Class RandomizedPCAScikitsLearnNode

Principal component analysis (PCA) using randomized SVD
This node has been automatically generated by wrapping the ``scikits.learn.decomposition.pca.RandomizedPCA`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
Linear dimensionality reduction using approximated Singular Value
Decomposition of the data and keeping only the most significant
singular vectors to project the data to a lower dimensional space.

This implementation uses a randomized SVD implementation and can
handle both scipy.sparse and numpy dense arrays as input.

**Parameters**

n_components: int
    Maximum number of components to keep: default is 50.

copy: bool
    If False, data passed to fit are overwritten

iterated_power: int, optional
    Number of iteration for the power method. 3 by default.

whiten: bool, optional
    When True (False by default) the ``components_`` vectors are divided
    by the singular values to ensure uncorrelated outputs with unit
    component-wise variances.

    Whitening will remove some information from the transformed signal
    (the relative variance scales of the components) but can sometime
    improve the predictive accuracy of the downstream estimators by
    making there data respect some hard-wired assumptions.

**Attributes**

components_: array, [n_components, n_features]
    Components with maximum variance.

explained_variance_ratio_: array, [n_components]
    Percentage of variance explained by each of the selected components.
    k is not set then all components are stored and the sum of
    explained variances is equal to 1.0

**Examples**

>>> import numpy as np
>>> from scikits.learn.decomposition import RandomizedPCA
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> pca = RandomizedPCA(n_components=2)
>>> pca.fit(X)
RandomizedPCA(copy=True, n_components=2, iterated_power=3, whiten=False)
>>> print pca.explained_variance_ratio_
[ 0.99244289  0.00755711]

See also

PCA
ProbabilisticPCA

**Notes**

References:


* Finding structure with randomness: Stochastic algorithms for
  constructing approximate matrix decompositions Halko, et al., 2009
  (arXiv:909)

* A randomized algorithm for the decomposition of matrices
  Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert

Overrides: object.__init__

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Returns: bool

A boolean indication whether the node can be trained.

Overrides: Node.is_trainable

Fit the model to the data X. This node has been automatically generated by wrapping the scikits.learn.decomposition.pca.RandomizedPCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X: array-like or scipy.sparse matrix, shape (n_samples, n_features)

Training vector, where n_samples in the number of samples and n_features is the number of features.

Returns

self : object

Returns the instance itself.

Overrides: Node.stop_training