fixed p-value for extreme case.

mllib/src/main/scala/org/apache/spark/mllib/stat/test/ChiSqTest.scala

@@ -154,8 +154,8 @@ private[stat] object ChiSqTest extends Logging {
           throw new IllegalArgumentException("Chi-squared statistic undefined for input vectors due"
             + " to 0.0 values in both observed and expected.")
         } else {
-          return new ChiSqTestResult(Double.PositiveInfinity, size - 1, Double.PositiveInfinity,
-            PEARSON.name, NullHypothesis.goodnessOfFit.toString)
+          return new ChiSqTestResult(0.0, size - 1, Double.PositiveInfinity, PEARSON.name,
+            NullHypothesis.goodnessOfFit.toString)
         }
       }
       if (scale == 1.0) {

mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala

@@ -66,7 +66,7 @@ class HypothesisTestSuite extends FunSuite with LocalSparkContext {
     val inf = Statistics.chiSqTest(observed, zeroExpected)
     assert(inf.statistic === Double.PositiveInfinity)
     assert(inf.degreesOfFreedom === 2)
-    assert(inf.pValue === Double.PositiveInfinity)
+    assert(inf.pValue === 0.0)
     assert(inf.method === ChiSqTest.PEARSON.name)
     assert(inf.nullHypothesis === ChiSqTest.NullHypothesis.goodnessOfFit.toString)