From fc97d5d2f4dc09fdaac7b30be91e96022a83a35d Mon Sep 17 00:00:00 2001 From: Tiramisu 1993 Date: Wed, 19 Jul 2017 23:25:35 +0800 Subject: [PATCH] restore some changed seed --- src/shogun/base/DynArray.h | 2 +- src/shogun/clustering/KMeansMiniBatch.cpp | 2 +- src/shogun/features/DataGenerator.cpp | 6 +++--- src/shogun/lib/SGVector.cpp | 2 +- src/shogun/mathematics/Math.h | 2 +- src/shogun/mathematics/Statistics.cpp | 6 +++--- src/shogun/mathematics/ajd/QDiag.cpp | 2 +- src/shogun/multiclass/LaRank.h | 2 +- src/shogun/optimization/liblinear/shogun_liblinear.cpp | 2 +- src/shogun/structure/TwoStateModel.cpp | 2 +- tests/unit/neuralnets/NeuralNetwork_unittest.cc | 8 ++++---- tests/unit/neuralnets/RBM_unittest.cc | 2 +- 12 files changed, 19 insertions(+), 19 deletions(-) diff --git a/src/shogun/base/DynArray.h b/src/shogun/base/DynArray.h index d5041e5f095..bb5a941604c 100644 --- a/src/shogun/base/DynArray.h +++ b/src/shogun/base/DynArray.h @@ -448,7 +448,7 @@ template class DynArray /** randomizes the array (not thread safe!) */ void shuffle() { - auto m_rng = std::unique_ptr(new CRandom(sg_random_seed)); + auto m_rng = std::unique_ptr(new CRandom()); for (index_t i=0; i<=current_num_elements-1; ++i) CMath::swap( array[i], diff --git a/src/shogun/clustering/KMeansMiniBatch.cpp b/src/shogun/clustering/KMeansMiniBatch.cpp index 4598e4df467..21447aa8701 100644 --- a/src/shogun/clustering/KMeansMiniBatch.cpp +++ b/src/shogun/clustering/KMeansMiniBatch.cpp @@ -131,7 +131,7 @@ SGVector CKMeansMiniBatch::mbchoose_rand(int32_t b, int32_t num) { SGVector chosen=SGVector(num); SGVector ret=SGVector(b); - auto rng = std::unique_ptr(new CRandom(sg_random_seed)); + auto rng = std::unique_ptr(new CRandom()); chosen.zero(); int32_t ch=0; while (ch CDataGenerator::generate_checkboard_data(int32_t num_classes int32_t dim, int32_t num_points, float64_t overlap) { int32_t points_per_class = num_points / num_classes; - auto m_rng = std::unique_ptr(new CRandom(sg_random_seed)); + auto m_rng = std::unique_ptr(new CRandom()); int32_t grid_size = (int32_t ) CMath::ceil(CMath::sqrt((float64_t ) num_classes)); float64_t cell_size = (float64_t ) 1 / grid_size; @@ -88,7 +88,7 @@ SGMatrix CDataGenerator::generate_mean_data(index_t m, /* evtl. allocate space */ SGMatrix result=SGMatrix::get_allocated_matrix( dim, 2*m, target); - auto m_rng = std::unique_ptr(new CRandom(sg_random_seed)); + auto m_rng = std::unique_ptr(new CRandom()); /* fill matrix with normal data */ for (index_t i=0; i<2*m; ++i) @@ -110,7 +110,7 @@ SGMatrix CDataGenerator::generate_sym_mix_gauss(index_t m, /* evtl. allocate space */ SGMatrix result=SGMatrix::get_allocated_matrix( 2, m, target); - auto m_rng = std::unique_ptr(new CRandom(sg_random_seed)); + auto m_rng = std::unique_ptr(new CRandom()); /* rotation matrix */ SGMatrix rot=SGMatrix(2,2); rot(0, 0)=CMath::cos(angle); diff --git a/src/shogun/lib/SGVector.cpp b/src/shogun/lib/SGVector.cpp index 5ed8177c017..4071c8b75f0 100644 --- a/src/shogun/lib/SGVector.cpp +++ b/src/shogun/lib/SGVector.cpp @@ -614,7 +614,7 @@ void SGVector::vec1_plus_scalar_times_vec2(float32_t* vec1, template void SGVector::random_vector(T* vec, int32_t len, T min_value, T max_value) { - auto m_rng = std::unique_ptr(new CRandom(sg_random_seed)); + auto m_rng = std::unique_ptr(new CRandom()); for (int32_t i=0; irandom(min_value, max_value); } diff --git a/src/shogun/mathematics/Math.h b/src/shogun/mathematics/Math.h index 9d343f84a2e..348b88cc55d 100644 --- a/src/shogun/mathematics/Math.h +++ b/src/shogun/mathematics/Math.h @@ -1027,7 +1027,7 @@ class CMath : public CSGObject else { auto m_rng = - std::unique_ptr(new CRandom(sg_random_seed)); + std::unique_ptr(new CRandom()); for (index_t i = 0; i < v.vlen; ++i) swap(v[i], v[m_rng->random(i, v.vlen - 1)]); } diff --git a/src/shogun/mathematics/Statistics.cpp b/src/shogun/mathematics/Statistics.cpp index 943ac9cb8a9..47e4913a06e 100644 --- a/src/shogun/mathematics/Statistics.cpp +++ b/src/shogun/mathematics/Statistics.cpp @@ -325,7 +325,7 @@ SGVector CStatistics::sample_indices(int32_t sample_size, int32_t N) int32_t* idxs=SG_MALLOC(int32_t,N); int32_t i, rnd; int32_t* permuted_idxs=SG_MALLOC(int32_t,sample_size); - auto rng = std::unique_ptr(new CRandom(sg_random_seed)); + auto rng = std::unique_ptr(new CRandom()); // reservoir sampling for (i=0; i CStatistics::sample_from_gaussian(SGVector mean, int32_t dim=mean.vlen; Map mu(mean.vector, mean.vlen); Map c(cov.matrix, cov.num_rows, cov.num_cols); - auto rng = std::unique_ptr(new CRandom(sg_random_seed)); + auto rng = std::unique_ptr(new CRandom()); // generate samples, z, from N(0, I), DxN SGMatrix S(dim, N); @@ -775,7 +775,7 @@ SGMatrix CStatistics::sample_from_gaussian(SGVector mean, typedef SparseMatrix MatrixType; const MatrixType &c=EigenSparseUtil::toEigenSparse(cov); - auto rng = std::unique_ptr(new CRandom(sg_random_seed)); + auto rng = std::unique_ptr(new CRandom()); SimplicialLLT llt; diff --git a/src/shogun/mathematics/ajd/QDiag.cpp b/src/shogun/mathematics/ajd/QDiag.cpp index c8e2df57616..38ace2d5568 100644 --- a/src/shogun/mathematics/ajd/QDiag.cpp +++ b/src/shogun/mathematics/ajd/QDiag.cpp @@ -16,7 +16,7 @@ SGMatrix CQDiag::diagonalize(SGNDArray C, SGMatrix V; - auto rng = std::unique_ptr(new CRandom(sg_random_seed)); + auto rng = std::unique_ptr(new CRandom()); if (V0.num_rows == N && V0.num_cols == N) { V = V0.clone(); diff --git a/src/shogun/multiclass/LaRank.h b/src/shogun/multiclass/LaRank.h index ddfa33cdf93..b951f5d03f7 100644 --- a/src/shogun/multiclass/LaRank.h +++ b/src/shogun/multiclass/LaRank.h @@ -250,7 +250,7 @@ namespace shogun LaRankPattern & sample () { auto m_rng = - std::unique_ptr(new CRandom(sg_random_seed)); + std::unique_ptr(new CRandom()); ASSERT(!empty()) while (true) { diff --git a/src/shogun/optimization/liblinear/shogun_liblinear.cpp b/src/shogun/optimization/liblinear/shogun_liblinear.cpp index 5f24dc7e2d2..2d8637ffdc9 100644 --- a/src/shogun/optimization/liblinear/shogun_liblinear.cpp +++ b/src/shogun/optimization/liblinear/shogun_liblinear.cpp @@ -512,7 +512,7 @@ void Solver_MCSVM_CS::solve() } state->inited = true; } - auto m_rng = std::unique_ptr(new CRandom(sg_random_seed)); + auto m_rng = std::unique_ptr(new CRandom()); while(iter < max_iter && !CSignal::cancel_computations()) { double stopping = -CMath::INFTY; diff --git a/src/shogun/structure/TwoStateModel.cpp b/src/shogun/structure/TwoStateModel.cpp index b8a585e8436..5ddc43f6ddc 100644 --- a/src/shogun/structure/TwoStateModel.cpp +++ b/src/shogun/structure/TwoStateModel.cpp @@ -269,7 +269,7 @@ CHMSVMModel* CTwoStateModel::simulate_data(int32_t num_exm, int32_t exm_len, SGVector< int32_t > ll(num_exm*exm_len); ll.zero(); int32_t rnb, rl, rp; - auto m_rng = std::unique_ptr(new CRandom(sg_random_seed)); + auto m_rng = std::unique_ptr(new CRandom()); for ( int32_t i = 0 ; i < num_exm ; ++i) { SGVector< int32_t > lab(exm_len); diff --git a/tests/unit/neuralnets/NeuralNetwork_unittest.cc b/tests/unit/neuralnets/NeuralNetwork_unittest.cc index f1080283052..2c136c2dd53 100644 --- a/tests/unit/neuralnets/NeuralNetwork_unittest.cc +++ b/tests/unit/neuralnets/NeuralNetwork_unittest.cc @@ -56,7 +56,7 @@ TEST(NeuralNetwork, backpropagation_linear) { float64_t tolerance = 1e-9; - set_global_seed(100); + set_global_seed(10); CDynamicObjectArray* layers = new CDynamicObjectArray(); layers->append_element(new CNeuralInputLayer(5)); @@ -88,7 +88,7 @@ TEST(NeuralNetwork, neural_layers_builder) { float64_t tolerance = 1e-9; - set_global_seed(100); + set_global_seed(10); CNeuralLayers* layers = new CNeuralLayers(); layers->input(5) @@ -123,7 +123,7 @@ TEST(NeuralNetwork, backpropagation_logistic) { float64_t tolerance = 1e-9; - set_global_seed(100); + set_global_seed(10); CDynamicObjectArray* layers = new CDynamicObjectArray(); layers->append_element(new CNeuralInputLayer(5)); @@ -155,7 +155,7 @@ TEST(NeuralNetwork, backpropagation_softmax) { float64_t tolerance = 1e-9; - set_global_seed(100); + set_global_seed(10); CDynamicObjectArray* layers = new CDynamicObjectArray(); layers->append_element(new CNeuralInputLayer(5)); diff --git a/tests/unit/neuralnets/RBM_unittest.cc b/tests/unit/neuralnets/RBM_unittest.cc index 26e5e0b92b7..bc53de63ec5 100644 --- a/tests/unit/neuralnets/RBM_unittest.cc +++ b/tests/unit/neuralnets/RBM_unittest.cc @@ -84,7 +84,7 @@ TEST(RBM, gibbs_sampling) TEST(RBM, free_energy_binary) { - set_global_seed(10); + set_global_seed(100); int32_t num_visible = 5; int32_t num_hidden = 6;