvendra Mall [aut, cre],
   Khalid Kunji [aut],
   Melissa O'Neill [ctb]
@@ -17,5 +17,5 @@ License: GPL (>= 3)
 LazyLoad: yes
 Depends: foreach, plyr, doParallel
 NeedsCompilation: yes
-Packaged: 2022-09-25 11:49:58 UTC; rmall
-Date/Publication: 2022-09-26 07:10:10 UTC
+Packaged: 2023-04-13 05:20:19 UTC; raghvendra
+Date/Publication: 2023-04-14 08:50:14 UTC
diff --git a/MD5 b/MD5
@@ -1,4 +1,4 @@
-ac18e772aeba619feec6931cb70729b4 *DESCRIPTION
+2720e1c59e428d2938243ed322d7b0fc *DESCRIPTION
 43c6737dbf86ce00d8d71edb4821a5bb *NAMESPACE
 233f76ea882528c6cb2891a0ac721f99 *R/GBM.R
 3e0b1f671174b0f89196d4823fb55291 *R/GBM.test.R
@@ -36,11 +36,11 @@ f37253d913461c94cc1b0e9c2006d1e7 *man/normalize_matrix_colwise.Rd
 f8578d694b247191dea4b761dbe8d726 *src/Model.h
 67e9c9edc9444f9379a7e7527a6d83b4 *src/Prediction.h
 816a3253639f49c879b218a8fea94ac5 *src/R_init_RGBM.c
-be41e1925cb325134cd3eba9146c5e88 *src/pcg_basic.c
+bcb0a707738a401d2a3583b20429b38a *src/pcg_basic.c
 1c5d0bb60da87eae144c92908cfcbf43 *src/pcg_basic.h
 d91f2330e26658fe30e67f60224c57e6 *src/regression_stump.h
-a08dc7c53b556feca16fe58711654d11 *src/solver_test.cpp
+a5fd997114614edea413bb5ef40c0cdf *src/solver_test.cpp
 3584090f43282dab73019078bff9fbac *src/test_regression_stump.cpp
 6f6a9de713b8d64826d897c35cfab2d6 *src/test_regression_stump_R.cpp
-e3776b98991d41554f1b50d8ea35ac27 *src/train_regression_stump.cpp
+964b7a729a77685906901dd559897460 *src/train_regression_stump.cpp
 93aeef0c0baaf8509848a7780b5c38d1 *src/train_regression_stump_R.cpp
diff --git a/src/pcg_basic.c b/src/pcg_basic.c
@@ -66,10 +66,10 @@ uint32_t pcg32_random_r(pcg32_random_t* rng)
     return (xorshifted >> rot) | (xorshifted << ((-rot) & 31));
 }
 
-uint32_t pcg32_random()
-{
-    return pcg32_random_r(&pcg32_global);
-}
+//uint32_t pcg32_random()
+//{
+//    return pcg32_random_r(&pcg32_global);
+//}
 
 
 // pcg32_boundedrand(bound):

diff --git a/src/solver_test.cpp b/src/solver_test.cpp
@@ -42,7 +42,7 @@ int main() {
   // *never* have random sequences that coincide, at all) - the code below
   // shows three possible ways to do so.
   bool seed_given = false;
-  uint64_t seed;
+  uint64_t seed = 123;
   if (seed_given) {
     pcg32_srandom(42u, seed);
   } else {

diff --git a/src/train_regression_stump.cpp b/src/train_regression_stump.cpp
@@ -88,6 +88,7 @@ inline int compare(const void *a, const void *b) {
 	return 0;
 }
 
+static pcg32_random_t pcg32_global = PCG32_INITIALIZER;
 
 const Model train_regression_stump(const int N, const int P, const double *x,
 		const double *y, const double col_sampling_rate,
@@ -109,7 +110,7 @@ const Model train_regression_stump(const int N, const int P, const double *x,
 	// *never* have random sequences that coincide, at all) - the code below
 	// shows three possible ways to do so.
 	bool seed_given = false;
-  uint64_t seed;
+  uint64_t seed = 123;
   if (seed_given) {
     pcg32_srandom(42u, seed);
   } else {
@@ -268,7 +269,7 @@ const Model train_regression_stump(const int N, const int P, const double *x,
 	    P_unique_inbag = ceil(col_sampling_rate * P);
 	    P_already_bagged = 0;
 	    for (s = 0; s < P; s++) {
-	      if (((float)ldexp((double)pcg32_random(), -32)) * (P - s)
+	      if (((float)ldexp((double)pcg32_random_r(&pcg32_global), -32)) * (P - s)
               < P_unique_inbag - P_already_bagged) {
         //   if ((1.0 * rand() / RAND_MAX) * (P - s)
         //     < P_unique_inbag - P_already_bagged) {