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DenseFeatures.cpp
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DenseFeatures.cpp
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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) 1999-2010 Soeren Sonnenburg
* Written (W) 1999-2008 Gunnar Raetsch
* Written (W) 2011-2013 Heiko Strathmann
* Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
* Copyright (C) 2010 Berlin Institute of Technology
*/
#include <shogun/features/DenseFeatures.h>
#include <shogun/preprocessor/DensePreprocessor.h>
#include <shogun/io/SGIO.h>
#include <shogun/base/Parameter.h>
#include <shogun/mathematics/Math.h>
#include <string.h>
namespace shogun {
template<class ST> CDenseFeatures<ST>::CDenseFeatures(int32_t size) : CDotFeatures(size)
{
init();
}
template<class ST> CDenseFeatures<ST>::CDenseFeatures(const CDenseFeatures & orig) :
CDotFeatures(orig)
{
init();
set_feature_matrix(orig.feature_matrix);
initialize_cache();
if (orig.m_subset_stack != NULL)
{
SG_UNREF(m_subset_stack);
m_subset_stack=new CSubsetStack(*orig.m_subset_stack);
SG_REF(m_subset_stack);
}
}
template<class ST> CDenseFeatures<ST>::CDenseFeatures(SGMatrix<ST> matrix) :
CDotFeatures()
{
init();
set_feature_matrix(matrix);
}
template<class ST> CDenseFeatures<ST>::CDenseFeatures(ST* src, int32_t num_feat, int32_t num_vec) :
CDotFeatures()
{
init();
set_feature_matrix(SGMatrix<ST>(src, num_feat, num_vec));
}
template<class ST> CDenseFeatures<ST>::CDenseFeatures(CFile* loader) :
CDotFeatures()
{
init();
load(loader);
}
template<class ST> CFeatures* CDenseFeatures<ST>::duplicate() const
{
return new CDenseFeatures<ST>(*this);
}
template<class ST> CDenseFeatures<ST>::~CDenseFeatures()
{
free_features();
}
template<class ST> void CDenseFeatures<ST>::free_features()
{
m_subset_stack->remove_all_subsets();
free_feature_matrix();
SG_UNREF(feature_cache);
}
template<class ST> void CDenseFeatures<ST>::free_feature_matrix()
{
m_subset_stack->remove_all_subsets();
feature_matrix=SGMatrix<ST>();
num_vectors = 0;
num_features = 0;
}
template<class ST> ST* CDenseFeatures<ST>::get_feature_vector(int32_t num, int32_t& len, bool& dofree)
{
/* index conversion for subset, only for array access */
int32_t real_num=m_subset_stack->subset_idx_conversion(num);
len = num_features;
if (feature_matrix.matrix)
{
dofree = false;
return &feature_matrix.matrix[real_num * int64_t(num_features)];
}
ST* feat = NULL;
dofree = false;
if (feature_cache)
{
feat = feature_cache->lock_entry(real_num);
if (feat)
return feat;
else
feat = feature_cache->set_entry(real_num);
}
if (!feat)
dofree = true;
feat = compute_feature_vector(num, len, feat);
if (get_num_preprocessors())
{
int32_t tmp_len = len;
ST* tmp_feat_before = feat;
ST* tmp_feat_after = NULL;
for (int32_t i = 0; i < get_num_preprocessors(); i++)
{
CDensePreprocessor<ST>* p =
(CDensePreprocessor<ST>*) get_preprocessor(i);
// temporary hack
SGVector<ST> applied = p->apply_to_feature_vector(
SGVector<ST>(tmp_feat_before, tmp_len));
tmp_feat_after = applied.vector;
SG_UNREF(p);
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;
}
// note: tmp_feat_after should be checked as it is used by memcpy
if (tmp_feat_after)
{
memcpy(feat, tmp_feat_after, sizeof(ST) * tmp_len);
SG_FREE(tmp_feat_after);
len = tmp_len;
}
}
return feat;
}
template<class ST> void CDenseFeatures<ST>::set_feature_vector(SGVector<ST> vector, int32_t num)
{
/* index conversion for subset, only for array access */
int32_t real_num=m_subset_stack->subset_idx_conversion(num);
if (num>=get_num_vectors())
{
SG_ERROR("Index out of bounds (number of vectors %d, you "
"requested %d)\n", get_num_vectors(), num);
}
if (!feature_matrix.matrix)
SG_ERROR("Requires a in-memory feature matrix\n")
if (vector.vlen != num_features)
SG_ERROR(
"Vector not of length %d (has %d)\n", num_features, vector.vlen);
memcpy(&feature_matrix.matrix[real_num * int64_t(num_features)], vector.vector,
int64_t(num_features) * sizeof(ST));
}
template<class ST> SGVector<ST> CDenseFeatures<ST>::get_feature_vector(int32_t num)
{
/* index conversion for subset, only for array access */
int32_t real_num=m_subset_stack->subset_idx_conversion(num);
if (num >= get_num_vectors())
{
SG_ERROR("Index out of bounds (number of vectors %d, you "
"requested %d)\n", get_num_vectors(), real_num);
}
int32_t vlen;
bool do_free;
ST* vector= get_feature_vector(num, vlen, do_free);
return SGVector<ST>(vector, vlen, do_free);
}
template<class ST> void CDenseFeatures<ST>::free_feature_vector(ST* feat_vec, int32_t num, bool dofree)
{
if (feature_cache)
feature_cache->unlock_entry(m_subset_stack->subset_idx_conversion(num));
if (dofree)
SG_FREE(feat_vec);
}
template<class ST> void CDenseFeatures<ST>::free_feature_vector(SGVector<ST> vec, int32_t num)
{
free_feature_vector(vec.vector, num, false);
vec=SGVector<ST>();
}
template<class ST> void CDenseFeatures<ST>::vector_subset(int32_t* idx, int32_t idx_len)
{
if (m_subset_stack->has_subsets())
SG_ERROR("A subset is set, cannot call vector_subset\n")
ASSERT(feature_matrix.matrix)
ASSERT(idx_len<=num_vectors)
int32_t num_vec = num_vectors;
num_vectors = idx_len;
int32_t old_ii = -1;
for (int32_t i = 0; i < idx_len; i++)
{
int32_t ii = idx[i];
ASSERT(old_ii<ii)
if (ii < 0 || ii >= num_vec)
SG_ERROR("Index out of range: should be 0<%d<%d\n", ii, num_vec)
if (i == ii)
continue;
memcpy(&feature_matrix.matrix[int64_t(num_features) * i],
&feature_matrix.matrix[int64_t(num_features) * ii],
num_features * sizeof(ST));
old_ii = ii;
}
}
template<class ST> void CDenseFeatures<ST>::feature_subset(int32_t* idx, int32_t idx_len)
{
if (m_subset_stack->has_subsets())
SG_ERROR("A subset is set, cannot call feature_subset\n")
ASSERT(feature_matrix.matrix)
ASSERT(idx_len<=num_features)
int32_t num_feat = num_features;
num_features = idx_len;
for (int32_t i = 0; i < num_vectors; i++)
{
ST* src = &feature_matrix.matrix[int64_t(num_feat) * i];
ST* dst = &feature_matrix.matrix[int64_t(num_features) * i];
int32_t old_jj = -1;
for (int32_t j = 0; j < idx_len; j++)
{
int32_t jj = idx[j];
ASSERT(old_jj<jj)
if (jj < 0 || jj >= num_feat)
SG_ERROR(
"Index out of range: should be 0<%d<%d\n", jj, num_feat);
dst[j] = src[jj];
old_jj = jj;
}
}
}
template<class ST> SGMatrix<ST> CDenseFeatures<ST>::get_feature_matrix()
{
if (!m_subset_stack->has_subsets())
return feature_matrix;
SGMatrix<ST> submatrix(num_features, get_num_vectors());
/* copy a subset vector wise */
for (int32_t i=0; i<submatrix.num_cols; ++i)
{
int32_t real_i = m_subset_stack->subset_idx_conversion(i);
memcpy(&submatrix.matrix[i*int64_t(num_features)],
&feature_matrix.matrix[real_i * int64_t(num_features)],
num_features * sizeof(ST));
}
return submatrix;
}
template<class ST> SGMatrix<ST> CDenseFeatures<ST>::steal_feature_matrix()
{
SGMatrix<ST> st_feature_matrix=feature_matrix;
m_subset_stack->remove_all_subsets();
SG_UNREF(feature_cache);
clean_preprocessors();
free_feature_matrix();
return st_feature_matrix;
}
template<class ST> void CDenseFeatures<ST>::set_feature_matrix(SGMatrix<ST> matrix)
{
m_subset_stack->remove_all_subsets();
free_feature_matrix();
feature_matrix = matrix;
num_features = matrix.num_rows;
num_vectors = matrix.num_cols;
}
template<class ST> ST* CDenseFeatures<ST>::get_feature_matrix(int32_t &num_feat, int32_t &num_vec)
{
num_feat = num_features;
num_vec = num_vectors;
return feature_matrix.matrix;
}
template<class ST> CDenseFeatures<ST>* CDenseFeatures<ST>::get_transposed()
{
int32_t num_feat;
int32_t num_vec;
ST* fm = get_transposed(num_feat, num_vec);
return new CDenseFeatures<ST>(fm, num_feat, num_vec);
}
template<class ST> ST* CDenseFeatures<ST>::get_transposed(int32_t &num_feat, int32_t &num_vec)
{
num_feat = get_num_vectors();
num_vec = num_features;
int32_t old_num_vec=get_num_vectors();
ST* fm = SG_MALLOC(ST, int64_t(num_feat) * num_vec);
for (int32_t i=0; i<old_num_vec; i++)
{
SGVector<ST> vec=get_feature_vector(i);
for (int32_t j=0; j<vec.vlen; j++)
fm[j*int64_t(old_num_vec)+i]=vec.vector[j];
free_feature_vector(vec, i);
}
return fm;
}
template<class ST> void CDenseFeatures<ST>::copy_feature_matrix(SGMatrix<ST> src)
{
if (m_subset_stack->has_subsets())
SG_ERROR("A subset is set, cannot call copy_feature_matrix\n")
free_feature_matrix();
feature_matrix = src.clone();
num_features = src.num_rows;
num_vectors = src.num_cols;
initialize_cache();
}
template<class ST> void CDenseFeatures<ST>::obtain_from_dot(CDotFeatures* df)
{
m_subset_stack->remove_all_subsets();
int32_t num_feat = df->get_dim_feature_space();
int32_t num_vec = df->get_num_vectors();
ASSERT(num_feat>0 && num_vec>0)
free_feature_matrix();
feature_matrix = SGMatrix<ST>(num_feat, num_vec);
for (int32_t i = 0; i < num_vec; i++)
{
SGVector<float64_t> v = df->get_computed_dot_feature_vector(i);
ASSERT(num_feat==v.vlen)
for (int32_t j = 0; j < num_feat; j++)
feature_matrix.matrix[i * int64_t(num_feat) + j] = (ST) v.vector[j];
}
num_features = num_feat;
num_vectors = num_vec;
}
template<class ST> bool CDenseFeatures<ST>::apply_preprocessor(bool force_preprocessing)
{
if (m_subset_stack->has_subsets())
SG_ERROR("A subset is set, cannot call apply_preproc\n")
SG_DEBUG("force: %d\n", force_preprocessing)
if (feature_matrix.matrix && get_num_preprocessors())
{
for (int32_t i = 0; i < get_num_preprocessors(); i++)
{
if ((!is_preprocessed(i) || force_preprocessing))
{
set_preprocessed(i);
CDensePreprocessor<ST>* p =
(CDensePreprocessor<ST>*) get_preprocessor(i);
SG_INFO("preprocessing using preproc %s\n", p->get_name())
if (p->apply_to_feature_matrix(this).matrix == NULL)
{
SG_UNREF(p);
return false;
}
SG_UNREF(p);
}
}
return true;
}
else
{
if (!feature_matrix.matrix)
SG_ERROR("no feature matrix\n")
if (!get_num_preprocessors())
SG_ERROR("no preprocessors available\n")
return false;
}
}
template<class ST> int32_t CDenseFeatures<ST>::get_num_vectors() const
{
return m_subset_stack->has_subsets() ? m_subset_stack->get_size() : num_vectors;
}
template<class ST> int32_t CDenseFeatures<ST>::get_num_features() const { return num_features; }
template<class ST> void CDenseFeatures<ST>::set_num_features(int32_t num)
{
num_features = num;
initialize_cache();
}
template<class ST> void CDenseFeatures<ST>::set_num_vectors(int32_t num)
{
if (m_subset_stack->has_subsets())
SG_ERROR("A subset is set, cannot call set_num_vectors\n")
num_vectors = num;
initialize_cache();
}
template<class ST> void CDenseFeatures<ST>::initialize_cache()
{
if (m_subset_stack->has_subsets())
SG_ERROR("A subset is set, cannot call initialize_cache\n")
if (num_features && num_vectors)
{
SG_UNREF(feature_cache);
feature_cache = new CCache<ST>(get_cache_size(), num_features,
num_vectors);
SG_REF(feature_cache);
}
}
template<class ST> EFeatureClass CDenseFeatures<ST>::get_feature_class() const { return C_DENSE; }
template<class ST> bool CDenseFeatures<ST>::reshape(int32_t p_num_features, int32_t p_num_vectors)
{
if (m_subset_stack->has_subsets())
SG_ERROR("A subset is set, cannot call reshape\n")
if (p_num_features * p_num_vectors
== this->num_features * this->num_vectors)
{
num_features = p_num_features;
num_vectors = p_num_vectors;
return true;
} else
return false;
}
template<class ST> int32_t CDenseFeatures<ST>::get_dim_feature_space() const { return num_features; }
template<class ST> float64_t CDenseFeatures<ST>::dot(int32_t vec_idx1, CDotFeatures* df,
int32_t vec_idx2)
{
ASSERT(df)
ASSERT(df->get_feature_type() == get_feature_type())
ASSERT(df->get_feature_class() == get_feature_class())
CDenseFeatures<ST>* sf = (CDenseFeatures<ST>*) df;
int32_t len1, len2;
bool free1, free2;
ST* vec1 = get_feature_vector(vec_idx1, len1, free1);
ST* vec2 = sf->get_feature_vector(vec_idx2, len2, free2);
float64_t result = CMath::dot(vec1, vec2, len1);
free_feature_vector(vec1, vec_idx1, free1);
sf->free_feature_vector(vec2, vec_idx2, free2);
return result;
}
template<class ST> void CDenseFeatures<ST>::add_to_dense_vec(float64_t alpha, int32_t vec_idx1,
float64_t* vec2, int32_t vec2_len, bool abs_val)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
ST* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
if (abs_val)
{
for (int32_t i = 0; i < num_features; i++)
vec2[i] += alpha * CMath::abs(vec1[i]);
}
else
{
for (int32_t i = 0; i < num_features; i++)
vec2[i] += alpha * vec1[i];
}
free_feature_vector(vec1, vec_idx1, vfree);
}
template<>
void CDenseFeatures<float64_t>::add_to_dense_vec(float64_t alpha, int32_t vec_idx1,
float64_t* vec2, int32_t vec2_len, bool abs_val)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
float64_t* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
if (abs_val)
{
for (int32_t i = 0; i < num_features; i++)
vec2[i] += alpha * CMath::abs(vec1[i]);
}
else
{
SGVector<float64_t>::vec1_plus_scalar_times_vec2(vec2, alpha, vec1, num_features);
}
free_feature_vector(vec1, vec_idx1, vfree);
}
template<class ST> int32_t CDenseFeatures<ST>::get_nnz_features_for_vector(int32_t num)
{
return num_features;
}
template<class ST> void* CDenseFeatures<ST>::get_feature_iterator(int32_t vector_index)
{
if (vector_index>=get_num_vectors())
{
SG_ERROR("Index out of bounds (number of vectors %d, you "
"requested %d)\n", get_num_vectors(), vector_index);
}
dense_feature_iterator* iterator = SG_MALLOC(dense_feature_iterator, 1);
iterator->vec = get_feature_vector(vector_index, iterator->vlen,
iterator->vfree);
iterator->vidx = vector_index;
iterator->index = 0;
return iterator;
}
template<class ST> bool CDenseFeatures<ST>::get_next_feature(int32_t& index, float64_t& value,
void* iterator)
{
dense_feature_iterator* it = (dense_feature_iterator*) iterator;
if (!it || it->index >= it->vlen)
return false;
index = it->index++;
value = (float64_t) it->vec[index];
return true;
}
template<class ST> void CDenseFeatures<ST>::free_feature_iterator(void* iterator)
{
if (!iterator)
return;
dense_feature_iterator* it = (dense_feature_iterator*) iterator;
free_feature_vector(it->vec, it->vidx, it->vfree);
SG_FREE(it);
}
template<class ST> CFeatures* CDenseFeatures<ST>::copy_subset(SGVector<index_t> indices)
{
SGMatrix<ST> feature_matrix_copy(num_features, indices.vlen);
for (index_t i=0; i<indices.vlen; ++i)
{
index_t real_idx=m_subset_stack->subset_idx_conversion(indices.vector[i]);
memcpy(&feature_matrix_copy.matrix[i*num_features],
&feature_matrix.matrix[real_idx*num_features],
num_features*sizeof(ST));
}
CFeatures* result=new CDenseFeatures(feature_matrix_copy);
SG_REF(result);
return result;
}
template<class ST>
CFeatures* CDenseFeatures<ST>::copy_dimension_subset(SGVector<index_t> dims)
{
SG_DEBUG("Entering!\n");
// sanity checks
index_t max=CMath::max(dims.vector, dims.vlen);
index_t min=CMath::min(dims.vector, dims.vlen);
REQUIRE(max<num_features && min>=0,
"Provided dimensions is in the range [%d, %d] but they "
"have to be within [0, %d]! But it \n", min, max, num_features);
SGMatrix<ST> feature_matrix_copy(dims.vlen, get_num_vectors());
for (index_t i=0; i<dims.vlen; ++i)
{
for (index_t j=0; j<get_num_vectors(); ++j)
{
index_t real_idx=m_subset_stack->subset_idx_conversion(j);
feature_matrix_copy(i, j)=feature_matrix(dims[i], real_idx);
}
}
CFeatures* result=new CDenseFeatures(feature_matrix_copy);
SG_REF(result);
SG_DEBUG("Leaving!\n");
return result;
}
#ifndef SWIG // SWIG should skip this part
template<class ST>
CFeatures* CDenseFeatures<ST>::shallow_subset_copy()
{
CFeatures* shallow_copy_features=nullptr;
SG_SDEBUG("Using underlying feature matrix with %d dimensions and %d feature vectors!\n", num_features, num_vectors);
SGMatrix<ST> shallow_copy_matrix(feature_matrix);
shallow_copy_features=new CDenseFeatures<ST>(shallow_copy_matrix);
SG_REF(shallow_copy_features);
if (m_subset_stack->has_subsets())
shallow_copy_features->add_subset(m_subset_stack->get_last_subset()->get_subset_idx());
return shallow_copy_features;
}
#endif
template<class ST> ST* CDenseFeatures<ST>::compute_feature_vector(int32_t num, int32_t& len,
ST* target)
{
SG_NOTIMPLEMENTED
len = 0;
return NULL;
}
template<class ST> void CDenseFeatures<ST>::init()
{
num_vectors = 0;
num_features = 0;
feature_matrix = SGMatrix<ST>();
feature_cache = NULL;
set_generic<ST>();
/* not store number of vectors in subset */
SG_ADD(&num_vectors, "num_vectors", "Number of vectors.", MS_NOT_AVAILABLE);
SG_ADD(&num_features, "num_features", "Number of features.", MS_NOT_AVAILABLE);
SG_ADD(&feature_matrix, "feature_matrix",
"Matrix of feature vectors / 1 vector per column.", MS_NOT_AVAILABLE);
}
#define GET_FEATURE_TYPE(f_type, sg_type) \
template<> EFeatureType CDenseFeatures<sg_type>::get_feature_type() const \
{ \
return f_type; \
}
GET_FEATURE_TYPE(F_BOOL, bool)
GET_FEATURE_TYPE(F_CHAR, char)
GET_FEATURE_TYPE(F_BYTE, uint8_t)
GET_FEATURE_TYPE(F_BYTE, int8_t)
GET_FEATURE_TYPE(F_SHORT, int16_t)
GET_FEATURE_TYPE(F_WORD, uint16_t)
GET_FEATURE_TYPE(F_INT, int32_t)
GET_FEATURE_TYPE(F_UINT, uint32_t)
GET_FEATURE_TYPE(F_LONG, int64_t)
GET_FEATURE_TYPE(F_ULONG, uint64_t)
GET_FEATURE_TYPE(F_SHORTREAL, float32_t)
GET_FEATURE_TYPE(F_DREAL, float64_t)
GET_FEATURE_TYPE(F_LONGREAL, floatmax_t)
#undef GET_FEATURE_TYPE
template<> float64_t CDenseFeatures<bool>::dense_dot(int32_t vec_idx1,
const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
bool* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = 0;
for (int32_t i = 0; i < num_features; i++)
result += vec1[i] ? vec2[i] : 0;
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<> float64_t CDenseFeatures<char>::dense_dot(int32_t vec_idx1,
const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
char* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = 0;
for (int32_t i = 0; i < num_features; i++)
result += vec1[i] * vec2[i];
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<> float64_t CDenseFeatures<int8_t>::dense_dot(int32_t vec_idx1,
const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
int8_t* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = 0;
for (int32_t i = 0; i < num_features; i++)
result += vec1[i] * vec2[i];
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<> float64_t CDenseFeatures<uint8_t>::dense_dot(
int32_t vec_idx1, const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
uint8_t* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = 0;
for (int32_t i = 0; i < num_features; i++)
result += vec1[i] * vec2[i];
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<> float64_t CDenseFeatures<int16_t>::dense_dot(
int32_t vec_idx1, const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
int16_t* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = 0;
for (int32_t i = 0; i < num_features; i++)
result += vec1[i] * vec2[i];
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<> float64_t CDenseFeatures<uint16_t>::dense_dot(
int32_t vec_idx1, const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
uint16_t* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = 0;
for (int32_t i = 0; i < num_features; i++)
result += vec1[i] * vec2[i];
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<> float64_t CDenseFeatures<int32_t>::dense_dot(
int32_t vec_idx1, const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
int32_t* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = 0;
for (int32_t i = 0; i < num_features; i++)
result += vec1[i] * vec2[i];
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<> float64_t CDenseFeatures<uint32_t>::dense_dot(
int32_t vec_idx1, const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
uint32_t* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = 0;
for (int32_t i = 0; i < num_features; i++)
result += vec1[i] * vec2[i];
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<> float64_t CDenseFeatures<int64_t>::dense_dot(
int32_t vec_idx1, const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
int64_t* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = 0;
for (int32_t i = 0; i < num_features; i++)
result += vec1[i] * vec2[i];
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<> float64_t CDenseFeatures<uint64_t>::dense_dot(
int32_t vec_idx1, const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
uint64_t* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = 0;
for (int32_t i = 0; i < num_features; i++)
result += vec1[i] * vec2[i];
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<> float64_t CDenseFeatures<float32_t>::dense_dot(
int32_t vec_idx1, const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
float32_t* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = 0;
for (int32_t i = 0; i < num_features; i++)
result += vec1[i] * vec2[i];
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<> float64_t CDenseFeatures<float64_t>::dense_dot(
int32_t vec_idx1, const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
float64_t* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = CMath::dot(vec1, vec2, num_features);
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<> float64_t CDenseFeatures<floatmax_t>::dense_dot(
int32_t vec_idx1, const float64_t* vec2, int32_t vec2_len)
{
ASSERT(vec2_len == num_features)
int32_t vlen;
bool vfree;
floatmax_t* vec1 = get_feature_vector(vec_idx1, vlen, vfree);
ASSERT(vlen == num_features)
float64_t result = 0;
for (int32_t i = 0; i < num_features; i++)
result += vec1[i] * vec2[i];
free_feature_vector(vec1, vec_idx1, vfree);
return result;
}
template<class ST> bool CDenseFeatures<ST>::is_equal(CDenseFeatures* rhs)
{
if ( num_features != rhs->num_features || num_vectors != rhs->num_vectors )
return false;
ST* vec1;
ST* vec2;
int32_t v1len, v2len;
bool v1free, v2free, stop = false;
for (int32_t i = 0; i < num_vectors; i++)
{
vec1 = get_feature_vector(i, v1len, v1free);
vec2 = rhs->get_feature_vector(i, v2len, v2free);
if (v1len!=v2len)
stop = true;
for (int32_t j=0; j<v1len; j++)
{
if (vec1[j]!=vec2[j])
stop = true;
}
free_feature_vector(vec1, i, v1free);
free_feature_vector(vec2, i, v2free);
if (stop)
return false;
}
return true;
}
template<class ST> CFeatures* CDenseFeatures<ST>::create_merged_copy(
CList* others)
{
SG_DEBUG("entering %s::create_merged_copy()\n", get_name());
if (!others)
return NULL;