deploy: f907105

Author: LouisTier

_modules/torchsom/core/som.html

@@ -415,7 +415,7 @@ <h1>Source code for torchsom.core.som</h1><div class="highlight"><pre>
 <span></span><span class="sd">&quot;&quot;&quot;PyTorch implementation of classic Self Organizing Maps using batch learning.&quot;&quot;&quot;</span>
 
 <span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Optional</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
 
 <span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
 <span class="kn">import</span><span class="w"> </span><span class="nn">torch.nn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">nn</span>
@@ -848,7 +848,11 @@ <h1>Source code for torchsom.core.som</h1><div class="highlight"><pre>
         <span class="n">historical_outputs</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
         <span class="n">bmus_idx_map</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]],</span>
         <span class="n">min_buffer_threshold</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">50</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+        <span class="n">return_indices</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span>
+        <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
+        <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Collect historical samples similar to the query sample using SOM projection.</span>
 
 <span class="sd">        Args:</span>
@@ -857,6 +861,11 @@ <h1>Source code for torchsom.core.som</h1><div class="highlight"><pre>
 <span class="sd">            historical_outputs (torch.Tensor): Historical outputs tensor [num_samples]</span>
 <span class="sd">            bmus_idx_map (dict[tuple[int, int], list[int]]): BMU to data indices mapping</span>
 <span class="sd">            min_buffer_threshold (int): Minimum buffer threshold</span>
+<span class="sd">            return_indices (bool): If True, also return the indices of collected samples</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            If return_indices is False: (historical_data_buffer, historical_output_buffer)</span>
+<span class="sd">            If return_indices is True: (historical_data_buffer, historical_output_buffer, indices_tensor)</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">query_sample</span> <span class="o">=</span> <span class="n">query_sample</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
         <span class="n">bmu_pos</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">identify_bmus</span><span class="p">(</span><span class="n">query_sample</span><span class="p">)</span>
@@ -907,6 +916,8 @@ <h1>Source code for torchsom.core.som</h1><div class="highlight"><pre>
         <span class="p">)</span>
         <span class="n">historical_data_buffer</span> <span class="o">=</span> <span class="n">historical_samples</span><span class="p">[</span><span class="n">indices_tensor</span><span class="p">]</span>
         <span class="n">historical_output_buffer</span> <span class="o">=</span> <span class="n">historical_outputs</span><span class="p">[</span><span class="n">indices_tensor</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">return_indices</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">historical_data_buffer</span><span class="p">,</span> <span class="n">historical_output_buffer</span><span class="p">,</span> <span class="n">indices_tensor</span>
         <span class="k">return</span> <span class="n">historical_data_buffer</span><span class="p">,</span> <span class="n">historical_output_buffer</span></div>
 
 

api/core.html

@@ -672,7 +672,7 @@ <h1>Core API<a class="headerlink" href="#core-api" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torchsom.core.som.SOM.collect_samples">
-<span class="sig-name descname"><span class="pre">collect_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query_sample</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">historical_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">historical_outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bmus_idx_map</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_buffer_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchsom/core/som.html#SOM.collect_samples"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchsom.core.som.SOM.collect_samples" title="Link to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">collect_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query_sample</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">historical_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">historical_outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bmus_idx_map</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_buffer_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchsom/core/som.html#SOM.collect_samples"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchsom.core.som.SOM.collect_samples" title="Link to this definition"></a></dt>
 <dd><p>Collect historical samples similar to the query sample using SOM projection.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -682,10 +682,15 @@ <h1>Core API<a class="headerlink" href="#core-api" title="Link to this heading">
 <li><p><strong>historical_outputs</strong> (<em>torch.Tensor</em>) – Historical outputs tensor [num_samples]</p></li>
 <li><p><strong>bmus_idx_map</strong> (<em>dict</em><em>[</em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em><em>, </em><em>list</em><em>[</em><em>int</em><em>]</em><em>]</em>) – BMU to data indices mapping</p></li>
 <li><p><strong>min_buffer_threshold</strong> (<em>int</em>) – Minimum buffer threshold</p></li>
+<li><p><strong>return_indices</strong> (<em>bool</em>) – If True, also return the indices of collected samples</p></li>
 </ul>
 </dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>tuple[<em>Tensor</em>, <em>Tensor</em>]</p>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>(historical_data_buffer, historical_output_buffer)
+If return_indices is True: (historical_data_buffer, historical_output_buffer, indices_tensor)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>If return_indices is False</p>
 </dd>
 </dl>
 </dd></dl>