Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/11108#discussion_r56434972
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/HypothesisTestingExample.scala
---
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.{SparkConf, SparkContext}
+// $example on$
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.stat.Statistics
+import org.apache.spark.mllib.stat.test.ChiSqTestResult
+import org.apache.spark.rdd.RDD
+// $example off$
+
+object HypothesisTestingExample {
+
+ def main(args: Array[String]) {
+
+ val conf = new SparkConf().setAppName("HypothesisTestingExample")
+ val sc = new SparkContext(conf)
+
+ // $example on$
+ // a vector composed of the frequencies of events
+ val vec: Vector = Vectors.dense(0.1, 0.15, 0.2, 0.3, 0.25)
+
+ // compute the goodness of fit. If a second vector to test against is
not supplied
+ // as a parameter, the test runs against a uniform distribution.
+ val goodnessOfFitTestResult = Statistics.chiSqTest(vec)
+ // summary of the test including the p-value, degrees of freedom, test
statistic, the method
+ // used, and the null hypothesis.
+ println(goodnessOfFitTestResult)
+ println()
+
+ // a contingency matrix. Create a dense matrix ((1.0, 2.0), (3.0,
4.0), (5.0, 6.0))
+ val mat: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0,
6.0))
+
+ // conduct Pearson's independence test on the input contingency matrix
+ val independenceTestResult = Statistics.chiSqTest(mat)
+ // summary of the test including the p-value, degrees of freedom
+ println(independenceTestResult)
+ println()
+
+ val p1 = LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0))
+ val p2 = LabeledPoint(1.0, Vectors.dense(1.0, 2.0, 0.0))
+ val p3 = LabeledPoint(-1.0, Vectors.dense(-1.0, 0.0, -0.5))
+ val obs: RDD[LabeledPoint] = sc.parallelize(Seq(p1, p2, p3)) //
(feature, label) pairs.
+
+ // The contingency table is constructed from the raw (feature, label)
pairs and used to conduct
+ // the independence test. Returns an array containing the
ChiSquaredTestResult for every feature
+ // against the label.
+ val featureTestResults: Array[ChiSqTestResult] =
Statistics.chiSqTest(obs)
+ featureTestResults.zipWithIndex.foreach { result =>
--- End diff --
use `case (k, v) =>`
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