Github user sryza commented on a diff in the pull request:
https://github.com/apache/spark/pull/6994#discussion_r33188987
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/stat/test/KSTest.scala ---
@@ -0,0 +1,126 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.mllib.stat.test
+
+import org.apache.commons.math3.distribution.NormalDistribution
+import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest
+
+import org.apache.spark.{SparkException, Logging}
+import org.apache.spark.rdd.RDD
+
+
+/**
+ * Conduct the two-sided Kolmogorov Smirnov test for data sampled from a
+ * continuous distribution. By comparing the largest difference between
the empirical cumulative
+ * distribution of the sample data and the theoretical distribution we can
provide a test for the
+ * the null hypothesis that the sample data comes from that theoretical
distribution.
+ * For more information on KS Test:
https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test
+ */
+ private[stat] object KSTest {
+
+ // Null hypothesis for the type of KS test to be included in the result.
+ object NullHypothesis extends Enumeration {
+ type NullHypothesis = Value
+ val oneSampleTwoSided = Value("Sample follows theoretical
distribution.")
+ }
+
+ /**
+ * Calculate empirical cumulative distribution values needed for KS
statistic
+ * @param dat `RDD[Double]` on which to calculate empirical cumulative
distribution values
+ * @return and RDD of (Double, Double, Double), where the first element
in each tuple is the
+ * value, the second element is the ECDFV - 1 /n, and the third
element is the ECDFV,
+ * where ECDF stands for empirical cumulative distribution
function value
+ *
+ */
+ def empirical(dat: RDD[Double]): RDD[(Double, Double, Double)] = {
--- End diff --
Nit: replace dat with data
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