[jira] [Created] (SPARK-15656) ChiSqTest for goodness of fit doesn't test against a wrong uniform distribution by default

["Jieyuan Chen (JIRA)" <ji...@apache.org>]

Jieyuan Chen created SPARK-15656:
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             Summary: ChiSqTest for goodness of fit doesn't test against a wrong uniform distribution by default
                 Key: SPARK-15656
                 URL: https://issues.apache.org/jira/browse/SPARK-15656
             Project: Spark
          Issue Type: Bug
          Components: MLlib
    Affects Versions: 1.6.1, 1.5.1
            Reporter: Jieyuan Chen


I've been running a ChiSqTest to test whether my samples fit a uniform distribution.
The documentation says that If a second vector to test against is not supplied as a parameter, the test runs against a uniform distribution. But when I pass samples drawn from a normal distribution, the p-value calculated is 1.0, which is wrong.
The problem is that in ChiSqTest.scala, the `chiSquared` function will generate a wrong uniform distribution if the expected vector is not supplied.



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