Change weights to be Array[Double] in LR model.

Also ensure weights are initialized to a column vector.

3 files changed, 15 insertions, 11 deletions

diff --git a/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala
index 77f5a7ae24..2c5038757b 100644
--- a/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala
+++ b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala
@@ -57,9 +57,8 @@ object GradientDescent {
     val nexamples: Long = data.count()
     val miniBatchSize = nexamples * miniBatchFraction
 
-    // Initialize weights as a column matrix
-    var weights = new DoubleMatrix(1, initialWeights.length,
-      initialWeights:_*)
+    // Initialize weights as a column vector
+    var weights = new DoubleMatrix(initialWeights.length, 1, initialWeights:_*)
     var reg_val = 0.0
 
     for (i <- 1 to numIters) {
diff --git a/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala b/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala
index 664baf33a3..ab865af0c6 100644
--- a/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala
+++ b/mllib/src/main/scala/spark/mllib/regression/LogisticRegression.scala
@@ -28,20 +28,23 @@ import org.jblas.DoubleMatrix
  * Based on Matlab code written by John Duchi.
  */
 class LogisticRegressionModel(
-  val weights: DoubleMatrix,
+  val weights: Array[Double],
   val intercept: Double,
   val stochasticLosses: Array[Double]) extends RegressionModel {
 
+  // Create a column vector that can be used for predictions
+  private val weightsMatrix = new DoubleMatrix(weights.length, 1, weights:_*)
+
   override def predict(testData: spark.RDD[Array[Double]]) = {
     testData.map { x =>
-      val margin = new DoubleMatrix(1, x.length, x:_*).mmul(this.weights).get(0) + this.intercept
+      val margin = new DoubleMatrix(1, x.length, x:_*).mmul(weightsMatrix).get(0) + this.intercept
       1.0/ (1.0 + math.exp(margin * -1))
     }
   }
 
   override def predict(testData: Array[Double]): Double = {
     val dataMat = new DoubleMatrix(1, testData.length, testData:_*)
-    val margin = dataMat.mmul(this.weights).get(0) + this.intercept
+    val margin = dataMat.mmul(weightsMatrix).get(0) + this.intercept
     1.0/ (1.0 + math.exp(margin * -1))
   }
 }
@@ -123,12 +126,14 @@ class LogisticRegression private (var stepSize: Double, var miniBatchFraction: D
       initalWeightsWithIntercept,
       miniBatchFraction)
 
-    val weightsScaled = weights.getRange(1, weights.length)
-    val intercept = weights.get(0)
+    val weightsArray = weights.toArray()
+
+    val intercept = weightsArray(0)
+    val weightsScaled = weightsArray.tail
 
     val model = new LogisticRegressionModel(weightsScaled, intercept, stochasticLosses)
 
-    logInfo("Final model weights " + model.weights)
+    logInfo("Final model weights " + model.weights.mkString(","))
     logInfo("Final model intercept " + model.intercept)
     logInfo("Last 10 stochastic losses " + model.stochasticLosses.takeRight(10).mkString(", "))
     model
diff --git a/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
index 2ff248d256..47191d9a5a 100644
--- a/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
@@ -75,7 +75,7 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
 
     val model = lr.train(testRDD)
 
-    val weight0 = model.weights.get(0)
+    val weight0 = model.weights(0)
     assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
     assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
   }
@@ -99,7 +99,7 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
 
     val model = lr.train(testRDD, initialWeights)
 
-    val weight0 = model.weights.get(0)
+    val weight0 = model.weights(0)
     assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
     assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
   }