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</ul> - </div> - </div> -</div> + <!--</ul>--> + </div><!-- /.navbar-collapse --> </div> +</nav> - <div class="col-md-8"> - <div class="page-header"> - <h1>Distance Metrics </h1> - </div> - <h3 id="distance-metrics-supported-by-mahout">Distance Metrics Supported By Mahout</h3> + <div class="container mt-5 pb-4"> + + <div class="row"> + + <div class="col-lg-8"> + <h3 id="distance-metrics-supported-by-mahout">Distance Metrics Supported By Mahout</h3> <div class="table-striped"> <table class="table"> @@ -350,7 +207,7 @@ Weighted Manhattan--> <p>In Mahout one can access the distant metrics directly to measure the distance between two arbitrary vectors, or can specify which distance metric to use as part of an algorithm. In the latter case the distance metric is called -by <code>Symbol</code>, we never pass Distance metrics directly to an algorithm. This design choice, in part has to do with +by <code class="highlighter-rouge">Symbol</code>, we never pass Distance metrics directly to an algorithm. This design choice, in part has to do with serialization of object and keeping the engine bindings as simple as possible. Behind the scenes, the only thing that is serialized and sent to the workers is a number which specifies what distant metric to use- this is much more abstract and easier to maintain on the back end than making sure each function can be serialized by any arbitrary engine. @@ -362,17 +219,18 @@ and recompile.</p> <p><strong>Meausring the distance between two vectors</strong></p> -<pre><code>import org.apache.mahout.math.algorithms.common.distance._ +<div class="highlighter-rouge"><pre class="highlight"><code>import org.apache.mahout.math.algorithms.common.distance._ val v1 = dvec(1.0, 1.5, -1.2, 3.5) val v2 = dvec(0.1, -1.4, 10.5, 3.2) Cosine.distance(v1, v2) </code></pre> +</div> <p><strong>Using distance in clustering</strong></p> -<pre><code>import org.apache.mahout.math.algorithms.clustering.CanopyClustering +<div class="highlighter-rouge"><pre class="highlight"><code>import org.apache.mahout.math.algorithms.clustering.CanopyClustering val drmA = drmParallelize(dense((1.0, 1.2, 1.3, 1.4), (1.1, 1.5, 2.5, 1.0), @@ -382,34 +240,28 @@ val drmA = drmParallelize(dense((1.0, 1.2, 1.3, 1.4), val model = new CanopyClustering().fit(drmA, 'distanceMeasure -> 'Cosine) </code></pre> - - </div> </div> - </div> - + </div> -</div> -<div id="footer"> - <div class="container"> - <p>© 2017 Apache Mahout - with help from <a href="http://jekyllbootstrap.com"; target="_blank" title="The Definitive Jekyll Blogging Framework">Jekyll Bootstrap</a> - and <a href="http://getbootstrap.com"; target="_blank">Bootstrap</a> - </p> </div> -</div> - - +</div> + <footer class="footer bg-light"> + <div class="container text-center small"> + Copyright © 2014-2017 The Apache Software Foundation, Licensed under the Apache License, Version 2.0. + </div> +</footer> + <script src="/assets/vendor/jquery/jquery-slim.min.js"></script> + <script src="/assets/vendor/popper/popper.min.js"></script> + <script src="/assets/vendor/bootstrap/js/bootstrap.min.js"></script> + <script src="/assets/header.js"></script> + <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; type="text/javascript"></script> -<!-- Latest compiled and minified JavaScript, requires jQuery 1.x (2.x not supported in IE8) --> -<!-- Placed at the end of the document so the pages load faster --> -<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js";></script> -<script src="/assets/themes/mahout3/js/bootstrap.min.js"></script> </body> -</html> +</html>
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One-Hot-Encoding)</a></li> - <li> <a href="/algorithms/preprocessors/StandardScaler.html">StandardScaler</a></li> - <li> <a href="/algorithms/preprocessors/MeanCenter.html">MeanCenter</a></li> - </ul> - </div> - <a href="#regression" class="list-group-item list-group-item-success" data-toggle="collapse" data-parent="#AlgoMenu"><b>Regression</b><i class="fa fa-caret-down"></i></a> - <div class="collapse" id="regression"> - <ul class="nav sidebar-nav"> - <a href="#serial-correlation" class="list-group-item list-group-item-success" data-toggle="collapse" data-parent="#regression"><b>• Serial Correlation</b><i class="fa fa-caret-down"></i></a> - <div class="collapse" id="serial-correlation"> - <ul class="nav sidebar-nav"> - <li> <a href="/algorithms/regression/serial-correlation/cochrane-orcutt.html">Cochrane-Orcutt Procedure</a></li> - <li> <a href="/algorithms/regression/serial-correlation/dw-test.html">Durbin Watson Test</a></li> - </ul> - </div> - <li> <a href="/algorithms/regression/ols.html">Ordinary Least Squares (Closed Form)</a></li> - <li> <a href="/algorithms/regression/fittness-tests.html">Fitness Tests</a></li> </ul> - </div> - <a href="#reccomenders" class="list-group-item list-group-item-success" data-toggle="collapse" data-parent="#AlgoMenu"><b>Reccomenders</b><i class="fa fa-caret-down"></i></a> - <div class="collapse" id="reccomenders"> - <ul class="nav sidebar-nav"> - <li> <a href="/algorithms/reccomenders">Reccomender Overview</a></li> - <li> <a href="/algorithms/reccomenders/cco.html">CCO</a></li> - <li> <a href="/algorithms/reccomenders/d-als.html">Distributed Alternating Least Squares</a></li> - </ul> - </div> - </div> - <span><b>Map Reduce Algorithms</b> (deprecated)</span> - <div class="list-group panel"> - <a href="#classification" class="list-group-item list-group-item-success" data-toggle="collapse" data-parent="#AlgoMenu"><b>Classification</b><i class="fa fa-caret-down"></i></a> - <div class="collapse" id="classification"> - <ul class="nav sidebar-nav"> - <li> <a href="/algorithms/map-reduce/classification/bayesian.html">Bayesian</a></li> - <li> <a href="/algorithms/map-reduce/classification/class-discovery.html">Class Discovery</a></li> - <li> <a href="/algorithms/map-reduce/classification/classifyingyourdata.html">Classifying Your Data</a></li> - <li> <a href="/algorithms/map-reduce/classification/collocations.html">Collocation</a></li> - <li> <a href="/algorithms/map-reduce/classification/gaussian-discriminative-analysis.html">Gaussian Discriminative Analysis</a></li> - <li> <a href="/algorithms/map-reduce/classification/hidden-markov-models.html">Hidden Markov Models</a></li> - <li> <a href="/algorithms/map-reduce/classification/independent-component-analysis.html">Independent Component Analysis</a></li> - <li> <a href="/algorithms/map-reduce/classification/locally-weighted-linear-regression.html">Locally Weighted Linear Regression</a></li> - <li> <a href="/algorithms/map-reduce/classification/logistic-regression.html">Logistic Regression</a></li> - <li> <a href="/algorithms/map-reduce/classification/mahout-collections.html">Mahout Collections</a></li> - <li> <a href="/algorithms/map-reduce/classification/mlp.html">Multilayer Perceptron</a></li> - <li> <a href="/algorithms/map-reduce/classification/naivebayes.html">Naive Bayes</a></li> - <li> <a href="/algorithms/map-reduce/classification/neural-network.html">Neural Networks</a></li> - <li> <a href="/algorithms/map-reduce/classification/partial-implementation.html">Partial Implementation</a></li> - <li> <a href="/algorithms/map-reduce/classification/random-forrests.html">Random Forrests</a></li> - <li> <a href="/algorithms/map-reduce/classification/restricted-boltzman-machines.html">Restricted Boltzman Machines</a></li> - <li> <a href="/algorithms/map-reduce/classification/support-vector-machines.html">Support Vector Machines</a></li> - </ul> - </div> - <a href="#mr-clustering" class="list-group-item list-group-item-success" data-toggle="collapse" data-parent="#AlgoMenu"><b>Clustering</b><i class="fa fa-caret-down"></i></a> - <div class="collapse" id="mr-clustering"> - <ul class="nav sidebar-nav"> - <li> <a href="/algorithms/map-reduce/clustering/canopy-clustering.html">Canopy Clustering</a></li> - <li> <a href="/algorithms/map-reduce/clustering/cluster-dumper.html">Cluster Dumper</a></li> - <li> <a href="/algorithms/map-reduce/clustering/expectation-maximization.html">Expectation Maximization</a></li> - <li> <a href="/algorithms/map-reduce/clustering/fuzzy-k-means.html">Fuzzy K-Means</a></li> - <li> <a href="/algorithms/map-reduce/clustering/hierarchical-clustering.html">Hierarchical Clustering</a></li> - <li> <a href="/algorithms/map-reduce/clustering/k-means-clustering.html">K-Means Clustering</a></li> - <li> <a href="/algorithms/map-reduce/clustering/latent-dirichlet-allocation.html">Latent Dirichlet Allocation</a></li> - <li> <a href="/algorithms/map-reduce/clustering/llr---log-likelihood-ratio.html">Log Likelihood Ratio</a></li> - <li> <a href="/algorithms/map-reduce/clustering/spectral-clustering.html">Spectral Clustering</a></li> - <li> <a href="/algorithms/map-reduce/clustering/streaming-k-means.html">Streaming K-Means</a></li> - 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