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January 28, 2014 22:41
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Example for numerical problems in libncbm.cpp (not terminating)
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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 | |
*/ | |
#include <shogun/lib/config.h> | |
#include <gtest/gtest.h> | |
#include <shogun/lib/SGVector.h> | |
#include <shogun/lib/SGSparseVector.h> | |
#include <shogun/lib/SGSparseMatrix.h> | |
#include <shogun/features/SparseFeatures.h> | |
#include <shogun/structure/MulticlassSOLabels.h> | |
#include <shogun/labels/StructuredLabels.h> | |
#include <shogun/structure/MulticlassModel.h> | |
#include <shogun/structure/DualLibQPBMSOSVM.h> | |
using namespace shogun; | |
SGVector<float64_t> create_test_labels(int32_t N) | |
{ | |
SGVector<float64_t> labs(N); | |
for (int32_t i=0; i<N; i++) | |
labs[i] = float64_t(i/3); | |
return labs; | |
} | |
SGSparseMatrix<float64_t> create_test_features(int32_t N, int32_t feat_dim, int32_t num_feat) | |
{ | |
SGSparseMatrix<float64_t> feats(feat_dim, N); | |
for (int32_t i=0; i<N; i++) | |
{ | |
feats.sparse_matrix[i] = SGSparseVector<float64_t>(num_feat); | |
int32_t f = 0; | |
ASSERT(f < num_feat); | |
feats.sparse_matrix[i].features[f].feat_index = 0; | |
feats.sparse_matrix[i].features[f].entry = i; | |
f++; | |
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++; | |
ASSERT(f < num_feat); | |
feats.sparse_matrix[i].features[f].feat_index = 2; | |
feats.sparse_matrix[i].features[f].entry = i%2; | |
f++; | |
ASSERT(f < num_feat); | |
feats.sparse_matrix[i].features[f].feat_index = 3; | |
feats.sparse_matrix[i].features[f].entry = i%3; | |
f++; | |
} | |
return feats; | |
} | |
TEST(DualLibQPBMSOSVMNCBM,train_small_problem_and_predict) | |
{ | |
// toy data | |
int32_t N = 100; | |
int32_t feat_dim = 5; | |
int32_t num_feat = 4; | |
// Create train labels | |
SGVector<float64_t> labs = create_test_labels(N); | |
CMulticlassSOLabels* labels = new CMulticlassSOLabels(labs); | |
// Create train features | |
SGSparseMatrix<float64_t> feats = create_test_features(N, feat_dim, num_feat); | |
CSparseFeatures< float64_t >* features = new CSparseFeatures< float64_t >(feats); | |
// initialization | |
float64_t lambda=1e3, eps=0.01; | |
bool icp=1; | |
uint32_t cp_models=2; | |
ESolver solver=NCBM; | |
// Create SO model, SO-SVM | |
CMulticlassModel* model = new CMulticlassModel(features, labels); | |
CDualLibQPBMSOSVM* sosvm = new CDualLibQPBMSOSVM(model, labels, lambda); | |
SG_REF(sosvm); | |
sosvm->set_cleanAfter(1); | |
sosvm->set_cleanICP(icp); | |
sosvm->set_TolRel(eps); | |
sosvm->set_cp_models(cp_models); | |
sosvm->set_solver(solver); | |
// sosvm->set_verbose(true); | |
sosvm->set_BufSize(100); | |
sosvm->train(); | |
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); | |
ASSERT_LE(res.nCP, 100); | |
ASSERT_LE(res.nzA, 100); | |
// ASSERT_LE(res.exitflag, 0); | |
CStructuredLabels* out = CLabelsFactory::to_structured(sosvm->apply()); | |
SG_REF(out); | |
SG_SPRINT("\n"); | |
// Compute error | |
//------------------------------------------------------------------------- | |
float64_t error=0.0; | |
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); | |
} | |
// SG_SPRINT("Error = %lf %% \n", error/num_feat*100); | |
// Free memory | |
SG_UNREF(sosvm); | |
SG_UNREF(out); | |
} |
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