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‎dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

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‎dev/_modules/skopt/callbacks.html

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@@ -348,6 +348,25 @@ <h1>Source code for skopt.callbacks</h1><div class="highlight"><pre>
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<span class="k">return</span> <span class="kc">None</span></div>
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<span class="k">class</span> <span class="nc">HollowIterationsStopper</span><span class="p">(</span><span class="n">EarlyStopper</span><span class="p">):</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> Stop if the improvement over the last n iterations is below a threshold.</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_iterations</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
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<span class="nb">super</span><span class="p">(</span><span class="n">HollowIterationsStopper</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">n_iterations</span> <span class="o">=</span> <span class="n">n_iterations</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">threshold</span><span class="p">)</span>
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<span class="k">def</span> <span class="nf">_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">result</span><span class="p">):</span>
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<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">func_vals</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_iterations</span><span class="p">:</span>
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<span class="k">return</span> <span class="kc">False</span>
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<span class="n">cummin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="o">.</span><span class="n">accumulate</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">func_vals</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">cummin</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">n_iterations</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">cummin</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span>
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<div class="viewcode-block" id="DeadlineStopper"><a class="viewcode-back" href="../../modules/generated/skopt.callbacks.DeadlineStopper.html#skopt.callbacks.DeadlineStopper">[docs]</a><span class="k">class</span> <span class="nc">DeadlineStopper</span><span class="p">(</span><span class="n">EarlyStopper</span><span class="p">):</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> Stop the optimization before running out of a fixed budget of time.</span>

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

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fun: 0.2071864923643295
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func_vals: array([0.20718649])
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models: []
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random_state: RandomState(MT19937) at 0x7F47401A3640
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random_state: RandomState(MT19937) at 0x7F0595687640
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space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
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specs: {'args': {'self': <skopt.optimizer.optimizer.Optimizer object at 0x7f4721006400>, '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'}
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specs: {'args': {'self': <skopt.optimizer.optimizer.Optimizer object at 0x7f057647fee0>, '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'}
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x: [-0.7315058981975282]
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x_iters: [[-0.7315058981975282]]
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**Total running time of the script:** ( 0 minutes 2.977 seconds)
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**Estimated memory usage:** 14 MB
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‎dev/_sources/auto_examples/bayesian-optimization.rst.txt

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normalize_y=True, random_state=822569775), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=0.01),
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n_restarts_optimizer=2, noise=0.010000000000000002,
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normalize_y=True, random_state=822569775)]
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random_state: RandomState(MT19937) at 0x7F47213E7040
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random_state: RandomState(MT19937) at 0x7F05764D1C40
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space: Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')])
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specs: {'args': {'func': <function f at 0x7f471a7d9dc0>, 'dimensions': Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5),
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specs: {'args': {'func': <function f at 0x7f057d9dd940>, 'dimensions': Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5),
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n_restarts_optimizer=2, noise=0.010000000000000002,
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normalize_y=True, 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 0x7F47213E7040, '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'}
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normalize_y=True, 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 0x7F05764D1C40, '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'}
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x: [0.9015772757452813]
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x_iters: [[-0.009345334109402526], [1.2713537644662787], [0.4484475787090836], [1.0854396754496047], [1.4426790855107496], [1.0722894741900921], [1.1040477623455258], [1.0332536348062722], [0.9564594121745684], [0.9335831126057479], [0.9616485252899714], [0.9464152293452086], [0.9015772757452813], [0.9186215428762252], [0.8568591231716587]]
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 3.208 seconds)
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**Total running time of the script:** ( 0 minutes 3.233 seconds)
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‎dev/_sources/auto_examples/exploration-vs-exploitation.rst.txt

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**Total running time of the script:** ( 0 minutes 33.498 seconds)
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‎dev/_sources/auto_examples/hyperparameter-optimization.rst.txt

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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 25.526 seconds)
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**Estimated memory usage:** 33 MB
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**Estimated memory usage:** 35 MB
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.. _sphx_glr_download_auto_examples_hyperparameter-optimization.py:

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

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normalize_y=True, random_state=655685735), GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5) + WhiteKernel(noise_level=1),
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random_state: RandomState(MT19937) at 0x7F057D979B40
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space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
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specs: {'args': {'func': <function obj_fun at 0x7f4729f6c9d0>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5),
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specs: {'args': {'func': <function obj_fun at 0x7f057d142040>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5),
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normalize_y=True, random_state=655685735), 'n_calls': 10, 'n_random_starts': 3, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7F47284B3B40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f4728487e80>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
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normalize_y=True, random_state=655685735), 'n_calls': 10, 'n_random_starts': 3, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7F057D979B40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f057d9c4b80>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
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x: [-18.660711532766314]
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x_iters: [[-20.0], [5.857990176187936], [-11.97095004855501], [5.450171667295798], [10.524218504753247], [-17.111120629189685], [7.251301454120835], [-19.167098759622625], [-18.660711532766314], [-18.28429711235809]]
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random_state: RandomState(MT19937) at 0x7F057C59FC40
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space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
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specs: {'args': {'func': <function obj_fun at 0x7f4729f6c9d0>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5),
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specs: {'args': {'func': <function obj_fun at 0x7f057d142040>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(kernel=1**2 * Matern(length_scale=1, nu=2.5),
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normalize_y=True, random_state=655685735), 'n_calls': 10, 'n_random_starts': 3, 'n_initial_points': 10, 'initial_point_generator': 'random', 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [[-20.0], [5.857990176187936], [-11.97095004855501], [5.450171667295798], [10.524218504753247], [-17.111120629189685], [7.251301454120835], [-19.167098759622625], [-18.660711532766314], [-18.28429711235809]], 'y0': array([-0.04682088, -0.08228249, -0.00653801, -0.07133619, 0.09063509,
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0.07662367, 0.08260541, -0.13236828, -0.17524445, 0.10024491]), 'random_state': RandomState(MT19937) at 0x7F47280BCC40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f4728487e80>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
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0.07662367, 0.08260541, -0.13236828, -0.17524445, 0.10024491]), 'random_state': RandomState(MT19937) at 0x7F057C59FC40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f057d9c4b80>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
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x: [-18.660711532766314]
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.. _sphx_glr_download_auto_examples_interruptible-optimization.py:

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

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

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.. _sphx_glr_download_auto_examples_parallel-optimization.py:

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

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

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

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

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Computation times
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=================
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**10:15.486** total execution time for **auto_examples_plots** files:
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+-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
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| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``) | 07:58.670 | 86.8 MB |
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| :ref:`sphx_glr_auto_examples_plots_visualizing-results.py` (``visualizing-results.py``) | 08:50.775 | 87.3 MB |
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+-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
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| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``) | 00:47.613 | 7.7 MB |
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| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot.py` (``partial-dependence-plot.py``) | 00:55.303 | 8.6 MB |
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+-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
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| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:15.078 | 36.8 MB |
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| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-with-categorical.py` (``partial-dependence-plot-with-categorical.py``) | 00:18.898 | 37.1 MB |
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+-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+
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| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-2D.py` (``partial-dependence-plot-2D.py``) | 00:10.380 | 7.7 MB |
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| :ref:`sphx_glr_auto_examples_plots_partial-dependence-plot-2D.py` (``partial-dependence-plot-2D.py``) | 00:10.510 | 7.9 MB |
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+-----------------------------------------------------------------------------------------------------------------------------------+-----------+---------+

‎dev/_sources/auto_examples/plots/visualizing-results.rst.txt

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**Total running time of the script:** ( 8 minutes 50.775 seconds)
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‎dev/_sources/auto_examples/sampler/initial-sampling-method-integer.rst.txt

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‎dev/_sources/auto_examples/sampler/initial-sampling-method.rst.txt

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.. rst-class:: sphx-glr-timing
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‎dev/_sources/auto_examples/sampler/sampling_comparison.rst.txt

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