Class PCAScikitsLearnNode

Principal component analysis (PCA)

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

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.

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

**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 amount of variance that needs to be explained is greater than the
    percentage specified by n_components

copy : bool
    If False, data passed to fit are overwritten and running
    fit(X).transform(X) will not yield the expected results,
    use fit_transform(X) instead.

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]
    Principal axes in feature space, representing the directions of
    maximum variance in the data.

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

``mean_`` : array, [n_features]
    Per-feature empirical mean, estimated from the training set.

``n_components_`` : int
    The estimated number of components. Relevant when n_components is set
    to 'mle' or a number between 0 and 1 to select using explained
    variance.

``noise_variance_`` : float
    The estimated noise covariance following the Probabilistic PCA model
    from Tipping and Bishop 1999. See "Pattern Recognition and
    Machine Learning" by C. Bishop, 12.2.1 p. 574 or
    http://www.miketipping.com/papers/met-mppca.pdf. It is required to
    computed the estimated data covariance and score samples.

**Notes**

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

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

Implements the probabilistic PCA model from:

M. Tipping and C. Bishop, Probabilistic Principal Component Analysis,
Journal of the Royal Statistical Society, Series B, 61, Part 3, pp. 611-622
via the score and score_samples methods.
See http://www.miketipping.com/papers/met-mppca.pdf

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 sklearn.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_) # doctest: +ELLIPSIS
[ 0.99244...  0.00755...]

See also

RandomizedPCA
KernelPCA
SparsePCA
TruncatedSVD

Overrides: object.__init__

Apply the dimensionality reduction on X.

This node has been automatically generated by wrapping the sklearn.decomposition.pca.PCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

X is projected on the first principal components previous extracted from a training set.

Parameters

X : array-like, shape (n_samples, n_features)

New data, where n_samples is the number of samples and n_features is the number of features.

Returns

X_new : array-like, shape (n_samples, n_components)

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit the model with X.

This node has been automatically generated by wrapping the sklearn.decomposition.pca.PCA 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 data, 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