void CFactorGraphModel::w_to_fparams(SGVector<float64_t> w)
{
// if nothing changed
if (m_w_cache.equals(w))
return;
if (m_verbose)
SG_SPRINT("****** update m_w_cache!\n");
ASSERT(w.size() == m_w_cache.size());
m_w_cache = w.clone();
int32_t offset = 0;
for (int32_t fi = 0; fi < m_factor_types->get_num_elements(); ++fi)
{
CFactorType* ftype = dynamic_cast<CFactorType*>(m_factor_types->get_element(fi));
int32_t w_dim = ftype->get_w_dim();
offset += w_dim;
SGVector<float64_t> fw(w_dim);
SGVector<int32_t> fw_map = get_params_mapping(ftype->get_type_id());
for (int32_t wi = 0; wi < w_dim; wi++)
fw[wi] = m_w_cache[fw_map[wi]];
ftype->set_w(fw);
SG_UNREF(ftype);
}
ASSERT(offset == m_w_cache.size());
}
float64_t CMahalanobisDistance::compute(int32_t idx_a, int32_t idx_b)
{
SGVector<float64_t> bvec = ((CDenseFeatures<float64_t>*) rhs)->
get_feature_vector(idx_b);
SGVector<float64_t> diff;
SGVector<float64_t> avec;
if (use_mean)
diff = mean.clone();
else
{
avec = ((CDenseFeatures<float64_t>*) lhs)->get_feature_vector(idx_a);
diff=avec.clone();
}
ASSERT(diff.vlen == bvec.vlen);
for (int32_t i=0; i < diff.vlen; i++)
diff[i] = bvec.vector[i] - diff[i];
SGVector<float64_t> v = diff.clone();
cblas_dgemv(CblasColMajor, CblasNoTrans,
icov.num_rows, icov.num_cols, 1.0, icov.matrix,
diff.vlen, diff.vector, 1, 0.0, v.vector, 1);
float64_t result = cblas_ddot(v.vlen, v.vector, 1, diff.vector, 1);
if (!use_mean)
((CDenseFeatures<float64_t>*) lhs)->free_feature_vector(avec, idx_a);
((CDenseFeatures<float64_t>*) rhs)->free_feature_vector(bvec, idx_b);
if (disable_sqrt)
return result;
else
return CMath::sqrt(result);
}
void CFactorDataSource::set_data(SGVector<float64_t> dense)
{
m_dense = dense.clone();
}
void CFactor::set_data(SGVector<float64_t> data_dense)
{
m_data = data_dense.clone();
m_is_data_dep = true;
}
void CFactor::set_variables(SGVector<int32_t> vars)
{
m_var_index = vars.clone();
}
}
bool CKNN::train_machine(CFeatures* data)
{
ASSERT(m_labels)
ASSERT(distance)
if (data)
{
if (m_labels->get_num_labels() != data->get_num_vectors())
SG_ERROR("Number of training vectors does not match number of labels\n")
distance->init(data, data);
}
SGVector<int32_t> lab=((CMulticlassLabels*) m_labels)->get_int_labels();
m_train_labels=lab.clone();
ASSERT(m_train_labels.vlen>0)
int32_t max_class=m_train_labels[0];
int32_t min_class=m_train_labels[0];
for (int32_t i=1; i<m_train_labels.vlen; i++)
{
max_class=CMath::max(max_class, m_train_labels[i]);
min_class=CMath::min(min_class, m_train_labels[i]);
}
for (int32_t i=0; i<m_train_labels.vlen; i++)
m_train_labels[i]-=min_class;
m_min_label=min_class;
void CFactorGraph::set_cardinalities(SGVector<int32_t> cards)
{
m_cardinalities = cards.clone();
}
