void CExactInferenceMethod::update_all()
{
if (m_labels)
m_label_vector =
((CRegressionLabels*) m_labels)->get_labels().clone();
if (m_features && m_features->has_property(FP_DOT) && m_features->get_num_vectors())
m_feature_matrix =
((CDotFeatures*)m_features)->get_computed_dot_feature_matrix();
else if (m_features && m_features->get_feature_class() == C_COMBINED)
{
CDotFeatures* feat =
(CDotFeatures*)((CCombinedFeatures*)m_features)->
get_first_feature_obj();
if (feat->get_num_vectors())
m_feature_matrix = feat->get_computed_dot_feature_matrix();
SG_UNREF(feat);
}
update_data_means();
if (m_kernel)
update_train_kernel();
if (m_ktrtr.num_cols*m_ktrtr.num_rows)
{
update_chol();
update_alpha();
}
}
bool CGMM::train(CFeatures* data)
{
ASSERT(m_n != 0);
if (m_components)
cleanup();
/** init features with data if necessary and assure type is correct */
if (data)
{
if (!data->has_property(FP_DOT))
SG_ERROR("Specified features are not of type CDotFeatures\n");
set_features(data);
}
CDotFeatures* dotdata = (CDotFeatures *) data;
int32_t num_vectors = dotdata->get_num_vectors();
int32_t num_dim = dotdata->get_dim_feature_space();
CEuclidianDistance* dist = new CEuclidianDistance();
CKMeans* init_k_means = new CKMeans(m_n, dist);
init_k_means->train(dotdata);
float64_t* init_means;
int32_t init_mean_dim;
int32_t init_mean_size;
init_k_means->get_cluster_centers(&init_means, &init_mean_dim, &init_mean_size);
float64_t* init_cov;
int32_t init_cov_rows;
int32_t init_cov_cols;
dotdata->get_cov(&init_cov, &init_cov_rows, &init_cov_cols);
m_coefficients = new float64_t[m_coef_size];
m_components = new CGaussian*[m_n];
for (int i=0; i<m_n; i++)
{
m_coefficients[i] = 1.0/m_coef_size;
m_components[i] = new CGaussian(&(init_means[i*init_mean_dim]), init_mean_dim,
init_cov, init_cov_rows, init_cov_cols);
}
/** question of faster vs. less memory using */
float64_t* pdfs = new float64_t[num_vectors*m_n];
float64_t* T = new float64_t[num_vectors*m_n];
int32_t iter = 0;
float64_t e_log_likelihood_change = m_minimal_change + 1;
float64_t e_log_likelihood_old = 0;
float64_t e_log_likelihood_new = -FLT_MAX;
while (iter<m_max_iter && e_log_likelihood_change>m_minimal_change)
{
e_log_likelihood_old = e_log_likelihood_new;
e_log_likelihood_new = 0;
/** Precomputing likelihoods */
float64_t* point;
int32_t point_len;
for (int i=0; i<num_vectors; i++)
{
dotdata->get_feature_vector(&point, &point_len, i);
for (int j=0; j<m_n; j++)
pdfs[i*m_n+j] = m_components[j]->compute_PDF(point, point_len);
delete[] point;
}
for (int i=0; i<num_vectors; i++)
{
float64_t sum = 0;
for (int j=0; j<m_n; j++)
sum += m_coefficients[j]*pdfs[i*m_n+j];
for (int j=0; j<m_n; j++)
{
T[i*m_n+j] = (m_coefficients[j]*pdfs[i*m_n+j])/sum;
e_log_likelihood_new += T[i*m_n+j]*CMath::log(m_coefficients[j]*pdfs[i*m_n+j]);
}
}
/** Not sure if getting the abs value is a good idea */
e_log_likelihood_change = CMath::abs(e_log_likelihood_new - e_log_likelihood_old);
/** Updates */
float64_t T_sum;
float64_t* mean_sum;
float64_t* cov_sum;
for (int i=0; i<m_n; i++)
{
T_sum = 0;
mean_sum = new float64_t[num_dim];
memset(mean_sum, 0, num_dim*sizeof(float64_t));
for (int j=0; j<num_vectors; j++)
{
T_sum += T[j*m_n+i];
dotdata->get_feature_vector(&point, &point_len, j);
CMath::add<float64_t>(mean_sum, T[j*m_n+i], point, 1, mean_sum, point_len);
delete[] point;
}
m_coefficients[i] = T_sum/num_vectors;
for (int j=0; j<num_dim; j++)
mean_sum[j] /= T_sum;
m_components[i]->set_mean(mean_sum, num_dim);
cov_sum = new float64_t[num_dim*num_dim];
memset(cov_sum, 0, num_dim*num_dim*sizeof(float64_t));
for (int j=0; j<num_vectors; j++)
{
dotdata->get_feature_vector(&point, &point_len, j);
CMath::add<float64_t>(point, 1, point, -1, mean_sum, point_len);
cblas_dger(CblasRowMajor, num_dim, num_dim, T[j*m_n+i], point, 1, point,
1, (double*) cov_sum, num_dim);
delete[] point;
}
for (int j=0; j<num_dim*num_dim; j++)
cov_sum[j] /= T_sum;
m_components[i]->set_cov(cov_sum, num_dim, num_dim);
delete[] mean_sum;
delete[] cov_sum;
}
iter++;
}
delete[] pdfs;
delete[] T;
return true;
}