Class NMFScikitsLearnNode

Non-Negative matrix factorization by Projected Gradient (NMF)
This node has been automatically generated by wrapping the ``scikits.learn.decomposition.nmf.NMF`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
**Parameters**

X: array, [n_samples, n_features]
    Data the model will be fit to.

n_components: int or None
    Number of components
    if n_components is not set all components are kept

init:  'nndsvd' |  'nndsvda' | 'nndsvdar' | int | RandomState
    Method used to initialize the procedure.
    Default: 'nndsvdar'
    Valid options:

        - 'nndsvd': default Nonnegative Double Singular Value
        -     Decomposition (NNDSVD) initialization (better for sparseness)
        - 'nndsvda': NNDSVD with zeros filled with the average of X
        -     (better when sparsity is not desired)
        - 'nndsvdar': NNDSVD with zeros filled with small random values
        -     (generally faster, less accurate alternative to NNDSVDa
        -     for when sparsity is not desired)
        - int seed or RandomState: non-negative random matrices


sparseness: 'data' | 'components' | None
    Where to enforce sparsity in the model.
    Default: None

beta: double
    Degree of sparseness, if sparseness is not None. Larger values mean
    more sparseness.
    Default: 1

eta: double
    Degree of correctness to mantain, if sparsity is not None. Smaller
    values mean larger error.
    Default: 0.1

tol: double
    Tolerance value used in stopping conditions.
    Default: 1e-4

max_iter: int
    Number of iterations to compute.
    Default: 200

nls_max_iter: int
    Number of iterations in NLS subproblem.
    Default: 2000

**Attributes**

components_: array, [n_components, n_features]
    Non-negative components of the data
reconstruction_err_: number
    Frobenius norm of the matrix difference between the
    training data and the reconstructed data from the
    fit produced by the model. || X - WH ||_2

**Examples**


>>> import numpy as np
>>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
>>> from scikits.learn.decomposition import ProjectedGradientNMF
>>> model = ProjectedGradientNMF(n_components=2, init=0)
>>> model.fit(X) #doctest: +ELLIPSIS
ProjectedGradientNMF(nls_max_iter=2000, eta=0.1, max_iter=200,
           init=<mtrand.RandomState object at 0x...>, beta=1,
           sparseness=None, n_components=2, tol=0.0001)
>>> model.components_
array([[ 0.77032744,  0.11118662],
       [ 0.38526873,  0.38228063]])
>>> model.reconstruction_err_ #doctest: +ELLIPSIS
0.00746...
>>> model = ProjectedGradientNMF(n_components=2, init=0,
...                              sparseness='components')
>>> model.fit(X) #doctest: +ELLIPSIS
ProjectedGradientNMF(nls_max_iter=2000, eta=0.1, max_iter=200,
           init=<mtrand.RandomState object at 0x...>, beta=1,
           sparseness='components', n_components=2, tol=0.0001)
>>> model.components_
array([[ 1.67481991,  0.29614922],
       [-0.        ,  0.4681982 ]])
>>> model.reconstruction_err_ #doctest: +ELLIPSIS
0.513...

**Notes**

This implements C.-J. Lin. Projected gradient methods
for non-negative matrix factorization. Neural
Computation, 19(2007), 2756-2779.
http://www.csie.ntu.edu.tw/~cjlin/nmf/

NNDSVD is introduced in
C. Boutsidis, E. Gallopoulos: SVD based
initialization: A head start for nonnegative
matrix factorization - Pattern Recognition, 2008
http://www.cs.rpi.edu/~boutsc/files/nndsvd.pdf

Overrides: object.__init__

Transform the data X according to the fitted NMF model This node has been automatically generated by wrapping the scikits.learn.decomposition.nmf.NMF class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X: array, [n_samples, n_features]

Data matrix to be transformed by the model

Returns

data: array, [n_samples, n_components]

Transformed data

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Returns: bool

A boolean indication whether the node can be trained.

Overrides: Node.is_trainable

Learn a NMF model for the data X. This node has been automatically generated by wrapping the scikits.learn.decomposition.nmf.NMF class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X: array, [n_samples, n_features]

Data matrix to be decomposed

Returns

self

Overrides: Node.stop_training