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SHOGUN
4.2.0
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SHOGUN
4.2.0
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Class that models Soft-Max likelihood.
\( \text{softmax}_i(f)=\frac{\exp{f_i}}{\sum\exp{f_i}} \)
Code adapted from https://gist.github.com/yorkerlin/8a36e8f9b298aa0246a4 and GPstuff - Gaussian process models for Bayesian analysis http://becs.aalto.fi/en/research/bayes/gpstuff/
The reference pseudo code is the algorithm 3.4 of the GPML textbook
The implementation of predictive statistics is based on the mc sampler. The basic idea of the sampler is that first generating samples from the posterior Gaussian distribution given by mu and s2 and then using the samplers to estimate the predictive marginal distribution.
Definition at line 79 of file SoftMaxLikelihood.h.
Public Member Functions | |
| CSoftMaxLikelihood () | |
| virtual | ~CSoftMaxLikelihood () |
| virtual const char * | get_name () const |
| virtual SGVector< float64_t > | get_predictive_means (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const |
| virtual SGVector< float64_t > | get_predictive_variances (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const |
| virtual SGVector< float64_t > | get_predictive_log_probabilities (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) |
| virtual SGVector< float64_t > | get_log_probability_f (const CLabels *lab, SGVector< float64_t > func) const |
| virtual SGVector< float64_t > | get_log_probability_derivative_f (const CLabels *lab, SGVector< float64_t > func, index_t i) const |
| virtual SGVector< float64_t > | get_log_zeroth_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const |
| virtual float64_t | get_first_moment (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab, index_t i) const |
| virtual float64_t | get_second_moment (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab, index_t i) const |
| virtual bool | supports_multiclass () const |
| virtual void | set_num_samples (index_t num_samples) |
| virtual ELikelihoodModelType | get_model_type () const |
| virtual SGVector< float64_t > | get_log_probability_fmatrix (const CLabels *lab, SGMatrix< float64_t > F) const |
| virtual SGVector< float64_t > | get_first_derivative (const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const |
| virtual SGVector< float64_t > | get_second_derivative (const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const |
| virtual SGVector< float64_t > | get_third_derivative (const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const |
| virtual SGVector< float64_t > | get_first_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const |
| virtual SGVector< float64_t > | get_second_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const |
| virtual bool | supports_regression () const |
| virtual bool | supports_binary () const |
| 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) |
| bool | has (const std::string &name) const |
| template<typename T > | |
| bool | has (const Tag< T > &tag) const |
| template<typename T , typename U = void> | |
| bool | has (const std::string &name) const |
| template<typename T > | |
| void | set (const Tag< T > &_tag, const T &value) |
| template<typename T , typename U = void> | |
| void | set (const std::string &name, const T &value) |
| template<typename T > | |
| T | get (const Tag< T > &_tag) const |
| template<typename T , typename U = void> | |
| T | get (const std::string &name) const |
| 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 |
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) |
| template<typename T > | |
| void | register_param (Tag< T > &_tag, const T &value) |
| template<typename T > | |
| void | register_param (const std::string &name, const T &value) |
default constructor
Definition at line 50 of file SoftMaxLikelihood.cpp.
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virtual |
destructor
Definition at line 55 of file SoftMaxLikelihood.cpp.
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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 630 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 747 of file SGObject.cpp.
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virtualinherited |
A deep copy. All the instance variables will also be copied.
Definition at line 231 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 651 of file SGObject.cpp.
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Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
| _tag | name and type information of parameter |
Definition at line 367 of file SGObject.h.
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Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
| name | name of the parameter |
Definition at line 388 of file SGObject.h.
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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 in CVariationalLikelihood, CStudentsTLikelihood, and CGaussianLikelihood.
Definition at line 192 of file LikelihoodModel.h.
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virtual |
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 \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
NOTE: NOT IMPLEMENTED
| 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 225 of file SoftMaxLikelihood.h.
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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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get derivative of log likelihood \(log(p(y|f))\) with respect to location function \(f\)
| lab | labels \(y_i\), an integer between 1 and C (ie. num of classes) |
| func | function location |
| i | index, choices are 1, 2, and 3 for first, second, and third derivatives respectively |
Implements CLikelihoodModel.
Definition at line 103 of file SoftMaxLikelihood.cpp.
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virtual |
returns the logarithm of the point-wise likelihood \(log(p(y_i|f_i))\) for each label \(y_i\), an integer between 1 and C (ie. number of classes).
One can evaluate log-likelihood like: \(log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\)
| lab | labels \(y_i\), an integer between 1 and C (ie. num of classes) |
| func | values of the function \(f_i\) |
Implements CLikelihoodModel.
Definition at line 67 of file SoftMaxLikelihood.cpp.
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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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virtual |
returns the zeroth moment of a given (unnormalized) probability distribution:
NOTE: NOT IMPLEMENTED
| 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 204 of file SoftMaxLikelihood.h.
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virtualinherited |
get model type
Reimplemented in CStudentsTLikelihood, CGaussianLikelihood, CVariationalLikelihood, CProbitLikelihood, and CLogitLikelihood.
Definition at line 139 of file LikelihoodModel.h.
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Definition at line 531 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 555 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 568 of file SGObject.cpp.
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returns the name of the likelihood model
Implements CSGObject.
Definition at line 92 of file SoftMaxLikelihood.h.
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returns the logarithm of the predictive density of \(y_*\): The implementation is based on a simple Monte Carlo sampler from the pseudo code.
\[ log(p(y_*|X,y,x_*)) = log\left(\int p(y_*|f_*) p(f_*|X,y,x_*) df_*\right) \]
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 \(y_*\) |
Note that the log_probability vector should be a column-marjor linearized C-by-n matrix, where C is the number of classes and n is the number of samplers
Reimplemented from CLikelihoodModel.
Definition at line 303 of file SoftMaxLikelihood.cpp.
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virtual |
returns mean of the predictive marginal \(p(y_*|X,y,x_*)\) The implementation is based on a simple Monte Carlo sampler from the pseudo code.
| 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 \(y_*\) |
Note that the mean vector should be a column-marjor linearized C-by-n matrix, where C is the number of classes and n is the number of samplers
Implements CLikelihoodModel.
Definition at line 335 of file SoftMaxLikelihood.cpp.
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returns variance of the predictive marginal \(p(y_*|X,y,x_*)\) The implementation is based on a simple Monte Carlo sampler from the pseudo code.
| 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 \(y_*\) |
Note that the variance vector should be a column-marjor linearized C-by-n matrix, where C is the number of classes and n is the number of samplers
Implements CLikelihoodModel.
Definition at line 342 of file SoftMaxLikelihood.cpp.
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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 in CVariationalLikelihood, CStudentsTLikelihood, and CGaussianLikelihood.
Definition at line 210 of file LikelihoodModel.h.
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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 \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
NOTE: NOT IMPLEMENTED
| 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 246 of file SoftMaxLikelihood.h.
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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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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 in CVariationalLikelihood, CStudentsTLikelihood, and CGaussianLikelihood.
Definition at line 227 of file LikelihoodModel.h.
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Checks if object has a class parameter identified by a name.
| name | name of the parameter |
Definition at line 289 of file SGObject.h.
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Checks if object has a class parameter identified by a Tag.
| tag | tag of the parameter containing name and type information |
Definition at line 301 of file SGObject.h.
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Checks if a type exists for a class parameter identified by a name.
| name | name of the parameter |
Definition at line 312 of file SGObject.h.
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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 329 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 |
Definition at line 402 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 459 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 454 of file SGObject.cpp.
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Definition at line 295 of file SGObject.cpp.
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prints all parameter registered for model selection and their type
Definition at line 507 of file SGObject.cpp.
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prints registered parameters out
| prefix | prefix for members |
Definition at line 341 of file SGObject.cpp.
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Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.
| _tag | name and type information of parameter |
| value | value of the parameter |
Definition at line 439 of file SGObject.h.
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Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.
| name | name of the parameter |
| value | value of the parameter along with type information |
Definition at line 452 of file SGObject.h.
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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 347 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 469 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 464 of file SGObject.cpp.
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Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
| _tag | name and type information of parameter |
| value | value of the parameter |
Definition at line 328 of file SGObject.h.
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Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
| name | name of the parameter |
| value | value of the parameter along with type information |
Definition at line 354 of file SGObject.h.
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Definition at line 74 of file SGObject.cpp.
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Definition at line 79 of file SGObject.cpp.
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Definition at line 84 of file SGObject.cpp.
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Definition at line 89 of file SGObject.cpp.
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Definition at line 94 of file SGObject.cpp.
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Definition at line 99 of file SGObject.cpp.
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Definition at line 104 of file SGObject.cpp.
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Definition at line 109 of file SGObject.cpp.
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Definition at line 114 of file SGObject.cpp.
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Definition at line 119 of file SGObject.cpp.
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Definition at line 124 of file SGObject.cpp.
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Definition at line 129 of file SGObject.cpp.
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Definition at line 134 of file SGObject.cpp.
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Definition at line 139 of file SGObject.cpp.
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Definition at line 144 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 274 of file SGObject.cpp.
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set the version object
| version | version object to use |
Definition at line 316 of file SGObject.cpp.
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set the num_samples used in the mc sampler
| num_samples | number of samples to be generated |
Definition at line 224 of file SoftMaxLikelihood.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 225 of file SGObject.cpp.
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return whether likelihood function supports binary classification
Reimplemented in CVariationalLikelihood, CProbitLikelihood, and CLogitLikelihood.
Definition at line 329 of file LikelihoodModel.h.
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return whether likelihood function supports multiclass classification
Reimplemented from CLikelihoodModel.
Definition at line 257 of file SoftMaxLikelihood.h.
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return whether likelihood function supports regression
Reimplemented in CStudentsTLikelihood, CGaussianLikelihood, and CVariationalLikelihood.
Definition at line 323 of file LikelihoodModel.h.
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unset generic type
this has to be called in classes specializing a template class
Definition at line 336 of file SGObject.cpp.
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Updates the hash of current parameter combination
Definition at line 281 of file SGObject.cpp.
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io
Definition at line 537 of file SGObject.h.
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parameters wrt which we can compute gradients
Definition at line 552 of file SGObject.h.
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Hash of parameter values
Definition at line 555 of file SGObject.h.
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model selection parameters
Definition at line 549 of file SGObject.h.
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parameters
Definition at line 546 of file SGObject.h.
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parallel
Definition at line 540 of file SGObject.h.
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version
Definition at line 543 of file SGObject.h.