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<html><head><meta charset="UTF-8" /><title>lambda-ml.factorization documentation</title><link rel="stylesheet" type="text/css" href="css/default.css" /><link rel="stylesheet" type="text/css" href="css/highlight.css" /><script type="text/javascript" src="js/highlight.min.js"></script><script type="text/javascript" src="js/jquery.min.js"></script><script type="text/javascript" src="js/page_effects.js"></script><script>hljs.initHighlightingOnLoad();</script></head><body><div id="header"><h2>Generated by <a href="https://github.com/weavejester/codox">Codox</a></h2><h1><a href="index.html"><span class="project-title"><span class="project-name">Lambda-ml</span> <span class="project-version">0.1.1</span></span></a></h1></div><div class="sidebar primary"><h3 class="no-link"><span class="inner">Project</span></h3><ul class="index-link"><li class="depth-1 "><a href="index.html"><div class="inner">Index</div></a></li></ul><h3 class="no-link"><span class="inner">Namespaces</span></h3><ul><li 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class="top"></span><span class="bottom"></span></span><span>util</span></div></a></li></ul></div><div class="sidebar secondary"><h3><a href="#top"><span class="inner">Public Vars</span></a></h3><ul><li class="depth-1"><a href="lambda-ml.factorization.html#var-cost"><div class="inner"><span>cost</span></div></a></li><li class="depth-1"><a href="lambda-ml.factorization.html#var-factorizations"><div class="inner"><span>factorizations</span></div></a></li><li class="depth-1"><a href="lambda-ml.factorization.html#var-init-factors"><div class="inner"><span>init-factors</span></div></a></li></ul></div><div class="namespace-docs" id="content"><h1 class="anchor" id="top">lambda-ml.factorization</h1><div class="doc"><div class="markdown"><p>Unsupervised learning with non-negative matrix factorization.</p>

<p>Example usage:</p>

<pre><code>(def data [[1 2 3] [4 5 6]])

(let [dims 2]

(-&gt; (factorizations data dims)

(nth 300)

((fn [x] (map #(mapv vec %) x)))))

;;=&gt; ([[0.20900693256125408 0.2000948450048419]

;;=&gt; [0.8547267961216941 0.32426625588317753]]

;;=&gt; [[4.601094573778913 3.4274218917618486 2.1966425686791777]

;;=&gt; [0.20523936453382804 6.391048036139935 12.709895897835892]])

</code></pre></div></div><div class="public anchor" id="var-cost"><h3>cost</h3><div class="usage"><code>(cost a b)</code></div><div class="doc"><div class="markdown"></div></div><div class="src-link"><a href="https://github.com/cloudkj/lambda-ml/blob/master/src/lambda_ml/factorization.clj#L24">view source</a></div></div><div class="public anchor" id="var-factorizations"><h3>factorizations</h3><div class="usage"><code>(factorizations v dims)</code><code>(factorizations v w h)</code></div><div class="doc"><div class="markdown"><p>Returns a lazy seq of factorizations of the input matrix v. For an m-by-n input matrix, each factorization is a pair of latent matrices with dimensions m-by-dims and dims-by-n.</p></div></div><div class="src-link"><a href="https://github.com/cloudkj/lambda-ml/blob/master/src/lambda_ml/factorization.clj#L28">view source</a></div></div><div class="public anchor" id="var-init-factors"><h3>init-factors</h3><div class="usage"><code>(init-factors rows cols)</code></div><div class="doc"><div class="markdown"></div></div><div class="src-link"><a href="https://github.com/cloudkj/lambda-ml/blob/master/src/lambda_ml/factorization.clj#L20">view source</a></div></div></div></body></html>