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SHOGUN
4.1.0
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SHOGUN
4.1.0
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Class that contains certain functions related to statistics, such as probability/cumulative distribution functions, different statistics, etc.
Definition at line 32 of file Statistics.h.
Classes | |
| struct | SigmoidParamters |
Public Member Functions | |
| virtual const char * | get_name () const |
| template<> | |
| floatmax_t | mean (SGVector< complex128_t > vec) |
| mean not implemented for complex128_t, returns 0.0 instead More... | |
| virtual CSGObject * | shallow_copy () const |
| virtual CSGObject * | deep_copy () const |
| virtual bool | is_generic (EPrimitiveType *generic) const |
| template<class T > | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| void | unset_generic () |
| virtual void | print_serializable (const char *prefix="") |
| virtual bool | save_serializable (CSerializableFile *file, const char *prefix="") |
| virtual bool | load_serializable (CSerializableFile *file, const char *prefix="") |
| 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 void | update_parameter_hash () |
| virtual bool | parameter_hash_changed () |
| virtual bool | equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false) |
| virtual CSGObject * | clone () |
Public Attributes | |
| SGIO * | io |
| Parallel * | parallel |
| Version * | version |
| Parameter * | m_parameters |
| Parameter * | m_model_selection_parameters |
| Parameter * | m_gradient_parameters |
| uint32_t | m_hash |
Static Public Attributes | |
| static const float64_t | ERFC_CASE1 =0.0492 |
| static const float64_t | ERFC_CASE2 =-11.3137 |
Protected Member Functions | |
| 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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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 597 of file SGObject.cpp.
Evaluates the CDF of the chi square distribution with parameter k at \(x\). Based on Wikipedia definition.
| x | position to evaluate |
| k | parameter |
Definition at line 1751 of file Statistics.cpp.
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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 714 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 335 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 311 of file Statistics.cpp.
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A deep copy. All the instance variables will also be copied.
Definition at line 198 of file SGObject.cpp.
Derivative of the log gamma function.
| x | input |
Definition at line 2071 of file Statistics.cpp.
Definition at line 2035 of file Statistics.cpp.
method to make ALGLIB integration easier
Definition at line 649 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) |
| tolerant | allows linient check on float equality (within accuracy) |
Definition at line 618 of file SGObject.cpp.
Use to estimates erfc(x) valid for -100 < x < -8
| x | real value |
Definition at line 1763 of file Statistics.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 1809 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 1848 of file Statistics.cpp.
Evaluates the CDF of the F-distribution with parameters \(d1,d2\) at \(x\). Based on Wikipedia definition.
| x | position to evaluate |
| d1 | parameter 1 |
| d2 | parameter 2 |
Definition at line 1757 of file Statistics.cpp.
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fisher's test for multiple 2x3 tables
| tables |
Definition at line 1891 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 2336 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 1508 of file Statistics.cpp.
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Definition at line 498 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 522 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 535 of file SGObject.cpp.
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method to make ALGLIB integration easier
Definition at line 661 of file Statistics.h.
method to make ALGLIB integration easier
Definition at line 664 of file Statistics.h.
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Continued fraction expansion #1 for incomplete beta integral
Taken from ALGLIB under gpl2+
Definition at line 1175 of file Statistics.cpp.
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Continued fraction expansion #2 for incomplete beta integral
Taken from ALGLIB under gpl2+
Definition at line 1278 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 1121 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 862 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 1383 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 1424 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 1514 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 410 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 1522 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 1004 of file Statistics.cpp.
same as other version, but with custom mean and variance
Definition at line 998 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 362 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 296 of file SGObject.cpp.
method to make ALGLIB integration easier
Definition at line 655 of file Statistics.h.
method to make ALGLIB integration easier
Definition at line 658 of file Statistics.h.
Definition at line 275 of file Statistics.h.
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Definition at line 282 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 1691 of file Statistics.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 |
Definition at line 369 of file SGObject.cpp.
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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 occurs. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 426 of file SGObject.cpp.
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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 occurs. |
Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 421 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 2174 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 2197 of file Statistics.cpp.
The log determinant of a dense matrix
If determinant of the input matrix is positive, it returns the logarithm of the value. If not, it returns CMath::INFTY Note that the input matrix is not required to be symmetric positive definite. This method is slower than log_det() if input matrix is known to be symmetric positive definite
It is adapted from Gaussian Process Machine Learning Toolbox http://www.gaussianprocess.org/gpml/code/matlab/doc/
| A | input matrix |
Definition at line 2120 of file Statistics.cpp.
Calculates mean of given values. Given \(\{x_1, ..., x_m\}\), this is \(\frac{1}{m}\sum_{i=1}^m x_i\)
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 218 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 192 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 300 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 255 of file Statistics.cpp.
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Calculates mean of given values. Given \(\{x_1, ..., x_m\}\), this is \(\frac{1}{m}\sum_{i=1}^m x_i\)
| vec | vector of values |
Definition at line 44 of file Statistics.h.
| floatmax_t mean | ( | SGVector< complex128_t > | vec | ) |
mean not implemented for complex128_t, returns 0.0 instead
Definition at line 669 of file Statistics.h.
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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 40 of file Statistics.cpp.
Definition at line 2014 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 1681 of file Statistics.cpp.
method to make ALGLIB integration easier
Definition at line 652 of file Statistics.h.
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Definition at line 262 of file SGObject.cpp.
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prints all parameter registered for model selection and their type
Definition at line 474 of file SGObject.cpp.
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prints registered parameters out
| prefix | prefix for members |
Definition at line 308 of file SGObject.cpp.
Definition at line 2025 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 2217 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 2267 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 2045 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 |
Definition at line 314 of file SGObject.cpp.
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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 occurs. |
Reimplemented in CKernel.
Definition at line 436 of file SGObject.cpp.
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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 occurs. |
Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 431 of file SGObject.cpp.
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Definition at line 41 of file SGObject.cpp.
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Definition at line 46 of file SGObject.cpp.
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Definition at line 51 of file SGObject.cpp.
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Definition at line 56 of file SGObject.cpp.
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Definition at line 61 of file SGObject.cpp.
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Definition at line 66 of file SGObject.cpp.
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Definition at line 71 of file SGObject.cpp.
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Definition at line 76 of file SGObject.cpp.
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Definition at line 81 of file SGObject.cpp.
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Definition at line 86 of file SGObject.cpp.
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Definition at line 91 of file SGObject.cpp.
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Definition at line 96 of file SGObject.cpp.
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Definition at line 101 of file SGObject.cpp.
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Definition at line 106 of file SGObject.cpp.
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Definition at line 111 of file SGObject.cpp.
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set generic type to T
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set the parallel object
| parallel | parallel object to use |
Definition at line 241 of file SGObject.cpp.
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set the version object
| version | version object to use |
Definition at line 283 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 192 of file SGObject.cpp.
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 295 of file Statistics.cpp.
Definition at line 292 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 303 of file SGObject.cpp.
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Updates the hash of current parameter combination
Definition at line 248 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 204 of file Statistics.cpp.
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Magic number for computing lnormal_cdf
Definition at line 620 of file Statistics.h.
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Magic number for computing lnormal_cdf
Definition at line 623 of file Statistics.h.
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io
Definition at line 369 of file SGObject.h.
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parameters wrt which we can compute gradients
Definition at line 384 of file SGObject.h.
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Hash of parameter values
Definition at line 387 of file SGObject.h.
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inherited |
model selection parameters
Definition at line 381 of file SGObject.h.
|
inherited |
parameters
Definition at line 378 of file SGObject.h.
|
inherited |
parallel
Definition at line 372 of file SGObject.h.
|
inherited |
version
Definition at line 375 of file SGObject.h.