C++ CTime::start方法代码示例

CSimpleFeatures<float64_t>* CKernelLocallyLinearEmbedding::embed_kernel(CKernel* kernel)
{
	CTime* time = new CTime();
	
	time->start();
	SGMatrix<float64_t> kernel_matrix = kernel->get_kernel_matrix();
	SG_DEBUG("Kernel matrix computation took %fs\n",time->cur_time_diff());
	
	time->start();
	SGMatrix<int32_t> neighborhood_matrix = get_neighborhood_matrix(kernel_matrix,m_k);
	SG_DEBUG("Neighbors finding took %fs\n",time->cur_time_diff());
	
	time->start();
	SGMatrix<float64_t> M_matrix = construct_weight_matrix(kernel_matrix,neighborhood_matrix);
	SG_DEBUG("Weights computation took %fs\n",time->cur_time_diff());
	kernel_matrix.destroy_matrix();
	neighborhood_matrix.destroy_matrix();

	time->start();
	SGMatrix<float64_t> nullspace = construct_embedding(M_matrix,m_target_dim);
	SG_DEBUG("Embedding construction took %fs\n",time->cur_time_diff());
	M_matrix.destroy_matrix();

	delete time;

	return new CSimpleFeatures<float64_t>(nullspace);
}

SGVector<complex128_t> CCGMShiftedFamilySolver::solve_shifted_weighted(
	CLinearOperator<SGVector<float64_t>, SGVector<float64_t> >* A, SGVector<float64_t> b,
	SGVector<complex128_t> shifts, SGVector<complex128_t> weights)
{
	SG_DEBUG("Entering\n");

	// sanity check
	REQUIRE(A, "Operator is NULL!\n");
	REQUIRE(A->get_dimension()==b.vlen, "Dimension mismatch! [%d vs %d]\n",
		A->get_dimension(), b.vlen);
	REQUIRE(shifts.vector,"Shifts are not initialized!\n");
	REQUIRE(weights.vector,"Weights are not initialized!\n");
	REQUIRE(shifts.vlen==weights.vlen, "Number of shifts and number of "
		"weights are not equal! [%d vs %d]\n", shifts.vlen, weights.vlen);

	// the solution matrix, one column per shift, initial guess 0 for all
	MatrixXcd x_sh=MatrixXcd::Zero(b.vlen, shifts.vlen);
	MatrixXcd p_sh=MatrixXcd::Zero(b.vlen, shifts.vlen);

	// non-shifted direction
	SGVector<float64_t> p_(b.vlen);

	// the rest of the part hinges on eigen3 for computing norms
	Map<VectorXd> b_map(b.vector, b.vlen);
	Map<VectorXd> p(p_.vector, p_.vlen);

	// residual r_i=b-Ax_i, here x_0=[0], so r_0=b
	VectorXd r=b_map;

	// initial direction is same as residual
	p=r;
	p_sh=r.replicate(1, shifts.vlen).cast<complex128_t>();

	// non shifted initializers
	float64_t r_norm2=r.dot(r);
	float64_t beta_old=1.0;
	float64_t alpha=1.0;

	// shifted quantities
	SGVector<complex128_t> alpha_sh(shifts.vlen);
	SGVector<complex128_t> beta_sh(shifts.vlen);
	SGVector<complex128_t> zeta_sh_old(shifts.vlen);
	SGVector<complex128_t> zeta_sh_cur(shifts.vlen);
	SGVector<complex128_t> zeta_sh_new(shifts.vlen);

	// shifted initializers
	zeta_sh_old.set_const(1.0);
	zeta_sh_cur.set_const(1.0);

	// the iterator for this iterative solver
	IterativeSolverIterator<float64_t> it(r, m_max_iteration_limit,
		m_relative_tolerence, m_absolute_tolerence);

	// start the timer
	CTime time;
	time.start();

	// set the residuals to zero
	if (m_store_residuals)
		m_residuals.set_const(0.0);

	// CG iteration begins
	for (it.begin(r); !it.end(r); ++it)
	{

		SG_DEBUG("CG iteration %d, residual norm %f\n",
				it.get_iter_info().iteration_count,
				it.get_iter_info().residual_norm);

		if (m_store_residuals)
		{
			m_residuals[it.get_iter_info().iteration_count]
				=it.get_iter_info().residual_norm;
		}

		// apply linear operator to the direction vector
		SGVector<float64_t> Ap_=A->apply(p_);
		Map<VectorXd> Ap(Ap_.vector, Ap_.vlen);

		// compute p^{T}Ap, if zero, failure
		float64_t p_dot_Ap=p.dot(Ap);
		if (p_dot_Ap==0.0)
			break;

		// compute the beta parameter of CG_M
		float64_t beta=-r_norm2/p_dot_Ap;

		// compute the zeta-shifted parameter of CG_M
		compute_zeta_sh_new(zeta_sh_old, zeta_sh_cur, shifts, beta_old, beta,
			alpha, zeta_sh_new);

		// compute beta-shifted parameter of CG_M
		compute_beta_sh(zeta_sh_new, zeta_sh_cur, beta, beta_sh);

		// update the solution vector and residual
		for (index_t i=0; i<shifts.vlen; ++i)
			x_sh.col(i)-=beta_sh[i]*p_sh.col(i);

		// r_{i}=r_{i-1}+\beta_{i}Ap
		r+=beta*Ap;
//.........这里部分代码省略.........

bool CShareBoost::train_machine(CFeatures* data)
{
	if (data)
		set_features(data);

	if (m_features == NULL)
		SG_ERROR("No features given for training\n")
	if (m_labels == NULL)
		SG_ERROR("No labels given for training\n")

	if (m_nonzero_feas <= 0)
		SG_ERROR("Set a valid (> 0) number of non-zero features to seek before training\n")
	if (m_nonzero_feas >= dynamic_cast<CDenseFeatures<float64_t>*>(m_features)->get_num_features())
		SG_ERROR("It doesn't make sense to use ShareBoost with num non-zero features >= num features in the data\n")

	m_fea = dynamic_cast<CDenseFeatures<float64_t> *>(m_features)->get_feature_matrix();
	m_rho = SGMatrix<float64_t>(m_multiclass_strategy->get_num_classes(), m_fea.num_cols);
	m_rho_norm = SGVector<float64_t>(m_fea.num_cols);
	m_pred = SGMatrix<float64_t>(m_fea.num_cols, m_multiclass_strategy->get_num_classes());
	m_pred.zero();

	m_activeset = SGVector<int32_t>(m_fea.num_rows);
	m_activeset.vlen = 0;

	m_machines->reset_array();
	for (int32_t i=0; i < m_multiclass_strategy->get_num_classes(); ++i)
		m_machines->push_back(new CLinearMachine());

	CTime *timer = new CTime();

	float64_t t_compute_pred = 0; // t of 1st round is 0, since no pred to compute
	for (int32_t t=0; t < m_nonzero_feas; ++t)
	{
		timer->start();
		compute_rho();
		int32_t i_fea = choose_feature();
		m_activeset.vector[m_activeset.vlen] = i_fea;
		m_activeset.vlen += 1;
		float64_t t_choose_feature = timer->cur_time_diff();
		timer->start();
		optimize_coefficients();
		float64_t t_optimize = timer->cur_time_diff();

		SG_SDEBUG(" SB[round %03d]: (%8.4f + %8.4f) sec.\n", t,
				t_compute_pred + t_choose_feature, t_optimize);

		timer->start();
		compute_pred();
		t_compute_pred = timer->cur_time_diff();
	}

	SG_UNREF(timer);

	// release memory
	m_fea = SGMatrix<float64_t>();
	m_rho = SGMatrix<float64_t>();
	m_rho_norm = SGVector<float64_t>();
	m_pred = SGMatrix<float64_t>();

	return true;
}

SGVector<float64_t> CConjugateGradientSolver::solve(
	CLinearOperator<float64_t>* A, SGVector<float64_t> b)
{
	SG_DEBUG("CConjugateGradientSolve::solve(): Entering..\n");

	// sanity check
	REQUIRE(A, "Operator is NULL!\n");
	REQUIRE(A->get_dimension()==b.vlen, "Dimension mismatch!\n");

	// the final solution vector, initial guess is 0
	SGVector<float64_t> result(b.vlen);
	result.set_const(0.0);

	// the rest of the part hinges on eigen3 for computing norms
	Map<VectorXd> x(result.vector, result.vlen);
	Map<VectorXd> b_map(b.vector, b.vlen);

	// direction vector
	SGVector<float64_t> p_(result.vlen);
	Map<VectorXd> p(p_.vector, p_.vlen);

	// residual r_i=b-Ax_i, here x_0=[0], so r_0=b
	VectorXd r=b_map;

	// initial direction is same as residual
	p=r;

	// the iterator for this iterative solver
	IterativeSolverIterator<float64_t> it(b_map, m_max_iteration_limit,
		m_relative_tolerence, m_absolute_tolerence);

	// CG iteration begins
	float64_t r_norm2=r.dot(r);

	// start the timer
	CTime time;
	time.start();

	// set the residuals to zero
	if (m_store_residuals)
		m_residuals.set_const(0.0);

	for (it.begin(r); !it.end(r); ++it)
	{
		SG_DEBUG("CG iteration %d, residual norm %f\n",
			it.get_iter_info().iteration_count,
			it.get_iter_info().residual_norm);

		if (m_store_residuals)
		{
			m_residuals[it.get_iter_info().iteration_count]
				=it.get_iter_info().residual_norm;
		}

		// apply linear operator to the direction vector
		SGVector<float64_t> Ap_=A->apply(p_);
		Map<VectorXd> Ap(Ap_.vector, Ap_.vlen);

		// compute p^{T}Ap, if zero, failure
		float64_t p_dot_Ap=p.dot(Ap);
		if (p_dot_Ap==0.0)
			break;

		// compute the alpha parameter of CG
		float64_t alpha=r_norm2/p_dot_Ap;

		// update the solution vector and residual
		// x_{i}=x_{i-1}+\alpha_{i}p
		x+=alpha*p;

		// r_{i}=r_{i-1}-\alpha_{i}p
		r-=alpha*Ap;

		// compute new ||r||_{2}, if zero, converged
		float64_t r_norm2_i=r.dot(r);
		if (r_norm2_i==0.0)
			break;

		// compute the beta parameter of CG
		float64_t beta=r_norm2_i/r_norm2;

		// update direction, and ||r||_{2}
		r_norm2=r_norm2_i;
		p=r+beta*p;
	}

	float64_t elapsed=time.cur_time_diff();

	if (!it.succeeded(r))
		SG_WARNING("Did not converge!\n");

	SG_INFO("Iteration took %ld times, residual norm=%.20lf, time elapsed=%lf\n",
		it.get_iter_info().iteration_count, it.get_iter_info().residual_norm, elapsed);

	SG_DEBUG("CConjugateGradientSolve::solve(): Leaving..\n");
	return result;
}

int main()
{
	init_shogun(&print_message, &print_warning,
			&print_error);
	try
	{
		uint256_t* a;
		uint32_t* b;
		CTime t;
		t.io->set_loglevel(MSG_DEBUG);

		SG_SPRINT("gen data..");
		t.start();
		gen_ints(a,b, LEN);
		t.cur_time_diff(true);

		SG_SPRINT("qsort..");
		t.start();
		CMath::qsort_index(a, b, LEN);
		t.cur_time_diff(true);

		SG_SPRINT("\n\n");
		for (uint32_t i=0; i<10; i++)
		{
			SG_SPRINT("a[%d]=", i);
			a[i].print_hex();
			SG_SPRINT("\n");
		}

		SG_SPRINT("\n\n");

		a[0]=(uint64_t[4]) {1,2,3,4};
		uint64_t val[4]={5,6,7,8};
		a[1]=val;
		a[2]=a[0];
		CMath::swap(a[0],a[1]);

		printf("a[0]==a[1] %d\n", (int) (a[0] == a[1]));
		printf("a[0]<a[1] %d\n", (int) (a[0] < a[1]));
		printf("a[0]<=a[1] %d\n", (int) (a[0] <= a[1]));
		printf("a[0]>a[1] %d\n", (int) (a[0] > a[1]));
		printf("a[0]>=a[1] %d\n", (int) (a[0] >= a[1]));

		printf("a[0]==a[0] %d\n", (int) (a[0] == a[0]));
		printf("a[0]<a[0] %d\n", (int) (a[0] < a[0]));
		printf("a[0]<=a[0] %d\n", (int) (a[0] <= a[0]));
		printf("a[0]>a[0] %d\n", (int) (a[0] > a[0]));
		printf("a[0]>=a[0] %d\n", (int) (a[0] >= a[0]));

		SG_SPRINT("\n\n");
		for (uint32_t i=0; i<10 ; i++)
		{
			SG_SPRINT("a[%d]=", i);
			a[i].print_hex();
			printf("\n");
		}

		delete[] a;
		delete[] b;
	}
	catch(ShogunException & sh)
	{
		SG_SPRINT("%s",sh.get_exception_string());
	}

	exit_shogun();
}

CFeatures* CLocallyLinearEmbedding::apply(CFeatures* features)
{
	ASSERT(features);
	// check features
	if (!(features->get_feature_class()==C_DENSE &&
	      features->get_feature_type()==F_DREAL))
	{
		SG_ERROR("Given features are not of SimpleRealFeatures type.\n");
	}
	// shorthand for simplefeatures
	CDenseFeatures<float64_t>* simple_features = (CDenseFeatures<float64_t>*) features;
	SG_REF(features);

	// get and check number of vectors
	int32_t N = simple_features->get_num_vectors();
	if (m_k>=N)
		SG_ERROR("Number of neighbors (%d) should be less than number of objects (%d).\n",
		         m_k, N);

	// compute distance matrix
	SG_DEBUG("Computing distance matrix\n");
	ASSERT(m_distance);
	CTime* time = new CTime();
	time->start();
	m_distance->init(simple_features,simple_features);
	SGMatrix<float64_t> distance_matrix = m_distance->get_distance_matrix();
	m_distance->remove_lhs_and_rhs();
	SG_DEBUG("Distance matrix computation took %fs\n",time->cur_time_diff());
	SG_DEBUG("Calculating neighborhood matrix\n");
	SGMatrix<int32_t> neighborhood_matrix;

	time->start();
	if (m_auto_k)
	{
		neighborhood_matrix = get_neighborhood_matrix(distance_matrix,m_max_k);
		m_k = estimate_k(simple_features,neighborhood_matrix);
		SG_DEBUG("Estimated k with value of %d\n",m_k);
	}
	else
		neighborhood_matrix = get_neighborhood_matrix(distance_matrix,m_k);

	SG_DEBUG("Neighbors finding took %fs\n",time->cur_time_diff());

	// init W (weight) matrix
	float64_t* W_matrix = SG_CALLOC(float64_t, N*N);

	// construct weight matrix
	SG_DEBUG("Constructing weight matrix\n");
	time->start();
	SGMatrix<float64_t> weight_matrix = construct_weight_matrix(simple_features,W_matrix,neighborhood_matrix);
	SG_DEBUG("Weight matrix construction took %.5fs\n", time->cur_time_diff());

	// find null space of weight matrix
	SG_DEBUG("Finding nullspace\n");
	time->start();
	SGMatrix<float64_t> new_feature_matrix = construct_embedding(weight_matrix,m_target_dim);
	SG_DEBUG("Eigenproblem solving took %.5fs\n", time->cur_time_diff());
	delete time;

	SG_UNREF(features);
	return (CFeatures*)(new CDenseFeatures<float64_t>(new_feature_matrix));
}