<?xml version="1.0" encoding="ascii"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
<title>pyspark.mllib.linalg.Vectors</title>
<link rel="stylesheet" href="epydoc.css" type="text/css" />
<script type="text/javascript" src="epydoc.js"></script>
</head>
<body bgcolor="white" text="black" link="blue" vlink="#204080"
alink="#204080">
<!-- ==================== NAVIGATION BAR ==================== -->
<table class="navbar" border="0" width="100%" cellpadding="0"
bgcolor="#a0c0ff" cellspacing="0">
<tr valign="middle">
<!-- Home link -->
<th> <a
href="pyspark-module.html">Home</a> </th>
<!-- Tree link -->
<th> <a
href="module-tree.html">Trees</a> </th>
<!-- Index link -->
<th> <a
href="identifier-index.html">Indices</a> </th>
<!-- Help link -->
<th> <a
href="help.html">Help</a> </th>
<!-- Project homepage -->
<th class="navbar" align="right" width="100%">
<table border="0" cellpadding="0" cellspacing="0">
<tr><th class="navbar" align="center"
><a class="navbar" target="_top" href="http://spark.apache.org">Spark 1.0.0 Python API Docs</a></th>
</tr></table></th>
</tr>
</table>
<table width="100%" cellpadding="0" cellspacing="0">
<tr valign="top">
<td width="100%">
<span class="breadcrumbs">
<a href="pyspark-module.html">Package pyspark</a> ::
<a href="pyspark.mllib-module.html">Package mllib</a> ::
<a href="pyspark.mllib.linalg-module.html">Module linalg</a> ::
Class Vectors
</span>
</td>
<td>
<table cellpadding="0" cellspacing="0">
<!-- hide/show private -->
<tr><td align="right"><span class="options"
>[<a href="frames.html" target="_top">frames</a
>] | <a href="pyspark.mllib.linalg.Vectors-class.html"
target="_top">no frames</a>]</span></td></tr>
</table>
</td>
</tr>
</table>
<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class Vectors</h1><p class="nomargin-top"><span class="codelink"><a href="pyspark.mllib.linalg-pysrc.html#Vectors">source code</a></span></p>
<pre class="base-tree">
object --+
|
<strong class="uidshort">Vectors</strong>
</pre>
<hr />
<p>Factory methods for working with vectors. Note that dense vectors are
simply represented as NumPy array objects, so there is no need to covert
them for use in MLlib. For sparse vectors, the factory methods in this
class create an MLlib-compatible type, or users can pass in SciPy's
<code>scipy.sparse</code> column vectors.</p>
<!-- ==================== INSTANCE METHODS ==================== -->
<a name="section-InstanceMethods"></a>
<table class="summary" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
<td align="left" colspan="2" class="table-header">
<span class="table-header">Instance Methods</span></td>
</tr>
<tr>
<td colspan="2" class="summary">
<p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
<code>__delattr__</code>,
<code>__format__</code>,
<code>__getattribute__</code>,
<code>__hash__</code>,
<code>__init__</code>,
<code>__new__</code>,
<code>__reduce__</code>,
<code>__reduce_ex__</code>,
<code>__repr__</code>,
<code>__setattr__</code>,
<code>__sizeof__</code>,
<code>__str__</code>,
<code>__subclasshook__</code>
</p>
</td>
</tr>
</table>
<!-- ==================== STATIC METHODS ==================== -->
<a name="section-StaticMethods"></a>
<table class="summary" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
<td align="left" colspan="2" class="table-header">
<span class="table-header">Static Methods</span></td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="pyspark.mllib.linalg.Vectors-class.html#sparse" class="summary-sig-name">sparse</a>(<span class="summary-sig-arg">size</span>,
<span class="summary-sig-arg">*args</span>)</span><br />
Create a sparse vector, using either a dictionary, a list of
(index, value) pairs, or two separate arrays of indices and
values (sorted by index).</td>
<td align="right" valign="top">
<span class="codelink"><a href="pyspark.mllib.linalg-pysrc.html#Vectors.sparse">source code</a></span>
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="pyspark.mllib.linalg.Vectors-class.html#dense" class="summary-sig-name">dense</a>(<span class="summary-sig-arg">elements</span>)</span><br />
Create a dense vector of 64-bit floats from a Python list.</td>
<td align="right" valign="top">
<span class="codelink"><a href="pyspark.mllib.linalg-pysrc.html#Vectors.dense">source code</a></span>
</td>
</tr>
</table>
</td>
</tr>
</table>
<!-- ==================== PROPERTIES ==================== -->
<a name="section-Properties"></a>
<table class="summary" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
<td align="left" colspan="2" class="table-header">
<span class="table-header">Properties</span></td>
</tr>
<tr>
<td colspan="2" class="summary">
<p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
<code>__class__</code>
</p>
</td>
</tr>
</table>
<!-- ==================== METHOD DETAILS ==================== -->
<a name="section-MethodDetails"></a>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
<td align="left" colspan="2" class="table-header">
<span class="table-header">Method Details</span></td>
</tr>
</table>
<a name="sparse"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">sparse</span>(<span class="sig-arg">size</span>,
<span class="sig-arg">*args</span>)</span>
<br /><em class="fname">Static Method</em>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="pyspark.mllib.linalg-pysrc.html#Vectors.sparse">source code</a></span>
</td>
</tr></table>
<pre class="literalblock">
Create a sparse vector, using either a dictionary, a list of
(index, value) pairs, or two separate arrays of indices and
values (sorted by index).
@param size: Size of the vector.
@param args: Non-zero entries, as a dictionary, list of tupes,
or two sorted lists containing indices and values.
>>> print Vectors.sparse(4, {1: 1.0, 3: 5.5})
[1: 1.0, 3: 5.5]
>>> print Vectors.sparse(4, [(1, 1.0), (3, 5.5)])
[1: 1.0, 3: 5.5]
>>> print Vectors.sparse(4, [1, 3], [1.0, 5.5])
[1: 1.0, 3: 5.5]
</pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="dense"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">dense</span>(<span class="sig-arg">elements</span>)</span>
<br /><em class="fname">Static Method</em>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="pyspark.mllib.linalg-pysrc.html#Vectors.dense">source code</a></span>
</td>
</tr></table>
<p>Create a dense vector of 64-bit floats from a Python list. Always
returns a NumPy array.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>Vectors.dense([1, 2, 3])
<span class="py-output">array([ 1., 2., 3.])</span></pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<br />
<!-- ==================== NAVIGATION BAR ==================== -->
<table class="navbar" border="0" width="100%" cellpadding="0"
bgcolor="#a0c0ff" cellspacing="0">
<tr valign="middle">
<!-- Home link -->
<th> <a
href="pyspark-module.html">Home</a> </th>
<!-- Tree link -->
<th> <a
href="module-tree.html">Trees</a> </th>
<!-- Index link -->
<th> <a
href="identifier-index.html">Indices</a> </th>
<!-- Help link -->
<th> <a
href="help.html">Help</a> </th>
<!-- Project homepage -->
<th class="navbar" align="right" width="100%">
<table border="0" cellpadding="0" cellspacing="0">
<tr><th class="navbar" align="center"
><a class="navbar" target="_top" href="http://spark.apache.org">Spark 1.0.0 Python API Docs</a></th>
</tr></table></th>
</tr>
</table>
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
Generated by Epydoc 3.0.1 on Fri Jul 4 18:52:26 2014
</td>
<td align="right" class="footer">
<a target="mainFrame" href="http://epydoc.sourceforge.net"
>http://epydoc.sourceforge.net</a>
</td>
</tr>
</table>
<script type="text/javascript">
<!--
// Private objects are initially displayed (because if
// javascript is turned off then we want them to be
// visible); but by default, we want to hide them. So hide
// them unless we have a cookie that says to show them.
checkCookie();
// -->
</script>
</body>
</html>
