| /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
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| 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
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| 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
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