Making Spectral Embedding Deterministic

Hasil-Sharma · Hasil-Sharma · commit b5f91f2a9ef2 · 2015-02-15T22:30:54.000+05:30
diff --git a/sklearn/manifold/spectral_embedding_.py b/sklearn/manifold/spectral_embedding_.py
@@ -181,7 +181,7 @@ def spectral_embedding(adjacency, n_components=8, eigen_solver=None,
     Notes
     -----
     Spectral embedding is most useful when the graph has one connected
-    component. If there graph has many components, the first few u
+    component. If there graph has many components, the first few eigenvectors
     will simply uncover the connected components of the graph.
 
     References
@@ -294,7 +294,7 @@ def spectral_embedding(adjacency, n_components=8, eigen_solver=None,
             # lobpcg needs native floats
             laplacian = laplacian.astype(np.float)
             laplacian = _set_diag(laplacian, 1)
-            # We increase the number of u requested, as lobpcg
+            # We increase the number of eigenvectors requested, as lobpcg
             # doesn't behave well in low dimension
             X = random_state.rand(laplacian.shape[0], n_components + 1)
             X[:, 0] = dd.ravel()
