deploy: 793bbf8

Author: Till-M

master/reference/acquisition.html

@@ -1289,7 +1289,7 @@ <h1 id="module-bayes_opt.acquisition"><span id="bayes-opt-acquisition"></span><a
 
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.acquisition.AcquisitionFunction.suggest">
-<span class="sig-name descname"><span class="pre">suggest</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.acquisition.AcquisitionFunction.suggest.gp" title="bayes_opt.acquisition.AcquisitionFunction.suggest.gp (Python parameter) — A fitted Gaussian Process."><span class="n"><span class="pre">gp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor" title="(in scikit-learn v1.6)"><span class="pre">GaussianProcessRegressor</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.acquisition.AcquisitionFunction.suggest.target_space" title="bayes_opt.acquisition.AcquisitionFunction.suggest.target_space (Python parameter) — The target space to probe."><span class="n"><span class="pre">target_space</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="target_space.html#bayes_opt.TargetSpace" title="bayes_opt.target_space.TargetSpace (Python class)"><span class="pre">TargetSpace</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.acquisition.AcquisitionFunction.suggest.n_random" title="bayes_opt.acquisition.AcquisitionFunction.suggest.n_random (Python parameter) — Number of random samples to use."><span class="n"><span class="pre">n_random</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.acquisition.AcquisitionFunction.suggest.n_smart" title="bayes_opt.acquisition.AcquisitionFunction.suggest.n_smart (Python parameter) — Controls the number of runs for the smart optimization."><span class="n"><span class="pre">n_smart</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.acquisition.AcquisitionFunction.suggest.fit_gp" title="bayes_opt.acquisition.AcquisitionFunction.suggest.fit_gp (Python parameter) — Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."><span class="n"><span class="pre">fit_gp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.acquisition.AcquisitionFunction.suggest.random_state" title="bayes_opt.acquisition.AcquisitionFunction.suggest.random_state (Python parameter) — Random state to use for the optimization."><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v2.2)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.acquisition.AcquisitionFunction.suggest" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">suggest</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.acquisition.AcquisitionFunction.suggest.gp" title="bayes_opt.acquisition.AcquisitionFunction.suggest.gp (Python parameter) — A fitted Gaussian Process."><span class="n"><span class="pre">gp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor" title="(in scikit-learn v1.7)"><span class="pre">GaussianProcessRegressor</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.acquisition.AcquisitionFunction.suggest.target_space" title="bayes_opt.acquisition.AcquisitionFunction.suggest.target_space (Python parameter) — The target space to probe."><span class="n"><span class="pre">target_space</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="target_space.html#bayes_opt.TargetSpace" title="bayes_opt.target_space.TargetSpace (Python class)"><span class="pre">TargetSpace</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.acquisition.AcquisitionFunction.suggest.n_random" title="bayes_opt.acquisition.AcquisitionFunction.suggest.n_random (Python parameter) — Number of random samples to use."><span class="n"><span class="pre">n_random</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.acquisition.AcquisitionFunction.suggest.n_smart" title="bayes_opt.acquisition.AcquisitionFunction.suggest.n_smart (Python parameter) — Controls the number of runs for the smart optimization."><span class="n"><span class="pre">n_smart</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.acquisition.AcquisitionFunction.suggest.fit_gp" title="bayes_opt.acquisition.AcquisitionFunction.suggest.fit_gp (Python parameter) — Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."><span class="n"><span class="pre">fit_gp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.acquisition.AcquisitionFunction.suggest.random_state" title="bayes_opt.acquisition.AcquisitionFunction.suggest.random_state (Python parameter) — Random state to use for the optimization."><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v2.2)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.acquisition.AcquisitionFunction.suggest" title="Link to this definition">¶</a></dt>
 <dd><p>Suggest a promising point to probe next.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="bayes_opt.acquisition.AcquisitionFunction.suggest-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.acquisition.AcquisitionFunction.suggest-parameters" title="Permalink to this headline">¶</a></dt>