Package mdp :: Package nodes :: Class GNBScikitsLearnNode

Class GNBScikitsLearnNode

Gaussian Naive Bayes (GNB) This node has been automatically generated by wrapping the scikits.learn.naive_bayes.GNB 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]: Training vector, where n_samples in the number of samples and n_features is the number of features.
y : array, shape = [n_samples]: Target vector relative to X

Attributes

proba_y : array, shape = [n_classes]: probability of each class.
theta : array, shape [n_classes * n_features]: mean of each feature for the different class
sigma : array, shape [n_classes * n_features]: variance of each feature for the different class

Methods

fit(X, y) : self: Fit the model
predict(X) : array: Predict using the model.
predict_proba(X) : array: Predict the probability of each class using the model.
predict_log_proba(X) : array: Predict the log-probability of each class using the model.

Examples

>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> Y = np.array([1, 1, 1, 2, 2, 2])
>>> from scikits.learn.naive_bayes import GNB
>>> clf = GNB()
>>> clf.fit(X, Y)
GNB()
>>> print clf.predict([[-0.8, -1]])
[1]

See also

Instance Methods

[hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Initializes an object of type 'ScikitsNode'.

list

_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.

_label(self, x)

_stop_training(self, **kwargs)
Transform the data and labels lists to array objects and reshape them.

label(self, x)
Perform classification on an array of test vectors X. This node has been automatically generated by wrapping the scikits.learn.naive_bayes.GNB class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

stop_training(self, **kwargs)
Fit Gaussian Naive Bayes according to X, y This node has been automatically generated by wrapping the scikits.learn.naive_bayes.GNB 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 ClassifierCumulator

_check_train_args(self, x, labels)

_train(self, x, labels)
Cumulate all input data in a one dimensional list.

train(self, x, labels)
Cumulate all input data in a one dimensional list.

Inherited from ClassifierNode

_execute(self, x)

_prob(self, x, *args, **kargs)

execute(self, x)
Process the data contained in x.

prob(self, x, *args, **kwargs)
This function does classification or regression on a test vector T given a model with probability information. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

rank(self, x, threshold=None)
Returns ordered list with all labels ordered according to prob(x) (e.g., [[3 1 2], [2 1 3], ...]).

Inherited from PreserveDimNode

_set_input_dim(self, n)

_set_output_dim(self, n)

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)

_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)

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)

Initializes an object of type 'ScikitsNode'.

Parameters:

input_dim (int) - Dimensionality of the input. Default is None.
output_dim (int) - Dimensionality of the output. Default is None.
dtype (numpy.dtype or str) - Datatype of the input. Default is None.

Overrides: object.__init__

_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.

Returns: list: The list of dtypes supported by this node.
Overrides: Node._get_supported_dtypes

_label(self, x)

Overrides: ClassifierNode._label

_stop_training(self, **kwargs)

Transform the data and labels lists to array objects and reshape them.

Overrides: Node._stop_training

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

label(self, x)

Perform classification on an array of test vectors X. This node has been automatically generated by wrapping the scikits.learn.naive_bayes.GNB 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]

Returns

C : array, shape = [n_samples]

Overrides: ClassifierNode.label

stop_training(self, **kwargs)

Fit Gaussian Naive Bayes according to X, y This node has been automatically generated by wrapping the scikits.learn.naive_bayes.GNB 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]: Training vectors, where n_samples is the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples]: Target values.

Returns

self : object: Returns self.

Overrides: Node.stop_training

Class GNBScikitsLearnNode

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

_get_supported_dtypes(self)

_label(self, x)

_stop_training(self, **kwargs)

is_invertible() Static Method

is_trainable() Static Method

label(self, x)

stop_training(self, **kwargs)

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

is_invertible()
Static Method

is_trainable()
Static Method