Class ProbabilisticPCAScikitsLearnNode

Additional layer on top of PCA that adds a probabilistic evaluation
This node has been automatically generated by wrapping the ``scikits.learn.decomposition.pca.ProbabilisticPCA`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
Principal component analysis (PCA)

Linear dimensionality reduction using 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 the scipy.linalg implementation of the singular
value decomposition. It only works for dense arrays and is not scalable to
large dimensional data.

The time complexity of this implementation is O(n ** 3) assuming
n ~ n_samples ~ n_features.

**Parameters**

n_components: int, none or string
    Number of components to keep.
    if n_components is not set all components are kept:

        - n_components == min(n_samples, n_features)


    if n_components == 'mle', Minka's MLE is used to guess the dimension

    if 0 < n_components < 1, select the number of components such that
                             the explained variance ratio is greater
                             than n_components

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

whiten: bool, optional
    When True (False by default) the ``components_`` vectors are divided
    by n_samples times 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

**Notes**

For n_components='mle', this class uses the method of Thomas P. Minka:

Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604

Due to implementation subtleties of the Singular Value Decomposition (SVD),
which is used in this implementation, running fit twice on the same matrix
can lead to principal components with signs flipped (change in direction).
For this reason, it is important to always use the same estimator object to
transform data in a consistent fashion.

**Examples**

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

See also

ProbabilisticPCA
RandomizedPCA

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

Additionally to PCA.fit, learns a covariance model This node has been automatically generated by wrapping the scikits.learn.decomposition.pca.ProbabilisticPCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X: array of shape(n_samples, n_dim)

The data to fit

homoscedastic: bool, optional,

If True, average variance across remaining dimensions

Overrides: Node.stop_training