Package mdp :: Package nodes :: Class OneClassSVMScikitsLearnNode

Class OneClassSVMScikitsLearnNode

Unsupervised Outliers Detection. This node has been automatically generated by wrapping the scikits.learn.svm.classes.OneClassSVM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Estimate the support of a high-dimensional distribution.

Parameters

kernel : string, optional: Specifies the kernel type to be used in the algorithm. Can be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'. If none is given 'rbf' will be used.
nu : float, optional: An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. By default 0.5 will be taken.
degree : int, optional: Degree of kernel function. Significant only in poly, rbf, sigmoid.
gamma : float, optional: kernel coefficient for rbf and poly, by default 1/n_features will be taken.
coef0 : float, optional: Independent term in kernel function. It is only significant in poly/sigmoid.
tol: float, optional: precision for stopping criteria
shrinking: boolean, optional: wether to use the shrinking heuristic.
cache_size: float, optional: specify the size of the cache (in MB)

Attributes

support_ : array-like, shape = [n_SV]: Index of support vectors.
support_vectors_ : array-like, shape = [nSV, n_features]: Support vectors.
dual_coef_ : array, shape = [n_classes-1, n_SV]: Coefficient of the support vector in the decision function.
coef_ : array, shape = [n_classes-1, n_features]: Weights asigned to the features (coefficients in the primal problem). This is only available in the case of linear kernel.
intercept_ : array, shape = [n_classes-1]: Constants in decision function.

Instance Methods

[hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Unsupervised Outliers Detection. This node has been automatically generated by wrapping the scikits.learn.svm.classes.OneClassSVM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Estimate the support of a high-dimensional distribution.

_execute(self, x)

_get_supported_dtypes(self)
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.

_stop_training(self, **kwargs)
Concatenate the collected data in a single array.

execute(self, x)
This function does classification or regression on an array of test vectors X. This node has been automatically generated by wrapping the scikits.learn.svm.classes.OneClassSVM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. For a classification model, the predicted class for each sample in X is returned. For a regression model, the function value of X calculated is returned.

stop_training(self, **kwargs)
Detects the soft boundary of the set of samples X. This node has been automatically generated by wrapping the scikits.learn.svm.classes.OneClassSVM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

Inherited from Cumulator

_train(self, *args)
Collect all input data in a list.

train(self, *args)
Collect all input data in a list.

Inherited from Node

__add__(self, other)

__call__(self, x, *args, **kwargs)
Calling an instance of Node is equivalent to calling its execute method.

__repr__(self)
repr(x)

__str__(self)
str(x)

_check_input(self, x)

_check_output(self, y)

_check_train_args(self, x, *args, **kwargs)

_get_train_seq(self)

_if_training_stop_training(self)

_inverse(self, x)

_pre_execution_checks(self, x)
This method contains all pre-execution checks.

_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.

_refcast(self, x)
Helper function to cast arrays to the internal dtype.

_set_dtype(self, t)

_set_input_dim(self, n)

_set_output_dim(self, n)

copy(self, protocol=None)
Return a deep copy of the node.

get_current_train_phase(self)
Return the index of the current training phase.

get_dtype(self)
Return dtype.

get_input_dim(self)
Return input dimensions.

get_output_dim(self)
Return output dimensions.

get_remaining_train_phase(self)
Return the number of training phases still to accomplish.

get_supported_dtypes(self)
Return dtypes supported by the node as a list of numpy.dtype objects.

has_multiple_training_phases(self)
Return True if the node has multiple training phases.

inverse(self, y, *args, **kwargs)
Invert y.

is_training(self)
Return True if the node is in the training phase, False otherwise.

save(self, filename, protocol=-1)
Save a pickled serialization of the node to filename. If filename is None, return a string.

set_dtype(self, t)
Set internal structures' dtype.

set_input_dim(self, n)
Set input dimensions.

set_output_dim(self, n)
Set output dimensions.

Static Methods

[hide private]

is_invertible()
Return True if the node can be inverted, False otherwise.

bool

is_trainable()
Return True if the node can be trained, False otherwise.

Properties

[hide private]

Inherited from object: __class__

Inherited from Node

_train_seq
List of tuples:

dtype
dtype

input_dim
Input dimensions

output_dim
Output dimensions

supported_dtypes
Supported dtypes

Method Details

[hide private]

init(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)

Parameters

kernel : string, optional: Specifies the kernel type to be used in the algorithm. Can be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'. If none is given 'rbf' will be used.
nu : float, optional: An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. By default 0.5 will be taken.
degree : int, optional: Degree of kernel function. Significant only in poly, rbf, sigmoid.
gamma : float, optional: kernel coefficient for rbf and poly, by default 1/n_features will be taken.
coef0 : float, optional: Independent term in kernel function. It is only significant in poly/sigmoid.
tol: float, optional: precision for stopping criteria
shrinking: boolean, optional: wether to use the shrinking heuristic.
cache_size: float, optional: specify the size of the cache (in MB)

Attributes

support_ : array-like, shape = [n_SV]: Index of support vectors.
support_vectors_ : array-like, shape = [nSV, n_features]: Support vectors.
dual_coef_ : array, shape = [n_classes-1, n_SV]: Coefficient of the support vector in the decision function.
coef_ : array, shape = [n_classes-1, n_features]: Weights asigned to the features (coefficients in the primal problem). This is only available in the case of linear kernel.
intercept_ : array, shape = [n_classes-1]: Constants in decision function.

Overrides: object.__init__

_execute(self, x)

Overrides: Node._execute

_get_supported_dtypes(self)

Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.

Overrides: Node._get_supported_dtypes

_stop_training(self, **kwargs)

Concatenate the collected data in a single array.

Overrides: Node._stop_training

execute(self, x)

This function does classification or regression on an array of test vectors X. This node has been automatically generated by wrapping the scikits.learn.svm.classes.OneClassSVM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. For a classification model, the predicted class for each sample in X is returned. For a regression model, the function value of X calculated is returned.

For an one-class model, +1 or -1 is returned.

Parameters

X : array-like, shape = [n_samples, n_features]

Returns

C : array, shape = [n_samples]

Overrides: Node.execute

is_invertible()
Static Method

Return True if the node can be inverted, False otherwise.

Overrides: Node.is_invertible: (inherited documentation)

is_trainable()
Static Method

Return True if the node can be trained, False otherwise.

Returns: bool: A boolean indicating whether the node can be trained.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

Detects the soft boundary of the set of samples X. This node has been automatically generated by wrapping the scikits.learn.svm.classes.OneClassSVM class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X : array-like, shape = [n_samples, n_features]: Set of samples, where n_samples is the number of samples and n_features is the number of features.

Returns

self : object: Returns self.

Notes

If X is not a C-ordered contiguous array, it is copied.

Overrides: Node.stop_training

Class OneClassSVMScikitsLearnNode

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs) (Constructor)

_execute(self, x)

_get_supported_dtypes(self)

_stop_training(self, **kwargs)

execute(self, x)

is_invertible() Static Method

is_trainable() Static Method

stop_training(self, **kwargs)

init(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)

is_invertible()
Static Method

is_trainable()
Static Method