Class
NumCosmoMathData
Description [src]
abstract class NumCosmoMath.Data : GObject.Object
{
/* No available fields */
}Abstract class for implementing data objects.
The NcmData object represent generic data. This is the root object used when
building a statistical analysis. Every implementation of NcmData envolves
the methods described in NcmDataClass.
A NcmData must implement, at least, the method ncm_data_m2lnL_val() or
ncm_data_leastsquares_f() to perform respectively likelihood or least
squares analysis.
Instance methods
ncm_data_bootstrap_create
Creates a bootstrap object inside of data. Uses the default bsize == fsize.
ncm_data_fisher_matrix
Calculates the Fisher-information matrix I. Note that this is an
additive quantity, i.e., the Fisher-information matrix of different
and uncorrrelated data sets can be added.
ncm_data_fisher_matrix_bias
Calculates the Fisher-information matrix I and the bias vector f
assuming that the true theoretical model is f_true. Note that these
are additive quantities, i.e., the Fisher-information matrix and
the bias vector of different and uncorrrelated data sets can be added.
ncm_data_has_mean_vector
This method returns TRUE if the likelihood implements
the ncm_data_mean_vector() virtual method.
ncm_data_inv_cov_UH
Given the Cholesky decomposition of the inverse covariance $C^{-1} = L\cdot U$ this function returns in-place the product $U\cdot H$.
ncm_data_inv_cov_Uf
Given the Cholesky decomposition of the inverse covariance $C^{-1} = L\cdot U$ this function returns in-place the product $U\cdot\vec{f}$.
ncm_data_leastsquares_f
Calculates the least squares vector $\vec{f}$ using the models contained in
mset and set the results in f.
ncm_data_m2lnL_val
Calculates the value of $-2\ln(L)$, where $L$ represents the likelihood of
the data given the models in mset. The result is stored in m2lnL.
ncm_data_mean_vector
Calculates the Gaussian mean vector (for non-Gaussian distribution it should calculate the Gaussian approximated mean of the actual distribution).
ncm_data_resample
Resample data in data from the models contained in mset.
During the resampling the data is marked as resampling
and prepare is called.
ncm_data_take_desc
Sets the data description desc without copying it, the desc memory will
be freed (g_free) when the object is freed.
Signals
Signals inherited from GObject (1)
GObject::notify
The notify signal is emitted on an object when one of its properties has its value set through g_object_set_property(), g_object_set(), et al.
Class structure
struct NumCosmoMathDataClass {
guint (* get_length) (
NcmData* data
);
guint (* get_dof) (
NcmData* data
);
void (* begin) (
NcmData* data
);
void (* prepare) (
NcmData* data,
NcmMSet* mset
);
void (* resample) (
NcmData* data,
NcmMSet* mset,
NcmRNG* rng
);
void (* leastsquares_f) (
NcmData* data,
NcmMSet* mset,
NcmVector* f
);
void (* m2lnL_val) (
NcmData* data,
NcmMSet* mset,
gdouble* m2lnL
);
void (* mean_vector) (
NcmData* data,
NcmMSet* mset,
NcmVector* mu
);
void (* inv_cov_UH) (
NcmData* data,
NcmMSet* mset,
NcmMatrix* H
);
void (* inv_cov_Uf) (
NcmData* data,
NcmMSet* mset,
NcmVector* f
);
void (* fisher_matrix_bias) (
NcmData* data,
NcmMSet* mset,
NcmVector* f_true,
NcmMatrix** IM,
NcmVector** delta_theta
);
}No description available.
Class members
get_length: guint (* get_length) ( NcmData* data )No description available.
get_dof: guint (* get_dof) ( NcmData* data )No description available.
begin: void (* begin) ( NcmData* data )No description available.
prepare: void (* prepare) ( NcmData* data, NcmMSet* mset )No description available.
resample: void (* resample) ( NcmData* data, NcmMSet* mset, NcmRNG* rng )No description available.
leastsquares_f: void (* leastsquares_f) ( NcmData* data, NcmMSet* mset, NcmVector* f )No description available.
m2lnL_val: void (* m2lnL_val) ( NcmData* data, NcmMSet* mset, gdouble* m2lnL )No description available.
mean_vector: void (* mean_vector) ( NcmData* data, NcmMSet* mset, NcmVector* mu )No description available.
inv_cov_UH: void (* inv_cov_UH) ( NcmData* data, NcmMSet* mset, NcmMatrix* H )No description available.
inv_cov_Uf: void (* inv_cov_Uf) ( NcmData* data, NcmMSet* mset, NcmVector* f )No description available.
fisher_matrix_bias: void (* fisher_matrix_bias) ( NcmData* data, NcmMSet* mset, NcmVector* f_true, NcmMatrix** IM, NcmVector** delta_theta )No description available.
Virtual methods
NumCosmoMath.DataClass.fisher_matrix
Calculates the Fisher-information matrix I. Note that this is an
additive quantity, i.e., the Fisher-information matrix of different
and uncorrrelated data sets can be added.
NumCosmoMath.DataClass.fisher_matrix_bias
Calculates the Fisher-information matrix I and the bias vector f
assuming that the true theoretical model is f_true. Note that these
are additive quantities, i.e., the Fisher-information matrix and
the bias vector of different and uncorrrelated data sets can be added.
NumCosmoMath.DataClass.inv_cov_UH
Given the Cholesky decomposition of the inverse covariance $C^{-1} = L\cdot U$ this function returns in-place the product $U\cdot H$.
NumCosmoMath.DataClass.inv_cov_Uf
Given the Cholesky decomposition of the inverse covariance $C^{-1} = L\cdot U$ this function returns in-place the product $U\cdot\vec{f}$.
NumCosmoMath.DataClass.leastsquares_f
Calculates the least squares vector $\vec{f}$ using the models contained in
mset and set the results in f.
NumCosmoMath.DataClass.m2lnL_val
Calculates the value of $-2\ln(L)$, where $L$ represents the likelihood of
the data given the models in mset. The result is stored in m2lnL.
NumCosmoMath.DataClass.mean_vector
Calculates the Gaussian mean vector (for non-Gaussian distribution it should calculate the Gaussian approximated mean of the actual distribution).
NumCosmoMath.DataClass.prepare
Prepare all models in data necessary for the statistical calculations.
NumCosmoMath.DataClass.resample
Resample data in data from the models contained in mset.
During the resampling the data is marked as resampling
and prepare is called.