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modify a method name and fix the indentation issue in header file
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@yorkerlin
yorkerlin committed Mar 21, 2014
1 parent 700a727 commit afcd973
Showing 1 changed file with 140 additions and 141 deletions.
281 changes: 140 additions & 141 deletions src/shogun/machine/gp/LaplacianInferenceMethodWithLBFGS.h
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* The views and conclusions contained in the software and documentation are those
* of the authors and should not be interpreted as representing official policies,
* either expressed or implied, of the Shogun Development Team.
*
* Code adapted from Gaussian Process Machine Learning Toolbox
* http://www.gaussianprocess.org/gpml/code/matlab/doc/
*/
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class CLaplacianInferenceMethodWithLBFGS: public CLaplacianInferenceMethod
{
public:
/* default constructor */
CLaplacianInferenceMethodWithLBFGS();

/* constructor
*
* @param kernel covariance function
* @param features features to use in inference
* @param mean mean function
* @param labels labels of the features
* @param model Likelihood model to use
*/
CLaplacianInferenceMethodWithLBFGS(CKernel* kernel,
CFeatures* features,
CMeanFunction* mean,
CLabels* labels,
CLikelihoodModel* model);

virtual ~CLaplacianInferenceMethodWithLBFGS();

/* returns the name of the inference method
*
* @return name LaplacianWithLBFGS
*/
virtual const char* get_name() const
{return "LaplacianInferenceMethodWithLBFGS";}

/* set L-BFGS parameters
* For details please see shogun/optimization/lbfgs/lbfgs.h
* @param m The number of corrections to approximate the inverse hessian matrix.
* Default value is 100.
* @param max_linesearch The maximum number of trials to do line search for each L-BFGS update.
* Default value is 1000.
* @param linesearch The line search algorithm.
* Default value is using the Morethuente line search
* @param max_iterations The maximum number of iterations for L-BFGS update.
* Default value is 1000.
* @param delta Delta for convergence test based on the change of function value.
* Default value is 0.
* @param past Distance for delta-based convergence test.
* Default value is 0.
* @param epsilon Epsilon for convergence test based on the change of gradient.
* Default value is 1e-5
* @param enable_newton_if_fail if LBFGS optimizer fails, should we use Newton method.
* Default value is true.
* @param min_step The minimum step of the line search.
* The default value is 1e-20
* @param max_step The maximum step of the line search.
* The default value is 1e+20
* @param ftol A parameter used in Armijo condition.
* Default value is 1e-4
* @param wolfe A parameter used in curvature condition.
* Default value is 0.9
* @param gtol A parameter used in Morethuente linesearch to control the accuracy.
* Default value is 0.9
* @param xtol The machine precision for floating-point values.
* Default value is 1e-16.
* @param orthantwise_c Coeefficient for the L1 norm of variables.
* This parameter should be set to zero for standard minimization problems.
* Setting this parameter to a positive value activates
* Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) method. Default value is 0.
* @param orthantwise_start Start index for computing L1 norm of the variables.
* This parameter is valid only for OWL-QN method. Default value is 0.
* @param orthantwise_end End index for computing L1 norm of the variables.
* Default value is 1.
*/
virtual void set_lbfgs_parameter(int m = 100,
int max_linesearch = 1000,
int linesearch = LBFGS_LINESEARCH_DEFAULT,
int max_iterations = 1000,
float64_t delta = 0.0,
int past = 0,
float64_t epsilon = 1e-5,
bool enable_newton_if_fail = true,
float64_t min_step = 1e-20,
float64_t max_step = 1e+20,
float64_t ftol = 1e-4,
float64_t wolfe = 0.9,
float64_t gtol = 0.9,
float64_t xtol = 1e-16,
float64_t orthantwise_c = 0.0,
int orthantwise_start = 0,
int orthantwise_end = 1);
/* default constructor */
CLaplacianInferenceMethodWithLBFGS();

/* constructor
*
* @param kernel covariance function
* @param features features to use in inference
* @param mean mean function
* @param labels labels of the features
* @param model Likelihood model to use
*/
CLaplacianInferenceMethodWithLBFGS(CKernel* kernel,
CFeatures* features,
CMeanFunction* mean,
CLabels* labels,
CLikelihoodModel* model);

virtual ~CLaplacianInferenceMethodWithLBFGS();

/* returns the name of the inference method
*
* @return name LaplacianWithLBFGS
*/
virtual const char* get_name() const
{return "LaplacianInferenceMethodWithLBFGS";}

/* set L-BFGS parameters
* For details please see shogun/optimization/lbfgs/lbfgs.h
* @param m The number of corrections to approximate the inverse hessian matrix.
* Default value is 100.
* @param max_linesearch The maximum number of trials to do line search for each L-BFGS update.
* Default value is 1000.
* @param linesearch The line search algorithm.
* Default value is using the Morethuente line search
* @param max_iterations The maximum number of iterations for L-BFGS update.
* Default value is 1000.
* @param delta Delta for convergence test based on the change of function value.
* Default value is 0.
* @param past Distance for delta-based convergence test.
* Default value is 0.
* @param epsilon Epsilon for convergence test based on the change of gradient.
* Default value is 1e-5
* @param enable_newton_if_fail if LBFGS optimizer fails, should we use Newton method.
* Default value is true.
* @param min_step The minimum step of the line search.
* The default value is 1e-20
* @param max_step The maximum step of the line search.
* The default value is 1e+20
* @param ftol A parameter used in Armijo condition.
* Default value is 1e-4
* @param wolfe A parameter used in curvature condition.
* Default value is 0.9
* @param gtol A parameter used in Morethuente linesearch to control the accuracy.
* Default value is 0.9
* @param xtol The machine precision for floating-point values.
* Default value is 1e-16.
* @param orthantwise_c Coeefficient for the L1 norm of variables.
* This parameter should be set to zero for standard minimization problems.
* Setting this parameter to a positive value activates
* Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) method. Default value is 0.
* @param orthantwise_start Start index for computing L1 norm of the variables.
* This parameter is valid only for OWL-QN method. Default value is 0.
* @param orthantwise_end End index for computing L1 norm of the variables.
* Default value is 1.
*/
virtual void set_lbfgs_parameters(int m = 100,
int max_linesearch = 1000,
int linesearch = LBFGS_LINESEARCH_DEFAULT,
int max_iterations = 1000,
float64_t delta = 0.0,
int past = 0,
float64_t epsilon = 1e-5,
bool enable_newton_if_fail = true,
float64_t min_step = 1e-20,
float64_t max_step = 1e+20,
float64_t ftol = 1e-4,
float64_t wolfe = 0.9,
float64_t gtol = 0.9,
float64_t xtol = 1e-16,
float64_t orthantwise_c = 0.0,
int orthantwise_start = 0,
int orthantwise_end = 1);


protected:
/* update alpha using the LBFGS method*/
virtual void update_alpha();
/* update alpha using the LBFGS method*/
virtual void update_alpha();

private:
/* lbfgs_parameter for the LBFGS build-in function*/
lbfgs_parameter_t m_lbfgs_param;

/* a parameter used to compute function value and gradient for LBFGS update*/
SGVector<float64_t> * m_mean_f;
/* a parameter used to compute function value and gradient for LBFGS update*/
SGVector<float64_t> * m_mean_f;

/* should we enable the original Newton method
* if the L-BFGS method fails
* */
bool m_enable_newton_if_fail;
/* should we enable the original Newton method
* if the L-BFGS method fails
* */
bool m_enable_newton_if_fail;

/* The number of corrections to approximate the inverse hessian matrix.*/
int m_m;
/* The number of corrections to approximate the inverse hessian matrix.*/
int m_m;

/* The maximum number of trials to do line search for each L-BFGS update.*/
int m_max_linesearch;
/* The maximum number of trials to do line search for each L-BFGS update.*/
int m_max_linesearch;

/* The line search algorithm.*/
int m_linesearch;
/* The line search algorithm.*/
int m_linesearch;

/* The maximum number of iterations for L-BFGS update.*/
int m_max_iterations;
/* The maximum number of iterations for L-BFGS update.*/
int m_max_iterations;

/* Delta for convergence test based on the change of function value.*/
float64_t m_delta;
/* Delta for convergence test based on the change of function value.*/
float64_t m_delta;

/* Distance for delta-based convergence test.*/
int m_past;
/* Distance for delta-based convergence test.*/
int m_past;

/* Epsilon for convergence test based on the change of gradient.*/
float64_t m_epsilon;
/* Epsilon for convergence test based on the change of gradient.*/
float64_t m_epsilon;

/* The minimum step of the line search.*/
float64_t m_min_step;
/* The minimum step of the line search.*/
float64_t m_min_step;

/* The maximum step of the line search.*/
float64_t m_max_step;
/* The maximum step of the line search.*/
float64_t m_max_step;

/* A parameter used in Armijo condition.*/
float64_t m_ftol;
/* A parameter used in Armijo condition.*/
float64_t m_ftol;

/* A parameter used in curvature condition.*/
float64_t m_wolfe;
/* A parameter used in curvature condition.*/
float64_t m_wolfe;

/* A parameter used in Morethuente linesearch to control the accuracy.*/
float64_t m_gtol;
/* A parameter used in Morethuente linesearch to control the accuracy.*/
float64_t m_gtol;

/* The machine precision for floating-point values.*/
float64_t m_xtol;
/* The machine precision for floating-point values.*/
float64_t m_xtol;

/* Coeefficient for the L1 norm of variables.*/
float64_t m_orthantwise_c;
/* Coeefficient for the L1 norm of variables.*/
float64_t m_orthantwise_c;

/* Start index for computing L1 norm of the variables.*/
int m_orthantwise_start;
/* Start index for computing L1 norm of the variables.*/
int m_orthantwise_start;

/* End index for computing L1 norm of the variables.*/
int m_orthantwise_end;
/* End index for computing L1 norm of the variables.*/
int m_orthantwise_end;

void init();
void init();

/* helper function is passed to the LBFGS API
* Note that this function should be static and
* private.
* */
static float64_t evaluate(void *obj,
const float64_t *alpha,
float64_t *gradient,
const int dim,
const float64_t step);
/* helper function is passed to the LBFGS API
* Note that this function should be static and
* private.
* */
static float64_t evaluate(void *obj,
const float64_t *alpha,
float64_t *gradient,
const int dim,
const float64_t step);

/* compute the gradient given the current alpha*/
void get_gradient_wrt_alpha(Eigen::Map<Eigen::VectorXd>* alpha,
Eigen::Map<Eigen::VectorXd>* gradient);
/* compute the gradient given the current alpha*/
void get_gradient_wrt_alpha(Eigen::Map<Eigen::VectorXd>* alpha,
Eigen::Map<Eigen::VectorXd>* gradient);

/* compute the function value given the current alpha*/
void get_psi_wrt_alpha(Eigen::Map<Eigen::VectorXd>* alpha,
float64_t* psi);
/* compute the function value given the current alpha*/
void get_psi_wrt_alpha(Eigen::Map<Eigen::VectorXd>* alpha,
float64_t* psi);
};

} /* namespace shogun */
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