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<div class="section" id="module-pymatgen.optimization.linear_assignment_numpy">
<span id="pymatgen-optimization-linear-assignment-numpy-module"></span><h1>pymatgen.optimization.linear_assignment_numpy module<a class="headerlink" href="#module-pymatgen.optimization.linear_assignment_numpy" title="Permalink to this headline">¶</a></h1>
<p>This module contains an algorithm to solve the Linear Assignment Problem.
It has the same functionality as linear_assignment.pyx, but is much slower
as it is vectorized in numpy rather than cython</p>
<dl class="py class">
<dt id="pymatgen.optimization.linear_assignment_numpy.LinearAssignment">
<em class="property">class </em><code class="sig-name descname">LinearAssignment</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">costs</span></em>, <em class="sig-param"><span class="n">epsilon</span><span class="o">=</span><span class="default_value">1e-06</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/materialsproject/pymatgen/blob/v2021.1.28/pymatgen/optimization/linear_assignment_numpy.py#L22-L244"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pymatgen.optimization.linear_assignment_numpy.LinearAssignment" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>This class finds the solution to the Linear Assignment Problem.
It finds a minimum cost matching between two sets, given a cost
matrix.</p>
<p>This class is an implementation of the LAPJV algorithm described in:
R. Jonker, A. Volgenant. A Shortest Augmenting Path Algorithm for
Dense and Sparse Linear Assignment Problems. Computing 38, 325-340
(1987)</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>costs</strong> – The cost matrix of the problem. cost[i,j] should be the
cost of matching x[i] to y[j]. The cost matrix may be
rectangular</p></li>
<li><p><strong>epsilon</strong> – Tolerance for determining if solution vector is < 0</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="pymatgen.optimization.linear_assignment_numpy.LinearAssignment.min_cost">
<em class="property">property </em><code class="sig-name descname">min_cost</code><a class="reference external" href="https://github.com/materialsproject/pymatgen/blob/v2021.1.28/pymatgen/optimization/linear_assignment_numpy.py"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pymatgen.optimization.linear_assignment_numpy.LinearAssignment.min_cost" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the cost of the best assignment</p>
</dd></dl>
</dd></dl>
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