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‎dev/.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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tags: 645f666f9bcd5a90fca523b33c5a78b7

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‎dev/_downloads/scikit-optimize-docs.pdf

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‎dev/_modules/index.html

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‎dev/_modules/skopt/learning/gaussian_process/gpr.html

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‎dev/_modules/skopt/optimizer/base.html

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‎dev/_modules/skopt/optimizer/forest.html

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‎dev/_modules/skopt/optimizer/gbrt.html

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‎dev/_modules/skopt/optimizer/gp.html

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‎dev/_modules/skopt/plots.html

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‎dev/_modules/skopt/sampler/base.html

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‎dev/_modules/skopt/sampler/halton.html

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‎dev/_modules/skopt/sampler/lhs.html

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‎dev/_modules/skopt/searchcv.html

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‎dev/_modules/skopt/space/transformers.html

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

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

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specs: {'args': {'func': <function f at 0x7fc1d1522310>, 'dimensions': Space([Real(low=-2.0, high=2.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
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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 0x7FC1D813B740, '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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‎dev/_sources/auto_examples/hyperparameter-optimization.rst.txt

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

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specs: {'args': {'func': <function obj_fun at 0x7fc1d800e700>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
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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 0x7F093FEDCD40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f0919445a90>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
172+
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 0x7FC1F805FD40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fc1d185f220>], '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: [20.0]
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x_iters: [[-20.0], [20.0], [20.0], [-20.0], [-20.0], [20.0], [-20.0], [20.0], [20.0], [20.0]]
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@@ -321,14 +321,14 @@ The previous results can then be used to continue the optimization process:
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n_restarts_optimizer=2, noise='gaussian',
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normalize_y=True, optimizer='fmin_l_bfgs_b',
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random_state=655685735)]
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random_state: RandomState(MT19937) at 0x7F093FEDCD40
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random_state: RandomState(MT19937) at 0x7FC1F805FD40
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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 0x7f0920060700>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
326+
specs: {'args': {'func': <function obj_fun at 0x7fc1d800e700>, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
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kernel=1**2 * Matern(length_scale=1, nu=2.5),
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n_restarts_optimizer=2, noise='gaussian',
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normalize_y=True, optimizer='fmin_l_bfgs_b',
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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,
331-
0.07662367, 0.08260541, -0.13236828, -0.17524445, 0.10024491]), 'random_state': RandomState(MT19937) at 0x7F093FEDCD40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f0919445a90>], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
331+
0.07662367, 0.08260541, -0.13236828, -0.17524445, 0.10024491]), 'random_state': RandomState(MT19937) at 0x7FC1F805FD40, 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7fc1d185f220>], '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: [20.0]
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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]]
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@@ -350,7 +350,7 @@ for more information on how the results get saved and possible caveats
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 3.270 seconds)
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**Total running time of the script:** ( 0 minutes 2.936 seconds)
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**Estimated memory usage:** 8 MB
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‎dev/_sources/auto_examples/optimizer-with-different-base-estimator.rst.txt

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@@ -292,9 +292,9 @@ Test different kernels
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 8.631 seconds)
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**Total running time of the script:** ( 0 minutes 8.334 seconds)
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**Estimated memory usage:** 12 MB
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**Estimated memory usage:** 14 MB
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.. _sphx_glr_download_auto_examples_optimizer-with-different-base-estimator.py:

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

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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 27.084 seconds)
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**Total running time of the script:** ( 0 minutes 27.367 seconds)
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**Estimated memory usage:** 28 MB
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**Estimated memory usage:** 33 MB
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.. _sphx_glr_download_auto_examples_parallel-optimization.py:

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