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| author | Reynold Xin <rxin@databricks.com> | 2015-03-20 14:13:02 -0400 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2015-03-20 14:13:02 -0400 |
| commit | db4d317ccfdd9bd1dc7e8beac54ebcc35966b7d5 (patch) | |
| tree | e6d6706f99b6564383b994750917858a0b1b1fc2 /examples/src | |
| parent | 6f80c3e8880340597f161f87e64697bec86cc586 (diff) | |
| download | spark-db4d317ccfdd9bd1dc7e8beac54ebcc35966b7d5.tar.gz spark-db4d317ccfdd9bd1dc7e8beac54ebcc35966b7d5.tar.bz2 spark-db4d317ccfdd9bd1dc7e8beac54ebcc35966b7d5.zip | |
[SPARK-6428][MLlib] Added explicit type for public methods and implemented hashCode when equals is defined.
I want to add a checker to turn public type checking on, since future pull requests can accidentally expose a non-public type. This is the first cleanup task.
Author: Reynold Xin <rxin@databricks.com>
Closes #5102 from rxin/mllib-hashcode-publicmethodtypes and squashes the following commits:
617f19e [Reynold Xin] Fixed Scala compilation error.
52bc2d5 [Reynold Xin] [MLlib] Added explicit type for public methods and implemented hashCode when equals is defined.
Diffstat (limited to 'examples/src')
| -rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala | 3 | ||||
| -rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala | 4 |
2 files changed, 4 insertions, 3 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala index 91a0a860d6..1f4ca4fbe7 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala @@ -175,7 +175,8 @@ object MovieLensALS { } /** Compute RMSE (Root Mean Squared Error). */ - def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], implicitPrefs: Boolean) = { + def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], implicitPrefs: Boolean) + : Double = { def mapPredictedRating(r: Double) = if (implicitPrefs) math.max(math.min(r, 1.0), 0.0) else r diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala index 91c9772744..9f22d40c15 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala @@ -116,7 +116,7 @@ object PowerIterationClusteringExample { sc.stop() } - def generateCircle(radius: Double, n: Int) = { + def generateCircle(radius: Double, n: Int): Seq[(Double, Double)] = { Seq.tabulate(n) { i => val theta = 2.0 * math.Pi * i / n (radius * math.cos(theta), radius * math.sin(theta)) @@ -147,7 +147,7 @@ object PowerIterationClusteringExample { /** * Gaussian Similarity: http://en.wikipedia.org/wiki/Radial_basis_function_kernel */ - def gaussianSimilarity(p1: (Double, Double), p2: (Double, Double), sigma: Double) = { + def gaussianSimilarity(p1: (Double, Double), p2: (Double, Double), sigma: Double): Double = { val coeff = 1.0 / (math.sqrt(2.0 * math.Pi) * sigma) val expCoeff = -1.0 / 2.0 * math.pow(sigma, 2.0) val ssquares = (p1._1 - p2._1) * (p1._1 - p2._1) + (p1._2 - p2._2) * (p1._2 - p2._2) |