Merge pull request scikit-learn#4683 from yanlend/patch-1

Typos in comments corrected

Author: ogrisel

sklearn/manifold/spectral_embedding_.py

@@ -23,7 +23,7 @@
 
 
 def _graph_connected_component(graph, node_id):
-    """Find the largest graph connected components the contains one
+    """Find the largest graph connected components that contains one
     given node
 
     Parameters
@@ -38,8 +38,8 @@ def _graph_connected_component(graph, node_id):
     Returns
     -------
     connected_components_matrix : array-like, shape: (n_samples,)
-        An array of bool value indicates the indexes of the nodes
-        belong to the largest connected components of the given query
+        An array of bool value indicating the indexes of the nodes
+        belonging to the largest connected components of the given query
         node
     """
     connected_components_matrix = np.zeros(
@@ -121,11 +121,11 @@ def _set_diag(laplacian, value):
 def spectral_embedding(adjacency, n_components=8, eigen_solver=None,
                        random_state=None, eigen_tol=0.0,
                        norm_laplacian=True, drop_first=True):
-    """Project the sample on the first eigen vectors of the graph Laplacian.
+    """Project the sample on the first eigenvectors of the graph Laplacian.
 
     The adjacency matrix is used to compute a normalized graph Laplacian
-    whose spectrum (especially the eigen vectors associated to the
-    smallest eigen values) has an interpretation in terms of minimal
+    whose spectrum (especially the eigenvectors associated to the
+    smallest eigenvalues) has an interpretation in terms of minimal
     number of cuts necessary to split the graph into comparably sized
     components.
 
@@ -135,7 +135,7 @@ def spectral_embedding(adjacency, n_components=8, eigen_solver=None,
     heat kernel of a euclidean distance matrix or a k-NN matrix).
 
     However care must taken to always make the affinity matrix symmetric
-    so that the eigen vector decomposition works as expected.
+    so that the eigenvector decomposition works as expected.
 
     Parameters
     ----------
@@ -152,7 +152,7 @@ def spectral_embedding(adjacency, n_components=8, eigen_solver=None,
 
     random_state : int seed, RandomState instance, or None (default)
         A pseudo random number generator used for the initialization of the
-        lobpcg eigen vectors decomposition when eigen_solver == 'amg'.
+        lobpcg eigenvectors decomposition when eigen_solver == 'amg'.
         By default, arpack is used.
 
     eigen_tol : float, optional, default=0.0
@@ -326,7 +326,7 @@ class SpectralEmbedding(BaseEstimator):
 
     random_state : int seed, RandomState instance, or None, default : None
         A pseudo random number generator used for the initialization of the
-        lobpcg eigen vectors decomposition when eigen_solver == 'amg'.
+        lobpcg eigenvectors decomposition when eigen_solver == 'amg'.
 
     affinity : string or callable, default : "nearest_neighbors"
         How to construct the affinity matrix.
@@ -383,11 +383,11 @@ def _pairwise(self):
         return self.affinity == "precomputed"
 
     def _get_affinity_matrix(self, X, Y=None):
-        """Caclulate the affinity matrix from data
+        """Calculate the affinity matrix from data
         Parameters
         ----------
         X : array-like, shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples
+            Training vector, where n_samples is the number of samples
             and n_features is the number of features.
 
             If affinity is "precomputed"
@@ -432,7 +432,7 @@ def fit(self, X, y=None):
         Parameters
         ----------
         X : array-like, shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples
+            Training vector, where n_samples is the number of samples
             and n_features is the number of features.
 
             If affinity is "precomputed"
@@ -469,7 +469,7 @@ def fit_transform(self, X, y=None):
         Parameters
         ----------
         X: array-like, shape (n_samples, n_features)
-            Training vector, where n_samples in the number of samples
+            Training vector, where n_samples is the number of samples
             and n_features is the number of features.
 
             If affinity is "precomputed"