[MRG] include default values in SparseCoder class doc
diff --git a/sklearn/decomposition/_dict_learning.py b/sklearn/decomposition/_dict_learning.py
index 0eb69f5b5a74c..29839157ca33f 100644
--- a/sklearn/decomposition/_dict_learning.py
+++ b/sklearn/decomposition/_dict_learning.py
@@ -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`.
 

This PR includes default values to the documentation of the SparseCoder class. It also changes the existing default values to the format "default=value".