COSMIT featuers -> features typo

Author: amueller

examples/cluster/plot_kmeans_stability_low_dim_dense.py

@@ -57,7 +57,7 @@ def make_data(random_state, n_samples_per_center, grid_size, scale):
     centers = np.array([[i, j]
                         for i in range(grid_size)
                         for j in range(grid_size)])
-    n_clusters_true, n_featues = centers.shape
+    n_clusters_true, n_features = centers.shape
 
     noise = random_state.normal(
         scale=scale, size=(n_samples_per_center, centers.shape[1]))
@@ -88,7 +88,7 @@ def make_data(random_state, n_samples_per_center, grid_size, scale):
         X, y = make_data(run_id, n_samples_per_center, grid_size, scale)
         for i, n_init in enumerate(n_init_range):
             km = factory(n_clusters=n_clusters, init=init, random_state=run_id,
-                    n_init=n_init, **params).fit(X)
+                         n_init=n_init, **params).fit(X)
             inertia[i, run_id] = km.inertia_
     p = pl.errorbar(n_init_range, inertia.mean(axis=1), inertia.std(axis=1))
     plots.append(p[0])

sklearn/decomposition/factor_analysis.py

@@ -132,7 +132,7 @@ def fit(self, X, y=None):
         else:
             if len(self.noise_variance_init) != n_features:
                 raise ValueError("noise_variance_init dimension does not "
-                                 "with number of featueres : %d != %d" %
+                                 "with number of features : %d != %d" %
                                  (len(self.noise_variance_init), n_features))
             psi = np.array(self.noise_variance_init)
 

sklearn/tests/test_common.py

@@ -185,7 +185,7 @@ def test_transformers():
                        SparseRandomProjection]:
             # Due to the jl lemma and very few samples, the number
             # of components of the random matrix projection will be greater
-            # than the number of featuers.
+            # than the number of features.
             # So we impose a smaller number (avoid "auto" mode)
             trans.n_components = 1
 
@@ -255,7 +255,7 @@ def test_transformers_sparse_data():
                            SparseRandomProjection]:
                 # Due to the jl lemma and very few samples, the number
                 # of components of the random matrix projection will be greater
-                # than the number of featuers.
+                # than the number of features.
                 # So we impose a smaller number (avoid "auto" mode)
                 trans = Trans(n_components=np.int(X.shape[1] / 4))
             else:
@@ -312,7 +312,7 @@ def test_estimators_nan_inf():
                     # Due to the jl lemma and very few samples, the number
                     # of components of the random matrix projection will be
                     # greater
-                    # than the number of featuers.
+                    # than the number of features.
                     # So we impose a smaller number (avoid "auto" mode)
                     est = Est(n_components=1)