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# Sphinx build info version 1
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@@ -353,10 +353,15 @@ <h1>Source code for skopt.learning.gaussian_process.gpr</h1><div class="highligh
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<span class="bp">self</span><span class="o">.</span><span class="n">K_inv_</span> <span class="o">=</span> <span class="n">L_inv</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">L_inv</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
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<span class="c1"># Fix deprecation warning #462</span>
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<span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">sklearn</span><span class="o">.</span><span class="n">__version__</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">])</span> <span class="o">&gt;=</span> <span class="mi">19</span><span class="p">:</span>
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<span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">sklearn</span><span class="o">.</span><span class="n">__version__</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">])</span> <span class="o">&gt;=</span> <span class="mi">23</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">y_train_std_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_y_train_std</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">y_train_mean_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_y_train_mean</span>
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<span class="k">elif</span> <span class="nb">int</span><span class="p">(</span><span class="n">sklearn</span><span class="o">.</span><span class="n">__version__</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">])</span> <span class="o">&gt;=</span> <span class="mi">19</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">y_train_mean_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_y_train_mean</span>
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<span class="k">return</span> <span class="bp">self</span></div>
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@@ -438,11 +443,14 @@ <h1>Source code for skopt.learning.gaussian_process.gpr</h1><div class="highligh
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<span class="k">else</span><span class="p">:</span> <span class="c1"># Predict based on GP posterior</span>
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<span class="n">K_trans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel_</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">X_train_</span><span class="p">)</span>
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<span class="n">y_mean</span> <span class="o">=</span> <span class="n">K_trans</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_</span><span class="p">)</span> <span class="c1"># Line 4 (y_mean = f_star)</span>
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<span class="n">y_mean</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_train_mean_</span> <span class="o">+</span> <span class="n">y_mean</span> <span class="c1"># undo normal.</span>
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<span class="c1"># undo normalisation</span>
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<span class="n">y_mean</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_train_std_</span> <span class="o">*</span> <span class="n">y_mean</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_train_mean_</span>
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<span class="k">if</span> <span class="n">return_cov</span><span class="p">:</span>
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<span class="n">v</span> <span class="o">=</span> <span class="n">cho_solve</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">L_</span><span class="p">,</span> <span class="kc">True</span><span class="p">),</span> <span class="n">K_trans</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="c1"># Line 5</span>
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<span class="n">y_cov</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel_</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">-</span> <span class="n">K_trans</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="c1"># Line 6</span>
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<span class="c1"># undo normalisation</span>
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<span class="n">y_cov</span> <span class="o">=</span> <span class="n">y_cov</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_train_std_</span><span class="o">**</span><span class="mi">2</span>
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<span class="k">return</span> <span class="n">y_mean</span><span class="p">,</span> <span class="n">y_cov</span>
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<span class="k">elif</span> <span class="n">return_std</span><span class="p">:</span>
@@ -459,17 +467,22 @@ <h1>Source code for skopt.learning.gaussian_process.gpr</h1><div class="highligh
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<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Predicted variances smaller than 0. &quot;</span>
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<span class="s2">&quot;Setting those variances to 0.&quot;</span><span class="p">)</span>
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<span class="n">y_var</span><span class="p">[</span><span class="n">y_var_negative</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
470+
<span class="c1"># undo normalisation</span>
471+
<span class="n">y_var</span> <span class="o">=</span> <span class="n">y_var</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_train_std_</span><span class="o">**</span><span class="mi">2</span>
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<span class="n">y_std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">y_var</span><span class="p">)</span>
463473

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<span class="k">if</span> <span class="n">return_mean_grad</span><span class="p">:</span>
465475
<span class="n">grad</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel_</span><span class="o">.</span><span class="n">gradient_x</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">X_train_</span><span class="p">)</span>
466476
<span class="n">grad_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">grad</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha_</span><span class="p">)</span>
467-
477+
<span class="c1"># undo normalisation</span>
478+
<span class="n">grad_mean</span> <span class="o">=</span> <span class="n">grad_mean</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_train_std_</span>
468479
<span class="k">if</span> <span class="n">return_std_grad</span><span class="p">:</span>
469480
<span class="n">grad_std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
470481
<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">y_std</span><span class="p">,</span> <span class="n">grad_std</span><span class="p">):</span>
471482
<span class="n">grad_std</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">K_trans</span><span class="p">,</span>
472483
<span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">K_inv</span><span class="p">,</span> <span class="n">grad</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">y_std</span>
484+
<span class="c1"># undo normalisation</span>
485+
<span class="n">grad_std</span> <span class="o">=</span> <span class="n">grad_std</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_train_std_</span><span class="o">**</span><span class="mi">2</span>
473486
<span class="k">return</span> <span class="n">y_mean</span><span class="p">,</span> <span class="n">y_std</span><span class="p">,</span> <span class="n">grad_mean</span><span class="p">,</span> <span class="n">grad_std</span>
474487

475488
<span class="k">if</span> <span class="n">return_std</span><span class="p">:</span>

‎0.8/_modules/skopt/learning/gbrt.html

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<meta name="Description" content="scikit-optimize: machine learning in Python">
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<title>skopt.learning.gbrt &mdash; scikit-optimize 0.8.0 documentation</title>
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<title>skopt.learning.gbrt &mdash; scikit-optimize 0.8.1 documentation</title>
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<link rel="canonical" href="https://scikit-optimize.github.io/_modules/skopt/learning/gbrt.html" />
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</div>
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<div class="alert alert-danger p-1 mb-2" role="alert">
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<p class="text-center mb-0">
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<strong>scikit-optimize 0.8.0</strong><br/>
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<strong>scikit-optimize 0.8.1</strong><br/>
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<a href="https://scikit-optimize.github.io/dev/versions.html">Other versions</a>
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</p>
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</div>

‎0.8/_modules/skopt/optimizer/base.html

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<meta name="Description" content="scikit-optimize: machine learning in Python">
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<title>skopt.optimizer.base &mdash; scikit-optimize 0.8.0 documentation</title>
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<title>skopt.optimizer.base &mdash; scikit-optimize 0.8.1 documentation</title>
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<link rel="canonical" href="https://scikit-optimize.github.io/_modules/skopt/optimizer/base.html" />
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</div>
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<div class="alert alert-danger p-1 mb-2" role="alert">
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<p class="text-center mb-0">
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<strong>scikit-optimize 0.8.0</strong><br/>
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<strong>scikit-optimize 0.8.1</strong><br/>
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<a href="https://scikit-optimize.github.io/dev/versions.html">Other versions</a>
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</p>
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</div>

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