[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

9 files changed, 15 insertions, 15 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)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index 78cdd47185..4df8016009 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -489,7 +489,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
      * b = \log{P(1) / P(0)} = \log{count_1 / count_0}
      * }}}
      */
-    val interceptTheory = Math.log(histogram(1).toDouble / histogram(0).toDouble)
+    val interceptTheory = math.log(histogram(1).toDouble / histogram(0).toDouble)
     val weightsTheory = Array(0.0, 0.0, 0.0, 0.0)
 
     assert(model.intercept ~== interceptTheory relTol 1E-5)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala
index 85fdd271b5..5c4af2b99e 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala
@@ -106,10 +106,10 @@ class NormalizerSuite extends FunSuite with MLlibTestSparkContext {
 
     assert((dataInf, dataInfRDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5))
 
-    assert(dataInf(0).toArray.map(Math.abs).max ~== 1.0 absTol 1E-5)
-    assert(dataInf(2).toArray.map(Math.abs).max ~== 1.0 absTol 1E-5)
-    assert(dataInf(3).toArray.map(Math.abs).max ~== 1.0 absTol 1E-5)
-    assert(dataInf(4).toArray.map(Math.abs).max ~== 1.0 absTol 1E-5)
+    assert(dataInf(0).toArray.map(math.abs).max ~== 1.0 absTol 1E-5)
+    assert(dataInf(2).toArray.map(math.abs).max ~== 1.0 absTol 1E-5)
+    assert(dataInf(3).toArray.map(math.abs).max ~== 1.0 absTol 1E-5)
+    assert(dataInf(4).toArray.map(math.abs).max ~== 1.0 absTol 1E-5)
 
     assert(dataInf(0) ~== Vectors.sparse(3, Seq((0, -0.86956522), (1, 1.0))) absTol 1E-5)
     assert(dataInf(1) ~== Vectors.dense(0.0, 0.0, 0.0) absTol 1E-5)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
index 3309713e91..27bb19f472 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala
@@ -96,7 +96,7 @@ class RowMatrixSuite extends FunSuite with MLlibTestSparkContext {
   }
 
   test("similar columns") {
-    val colMags = Vectors.dense(Math.sqrt(126), Math.sqrt(66), Math.sqrt(94))
+    val colMags = Vectors.dense(math.sqrt(126), math.sqrt(66), math.sqrt(94))
     val expected = BDM(
       (0.0, 54.0, 72.0),
       (0.0, 0.0, 78.0),
@@ -232,7 +232,7 @@ class RowMatrixSuite extends FunSuite with MLlibTestSparkContext {
         assert(summary.numNonzeros === Vectors.dense(3.0, 3.0, 4.0), "nnz mismatch")
         assert(summary.max === Vectors.dense(9.0, 7.0, 8.0), "max mismatch")
         assert(summary.min === Vectors.dense(0.0, 0.0, 1.0), "column mismatch.")
-        assert(summary.normL2 === Vectors.dense(Math.sqrt(126), Math.sqrt(66), Math.sqrt(94)),
+        assert(summary.normL2 === Vectors.dense(math.sqrt(126), math.sqrt(66), math.sqrt(94)),
           "magnitude mismatch.")
         assert(summary.normL1 === Vectors.dense(18.0, 12.0, 16.0), "L1 norm mismatch")
       }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
index 70c64775e4..c8f2adcf15 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala
@@ -89,7 +89,7 @@ class LBFGSSuite extends FunSuite with MLlibTestSparkContext with Matchers {
     // it requires 90 iterations in GD. No matter how hard we increase
     // the number of iterations in GD here, the lossGD will be always
     // larger than lossLBFGS. This is based on observation, no theoretically guaranteed
-    assert(Math.abs((lossGD.last - loss.last) / loss.last) < 0.02,
+    assert(math.abs((lossGD.last - loss.last) / loss.last) < 0.02,
       "LBFGS should match GD result within 2% difference.")
   }
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala
index 8e12340bbd..3b38bdf5ef 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala
@@ -26,7 +26,7 @@ import org.apache.spark.util.Utils
 class IsotonicRegressionSuite extends FunSuite with MLlibTestSparkContext with Matchers {
 
   private def round(d: Double) = {
-    Math.round(d * 100).toDouble / 100
+    math.round(d * 100).toDouble / 100
   }
 
   private def generateIsotonicInput(labels: Seq[Double]): Seq[(Double, Double, Double)] = {