included default values in SparseCoder class doc
@@ -952,7 +952,7 @@ class SparseCoder(SparseCodingMixin, BaseEstimator):
normalized to unit norm.
transform_algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', \
- 'threshold'}
+ 'threshold'}, default='omp'
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
@@ -963,12 +963,12 @@ class SparseCoder(SparseCodingMixin, BaseEstimator):
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
+ transform_n_nonzero_coefs : int, default=0.1*n_features
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
+ transform_alpha : float, default=1.
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
@@ -977,23 +977,23 @@ class SparseCoder(SparseCodingMixin, BaseEstimator):
the reconstruction error targeted. In this case, it overrides
`n_nonzero_coefs`.
- split_sign : bool, False by default
+ split_sign : bool, default=False
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 or None, optional (default=None)
+ n_jobs : int or None, default=None
Number of parallel jobs to run.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
- positive_code : bool
+ positive_code : bool, default=False
Whether to enforce positivity when finding the code.
.. versionadded:: 0.20
- transform_max_iter : int, optional (default=1000)
+ transform_max_iter : int, default=1000
Maximum number of iterations to perform if `algorithm='lasso_cd'` or
`lasso_lars`.
