@@ -163,13 +163,13 @@ and pass it to the minimizer:
163163 n_restarts_optimizer=2, noise='gaussian',
164164 normalize_y=True, optimizer='fmin_l_bfgs_b',
165165 random_state=655685735)]
166- random_state: RandomState(MT19937) at 0x7F7683F2C740
166+ random_state: RandomState(MT19937) at 0x7F9F4628D840
167167 space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
168- specs: {'args': {'func': <function obj_fun at 0x7f7686709d30 >, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
168+ specs: {'args': {'func': <function obj_fun at 0x7f9f52c4f310 >, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
169169 kernel=1**2 * Matern(length_scale=1, nu=2.5),
170170 n_restarts_optimizer=2, noise='gaussian',
171171 normalize_y=True, optimizer='fmin_l_bfgs_b',
172- random_state=655685735), 'n_calls': 10, 'n_random_starts': 0, 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7F7683F2C740 , 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f7601bee460 >], '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, 'acq_func': 'LCB', 'acq_optimizer': 'auto', 'x0': [-20.0], 'y0': None, 'random_state': RandomState(MT19937) at 0x7F9F4628D840 , 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f9ece08f4c0 >], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
173173 x: [20.0]
174174 x_iters: [[-20.0], [20.0], [20.0], [-20.0], [-20.0], [20.0], [-20.0], [20.0], [20.0], [20.0]]
175175
@@ -321,14 +321,14 @@ The previous results can then be used to continue the optimization process:
321321 n_restarts_optimizer=2, noise='gaussian',
322322 normalize_y=True, optimizer='fmin_l_bfgs_b',
323323 random_state=655685735)]
324- random_state: RandomState(MT19937) at 0x7F7683F2C740
324+ random_state: RandomState(MT19937) at 0x7F9F4628D840
325325 space: Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')])
326- specs: {'args': {'func': <function obj_fun at 0x7f7686709d30 >, '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 0x7f9f52c4f310 >, 'dimensions': Space([Real(low=-20.0, high=20.0, prior='uniform', transform='normalize')]), 'base_estimator': GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,
327327 kernel=1**2 * Matern(length_scale=1, nu=2.5),
328328 n_restarts_optimizer=2, noise='gaussian',
329329 normalize_y=True, optimizer='fmin_l_bfgs_b',
330330 random_state=655685735), 'n_calls': 10, 'n_random_starts': 0, '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 0x7F7683F2C740 , 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f7601bee460 >], '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 0x7F9F4628D840 , 'verbose': False, 'callback': [<skopt.callbacks.CheckpointSaver object at 0x7f9ece08f4c0 >], 'n_points': 10000, 'n_restarts_optimizer': 5, 'xi': 0.01, 'kappa': 1.96, 'n_jobs': 1, 'model_queue_size': None}, 'function': 'base_minimize'}
332332 x: [20.0]
333333 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]]
334334
@@ -350,7 +350,7 @@ for more information on how the results get saved and possible caveats
350350
351351.. rst-class :: sphx-glr-timing
352352
353- **Total running time of the script: ** ( 0 minutes 4.044 seconds)
353+ **Total running time of the script: ** ( 0 minutes 4.289 seconds)
354354
355355
356356.. _sphx_glr_download_auto_examples_interruptible-optimization.py :
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