[SPARK-7571] [MLLIB] rename Math to math

`scala.Math` is deprecated since 2.8. This PR only touchs `Math` usages in MLlib. dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #6092 from mengxr/SPARK-7571 and squashes the following commits:

fe8f8d3 [Xiangrui Meng] Math -> math

4 files changed, 6 insertions, 6 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 647226a0d1..93ba91167b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -175,7 +175,7 @@ class LogisticRegression
        * }}}
        */
       initialWeightsWithIntercept.toArray(numFeatures)
-        = Math.log(histogram(1).toDouble / histogram(0).toDouble)
+        = math.log(histogram(1).toDouble / histogram(0).toDouble)
     }
 
     val states = optimizer.iterations(new CachedDiffFunction(costFun),
@@ -285,7 +285,7 @@ class LogisticRegressionModel private[ml] (
     } else if (t == 1.0) {
       Double.PositiveInfinity
     } else {
-      Math.log(t / (1.0 - t))
+      math.log(t / (1.0 - t))
     }
     if (rawPrediction(1) > rawThreshold) 1 else 0
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
index 568b653056..c88410ac0f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
@@ -160,7 +160,7 @@ class GaussianMixture private (
     var llhp = 0.0            // previous log-likelihood
     
     var iter = 0
-    while(iter < maxIterations && Math.abs(llh-llhp) > convergenceTol) {
+    while (iter < maxIterations && math.abs(llh-llhp) > convergenceTol) {
       // create and broadcast curried cluster contribution function
       val compute = sc.broadcast(ExpectationSum.add(weights, gaussians)_)
       
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala
index 4766f77082..64d52bae00 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala
@@ -91,7 +91,7 @@ private[spark] object NNLS {
     val dir = ws.dir
     val lastDir = ws.lastDir
     val res = ws.res
-    val iterMax = Math.max(400, 20 * n)
+    val iterMax = math.max(400, 20 * n)
     var lastNorm = 0.0
     var iterno = 0
     var lastWall = 0 // Last iteration when we hit a bound constraint.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
index 0deef11b45..79747cc5d7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
@@ -32,7 +32,7 @@ private[stat] object KernelDensity {
 
     // This gets used in each Gaussian PDF computation, so compute it up front
     val logStandardDeviationPlusHalfLog2Pi =
-      Math.log(standardDeviation) + 0.5 * Math.log(2 * Math.PI)
+      math.log(standardDeviation) + 0.5 * math.log(2 * math.Pi)
 
     val (points, count) = samples.aggregate((new Array[Double](evaluationPoints.length), 0))(
       (x, y) => {
@@ -66,6 +66,6 @@ private[stat] object KernelDensity {
     val x0 = x - mean
     val x1 = x0 / standardDeviation
     val logDensity = -0.5 * x1 * x1 - logStandardDeviationPlusHalfLog2Pi
-    Math.exp(logDensity)
+    math.exp(logDensity)
   }
 }