Package mdp :: Package nodes :: Class NuSVCScikitsLearnNode

Class NuSVCScikitsLearnNode


NuSVC for sparse matrices (csr).
This node has been automatically generated by wrapping the ``scikits.learn.svm.sparse.classes.NuSVC`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
See :class:`scikits.learn.svm.NuSVC` for a complete list of parameters

**Notes**

For best results, this accepts a matrix in csr format
(scipy.sparse.csr), but should be able to convert from any array-like
object (including other sparse representations).

**Examples**

>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from scikits.learn.svm.sparse import NuSVC
>>> clf = NuSVC()
>>> clf.fit(X, y)
NuSVC(kernel='rbf', probability=False, degree=3, coef0=0.0, tol=0.001,
   cache_size=100.0, shrinking=True, nu=0.5, gamma=0.25)
>>> print clf.predict([[-0.8, -1]])
[ 1.]

Instance Methods

[hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
NuSVC for sparse matrices (csr).

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 or regression on an array of test vectors T. This node has been automatically generated by wrapping the scikits.learn.svm.sparse.classes.NuSVC 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 T is returned. For a regression model, the function value of T calculated is returned.

stop_training(self, **kwargs)
Fit the SVM model according to the given training data and parameters. This node has been automatically generated by wrapping the scikits.learn.svm.sparse.classes.NuSVC 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)


NuSVC for sparse matrices (csr).
This node has been automatically generated by wrapping the ``scikits.learn.svm.sparse.classes.NuSVC`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
See :class:`scikits.learn.svm.NuSVC` for a complete list of parameters

**Notes**

For best results, this accepts a matrix in csr format
(scipy.sparse.csr), but should be able to convert from any array-like
object (including other sparse representations).

**Examples**

>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from scikits.learn.svm.sparse import NuSVC
>>> clf = NuSVC()
>>> clf.fit(X, y)
NuSVC(kernel='rbf', probability=False, degree=3, coef0=0.0, tol=0.001,
   cache_size=100.0, shrinking=True, nu=0.5, gamma=0.25)
>>> print clf.predict([[-0.8, -1]])
[ 1.]

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)

This function does classification or regression on an array of test vectors T. This node has been automatically generated by wrapping the scikits.learn.svm.sparse.classes.NuSVC 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 T is returned. For a regression model, the function value of T calculated is returned.

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

Parameters

T : scipy.sparse.csr, shape = [n_samples, n_features]

Returns

C : array, shape = [n_samples]

Overrides: ClassifierNode.label

stop_training(self, **kwargs)

Fit the SVM model according to the given training data and parameters. This node has been automatically generated by wrapping the scikits.learn.svm.sparse.classes.NuSVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X : sparse matrix, 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 (integers in classification, real numbers in regression)

class_weight : {dict, 'auto'}, optional

Weights associated with classes in the form {class_label : weight}. If not given, all classes are supposed to have weight one.

The 'auto' mode uses the values of y to automatically adjust weights inversely proportional to class frequencies.

sample_weight : array-like, shape = [n_samples], optional

Weights applied to individual samples (1. for unweighted).

Returns

self : object: Returns an instance of self.

Notes

For maximum effiency, use a sparse matrix in csr format (scipy.sparse.csr_matrix)

Overrides: Node.stop_training

Class NuSVCScikitsLearnNode

__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