Pushing the docs to dev/ for branch: master, commit 3d1610cf1eb813f4ecbf7921f512a4a8a81d7e5d

Author: skoptci

dev/_downloads/0291a47bd3f63e064c434aea964d8e66/bayesian-optimization.ipynb

@@ -208,7 +208,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/0f0d53351b0405c47cf8d0ef671596f9/interruptible-optimization.ipynb

@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/227fbc11d592826e298cb4f679fc86b8/optimizer-with-different-base-estimator.ipynb

@@ -133,7 +133,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/2836dfc666978d3b8e63b1399cca6c8a/sampling_comparison.ipynb

@@ -165,7 +165,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/2aff3ba2ab1c0ff8a9636a15622dc4c4/parallel-optimization.ipynb

@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/327b9e59a928801e7cc2b01a757db508/partial-dependence-plot.ipynb

@@ -219,7 +219,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -129,7 +129,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/659de5944f8dc1f0424c48f86a240d84/hyperparameter-optimization.ipynb

@@ -154,7 +154,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/6bf99924122eedf93b886bae30f1182b/sklearn-gridsearchcv-replacement.ipynb

@@ -183,7 +183,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/6e32b232b2fb7887cfe8ea013442a759/visualizing-results.ipynb

@@ -439,7 +439,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/7157e2941c0003b55b6f20ca4563a9b7/exploration-vs-exploitation.ipynb

@@ -154,7 +154,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/78e1fe5890c6d232b4597f4b27e2ad13/partial-dependence-plot-with-categorical.ipynb

@@ -129,7 +129,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/b4f910a92676697b8c1c26c50df6d7af/strategy-comparison.ipynb

@@ -134,7 +134,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/b6b07db6d7bc35ac6c82ff6c997e4ad1/partial-dependence-plot-2D.ipynb

@@ -208,7 +208,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/b7daeffae1b2c218da61dcc9286972ee/ask-and-tell.ipynb

@@ -172,7 +172,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/c5d1fa871ae42002773189655de152a2/store-and-load-results.ipynb

@@ -255,7 +255,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/d34667f097c619f8afda4bc936e7af21/auto_examples_jupyter.zip

@@ -255,7 +255,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.1"
+      "version": "3.8.2"
     }
   },
   "nbformat": 4,

dev/_downloads/ed737baf9735731e081bcd30f558e314/initial-sampling-method-integer.ipynb

@@ -262,7 +262,7 @@ <h1>Source code for skopt.optimizer.gp</h1><div class="highlight"><pre>
 <span class="sd">          that of `&quot;EIps&quot;`</span>
 
 <span class="sd">    acq_optimizer : string, `&quot;sampling&quot;` or `&quot;lbfgs&quot;`, default: `&quot;lbfgs&quot;`</span>
-<span class="sd">        Method to minimize the acquistion function. The fit model</span>
+<span class="sd">        Method to minimize the acquisition function. The fit model</span>
 <span class="sd">        is updated with the optimal value obtained by optimizing `acq_func`</span>
 <span class="sd">        with `acq_optimizer`.</span>
 

dev/_downloads/faec476481e9ca7d36b78731fa5a6689/initial-sampling-method.ipynb

@@ -180,9 +180,9 @@ and report the value back to the optimizer:
               fun: 0.2071864923643295
         func_vals: array([0.20718649])
            models: []
-     random_state: RandomState(MT19937) at 0x7FA3CBC82A40
+     random_state: RandomState(MT19937) at 0x7F501B5C8B40
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'self': <skopt.optimizer.optimizer.Optimizer object at 0x7fa3adf63f40>, 'dimensions': [(-2.0, 2.0)], 'base_estimator': 'GP', 'n_random_starts': None, 'n_initial_points': 10, 'initial_point_generator': 'lhs', 'n_jobs': 1, 'acq_func': 'EI', 'acq_optimizer': 'sampling', 'random_state': None, 'model_queue_size': None, 'acq_func_kwargs': None, 'acq_optimizer_kwargs': None}, 'function': 'Optimizer'}
+            specs: {'args': {'self': <skopt.optimizer.optimizer.Optimizer object at 0x7f50040bffd0>, 'dimensions': [(-2.0, 2.0)], 'base_estimator': 'GP', 'n_random_starts': None, 'n_initial_points': 10, 'initial_point_generator': 'lhs', 'n_jobs': 1, 'acq_func': 'EI', 'acq_optimizer': 'sampling', 'random_state': None, 'model_queue_size': None, 'acq_func_kwargs': None, 'acq_optimizer_kwargs': None}, 'function': 'Optimizer'}
                 x: [-0.7315058981975282]
           x_iters: [[-0.7315058981975282]]
 
@@ -286,9 +286,9 @@ meantime:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  3.354 seconds)
+   **Total running time of the script:** ( 0 minutes  2.799 seconds)
 
-**Estimated memory usage:**  10 MB
+**Estimated memory usage:**  9 MB
 
 
 .. _sphx_glr_download_auto_examples_ask-and-tell.py:

dev/_downloads/scikit-optimize-docs.pdf

@@ -265,13 +265,13 @@ provide the following information:
                              n_restarts_optimizer=2, noise=0.010000000000000002,
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
                              random_state=822569775)]
-     random_state: RandomState(MT19937) at 0x7FA3ADECBC40
+     random_state: RandomState(MT19937) at 0x7F4FFDBD6440
             space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function f at 0x7fa3ae2a4ee0>, 'dimensions': Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
+            specs: {'args': {'func': <function f at 0x7f5004098160>, 'dimensions': Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
                              kernel=1**2 * Matern(length_scale=1, nu=2.5),
                              n_restarts_optimizer=2, noise=0.010000000000000002,
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
-                             random_state=822569775), 'n_calls': 15, 'n_random_starts': 5, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7FA3ADECBC40, 'verbose': False, 'callback': None, 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
+                             random_state=822569775), 'n_calls': 15, 'n_random_starts': 5, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'EI', 'acq_optimizer': 'auto', 'x0': None, 'y0': None, 'random_state': RandomState(MT19937) at 0x7F4FFDBD6440, 'verbose': False, 'callback': None, 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
                 x: [-0.35076964188527904]
           x_iters: [[-0.009345334109402526], [1.2713537644662787], [0.4484475787090836], [1.0854396754496047], [1.4426790855107496], [0.9698921802985794], [-0.4464493263345517], [-0.6474638284799423], [-0.35076964188527904], [-0.28714767658880325], [-0.2968537755362253], [-2.0], [2.0], [-1.3149517825054502], [-0.32181607448732485]]
 
@@ -303,7 +303,7 @@ the last iteration:
  .. code-block:: none
 
 
-    <matplotlib.axes._subplots.AxesSubplot object at 0x7fa3a3708760>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ff3049be0>
 
 
 
@@ -415,7 +415,7 @@ because the GP does not gain anything more by further exploration:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  4.051 seconds)
+   **Total running time of the script:** ( 0 minutes  3.618 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_modules/skopt/optimizer/gp.html

@@ -526,7 +526,7 @@ recalculated.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  37.273 seconds)
+   **Total running time of the script:** ( 0 minutes  31.215 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_sources/auto_examples/ask-and-tell.rst.txt

@@ -207,14 +207,14 @@ Convergence plot
  .. code-block:: none
 
 
-    <matplotlib.axes._subplots.AxesSubplot object at 0x7fa3b42cc130>
+    <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ffdbe8ca0>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  36.138 seconds)
+   **Total running time of the script:** ( 0 minutes  28.369 seconds)
 
 **Estimated memory usage:**  34 MB
 

dev/_sources/auto_examples/bayesian-optimization.rst.txt

@@ -163,13 +163,13 @@ and pass it to the minimizer:
                              n_restarts_optimizer=2, noise='gaussian',
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
                              random_state=655685735)]
-     random_state: RandomState(MT19937) at 0x7FA3AE2AD540
+     random_state: RandomState(MT19937) at 0x7F4FFDC79A40
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7fa3adf480d0>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
+            specs: {'args': {'func': <function obj_fun at 0x7f4ffd8c54c0>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
                              kernel=1**2 * Matern(length_scale=1, nu=2.5),
                              n_restarts_optimizer=2, noise='gaussian',
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
-                             random_state=655685735), 'n_calls': 10, 'n_random_starts': 0, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7FA3AE2AD540, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fa3a3768a60>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
+                             random_state=655685735), 'n_calls': 10, 'n_random_starts': 0, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7F4FFDC79A40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f4ff300df40>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
                 x: [20.0]
           x_iters: [[-20.0], [20.0], [20.0], [-20.0], [-20.0], [20.0], [-20.0], [20.0], [20.0], [20.0]]
 
@@ -321,14 +321,14 @@ The previous results can then be used to continue the optimization process:
                              n_restarts_optimizer=2, noise='gaussian',
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
                              random_state=655685735)]
-     random_state: RandomState(MT19937) at 0x7FA3AC97CB40
+     random_state: RandomState(MT19937) at 0x7F4FFDCC5C40
             space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
-            specs: {'args': {'func': <function obj_fun at 0x7fa3adf480d0>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
+            specs: {'args': {'func': <function obj_fun at 0x7f4ffd8c54c0>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
                              kernel=1**2 * Matern(length_scale=1, nu=2.5),
                              n_restarts_optimizer=2, noise='gaussian',
                              normalize_y=True, optimizer='fmin_l_bfgs_b',
                              random_state=655685735), 'n_calls': 10, 'n_random_starts': 0, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [[-20.0], [20.0], [20.0], [-20.0], [-20.0], [20.0], [-20.0], [20.0], [20.0], [20.0]], 'y0': array([-0.04682088, -0.08228249, -0.00653801, -0.07133619,  0.09063509,
-            0.07662367,  0.08260541, -0.13236828, -0.17524445,  0.10024491]), 'random_state': RandomState(MT19937) at 0x7FA3AC97CB40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fa3a3768a60>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
+            0.07662367,  0.08260541, -0.13236828, -0.17524445,  0.10024491]), 'random_state': RandomState(MT19937) at 0x7F4FFDCC5C40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f4ff300df40>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
                 x: [20.0]
           x_iters: [[-20.0], [20.0], [20.0], [-20.0], [-20.0], [20.0], [-20.0], [20.0], [20.0], [20.0], [20.0], [20.0], [-20.0], [-20.0], [-20.0], [-20.0], [-20.0], [-20.0], [-20.0], [-20.0]]
 
@@ -350,7 +350,7 @@ for more information on how the results get saved and possible caveats
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  3.614 seconds)
+   **Total running time of the script:** ( 0 minutes  3.049 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_sources/auto_examples/exploration-vs-exploitation.rst.txt

@@ -254,7 +254,7 @@ Test different kernels
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  11.210 seconds)
+   **Total running time of the script:** ( 0 minutes  9.662 seconds)
 
 **Estimated memory usage:**  12 MB
 

dev/_sources/auto_examples/hyperparameter-optimization.rst.txt

@@ -119,7 +119,7 @@ scikit-optimize.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  30.411 seconds)
+   **Total running time of the script:** ( 0 minutes  26.775 seconds)
 
 **Estimated memory usage:**  31 MB
 

dev/_sources/auto_examples/interruptible-optimization.rst.txt

@@ -281,9 +281,9 @@ Simple example to show the new 2D plots.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  13.303 seconds)
+   **Total running time of the script:** ( 0 minutes  15.208 seconds)
 
-**Estimated memory usage:**  9 MB
+**Estimated memory usage:**  11 MB
 
 
 .. _sphx_glr_download_auto_examples_plots_partial-dependence-plot-2D.py:

dev/_sources/auto_examples/optimizer-with-different-base-estimator.rst.txt

@@ -188,7 +188,7 @@ parsing a list as the pars argument:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  16.278 seconds)
+   **Total running time of the script:** ( 0 minutes  13.067 seconds)
 
 **Estimated memory usage:**  34 MB
 

dev/_sources/auto_examples/parallel-optimization.rst.txt

@@ -257,9 +257,9 @@ as the minimum argument:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  54.893 seconds)
+   **Total running time of the script:** ( 0 minutes  43.254 seconds)
 
-**Estimated memory usage:**  10 MB
+**Estimated memory usage:**  9 MB
 
 
 .. _sphx_glr_download_auto_examples_plots_partial-dependence-plot.py:

dev/_sources/auto_examples/plots/partial-dependence-plot-2D.rst.txt

@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**09:10.944** total execution time for **auto_examples_plots** files:
+**09:18.692** total execution time for **auto_examples_plots** files:
 
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``)                                           | 07:46.469 | 88.7 MB |
+| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``)                                           | 08:07.163 | 88.5 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``)                                   | 00:54.893 | 10.0 MB |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``)                                   | 00:43.254 | 8.9 MB  |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:16.278 | 34.5 MB |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-2D.py` (``partial-dependence-plot-2D.py``)                             | 00:15.208 | 10.6 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
-| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-2D.py` (``partial-dependence-plot-2D.py``)                             | 00:13.303 | 9.2 MB  |
+| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:13.067 | 33.6 MB |
 +-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+

dev/_sources/auto_examples/plots/partial-dependence-plot-with-categorical.rst.txt

@@ -360,9 +360,9 @@ in both the placement of samples and the partial dependence plots.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 7 minutes  46.469 seconds)
+   **Total running time of the script:** ( 8 minutes  7.163 seconds)
 
-**Estimated memory usage:**  89 MB
+**Estimated memory usage:**  88 MB
 
 
 .. _sphx_glr_download_auto_examples_plots_visualizing-results.py:

dev/_sources/auto_examples/plots/partial-dependence-plot.rst.txt

@@ -429,7 +429,7 @@ It can be seen that random has the worst performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  10.932 seconds)
+   **Total running time of the script:** ( 0 minutes  8.393 seconds)
 
 **Estimated memory usage:**  8 MB
 

dev/_sources/auto_examples/plots/sg_execution_times.rst.txt

@@ -340,7 +340,7 @@ It can be seen that random has the worst performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  10.948 seconds)
+   **Total running time of the script:** ( 0 minutes  8.241 seconds)
 
 **Estimated memory usage:**  8 MB