Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1814#discussion_r15920592
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala ---
@@ -35,38 +35,47 @@ import org.apache.spark.rdd.RDD
* @param withStd True by default. Scales the data to unit standard
deviation.
*/
@Experimental
-class StandardScaler(withMean: Boolean, withStd: Boolean) extends
VectorTransformer {
+class StandardScaler(withMean: Boolean, withStd: Boolean) {
def this() = this(false, true)
require(withMean || withStd, s"withMean and withStd both equal to false.
Doing nothing.")
- private var mean: BV[Double] = _
- private var factor: BV[Double] = _
-
/**
* Computes the mean and variance and stores as a model to be used for
later scaling.
*
* @param data The data used to compute the mean and variance to build
the transformation model.
- * @return This StandardScalar object.
+ * @return a StandardScalarModel
*/
- def fit(data: RDD[Vector]): this.type = {
+ def fit(data: RDD[Vector]): StandardScalerModel = {
val summary = data.treeAggregate(new MultivariateOnlineSummarizer)(
(aggregator, data) => aggregator.add(data),
(aggregator1, aggregator2) => aggregator1.merge(aggregator2))
- mean = summary.mean.toBreeze
- factor = summary.variance.toBreeze
- require(mean.length == factor.length)
+ val mean = summary.mean.toBreeze
+ val factor = summary.variance.toBreeze
+ require(mean.size == factor.size)
var i = 0
- while (i < factor.length) {
+ while (i < factor.size) {
factor(i) = if (factor(i) != 0.0) 1.0 / math.sqrt(factor(i)) else 0.0
i += 1
}
- this
+ new StandardScalerModel(withMean, withStd, mean, factor)
}
+}
+
+/**
+ * :: Experimental ::
+ * Represents a StandardScaler model that can transform vectors.
+ */
+@Experimental
+class StandardScalerModel private[mllib] (
+ val withMean: Boolean,
+ val withStd: Boolean,
+ val mean: BV[Double],
+ val factor: BV[Double]) extends VectorTransformer {
--- End diff --
done.
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