diff --git a/random_layer.py b/random_layer.py
index 713a795..2d63038 100644
--- a/random_layer.py
+++ b/random_layer.py
@@ -24,7 +24,7 @@
 from scipy.spatial.distance import cdist, pdist, squareform
 
 from sklearn.metrics import pairwise_distances
-from sklearn.utils import check_random_state, atleast2d_or_csr
+from sklearn.utils import check_random_state, check_array
 from sklearn.utils.extmath import safe_sparse_dot
 from sklearn.base import BaseEstimator, TransformerMixin
 
@@ -108,7 +108,7 @@ def fit(self, X, y=None):
         -------
         self
         """
-        X = atleast2d_or_csr(X)
+        X = check_array(X)
 
         self._generate_components(X)
 
@@ -130,7 +130,7 @@ def transform(self, X, y=None):
         -------
         X_new : numpy array of shape [n_samples, n_components]
         """
-        X = atleast2d_or_csr(X)
+        X = check_array(X)
 
         if (self.components_ is None):
             raise ValueError('No components initialized')
@@ -165,7 +165,7 @@ class RandomLayer(BaseRandomLayer):
     array of the same shape as its argument (the input activation array, of
     shape [n_samples, n_hidden]).  Functions provided are 'sine', 'tanh',
     'tribas', 'inv_tribas', 'sigmoid', 'hardlim', 'softlim', 'gaussian',
-    'multiquadric', or 'inv_multiquadric'.
+    'multiquadric', 'inv_multiquadric' and 'reclinear'.
 
     Parameters
     ----------
@@ -192,8 +192,8 @@ class RandomLayer(BaseRandomLayer):
 
         It must be one of 'tanh', 'sine', 'tribas', 'inv_tribas',
         'sigmoid', 'hardlim', 'softlim', 'gaussian', 'multiquadric',
-        'inv_multiquadric' or a callable.  If None is given, 'tanh'
-        will be used.
+        'inv_multiquadric', 'reclinear' or a callable.  If None is given,
+        'tanh' will be used.
 
         If a callable is given, it will be used to compute the activations.
 
@@ -241,6 +241,9 @@ class RandomLayer(BaseRandomLayer):
     _inv_multiquadric = (lambda x:
                          1.0/(np.sqrt(1.0 + pow(x, 2.0))))
 
+    # rectified linear: max(0, x)
+    _reclinear = (lambda x: np.maximum(0, x))
+
     # internal activation function table
     _internal_activation_funcs = {'sine': np.sin,
                                   'tanh': np.tanh,
@@ -252,6 +255,7 @@ class RandomLayer(BaseRandomLayer):
                                   'gaussian': _gaussian,
                                   'multiquadric': _multiquadric,
                                   'inv_multiquadric': _inv_multiquadric,
+                                  'reclinear': _reclinear
                                   }
 
     def __init__(self, n_hidden=20, alpha=0.5, random_state=None,