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<div class="section" id="python-bindings-for-nmslib">
<h1>Python bindings for NMSLIB<a class="headerlink" href="#python-bindings-for-nmslib" title="Permalink to this headline"></a></h1>
<div class="section" id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline"></a></h2>
<p>This project works with Python on version 2.7+ and 3.5+, and on Linux, OSX and the Windows operating systems. To install:</p>
<p><code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">nmslib</span></code></p>
<p>You may need to install Python dev-files. On Ubuntu, you can do it as follows:</p>
<p><code class="docutils literal notranslate"><span class="pre">sudo</span> <span class="pre">apt-get</span> <span class="pre">install</span> <span class="pre">python3-dev</span></code></p>
<p>Building on Windows requires Visual Studio 2015, see <a class="reference external" href="https://github.com/pybind/python_example#installation">this project for more information</a>.</p>
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<div class="section" id="example-usage">
<h2>Example Usage<a class="headerlink" href="#example-usage" title="Permalink to this headline"></a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">nmslib</span>
<span class="kn">import</span> <span class="nn">numpy</span>
<span class="c1"># create a random matrix to index</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10000</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="c1"># initialize a new index, using a HNSW index on Cosine Similarity</span>
<span class="n">index</span> <span class="o">=</span> <span class="n">nmslib</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;hnsw&#39;</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="s1">&#39;cosinesimil&#39;</span><span class="p">)</span>
<span class="n">index</span><span class="o">.</span><span class="n">addDataPointBatch</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">index</span><span class="o">.</span><span class="n">createIndex</span><span class="p">({</span><span class="s1">&#39;post&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">},</span> <span class="n">print_progress</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="c1"># query for the nearest neighbours of the first datapoint</span>
<span class="n">ids</span><span class="p">,</span> <span class="n">distances</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">knnQuery</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="n">k</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="c1"># get all nearest neighbours for all the datapoint</span>
<span class="c1"># using a pool of 4 threads to compute</span>
<span class="n">neighbours</span> <span class="o">=</span> <span class="n">index</span><span class="o">.</span><span class="n">knnQueryBatch</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">num_threads</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
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&copy; Copyright 2019, Bilegsaikhan Naidan, Leonid Boytsov, Yury Malkov. With contributions from David Novak, Lawrence Cayton, Wei Dong, Avrelin Nikita, Ben Frederickson, Dmitry Yashunin, Bob Poekert, @gregfriedland, @orgoro, Scott Gigante, Maxim Andreev, Daniel Lemire, Nathan Kurz, Alexander Ponomarenko.
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