Class LabelSpreadingScikitsLearnNode

LabelSpreading model for semi-supervised learning

This node has been automatically generated by wrapping the ``sklearn.semi_supervised.label_propagation.LabelSpreading`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

This model is similar to the basic Label Propgation algorithm,
but uses affinity matrix based on the normalized graph Laplacian
and soft clamping across the labels.

Read more in the :ref:`User Guide <label_propagation>`.

**Parameters**

kernel : {'knn', 'rbf'}
    String identifier for kernel function to use.
    Only 'rbf' and 'knn' kernels are currently supported.

gamma : float
  parameter for rbf kernel

n_neighbors : integer > 0
  parameter for knn kernel

alpha : float
  clamping factor

max_iter : float
  maximum number of iterations allowed

tol : float
  Convergence tolerance: threshold to consider the system at steady
  state

**Attributes**

``X_`` : array, shape = [n_samples, n_features]
    Input array.

``classes_`` : array, shape = [n_classes]
    The distinct labels used in classifying instances.

``label_distributions_`` : array, shape = [n_samples, n_classes]
    Categorical distribution for each item.

``transduction_`` : array, shape = [n_samples]
    Label assigned to each item via the transduction.

``n_iter_`` : int
    Number of iterations run.

**Examples**

>>> from sklearn import datasets
>>> from sklearn.semi_supervised import LabelSpreading
>>> label_prop_model = LabelSpreading()
>>> iris = datasets.load_iris()
>>> random_unlabeled_points = np.where(np.random.random_integers(0, 1,
...    size=len(iris.target)))
>>> labels = np.copy(iris.target)
>>> labels[random_unlabeled_points] = -1
>>> label_prop_model.fit(iris.data, labels)
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
LabelSpreading(...)

**References**

Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston,
Bernhard Schoelkopf. Learning with local and global consistency (2004)
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.3219

See Also

LabelPropagation : Unregularized graph based semi-supervised learning

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Performs inductive inference across the model.

This node has been automatically generated by wrapping the sklearn.semi_supervised.label_propagation.LabelSpreading 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

y : array_like, shape = [n_samples]

Predictions for input data

Overrides: ClassifierNode.label

Fit a semi-supervised label propagation model based

This node has been automatically generated by wrapping the sklearn.semi_supervised.label_propagation.LabelSpreading class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

All the input data is provided matrix X (labeled and unlabeled) and corresponding label matrix y with a dedicated marker value for unlabeled samples.

Parameters

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

A {n_samples by n_samples} size matrix will be created from this

y : array_like, shape = [n_samples]

n_labeled_samples (unlabeled points are marked as -1) All unlabeled samples will be transductively assigned labels

Returns

self : returns an instance of self.

Overrides: Node.stop_training