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author | Evan Sparks <sparks@cs.berkeley.edu> | 2013-08-16 18:00:20 -0700 |
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committer | Shivaram Venkataraman <shivaram@eecs.berkeley.edu> | 2013-08-18 15:03:13 -0700 |
commit | 07fe910669b2ec15b6b5c1e5186df5036d05b9b1 (patch) | |
tree | b1162a2155a84796b33cce277fb85d77a725a4f4 /mllib | |
parent | b291db712e73fdff0c02946bac96e330b089409d (diff) | |
download | spark-07fe910669b2ec15b6b5c1e5186df5036d05b9b1.tar.gz spark-07fe910669b2ec15b6b5c1e5186df5036d05b9b1.tar.bz2 spark-07fe910669b2ec15b6b5c1e5186df5036d05b9b1.zip |
Fixing typos in Java tests, and addressing alignment issues.
Diffstat (limited to 'mllib')
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); |