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Posted to commits@spark.apache.org by yh...@apache.org on 2016/12/28 22:35:26 UTC
[15/25] spark-website git commit: Update 2.1.0 docs to include
https://github.com/apache/spark/pull/16294
http://git-wip-us.apache.org/repos/asf/spark-website/blob/d2bcf185/site/docs/2.1.0/mllib-data-types.html
----------------------------------------------------------------------
diff --git a/site/docs/2.1.0/mllib-data-types.html b/site/docs/2.1.0/mllib-data-types.html
index 546d921..f7b5358 100644
--- a/site/docs/2.1.0/mllib-data-types.html
+++ b/site/docs/2.1.0/mllib-data-types.html
@@ -307,14 +307,14 @@
<ul id="markdown-toc">
- <li><a href="#local-vector" id="markdown-toc-local-vector">Local vector</a></li>
- <li><a href="#labeled-point" id="markdown-toc-labeled-point">Labeled point</a></li>
- <li><a href="#local-matrix" id="markdown-toc-local-matrix">Local matrix</a></li>
- <li><a href="#distributed-matrix" id="markdown-toc-distributed-matrix">Distributed matrix</a> <ul>
- <li><a href="#rowmatrix" id="markdown-toc-rowmatrix">RowMatrix</a></li>
- <li><a href="#indexedrowmatrix" id="markdown-toc-indexedrowmatrix">IndexedRowMatrix</a></li>
- <li><a href="#coordinatematrix" id="markdown-toc-coordinatematrix">CoordinateMatrix</a></li>
- <li><a href="#blockmatrix" id="markdown-toc-blockmatrix">BlockMatrix</a></li>
+ <li><a href="#local-vector">Local vector</a></li>
+ <li><a href="#labeled-point">Labeled point</a></li>
+ <li><a href="#local-matrix">Local matrix</a></li>
+ <li><a href="#distributed-matrix">Distributed matrix</a> <ul>
+ <li><a href="#rowmatrix">RowMatrix</a></li>
+ <li><a href="#indexedrowmatrix">IndexedRowMatrix</a></li>
+ <li><a href="#coordinatematrix">CoordinateMatrix</a></li>
+ <li><a href="#blockmatrix">BlockMatrix</a></li>
</ul>
</li>
</ul>
@@ -347,14 +347,14 @@ using the factory methods implemented in
<p>Refer to the <a href="api/scala/index.html#org.apache.spark.mllib.linalg.Vector"><code>Vector</code> Scala docs</a> and <a href="api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$"><code>Vectors</code> Scala docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.</span><span class="o">{</span><span class="nc">Vector</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">}</span>
+ <figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span></span><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.</span><span class="o">{</span><span class="nc">Vector</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">}</span>
<span class="c1">// Create a dense vector (1.0, 0.0, 3.0).</span>
<span class="k">val</span> <span class="n">dv</span><span class="k">:</span> <span class="kt">Vector</span> <span class="o">=</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">)</span>
<span class="c1">// Create a sparse vector (1.0, 0.0, 3.0) by specifying its indices and values corresponding to nonzero entries.</span>
<span class="k">val</span> <span class="n">sv1</span><span class="k">:</span> <span class="kt">Vector</span> <span class="o">=</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">0</span><span class="o">,</span> <span class="mi">2</span><span class="o">),</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">))</span>
<span class="c1">// Create a sparse vector (1.0, 0.0, 3.0) by specifying its nonzero entries.</span>
-<span class="k">val</span> <span class="n">sv2</span><span class="k">:</span> <span class="kt">Vector</span> <span class="o">=</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="nc">Seq</span><span class="o">((</span><span class="mi">0</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span> <span class="o">(</span><span class="mi">2</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">)))</span></code></pre></div>
+<span class="k">val</span> <span class="n">sv2</span><span class="k">:</span> <span class="kt">Vector</span> <span class="o">=</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="nc">Seq</span><span class="o">((</span><span class="mi">0</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span> <span class="o">(</span><span class="mi">2</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">)))</span></code></pre></figure>
<p><strong><em>Note:</em></strong>
Scala imports <code>scala.collection.immutable.Vector</code> by default, so you have to import
@@ -373,13 +373,13 @@ using the factory methods implemented in
<p>Refer to the <a href="api/java/org/apache/spark/mllib/linalg/Vector.html"><code>Vector</code> Java docs</a> and <a href="api/java/org/apache/spark/mllib/linalg/Vectors.html"><code>Vectors</code> Java docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span><span class="o">;</span>
+ <figure class="highlight"><pre><code class="language-java" data-lang="java"><span></span><span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span><span class="o">;</span>
<span class="c1">// Create a dense vector (1.0, 0.0, 3.0).</span>
<span class="n">Vector</span> <span class="n">dv</span> <span class="o">=</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">);</span>
<span class="c1">// Create a sparse vector (1.0, 0.0, 3.0) by specifying its indices and values corresponding to nonzero entries.</span>
-<span class="n">Vector</span> <span class="n">sv</span> <span class="o">=</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="k">new</span> <span class="kt">int</span><span class="o">[]</span> <span class="o">{</span><span class="mi">0</span><span class="o">,</span> <span class="mi">2</span><span class="o">},</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">});</span></code></pre></div>
+<span class="n">Vector</span> <span class="n">sv</span> <span class="o">=</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="k">new</span> <span class="kt">int</span><span class="o">[]</span> <span class="o">{</span><span class="mi">0</span><span class="o">,</span> <span class="mi">2</span><span class="o">},</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">});</span></code></pre></figure>
</div>
@@ -405,18 +405,18 @@ in <a href="api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vectors"><code>Ve
<p>Refer to the <a href="api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vectors"><code>Vectors</code> Python docs</a> for more details on the API.</p>
- <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+ <figure class="highlight"><pre><code class="language-python" data-lang="python"><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">scipy.sparse</span> <span class="kn">as</span> <span class="nn">sps</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">Vectors</span>
-<span class="c"># Use a NumPy array as a dense vector.</span>
+<span class="c1"># Use a NumPy array as a dense vector.</span>
<span class="n">dv1</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">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">])</span>
-<span class="c"># Use a Python list as a dense vector.</span>
+<span class="c1"># Use a Python list as a dense vector.</span>
<span class="n">dv2</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">]</span>
-<span class="c"># Create a SparseVector.</span>
+<span class="c1"># Create a SparseVector.</span>
<span class="n">sv1</span> <span class="o">=</span> <span class="n">Vectors</span><span class="o">.</span><span class="n">sparse</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">])</span>
-<span class="c"># Use a single-column SciPy csc_matrix as a sparse vector.</span>
-<span class="n">sv2</span> <span class="o">=</span> <span class="n">sps</span><span class="o">.</span><span class="n">csc_matrix</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="mf">1.0</span><span class="p">,</span> <span class="mf">3.0</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="mi">0</span><span class="p">,</span> <span class="mi">2</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="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">])),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span></code></pre></div>
+<span class="c1"># Use a single-column SciPy csc_matrix as a sparse vector.</span>
+<span class="n">sv2</span> <span class="o">=</span> <span class="n">sps</span><span class="o">.</span><span class="n">csc_matrix</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="mf">1.0</span><span class="p">,</span> <span class="mf">3.0</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="mi">0</span><span class="p">,</span> <span class="mi">2</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="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">])),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span></code></pre></figure>
</div>
</div>
@@ -438,14 +438,14 @@ For multiclass classification, labels should be class indices starting from zero
<p>Refer to the <a href="api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint"><code>LabeledPoint</code> Scala docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span>
+ <figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span></span><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span>
<span class="c1">// Create a labeled point with a positive label and a dense feature vector.</span>
<span class="k">val</span> <span class="n">pos</span> <span class="k">=</span> <span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">))</span>
<span class="c1">// Create a labeled point with a negative label and a sparse feature vector.</span>
-<span class="k">val</span> <span class="n">neg</span> <span class="k">=</span> <span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">0</span><span class="o">,</span> <span class="mi">2</span><span class="o">),</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">)))</span></code></pre></div>
+<span class="k">val</span> <span class="n">neg</span> <span class="k">=</span> <span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">0</span><span class="o">,</span> <span class="mi">2</span><span class="o">),</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">)))</span></code></pre></figure>
</div>
@@ -456,14 +456,14 @@ For multiclass classification, labels should be class indices starting from zero
<p>Refer to the <a href="api/java/org/apache/spark/mllib/regression/LabeledPoint.html"><code>LabeledPoint</code> Java docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span><span class="o">;</span>
+ <figure class="highlight"><pre><code class="language-java" data-lang="java"><span></span><span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span>
<span class="c1">// Create a labeled point with a positive label and a dense feature vector.</span>
-<span class="n">LabeledPoint</span> <span class="n">pos</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">));</span>
+<span class="n">LabeledPoint</span> <span class="n">pos</span> <span class="o">=</span> <span class="k">new</span> <span class="n">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">));</span>
<span class="c1">// Create a labeled point with a negative label and a sparse feature vector.</span>
-<span class="n">LabeledPoint</span> <span class="n">neg</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="k">new</span> <span class="kt">int</span><span class="o">[]</span> <span class="o">{</span><span class="mi">0</span><span class="o">,</span> <span class="mi">2</span><span class="o">},</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">}));</span></code></pre></div>
+<span class="n">LabeledPoint</span> <span class="n">neg</span> <span class="o">=</span> <span class="k">new</span> <span class="n">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</span><span class="o">,</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="k">new</span> <span class="kt">int</span><span class="o">[]</span> <span class="o">{</span><span class="mi">0</span><span class="o">,</span> <span class="mi">2</span><span class="o">},</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">}));</span></code></pre></figure>
</div>
@@ -474,14 +474,14 @@ For multiclass classification, labels should be class indices starting from zero
<p>Refer to the <a href="api/python/pyspark.mllib.html#pyspark.mllib.regression.LabeledPoint"><code>LabeledPoint</code> Python docs</a> for more details on the API.</p>
- <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">SparseVector</span>
+ <figure class="highlight"><pre><code class="language-python" data-lang="python"><span></span><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">SparseVector</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib.regression</span> <span class="kn">import</span> <span class="n">LabeledPoint</span>
-<span class="c"># Create a labeled point with a positive label and a dense feature vector.</span>
+<span class="c1"># Create a labeled point with a positive label and a dense feature vector.</span>
<span class="n">pos</span> <span class="o">=</span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">])</span>
-<span class="c"># Create a labeled point with a negative label and a sparse feature vector.</span>
-<span class="n">neg</span> <span class="o">=</span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">SparseVector</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">]))</span></code></pre></div>
+<span class="c1"># Create a labeled point with a negative label and a sparse feature vector.</span>
+<span class="n">neg</span> <span class="o">=</span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">SparseVector</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">]))</span></code></pre></figure>
</div>
</div>
@@ -508,11 +508,11 @@ examples stored in LIBSVM format.</p>
<p>Refer to the <a href="api/scala/index.html#org.apache.spark.mllib.util.MLUtils$"><code>MLUtils</code> Scala docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span>
+ <figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span></span><span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span>
<span class="k">import</span> <span class="nn">org.apache.spark.rdd.RDD</span>
-<span class="k">val</span> <span class="n">examples</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">LabeledPoint</span><span class="o">]</span> <span class="k">=</span> <span class="nc">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">)</span></code></pre></div>
+<span class="k">val</span> <span class="n">examples</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">LabeledPoint</span><span class="o">]</span> <span class="k">=</span> <span class="nc">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">)</span></code></pre></figure>
</div>
@@ -522,12 +522,12 @@ examples stored in LIBSVM format.</p>
<p>Refer to the <a href="api/java/org/apache/spark/mllib/util/MLUtils.html"><code>MLUtils</code> Java docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span>
+ <figure class="highlight"><pre><code class="language-java" data-lang="java"><span></span><span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</span><span class="o">;</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">examples</span> <span class="o">=</span>
- <span class="n">MLUtils</span><span class="o">.</span><span class="na">loadLibSVMFile</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">).</span><span class="na">toJavaRDD</span><span class="o">();</span></code></pre></div>
+ <span class="n">MLUtils</span><span class="o">.</span><span class="na">loadLibSVMFile</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">).</span><span class="na">toJavaRDD</span><span class="o">();</span></code></pre></figure>
</div>
@@ -537,9 +537,9 @@ examples stored in LIBSVM format.</p>
<p>Refer to the <a href="api/python/pyspark.mllib.html#pyspark.mllib.util.MLUtils"><code>MLUtils</code> Python docs</a> for more details on the API.</p>
- <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">MLUtils</span>
+ <figure class="highlight"><pre><code class="language-python" data-lang="python"><span></span><span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">MLUtils</span>
-<span class="n">examples</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="p">)</span></code></pre></div>
+<span class="n">examples</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s2">"data/mllib/sample_libsvm_data.txt"</span><span class="p">)</span></code></pre></figure>
</div>
</div>
@@ -570,13 +570,13 @@ matrices. Remember, local matrices in MLlib are stored in column-major order.</p
<p>Refer to the <a href="api/scala/index.html#org.apache.spark.mllib.linalg.Matrix"><code>Matrix</code> Scala docs</a> and <a href="api/scala/index.html#org.apache.spark.mllib.linalg.Matrices$"><code>Matrices</code> Scala docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.</span><span class="o">{</span><span class="nc">Matrix</span><span class="o">,</span> <span class="nc">Matrices</span><span class="o">}</span>
+ <figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span></span><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.</span><span class="o">{</span><span class="nc">Matrix</span><span class="o">,</span> <span class="nc">Matrices</span><span class="o">}</span>
<span class="c1">// Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))</span>
<span class="k">val</span> <span class="n">dm</span><span class="k">:</span> <span class="kt">Matrix</span> <span class="o">=</span> <span class="nc">Matrices</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="mi">2</span><span class="o">,</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">,</span> <span class="mf">5.0</span><span class="o">,</span> <span class="mf">2.0</span><span class="o">,</span> <span class="mf">4.0</span><span class="o">,</span> <span class="mf">6.0</span><span class="o">))</span>
<span class="c1">// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))</span>
-<span class="k">val</span> <span class="n">sm</span><span class="k">:</span> <span class="kt">Matrix</span> <span class="o">=</span> <span class="nc">Matrices</span><span class="o">.</span><span class="n">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="mi">2</span><span class="o">,</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">0</span><span class="o">,</span> <span class="mi">1</span><span class="o">,</span> <span class="mi">3</span><span class="o">),</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">0</span><span class="o">,</span> <span class="mi">2</span><span class="o">,</span> <span class="mi">1</span><span class="o">),</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">9</span><span class="o">,</span> <span class="mi">6</span><span class="o">,</span> <span class="mi">8</span><span class="o">))</span></code></pre></div>
+<span class="k">val</span> <span class="n">sm</span><span class="k">:</span> <span class="kt">Matrix</span> <span class="o">=</span> <span class="nc">Matrices</span><span class="o">.</span><span class="n">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="mi">2</span><span class="o">,</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">0</span><span class="o">,</span> <span class="mi">1</span><span class="o">,</span> <span class="mi">3</span><span class="o">),</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">0</span><span class="o">,</span> <span class="mi">2</span><span class="o">,</span> <span class="mi">1</span><span class="o">),</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">9</span><span class="o">,</span> <span class="mi">6</span><span class="o">,</span> <span class="mi">8</span><span class="o">))</span></code></pre></figure>
</div>
@@ -592,14 +592,14 @@ matrices. Remember, local matrices in MLlib are stored in column-major order.</p
<p>Refer to the <a href="api/java/org/apache/spark/mllib/linalg/Matrix.html"><code>Matrix</code> Java docs</a> and <a href="api/java/org/apache/spark/mllib/linalg/Matrices.html"><code>Matrices</code> Java docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Matrix</span><span class="o">;</span>
+ <figure class="highlight"><pre><code class="language-java" data-lang="java"><span></span><span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Matrix</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Matrices</span><span class="o">;</span>
<span class="c1">// Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))</span>
<span class="n">Matrix</span> <span class="n">dm</span> <span class="o">=</span> <span class="n">Matrices</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="mi">2</span><span class="o">,</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mf">1.0</span><span class="o">,</span> <span class="mf">3.0</span><span class="o">,</span> <span class="mf">5.0</span><span class="o">,</span> <span class="mf">2.0</span><span class="o">,</span> <span class="mf">4.0</span><span class="o">,</span> <span class="mf">6.0</span><span class="o">});</span>
<span class="c1">// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))</span>
-<span class="n">Matrix</span> <span class="n">sm</span> <span class="o">=</span> <span class="n">Matrices</span><span class="o">.</span><span class="na">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="mi">2</span><span class="o">,</span> <span class="k">new</span> <span class="kt">int</span><span class="o">[]</span> <span class="o">{</span><span class="mi">0</span><span class="o">,</span> <span class="mi">1</span><span class="o">,</span> <span class="mi">3</span><span class="o">},</span> <span class="k">new</span> <span class="kt">int</span><span class="o">[]</span> <span class="o">{</span><span class="mi">0</span><span class="o">,</span> <span class="mi">2</span><span class="o">,</span> <span class="mi">1</span><span class="o">},</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mi">9</span><span class="o">,</span> <span class="mi">6</span><span class="o"
>,</span> <span class="mi">8</span><span class="o">});</span></code></pre></div>
+<span class="n">Matrix</span> <span class="n">sm</span> <span class="o">=</span> <span class="n">Matrices</span><span class="o">.</span><span class="na">sparse</span><span class="o">(</span><span class="mi">3</span><span class="o">,</span> <span class="mi">2</span><span class="o">,</span> <span class="k">new</span> <span class="kt">int</span><span class="o">[]</span> <span class="o">{</span><span class="mi">0</span><span class="o">,</span> <span class="mi">1</span><span class="o">,</span> <span class="mi">3</span><span class="o">},</span> <span class="k">new</span> <span class="kt">int</span><span class="o">[]</span> <span class="o">{</span><span class="mi">0</span><span class="o">,</span> <span class="mi">2</span><span class="o">,</span> <span class="mi">1</span><span class="o">},</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mi">9</span><span class="o">,</span> <span class="mi">6</span><span class="o"
>,</span> <span class="mi">8</span><span class="o">});</span></code></pre></figure>
</div>
@@ -615,13 +615,13 @@ matrices. Remember, local matrices in MLlib are stored in column-major order.</p
<p>Refer to the <a href="api/python/pyspark.mllib.html#pyspark.mllib.linalg.Matrix"><code>Matrix</code> Python docs</a> and <a href="api/python/pyspark.mllib.html#pyspark.mllib.linalg.Matrices"><code>Matrices</code> Python docs</a> for more details on the API.</p>
- <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">Matrix</span><span class="p">,</span> <span class="n">Matrices</span>
+ <figure class="highlight"><pre><code class="language-python" data-lang="python"><span></span><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">Matrix</span><span class="p">,</span> <span class="n">Matrices</span>
-<span class="c"># Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))</span>
+<span class="c1"># Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))</span>
<span class="n">dm2</span> <span class="o">=</span> <span class="n">Matrices</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
-<span class="c"># Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))</span>
-<span class="n">sm</span> <span class="o">=</span> <span class="n">Matrices</span><span class="o">.</span><span class="n">sparse</span><span class="p">(</span><span class="mi">3</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="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span></code></pre></div>
+<span class="c1"># Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))</span>
+<span class="n">sm</span> <span class="o">=</span> <span class="n">Matrices</span><span class="o">.</span><span class="n">sparse</span><span class="p">(</span><span class="mi">3</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="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span></code></pre></figure>
</div>
@@ -670,7 +670,7 @@ For <a href="https://en.wikipedia.org/wiki/Singular_value_decomposition">singula
<p>Refer to the <a href="api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix"><code>RowMatrix</code> Scala docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span>
+ <figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span></span><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.RowMatrix</span>
<span class="k">val</span> <span class="n">rows</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">Vector</span><span class="o">]</span> <span class="k">=</span> <span class="o">...</span> <span class="c1">// an RDD of local vectors</span>
@@ -682,7 +682,7 @@ For <a href="https://en.wikipedia.org/wiki/Singular_value_decomposition">singula
<span class="k">val</span> <span class="n">n</span> <span class="k">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numCols</span><span class="o">()</span>
<span class="c1">// QR decomposition </span>
-<span class="k">val</span> <span class="n">qrResult</span> <span class="k">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">tallSkinnyQR</span><span class="o">(</span><span class="kc">true</span><span class="o">)</span></code></pre></div>
+<span class="k">val</span> <span class="n">qrResult</span> <span class="k">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">tallSkinnyQR</span><span class="o">(</span><span class="kc">true</span><span class="o">)</span></code></pre></figure>
</div>
@@ -693,20 +693,20 @@ created from a <code>JavaRDD<Vector></code> instance. Then we can compute
<p>Refer to the <a href="api/java/org/apache/spark/mllib/linalg/distributed/RowMatrix.html"><code>RowMatrix</code> Java docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</span><span class="o">;</span>
+ <figure class="highlight"><pre><code class="language-java" data-lang="java"><span></span><span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.RowMatrix</span><span class="o">;</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">Vector</span><span class="o">></span> <span class="n">rows</span> <span class="o">=</span> <span class="o">...</span> <span class="c1">// a JavaRDD of local vectors</span>
<span class="c1">// Create a RowMatrix from an JavaRDD<Vector>.</span>
-<span class="n">RowMatrix</span> <span class="n">mat</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">RowMatrix</span><span class="o">(</span><span class="n">rows</span><span class="o">.</span><span class="na">rdd</span><span class="o">());</span>
+<span class="n">RowMatrix</span> <span class="n">mat</span> <span class="o">=</span> <span class="k">new</span> <span class="n">RowMatrix</span><span class="o">(</span><span class="n">rows</span><span class="o">.</span><span class="na">rdd</span><span class="o">());</span>
<span class="c1">// Get its size.</span>
<span class="kt">long</span> <span class="n">m</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="na">numRows</span><span class="o">();</span>
<span class="kt">long</span> <span class="n">n</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="na">numCols</span><span class="o">();</span>
<span class="c1">// QR decomposition </span>
-<span class="n">QRDecomposition</span><span class="o"><</span><span class="n">RowMatrix</span><span class="o">,</span> <span class="n">Matrix</span><span class="o">></span> <span class="n">result</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="na">tallSkinnyQR</span><span class="o">(</span><span class="kc">true</span><span class="o">);</span></code></pre></div>
+<span class="n">QRDecomposition</span><span class="o"><</span><span class="n">RowMatrix</span><span class="o">,</span> <span class="n">Matrix</span><span class="o">></span> <span class="n">result</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="na">tallSkinnyQR</span><span class="o">(</span><span class="kc">true</span><span class="o">);</span></code></pre></figure>
</div>
@@ -717,20 +717,20 @@ created from an <code>RDD</code> of vectors.</p>
<p>Refer to the <a href="api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.RowMatrix"><code>RowMatrix</code> Python docs</a> for more details on the API.</p>
- <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg.distributed</span> <span class="kn">import</span> <span class="n">RowMatrix</span>
+ <figure class="highlight"><pre><code class="language-python" data-lang="python"><span></span><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg.distributed</span> <span class="kn">import</span> <span class="n">RowMatrix</span>
-<span class="c"># Create an RDD of vectors.</span>
+<span class="c1"># Create an RDD of vectors.</span>
<span class="n">rows</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">]])</span>
-<span class="c"># Create a RowMatrix from an RDD of vectors.</span>
+<span class="c1"># Create a RowMatrix from an RDD of vectors.</span>
<span class="n">mat</span> <span class="o">=</span> <span class="n">RowMatrix</span><span class="p">(</span><span class="n">rows</span><span class="p">)</span>
-<span class="c"># Get its size.</span>
-<span class="n">m</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numRows</span><span class="p">()</span> <span class="c"># 4</span>
-<span class="n">n</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numCols</span><span class="p">()</span> <span class="c"># 3</span>
+<span class="c1"># Get its size.</span>
+<span class="n">m</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numRows</span><span class="p">()</span> <span class="c1"># 4</span>
+<span class="n">n</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numCols</span><span class="p">()</span> <span class="c1"># 3</span>
-<span class="c"># Get the rows as an RDD of vectors again.</span>
-<span class="n">rowsRDD</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">rows</span></code></pre></div>
+<span class="c1"># Get the rows as an RDD of vectors again.</span>
+<span class="n">rowsRDD</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">rows</span></code></pre></figure>
</div>
@@ -754,7 +754,7 @@ its row indices.</p>
<p>Refer to the <a href="api/scala/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix"><code>IndexedRowMatrix</code> Scala docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.</span><span class="o">{</span><span class="nc">IndexedRow</span><span class="o">,</span> <span class="nc">IndexedRowMatrix</span><span class="o">,</span> <span class="nc">RowMatrix</span><span class="o">}</span>
+ <figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span></span><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.</span><span class="o">{</span><span class="nc">IndexedRow</span><span class="o">,</span> <span class="nc">IndexedRowMatrix</span><span class="o">,</span> <span class="nc">RowMatrix</span><span class="o">}</span>
<span class="k">val</span> <span class="n">rows</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">IndexedRow</span><span class="o">]</span> <span class="k">=</span> <span class="o">...</span> <span class="c1">// an RDD of indexed rows</span>
<span class="c1">// Create an IndexedRowMatrix from an RDD[IndexedRow].</span>
@@ -765,7 +765,7 @@ its row indices.</p>
<span class="k">val</span> <span class="n">n</span> <span class="k">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numCols</span><span class="o">()</span>
<span class="c1">// Drop its row indices.</span>
-<span class="k">val</span> <span class="n">rowMat</span><span class="k">:</span> <span class="kt">RowMatrix</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toRowMatrix</span><span class="o">()</span></code></pre></div>
+<span class="k">val</span> <span class="n">rowMat</span><span class="k">:</span> <span class="kt">RowMatrix</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toRowMatrix</span><span class="o">()</span></code></pre></figure>
</div>
@@ -780,21 +780,21 @@ its row indices.</p>
<p>Refer to the <a href="api/java/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.html"><code>IndexedRowMatrix</code> Java docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</span><span class="o">;</span>
+ <figure class="highlight"><pre><code class="language-java" data-lang="java"><span></span><span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.IndexedRow</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.RowMatrix</span><span class="o">;</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">IndexedRow</span><span class="o">></span> <span class="n">rows</span> <span class="o">=</span> <span class="o">...</span> <span class="c1">// a JavaRDD of indexed rows</span>
<span class="c1">// Create an IndexedRowMatrix from a JavaRDD<IndexedRow>.</span>
-<span class="n">IndexedRowMatrix</span> <span class="n">mat</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">IndexedRowMatrix</span><span class="o">(</span><span class="n">rows</span><span class="o">.</span><span class="na">rdd</span><span class="o">());</span>
+<span class="n">IndexedRowMatrix</span> <span class="n">mat</span> <span class="o">=</span> <span class="k">new</span> <span class="n">IndexedRowMatrix</span><span class="o">(</span><span class="n">rows</span><span class="o">.</span><span class="na">rdd</span><span class="o">());</span>
<span class="c1">// Get its size.</span>
<span class="kt">long</span> <span class="n">m</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="na">numRows</span><span class="o">();</span>
<span class="kt">long</span> <span class="n">n</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="na">numCols</span><span class="o">();</span>
<span class="c1">// Drop its row indices.</span>
-<span class="n">RowMatrix</span> <span class="n">rowMat</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="na">toRowMatrix</span><span class="o">();</span></code></pre></div>
+<span class="n">RowMatrix</span> <span class="n">rowMat</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="na">toRowMatrix</span><span class="o">();</span></code></pre></figure>
</div>
@@ -808,30 +808,30 @@ its row indices.</p>
<p>Refer to the <a href="api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.IndexedRowMatrix"><code>IndexedRowMatrix</code> Python docs</a> for more details on the API.</p>
- <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg.distributed</span> <span class="kn">import</span> <span class="n">IndexedRow</span><span class="p">,</span> <span class="n">IndexedRowMatrix</span>
+ <figure class="highlight"><pre><code class="language-python" data-lang="python"><span></span><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg.distributed</span> <span class="kn">import</span> <span class="n">IndexedRow</span><span class="p">,</span> <span class="n">IndexedRowMatrix</span>
-<span class="c"># Create an RDD of indexed rows.</span>
-<span class="c"># - This can be done explicitly with the IndexedRow class:</span>
+<span class="c1"># Create an RDD of indexed rows.</span>
+<span class="c1"># - This can be done explicitly with the IndexedRow class:</span>
<span class="n">indexedRows</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([</span><span class="n">IndexedRow</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span>
<span class="n">IndexedRow</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]),</span>
<span class="n">IndexedRow</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">]),</span>
<span class="n">IndexedRow</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">])])</span>
-<span class="c"># - or by using (long, vector) tuples:</span>
+<span class="c1"># - or by using (long, vector) tuples:</span>
<span class="n">indexedRows</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]),</span>
<span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">]),</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">])])</span>
-<span class="c"># Create an IndexedRowMatrix from an RDD of IndexedRows.</span>
+<span class="c1"># Create an IndexedRowMatrix from an RDD of IndexedRows.</span>
<span class="n">mat</span> <span class="o">=</span> <span class="n">IndexedRowMatrix</span><span class="p">(</span><span class="n">indexedRows</span><span class="p">)</span>
-<span class="c"># Get its size.</span>
-<span class="n">m</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numRows</span><span class="p">()</span> <span class="c"># 4</span>
-<span class="n">n</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numCols</span><span class="p">()</span> <span class="c"># 3</span>
+<span class="c1"># Get its size.</span>
+<span class="n">m</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numRows</span><span class="p">()</span> <span class="c1"># 4</span>
+<span class="n">n</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numCols</span><span class="p">()</span> <span class="c1"># 3</span>
-<span class="c"># Get the rows as an RDD of IndexedRows.</span>
+<span class="c1"># Get the rows as an RDD of IndexedRows.</span>
<span class="n">rowsRDD</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">rows</span>
-<span class="c"># Convert to a RowMatrix by dropping the row indices.</span>
-<span class="n">rowMat</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toRowMatrix</span><span class="p">()</span></code></pre></div>
+<span class="c1"># Convert to a RowMatrix by dropping the row indices.</span>
+<span class="n">rowMat</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toRowMatrix</span><span class="p">()</span></code></pre></figure>
</div>
@@ -857,7 +857,7 @@ with sparse rows by calling <code>toIndexedRowMatrix</code>. Other computations
<p>Refer to the <a href="api/scala/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix"><code>CoordinateMatrix</code> Scala docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.</span><span class="o">{</span><span class="nc">CoordinateMatrix</span><span class="o">,</span> <span class="nc">MatrixEntry</span><span class="o">}</span>
+ <figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span></span><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.</span><span class="o">{</span><span class="nc">CoordinateMatrix</span><span class="o">,</span> <span class="nc">MatrixEntry</span><span class="o">}</span>
<span class="k">val</span> <span class="n">entries</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">MatrixEntry</span><span class="o">]</span> <span class="k">=</span> <span class="o">...</span> <span class="c1">// an RDD of matrix entries</span>
<span class="c1">// Create a CoordinateMatrix from an RDD[MatrixEntry].</span>
@@ -868,7 +868,7 @@ with sparse rows by calling <code>toIndexedRowMatrix</code>. Other computations
<span class="k">val</span> <span class="n">n</span> <span class="k">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numCols</span><span class="o">()</span>
<span class="c1">// Convert it to an IndexRowMatrix whose rows are sparse vectors.</span>
-<span class="k">val</span> <span class="n">indexedRowMatrix</span> <span class="k">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toIndexedRowMatrix</span><span class="o">()</span></code></pre></div>
+<span class="k">val</span> <span class="n">indexedRowMatrix</span> <span class="k">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toIndexedRowMatrix</span><span class="o">()</span></code></pre></figure>
</div>
@@ -884,21 +884,21 @@ with sparse rows by calling <code>toIndexedRowMatrix</code>. Other computations
<p>Refer to the <a href="api/java/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.html"><code>CoordinateMatrix</code> Java docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</span><span class="o">;</span>
+ <figure class="highlight"><pre><code class="language-java" data-lang="java"><span></span><span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.CoordinateMatrix</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.MatrixEntry</span><span class="o">;</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">MatrixEntry</span><span class="o">></span> <span class="n">entries</span> <span class="o">=</span> <span class="o">...</span> <span class="c1">// a JavaRDD of matrix entries</span>
<span class="c1">// Create a CoordinateMatrix from a JavaRDD<MatrixEntry>.</span>
-<span class="n">CoordinateMatrix</span> <span class="n">mat</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">CoordinateMatrix</span><span class="o">(</span><span class="n">entries</span><span class="o">.</span><span class="na">rdd</span><span class="o">());</span>
+<span class="n">CoordinateMatrix</span> <span class="n">mat</span> <span class="o">=</span> <span class="k">new</span> <span class="n">CoordinateMatrix</span><span class="o">(</span><span class="n">entries</span><span class="o">.</span><span class="na">rdd</span><span class="o">());</span>
<span class="c1">// Get its size.</span>
<span class="kt">long</span> <span class="n">m</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="na">numRows</span><span class="o">();</span>
<span class="kt">long</span> <span class="n">n</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="na">numCols</span><span class="o">();</span>
<span class="c1">// Convert it to an IndexRowMatrix whose rows are sparse vectors.</span>
-<span class="n">IndexedRowMatrix</span> <span class="n">indexedRowMatrix</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="na">toIndexedRowMatrix</span><span class="o">();</span></code></pre></div>
+<span class="n">IndexedRowMatrix</span> <span class="n">indexedRowMatrix</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="na">toIndexedRowMatrix</span><span class="o">();</span></code></pre></figure>
</div>
@@ -912,32 +912,32 @@ calling <code>toRowMatrix</code>, or to an <code>IndexedRowMatrix</code> with sp
<p>Refer to the <a href="api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.CoordinateMatrix"><code>CoordinateMatrix</code> Python docs</a> for more details on the API.</p>
- <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg.distributed</span> <span class="kn">import</span> <span class="n">CoordinateMatrix</span><span class="p">,</span> <span class="n">MatrixEntry</span>
+ <figure class="highlight"><pre><code class="language-python" data-lang="python"><span></span><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg.distributed</span> <span class="kn">import</span> <span class="n">CoordinateMatrix</span><span class="p">,</span> <span class="n">MatrixEntry</span>
-<span class="c"># Create an RDD of coordinate entries.</span>
-<span class="c"># - This can be done explicitly with the MatrixEntry class:</span>
+<span class="c1"># Create an RDD of coordinate entries.</span>
+<span class="c1"># - This can be done explicitly with the MatrixEntry class:</span>
<span class="n">entries</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([</span><span class="n">MatrixEntry</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">),</span> <span class="n">MatrixEntry</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">),</span> <span class="n">MatrixEntry</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">3.7</span><span class="p">)])</span>
-<span class="c"># - or using (long, long, float) tuples:</span>
+<span class="c1"># - or using (long, long, float) tuples:</span>
<span class="n">entries</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">3.7</span><span class="p">)])</span>
-<span class="c"># Create an CoordinateMatrix from an RDD of MatrixEntries.</span>
+<span class="c1"># Create an CoordinateMatrix from an RDD of MatrixEntries.</span>
<span class="n">mat</span> <span class="o">=</span> <span class="n">CoordinateMatrix</span><span class="p">(</span><span class="n">entries</span><span class="p">)</span>
-<span class="c"># Get its size.</span>
-<span class="n">m</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numRows</span><span class="p">()</span> <span class="c"># 3</span>
-<span class="n">n</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numCols</span><span class="p">()</span> <span class="c"># 2</span>
+<span class="c1"># Get its size.</span>
+<span class="n">m</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numRows</span><span class="p">()</span> <span class="c1"># 3</span>
+<span class="n">n</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numCols</span><span class="p">()</span> <span class="c1"># 2</span>
-<span class="c"># Get the entries as an RDD of MatrixEntries.</span>
+<span class="c1"># Get the entries as an RDD of MatrixEntries.</span>
<span class="n">entriesRDD</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">entries</span>
-<span class="c"># Convert to a RowMatrix.</span>
+<span class="c1"># Convert to a RowMatrix.</span>
<span class="n">rowMat</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toRowMatrix</span><span class="p">()</span>
-<span class="c"># Convert to an IndexedRowMatrix.</span>
+<span class="c1"># Convert to an IndexedRowMatrix.</span>
<span class="n">indexedRowMat</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toIndexedRowMatrix</span><span class="p">()</span>
-<span class="c"># Convert to a BlockMatrix.</span>
-<span class="n">blockMat</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toBlockMatrix</span><span class="p">()</span></code></pre></div>
+<span class="c1"># Convert to a BlockMatrix.</span>
+<span class="n">blockMat</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toBlockMatrix</span><span class="p">()</span></code></pre></figure>
</div>
@@ -962,7 +962,7 @@ Users may change the block size by supplying the values through <code>toBlockMat
<p>Refer to the <a href="api/scala/index.html#org.apache.spark.mllib.linalg.distributed.BlockMatrix"><code>BlockMatrix</code> Scala docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.</span><span class="o">{</span><span class="nc">BlockMatrix</span><span class="o">,</span> <span class="nc">CoordinateMatrix</span><span class="o">,</span> <span class="nc">MatrixEntry</span><span class="o">}</span>
+ <figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span></span><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.</span><span class="o">{</span><span class="nc">BlockMatrix</span><span class="o">,</span> <span class="nc">CoordinateMatrix</span><span class="o">,</span> <span class="nc">MatrixEntry</span><span class="o">}</span>
<span class="k">val</span> <span class="n">entries</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">MatrixEntry</span><span class="o">]</span> <span class="k">=</span> <span class="o">...</span> <span class="c1">// an RDD of (i, j, v) matrix entries</span>
<span class="c1">// Create a CoordinateMatrix from an RDD[MatrixEntry].</span>
@@ -975,7 +975,7 @@ Users may change the block size by supplying the values through <code>toBlockMat
<span class="n">matA</span><span class="o">.</span><span class="n">validate</span><span class="o">()</span>
<span class="c1">// Calculate A^T A.</span>
-<span class="k">val</span> <span class="n">ata</span> <span class="k">=</span> <span class="n">matA</span><span class="o">.</span><span class="n">transpose</span><span class="o">.</span><span class="n">multiply</span><span class="o">(</span><span class="n">matA</span><span class="o">)</span></code></pre></div>
+<span class="k">val</span> <span class="n">ata</span> <span class="k">=</span> <span class="n">matA</span><span class="o">.</span><span class="n">transpose</span><span class="o">.</span><span class="n">multiply</span><span class="o">(</span><span class="n">matA</span><span class="o">)</span></code></pre></figure>
</div>
@@ -988,14 +988,14 @@ Users may change the block size by supplying the values through <code>toBlockMat
<p>Refer to the <a href="api/java/org/apache/spark/mllib/linalg/distributed/BlockMatrix.html"><code>BlockMatrix</code> Java docs</a> for details on the API.</p>
- <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</span><span class="o">;</span>
+ <figure class="highlight"><pre><code class="language-java" data-lang="java"><span></span><span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.BlockMatrix</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.CoordinateMatrix</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix</span><span class="o">;</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">MatrixEntry</span><span class="o">></span> <span class="n">entries</span> <span class="o">=</span> <span class="o">...</span> <span class="c1">// a JavaRDD of (i, j, v) Matrix Entries</span>
<span class="c1">// Create a CoordinateMatrix from a JavaRDD<MatrixEntry>.</span>
-<span class="n">CoordinateMatrix</span> <span class="n">coordMat</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">CoordinateMatrix</span><span class="o">(</span><span class="n">entries</span><span class="o">.</span><span class="na">rdd</span><span class="o">());</span>
+<span class="n">CoordinateMatrix</span> <span class="n">coordMat</span> <span class="o">=</span> <span class="k">new</span> <span class="n">CoordinateMatrix</span><span class="o">(</span><span class="n">entries</span><span class="o">.</span><span class="na">rdd</span><span class="o">());</span>
<span class="c1">// Transform the CoordinateMatrix to a BlockMatrix</span>
<span class="n">BlockMatrix</span> <span class="n">matA</span> <span class="o">=</span> <span class="n">coordMat</span><span class="o">.</span><span class="na">toBlockMatrix</span><span class="o">().</span><span class="na">cache</span><span class="o">();</span>
@@ -1004,7 +1004,7 @@ Users may change the block size by supplying the values through <code>toBlockMat
<span class="n">matA</span><span class="o">.</span><span class="na">validate</span><span class="o">();</span>
<span class="c1">// Calculate A^T A.</span>
-<span class="n">BlockMatrix</span> <span class="n">ata</span> <span class="o">=</span> <span class="n">matA</span><span class="o">.</span><span class="na">transpose</span><span class="o">().</span><span class="na">multiply</span><span class="o">(</span><span class="n">matA</span><span class="o">);</span></code></pre></div>
+<span class="n">BlockMatrix</span> <span class="n">ata</span> <span class="o">=</span> <span class="n">matA</span><span class="o">.</span><span class="na">transpose</span><span class="o">().</span><span class="na">multiply</span><span class="o">(</span><span class="n">matA</span><span class="o">);</span></code></pre></figure>
</div>
@@ -1016,31 +1016,31 @@ can be created from an <code>RDD</code> of sub-matrix blocks, where a sub-matrix
<p>Refer to the <a href="api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.BlockMatrix"><code>BlockMatrix</code> Python docs</a> for more details on the API.</p>
- <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">Matrices</span>
+ <figure class="highlight"><pre><code class="language-python" data-lang="python"><span></span><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">Matrices</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib.linalg.distributed</span> <span class="kn">import</span> <span class="n">BlockMatrix</span>
-<span class="c"># Create an RDD of sub-matrix blocks.</span>
+<span class="c1"># Create an RDD of sub-matrix blocks.</span>
<span class="n">blocks</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">Matrices</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">])),</span>
<span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">Matrices</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">]))])</span>
-<span class="c"># Create a BlockMatrix from an RDD of sub-matrix blocks.</span>
+<span class="c1"># Create a BlockMatrix from an RDD of sub-matrix blocks.</span>
<span class="n">mat</span> <span class="o">=</span> <span class="n">BlockMatrix</span><span class="p">(</span><span class="n">blocks</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="c"># Get its size.</span>
-<span class="n">m</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numRows</span><span class="p">()</span> <span class="c"># 6</span>
-<span class="n">n</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numCols</span><span class="p">()</span> <span class="c"># 2</span>
+<span class="c1"># Get its size.</span>
+<span class="n">m</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numRows</span><span class="p">()</span> <span class="c1"># 6</span>
+<span class="n">n</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">numCols</span><span class="p">()</span> <span class="c1"># 2</span>
-<span class="c"># Get the blocks as an RDD of sub-matrix blocks.</span>
+<span class="c1"># Get the blocks as an RDD of sub-matrix blocks.</span>
<span class="n">blocksRDD</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">blocks</span>
-<span class="c"># Convert to a LocalMatrix.</span>
+<span class="c1"># Convert to a LocalMatrix.</span>
<span class="n">localMat</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toLocalMatrix</span><span class="p">()</span>
-<span class="c"># Convert to an IndexedRowMatrix.</span>
+<span class="c1"># Convert to an IndexedRowMatrix.</span>
<span class="n">indexedRowMat</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toIndexedRowMatrix</span><span class="p">()</span>
-<span class="c"># Convert to a CoordinateMatrix.</span>
-<span class="n">coordinateMat</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toCoordinateMatrix</span><span class="p">()</span></code></pre></div>
+<span class="c1"># Convert to a CoordinateMatrix.</span>
+<span class="n">coordinateMat</span> <span class="o">=</span> <span class="n">mat</span><span class="o">.</span><span class="n">toCoordinateMatrix</span><span class="p">()</span></code></pre></figure>
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