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@@ -245,7 +245,8 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
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<span class="sd"> - x_a and x_b are the samples</span>
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<span class="sd"> - a and b are the sample weights</span>
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<span class="sd"> When &#39;minkowski&#39; is used as a metric, :math:`d(x, y) = |x - y|^p`.</span>
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<span class="sd"> This implementation only supports metrics</span>
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<span class="sd"> of the form :math:`d(x, y) = |x - y|^p`.</span>
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<span class="sd"> Uses the algorithm detailed in [1]_</span>
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@@ -260,9 +261,8 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
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<span class="sd"> b : (nt,) ndarray, float64, optional</span>
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<span class="sd"> Target histogram (default is uniform weight)</span>
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<span class="sd"> metric: str, optional (default=&#39;sqeuclidean&#39;)</span>
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<span class="sd"> Metric to be used. Only strings listed in :func:`ot.dist` are accepted.</span>
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<span class="sd"> Due to implementation details, this function runs faster when</span>
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<span class="sd"> `&#39;sqeuclidean&#39;`, `&#39;cityblock&#39;`, or `&#39;euclidean&#39;` metrics are used.</span>
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<span class="sd"> Metric to be used. Only works with either of the strings</span>
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<span class="sd"> `&#39;sqeuclidean&#39;`, `&#39;minkowski&#39;`, `&#39;cityblock&#39;`, or `&#39;euclidean&#39;`.</span>
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<span class="sd"> p: float, optional (default=1.0)</span>
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<span class="sd"> The p-norm to apply for if metric=&#39;minkowski&#39;</span>
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<span class="sd"> dense: boolean, optional (default=True)</span>
@@ -327,6 +327,12 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
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<span class="s2">&quot;emd_1d should only be used with monodimensional data&quot;</span>
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<span class="k">assert</span> <span class="p">(</span><span class="n">x_b</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">x_b</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">x_b</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">),</span> \
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<span class="s2">&quot;emd_1d should only be used with monodimensional data&quot;</span>
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<span class="k">if</span> <span class="n">metric</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;sqeuclidean&#39;</span><span class="p">,</span> <span class="s1">&#39;minkowski&#39;</span><span class="p">,</span> <span class="s1">&#39;cityblock&#39;</span><span class="p">,</span> <span class="s1">&#39;euclidean&#39;</span><span class="p">]:</span>
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<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
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<span class="s2">&quot;Solver for EMD in 1d only supports metrics &quot;</span> <span class="o">+</span>
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<span class="s2">&quot;from the following list: &quot;</span> <span class="o">+</span>
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<span class="s2">&quot;`[&#39;sqeuclidean&#39;, &#39;minkowski&#39;, &#39;cityblock&#39;, &#39;euclidean&#39;]`&quot;</span>
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<span class="p">)</span>
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<span class="c1"># if empty array given then use uniform distributions</span>
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<span class="k">if</span> <span class="n">a</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">a</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
@@ -396,7 +402,8 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
396402
<span class="sd"> - x_a and x_b are the samples</span>
397403
<span class="sd"> - a and b are the sample weights</span>
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399-
<span class="sd"> When &#39;minkowski&#39; is used as a metric, :math:`d(x, y) = |x - y|^p`.</span>
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<span class="sd"> This implementation only supports metrics</span>
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<span class="sd"> of the form :math:`d(x, y) = |x - y|^p`.</span>
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<span class="sd"> Uses the algorithm detailed in [1]_</span>
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@@ -411,10 +418,8 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
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<span class="sd"> b : (nt,) ndarray, float64, optional</span>
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<span class="sd"> Target histogram (default is uniform weight)</span>
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<span class="sd"> metric: str, optional (default=&#39;sqeuclidean&#39;)</span>
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<span class="sd"> Metric to be used. Only strings listed in :func:`ot.dist` are accepted.</span>
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<span class="sd"> Due to implementation details, this function runs faster when</span>
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<span class="sd"> `&#39;sqeuclidean&#39;`, `&#39;minkowski&#39;`, `&#39;cityblock&#39;`, or `&#39;euclidean&#39;` metrics</span>
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<span class="sd"> are used.</span>
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<span class="sd"> Metric to be used. Only works with either of the strings</span>
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<span class="sd"> `&#39;sqeuclidean&#39;`, `&#39;minkowski&#39;`, `&#39;cityblock&#39;`, or `&#39;euclidean&#39;`.</span>
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<span class="sd"> p: float, optional (default=1.0)</span>
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<span class="sd"> The p-norm to apply for if metric=&#39;minkowski&#39;</span>
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<span class="sd"> dense: boolean, optional (default=True)</span>

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