DOC Improves doc formatting in SparseCoder and DictionaryLearning
@@ -956,13 +956,14 @@ class SparseCoder(SparseCodingMixin, BaseEstimator):
transform_algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', \
'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
- lasso_cd: uses the coordinate descent method to compute the
+
+ - 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
+ - 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, default=0.1*n_features
@@ -1114,13 +1115,14 @@ class DictionaryLearning(SparseCodingMixin, BaseEstimator):
transform_algorithm : {'lasso_lars', 'lasso_cd', 'lars', 'omp', \
'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
- lasso_cd: uses the coordinate descent method to compute the
+
+ - 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
+ - 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
