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author | Xiangrui Meng <meng@databricks.com> | 2015-05-12 14:39:03 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-05-12 14:39:11 -0700 |
commit | 2555517c97ff4ccfc9d691ddf06c408a0ce28f2e (patch) | |
tree | bf6723a717a51e1bd341985f7320e31577ef5b09 /mllib/src | |
parent | 32819fcb7c862d66a98329c0c238e18fa56238a8 (diff) | |
download | spark-2555517c97ff4ccfc9d691ddf06c408a0ce28f2e.tar.gz spark-2555517c97ff4ccfc9d691ddf06c408a0ce28f2e.tar.bz2 spark-2555517c97ff4ccfc9d691ddf06c408a0ce28f2e.zip |
[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
(cherry picked from commit a4874b0d1820efd24071108434a4d89429473fe3)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
Diffstat (limited to 'mllib/src')
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)] = { |