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| author | Shivaram Venkataraman <shivaram@eecs.berkeley.edu> | 2013-07-17 16:08:34 -0700 |
|---|---|---|
| committer | Shivaram Venkataraman <shivaram@eecs.berkeley.edu> | 2013-07-17 16:08:34 -0700 |
| commit | 217667174e267adba5469cf26b3e4418e3d1cc90 (patch) | |
| tree | 7f580df62e53ef59ac158b42538ef010d42312b2 /mllib | |
| parent | 45f3c855181539306d5610c5aa265f24b431c142 (diff) | |
| download | spark-217667174e267adba5469cf26b3e4418e3d1cc90.tar.gz spark-217667174e267adba5469cf26b3e4418e3d1cc90.tar.bz2 spark-217667174e267adba5469cf26b3e4418e3d1cc90.zip | |
Return Array[Double] from SGD instead of DoubleMatrix
Diffstat (limited to 'mllib')
| -rw-r--r-- | mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala | 4 | ||||
| -rw-r--r-- | mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala | 6 |
2 files changed, 4 insertions, 6 deletions
diff --git a/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala index 2c5038757b..4c996c0903 100644 --- a/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala +++ b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala @@ -50,7 +50,7 @@ object GradientDescent { stepSize: Double, numIters: Int, initialWeights: Array[Double], - miniBatchFraction: Double=1.0) : (DoubleMatrix, Array[Double]) = { + miniBatchFraction: Double=1.0) : (Array[Double], Array[Double]) = { val stochasticLossHistory = new ArrayBuffer[Double](numIters) @@ -75,6 +75,6 @@ object GradientDescent { reg_val = update._2 } - (weights, stochasticLossHistory.toArray) + (weights.toArray, stochasticLossHistory.toArray) } } diff --git a/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala b/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala index ab865af0c6..711e205c39 100644 --- a/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala +++ b/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala @@ -126,10 +126,8 @@ class LogisticRegression private (var stepSize: Double, var miniBatchFraction: D initalWeightsWithIntercept, miniBatchFraction) - val weightsArray = weights.toArray() - - val intercept = weightsArray(0) - val weightsScaled = weightsArray.tail + val intercept = weights(0) + val weightsScaled = weights.tail val model = new LogisticRegressionModel(weightsScaled, intercept, stochasticLosses) |