C++ CDotFeatures::get_num_vectors方法代码示例

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;
//.........这里部分代码省略.........