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SHOGUN
v3.0.0
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SHOGUN
v3.0.0
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Class that contains certain functions related to statistics, such as probability/cumulative distribution functions, different statistics, etc.
Definition at line 31 of file Statistics.h.
Classes | |
| struct | SigmoidParamters |
Public Member Functions | |
| virtual const char * | get_name () const |
| virtual CSGObject * | shallow_copy () const |
| virtual CSGObject * | deep_copy () const |
| virtual bool | is_generic (EPrimitiveType *generic) const |
| template<class T > | |
| void | set_generic () |
| void | unset_generic () |
| virtual void | print_serializable (const char *prefix="") |
| virtual bool | save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter()) |
| virtual bool | load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter()) |
| DynArray< TParameter * > * | load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="") |
| DynArray< TParameter * > * | load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="") |
| void | map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos) |
| void | set_global_io (SGIO *io) |
| SGIO * | get_global_io () |
| void | set_global_parallel (Parallel *parallel) |
| Parallel * | get_global_parallel () |
| void | set_global_version (Version *version) |
| Version * | get_global_version () |
| SGStringList< char > | get_modelsel_names () |
| void | print_modsel_params () |
| char * | get_modsel_param_descr (const char *param_name) |
| index_t | get_modsel_param_index (const char *param_name) |
| void | build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict) |
| virtual bool | update_parameter_hash () |
| virtual bool | equals (CSGObject *other, float64_t accuracy=0.0) |
| virtual CSGObject * | clone () |
Public Attributes | |
| SGIO * | io |
| Parallel * | parallel |
| Version * | version |
| Parameter * | m_parameters |
| Parameter * | m_model_selection_parameters |
| Parameter * | m_gradient_parameters |
| ParameterMap * | m_parameter_map |
| uint32_t | m_hash |
Protected Member Functions | |
| virtual TParameter * | migrate (DynArray< TParameter * > *param_base, const SGParamInfo *target) |
| virtual void | one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL) |
| virtual void | load_serializable_pre () throw (ShogunException) |
| virtual void | load_serializable_post () throw (ShogunException) |
| virtual void | save_serializable_pre () throw (ShogunException) |
| virtual void | save_serializable_post () throw (ShogunException) |
Static Protected Member Functions | |
| static float64_t | ibetaf_incompletebetaps (float64_t a, float64_t b, float64_t x, float64_t maxgam) |
| static float64_t | ibetaf_incompletebetafe (float64_t a, float64_t b, float64_t x, float64_t big, float64_t biginv) |
| static float64_t | ibetaf_incompletebetafe2 (float64_t a, float64_t b, float64_t x, float64_t big, float64_t biginv) |
| static bool | equal (float64_t a, float64_t b) |
| static bool | not_equal (float64_t a, float64_t b) |
| static bool | less (float64_t a, float64_t b) |
| static bool | less_equal (float64_t a, float64_t b) |
| static bool | greater (float64_t a, float64_t b) |
| static bool | greater_equal (float64_t a, float64_t b) |
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inherited |
Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.
| dict | dictionary of parameters to be built. |
Definition at line 1196 of file SGObject.cpp.
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virtualinherited |
Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.
Definition at line 1313 of file SGObject.cpp.
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Calculates the sample mean of a given set of samples and also computes the confidence interval for the actual mean for a given p-value, assuming that the actual variance and mean are unknown (These are estimated by the samples). Based on Student's t-distribution.
Only for normally distributed data
| values | vector of values that are used for calculations |
| alpha | actual mean lies in confidence interval with (1-alpha)*100% |
| conf_int_low | lower confidence interval border is written here |
| conf_int_up | upper confidence interval border is written here |
Definition at line 341 of file Statistics.cpp.
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Computes the empirical estimate of the covariance matrix of the given data which is organized as num_cols variables with num_rows observations.
Data is centered before matrix is computed. May be done in place. In this case, the observation matrix is changed (centered).
Given sample matrix \(X\), first, column mean is removed to create \(\bar X\). Then \(\text{cov}(X)=(X-\bar X)^T(X - \bar X)\) is returned.
Needs SHOGUN to be compiled with LAPACK.
| observations | data matrix organized as one variable per column |
| in_place | optional, if set to true, observations matrix will be centered, if false, a copy will be created an centered. |
Definition at line 317 of file Statistics.cpp.
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virtualinherited |
A deep copy. All the instance variables will also be copied.
Definition at line 160 of file SGObject.h.
Derivative of the log gamma function.
| x | input |
Definition at line 1970 of file Statistics.cpp.
Definition at line 1934 of file Statistics.cpp.
method to make ALGLIB integration easier
Definition at line 592 of file Statistics.h.
Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!
May be overwritten but please do with care! Should not be necessary in most cases.
| other | object to compare with |
| accuracy | accuracy to use for comparison (optional) |
Definition at line 1217 of file SGObject.cpp.
Error function
The integral is
\[ \text{error\_function}(x)= \frac{2}{\sqrt{pi}}\int_0^x \exp (-t^2) dt \]
For \(0 \leq |x| < 1, \text{error\_function}(x) = x \frac{P4(x^2)}{Q5(x^2)}\) otherwise \(\text{error\_function}(x) = 1 - \text{error\_function\_complement}(x)\).
Taken from ALGLIB under gpl2+
Definition at line 1708 of file Statistics.cpp.
Complementary error function
\[ 1 - \text{error\_function}(x) = \text{error\_function\_complement}(x)= \frac{2}{\sqrt{\pi}}\int_x^\infty \exp\left(-t^2 \right)dt \]
For small \(x\), \(\text{error\_function\_complement}(x) = 1 - \text{error\_function}(x)\); otherwise rational approximations are computed.
Taken from ALGLIB under gpl2+
Definition at line 1747 of file Statistics.cpp.
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fisher's test for multiple 2x3 tables
| tables |
Definition at line 1790 of file Statistics.cpp.
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Converts a given vector of scores to calibrated probabilities by fitting a sigmoid function using the method described in Lin, H., Lin, C., and Weng, R. (2007). A note on Platt's probabilistic outputs for support vector machines.
This can be used to transform scores to probabilities as setting \(pf=x*a+b\) for a given score \(x\) and computing \(\frac{\exp(-f)}{1+}exp(-f)}\) if \(f\geq 0\) and \(\frac{1}{(1+\exp(f)}\) otherwise
| scores | scores to fit the sigmoid to |
Definition at line 2191 of file Statistics.cpp.
Evaluates the CDF of the gamma distribution with given parameters \(a, b\) at \(x\). Based on Wikipedia definition and ALGLIB routines.
| x | position to evaluate |
| a | shape parameter |
| b | scale parameter |
Definition at line 1514 of file Statistics.cpp.
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inherited |
Definition at line 1100 of file SGObject.cpp.
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Returns description of a given parameter string, if it exists. SG_ERROR otherwise
| param_name | name of the parameter |
Definition at line 1124 of file SGObject.cpp.
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Returns index of model selection parameter with provided index
| param_name | name of model selection parameter |
Definition at line 1137 of file SGObject.cpp.
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method to make ALGLIB integration easier
Definition at line 604 of file Statistics.h.
method to make ALGLIB integration easier
Definition at line 607 of file Statistics.h.
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staticprotected |
Continued fraction expansion #1 for incomplete beta integral
Taken from ALGLIB under gpl2+
Definition at line 1181 of file Statistics.cpp.
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staticprotected |
Continued fraction expansion #2 for incomplete beta integral
Taken from ALGLIB under gpl2+
Definition at line 1284 of file Statistics.cpp.
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Power series for incomplete beta integral. Use when \(bx\) is small and \(x\) not too close to \(1\).
Taken from ALGLIB under gpl2+
Definition at line 1127 of file Statistics.cpp.
Incomplete beta integral
Returns incomplete beta integral of the arguments, evaluated from zero to \(x\). The function is defined as
\[ \frac{\Gamma(a+b)}{\Gamma(a)\Gamma(b)}\int_0^x t^{a-1} (1-t)^{b-1} dt. \]
The domain of definition is \(0 \leq x \leq 1\). In this implementation \(a\) and \(b\) are restricted to positive values. The integral from \(x\) to \(1\) may be obtained by the symmetry relation
\[ 1-\text{incomplete\_beta}(a,b,x)=\text{incomplete\_beta}(b,a,1-x). \]
The integral is evaluated by a continued fraction expansion or, when \(b\cdot x\) is small, by a power series.
Taken from ALGLIB under gpl2+
Definition at line 868 of file Statistics.cpp.
Incomplete gamma integral
Given \(p\), the function finds \(x\) such that
\[ \text{incomplete\_gamma}(a,x)=\frac{1}{\Gamma(a)}}\int_0^x e^{-t} t^{a-1} dt. \]
In this implementation both arguments must be positive. The integral is evaluated by either a power series or continued fraction expansion, depending on the relative values of \(a\) and \(x\).
Taken from ALGLIB under gpl2+
Definition at line 1389 of file Statistics.cpp.
Complemented incomplete gamma integral
The function is defined by
\[ \text{incomplete\_gamma\_completed}(a,x)=1-\text{incomplete\_gamma}(a,x) = \frac{1}{\Gamma (a)}\int_x^\infty e^{-t} t^{a-1} dt \]
In this implementation both arguments must be positive. The integral is evaluated by either a power series or continued fraction expansion, depending on the relative values of \(a\) and \(x\).
Taken from ALGLIB under gpl2+
Definition at line 1430 of file Statistics.cpp.
Evaluates the inverse CDF of the gamma distribution with given parameters \(a\), \(b\) at \(x\), such that result equals \(\text{gamma\_cdf}(x,a,b)\).
| p | position to evaluate |
| a | shape parameter |
| b | scale parameter |
Definition at line 1520 of file Statistics.cpp.
Inverse of incomplete beta integral
Given \(y\), the function finds \(x\) such that
\(\text{inverse\_incomplete\_beta}( a, b, x ) = y .\)
The routine performs interval halving or Newton iterations to find the root of \(\text{inverse\_incomplete\_beta}( a, b, x )-y=0.\)
Taken from ALGLIB under gpl2+
Definition at line 416 of file Statistics.cpp.
Inverse of complemented incomplete gamma integral
Given \(p\), the function finds \(x\) such that
\(\text{inverse\_incomplete\_gamma\_completed}( a, x ) = p.\)
Starting with the approximate value \( x=a t^3\), where \( t = 1 - d - \text{ndtri}(p) \sqrt{d} \) and \( d = \frac{1}{9}a \)
The routine performs up to 10 Newton iterations to find the root of \( \text{inverse\_incomplete\_gamma\_completed}( a, x )-p=0\)
Taken from ALGLIB under gpl2+
Definition at line 1528 of file Statistics.cpp.
Inverse of Normal distribution function
Returns the argument, \(x\), for which the area under the Gaussian probability density function (integrated from minus infinity to \(x\)) is equal to \(y\).
For small arguments \(0 < y < \exp(-2)\), the program computes \(z = \sqrt{ -2.0 \log(y) }\); then the approximation is \(x = z - \frac{log(z)}{z} - \frac{1}{z} \frac{P(\frac{1}{z})}{ Q(\frac{1}{z}}\). There are two rational functions \(\frac{P}{Q}\), one for \(0 < y < \exp(-32)\) and the other for \(y\) up to \(\exp(-2)\). For larger arguments, \(w = y - 0.5\), and \(\frac{x}{\sqrt{2\pi}} = w + w^3 R(\frac{w^2)}{S(w^2)})\).
Taken from ALGLIB under gpl2+
Definition at line 1010 of file Statistics.cpp.
same as other version, but with custom mean and variance
Definition at line 1004 of file Statistics.cpp.
Functional inverse of Student's t distribution
Given probability \(p\), finds the argument \(t\) such that \(\text{student\_t}(k,t)=p\)
Taken from ALGLIB under gpl2+
Definition at line 368 of file Statistics.cpp.
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If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
| generic | set to the type of the generic if returning TRUE |
Definition at line 268 of file SGObject.cpp.
method to make ALGLIB integration easier
Definition at line 598 of file Statistics.h.
method to make ALGLIB integration easier
Definition at line 601 of file Statistics.h.
Definition at line 265 of file Statistics.h.
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Definition at line 272 of file Statistics.h.
returns logarithm of the cumulative distribution function (CDF) of Gaussian distribution \(N(0, 1)\):
\[ \text{lnormal\_cdf}(x)=log\left(\frac{1}{2}+ \frac{1}{2}\text{error\_function}(\frac{x}{\sqrt{2}})\right) \]
This method uses asymptotic expansion for \(x<-10.0\), otherwise it returns \(log(\text{normal\_cdf}(x))\).
| x | real value |
Definition at line 1692 of file Statistics.cpp.
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maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)
| file_version | parameter version of the file |
| current_version | version from which mapping begins (you want to use Version::get_version_parameter() for this in most cases) |
| file | file to load from |
| prefix | prefix for members |
Definition at line 673 of file SGObject.cpp.
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loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned
| param_info | information of parameter |
| file_version | parameter version of the file, must be <= provided parameter version |
| file | file to load from |
| prefix | prefix for members |
Definition at line 514 of file SGObject.cpp.
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Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
| file | where to load from |
| prefix | prefix for members |
| param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 345 of file SGObject.cpp.
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protectedvirtualinherited |
Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.
| ShogunException | Will be thrown if an error occurres. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 1029 of file SGObject.cpp.
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protectedvirtualinherited |
Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.
| ShogunException | Will be thrown if an error occurres. |
Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CDynamicArray< uint64_t >, and CDynamicObjectArray.
Definition at line 1024 of file SGObject.cpp.
The log determinant of a dense matrix
The log determinant of a positive definite symmetric real valued matrix is calculated as
\[ \text{log\_determinant}(M) = \text{log}(\text{determinant}(L)\times\text{determinant}(L')) = 2\times \sum_{i}\text{log}(L_{i,i}) \]
Where, \(M = L\times L'\) as per Cholesky decomposition.
| m | input matrix |
Definition at line 2019 of file Statistics.cpp.
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The log determinant of a sparse matrix
The log determinant of symmetric positive definite sparse matrix is calculated in a similar way as the dense case. But using cholesky decomposition on sparse matrices may suffer from fill-in phenomenon, i.e. the factors may not be as sparse. The SimplicialCholesky module for sparse matrix in eigen3 library uses an approach called approximate minimum degree reordering, or amd, which permutes the matrix beforehand and results in much sparser factors. If \(P\) is the permutation matrix, it computes \(\text{LLT}(P\times M\times P^{-1}) = L\times L'\).
| m | input sparse matrix |
Definition at line 2042 of file Statistics.cpp.
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Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match
| param_base | set of TParameter instances that are mapped to the provided target parameter infos |
| base_version | version of the parameter base |
| target_param_infos | set of SGParamInfo instances that specify the target parameter base |
Definition at line 711 of file SGObject.cpp.
Calculates mean of given values. Given \(\{x_1, ..., x_m\}\), this is \(\frac{1}{m}\sum_{i=1}^m x_i\)
Computes the mean for each row/col of matrix
| values | vector of values |
| col_wise | if true, every column vector will be used, row vectors otherwise |
Definition at line 224 of file Statistics.cpp.
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Calculates median of given values. Matrix is seen as a long vector for this. The median is the value that one gets when the input vector is sorted and then selects the middle value.
This method is just a wrapper for median(). See this method for license of QuickSelect and Torben.
| values | vector of values |
| modify | if false, array is modified while median is computed (Using QuickSelect). If true, median is computed without modifications, which is slower. There are two methods to choose from. |
| in_place | if set false, the vector is copied and then computed using QuickSelect. If set true, median is computed in-place using Torben method. |
Definition at line 198 of file Statistics.cpp.
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Calculates unbiased empirical standard deviation estimator of given values. Given \(\{x_1, ..., x_m\}\), this is \(\sqrt{\frac{1}{m-1}\sum_{i=1}^m (x-\bar{x})^2}\) where \(\bar x=\frac{1}{m}\sum_{i=1}^m x_i\)
Computes the variance for each row/col of matrix
| values | vector of values |
| col_wise | if true, every column vector will be used, row vectors otherwise |
Definition at line 306 of file Statistics.cpp.
Calculates unbiased empirical variance estimator of given values. Given \(\{x_1, ..., x_m\}\), this is \(\frac{1}{m-1}\sum_{i=1}^m (x-\bar{x})^2\) where \(\bar x=\frac{1}{m}\sum_{i=1}^m x_i\)
Computes the variance for each row/col of matrix
| values | vector of values |
| col_wise | if true, every column vector will be used, row vectors otherwise |
Definition at line 261 of file Statistics.cpp.
Calculates mean of given values. Given \(\{x_1, ..., x_m\}\), this is \(\frac{1}{m}\sum_{i=1}^m x_i\)
| values | vector of values |
Definition at line 34 of file Statistics.cpp.
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Calculates median of given values. The median is the value that one gets when the input vector is sorted and then selects the middle value.
QuickSelect method copyright: This Quickselect routine is based on the algorithm described in "Numerical recipes in C", Second Edition, Cambridge University Press, 1992, Section 8.5, ISBN 0-521-43108-5 This code by Nicolas Devillard - 1998. Public domain.
Torben method copyright: The following code is public domain. Algorithm by Torben Mogensen, implementation by N. Devillard. Public domain.
Both methods adapted to SHOGUN by Heiko Strathmann.
| values | vector of values |
| modify | if false, array is modified while median is computed (Using QuickSelect). If true, median is computed without modifications, which is slower. There are two methods to choose from. |
| in_place | if set false, the vector is copied and then computed using QuickSelect. If set true, median is computed in-place using Torben method. |
Definition at line 46 of file Statistics.cpp.
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creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.
If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass
| param_base | set of TParameter instances to use for migration |
| target | parameter info for the resulting TParameter |
Definition at line 918 of file SGObject.cpp.
Definition at line 1913 of file Statistics.cpp.
Normal distribution function
Returns the area under the Gaussian probability density function, integrated from minus infinity to \(x\):
\[ \text{normal\_cdf}(x)=\frac{1}{\sqrt{2\pi}} \int_{-\infty}^x \exp \left( -\frac{t^2}{2} \right) dt = \frac{1+\text{error\_function}(z) }{2} \]
where \( z = \frac{x}{\sqrt{2} \sigma}\) and \( \sigma \) is the standard deviation. Computation is via the functions \(\text{error\_function}\) and \(\text{error\_function\_completement}\).
Taken from ALGLIB under gpl2+ Custom variance added by Heiko Strathmann
Definition at line 1687 of file Statistics.cpp.
method to make ALGLIB integration easier
Definition at line 595 of file Statistics.h.
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This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)
| param_base | set of TParameter instances to use for migration |
| target | parameter info for the resulting TParameter |
| replacement | (used as output) here the TParameter instance which is returned by migration is created into |
| to_migrate | the only source that is used for migration |
| old_name | with this parameter, a name change may be specified |
Definition at line 858 of file SGObject.cpp.
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prints all parameter registered for model selection and their type
Definition at line 1076 of file SGObject.cpp.
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prints registered parameters out
| prefix | prefix for members |
Definition at line 280 of file SGObject.cpp.
Definition at line 1924 of file Statistics.cpp.
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Sampling from a multivariate Gaussian distribution with dense covariance matrix
Sampling is performed by taking samples from \(N(0, I)\), then using cholesky factor of the covariance matrix, \(\Sigma\) and performing
\[S_{N(\mu,\Sigma)}=S_{N(0,I)}*L^{T}+\mu\]
where \(\Sigma=L*L^{T}\) and \(\mu\) is the mean vector.
| mean | the mean vector |
| cov | the covariance matrix |
| N | number of samples |
| precision_matrix | if true, sample from N(mu,C^-1) |
Definition at line 2062 of file Statistics.cpp.
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Sampling from a multivariate Gaussian distribution with sparse covariance matrix
Sampling is performed in similar way as of dense covariance matrix, but direct cholesky factorization of sparse matrices could be inefficient. So, this method uses permutation matrix for factorization and then permutes back the final samples before adding the mean.
| mean | the mean vector |
| cov | the covariance matrix |
| N | number of samples |
| precision_matrix | if true, sample from N(mu,C^-1) |
Definition at line 2122 of file Statistics.cpp.
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sample indices
| sample_size | size of sample to pick |
| N | total number of indices |
Definition at line 1944 of file Statistics.cpp.
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Save this object to file.
| file | where to save the object; will be closed during returning if PREFIX is an empty string. |
| prefix | prefix for members |
| param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 286 of file SGObject.cpp.
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protectedvirtualinherited |
Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.
| ShogunException | Will be thrown if an error occurres. |
Reimplemented in CKernel.
Definition at line 1039 of file SGObject.cpp.
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protectedvirtualinherited |
Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.
| ShogunException | Will be thrown if an error occurres. |
Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CDynamicArray< uint64_t >, and CDynamicObjectArray.
Definition at line 1034 of file SGObject.cpp.
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set generic type to T
Definition at line 41 of file SGObject.cpp.
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set the parallel object
| parallel | parallel object to use |
Definition at line 220 of file SGObject.cpp.
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set the version object
| version | version object to use |
Definition at line 255 of file SGObject.cpp.
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A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 151 of file SGObject.h.
Calculates unbiased empirical standard deviation estimator of given values. Given \(\{x_1, ..., x_m\}\), this is \(\sqrt{\frac{1}{m-1}\sum_{i=1}^m (x-\bar{x})^2}\) where \(\bar x=\frac{1}{m}\sum_{i=1}^m x_i\)
| values | vector of values |
Definition at line 301 of file Statistics.cpp.
Definition at line 282 of file Statistics.h.
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unset generic type
this has to be called in classes specializing a template class
Definition at line 275 of file SGObject.cpp.
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Updates the hash of current parameter combination.
Definition at line 227 of file SGObject.cpp.
Calculates unbiased empirical variance estimator of given values. Given \(\{x_1, ..., x_m\}\), this is \(\frac{1}{m-1}\sum_{i=1}^m (x-\bar{x})^2\) where \(\bar x=\frac{1}{m}\sum_{i=1}^m x_i\)
| values | vector of values |
Definition at line 210 of file Statistics.cpp.
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io
Definition at line 514 of file SGObject.h.
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parameters wrt which we can compute gradients
Definition at line 529 of file SGObject.h.
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Hash of parameter values
Definition at line 535 of file SGObject.h.
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model selection parameters
Definition at line 526 of file SGObject.h.
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map for different parameter versions
Definition at line 532 of file SGObject.h.
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parameters
Definition at line 523 of file SGObject.h.
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parallel
Definition at line 517 of file SGObject.h.
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version
Definition at line 520 of file SGObject.h.