added cmiknn_mixed independence test

jakobrunge · Dec 20, 2024 · cc4425d · cc4425d
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commit cc4425d
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diff --git a/docs/_build/.doctrees/environment.pickle b/docs/_build/.doctrees/environment.pickle
diff --git a/docs/_build/.doctrees/index.doctree b/docs/_build/.doctrees/index.doctree
diff --git a/docs/_build/_modules/tigramite/data_processing.html b/docs/_build/_modules/tigramite/data_processing.html
@@ -1648,21 +1648,21 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
 
     <span class="nb">print</span><span class="p">(</span><span class="n">frame</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
 
-    <span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-    <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
-    <span class="n">Z</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">)]</span>
-    <span class="n">tau_max</span><span class="o">=</span><span class="mi">5</span>
-    <span class="n">frame</span><span class="o">.</span><span class="n">construct_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tau_max</span><span class="p">,</span>
-                        <span class="n">extraZ</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                        <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                        <span class="n">mask_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                        <span class="n">return_cleaned_xyz</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                        <span class="n">do_checks</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                        <span class="n">cut_off</span><span class="o">=</span><span class="s1">&#39;2xtau_max&#39;</span><span class="p">,</span>
-                        <span class="n">verbosity</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="n">ordinal_patt_array</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">array_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                        <span class="n">weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="c1"># X=[(0, 0)]</span>
+    <span class="c1"># Y=[(0, 0)]</span>
+    <span class="c1"># Z=[(0, -3)]</span>
+    <span class="c1"># tau_max=5</span>
+    <span class="c1"># frame.construct_array(X, Y, Z, tau_max,</span>
+    <span class="c1">#                     extraZ=None,</span>
+    <span class="c1">#                     mask=None,</span>
+    <span class="c1">#                     mask_type=None,</span>
+    <span class="c1">#                     return_cleaned_xyz=False,</span>
+    <span class="c1">#                     do_checks=True,</span>
+    <span class="c1">#                     cut_off=&#39;2xtau_max&#39;,</span>
+    <span class="c1">#                     verbosity=4)</span>
+
+    <span class="c1"># print(ordinal_patt_array(data, array_mask=None, dim=2, step=1,</span>
+    <span class="c1">#                     weights=False, verbosity=0)[0])</span>
 </pre></div>
 
           </div>

diff --git a/docs/_build/_modules/tigramite/independence_tests/cmiknn.html b/docs/_build/_modules/tigramite/independence_tests/cmiknn.html
@@ -615,12 +615,13 @@ <h1>Source code for tigramite.independence_tests.cmiknn</h1><div class="highligh
     <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">))</span>
     <span class="n">data</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span>
     <span class="nb">print</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-    <span class="n">dataframe</span> <span class="o">=</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
-    <span class="n">cmi</span><span class="o">.</span><span class="n">set_dataframe</span><span class="p">(</span><span class="n">dataframe</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">run_test</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">Y</span><span class="o">=</span><span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">alpha_or_thres</span><span class="o">=</span><span class="mf">0.5</span>  <span class="p">))</span>
+    <span class="c1"># dataframe = DataFrame(data=data)</span>
+    <span class="c1"># cmi.set_dataframe(dataframe)</span>
+    <span class="c1"># print(cmi.run_test(X=[(0, 0)], Y=[(1, 0)], alpha_or_thres=0.5  ))</span>
     <span class="c1"># print(cmi.get_model_selection_criterion(j=1, parents=[], tau_max=0))</span>
     <span class="c1"># print(cmi.get_model_selection_criterion(j=1, parents=[(0, 0)], tau_max=0))</span>
     <span class="c1"># print(cmi.get_model_selection_criterion(j=1, parents=[(0, 0), (2, 0)], tau_max=0))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">cmi</span><span class="o">.</span><span class="n">get_dependence_measure_raw</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="p">))</span>
 </pre></div>
 
           </div>