SHOGUN
4.1.0
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Class that models Logit likelihood and uses numerical integration to approximate the following variational expection of log likelihood
\[ \sum_{{i=1}^n}{E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)]} \]
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Definition at line 58 of file LogitVGLikelihood.h.
Public Attributes | |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
uint32_t | m_hash |
Protected Member Functions | |
virtual void | init_likelihood () |
virtual void | set_likelihood (CLikelihoodModel *lik) |
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) |
Protected Attributes | |
SGVector< float64_t > | m_mu |
SGVector< float64_t > | m_s2 |
SGVector< float64_t > | m_lab |
CLikelihoodModel * | m_likelihood |
Definition at line 50 of file LogitVGLikelihood.cpp.
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virtual |
Definition at line 56 of file LogitVGLikelihood.cpp.
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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.
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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 714 of file SGObject.cpp.
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virtualinherited |
A deep copy. All the instance variables will also be copied.
Definition at line 198 of file SGObject.cpp.
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.
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virtualinherited |
get derivative of log likelihood \(log(p(y|f))\) with respect to given parameter
lab | labels used |
func | function location |
param | parameter |
Reimplemented from CLikelihoodModel.
Definition at line 88 of file VariationalLikelihood.cpp.
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virtualinherited |
get derivative of log likelihood \(log(p(y|f))\) with respect to given hyperparameter Note that variational parameters (mu and sigma) are NOT considered as hyperparameters
param | parameter |
Implements CVariationalLikelihood.
Definition at line 101 of file NumericalVGLikelihood.cpp.
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virtualinherited |
returns the first moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
This method is useful for EP local likelihood approximation.
mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
lab | labels \(y_i\) |
i | index i |
Implements CLikelihoodModel.
Definition at line 140 of file VariationalLikelihood.cpp.
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virtualinherited |
returns the first moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\) for each \(f_i\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
Wrapper method which calls get_first_moment multiple times.
mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
lab | labels \(y_i\) |
Definition at line 72 of file LikelihoodModel.cpp.
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inherited |
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inherited |
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inherited |
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virtualinherited |
get derivative of log likelihood \(log(p(y|f))\) with respect to location function \(f\)
lab | labels used |
func | function location |
i | index, choices are 1, 2, and 3 for first, second, and third derivatives respectively |
Implements CLikelihoodModel.
Definition at line 125 of file VariationalLikelihood.cpp.
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virtualinherited |
Returns the logarithm of the point-wise likelihood \(log(p(y_i|f_i))\) for each label \(y_i\).
One can evaluate log-likelihood like: \( log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\)
lab | labels \(y_i\) |
func | values of the function \(f_i\) |
Implements CLikelihoodModel.
Definition at line 118 of file VariationalLikelihood.cpp.
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virtualinherited |
Returns the log-likelihood \(log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\) for each of the provided functions \( f \) in the given matrix.
Wrapper method which calls get_log_probability_f multiple times.
lab | labels \(y_i\) |
F | values of the function \(f_i\) where each column of the matrix is one function \( f \). |
Definition at line 51 of file LikelihoodModel.cpp.
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virtualinherited |
returns the zeroth moment of a given (unnormalized) probability distribution:
\[ log(Z_i) = log\left(\int p(y_i|f_i) \mathcal{N}(f_i|\mu,\sigma^2) df_i\right) \]
for each \(f_i\).
mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
lab | labels \(y_i\) |
Implements CLikelihoodModel.
Definition at line 132 of file VariationalLikelihood.cpp.
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virtualinherited |
get model type
Reimplemented from CLikelihoodModel.
Definition at line 112 of file VariationalLikelihood.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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virtual |
returns the name of the likelihood model
Reimplemented from CNumericalVGLikelihood.
Definition at line 69 of file LogitVGLikelihood.h.
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virtualinherited |
returns the logarithm of the predictive density of \(y_*\):
\[ log(p(y_*|X,y,x_*)) = log\left(\int p(y_*|f_*) p(f_*|X,y,x_*) df_*\right) \]
which approximately equals to
\[ log\left(\int p(y_*|f_*) \mathcal{N}(f_*|\mu,\sigma^2) df_*\right) \]
where normal distribution \(\mathcal{N}(\mu,\sigma^2)\) is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\).
NOTE: if lab equals to NULL, then each \(y_*\) equals to one.
mu | posterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
s2 | posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
lab | labels \(y_*\) |
Reimplemented in CSoftMaxLikelihood.
Definition at line 45 of file LikelihoodModel.cpp.
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virtualinherited |
returns mean of the predictive marginal \(p(y_*|X,y,x_*)\)
NOTE: if lab equals to NULL, then each \(y_*\) equals to one.
mu | posterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
s2 | posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
lab | labels \(y_*\) |
Implements CLikelihoodModel.
Definition at line 72 of file VariationalLikelihood.cpp.
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virtualinherited |
returns variance of the predictive marginal \(p(y_*|X,y,x_*)\)
NOTE: if lab equals to NULL, then each \(y_*\) equals to one.
mu | posterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
s2 | posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
lab | labels \(y_*\) |
Implements CLikelihoodModel.
Definition at line 80 of file VariationalLikelihood.cpp.
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virtualinherited |
get derivative of the first derivative of log likelihood with respect to function location, i.e. \(\frac{\partial log(p(y|f))}{\partial f}\) with respect to given parameter
lab | labels used |
func | function location |
param | parameter |
Reimplemented from CLikelihoodModel.
Definition at line 96 of file VariationalLikelihood.cpp.
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virtualinherited |
returns the second moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
This method is useful for EP local likelihood approximation.
mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
lab | labels \(y_i\) |
i | index i |
Implements CLikelihoodModel.
Definition at line 148 of file VariationalLikelihood.cpp.
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virtualinherited |
returns the second moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\) for each \(f_i\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
Wrapper method which calls get_second_moment multiple times.
mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
lab | labels \(y_i\) |
Definition at line 89 of file LikelihoodModel.cpp.
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virtualinherited |
get derivative of the second derivative of log likelihood with respect to function location, i.e. \(\frac{\partial^{2} log(p(y|f))}{\partial f^{2}}\) with respect to given parameter
lab | labels used |
func | function location |
param | parameter |
Reimplemented from CLikelihoodModel.
Definition at line 104 of file VariationalLikelihood.cpp.
returns the expection of the logarithm of a logit distribution wrt the variational distribution using numerical integration
For each sample i, using Gaussian-Hermite quadrature to approximate
\[ E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)] \]
given mu_i and sigma2_i
Implements CVariationalLikelihood.
Definition at line 134 of file NumericalVGLikelihood.cpp.
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virtualinherited |
get derivative of the variational expection of log LogitLikelihood using numerical integration with respect to given parameter
compute the derivative of
\[ E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)] \]
given mu_i and sigma2_i with repect to param using Gaussian-Hermite quadrature
param | parameter(mu or sigma2) |
Implements CVariationalLikelihood.
Definition at line 161 of file NumericalVGLikelihood.cpp.
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protectedvirtual |
The function used to initialize m_likelihood
Implements CNumericalVGLikelihood.
Definition at line 60 of file LogitVGLikelihood.cpp.
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virtualinherited |
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.
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virtualinherited |
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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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 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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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 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.
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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.
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virtualinherited |
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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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 occurs. |
Reimplemented in CKernel.
Definition at line 436 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 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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virtualinherited |
set the number of Gaussian Hermite point used to compute variational expection
n | number of Gaussian Hermite point |
The default value is 20.
Definition at line 91 of file NumericalVGLikelihood.cpp.
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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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protectedvirtualinherited |
this method used to set m_likelihood
Definition at line 49 of file VariationalLikelihood.cpp.
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virtualinherited |
set a non-negative noise factor in order to correct the variance if variance is close to zero or negative setting 0 means correction is not applied
noise_factor | noise factor |
The default value is 1e-6.
Reimplemented in CDualVariationalGaussianLikelihood.
Definition at line 60 of file VariationalGaussianLikelihood.cpp.
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virtualinherited |
set the variational Gaussian distribution given data and parameters
mu | mean of the variational Gaussian distribution |
s2 | variance of the variational Gaussian distribution |
lab | labels/data used |
Reimplemented from CVariationalGaussianLikelihood.
Definition at line 225 of file NumericalVGLikelihood.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.
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return whether likelihood function supports binary classification
Reimplemented from CLikelihoodModel.
Definition at line 162 of file VariationalLikelihood.cpp.
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return whether likelihood function supports computing the derivative wrt hyperparameter Note that variational parameters are NOT considered as hyperparameters
Implements CVariationalLikelihood.
Definition at line 77 of file LogitVGLikelihood.h.
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return whether likelihood function supports multiclass classification
Reimplemented from CLikelihoodModel.
Definition at line 168 of file VariationalLikelihood.cpp.
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return whether likelihood function supports regression
Reimplemented from CLikelihoodModel.
Definition at line 156 of file VariationalLikelihood.cpp.
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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.
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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.
the label of data
Definition at line 277 of file VariationalLikelihood.h.
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protectedinherited |
the distribution used to model data
Definition at line 280 of file VariationalLikelihood.h.
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model selection parameters
Definition at line 381 of file SGObject.h.
The mean of variational Gaussian distribution
Definition at line 79 of file VariationalGaussianLikelihood.h.
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parameters
Definition at line 378 of file SGObject.h.
The variance of variational Gaussian distribution
Definition at line 82 of file VariationalGaussianLikelihood.h.
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parallel
Definition at line 372 of file SGObject.h.
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version
Definition at line 375 of file SGObject.h.