diff options
Diffstat (limited to 'mllib')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala | 10 | ||||
| -rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala | 6 |
2 files changed, 8 insertions, 8 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala index 45dbf6044f..5a419d1640 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala @@ -94,16 +94,16 @@ class LogisticGradient extends Gradient { * :: DeveloperApi :: * Compute gradient and loss for a Least-squared loss function, as used in linear regression. * This is correct for the averaged least squares loss function (mean squared error) - * L = 1/n ||A weights-y||^2 + * L = 1/2n ||A weights-y||^2 * See also the documentation for the precise formulation. */ @DeveloperApi class LeastSquaresGradient extends Gradient { override def compute(data: Vector, label: Double, weights: Vector): (Vector, Double) = { val diff = dot(data, weights) - label - val loss = diff * diff + val loss = diff * diff / 2.0 val gradient = data.copy - scal(2.0 * diff, gradient) + scal(diff, gradient) (gradient, loss) } @@ -113,8 +113,8 @@ class LeastSquaresGradient extends Gradient { weights: Vector, cumGradient: Vector): Double = { val diff = dot(data, weights) - label - axpy(2.0 * diff, data, cumGradient) - diff * diff + axpy(diff, data, cumGradient) + diff * diff / 2.0 } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala index 03b71301e9..70b43ddb7d 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala @@ -52,7 +52,7 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { // create model val model = new StreamingLinearRegressionWithSGD() .setInitialWeights(Vectors.dense(0.0, 0.0)) - .setStepSize(0.1) + .setStepSize(0.2) .setNumIterations(25) // generate sequence of simulated data @@ -84,7 +84,7 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { // create model val model = new StreamingLinearRegressionWithSGD() .setInitialWeights(Vectors.dense(0.0)) - .setStepSize(0.1) + .setStepSize(0.2) .setNumIterations(25) // generate sequence of simulated data @@ -118,7 +118,7 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { // create model initialized with true weights val model = new StreamingLinearRegressionWithSGD() .setInitialWeights(Vectors.dense(10.0, 10.0)) - .setStepSize(0.1) + .setStepSize(0.2) .setNumIterations(25) // generate sequence of simulated data for testing |