The class implements the stochastic gradient descend (SGD) minimizer.
A good introduction to SGD can be found at http://cs231n.github.io/neural-networks-3/#sgd
Definition at line 45 of file SGDMinimizer.h.
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| | SGDMinimizer () |
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| | SGDMinimizer (FirstOrderStochasticCostFunction *fun) |
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| virtual | ~SGDMinimizer () |
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| virtual const char * | get_name () const |
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| virtual float64_t | minimize () |
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| virtual bool | supports_batch_update () const |
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| virtual void | set_gradient_updater (DescendUpdater *gradient_updater) |
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| virtual void | set_number_passes (int32_t num_passes) |
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| virtual void | set_learning_rate (LearningRate *learning_rate) |
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| virtual int32_t | get_iteration_counter () |
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| virtual void | set_cost_function (FirstOrderCostFunction *fun) |
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| virtual void | unset_cost_function (bool is_unref=true) |
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| virtual void | set_penalty_weight (float64_t penalty_weight) |
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| virtual void | set_penalty_type (Penalty *penalty_type) |
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| virtual CSGObject * | shallow_copy () const |
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| virtual CSGObject * | deep_copy () const |
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| virtual bool | is_generic (EPrimitiveType *generic) const |
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| template<class T > |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| template<> |
| void | set_generic () |
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| void | unset_generic () |
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| virtual void | print_serializable (const char *prefix="") |
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| virtual bool | save_serializable (CSerializableFile *file, const char *prefix="") |
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| virtual bool | load_serializable (CSerializableFile *file, const char *prefix="") |
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| void | set_global_io (SGIO *io) |
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| SGIO * | get_global_io () |
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| void | set_global_parallel (Parallel *parallel) |
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| Parallel * | get_global_parallel () |
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| void | set_global_version (Version *version) |
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| Version * | get_global_version () |
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| SGStringList< char > | get_modelsel_names () |
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| void | print_modsel_params () |
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| char * | get_modsel_param_descr (const char *param_name) |
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| index_t | get_modsel_param_index (const char *param_name) |
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| void | build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict) |
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| bool | has (const std::string &name) const |
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| template<typename T > |
| bool | has (const Tag< T > &tag) const |
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| template<typename T , typename U = void> |
| bool | has (const std::string &name) const |
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| template<typename T > |
| void | set (const Tag< T > &_tag, const T &value) |
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| template<typename T , typename U = void> |
| void | set (const std::string &name, const T &value) |
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| template<typename T > |
| T | get (const Tag< T > &_tag) const |
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| template<typename T , typename U = void> |
| T | get (const std::string &name) const |
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| virtual void | update_parameter_hash () |
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| virtual bool | parameter_hash_changed () |
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| virtual bool | equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false) |
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| virtual CSGObject * | clone () |
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Constructor
- Parameters
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| fun | stochastic cost function |
Definition at line 46 of file SGDMinimizer.cpp.
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.
- Parameters
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| dict | dictionary of parameters to be built. |
Definition at line 630 of file SGObject.cpp.
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.
- Returns
- an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed
Definition at line 747 of file SGObject.cpp.
A deep copy. All the instance variables will also be copied.
Definition at line 231 of file SGObject.cpp.
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protectedvirtualinherited |
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.
- Parameters
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| other | object to compare with |
| accuracy | accuracy to use for comparison (optional) |
| tolerant | allows linient check on float equality (within accuracy) |
- Returns
- true if all parameters were equal, false if not
Definition at line 651 of file SGObject.cpp.
| T get |
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const Tag< T > & |
_tag | ) |
const |
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inherited |
Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
- Parameters
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| _tag | name and type information of parameter |
- Returns
- value of the parameter identified by the input tag
Definition at line 367 of file SGObject.h.
| T get |
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const std::string & |
name | ) |
const |
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inherited |
Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
- Parameters
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| name | name of the parameter |
- Returns
- value of the parameter corresponding to the input name and type
Definition at line 388 of file SGObject.h.
get the io object
- Returns
- io object
Definition at line 268 of file SGObject.cpp.
get the parallel object
- Returns
- parallel object
Definition at line 310 of file SGObject.cpp.
get the version object
- Returns
- version object
Definition at line 323 of file SGObject.cpp.
| virtual int32_t get_iteration_counter |
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virtualinherited |
How many samples/mini-batch does the minimizer use?
- Returns
- the number of samples/mini-batches used in optimization
Definition at line 136 of file FirstOrderStochasticMinimizer.h.
- Returns
- vector of names of all parameters which are registered for model selection
Definition at line 531 of file SGObject.cpp.
| char * get_modsel_param_descr |
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const char * |
param_name | ) |
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inherited |
Returns description of a given parameter string, if it exists. SG_ERROR otherwise
- Parameters
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| param_name | name of the parameter |
- Returns
- description of the parameter
Definition at line 555 of file SGObject.cpp.
| index_t get_modsel_param_index |
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const char * |
param_name | ) |
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inherited |
Returns index of model selection parameter with provided index
- Parameters
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| param_name | name of model selection parameter |
- Returns
- index of model selection parameter with provided name, -1 if there is no such
Definition at line 568 of file SGObject.cpp.
| virtual const char* get_name |
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const |
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virtual |
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protectedvirtualinherited |
Get the penalty given target variables For L2 penalty, the target variable is \(w\) and the value of penalty is \(\lambda \frac{w^t w}{2}\), where \(\lambda\) is the weight of penalty
- Parameters
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| var | the variable used in regularization |
Definition at line 69 of file FirstOrderMinimizer.cpp.
| bool has |
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const std::string & |
name | ) |
const |
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inherited |
Checks if object has a class parameter identified by a name.
- Parameters
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| name | name of the parameter |
- Returns
- true if the parameter exists with the input name
Definition at line 289 of file SGObject.h.
| bool has |
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const Tag< T > & |
tag | ) |
const |
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inherited |
Checks if object has a class parameter identified by a Tag.
- Parameters
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| tag | tag of the parameter containing name and type information |
- Returns
- true if the parameter exists with the input tag
Definition at line 301 of file SGObject.h.
| bool has |
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const std::string & |
name | ) |
const |
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inherited |
Checks if a type exists for a class parameter identified by a name.
- Parameters
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| name | name of the parameter |
- Returns
- true if the parameter exists with the input name and type
Definition at line 312 of file SGObject.h.
| void init_minimization |
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protectedvirtual |
| bool is_generic |
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EPrimitiveType * |
generic | ) |
const |
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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.
- Parameters
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| generic | set to the type of the generic if returning TRUE |
- Returns
- TRUE if a class template.
Definition at line 329 of file SGObject.cpp.
Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
- Parameters
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| file | where to load from |
| prefix | prefix for members |
- Returns
- TRUE if done, otherwise FALSE
Definition at line 402 of file SGObject.cpp.
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protectedvirtualinherited |
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protectedvirtualinherited |
| bool parameter_hash_changed |
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virtualinherited |
- Returns
- whether parameter combination has changed since last update
Definition at line 295 of file SGObject.cpp.
| void print_modsel_params |
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inherited |
prints all parameter registered for model selection and their type
Definition at line 507 of file SGObject.cpp.
| void print_serializable |
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const char * |
prefix = "" | ) |
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virtualinherited |
prints registered parameters out
- Parameters
-
Definition at line 341 of file SGObject.cpp.
| void register_param |
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Tag< T > & |
_tag, |
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const T & |
value |
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) |
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protectedinherited |
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.
- Parameters
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| _tag | name and type information of parameter |
| value | value of the parameter |
Definition at line 439 of file SGObject.h.
| void register_param |
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const std::string & |
name, |
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const T & |
value |
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) |
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protectedinherited |
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.
- Parameters
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| name | name of the parameter |
| value | value of the parameter along with type information |
Definition at line 452 of file SGObject.h.
Save this object to file.
- Parameters
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| file | where to save the object; will be closed during returning if PREFIX is an empty string. |
| prefix | prefix for members |
- Returns
- TRUE if done, otherwise FALSE
Definition at line 347 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.
- Exceptions
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Reimplemented in CKernel.
Definition at line 469 of file SGObject.cpp.
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protectedvirtualinherited |
| void set |
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const Tag< T > & |
_tag, |
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const T & |
value |
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) |
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inherited |
Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
- Parameters
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| _tag | name and type information of parameter |
| value | value of the parameter |
Definition at line 328 of file SGObject.h.
| void set |
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const std::string & |
name, |
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const T & |
value |
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) |
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inherited |
Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
- Parameters
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| name | name of the parameter |
| value | value of the parameter along with type information |
Definition at line 354 of file SGObject.h.
| void set_global_io |
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SGIO * |
io | ) |
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inherited |
| void set_global_parallel |
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Parallel * |
parallel | ) |
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inherited |
set the parallel object
- Parameters
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| parallel | parallel object to use |
Definition at line 274 of file SGObject.cpp.
| void set_global_version |
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Version * |
version | ) |
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inherited |
set the version object
- Parameters
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| version | version object to use |
Definition at line 316 of file SGObject.cpp.
| void set_number_passes |
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int32_t |
num_passes | ) |
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virtualinherited |
Set the number of times to go through all data points (samples) For example, num_passes=1 means go through all data points once.
Recall that a stochastic cost function \(f(w)\) can be written as \(\sum_i{ f_i(w) }\), where \(f_i(w)\) is the differentiable function for the i-th sample.
- Parameters
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| num_passes | the number of times |
Definition at line 55 of file FirstOrderStochasticMinimizer.cpp.
| void set_penalty_type |
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Penalty * |
penalty_type | ) |
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virtualinherited |
Set the type of penalty For example, L2 penalty
- Parameters
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| penalty_type | the type of penalty. If NULL is given, regularization is not enabled. |
Definition at line 53 of file FirstOrderMinimizer.cpp.
| void set_penalty_weight |
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float64_t |
penalty_weight | ) |
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virtualinherited |
Set the weight of penalty
- Parameters
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| penalty_weight | the weight of penalty, which is positive |
Definition at line 63 of file FirstOrderMinimizer.cpp.
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.
| virtual bool supports_batch_update |
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| ) |
const |
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virtualinherited |
| virtual void unset_cost_function |
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bool |
is_unref = true | ) |
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virtualinherited |
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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protectedvirtualinherited |
Add gradient of the penalty wrt target variables to unpenalized gradient For least sqaure with L2 penalty,
\[ L2f(w)=f(w) + L2(w) \]
where \( f(w)=\sum_i{(y_i-w^T x_i)^2}\) is the least sqaure cost function and \(L2(w)=\lambda \frac{w^t w}{2}\) is the L2 penalty
Target variables is \(w\) Unpenalized gradient is \(\frac{\partial f(w) }{\partial w}\) Gradient of the penalty wrt target variables is \(\frac{\partial L2(w) }{\partial w}\)
- Parameters
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| gradient | unpenalized gradient wrt its target variable |
| var | the target variable |
Definition at line 81 of file FirstOrderMinimizer.cpp.
| void update_parameter_hash |
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| ) |
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virtualinherited |
Updates the hash of current parameter combination
Definition at line 281 of file SGObject.cpp.
parameters wrt which we can compute gradients
Definition at line 552 of file SGObject.h.
Hash of parameter values
Definition at line 555 of file SGObject.h.
model selection parameters
Definition at line 549 of file SGObject.h.
The documentation for this class was generated from the following files:
