/frameworks/ml/bordeaux/learning/stochastic_linear_ranker/native/ |
H A D | sparse_weight_vector.h | 37 class SparseWeightVector { class in namespace:learning_stochastic_linear 42 SparseWeightVector() { function in class:learning_stochastic_linear::SparseWeightVector 45 ~SparseWeightVector() {} 46 explicit SparseWeightVector(const SparseWeightVector<Key, Hash> &other) { argument 49 void operator=(const SparseWeightVector<Key, Hash> &other) { argument 52 void CopyFrom(const SparseWeightVector<Key, Hash> &other) { argument 116 void LoadWeightVector(const SparseWeightVector<Key, Hash> &vec) { argument 133 const SparseWeightVector<Key, Hash> &w1, 136 const SparseWeightVector<Ke 191 operator <<(std::ostream &stream, const SparseWeightVector<Key, Hash> &vector) argument [all...] |
H A D | sparse_weight_vector.cpp | 35 bool SparseWeightVector<Key, Hash>::IsValid() const { 48 void SparseWeightVector<Key, Hash>::AdditiveWeightUpdate( argument 50 const SparseWeightVector<Key, Hash> &w1, 62 void SparseWeightVector<Key, Hash>::AdditiveSquaredWeightUpdate( argument 64 const SparseWeightVector<Key, Hash> &w1, 77 void SparseWeightVector<Key, Hash>::AdditiveInvSqrtWeightUpdate( argument 79 const SparseWeightVector<Key, Hash> &w1, 94 void SparseWeightVector<Key, Hash>::AdditiveWeightUpdateBounded( argument 96 const SparseWeightVector<Key, Hash> &w1, 122 void SparseWeightVector<Ke argument 134 MultWeightUpdateBounded( const SparseWeightVector<Key, Hash> &w1) argument 194 DotProduct( const SparseWeightVector<Key, Hash> &w1) const argument [all...] |
H A D | stochastic_linear_ranker.h | 169 void LoadWeights(const SparseWeightVector<Key, Hash> &model) { argument 173 void SaveWeights(SparseWeightVector<Key, Hash> *model) { 177 double ScoreSample(const SparseWeightVector<Key, Hash> &sample) { argument 201 int UpdateClassifier(const SparseWeightVector<Key, Hash> &positive, 202 const SparseWeightVector<Key, Hash> &negative); 205 SparseWeightVector<Key, Hash> weight_; 221 SparseWeightVector<Key, Hash> current_negative_; 235 void UpdateSubGradient(const SparseWeightVector<Key, Hash> &positive, 236 const SparseWeightVector<Key, Hash> &negative,
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H A D | stochastic_linear_ranker.cpp | 26 const SparseWeightVector<Key, Hash> &positive, 27 const SparseWeightVector<Key, Hash> &negative, 32 SparseWeightVector<Key, Hash> gradient; 63 const SparseWeightVector<Key, Hash> &positive, 64 const SparseWeightVector<Key, Hash> &negative) { 67 SparseWeightVector<Key, Hash> weight_backup; 25 UpdateSubGradient( const SparseWeightVector<Key, Hash> &positive, const SparseWeightVector<Key, Hash> &negative, const double learning_rate, const double positive_score, const double negative_score, const int32 gradient_l0_norm) argument 62 UpdateClassifier( const SparseWeightVector<Key, Hash> &positive, const SparseWeightVector<Key, Hash> &negative) argument
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/frameworks/ml/bordeaux/learning/stochastic_linear_ranker/jni/ |
H A D | jni_stochastic_linear_ranker.cpp | 28 using learning_stochastic_linear::SparseWeightVector; 31 const int length, SparseWeightVector<string> * sample) { 52 SparseWeightVector<string> *sample) { 54 SparseWeightVector<string>::Wmap w_ = sample->GetMap(); 56 for ( SparseWeightVector<string>::Witer_const iter = w_.begin(); 84 SparseWeightVector<string> model; 235 SparseWeightVector<string> M_weights; 238 SparseWeightVector<string>::Wmap w_map = M_weights.GetMap(); 377 SparseWeightVector<string> M_weights; 379 SparseWeightVector<strin [all...] |