Class DictionaryLearningScikitsLearnNode

Dictionary learning

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

Finds a dictionary (a set of atoms) that can best be used to represent data
using a sparse code.

Solves the optimization problem::


    (U^*,V^*) = argmin 0.5 || Y - U V ||_2^2 + alpha * || U ||_1
                (U,V)
                with || V_k ||_2 = 1 for all  0 <= k < n_components

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

**Parameters**

n_components : int,
    number of dictionary elements to extract

alpha : float,
    sparsity controlling parameter

max_iter : int,
    maximum number of iterations to perform

tol : float,
    tolerance for numerical error

fit_algorithm : {'lars', 'cd'}
    lars: uses the least angle regression method to solve the lasso problem
    (linear_model.lars_path)
    cd: uses the coordinate descent method to compute the
    Lasso solution (linear_model.Lasso). Lars will be faster if
    the estimated components are sparse.

    .. versionadded:: 0.17
       *cd* coordinate descent method to improve speed.

transform_algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp',     'threshold'}
    Algorithm used to transform the data
    lars: uses the least angle regression method (linear_model.lars_path)
    lasso_lars: uses Lars to compute the Lasso solution
    lasso_cd: uses the coordinate descent method to compute the
    Lasso solution (linear_model.Lasso). lasso_lars will be faster if
    the estimated components are sparse.
    omp: uses orthogonal matching pursuit to estimate the sparse solution
    threshold: squashes to zero all coefficients less than alpha from
    the projection ``dictionary * X'``

    .. versionadded:: 0.17
       *lasso_cd* coordinate descent method to improve speed.

transform_n_nonzero_coefs : int, ``0.1 * n_features`` by default
    Number of nonzero coefficients to target in each column of the
    solution. This is only used by `algorithm='lars'` and `algorithm='omp'`
    and is overridden by `alpha` in the `omp` case.

transform_alpha : float, 1. by default
    If `algorithm='lasso_lars'` or `algorithm='lasso_cd'`, `alpha` is the
    penalty applied to the L1 norm.
    If `algorithm='threshold'`, `alpha` is the absolute value of the
    threshold below which coefficients will be squashed to zero.
    If `algorithm='omp'`, `alpha` is the tolerance parameter: the value of
    the reconstruction error targeted. In this case, it overrides
    `n_nonzero_coefs`.

split_sign : bool, False by default
    Whether to split the sparse feature vector into the concatenation of
    its negative part and its positive part. This can improve the
    performance of downstream classifiers.

n_jobs : int,
    number of parallel jobs to run

code_init : array of shape (n_samples, n_components),
    initial value for the code, for warm restart

dict_init : array of shape (n_components, n_features),
    initial values for the dictionary, for warm restart

verbose :

    - degree of verbosity of the printed output


random_state : int or RandomState
    Pseudo number generator state used for random sampling.

**Attributes**

``components_`` : array, [n_components, n_features]
    dictionary atoms extracted from the data

``error_`` : array
    vector of errors at each iteration

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

**Notes**

**References:**

J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009: Online dictionary learning
for sparse coding (http://www.di.ens.fr/sierra/pdfs/icml09.pdf)

See also

SparseCoder
MiniBatchDictionaryLearning
SparsePCA
MiniBatchSparsePCA

Overrides: object.__init__

Encode the data as a sparse combination of the dictionary atoms.

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

Coding method is determined by the object parameter transform_algorithm.

Parameters

X : array of shape (n_samples, n_features)

Test data to be transformed, must have the same number of features as the data used to train the model.

Returns

X_new : array, shape (n_samples, n_components)

Transformed data

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit the model from data in X.

This node has been automatically generated by wrapping the sklearn.decomposition.dict_learning.DictionaryLearning 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 vector, where n_samples in the number of samples and n_features is the number of features.

Returns

self: object

Returns the object itself

Overrides: Node.stop_training