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‎objects.inv‎

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‎reference/spotpython/utils/sampling/index.html‎

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<h2 id="spotpython.utils.sampling.mmphi_intensive_update" class="doc doc-heading">
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<code class="highlight language-python"><span class="n">mmphi_intensive_update</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">new_point</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">2.0</span><span class="p">)</span></code>
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<a href="#spotpython.utils.sampling.mmphi_intensive_update" class="headerlink" title="Permanent link">&para;</a></h2>
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<div class="doc doc-contents ">
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<p>Updates the Morris-Mitchell intensive criterion for n+1 points by adding a new point to the design.</p>
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<p><span class="doc-section-title">Parameters:</span></p>
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<table>
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<thead>
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<tr>
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<th>Name</th>
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<th>Type</th>
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<th>Description</th>
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<th>Default</th>
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<td><code>X</code></td>
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<td>
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<code><span title="numpy.ndarray">ndarray</span></code>
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<td>
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<div class="doc-md-description">
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<p>Existing sampling plan (shape: (n, d)).</p>
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</div>
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</td>
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<td>
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<em>required</em>
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</td>
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</tr>
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<tr class="doc-section-item">
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<td><code>new_point</code></td>
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<td>
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<code><span title="numpy.ndarray">ndarray</span></code>
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<td>
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<div class="doc-md-description">
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<p>New point to add (shape: (d,)).</p>
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</div>
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</td>
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<em>required</em>
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<td><code>J</code></td>
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<td>
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<code><span title="numpy.ndarray">ndarray</span></code>
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<td>
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<div class="doc-md-description">
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<p>Multiplicities of distances for the existing design.</p>
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</div>
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</td>
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<td>
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<em>required</em>
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<td><code>d</code></td>
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<code><span title="numpy.ndarray">ndarray</span></code>
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<td>
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<div class="doc-md-description">
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<p>Unique distances for the existing design.</p>
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</div>
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</td>
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<em>required</em>
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<td><code>q</code></td>
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<code>float</code>
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<td>
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<div class="doc-md-description">
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<p>Exponent used in the computation of the metric. Defaults to 2.0.</p>
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</div>
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</td>
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<td>
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<code>2.0</code>
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<td><code>p</code></td>
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<td>
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<code>float</code>
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<td>
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<div class="doc-md-description">
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<p>Distance norm to use (e.g., p=1 for Manhattan, p=2 for Euclidean). Defaults to 2.0.</p>
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<td>
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<code>2.0</code>
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<p><span class="doc-section-title">Returns:</span></p>
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<table>
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<thead>
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<tr>
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<th>Type</th>
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<th>Description</th>
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<code>tuple[float, <span title="numpy.ndarray">ndarray</span>, <span title="numpy.ndarray">ndarray</span>]</code>
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<td>
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<div class="doc-md-description">
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<p>tuple[float, np.ndarray, np.ndarray]: Updated intensive_phiq, updated_J, updated_d.</p>
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</div>
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</td>
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<details class="quote">
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<summary>Source code in <code>spotpython/utils/sampling.py</code></summary>
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<div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">956</span>
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<span class="normal">994</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">mmphi_intensive_update</span><span class="p">(</span><span class="n">X</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">new_point</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">J</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">d</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">q</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">,</span> <span class="n">p</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span>
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<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> Updates the Morris-Mitchell intensive criterion for n+1 points by adding a new point to the design.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> X (np.ndarray): Existing sampling plan (shape: (n, d)).</span>
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<span class="sd"> new_point (np.ndarray): New point to add (shape: (d,)).</span>
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<span class="sd"> J (np.ndarray): Multiplicities of distances for the existing design.</span>
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<span class="sd"> d (np.ndarray): Unique distances for the existing design.</span>
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<span class="sd"> q (float): Exponent used in the computation of the metric. Defaults to 2.0.</span>
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<span class="sd"> p (float): Distance norm to use (e.g., p=1 for Manhattan, p=2 for Euclidean). Defaults to 2.0.</span>
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<span class="sd"> Returns:</span>
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<span class="sd"> tuple[float, np.ndarray, np.ndarray]: Updated intensive_phiq, updated_J, updated_d.</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="n">n_points</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
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<span class="k">if</span> <span class="n">n_points</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
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<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The existing design must contain at least one point.&quot;</span><span class="p">)</span>
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<span class="c1"># Compute distances between the new point and all existing points</span>
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<span class="n">new_distances</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">new_point</span> <span class="o">-</span> <span class="n">X</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="nb">ord</span><span class="o">=</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_points</span><span class="p">)])</span>
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<span class="c1"># Combine old distances and new distances into a single list</span>
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<span class="n">all_distances</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="k">for</span> <span class="n">dist</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">J</span><span class="p">):</span>
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<span class="n">all_distances</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">dist</span><span class="p">]</span> <span class="o">*</span> <span class="n">count</span><span class="p">)</span>
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<span class="n">all_distances</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">new_distances</span><span class="p">)</span>
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<span class="c1"># Find unique distances and their counts</span>
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<span class="n">updated_d</span><span class="p">,</span> <span class="n">updated_J</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">all_distances</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<span class="c1"># Calculate the number of unique pairs of points</span>
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<span class="n">M</span> <span class="o">=</span> <span class="p">(</span><span class="n">n_points</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">n_points</span> <span class="o">/</span> <span class="mi">2</span>
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<span class="c1"># Compute the updated intensive_phiq</span>
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<span class="n">sum_term</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">updated_J</span> <span class="o">*</span> <span class="p">(</span><span class="n">updated_d</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="n">q</span><span class="p">)))</span>
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<span class="n">intensive_phiq</span> <span class="o">=</span> <span class="p">(</span><span class="n">sum_term</span> <span class="o">/</span> <span class="n">M</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">q</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">intensive_phiq</span><span class="p">,</span> <span class="n">updated_J</span><span class="p">,</span> <span class="n">updated_d</span>
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</code></pre></div></td></tr></table></div>
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</details>
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<div class="doc doc-object doc-function">
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<h2 id="spotpython.utils.sampling.mmsort" class="doc doc-heading">
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<code class="highlight language-python"><span class="n">mmsort</span><span class="p">(</span><span class="n">X3D</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span></code>
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‎search/search_index.json‎

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