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<center><b>
[ <a href="module-tree.html">Module Hierarchy</a>
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<h1 class="epydoc">Class Hierarchy</h1>
<ul class="nomargin-top">
<li> <strong class="uidlink">collections.Iterable</strong>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.util.MLUtils-class.html">pyspark.mllib.util.MLUtils</a></strong>:
<em class="summary">Helper methods to load, save and pre-process data used in MLlib.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.sql.SQLContext-class.html">pyspark.sql.SQLContext</a></strong>:
<em class="summary">Main entry point for SparkSQL functionality.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.storagelevel.StorageLevel-class.html">pyspark.storagelevel.StorageLevel</a></strong>:
<em class="summary">Flags for controlling the storage of an RDD.</em>
</li>
<li> <strong class="uidlink">object</strong>:
<em class="summary">The most base type</em>
<ul>
<li> <strong class="uidlink"><a href="pyspark.mllib.recommendation.ALS-class.html">pyspark.mllib.recommendation.ALS</a></strong>
</li>
<li> <strong class="uidlink"><a href="pyspark.accumulators.Accumulator-class.html">pyspark.accumulators.Accumulator</a></strong>:
<em class="summary">A shared variable that can be accumulated, i.e., has a commutative
and associative "add" operation.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.accumulators.AccumulatorParam-class.html">pyspark.accumulators.AccumulatorParam</a></strong>:
<em class="summary">Helper object that defines how to accumulate values of a given
type.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.broadcast.Broadcast-class.html">pyspark.broadcast.Broadcast</a></strong>:
<em class="summary">A broadcast variable created with <a
href="pyspark.context.SparkContext-class.html#broadcast"
class="link">SparkContext.broadcast()</a>.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.clustering.KMeans-class.html">pyspark.mllib.clustering.KMeans</a></strong>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.clustering.KMeansModel-class.html">pyspark.mllib.clustering.KMeansModel</a></strong>:
<em class="summary">A clustering model derived from the k-means method.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.regression.LabeledPoint-class.html">pyspark.mllib.regression.LabeledPoint</a></strong>:
<em class="summary">The features and labels of a data point.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.regression.LassoWithSGD-class.html">pyspark.mllib.regression.LassoWithSGD</a></strong>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.regression.LinearModel-class.html">pyspark.mllib.regression.LinearModel</a></strong>:
<em class="summary">A linear model that has a vector of coefficients and an intercept.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.regression.LinearRegressionWithSGD-class.html">pyspark.mllib.regression.LinearRegressionWithSGD</a></strong>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.classification.LogisticRegressionWithSGD-class.html">pyspark.mllib.classification.LogisticRegressionWithSGD</a></strong>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.recommendation.MatrixFactorizationModel-class.html">pyspark.mllib.recommendation.MatrixFactorizationModel</a></strong>:
<em class="summary">A matrix factorisation model trained by regularized alternating
least-squares.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.classification.NaiveBayes-class.html">pyspark.mllib.classification.NaiveBayes</a></strong>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.classification.NaiveBayesModel-class.html">pyspark.mllib.classification.NaiveBayesModel</a></strong>:
<em class="summary">Model for Naive Bayes classifiers.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.rdd.RDD-class.html">pyspark.rdd.RDD</a></strong>:
<em class="summary">A Resilient Distributed Dataset (RDD), the basic abstraction in
Spark.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.regression.RidgeRegressionWithSGD-class.html">pyspark.mllib.regression.RidgeRegressionWithSGD</a></strong>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.classification.SVMWithSGD-class.html">pyspark.mllib.classification.SVMWithSGD</a></strong>
</li>
<li> <strong class="uidlink">pyspark.serializers.Serializer</strong>
</li>
<li> <strong class="uidlink"><a href="pyspark.conf.SparkConf-class.html">pyspark.conf.SparkConf</a></strong>:
<em class="summary">Configuration for a Spark application.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.context.SparkContext-class.html">pyspark.context.SparkContext</a></strong>:
<em class="summary">Main entry point for Spark functionality.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.files.SparkFiles-class.html">pyspark.files.SparkFiles</a></strong>:
<em class="summary">Resolves paths to files added through <a
href="pyspark.context.SparkContext-class.html#addFile"
class="link">SparkContext.addFile()</a>.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.linalg.SparseVector-class.html">pyspark.mllib.linalg.SparseVector</a></strong>:
<em class="summary">A simple sparse vector class for passing data to MLlib.</em>
</li>
<li> <strong class="uidlink"><a href="pyspark.statcounter.StatCounter-class.html">pyspark.statcounter.StatCounter</a></strong>
</li>
<li> <strong class="uidlink"><a href="pyspark.mllib.linalg.Vectors-class.html">pyspark.mllib.linalg.Vectors</a></strong>:
<em class="summary">Factory methods for working with vectors.</em>
</li>
<li> <strong class="uidlink">dict</strong>:
<em class="summary">dict() -> new empty dictionary
dict(mapping) -> new dictionary initialized from a mapping object's
(key, value) pairs
dict(iterable) -> new dictionary initialized as if via:
d = {}
for k, v in iterable:
d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs
in the keyword argument list.</em>
</li>
</ul>
</li>
</ul>
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