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<p class="subTitle">org.apache.spark.mllib.optimization</p>
<h2 title="Class GradientDescent" class="title">Class GradientDescent</h2>
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<pre>public class <strong>GradientDescent</strong>
extends Object
implements <a href="../../../../../org/apache/spark/mllib/optimization/Optimizer.html" title="interface in org.apache.spark.mllib.optimization">Optimizer</a>, <a href="../../../../../org/apache/spark/Logging.html" title="interface in org.apache.spark">Logging</a></pre>
<div class="block">Class used to solve an optimization problem using Gradient Descent.</div>
<dl><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../serialized-form.html#org.apache.spark.mllib.optimization.GradientDescent">Serialized Form</a></dd></dl>
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<caption><span>Methods</span><span class="tabEnd"> </span></caption>
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<th class="colFirst" scope="col">Modifier and Type</th>
<th class="colLast" scope="col">Method and Description</th>
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<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html#optimize(org.apache.spark.rdd.RDD, org.apache.spark.mllib.linalg.Vector)">optimize</a></strong>(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple2<Object,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>>> data,
<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> initialWeights)</code>
<div class="block">:: DeveloperApi ::
Runs gradient descent on the given training data.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static scala.Tuple2<<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>,double[]></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html#runMiniBatchSGD(org.apache.spark.rdd.RDD, org.apache.spark.mllib.optimization.Gradient, org.apache.spark.mllib.optimization.Updater, double, int, double, double, org.apache.spark.mllib.linalg.Vector)">runMiniBatchSGD</a></strong>(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple2<Object,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>>> data,
<a href="../../../../../org/apache/spark/mllib/optimization/Gradient.html" title="class in org.apache.spark.mllib.optimization">Gradient</a> gradient,
<a href="../../../../../org/apache/spark/mllib/optimization/Updater.html" title="class in org.apache.spark.mllib.optimization">Updater</a> updater,
double stepSize,
int numIterations,
double regParam,
double miniBatchFraction,
<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> initialWeights)</code>
<div class="block">Run stochastic gradient descent (SGD) in parallel using mini batches.</div>
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</tr>
<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html#setGradient(org.apache.spark.mllib.optimization.Gradient)">setGradient</a></strong>(<a href="../../../../../org/apache/spark/mllib/optimization/Gradient.html" title="class in org.apache.spark.mllib.optimization">Gradient</a> gradient)</code>
<div class="block">Set the gradient function (of the loss function of one single data example)
to be used for SGD.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html#setMiniBatchFraction(double)">setMiniBatchFraction</a></strong>(double fraction)</code>
<div class="block">:: Experimental ::
Set fraction of data to be used for each SGD iteration.</div>
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</tr>
<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html#setNumIterations(int)">setNumIterations</a></strong>(int iters)</code>
<div class="block">Set the number of iterations for SGD.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html#setRegParam(double)">setRegParam</a></strong>(double regParam)</code>
<div class="block">Set the regularization parameter.</div>
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<tr class="altColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html#setStepSize(double)">setStepSize</a></strong>(double step)</code>
<div class="block">Set the initial step size of SGD for the first step.</div>
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</tr>
<tr class="rowColor">
<td class="colFirst"><code><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html#setUpdater(org.apache.spark.mllib.optimization.Updater)">setUpdater</a></strong>(<a href="../../../../../org/apache/spark/mllib/optimization/Updater.html" title="class in org.apache.spark.mllib.optimization">Updater</a> updater)</code>
<div class="block">Set the updater function to actually perform a gradient step in a given direction.</div>
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<code><a href="../../../../../org/apache/spark/Logging.html#initialized()">initialized</a>, <a href="../../../../../org/apache/spark/Logging.html#initializeIfNecessary()">initializeIfNecessary</a>, <a href="../../../../../org/apache/spark/Logging.html#initializeLogging()">initializeLogging</a>, <a href="../../../../../org/apache/spark/Logging.html#initLock()">initLock</a>, <a href="../../../../../org/apache/spark/Logging.html#isTraceEnabled()">isTraceEnabled</a>, <a href="../../../../../org/apache/spark/Logging.html#log_()">log_</a>, <a href="../../../../../org/apache/spark/Logging.html#log()">log</a>, <a href="../../../../../org/apache/spark/Logging.html#logDebug(scala.Function0)">logDebug</a>, <a href="../../../../../org/apache/spark/Logging.html#logDebug(scala.Function0, java.lang.Throwable)">logDebug</a>, <a href="../../../../../org/apache/spark/Logging.html#logError(scala.Function0)">logError</a>, <a href="../../../../../org/apache/spark/Logging.html#logError(scala.Function0, java.lang.Throwable)">logError</a>, <a href="../../../../../org/apache/spark/Logging.html#logInfo(scala.Function0)">logInfo</a>, <a href="../../../../../org/apache/spark/Logging.html#logInfo(scala.Function0, java.lang.Throwable)">logInfo</a>, <a href="../../../../../org/apache/spark/Logging.html#logTrace(scala.Function0)">logTrace</a>, <a href="../../../../../org/apache/spark/Logging.html#logTrace(scala.Function0, java.lang.Throwable)">logTrace</a>, <a href="../../../../../org/apache/spark/Logging.html#logWarning(scala.Function0)">logWarning</a>, <a href="../../../../../org/apache/spark/Logging.html#logWarning(scala.Function0, java.lang.Throwable)">logWarning</a></code></li>
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<h3>Method Detail</h3>
<a name="runMiniBatchSGD(org.apache.spark.rdd.RDD, org.apache.spark.mllib.optimization.Gradient, org.apache.spark.mllib.optimization.Updater, double, int, double, double, org.apache.spark.mllib.linalg.Vector)">
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<h4>runMiniBatchSGD</h4>
<pre>public static scala.Tuple2<<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>,double[]> runMiniBatchSGD(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple2<Object,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>>> data,
<a href="../../../../../org/apache/spark/mllib/optimization/Gradient.html" title="class in org.apache.spark.mllib.optimization">Gradient</a> gradient,
<a href="../../../../../org/apache/spark/mllib/optimization/Updater.html" title="class in org.apache.spark.mllib.optimization">Updater</a> updater,
double stepSize,
int numIterations,
double regParam,
double miniBatchFraction,
<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> initialWeights)</pre>
<div class="block">Run stochastic gradient descent (SGD) in parallel using mini batches.
In each iteration, we sample a subset (fraction miniBatchFraction) of the total data
in order to compute a gradient estimate.
Sampling, and averaging the subgradients over this subset is performed using one standard
spark map-reduce in each iteration.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - - Input data for SGD. RDD of the set of data examples, each of
the form (label, [feature values]).</dd><dd><code>gradient</code> - - Gradient object (used to compute the gradient of the loss function of
one single data example)</dd><dd><code>updater</code> - - Updater function to actually perform a gradient step in a given direction.</dd><dd><code>stepSize</code> - - initial step size for the first step</dd><dd><code>numIterations</code> - - number of iterations that SGD should be run.</dd><dd><code>regParam</code> - - regularization parameter</dd><dd><code>miniBatchFraction</code> - - fraction of the input data set that should be used for
one iteration of SGD. Default value 1.0.
<p></dd>
<dt><span class="strong">Returns:</span></dt><dd>A tuple containing two elements. The first element is a column matrix containing
weights for every feature, and the second element is an array containing the
stochastic loss computed for every iteration.</dd></dl>
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<h4>setStepSize</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a> setStepSize(double step)</pre>
<div class="block">Set the initial step size of SGD for the first step. Default 1.0.
In subsequent steps, the step size will decrease with stepSize/sqrt(t)</div>
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<a name="setMiniBatchFraction(double)">
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<h4>setMiniBatchFraction</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a> setMiniBatchFraction(double fraction)</pre>
<div class="block">:: Experimental ::
Set fraction of data to be used for each SGD iteration.
Default 1.0 (corresponding to deterministic/classical gradient descent)</div>
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<h4>setNumIterations</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a> setNumIterations(int iters)</pre>
<div class="block">Set the number of iterations for SGD. Default 100.</div>
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<h4>setRegParam</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a> setRegParam(double regParam)</pre>
<div class="block">Set the regularization parameter. Default 0.0.</div>
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<h4>setGradient</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a> setGradient(<a href="../../../../../org/apache/spark/mllib/optimization/Gradient.html" title="class in org.apache.spark.mllib.optimization">Gradient</a> gradient)</pre>
<div class="block">Set the gradient function (of the loss function of one single data example)
to be used for SGD.</div>
</li>
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<a name="setUpdater(org.apache.spark.mllib.optimization.Updater)">
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<h4>setUpdater</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/optimization/GradientDescent.html" title="class in org.apache.spark.mllib.optimization">GradientDescent</a> setUpdater(<a href="../../../../../org/apache/spark/mllib/optimization/Updater.html" title="class in org.apache.spark.mllib.optimization">Updater</a> updater)</pre>
<div class="block">Set the updater function to actually perform a gradient step in a given direction.
The updater is responsible to perform the update from the regularization term as well,
and therefore determines what kind or regularization is used, if any.</div>
</li>
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<a name="optimize(org.apache.spark.rdd.RDD, org.apache.spark.mllib.linalg.Vector)">
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<h4>optimize</h4>
<pre>public <a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> optimize(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a><scala.Tuple2<Object,<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>>> data,
<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> initialWeights)</pre>
<div class="block">:: DeveloperApi ::
Runs gradient descent on the given training data.</div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/optimization/Optimizer.html#optimize(org.apache.spark.rdd.RDD, org.apache.spark.mllib.linalg.Vector)">optimize</a></code> in interface <code><a href="../../../../../org/apache/spark/mllib/optimization/Optimizer.html" title="interface in org.apache.spark.mllib.optimization">Optimizer</a></code></dd>
<dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - training data</dd><dd><code>initialWeights</code> - initial weights</dd>
<dt><span class="strong">Returns:</span></dt><dd>solution vector</dd></dl>
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