aboutsummaryrefslogtreecommitdiff
diff options
context:
space:
mode:
-rw-r--r--mllib/src/main/scala/spark/mllib/util/LinearDataGenerator.scala14
-rw-r--r--mllib/src/test/java/spark/mllib/regression/JavaLassoSuite.java6
-rw-r--r--mllib/src/test/java/spark/mllib/regression/JavaLinearRegressionSuite.java6
-rw-r--r--mllib/src/test/java/spark/mllib/regression/JavaRidgeRegressionSuite.java6
4 files changed, 16 insertions, 16 deletions
diff --git a/mllib/src/main/scala/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/spark/mllib/util/LinearDataGenerator.scala
index 8fe3ab4754..20e1656beb 100644
--- a/mllib/src/main/scala/spark/mllib/util/LinearDataGenerator.scala
+++ b/mllib/src/main/scala/spark/mllib/util/LinearDataGenerator.scala
@@ -91,13 +91,13 @@ object LinearDataGenerator {
* @return RDD of LabeledPoint containing sample data.
*/
def generateLinearRDD(
- sc: SparkContext,
- nexamples: Int,
- nfeatures: Int,
- eps: Double,
- weights: Array[Double] = Array[Double](),
- nparts: Int = 2,
- intercept: Double = 0.0) : RDD[LabeledPoint] = {
+ sc: SparkContext,
+ nexamples: Int,
+ nfeatures: Int,
+ eps: Double,
+ weights: Array[Double] = Array[Double](),
+ nparts: Int = 2,
+ intercept: Double = 0.0) : RDD[LabeledPoint] = {
org.jblas.util.Random.seed(42)
// Random values distributed uniformly in [-0.5, 0.5]
val w = DoubleMatrix.rand(nfeatures, 1).subi(0.5)
diff --git a/mllib/src/test/java/spark/mllib/regression/JavaLassoSuite.java b/mllib/src/test/java/spark/mllib/regression/JavaLassoSuite.java
index 8d692c2d0d..428902e85c 100644
--- a/mllib/src/test/java/spark/mllib/regression/JavaLassoSuite.java
+++ b/mllib/src/test/java/spark/mllib/regression/JavaLassoSuite.java
@@ -67,11 +67,11 @@ public class JavaLassoSuite implements Serializable {
List<LabeledPoint> validationData =
LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 17);
- LassoWithSGD svmSGDImpl = new LassoWithSGD();
- svmSGDImpl.optimizer().setStepSize(1.0)
+ LassoWithSGD lassoSGDImpl = new LassoWithSGD();
+ lassoSGDImpl.optimizer().setStepSize(1.0)
.setRegParam(0.01)
.setNumIterations(20);
- LassoModel model = svmSGDImpl.run(testRDD.rdd());
+ LassoModel model = lassoSGDImpl.run(testRDD.rdd());
int numAccurate = validatePrediction(validationData, model);
Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
diff --git a/mllib/src/test/java/spark/mllib/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/spark/mllib/regression/JavaLinearRegressionSuite.java
index d2d8a62980..9642e89844 100644
--- a/mllib/src/test/java/spark/mllib/regression/JavaLinearRegressionSuite.java
+++ b/mllib/src/test/java/spark/mllib/regression/JavaLinearRegressionSuite.java
@@ -67,11 +67,11 @@ public class JavaLinearRegressionSuite implements Serializable {
List<LabeledPoint> validationData =
LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 17);
- LinearRegressionWithSGD svmSGDImpl = new LinearRegressionWithSGD();
- svmSGDImpl.optimizer().setStepSize(1.0)
+ LinearRegressionWithSGD linSGDImpl = new LinearRegressionWithSGD();
+ linSGDImpl.optimizer().setStepSize(1.0)
.setRegParam(0.01)
.setNumIterations(20);
- LinearRegressionModel model = svmSGDImpl.run(testRDD.rdd());
+ LinearRegressionModel model = linSGDImpl.run(testRDD.rdd());
int numAccurate = validatePrediction(validationData, model);
Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
diff --git a/mllib/src/test/java/spark/mllib/regression/JavaRidgeRegressionSuite.java b/mllib/src/test/java/spark/mllib/regression/JavaRidgeRegressionSuite.java
index 72ab875985..5df6d8076d 100644
--- a/mllib/src/test/java/spark/mllib/regression/JavaRidgeRegressionSuite.java
+++ b/mllib/src/test/java/spark/mllib/regression/JavaRidgeRegressionSuite.java
@@ -67,11 +67,11 @@ public class JavaRidgeRegressionSuite implements Serializable {
List<LabeledPoint> validationData =
LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 17);
- RidgeRegressionWithSGD svmSGDImpl = new RidgeRegressionWithSGD();
- svmSGDImpl.optimizer().setStepSize(1.0)
+ RidgeRegressionWithSGD ridgeSGDImpl = new RidgeRegressionWithSGD();
+ ridgeSGDImpl.optimizer().setStepSize(1.0)
.setRegParam(0.01)
.setNumIterations(20);
- RidgeRegressionModel model = svmSGDImpl.run(testRDD.rdd());
+ RidgeRegressionModel model = ridgeSGDImpl.run(testRDD.rdd());
int numAccurate = validatePrediction(validationData, model);
Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);