Made std dev optional in standard scaler.

src/main/scala/nodes/misc/StandardScaler.scala

@@ -13,7 +13,7 @@ import utils.MLlibUtils
  * @param mean column mean values
  * @param std column standard deviation values
  */
-class StandardScalerModel(val mean: DenseVector[Double], val std: DenseVector[Double])
+class StandardScalerModel(val mean: DenseVector[Double], val std: Option[DenseVector[Double]] = None)
     extends Transformer[DenseVector[Double], DenseVector[Double]] {
   /**
    * Applies standardization transformation on a vector.
@@ -23,22 +23,19 @@ class StandardScalerModel(val mean: DenseVector[Double], val std: DenseVector[Do
    *         for the column with zero std.
    */
   override def apply(in: DenseVector[Double]): DenseVector[Double] = {
-    val values = in.copy
-    val size = values.length
-    var i = 0
-    while (i < size) {
-      values(i) = if (std(i) != 0.0) (values(i) - mean(i)) * (1.0 / std(i)) else 0.0
-      i += 1
-    }
-    values
+    val out = in - mean
+    std.foreach(x => {
+      out :/= x
+    })
+    out
   }
 }
 
 /**
  * Standardizes features by removing the mean and scaling to unit std using column summary
  * statistics on the samples in the training set.
  */
-class StandardScaler(eps: Double = 1E-12) extends Estimator[DenseVector[Double], DenseVector[Double]]{
+class StandardScaler(normalizeStdDev: Boolean = true, eps: Double = 1E-12) extends Estimator[DenseVector[Double], DenseVector[Double]]{
   /**
    * Computes the mean and variance and stores as a model to be used for later scaling.
    *
@@ -49,9 +46,15 @@ class StandardScaler(eps: Double = 1E-12) extends Estimator[DenseVector[Double],
     val summary = data.treeAggregate(new MultivariateOnlineSummarizer)(
       (aggregator, data) => aggregator.add(MLlibUtils.breezeVectorToMLlib(data)),
       (aggregator1, aggregator2) => aggregator1.merge(aggregator2))
-    new StandardScalerModel(
-      MLlibUtils.mllibVectorToDenseBreeze(summary.mean),
-      sqrt(MLlibUtils.mllibVectorToDenseBreeze(summary.variance))
-          .map(r => if(r.isNaN | r.isInfinite | math.abs(r) < eps) 1.0 else r))
+    if (normalizeStdDev) {
+      new StandardScalerModel(
+        MLlibUtils.mllibVectorToDenseBreeze(summary.mean),
+        Some(sqrt(MLlibUtils.mllibVectorToDenseBreeze(summary.variance))
+            .map(r => if (r.isNaN | r.isInfinite | math.abs(r) < eps) 1.0 else r)))
+    } else {
+      new StandardScalerModel(
+        MLlibUtils.mllibVectorToDenseBreeze(summary.mean),
+        None)
+    }
   }
 }