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Merge pull request #1813 from tklein23/libbmrm_memory_corruption
Fixing integer overflows and memory corruption in libbmrm
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| Original file line number | Diff line number | Diff line change |
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| /* | ||
| * This program is free software; you can redistribute it and/or modify | ||
| * it under the terms of the GNU General Public License as published by | ||
| * the Free Software Foundation; either version 3 of the License, or | ||
| * (at your option) any later version. | ||
| * | ||
| * Written (W) 2014 Thoralf Klein | ||
| */ | ||
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| #include <shogun/lib/config.h> | ||
| #include <gtest/gtest.h> | ||
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| #include <shogun/lib/SGVector.h> | ||
| #include <shogun/lib/SGSparseVector.h> | ||
| #include <shogun/lib/SGSparseMatrix.h> | ||
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| #include <shogun/features/SparseFeatures.h> | ||
| #include <shogun/structure/MulticlassSOLabels.h> | ||
| #include <shogun/labels/StructuredLabels.h> | ||
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| #include <shogun/structure/MulticlassModel.h> | ||
| #include <shogun/structure/DualLibQPBMSOSVM.h> | ||
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| using namespace shogun; | ||
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| TEST(DualLibQPBMSOSVM,train_bmrm_small_buffer) | ||
| { | ||
| // toy data | ||
| int32_t N = 100; | ||
| int32_t feat_dim = 5; | ||
| int32_t num_feat = 4; | ||
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| SGVector<float64_t> labs(N); | ||
| SGSparseMatrix<float64_t> feats(feat_dim, N); | ||
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| for (int32_t i=0; i<N; i++) | ||
| { | ||
| feats.sparse_matrix[i] = SGSparseVector<float64_t>(num_feat); | ||
| int32_t f = 0; | ||
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| ASSERT(f < num_feat); | ||
| feats.sparse_matrix[i].features[f].feat_index = 0; | ||
| feats.sparse_matrix[i].features[f].entry = i; | ||
| f++; | ||
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| ASSERT(f < num_feat); | ||
| feats.sparse_matrix[i].features[f].feat_index = 1; | ||
| feats.sparse_matrix[i].features[f].entry = i%2 - 0.5; | ||
| f++; | ||
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| ASSERT(f < num_feat); | ||
| feats.sparse_matrix[i].features[f].feat_index = 2; | ||
| feats.sparse_matrix[i].features[f].entry = i%2; | ||
| f++; | ||
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| ASSERT(f < num_feat); | ||
| feats.sparse_matrix[i].features[f].feat_index = 3; | ||
| feats.sparse_matrix[i].features[f].entry = i%3; | ||
| f++; | ||
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| labs[i] = float64_t(i/3); | ||
| } | ||
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| // initialization | ||
| float64_t lambda=1e3, eps=0.01; | ||
| bool icp=1; | ||
| uint32_t cp_models=1; | ||
| ESolver solver=BMRM; | ||
|
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| // Create train labels | ||
| CMulticlassSOLabels* labels = new CMulticlassSOLabels(labs); | ||
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| // Create train features | ||
| CSparseFeatures< float64_t >* features = new CSparseFeatures< float64_t >(feats); | ||
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| // Create structured model | ||
| CMulticlassModel* model = new CMulticlassModel(features, labels); | ||
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| // Create SO-SVM, train | ||
| CDualLibQPBMSOSVM* sosvm = new CDualLibQPBMSOSVM(model, labels, lambda); | ||
| SG_REF(sosvm); | ||
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| sosvm->set_cleanAfter(10); | ||
| sosvm->set_cleanICP(icp); | ||
| sosvm->set_TolRel(eps); | ||
| sosvm->set_cp_models(cp_models); | ||
| sosvm->set_solver(solver); | ||
|
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| // sosvm->set_verbose(true); | ||
| sosvm->set_BufSize(2); | ||
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| sosvm->train(); | ||
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| BmrmStatistics res = sosvm->get_result(); | ||
| //SG_SPRINT("result = { Fp=%lf, Fd=%lf, nIter=%d, nCP=%d, nzA=%d, exitflag=%d }\n", | ||
| // res.Fp, res.Fd, res.nIter, res.nCP, res.nzA, res.exitflag); | ||
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| ASSERT_LE(res.nCP, 2); | ||
| ASSERT_LE(res.nzA, 2); | ||
| ASSERT_LE(res.exitflag, 0); | ||
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| CStructuredLabels* out = CLabelsFactory::to_structured(sosvm->apply()); | ||
| SG_REF(out); | ||
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| SG_SPRINT("\n"); | ||
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| // Compute error | ||
| //------------------------------------------------------------------------- | ||
| float64_t error=0.0; | ||
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| for (int32_t i=0; i<num_feat; ++i) | ||
| { | ||
| CRealNumber* rn = CRealNumber::obtain_from_generic( out->get_label(i) ); | ||
| error+=(rn->value==labs.get_element(i)) ? 0.0 : 1.0; | ||
| SG_UNREF(rn); | ||
| } | ||
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| // SG_SPRINT("Error = %lf %% \n", error/num_feat*100); | ||
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| // Free memory | ||
| SG_UNREF(sosvm); | ||
| SG_UNREF(out); | ||
| } |