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@@ -45,53 +45,53 @@ performance (latency) and predictive power (MSE or Hamming Loss).
          learning_rate='optimal', loss='modified_huber', n_iter=5, n_jobs=1,
          penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True,
          verbose=0, warm_start=False)
-  Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.024681s
+  Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.024902s
 
   Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
          eta0=0.0, fit_intercept=True, l1_ratio=0.5, learning_rate='optimal',
          loss='modified_huber', n_iter=5, n_jobs=1, penalty='elasticnet',
          power_t=0.5, random_state=None, shuffle=True, verbose=0,
          warm_start=False)
-  Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.019110s
+  Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.019103s
 
   Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
          eta0=0.0, fit_intercept=True, l1_ratio=0.75,
          learning_rate='optimal', loss='modified_huber', n_iter=5, n_jobs=1,
          penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True,
          verbose=0, warm_start=False)
-  Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.015952s
+  Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.015135s
 
   Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
          eta0=0.0, fit_intercept=True, l1_ratio=0.9, learning_rate='optimal',
          loss='modified_huber', n_iter=5, n_jobs=1, penalty='elasticnet',
          power_t=0.5, random_state=None, shuffle=True, verbose=0,
          warm_start=False)
-  Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.013032s
+  Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.013826s
 
   Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
      kernel='rbf', max_iter=-1, nu=0.1, shrinking=True, tol=0.001,
      verbose=False)
-  Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000399s
+  Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000400s
 
   Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
      kernel='rbf', max_iter=-1, nu=0.25, shrinking=True, tol=0.001,
      verbose=False)
-  Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000720s
+  Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000703s
 
   Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
      kernel='rbf', max_iter=-1, nu=0.5, shrinking=True, tol=0.001,
      verbose=False)
-  Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001238s
+  Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001192s
 
   Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
      kernel='rbf', max_iter=-1, nu=0.75, shrinking=True, tol=0.001,
      verbose=False)
-  Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001751s
+  Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001682s
 
   Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
      kernel='rbf', max_iter=-1, nu=0.9, shrinking=True, tol=0.001,
      verbose=False)
-  Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001986s
+  Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001927s
 
   Benchmarking GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls',
                max_depth=3, max_features=None, max_leaf_nodes=None,
@@ -105,7 +105,7 @@ performance (latency) and predictive power (MSE or Hamming Loss).
                min_samples_leaf=1, min_samples_split=2,
                min_weight_fraction_leaf=0.0, n_estimators=50, presort='auto',
                random_state=None, subsample=1.0, verbose=0, warm_start=False)
-  Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000203s
+  Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000202s
 
   Benchmarking GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls',
                max_depth=3, max_features=None, max_leaf_nodes=None,
@@ -121,15 +121,15 @@ performance (latency) and predictive power (MSE or Hamming Loss).
                min_weight_fraction_leaf=0.0, n_estimators=200,
                presort='auto', random_state=None, subsample=1.0, verbose=0,
                warm_start=False)
-  Complexity: 200 | MSE: 6.1836 | Pred. Time: 0.000452s
+  Complexity: 200 | MSE: 6.1836 | Pred. Time: 0.000460s
 
   Benchmarking GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls',
                max_depth=3, max_features=None, max_leaf_nodes=None,
                min_samples_leaf=1, min_samples_split=2,
                min_weight_fraction_leaf=0.0, n_estimators=500,
                presort='auto', random_state=None, subsample=1.0, verbose=0,
                warm_start=False)
-  Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.000972s
+  Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.000993s
 
 
 
@@ -138,6 +138,6 @@ performance (latency) and predictive power (MSE or Hamming Loss).
 .. literalinclude:: plot_model_complexity_influence.py
     :lines: 16-
 
-**Total running time of the example:**  24.70 seconds
-( 0 minutes  24.70 seconds)
+**Total running time of the example:**  23.85 seconds
+( 0 minutes  23.85 seconds)
 
@@ -55,46 +55,46 @@ feeding them to the learner.
 **Script output**::
 
   Test set is 878 documents (108 positive)
-    Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.904 in 1.77s (  543 docs/s)
-            Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.78s (  541 docs/s)
-                   SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.846 in 1.78s (  540 docs/s)
-        NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.82s (  529 docs/s)
+    Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.904 in 1.79s (  538 docs/s)
+            Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.79s (  536 docs/s)
+                   SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.846 in 1.80s (  535 docs/s)
+        NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.83s (  525 docs/s)
 
 
-    Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.948 in 5.46s (  716 docs/s)
-            Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 5.47s (  715 docs/s)
-                   SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.933 in 5.47s (  714 docs/s)
-        NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 5.54s (  705 docs/s)
+    Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.948 in 5.35s (  731 docs/s)
+            Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 5.35s (  730 docs/s)
+                   SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.933 in 5.36s (  730 docs/s)
+        NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 5.39s (  725 docs/s)
 
 
-    Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.944 in 9.73s (  701 docs/s)
-            Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 9.74s (  700 docs/s)
-                   SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.950 in 9.74s (  700 docs/s)
-        NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 9.80s (  696 docs/s)
+    Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.944 in 8.85s (  770 docs/s)
+            Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 8.85s (  770 docs/s)
+                   SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.950 in 8.86s (  770 docs/s)
+        NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 8.89s (  766 docs/s)
 
 
-    Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 14.14s (  690 docs/s)
-            Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 14.15s (  689 docs/s)
-                   SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.959 in 14.15s (  689 docs/s)
-        NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 14.19s (  687 docs/s)
+    Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 12.36s (  789 docs/s)
+            Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 12.37s (  789 docs/s)
+                   SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.959 in 12.37s (  788 docs/s)
+        NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 12.41s (  786 docs/s)
 
 
-    Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.965 in 17.20s (  678 docs/s)
-            Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 17.21s (  678 docs/s)
-                   SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.957 in 17.21s (  678 docs/s)
-        NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 17.25s (  677 docs/s)
+    Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.965 in 15.53s (  752 docs/s)
+            Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 15.53s (  752 docs/s)
+                   SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.957 in 15.53s (  751 docs/s)
+        NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 15.57s (  750 docs/s)
 
 
-    Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.967 in 20.98s (  696 docs/s)
-            Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 20.99s (  696 docs/s)
-                   SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 20.99s (  696 docs/s)
-        NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 21.03s (  695 docs/s)
+    Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.967 in 19.09s (  766 docs/s)
+            Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 19.10s (  765 docs/s)
+                   SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 19.10s (  765 docs/s)
+        NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 19.14s (  764 docs/s)
 
 
-    Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.966 in 24.45s (  709 docs/s)
-            Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 24.46s (  709 docs/s)
-                   SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 24.46s (  709 docs/s)
-        NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 24.50s (  708 docs/s)
+    Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.966 in 22.38s (  775 docs/s)
+            Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 22.39s (  775 docs/s)
+                   SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 22.39s (  775 docs/s)
+        NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 22.43s (  774 docs/s)
 
 
 
@@ -103,6 +103,6 @@ feeding them to the learner.
 .. literalinclude:: plot_out_of_core_classification.py
     :lines: 25-
 
-**Total running time of the example:**  26.52 seconds
-( 0 minutes  26.52 seconds)
+**Total running time of the example:**  24.46 seconds
+( 0 minutes  24.46 seconds)
 
@@ -72,6 +72,6 @@ risk of over-fitting the data.
 .. literalinclude:: plot_outlier_detection_housing.py
     :lines: 48-
 
-**Total running time of the example:**  4.92 seconds
-( 0 minutes  4.92 seconds)
+**Total running time of the example:**  4.86 seconds
+( 0 minutes  4.86 seconds)
 
@@ -58,7 +58,7 @@ The plots represent the distribution of the prediction latency as a boxplot.
   benchmarking with 100 features
   benchmarking with 250 features
   benchmarking with 500 features
-  example run in 2.98s
+  example run in 3.04s
 
 
 
@@ -67,6 +67,6 @@ The plots represent the distribution of the prediction latency as a boxplot.
 .. literalinclude:: plot_prediction_latency.py
     :lines: 15-
 
-**Total running time of the example:**  2.99 seconds
-( 0 minutes  2.99 seconds)
+**Total running time of the example:**  3.04 seconds
+( 0 minutes  3.04 seconds)
 
@@ -62,7 +62,7 @@ References
 
    Area under the ROC curve : 0.993919
 
-  time elapsed: 6.92s
+  time elapsed: 6.82s
 
 
 
@@ -71,6 +71,6 @@ References
 .. literalinclude:: plot_species_distribution_modeling.py
     :lines: 38-
 
-**Total running time of the example:**  6.92 seconds
-( 0 minutes  6.92 seconds)
+**Total running time of the example:**  6.83 seconds
+( 0 minutes  6.83 seconds)
 
@@ -91,6 +91,6 @@ axis.
 .. literalinclude:: plot_stock_market.py
     :lines: 62-
 
-**Total running time of the example:**  10.90 seconds
-( 0 minutes  10.90 seconds)
+**Total running time of the example:**  10.21 seconds
+( 0 minutes  10.21 seconds)
 
@@ -51,6 +51,6 @@ contributed to fewer projections than the central disk.
 .. literalinclude:: plot_tomography_l1_reconstruction.py
     :lines: 37-
 
-**Total running time of the example:**  8.91 seconds
-( 0 minutes  8.91 seconds)
+**Total running time of the example:**  9.49 seconds
+( 0 minutes  9.49 seconds)
 
@@ -56,6 +56,6 @@ representation of the checkerboard structure.
 .. literalinclude:: plot_spectral_biclustering.py
     :lines: 18-
 
-**Total running time of the example:**  0.76 seconds
-( 0 minutes  0.76 seconds)
+**Total running time of the example:**  0.77 seconds
+( 0 minutes  0.77 seconds)
 
@@ -118,6 +118,6 @@ but not where it is transposed-sigmoid (e.g., Gaussian naive Bayes).
 .. literalinclude:: plot_calibration_curve.py
     :lines: 44-
 
-**Total running time of the example:**  2.45 seconds
-( 0 minutes  2.45 seconds)
+**Total running time of the example:**  2.38 seconds
+( 0 minutes  2.38 seconds)
 
@@ -64,6 +64,6 @@ with different biases per method:
 .. literalinclude:: plot_compare_calibration.py
     :lines: 50-
 
-**Total running time of the example:**  2.49 seconds
-( 0 minutes  2.49 seconds)
+**Total running time of the example:**  2.51 seconds
+( 0 minutes  2.51 seconds)
 
@@ -34,6 +34,6 @@ set.
 .. literalinclude:: plot_classifier_comparison.py
     :lines: 23-
 
-**Total running time of the example:**  8.36 seconds
-( 0 minutes  8.36 seconds)
+**Total running time of the example:**  8.32 seconds
+( 0 minutes  8.32 seconds)
 
@@ -22,6 +22,6 @@ Shows how shrinkage improves classification.
 .. literalinclude:: plot_lda.py
     :lines: 8-
 
-**Total running time of the example:**  5.56 seconds
-( 0 minutes  5.56 seconds)
+**Total running time of the example:**  5.58 seconds
+( 0 minutes  5.58 seconds)
 
@@ -45,15 +45,15 @@ value of k on various overlapping sub-samples of the dataset.
   Computing adjusted_rand_score for 10 values of n_clusters and n_samples=100
   done in 0.088s
   Computing v_measure_score for 10 values of n_clusters and n_samples=100
-  done in 0.024s
+  done in 0.025s
   Computing adjusted_mutual_info_score for 10 values of n_clusters and n_samples=100
-  done in 0.391s
+  done in 0.388s
   Computing mutual_info_score for 10 values of n_clusters and n_samples=100
-  done in 0.016s
+  done in 0.017s
   Computing adjusted_rand_score for 10 values of n_clusters and n_samples=1000
-  done in 0.058s
+  done in 0.057s
   Computing v_measure_score for 10 values of n_clusters and n_samples=1000
-  done in 0.035s
+  done in 0.036s
   Computing adjusted_mutual_info_score for 10 values of n_clusters and n_samples=1000
   done in 0.230s
   Computing mutual_info_score for 10 values of n_clusters and n_samples=1000
@@ -66,6 +66,6 @@ value of k on various overlapping sub-samples of the dataset.
 .. literalinclude:: plot_adjusted_for_chance_measures.py
     :lines: 23-
 
-**Total running time of the example:**  1.04 seconds
-( 0 minutes  1.04 seconds)
+**Total running time of the example:**  1.05 seconds
+( 0 minutes  1.05 seconds)
 
@@ -57,6 +57,6 @@ percolation instability.
 .. literalinclude:: plot_agglomerative_clustering.py
     :lines: 23-
 
-**Total running time of the example:**  7.49 seconds
-( 0 minutes  7.49 seconds)
+**Total running time of the example:**  7.50 seconds
+( 0 minutes  7.50 seconds)
 
@@ -84,6 +84,6 @@ thus the clustering puts them in the same cluster.
 .. literalinclude:: plot_agglomerative_clustering_metrics.py
     :lines: 35-
 
-**Total running time of the example:**  1.34 seconds
-( 0 minutes  1.34 seconds)
+**Total running time of the example:**  1.40 seconds
+( 0 minutes  1.40 seconds)