# sparse_encode#

sklearn.decomposition.sparse_encode(X, dictionary, *, gram=None, cov=None, algorithm='lasso_lars', n_nonzero_coefs=None, alpha=None, copy_cov=True, init=None, max_iter=1000, n_jobs=None, check_input=True, verbose=0, positive=False)[source]#

Sparse coding.

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 User Guide.

Parameters:
Xarray-like of shape (n_samples, n_features)

Data matrix.

dictionaryarray-like of shape (n_components, n_features)

The dictionary matrix against which to solve the sparse coding of the data. Some of the algorithms assume normalized rows for meaningful output.

gramarray-like of shape (n_components, n_components), default=None

Precomputed Gram matrix, `dictionary * dictionary'`.

covarray-like of shape (n_components, n_samples), default=None

Precomputed covariance, `dictionary' * X`.

algorithm{‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’}, default=’lasso_lars’

The algorithm used:

• `'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 regularization from the projection `dictionary * data'`.

n_nonzero_coefsint, default=None

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. If `None`, then `n_nonzero_coefs=int(n_features / 10)`.

alphafloat, default=None

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`. If `None`, default to 1.

copy_covbool, default=True

Whether to copy the precomputed covariance matrix; if `False`, it may be overwritten.

initndarray of shape (n_samples, n_components), default=None

Initialization value of the sparse codes. Only used if `algorithm='lasso_cd'`.

max_iterint, default=1000

Maximum number of iterations to perform if `algorithm='lasso_cd'` or `'lasso_lars'`.

n_jobsint, default=None

Number of parallel jobs to run. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See Glossary for more details.

check_inputbool, default=True

If `False`, the input arrays X and dictionary will not be checked.

verboseint, default=0

Controls the verbosity; the higher, the more messages.

positivebool, default=False

Whether to enforce positivity when finding the encoding.

Returns:
codendarray of shape (n_samples, n_components)

The sparse codes.

`sklearn.linear_model.lars_path`

Compute Least Angle Regression or Lasso path using LARS algorithm.

`sklearn.linear_model.orthogonal_mp`

Solves Orthogonal Matching Pursuit problems.

`sklearn.linear_model.Lasso`

Train Linear Model with L1 prior as regularizer.

`SparseCoder`

Find a sparse representation of data from a fixed precomputed dictionary.

Examples

```>>> import numpy as np
>>> from sklearn.decomposition import sparse_encode
>>> X = np.array([[-1, -1, -1], [0, 0, 3]])
>>> dictionary = np.array(
...     [[0, 1, 0],
...      [-1, -1, 2],
...      [1, 1, 1],
...      [0, 1, 1],
...      [0, 2, 1]],
...    dtype=np.float64
... )
>>> sparse_encode(X, dictionary, alpha=1e-10)
array([[ 0.,  0., -1.,  0.,  0.],
[ 0.,  1.,  1.,  0.,  0.]])
```