Package mdp :: Package nodes :: Class QDAScikitsLearnNode

Class QDAScikitsLearnNode

Quadratic Discriminant Analysis (QDA) This node has been automatically generated by wrapping the scikits.learn.qda.QDA 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
priors : array, optional, shape = [n_classes]: Priors on classes

Attributes

means_ : array-like, shape = [n_classes, n_features]: Class means
priors_ : array-like, shape = [n_classes]: Class priors (sum to 1)
covariances_ : list of array-like, shape = [n_features, n_features]: Covariance matrices of each class

Examples

>>> from scikits.learn.qda import QDA
>>> 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])
>>> clf = QDA()
>>> clf.fit(X, y)
QDA(priors=None)
>>> print clf.predict([[-0.8, -1]])
[1]

See also

LDA

Instance Methods

[hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Quadratic Discriminant Analysis (QDA) This node has been automatically generated by wrapping the scikits.learn.qda.QDA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

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)
This function does classification on an array of test vectors X. This node has been automatically generated by wrapping the scikits.learn.qda.QDA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. The predicted class C for each sample in X is returned.

stop_training(self, **kwargs)
Fit the QDA model according to the given training data and 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)

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
priors : array, optional, shape = [n_classes]: Priors on classes

Attributes

means_ : array-like, shape = [n_classes, n_features]: Class means
priors_ : array-like, shape = [n_classes]: Class priors (sum to 1)
covariances_ : list of array-like, shape = [n_features, n_features]: Covariance matrices of each class

Examples

>>> from scikits.learn.qda import QDA
>>> 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])
>>> clf = QDA()
>>> clf.fit(X, y)
QDA(priors=None)
>>> print clf.predict([[-0.8, -1]])
[1]

_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)

This function does classification on an array of test vectors X. This node has been automatically generated by wrapping the scikits.learn.qda.QDA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. The predicted class C for each sample in X is returned.

Parameters

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

Returns

C : array, shape = [n_samples]

Overrides: ClassifierNode.label

stop_training(self, **kwargs)


Fit the QDA model according to the given training data and parameters.
This node has been automatically generated by wrapping the ``scikits.learn.qda.QDA`` 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 values (integers)
store_covariances : boolean
    If True the covariance matrices are computed and stored in
    self.covariances_ attribute.

Overrides: Node.stop_training

Class QDAScikitsLearnNode

__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