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‎.buildinfo‎

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# Sphinx build info version 1
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# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: dbb24acebc514779a1d752efb62c23c6
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config: 085436ad50aa002898f470a7ac7a6d5e
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip‎

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‎_downloads/77b9e52ede6dc6a82f8015e94df3d763/plot_iris_example.zip‎

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‎_downloads/b4cf190b888ee49e92905caa55256121/plot_iris_example.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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"version": "3.12.3"
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}
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},
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"nbformat": 4,

‎_images/sphx_glr_plot_iris_example_002.png‎

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‎_images/sphx_glr_plot_iris_example_003.png‎

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‎_modules/index.html‎

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document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
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</script>
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‎_modules/radius_clustering/radius_clustering.html‎

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<span class="kn">import</span> <span class="nn">os</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">scipy.spatial</span> <span class="k">as</span> <span class="nn">sp_spatial</span>
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<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">pairwise_distances</span>
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<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClusterMixin</span>
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<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">check_array</span>
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<span class="sd"> -----------</span>
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<span class="sd"> X : array-like, shape (n_samples, n_features)</span>
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<span class="sd"> The input data.</span>
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<span class="sd"> centers : list</span>
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<span class="sd"> centers\_ : list</span>
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<span class="sd"> The indices of the cluster centers.</span>
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<span class="sd"> labels\_ : array-like, shape (n_samples,)</span>
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<span class="sd"> The cluster labels for each point in the input data.</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">manner</span> <span class="o">=</span> <span class="n">manner</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">threshold</span>
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<span class="k">def</span> <span class="nf">_check_symmetric</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">):</span>
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<span class="k">if</span> <span class="n">a</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">2</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;Input must be a 2D array.&quot;</span><span class="p">)</span>
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<span class="k">if</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
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<span class="k">return</span> <span class="kc">False</span>
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<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">a</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">tol</span><span class="p">)</span>
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<div class="viewcode-block" id="RadiusClustering.fit">
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<a class="viewcode-back" href="../../api.html#radius_clustering.RadiusClustering.fit">[docs]</a>
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<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
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<span class="c1"># Create dist and adj matrices</span>
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<span class="n">dist_mat</span> <span class="o">=</span> <span class="n">sp_spatial</span><span class="o">.</span><span class="n">distance_matrix</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
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<span class="n">dist_mat</span> <span class="o">=</span> <span class="n">pairwise_distances</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;euclidean&quot;</span><span class="p">)</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="n">dist_mat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span>
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<span class="n">adj_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">triu</span><span class="p">((</span><span class="n">dist_mat</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">),</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">nb_edges</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">adj_mask</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">edges</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">adj_mask</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nb_edges</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">centers_</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">labels_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">centers_</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">effective_radius</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_mds_exec_time</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="k">return</span> <span class="bp">self</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">edges</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">adj_mask</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint32</span><span class="p">)</span> <span class="c1">#TODO: changer en uint32</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">dist_mat</span> <span class="o">=</span> <span class="n">dist_mat</span>
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<span class="sd"> This function uses the approximation method to solve the MDS problem.</span>
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<span class="sd"> See [casado]_ for more details.</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="n">result</span> <span class="o">=</span> <span class="n">solve_mds</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">edges</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">nb_edges</span><span class="p">,</span> <span class="s2">&quot;test&quot;</span><span class="p">)</span>
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<span class="n">result</span> <span class="o">=</span> <span class="n">solve_mds</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">edges</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">nb_edges</span><span class="p">,</span> <span class="s2">&quot;test&quot;</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">centers_</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">result</span><span class="p">[</span><span class="s2">&quot;solution_set&quot;</span><span class="p">]]</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_mds_exec_time</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s2">&quot;Time&quot;</span><span class="p">]</span>
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@@ -643,8 +664,8 @@ <h1>Source code for radius_clustering.radius_clustering</h1><div class="highligh
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‎_sources/auto_examples/plot_iris_example.rst.txt‎

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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** (0 minutes 0.486 seconds)
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**Total running time of the script:** (0 minutes 0.485 seconds)
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.. _sphx_glr_download_auto_examples_plot_iris_example.py:

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