secml.ml.stats¶
CDensityEstimation¶
-
class
secml.ml.stats.c_density_estimation.
CDensityEstimation
(bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=1e-08, breadth_first=True, leaf_size=40, metric_params=None)[source]¶ Bases:
secml.core.c_creator.CCreator
Kernel Density Estimation
- Parameters
- bandwidthfloat, optional
The bandwidth of the kernel. Default 1.
- algorithmstr, optional
The tree algorithm to use. Valid options are [‘kd_tree’|’ball_tree’|’auto’]. Default is ‘auto’.
- kernelstr, optional
The kernel to use. Valid kernels are [‘gaussian’|’tophat’|’epanechnikov’|’exponential’|’linear’|’cosine’]. Default is ‘gaussian’.
- metricstr, optional
The distance metric to use. Note that not all metrics are valid with all algorithms. Refer to the documentation of BallTree and KDTree for a description of available algorithms. Note that the normalization of the density output is correct only for the Euclidean distance metric. Default is ‘euclidean’.
- atolfloat, optional
The desired absolute tolerance of the result. A larger tolerance will generally lead to faster execution. Default is 0.
- rtolfloat, optional
The desired relative tolerance of the result. A larger tolerance will generally lead to faster execution. Default is 1E-8.
- breadth_firstbool, optional
If true (default), use a breadth-first approach to the problem. Otherwise use a depth-first approach.
- leaf_sizeint, optional
Specify the leaf size of the underlying tree. See BallTree or KDTree for details. Default is 40.
- metric_paramsdict, optional
Additional parameters to be passed to the tree for use with the metric. For more information, see the documentation of BallTree or KDTree.
- Attributes
class_type
Defines class type.
logger
Logger for current object.
verbose
Verbosity level of logger output.
Methods
copy
(self)Returns a shallow copy of current class.
create
([class_item])This method creates an instance of a class with given type.
deepcopy
(self)Returns a deep copy of current class.
estimate_density
(self, x[, n_points])Estimate density of input array.
get_class_from_type
(class_type)Return the class associated with input type.
get_params
(self)Returns the dictionary of class parameters.
get_state
(self)Returns the object state dictionary.
get_subclasses
()Get all the subclasses of the calling class.
list_class_types
()This method lists all types of available subclasses of calling one.
load
(path)Loads object from file.
load_state
(self, path)Sets the object state from file.
save
(self, path)Save class object to file.
save_state
(self, path)Store the object state to file.
set
(self, param_name, param_value[, copy])Set a parameter of the class.
set_params
(self, params_dict[, copy])Set all parameters passed as a dictionary {key: value}.
set_state
(self, state_dict[, copy])Sets the object state using input dictionary.
timed
([msg])Timer decorator.
CDistributionGaussian¶
-
class
secml.ml.stats.c_distribution_gaussian.
CDistributionGaussian
(mean=0, cov=1)[source]¶ Bases:
secml.core.c_creator.CCreator
A multivariate normal random variable.
- Parameters
- meanscalar, optional
Mean of the distribution (default zero)
- covarray_like or scalar, optional
Covariance matrix of the distribution (default one)
- Attributes
class_type
Defines class type.
logger
Logger for current object.
verbose
Verbosity level of logger output.
Methods
copy
(self)Returns a shallow copy of current class.
create
([class_item])This method creates an instance of a class with given type.
deepcopy
(self)Returns a deep copy of current class.
get_class_from_type
(class_type)Return the class associated with input type.
get_params
(self)Returns the dictionary of class parameters.
get_state
(self)Returns the object state dictionary.
get_subclasses
()Get all the subclasses of the calling class.
list_class_types
()This method lists all types of available subclasses of calling one.
load
(path)Loads object from file.
load_state
(self, path)Sets the object state from file.
logpdf
(self, data)Log of the probability density function.
pdf
(self, data)Probability density function.
save
(self, path)Save class object to file.
save_state
(self, path)Store the object state to file.
set
(self, param_name, param_value[, copy])Set a parameter of the class.
set_params
(self, params_dict[, copy])Set all parameters passed as a dictionary {key: value}.
set_state
(self, state_dict[, copy])Sets the object state using input dictionary.
timed
([msg])Timer decorator.