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<div class="subTitle">org.apache.spark.mllib.optimization</div>
<h2 title="Class LogisticGradient" class="title">Class LogisticGradient</h2>
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<li>Object</li>
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<li><a href="../../../../../org/apache/spark/mllib/optimization/Gradient.html" title="class in org.apache.spark.mllib.optimization">org.apache.spark.mllib.optimization.Gradient</a></li>
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<pre>public class <span class="strong">LogisticGradient</span>
extends <a href="../../../../../org/apache/spark/mllib/optimization/Gradient.html" title="class in org.apache.spark.mllib.optimization">Gradient</a></pre>
<div class="block">:: DeveloperApi ::
Compute gradient and loss for a logistic loss function, as used in binary classification.
See also the documentation for the precise formulation.</div>
<dl><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../serialized-form.html#org.apache.spark.mllib.optimization.LogisticGradient">Serialized Form</a></dd></dl>
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<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/mllib/optimization/LogisticGradient.html#LogisticGradient()">LogisticGradient</a></strong>()</code> </td>
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<td class="colFirst"><code>scala.Tuple2<<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>,Object></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/optimization/LogisticGradient.html#compute(org.apache.spark.mllib.linalg.Vector, double, org.apache.spark.mllib.linalg.Vector)">compute</a></strong>(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> data,
double label,
<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> weights)</code>
<div class="block">Compute the gradient and loss given the features of a single data point.</div>
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<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/optimization/LogisticGradient.html#compute(org.apache.spark.mllib.linalg.Vector, double, org.apache.spark.mllib.linalg.Vector, org.apache.spark.mllib.linalg.Vector)">compute</a></strong>(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> data,
double label,
<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> weights,
<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> cumGradient)</code>
<div class="block">Compute the gradient and loss given the features of a single data point,
add the gradient to a provided vector to avoid creating new objects, and return loss.</div>
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<h4>LogisticGradient</h4>
<pre>public LogisticGradient()</pre>
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<h4>compute</h4>
<pre>public scala.Tuple2<<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a>,Object> compute(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> data,
double label,
<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> weights)</pre>
<div class="block"><strong>Description copied from class: <code><a href="../../../../../org/apache/spark/mllib/optimization/Gradient.html#compute(org.apache.spark.mllib.linalg.Vector, double, org.apache.spark.mllib.linalg.Vector)">Gradient</a></code></strong></div>
<div class="block">Compute the gradient and loss given the features of a single data point.
<p></div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/optimization/Gradient.html#compute(org.apache.spark.mllib.linalg.Vector, double, org.apache.spark.mllib.linalg.Vector)">compute</a></code> in class <code><a href="../../../../../org/apache/spark/mllib/optimization/Gradient.html" title="class in org.apache.spark.mllib.optimization">Gradient</a></code></dd>
<dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - features for one data point</dd><dd><code>label</code> - label for this data point</dd><dd><code>weights</code> - weights/coefficients corresponding to features
<p></dd>
<dt><span class="strong">Returns:</span></dt><dd>(gradient: Vector, loss: Double)</dd></dl>
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<h4>compute</h4>
<pre>public double compute(<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> data,
double label,
<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> weights,
<a href="../../../../../org/apache/spark/mllib/linalg/Vector.html" title="interface in org.apache.spark.mllib.linalg">Vector</a> cumGradient)</pre>
<div class="block"><strong>Description copied from class: <code><a href="../../../../../org/apache/spark/mllib/optimization/Gradient.html#compute(org.apache.spark.mllib.linalg.Vector, double, org.apache.spark.mllib.linalg.Vector, org.apache.spark.mllib.linalg.Vector)">Gradient</a></code></strong></div>
<div class="block">Compute the gradient and loss given the features of a single data point,
add the gradient to a provided vector to avoid creating new objects, and return loss.
<p></div>
<dl>
<dt><strong>Specified by:</strong></dt>
<dd><code><a href="../../../../../org/apache/spark/mllib/optimization/Gradient.html#compute(org.apache.spark.mllib.linalg.Vector, double, org.apache.spark.mllib.linalg.Vector, org.apache.spark.mllib.linalg.Vector)">compute</a></code> in class <code><a href="../../../../../org/apache/spark/mllib/optimization/Gradient.html" title="class in org.apache.spark.mllib.optimization">Gradient</a></code></dd>
<dt><span class="strong">Parameters:</span></dt><dd><code>data</code> - features for one data point</dd><dd><code>label</code> - label for this data point</dd><dd><code>weights</code> - weights/coefficients corresponding to features</dd><dd><code>cumGradient</code> - the computed gradient will be added to this vector
<p></dd>
<dt><span class="strong">Returns:</span></dt><dd>loss</dd></dl>
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