Class interpm_krige_optim (o2scl)

O2scl : Class List

template<class vec_t = boost::numeric::ublas::vector<double>, class mat_x_t = o2scl::const_matrix_view_table<>, class mat_x_row_t = const const_matrix_row_gen<o2scl::const_matrix_view_table<>>, class mat_y_t = o2scl::matrix_view_table<>, class mat_y_col_t = const matrix_column_gen<o2scl::matrix_view_table<>>, class mat_inv_kxx_t = boost::numeric::ublas::matrix<double>, class mat_inv_t = o2scl_linalg::matrix_invert_det_cholesky<boost::numeric::ublas::matrix<double>>, class vec3_t = std::vector<std::vector<std::vector<double>>>>
class interpm_krige_optim : public o2scl::interpm_base<boost::numeric::ublas::vector<double>, o2scl::const_matrix_view_table<>, o2scl::matrix_view_table<>>

Multi-dimensional interpolation using Kriging (a.k.a. Gaussian process interpolation)

See also the Higher-dimensional Interpolation section of the User’s guide.

Settings

bool timing

If true, output timing results.

bool full_min

If true, use the full minimizer.

bool skip_optim

If true, skip optimization.

bool rescale

If true, then the data will be automatically rescaled (default true)

bool keep_matrix

If true, keep \( K^{-1} \) (default true)

Minimizers and settings

mmin_simp2<multi_funct, ubvector> def_mmin

Default minimizer.

diff_evo_adapt alt_mmin

Alternate minimizer.

bool use_alt_mmin

If true, use the alternate minimizer.

inline void set_mmin(mmin_base<multi_funct, multi_funct, ubvector> &mb)

Set the minimizer to use.

Function to minimize and related options

size_t mode

Function to minimize (default mode_loo_cv)

size_t loo_npts

Number of points to test for cross validation (default 100)

static const size_t mode_loo_cv_bf = 1

Leave-one-out cross validation (brute force version)

static const size_t mode_max_lml = 2

Minus Log-marginal-likelihood.

static const size_t mode_loo_cv = 3

Leave-one-out cross validation (default)

static const size_t mode_final = 10

No optimization (for internal use)

Covariance function

std::vector<std::shared_ptr<mcovar_base<vec_t, mat_x_row_t>>> cf

Pointer to the covariance function.

inline int set_covar(std::vector<std::shared_ptr<mcovar_base<vec_t, mat_x_row_t>>> covar, vec3_t &param_lists)

Set the covariance function and parameter lists.

Other functions

inline virtual int addl_const(size_t iout, double &ret)

Additional constraints to add to the fit.

inline virtual double qual_fun(size_t iout, int &success)

Function to optimize the covariance parameters.

inline double min_fun(size_t iout, size_t n, const ubvector &v, double max_val)

Minimization function for the covariance parameters.

Constructor and destructor

inline interpm_krige_optim()

inline virtual ~interpm_krige_optim()

Basic interpolation functions

inline virtual int set_data(size_t n_in, size_t n_out, size_t n_pts, mat_x_t &user_x, mat_y_t &user_y)

Initialize the data for the interpolation.

inline virtual int eval(const vec_t &xp, vec_t &yp) const

Evaluate the interpolation at point x, returning y.

inline virtual int eval_unc(const vec_t &xp, vec_t &yp, vec_t &y_unc) const

Evaluate the interpolation at point x, returning y and the uncertainties in y_unc.

template<class vec4_t>
inline int eval_tl(const vec_t &x0, vec4_t &y0) const

Given input vector x store the result of the interpolation in y.

Uncertainties and derivatives

template<class vec4_t>
inline void sigma(const vec_t &x0, vec4_t &dy0) const

Return the interpolation uncertainty from the Gaussian process.

template<class vec2_t, class vec4_t>
inline void deriv(const vec2_t &x0, vec4_t &y0, size_t ix)

Given input vector x store the result of the interpolation in y.

template<class vec2_t, class vec4_t>
inline void deriv2(const vec2_t &x0, vec4_t &y0, size_t ix, size_t iy)

Given input vector x store the result of the interpolation in y.

Public Types

typedef boost::numeric::ublas::matrix<double> ubmatrix

Internal matrix type for the Nelder-Mead simplex.

typedef interpm_krige_optim<vec_t, mat_x_t, mat_x_row_t, mat_y_t, mat_y_col_t, mat_inv_kxx_t, mat_inv_t, vec3_t> class_t

Typedef for this type.

Protected Attributes

std::vector<ubvector> Kinvf

Inverse covariance matrix times function vector.

std::vector<mat_inv_kxx_t> inv_KXX

The inverse of the covariance matrix for each output quantity.

mat_inv_t mi

The matrix inversion object.

vec3_t plists

The list of parameter values.

The first index runs over output quantities, the second over different sets of values, and the third over parameters.

std::vector<double> qual

The quality factor of the optimization for each output function.

mmin_base<multi_funct, multi_funct, ubvector> *mp

Pointer to the user-specified minimizer.

mat_x_t x

The input data.

mat_y_t y

The output data.

bool data_set

True if the data has been specified.

ubvector mean_y

The output means for rescaling.

ubvector std_y

The output standard deviations for rescaling.