_NcmFitLSJ ()

void
(*_NcmFitLSJ) (NcmFit *fit,
               NcmMatrix *J);

_NcmFitLSFJ ()

void
(*_NcmFitLSFJ) (NcmFit *fit,
                NcmVector *f,
                NcmMatrix *J);

_NcmFitM2lnLGrad ()

void
(*_NcmFitM2lnLGrad) (NcmFit *fit,
                     NcmVector *grad);

_NcmFitM2lnLValGrad ()

void
(*_NcmFitM2lnLValGrad) (NcmFit *fit,
                        gdouble *m2lnL,
                        NcmVector *grad);

NcmFitWriter ()

void
(*NcmFitWriter) (NcmFit *fit,
                 const gchar *msg);

NcmFitUpdater ()

void
(*NcmFitUpdater) (NcmFit *fit,
                  guint n);

NcmFitUpdateChange ()

void
(*NcmFitUpdateChange) (NcmFit *fit,
                       const gchar *msg);

ncm_fit_factory ()

NcmFit *
ncm_fit_factory (NcmFitType ftype,
                 gchar *algo_name,
                 NcmLikelihood *lh,
                 NcmMSet *mset,
                 NcmFitGradType gtype);

Creates a new NcmFit object.

ncm_fit_ref ()

NcmFit *
ncm_fit_ref (NcmFit *fit);

Increases the reference count of fit .

ncm_fit_copy_new ()

NcmFit *
ncm_fit_copy_new (NcmFit *fit,
                  NcmLikelihood *lh,
                  NcmMSet *mset,
                  NcmFitGradType gtype);

Duplicates the NcmFit object with new references for its contents.

ncm_fit_dup ()

NcmFit *
ncm_fit_dup (NcmFit *fit,
             NcmSerialize *ser);

Duplicates the NcmFit object duplicating all its contents.

ncm_fit_free ()

void
ncm_fit_free (NcmFit *fit);

Atomically decrements the reference count of fit by one. If the reference count drops to 0, all memory allocated by fit is released.

ncm_fit_clear ()

void
ncm_fit_clear (NcmFit **fit);

The reference count of fit is decreased and the pointer is set to NULL.

ncm_fit_set_sub_fit ()

void
ncm_fit_set_sub_fit (NcmFit *fit,
                     NcmFit *sub_fit);

Sets a NcmFit object to be used as subsidiary fit.

ncm_fit_get_sub_fit ()

NcmFit *
ncm_fit_get_sub_fit (NcmFit *fit);

Gets a NcmFit object to be used as subsidiary fit.

ncm_fit_has_sub_fit ()

gboolean
ncm_fit_has_sub_fit (NcmFit *fit);

Checks if fit has a subsidiary fit.

ncm_fit_set_grad_type ()

void
ncm_fit_set_grad_type (NcmFit *fit,
                       NcmFitGradType gtype);

Sets the differentiation method to be used.

ncm_fit_set_maxiter ()

void
ncm_fit_set_maxiter (NcmFit *fit,
                     guint maxiter);

Sets the maximum number of iterations.

ncm_fit_set_m2lnL_reltol ()

void
ncm_fit_set_m2lnL_reltol (NcmFit *fit,
                          gdouble tol);

Sets the relative tolerance for the m2lnL.

ncm_fit_set_m2lnL_abstol ()

void
ncm_fit_set_m2lnL_abstol (NcmFit *fit,
                          gdouble tol);

Sets the absolute tolerance for the m2lnL.

ncm_fit_set_params_reltol ()

void
ncm_fit_set_params_reltol (NcmFit *fit,
                           gdouble tol);

Sets the relative tolerance for the fitted parameters.

ncm_fit_set_messages ()

void
ncm_fit_set_messages (NcmFit *fit,
                      NcmFitRunMsgs mtype);

Sets the log level for the messages to be printed during the fit.

ncm_fit_get_grad_type ()

NcmFitGradType
ncm_fit_get_grad_type (NcmFit *fit);

Gets the differentiation method to be used.

ncm_fit_get_maxiter ()

guint
ncm_fit_get_maxiter (NcmFit *fit);

Gets the maximum number of iterations.

ncm_fit_get_m2lnL_reltol ()

gdouble
ncm_fit_get_m2lnL_reltol (NcmFit *fit);

Gets the relative tolerance for the m2lnL.

ncm_fit_get_m2lnL_abstol ()

gdouble
ncm_fit_get_m2lnL_abstol (NcmFit *fit);

Gets the absolute tolerance for the m2lnL.

ncm_fit_get_params_reltol ()

gdouble
ncm_fit_get_params_reltol (NcmFit *fit);

Gets the relative tolerance for the fitted parameters.

ncm_fit_get_messages ()

NcmFitRunMsgs
ncm_fit_get_messages (NcmFit *fit);

Gets the log level for the messages to be printed during the fit.

ncm_fit_is_least_squares ()

gboolean
ncm_fit_is_least_squares (NcmFit *fit);

Indicates if the least squares fitting is being used (TRUE) or not (FALSE).

ncm_fit_peek_mset ()

NcmMSet *
ncm_fit_peek_mset (NcmFit *fit);

Peeks the NcmMSet object.

ncm_fit_peek_state ()

NcmFitState *
ncm_fit_peek_state (NcmFit *fit);

Peeks the NcmFitState object.

ncm_fit_peek_likelihood ()

NcmLikelihood *
ncm_fit_peek_likelihood (NcmFit *fit);

Peeks the NcmLikelihood object.

ncm_fit_peek_diff ()

NcmDiff *
ncm_fit_peek_diff (NcmFit *fit);

Peeks the NcmDiff object.

ncm_fit_params_set ()

void
ncm_fit_params_set (NcmFit *fit,
                    guint i,
                    const gdouble x);

Sets the parameters vector.

ncm_fit_params_set_vector ()

void
ncm_fit_params_set_vector (NcmFit *fit,
                           NcmVector *x);

Sets the parameters vector.

ncm_fit_params_set_vector_offset ()

void
ncm_fit_params_set_vector_offset (NcmFit *fit,
                                  NcmVector *x,
                                  guint offset);

Sets the parameters from vector x starting at offset .

ncm_fit_params_set_array ()

void
ncm_fit_params_set_array (NcmFit *fit,
                          const gdouble *x);

Sets the parameters from array x .

ncm_fit_params_set_gsl_vector ()

void
ncm_fit_params_set_gsl_vector (NcmFit *fit,
                               const gsl_vector *x);

Sets the parameters from a gsl_vector x .

[skip]

ncm_fit_params_update ()

void
ncm_fit_params_update (NcmFit *fit);

Updates the parameters vector.

ncm_fit_add_equality_constraint ()

void
ncm_fit_add_equality_constraint (NcmFit *fit,
                                 NcmMSetFunc *func,
                                 const gdouble tot);

Adds an equality constraint with the function func and the tolerance tot .

ncm_fit_add_inequality_constraint ()

void
ncm_fit_add_inequality_constraint (NcmFit *fit,
                                   NcmMSetFunc *func,
                                   const gdouble tot);

Adds an inequality constraint with the function func and the tolerance tot .

ncm_fit_remove_equality_constraints ()

void
ncm_fit_remove_equality_constraints (NcmFit *fit);

Removes all equality constraints.

ncm_fit_remove_inequality_constraints ()

void
ncm_fit_remove_inequality_constraints (NcmFit *fit);

Removes all inequality constraints.

ncm_fit_equality_constraints_len ()

guint
ncm_fit_equality_constraints_len (NcmFit *fit);

Gets the number of equality constraints.

ncm_fit_inequality_constraints_len ()

guint
ncm_fit_inequality_constraints_len (NcmFit *fit);

Gets the number of inequality constraints.

ncm_fit_get_equality_constraint ()

void
ncm_fit_get_equality_constraint (NcmFit *fit,
                                 guint i,
                                 NcmMSetFunc **func,
                                 gdouble *tot);

Gets the equality constraint at index i .

ncm_fit_get_inequality_constraint ()

void
ncm_fit_get_inequality_constraint (NcmFit *fit,
                                   guint i,
                                   NcmMSetFunc **func,
                                   gdouble *tot);

Gets the inequality constraint at index i .

ncm_fit_get_desc ()

const gchar *
ncm_fit_get_desc (NcmFit *fit);

Gets the fit object description.

ncm_fit_set_logger ()

void
ncm_fit_set_logger (NcmFit *fit,
                    NcmFitWriter writer,
                    NcmFitUpdater updater,
                    NcmFitUpdateChange start_update,
                    NcmFitUpdateChange end_update);

Sets the logger functions. The writer function is called to write the messages to the log. The updater function is called to update the parameters. The start_update is called before the minimization starts and the end_update is called after the minimization ends.

ncm_fit_log_info ()

void
ncm_fit_log_info (NcmFit *fit);

Prints to the log the data set and the model set.

ncm_fit_log_covar ()

void
ncm_fit_log_covar (NcmFit *fit);

Prints to the log file the names and indices of the fitted parameters, their best-fit values, standard deviations and correlation matrix.

ncm_fit_log_start ()

void
ncm_fit_log_start (NcmFit *fit);

This function prints in the log the initial state.

ncm_fit_log_state ()

void
ncm_fit_log_state (NcmFit *fit);

This function prints in the log the current state.

ncm_fit_log_step ()

void
ncm_fit_log_step (NcmFit *fit);

This function prints in the log one step of the minimization.

ncm_fit_log_step_error ()

void
ncm_fit_log_step_error (NcmFit *fit,
                        const gchar *strerror,
                        ...);

This function prints in the log the error message.

ncm_fit_log_end ()

void
ncm_fit_log_end (NcmFit *fit);

This function prints in the log the precision with which the best-fit was found.

ncm_fit_data_m2lnL_val ()

void
ncm_fit_data_m2lnL_val (NcmFit *fit,
                        gdouble *data_m2lnL);

This function computes minus two times the logarithm base e of the likelihood using only the data set and not considering any prior. The result is set on data_m2lnL .

ncm_fit_priors_m2lnL_val ()

void
ncm_fit_priors_m2lnL_val (NcmFit *fit,
                          gdouble *priors_m2lnL);

This function computes minus two times the logarithm base e of the likelihood using the data set and taking into account the assumed priors. The result is set on priors_m2lnL .

ncm_fit_m2lnL_val ()

void
ncm_fit_m2lnL_val (NcmFit *fit,
                   gdouble *m2lnL);

Computes minus two times the logarithm base e of the likelihood.

ncm_fit_ls_f ()

void
ncm_fit_ls_f (NcmFit *fit,
              NcmVector *f);

Computes the residuals vector.

ncm_fit_m2lnL_grad ()

void
ncm_fit_m2lnL_grad (NcmFit *fit,
                    NcmVector *df);

Computes the gradient of the minus two times the logarithm base e of the likelihood.

ncm_fit_m2lnL_val_grad ()

void
ncm_fit_m2lnL_val_grad (NcmFit *fit,
                        gdouble *result,
                        NcmVector *df);

Computes the minus two times the logarithm base e of the likelihood and its gradient.

ncm_fit_ls_J ()

void
ncm_fit_ls_J (NcmFit *fit,
              NcmMatrix *J);

Computes the Jacobian matrix for the least squares problem.

ncm_fit_ls_f_J ()

void
ncm_fit_ls_f_J (NcmFit *fit,
                NcmVector *f,
                NcmMatrix *J);

Computes the residuals vector and the Jacobian matrix for the least squares problem.

ncm_fit_obs_fisher ()

void
ncm_fit_obs_fisher (NcmFit *fit);

Computes the covariance matrix using the inverse of the Hessian matrix $\partial_i\partial_j -\ln(L)$, where the derivatives are taken with respect to the free parameters. This function does not use the gradient defined in the fit object, it always uses the accurate numerical differentiation methods implemented in the NcmDiff object.

It sets both the covariance matrix and the Hessian matrix in the NcmFitState object associated to the fit object.

ncm_fit_ls_fisher ()

void
ncm_fit_ls_fisher (NcmFit *fit);

Computes the covariance matrix using the jacobian matrix and the least squares problem, see ncm_fit_ls_J(). Note that this function uses the gradient defined in the fit object using ncm_fit_set_grad_type() to compute the jacobian matrix.

This function is similar to ncm_fit_fisher() but it computes the covariance using the full jacobian matrix instead adding the individual contributions of each NcmData in the data set.

It sets both the covariance matrix in the NcmFitState object associated to the fit object.

ncm_fit_fisher ()

void
ncm_fit_fisher (NcmFit *fit);

Calculates the covariance from the Fisher matrix, see ncm_dataset_fisher_matrix(). Note that this function does not use the gradient defined in the fit object, it always uses the accurate numerical differentiation methods implemented in the NcmDiff object.

It sets both the covariance matrix in the NcmFitState object associated to the fit object.

ncm_fit_fisher_bias ()

NcmVector *
ncm_fit_fisher_bias (NcmFit *fit,
                     NcmVector *f_true);

Calculates the covariance from the Fisher matrix and the bias vector, see ncm_dataset_fisher_matrix_bias(). The bias vector is calculated using the the theory vector f_true as the true model expectation values.

Note that this function does not use the gradient defined in the fit object, it always uses the accurate numerical differentiation methods implemented in the NcmDiff object.

It sets the covariance matrix in the NcmFitState object associated to the fit object. Moreover, it computes the final bias vector, that is, the inverse of the Fisher matrix times the bias vector returns it.

ncm_fit_numdiff_m2lnL_covar ()

void
ncm_fit_numdiff_m2lnL_covar (NcmFit *fit);

Calcualtes the covariance matrix using the inverse of the Hessian matrix $\partial_i\partial_j -\ln(L)$, where the derivatives are taken with respect to the free parameters.

ncm_fit_numdiff_m2lnL_lndet_covar ()

gdouble
ncm_fit_numdiff_m2lnL_lndet_covar (NcmFit *fit);

Calculates the logarithm of the determinant of the covariance matrix using the inverse of the Hessian matrix $\partial_i\partial_j -\ln(L)$, where the derivatives are taken with respect to the free parameters.

It sets the covariance matrix in the NcmFitState object associated to the fit object.

ncm_fit_get_covar ()

NcmMatrix *
ncm_fit_get_covar (NcmFit *fit);

Returns a copy of the covariance matrix (pre-calculated by, e.g, ncm_fit_numdiff_m2lnL_covar()).

ncm_fit_covar_var ()

gdouble
ncm_fit_covar_var (NcmFit *fit,
                   NcmModelID mid,
                   guint pid);

Computes the variance of the fitted parameter pid of the model mid .

ncm_fit_covar_sd ()

gdouble
ncm_fit_covar_sd (NcmFit *fit,
                  NcmModelID mid,
                  guint pid);

Computes the standard deviation of the fitted parameter pid of the model mid .

ncm_fit_covar_cov ()

gdouble
ncm_fit_covar_cov (NcmFit *fit,
                   NcmModelID mid1,
                   guint pid1,
                   NcmModelID mid2,
                   guint pid2);

Computes the covariance between the parameters pid1 and pid2 of the models mid1 and mid2 , respectively.

ncm_fit_covar_cor ()

gdouble
ncm_fit_covar_cor (NcmFit *fit,
                   NcmModelID mid1,
                   guint pid1,
                   NcmModelID mid2,
                   guint pid2);

Computes the correlation between the parameters pid1 and pid2 of the models mid1 and mid2 , respectively.

ncm_fit_covar_fparam_var ()

gdouble
ncm_fit_covar_fparam_var (NcmFit *fit,
                          guint fpi);

Computes the variance of the fitted parameter fpi . This index refers to the list of all FREE parameters set in the MSet.

See also the similar function ncm_fit_covar_var() to which one has to provide the respective model of the parameter.

ncm_fit_covar_fparam_sd ()

gdouble
ncm_fit_covar_fparam_sd (NcmFit *fit,
                         guint fpi);

Computes the standard deviation of the fitted parameter fpi . This index refers to the list of all FREE parameters set in the MSet.

See also the similar function ncm_fit_covar_sd() to which one has to provide the respective model of the parameter.

ncm_fit_covar_fparam_cov ()

gdouble
ncm_fit_covar_fparam_cov (NcmFit *fit,
                          guint fpi1,
                          guint fpi2);

Computes the covariance between the fitted parameters fpi1 and fpi2 . These indices refers to the list of all FREE parameters set in the MSet.

See also the similar function ncm_fit_covar_cov() to which one has to provide the respective models of the parameters.

ncm_fit_covar_fparam_cor ()

gdouble
ncm_fit_covar_fparam_cor (NcmFit *fit,
                          guint fpi1,
                          guint fpi2);

Computes the correlation between the fitted parameters fpi1 and fpi2 . These indices refers to the list of all FREE parameters set in the MSet.

See also the similar function ncm_fit_covar_cor() to which one has to provide the respective models of the parameters.

ncm_fit_reset ()

void
ncm_fit_reset (NcmFit *fit);

Resets the fit.

ncm_fit_run ()

gboolean
ncm_fit_run (NcmFit *fit,
             NcmFitRunMsgs mtype);

Computes the minimization.

ncm_fit_run_restart ()

gboolean
ncm_fit_run_restart (NcmFit *fit,
                     NcmFitRunMsgs mtype,
                     const gdouble abstol,
                     const gdouble reltol,
                     NcmMSet *save_mset,
                     const gchar *mset_file);

Re-runs the fit until the difference between fits are less than the required tolerance, i.e., $$ m2lnL_{i-1} - m2lnL_i < \mathrm{abstol} + \mathrm{reltol}\vert m2lnL_{i-1}\vert. $$

ncm_fit_lr_test_range ()

NcmMatrix *
ncm_fit_lr_test_range (NcmFit *fit,
                       NcmModelID mid,
                       guint pid,
                       gdouble start,
                       gdouble stop,
                       guint nsteps);

Computes the likelihood ratio test for the parameter pid of the model mid in the interval [start , stop ] subdivided by nsteps . The function returns a NcmMatrix with the following columns:

ncm_fit_lr_test ()

gdouble
ncm_fit_lr_test (NcmFit *fit,
                 NcmModelID mid,
                 guint pid,
                 gdouble val,
                 gint dof);

Computes the likelihood ratio test for the parameter pid of the model mid with the value val . The function returns the probability of the null hypothesis assuming a chi-squared distribution with dof degrees of freedom. That is, it computes the left tail of the chi-squared distribution with dof degrees of freedom.

ncm_fit_chisq_test ()

gdouble
ncm_fit_chisq_test (NcmFit *fit,
                    size_t bins);

ncm_fit_function_error ()

void
ncm_fit_function_error (NcmFit *fit,
                        NcmMSetFunc *func,
                        gdouble *x,
                        gdouble *f,
                        gdouble *sigma_f);

Propagates the errors in the free parameters to the function func . If no free parameters are set in the NcmMSet object associated to the fit object, the computed error is 0.

Note that the covariance matrix must be calculated before calling this function. Otherwise, it will raise an error.

NCM_TYPE_FIT

#define NCM_TYPE_FIT (ncm_fit_get_type ())

enum NcmFitType

Defines the subclasse of NcmFit to be used.

enum NcmFitGradType

Defines the type of gradient calculation.

enum NcmFitRunMsgs

Defines the type of messages to be printed during the fit.

struct NcmFitClass

struct NcmFitClass {
};

NCM_FIT_DEFAULT_M2LNL_RELTOL

#define NCM_FIT_DEFAULT_M2LNL_RELTOL (1e-8)

NCM_FIT_DEFAULT_M2LNL_ABSTOL

#define NCM_FIT_DEFAULT_M2LNL_ABSTOL (0.0)

NCM_FIT_DEFAULT_PARAMS_RELTOL

#define NCM_FIT_DEFAULT_PARAMS_RELTOL (1e-5)

NCM_FIT_DEFAULT_MAXITER

#define NCM_FIT_DEFAULT_MAXITER 100000

NcmFit

typedef struct _NcmFit NcmFit;

The “equality-constraints” property

  “equality-constraints”     NcmObjArray *

Equality constraints array.

Owner: NcmFit

Flags: Read / Write

The “equality-constraints-tot” property

  “equality-constraints-tot” NcmVector *

Equality constraints tolerance.

Owner: NcmFit

Flags: Read / Write

The “grad-type” property

  “grad-type”                NcmFitGradType

Differentiation method.

Owner: NcmFit

Flags: Read / Write / Construct

Default value: NCM_FIT_GRAD_NUMDIFF_FORWARD

The “inequality-constraints” property

  “inequality-constraints”   NcmObjArray *

Inequality constraints array.

Owner: NcmFit

Flags: Read / Write

The “inequality-constraints-tot” property

  “inequality-constraints-tot” NcmVector *

Inequality constraints tolerance.

Owner: NcmFit

Flags: Read / Write

The “likelihood” property

  “likelihood”               NcmLikelihood *

Likelihood object.

Owner: NcmFit

Flags: Read / Write / Construct Only

The “m2lnL-abstol” property

  “m2lnL-abstol”             double

Absolute tolarence in m2lnL.

Owner: NcmFit

Flags: Read / Write / Construct

Allowed values: >= 0

Default value: 0

The “m2lnL-reltol” property

  “m2lnL-reltol”             double

Relative tolarence in m2lnL.

Owner: NcmFit

Flags: Read / Write / Construct

Allowed values: >= 0

Default value: 1e-08

The “maxiter” property

  “maxiter”                  guint

Maximum number of interations.

Owner: NcmFit

Flags: Read / Write / Construct

Default value: 100000

The “mset” property

  “mset”                     NcmMSet *

Model set object.

Owner: NcmFit

Flags: Read / Write / Construct Only

The “params-reltol” property

  “params-reltol”            double

Relative tolarence in fitted parameters.

Owner: NcmFit

Flags: Read / Write / Construct

Allowed values: >= 0

Default value: 1e-05

The “state” property

  “state”                    NcmFitState *

Fit state object.

Owner: NcmFit

Flags: Read / Write / Construct Only

The “sub-fit” property

  “sub-fit”                  NcmFit *

Subsidiary fit.

Owner: NcmFit

Flags: Read / Write