Class SparseCoderScikitsLearnNode

Sparse coding

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

Finds a sparse representation of data against a fixed, precomputed
dictionary.

Each row of the result is the solution to a sparse coding problem.
The goal is to find a sparse array `code` such that::


    X ~= code * dictionary

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

**Parameters**

dictionary : array, [n_components, n_features]
    The dictionary atoms used for sparse coding. Lines are assumed to be
    normalized to unit norm.

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'``


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

**Attributes**

``components_`` : array, [n_components, n_features]
    The unchanged dictionary atoms

See also

DictionaryLearning
MiniBatchDictionaryLearning
SparsePCA
MiniBatchSparsePCA
sparse_encode

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.SparseCoder 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

Do nothing and return the estimator unchanged

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

This method is just there to implement the usual API and hence work in pipelines.

Overrides: Node.stop_training