Repository: spark Updated Branches: refs/heads/branch-1.4 32819fcb7 -> 2555517c9
[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 <m...@databricks.com> Closes #6092 from mengxr/SPARK-7571 and squashes the following commits: fe8f8d3 [Xiangrui Meng] Math -> math (cherry picked from commit a4874b0d1820efd24071108434a4d89429473fe3) Signed-off-by: Xiangrui Meng <m...@databricks.com> Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/2555517c Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/2555517c Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/2555517c Branch: refs/heads/branch-1.4 Commit: 2555517c97ff4ccfc9d691ddf06c408a0ce28f2e Parents: 32819fc Author: Xiangrui Meng <m...@databricks.com> Authored: Tue May 12 14:39:03 2015 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Tue May 12 14:39:11 2015 -0700 ---------------------------------------------------------------------- .../apache/spark/ml/classification/LogisticRegression.scala | 4 ++-- .../org/apache/spark/mllib/clustering/GaussianMixture.scala | 2 +- .../scala/org/apache/spark/mllib/optimization/NNLS.scala | 2 +- .../scala/org/apache/spark/mllib/stat/KernelDensity.scala | 4 ++-- .../spark/ml/classification/LogisticRegressionSuite.scala | 2 +- .../org/apache/spark/mllib/feature/NormalizerSuite.scala | 8 ++++---- .../spark/mllib/linalg/distributed/RowMatrixSuite.scala | 4 ++-- .../org/apache/spark/mllib/optimization/LBFGSSuite.scala | 2 +- .../spark/mllib/regression/IsotonicRegressionSuite.scala | 2 +- 9 files changed, 15 insertions(+), 15 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/2555517c/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala ---------------------------------------------------------------------- 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 647226a..93ba911 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 } http://git-wip-us.apache.org/repos/asf/spark/blob/2555517c/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala ---------------------------------------------------------------------- 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 568b653..c88410a 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)_) http://git-wip-us.apache.org/repos/asf/spark/blob/2555517c/mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLS.scala ---------------------------------------------------------------------- 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 4766f77..64d52ba 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. http://git-wip-us.apache.org/repos/asf/spark/blob/2555517c/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala ---------------------------------------------------------------------- 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 0deef11..79747cc 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) } } http://git-wip-us.apache.org/repos/asf/spark/blob/2555517c/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala ---------------------------------------------------------------------- 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 78cdd47..4df8016 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) http://git-wip-us.apache.org/repos/asf/spark/blob/2555517c/mllib/src/test/scala/org/apache/spark/mllib/feature/NormalizerSuite.scala ---------------------------------------------------------------------- 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 85fdd27..5c4af2b 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) http://git-wip-us.apache.org/repos/asf/spark/blob/2555517c/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala ---------------------------------------------------------------------- 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 3309713..27bb19f 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") } http://git-wip-us.apache.org/repos/asf/spark/blob/2555517c/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---------------------------------------------------------------------- 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 70c6477..c8f2adc 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.") } http://git-wip-us.apache.org/repos/asf/spark/blob/2555517c/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala ---------------------------------------------------------------------- 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 8e12340..3b38bdf 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)] = { --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org