Github user sryza commented on a diff in the pull request:
https://github.com/apache/spark/pull/7278#discussion_r35342576
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
---
@@ -196,4 +196,33 @@ object Statistics {
: KolmogorovSmirnovTestResult = {
KolmogorovSmirnovTest.testOneSample(data, distName, params: _*)
}
+
+ /**
+ * Conduct a 1-sample Anderson-Darling test for the null hypothesis that
the data
+ * comes from a given theoretical distribution. The Anderson-Darling
test is an alternative
+ * to the Kolmogorov-Smirnov test, and is more adequate at identifying
departures from the
+ * theoretical distribution at the tails. The implementation returns an
+ * `AndersonDarlingTestResult`, which includes the statistic, the
critical values at varying
+ * significance levels, and the null hypothesis. Note that the critical
values are calculated
+ * assuming the parameters have been calculated from the data sample.
+ * If the parameters for the theoretical distribution are not in a valid
domain, throws an
+ * exception.
+ * @param data the data to be tested
+ * @param distName name of the theoretical distribution to test against.
Currently
+ * supports Normal ("norm"), Exponential ("exp"), Gumbel
("gumbel"),
+ * Logistic ("logistic"), and Weibull ("weibull")
distributions
+ * @param params provides the parameters for the theoretical
distribution.
+ * The order of parameters are as follow
+ * Normal -> [mu, sigma] (location, scale)
+ * Exponential -> [1 / lambda] (scale)
+ * Gumbel -> [mu, beta] (location, scale)
+ * Logistic -> [mu, s] (location, scale)
+ * Weibull -> [lambda, k] (scale, shape)
+ * @return
--- End diff --
Give something short for @return
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