template<class ST> void CSparseFeatures<ST>::add_to_dense_vec(float64_t alpha, int32_t num, float64_t* vec, int32_t dim, bool abs_val)
{
REQUIRE(vec, "add_to_dense_vec(num=%d,dim=%d): vec must not be NULL\n",
num, dim);
REQUIRE(dim>=get_num_features(),
"add_to_dense_vec(num=%d,dim=%d): dim should contain number of features %d\n",
num, dim, get_num_features());
SGSparseVector<ST> sv=get_sparse_feature_vector(num);
if (sv.features)
{
if (abs_val)
{
for (int32_t i=0; i<sv.num_feat_entries; i++)
{
vec[sv.features[i].feat_index]+=alpha
*CMath::abs(sv.features[i].entry);
}
}
else
{
for (int32_t i=0; i<sv.num_feat_entries; i++)
{
vec[sv.features[i].feat_index]+=alpha
*sv.features[i].entry;
}
}
}
free_sparse_feature_vector(num);
}
template< class ST > void CMatrixFeatures< ST >::get_feature_vector_col(
SGVector< ST > out,
int32_t num,
int32_t col) const
{
if ( num < 0 || num >= get_num_vectors() )
{
SG_ERROR("The index of the feature vector to get must be between "
"0 and %d (get_num_vectors()-1)\n", get_num_vectors()-1);
}
// Shorthands for the dimensions of the feature vector to get
int32_t num_cols = m_features[num].num_cols;
int32_t num_rows = m_features[num].num_rows;
if ( col < 0 || col >= num_cols )
{
SG_ERROR("The index of the column to get must be between "
"0 and %d (#columns of the feature vector)\n", num_cols);
}
if ( out.vlen < get_num_features() )
{
SG_ERROR("The vector out must have space to hold at least "
"%d (get_num_features()) elements\n", get_num_features());
}
int32_t start = col*num_rows;
for ( int32_t i = 0 ; i < get_num_features(); ++i )
{
out[i] = m_features[num][start + i];
}
}
template<class ST> ST CSparseFeatures<ST>::get_feature(int32_t num, int32_t index)
{
REQUIRE(index>=0 && index<get_num_features(),
"get_feature(num=%d,index=%d): index exceeds [0;%d]\n",
num, index, get_num_features()-1);
SGSparseVector<ST> sv=get_sparse_feature_vector(num);
ST ret = sv.get_feature(index);
free_sparse_feature_vector(num);
return ret;
}
template<class ST> void CSparseFeatures<ST>::set_sparse_feature_matrix(SGSparseMatrix<ST> sm)
{
if (m_subset_stack->has_subsets())
SG_ERROR("Not allowed with subset\n");
sparse_feature_matrix=sm;
// TODO: check should be implemented in sparse matrix class
for (int32_t j=0; j<get_num_vectors(); j++) {
SGSparseVector<ST> sv=get_sparse_feature_vector(j);
REQUIRE(get_num_features() >= sv.get_num_dimensions(),
"sparse_matrix[%d] check failed (matrix features %d >= vector dimension %d)\n",
j, get_num_features(), sv.get_num_dimensions());
}
}
template<class ST> SGVector<ST> CSparseFeatures<ST>::get_full_feature_vector(int32_t num)
{
SGSparseVector<ST> sv=get_sparse_feature_vector(num);
SGVector<ST> dense = sv.get_dense(get_num_features());
free_sparse_feature_vector(num);
return dense;
}
template< class ST > void CMatrixFeatures< ST >::set_feature_vector(
SGMatrix< ST > const vec,
int32_t num)
{
if ( num < 0 || num >= get_num_vectors() )
{
SG_ERROR("The index of the feature vector to set must be between "
"0 and %d (get_num_vectors()-1)\n", get_num_vectors()-1);
}
if ( get_num_features() != 0 && vec.num_rows != get_num_features() )
{
SG_ERROR("The feature vector to set must have the same features "
"as the rest of the MatrixFeatures, %d "
"(get_num_features())\n", get_num_features());
}
m_features.set_matrix(num, vec);
}
template<class ST> SGMatrix<ST> CSparseFeatures<ST>::get_full_feature_matrix()
{
SGMatrix<ST> full(get_num_features(), get_num_vectors());
full.zero();
SG_INFO("converting sparse features to full feature matrix of %d x %d"
" entries\n", sparse_feature_matrix.num_vectors, get_num_features())
for (int32_t v=0; v<full.num_cols; v++)
{
int32_t idx=m_subset_stack->subset_idx_conversion(v);
SGSparseVector<ST> current=sparse_feature_matrix[idx];
for (int32_t f=0; f<current.num_feat_entries; f++)
{
int64_t offs=(v*get_num_features())
+current.features[f].feat_index;
full.matrix[offs]=current.features[f].entry;
}
}
return full;
}
CFeatures* CFeatureSelection<ST>::apply(CFeatures* features)
{
SG_DEBUG("Entering!\n");
// remove previously computed feature subsets
m_subset->remove_all_subsets();
// sanity checks
REQUIRE(features, "Features cannot be NULL!\n");
REQUIRE(features->get_num_vectors()>0,
"Number of feature vectors has to be positive!\n");
REQUIRE(m_target_dim>0, "Target dimension (%d) has to be positive! Set "
"a higher number via set_target_dim().\n", m_target_dim);
index_t num_features=get_num_features(features);
REQUIRE(num_features>0, "Invalid number of features (%d)! Most likely "
"feature selection cannot be performed for %s!\n",
num_features, features->get_name());
REQUIRE(num_features>m_target_dim,
"Number of original features (dimensions of the feature vectors) "
"(%d) has to be greater that the target dimension (%d)!\n",
num_features, m_target_dim);
// this method makes a deep copy of the feature object and performs
// feature selection on it. This is already SG_REF'ed because of the
// implementation of clone()
CFeatures* feats_copy=(CFeatures*)features->clone();
switch (m_algorithm)
{
case BACKWARD_ELIMINATION:
return apply_backward_elimination(feats_copy);
default:
SG_ERROR("Specified algorithm not yet supported!\n");
return features;
}
SG_DEBUG("Leaving!\n");
}
template<class ST> SGSparseVector<ST> CSparseFeatures<ST>::get_sparse_feature_vector(int32_t num)
{
REQUIRE(num>=0 && num<get_num_vectors(),
"get_sparse_feature_vector(num=%d): num exceeds [0;%d]\n",
num, get_num_vectors()-1);
index_t real_num=m_subset_stack->subset_idx_conversion(num);
if (sparse_feature_matrix.sparse_matrix)
{
return sparse_feature_matrix[real_num];
}
else
{
SGSparseVector<ST> result;
if (feature_cache)
{
result.features=feature_cache->lock_entry(num);
if (result.features)
return result;
else
{
result.features=feature_cache->set_entry(num);
}
}
//if (!result.features)
// result.do_free=true;
result.features=compute_sparse_feature_vector(num,
result.num_feat_entries, result.features);
if (get_num_preprocessors())
{
int32_t tmp_len=result.num_feat_entries;
SGSparseVectorEntry<ST>* tmp_feat_before=result.features;
SGSparseVectorEntry<ST>* tmp_feat_after = NULL;
for (int32_t i=0; i<get_num_preprocessors(); i++)
{
//tmp_feat_after=((CSparsePreprocessor<ST>*) get_preproc(i))->apply_to_feature_vector(tmp_feat_before, tmp_len);
if (i!=0) // delete feature vector, except for the the first one, i.e., feat
SG_FREE(tmp_feat_before);
tmp_feat_before=tmp_feat_after;
}
if (tmp_feat_after)
{
memcpy(result.features, tmp_feat_after,
sizeof(SGSparseVectorEntry<ST>)*tmp_len);
SG_FREE(tmp_feat_after);
result.num_feat_entries=tmp_len;
}
SG_DEBUG("len: %d len2: %d\n", result.num_feat_entries, get_num_features())
}
return result ;
}
}
CFeatures* CFeatureSelection<ST>::apply_backward_elimination(CFeatures* features)
{
SG_DEBUG("Entering!\n");
// precompute whenever appropriate for performing the rest of the tasks
precompute();
// NULL check for features is handled in get_num_features
index_t num_features=get_num_features(features);
SG_DEBUG("Initial number of features %d!\n", num_features);
// the main loop
while (num_features>m_target_dim)
{
// tune the measurement parameters whenever necessary based on current
// features
adapt_params(features);
// compute the measures for each of the current dimensions
SGVector<float64_t> measures(num_features);
for (index_t i=0; i<num_features; ++i)
measures[i]=compute_measures(features, i);
if (io->get_loglevel()==MSG_DEBUG || io->get_loglevel()==MSG_GCDEBUG)
measures.display_vector("measures");
// rank the measures
SGVector<index_t> argsorted=CMath::argsort(measures);
if (io->get_loglevel()==MSG_DEBUG || io->get_loglevel()==MSG_GCDEBUG)
argsorted.display_vector("argsorted");
// make sure that we don't end up with lesser feats than target dim
index_t to_remove;
if (m_policy==N_SMALLEST || m_policy==N_LARGEST)
to_remove=m_num_remove;
else
to_remove=num_features*m_num_remove*0.01;
index_t can_remove=num_features-m_target_dim;
// if policy is to remove N feats corresponding to smallest/largest
// measures, we just replace N with can_remove. if policy is to remove
// N% feats, then we change the policy temporarily and remove a fixed
// can_remove number of feats instead
index_t orig_remove=m_num_remove;
EFeatureRemovalPolicy orig_policy=m_policy;
if (to_remove>can_remove)
{
m_num_remove=can_remove;
SG_DEBUG("Can only remove %d features in this iteration!\n",
can_remove);
if (m_policy==PERCENTILE_SMALLEST)
m_policy=N_SMALLEST;
else if (m_policy==PERCENTILE_LARGEST)
m_policy=N_LARGEST;
}
// remove appropriate number of features based on the measures and the
// removal policy. this internally update the subset for selected
// features as well
features=remove_feats(features, argsorted);
// restore original removal policy and numbers if necessary for the
// sake of consistency
if (to_remove>can_remove)
{
m_policy=orig_policy;
m_num_remove=orig_remove;
}
// update the number of features
num_features=get_num_features(features);
SG_DEBUG("Current number of features %d!\n", num_features);
}
// sanity check
ASSERT(m_subset->get_size()==m_target_dim);
SG_DEBUG("Leaving!\n");
return features;
}
