Google Git
Sign in
apache / spark-website / 362ef752f88697ba41adaa795570a250eb8ee106 / . / site / docs / 3.3.1 / api / python / reference / api / pyspark.mllib.linalg.DenseVector.html
blob: 6269e3b6d4b9fcdea9dfa399e4fbad9b5f8251f6 [file] [log] [blame]
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<title>DenseVector &#8212; PySpark 3.3.1 documentation</title>
<link rel="stylesheet" href="../../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
<link rel="stylesheet"
href="../../_static/vendor/fontawesome/5.13.0/css/all.min.css">
<link rel="preload" as="font" type="font/woff2" crossorigin
href="../../_static/vendor/fontawesome/5.13.0/webfonts/fa-solid-900.woff2">
<link rel="preload" as="font" type="font/woff2" crossorigin
href="../../_static/vendor/fontawesome/5.13.0/webfonts/fa-brands-400.woff2">
<link rel="stylesheet"
href="../../_static/vendor/open-sans_all/1.44.1/index.css">
<link rel="stylesheet"
href="../../_static/vendor/lato_latin-ext/1.44.1/index.css">
<link rel="stylesheet" href="../../_static/basic.css" type="text/css" />
<link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
<link rel="stylesheet" type="text/css" href="../../_static/css/pyspark.css" />
<link rel="preload" as="script" href="../../_static/js/index.3da636dd464baa7582d2.js">
<script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
<script src="../../_static/jquery.js"></script>
<script src="../../_static/underscore.js"></script>
<script src="../../_static/doctools.js"></script>
<script src="../../_static/language_data.js"></script>
<script src="../../_static/copybutton.js"></script>
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
<script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
<script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true, "ignoreClass": "document", "processClass": "math|output_area"}})</script>
<link rel="search" title="Search" href="../../search.html" />
<link rel="next" title="SparseVector" href="pyspark.mllib.linalg.SparseVector.html" />
<link rel="prev" title="Vector" href="pyspark.mllib.linalg.Vector.html" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="docsearch:language" content="en" />
</head>
<body data-spy="scroll" data-target="#bd-toc-nav" data-offset="80">
<nav class="navbar navbar-light navbar-expand-lg bg-light fixed-top bd-navbar" id="navbar-main">
<div class="container-xl">
<a class="navbar-brand" href="../../index.html">
<img src="../../_static/spark-logo-reverse.png" class="logo" alt="logo" />
</a>
<button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbar-menu" aria-controls="navbar-menu" aria-expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
<div id="navbar-menu" class="col-lg-9 collapse navbar-collapse">
<ul id="navbar-main-elements" class="navbar-nav mr-auto">
<li class="nav-item ">
<a class="nav-link" href="../../getting_started/index.html">Getting Started</a>
</li>
<li class="nav-item ">
<a class="nav-link" href="../../user_guide/index.html">User Guide</a>
</li>
<li class="nav-item active">
<a class="nav-link" href="../index.html">API Reference</a>
</li>
<li class="nav-item ">
<a class="nav-link" href="../../development/index.html">Development</a>
</li>
<li class="nav-item ">
<a class="nav-link" href="../../migration_guide/index.html">Migration Guide</a>
</li>
</ul>
<ul class="navbar-nav">
</ul>
</div>
</div>
</nav>
<div class="container-xl">
<div class="row">
<div class="col-12 col-md-3 bd-sidebar"><form class="bd-search d-flex align-items-center" action="../../search.html" method="get">
<i class="icon fas fa-search"></i>
<input type="search" class="form-control" name="q" id="search-input" placeholder="Search the docs ..." aria-label="Search the docs ..." autocomplete="off" >
</form>
<nav class="bd-links" id="bd-docs-nav" aria-label="Main navigation">
<div class="bd-toc-item active">
<ul class="nav bd-sidenav">
<li class="">
<a href="../pyspark.sql/index.html">Spark SQL</a>
</li>
<li class="">
<a href="../pyspark.pandas/index.html">Pandas API on Spark</a>
</li>
<li class="">
<a href="../pyspark.ss/index.html">Structured Streaming</a>
</li>
<li class="">
<a href="../pyspark.ml.html">MLlib (DataFrame-based)</a>
</li>
<li class="">
<a href="../pyspark.streaming.html">Spark Streaming</a>
</li>
<li class="active">
<a href="../pyspark.mllib.html">MLlib (RDD-based)</a>
</li>
<li class="">
<a href="../pyspark.html">Spark Core</a>
</li>
<li class="">
<a href="../pyspark.resource.html">Resource Management</a>
</li>
</ul>
</nav>
</div>
<div class="d-none d-xl-block col-xl-2 bd-toc">
<nav id="bd-toc-nav">
<ul class="nav section-nav flex-column">
</ul>
</nav>
</div>
<main class="col-12 col-md-9 col-xl-7 py-md-5 pl-md-5 pr-md-4 bd-content" role="main">
<div>
<div class="section" id="densevector">
<h1>DenseVector<a class="headerlink" href="#densevector" title="Permalink to this headline"></a></h1>
<dl class="py class">
<dt id="pyspark.mllib.linalg.DenseVector">
<em class="property">class </em><code class="sig-prename descclassname">pyspark.mllib.linalg.</code><code class="sig-name descname">DenseVector</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ar</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>bytes<span class="p">, </span>numpy.ndarray<span class="p">, </span>Iterable<span class="p">[</span>float<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/pyspark/mllib/linalg.html#DenseVector"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.linalg.DenseVector" title="Permalink to this definition"></a></dt>
<dd><p>A dense vector represented by a value array. We use numpy array for
storage and arithmetics will be delegated to the underlying numpy
array.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">u</span> <span class="o">=</span> <span class="n">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="p">([</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">+</span> <span class="n">u</span>
<span class="go">DenseVector([4.0, 6.0])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="mi">2</span> <span class="o">-</span> <span class="n">v</span>
<span class="go">DenseVector([1.0, 0.0])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">/</span> <span class="mi">2</span>
<span class="go">DenseVector([0.5, 1.0])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">*</span> <span class="n">u</span>
<span class="go">DenseVector([3.0, 8.0])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">u</span> <span class="o">/</span> <span class="n">v</span>
<span class="go">DenseVector([3.0, 2.0])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">u</span> <span class="o">%</span> <span class="mi">2</span>
<span class="go">DenseVector([1.0, 0.0])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="o">-</span><span class="n">v</span>
<span class="go">DenseVector([-1.0, -2.0])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.linalg.DenseVector.asML" title="pyspark.mllib.linalg.DenseVector.asML"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asML</span></code></a>()</p></td>
<td><p>Convert this vector to the new mllib-local representation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.linalg.DenseVector.dot" title="pyspark.mllib.linalg.DenseVector.dot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dot</span></code></a>(other)</p></td>
<td><p>Compute the dot product of two Vectors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.linalg.DenseVector.norm" title="pyspark.mllib.linalg.DenseVector.norm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">norm</span></code></a>(p)</p></td>
<td><p>Calculates the norm of a DenseVector.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.linalg.DenseVector.numNonzeros" title="pyspark.mllib.linalg.DenseVector.numNonzeros"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numNonzeros</span></code></a>()</p></td>
<td><p>Number of nonzero elements.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.linalg.DenseVector.parse" title="pyspark.mllib.linalg.DenseVector.parse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parse</span></code></a>(s)</p></td>
<td><p>Parse string representation back into the DenseVector.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.linalg.DenseVector.squared_distance" title="pyspark.mllib.linalg.DenseVector.squared_distance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">squared_distance</span></code></a>(other)</p></td>
<td><p>Squared distance of two Vectors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.linalg.DenseVector.toArray" title="pyspark.mllib.linalg.DenseVector.toArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">toArray</span></code></a>()</p></td>
<td><p>Returns an numpy.ndarray</p></td>
</tr>
</tbody>
</table>
<p class="rubric">Attributes</p>
<table class="longtable table autosummary">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.linalg.DenseVector.values" title="pyspark.mllib.linalg.DenseVector.values"><code class="xref py py-obj docutils literal notranslate"><span class="pre">values</span></code></a></p></td>
<td><p>Returns a list of values</p></td>
</tr>
</tbody>
</table>
<p class="rubric">Methods Documentation</p>
<dl class="py method">
<dt id="pyspark.mllib.linalg.DenseVector.asML">
<code class="sig-name descname">asML</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="pyspark.ml.linalg.DenseVector.html#pyspark.ml.linalg.DenseVector" title="pyspark.ml.linalg.DenseVector">pyspark.ml.linalg.DenseVector</a><a class="reference internal" href="../../_modules/pyspark/mllib/linalg.html#DenseVector.asML"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.linalg.DenseVector.asML" title="Permalink to this definition"></a></dt>
<dd><p>Convert this vector to the new mllib-local representation.
This does NOT copy the data; it copies references.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 2.0.0.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><dl class="simple">
<dt><a class="reference internal" href="pyspark.ml.linalg.DenseVector.html#pyspark.ml.linalg.DenseVector" title="pyspark.ml.linalg.DenseVector"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.ml.linalg.DenseVector</span></code></a></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="pyspark.mllib.linalg.DenseVector.dot">
<code class="sig-name descname">dot</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">other</span><span class="p">:</span> <span class="n">Iterable<span class="p">[</span>float<span class="p">]</span></span></em><span class="sig-paren">)</span> &#x2192; numpy.float64<a class="reference internal" href="../../_modules/pyspark/mllib/linalg.html#DenseVector.dot"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.linalg.DenseVector.dot" title="Permalink to this definition"></a></dt>
<dd><p>Compute the dot product of two Vectors. We support
(Numpy array, list, SparseVector, or SciPy sparse)
and a target NumPy array that is either 1- or 2-dimensional.
Equivalent to calling numpy.dot of the two vectors.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span> <span class="o">=</span> <span class="n">DenseVector</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">dense</span><span class="p">)</span>
<span class="go">5.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">SparseVector</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]))</span>
<span class="go">4.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="go">5.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
<span class="go">5.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span><span class="o">.</span><span class="n">dot</span><span class="p">([</span><span class="mf">1.</span><span class="p">,])</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="o">...</span>
<span class="gr">AssertionError</span>: <span class="n">dimension mismatch</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">))</span>
<span class="go">array([ 5., 11.])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">))</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="o">...</span>
<span class="gr">AssertionError</span>: <span class="n">dimension mismatch</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt id="pyspark.mllib.linalg.DenseVector.norm">
<code class="sig-name descname">norm</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">p</span><span class="p">:</span> <span class="n">NormType</span></em><span class="sig-paren">)</span> &#x2192; numpy.float64<a class="reference internal" href="../../_modules/pyspark/mllib/linalg.html#DenseVector.norm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.linalg.DenseVector.norm" title="Permalink to this definition"></a></dt>
<dd><p>Calculates the norm of a DenseVector.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">DenseVector</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="go">3.7...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="go">6.0</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt id="pyspark.mllib.linalg.DenseVector.numNonzeros">
<code class="sig-name descname">numNonzeros</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="../../_modules/pyspark/mllib/linalg.html#DenseVector.numNonzeros"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.linalg.DenseVector.numNonzeros" title="Permalink to this definition"></a></dt>
<dd><p>Number of nonzero elements. This scans all active values and count non zeros</p>
</dd></dl>
<dl class="py method">
<dt id="pyspark.mllib.linalg.DenseVector.parse">
<em class="property">static </em><code class="sig-name descname">parse</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">s</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="#pyspark.mllib.linalg.DenseVector" title="pyspark.mllib.linalg.DenseVector">pyspark.mllib.linalg.DenseVector</a><a class="reference internal" href="../../_modules/pyspark/mllib/linalg.html#DenseVector.parse"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.linalg.DenseVector.parse" title="Permalink to this definition"></a></dt>
<dd><p>Parse string representation back into the DenseVector.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">DenseVector</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="s1">&#39; [ 0.0,1.0,2.0, 3.0]&#39;</span><span class="p">)</span>
<span class="go">DenseVector([0.0, 1.0, 2.0, 3.0])</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt id="pyspark.mllib.linalg.DenseVector.squared_distance">
<code class="sig-name descname">squared_distance</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">other</span><span class="p">:</span> <span class="n">Iterable<span class="p">[</span>float<span class="p">]</span></span></em><span class="sig-paren">)</span> &#x2192; numpy.float64<a class="reference internal" href="../../_modules/pyspark/mllib/linalg.html#DenseVector.squared_distance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.linalg.DenseVector.squared_distance" title="Permalink to this definition"></a></dt>
<dd><p>Squared distance of two Vectors.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">dense1</span> <span class="o">=</span> <span class="n">DenseVector</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense1</span><span class="o">.</span><span class="n">squared_distance</span><span class="p">(</span><span class="n">dense1</span><span class="p">)</span>
<span class="go">0.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense1</span><span class="o">.</span><span class="n">squared_distance</span><span class="p">(</span><span class="n">dense2</span><span class="p">)</span>
<span class="go">2.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense3</span> <span class="o">=</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense1</span><span class="o">.</span><span class="n">squared_distance</span><span class="p">(</span><span class="n">dense3</span><span class="p">)</span>
<span class="go">2.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">sparse1</span> <span class="o">=</span> <span class="n">SparseVector</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense1</span><span class="o">.</span><span class="n">squared_distance</span><span class="p">(</span><span class="n">sparse1</span><span class="p">)</span>
<span class="go">2.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense1</span><span class="o">.</span><span class="n">squared_distance</span><span class="p">([</span><span class="mf">1.</span><span class="p">,])</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="o">...</span>
<span class="gr">AssertionError</span>: <span class="n">dimension mismatch</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dense1</span><span class="o">.</span><span class="n">squared_distance</span><span class="p">(</span><span class="n">SparseVector</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,]))</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="o">...</span>
<span class="gr">AssertionError</span>: <span class="n">dimension mismatch</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt id="pyspark.mllib.linalg.DenseVector.toArray">
<code class="sig-name descname">toArray</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; numpy.ndarray<a class="reference internal" href="../../_modules/pyspark/mllib/linalg.html#DenseVector.toArray"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.linalg.DenseVector.toArray" title="Permalink to this definition"></a></dt>
<dd><p>Returns an numpy.ndarray</p>
</dd></dl>
<p class="rubric">Attributes Documentation</p>
<dl class="py attribute">
<dt id="pyspark.mllib.linalg.DenseVector.values">
<code class="sig-name descname">values</code><a class="headerlink" href="#pyspark.mllib.linalg.DenseVector.values" title="Permalink to this definition"></a></dt>
<dd><p>Returns a list of values</p>
</dd></dl>
</dd></dl>
</div>
</div>
<div class='prev-next-bottom'>
<a class='left-prev' id="prev-link" href="pyspark.mllib.linalg.Vector.html" title="previous page">Vector</a>
<a class='right-next' id="next-link" href="pyspark.mllib.linalg.SparseVector.html" title="next page">SparseVector</a>
</div>
</main>
</div>
</div>
<script src="../../_static/js/index.3da636dd464baa7582d2.js"></script>
<footer class="footer mt-5 mt-md-0">
<div class="container">
<p>
&copy; Copyright .<br/>
Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.0.4.<br/>
</p>
</div>
</footer>
</body>
</html>