Update SVM.scala

mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala

@@ -121,7 +121,7 @@ object SVMModel extends Loader[SVMModel] {
  * regularization is used, which can be changed via [[SVMWithSGD.optimizer]].
  * NOTE: Labels used in SVM should be {0, 1}.
  */
-class SVMWithSGD private (
+class SVMWithSGD (
     private var stepSize: Double,
     private var numIterations: Int,
     private var regParam: Double,
@@ -152,39 +152,6 @@ class SVMWithSGD private (
  * Top-level methods for calling SVM. NOTE: Labels used in SVM should be {0, 1}.
  */
 object SVMWithSGD {
-
-  /**
-   * Train a SVM model given an RDD of (label, features) pairs. We run a fixed number
-   * of iterations of gradient descent using the specified step size. Each iteration uses
-   * `miniBatchFraction` fraction of the data to calculate the gradient. The weights used in
-   * gradient descent are initialized using the initial weights provided.
-   *
-   * NOTE: Labels used in SVM should be {0, 1}.
-   *
-   * @param input RDD of (label, array of features) pairs.
-   * @param numIterations Number of iterations of gradient descent to run.
-   * @param stepSize Step size to be used for each iteration of gradient descent.
-   * @param regParam Regularization parameter.
-   * @param miniBatchFraction Fraction of data to be used per iteration.
-   * @param initialWeights Initial set of weights to be used. Array should be equal in size to
-   *        the number of features in the data.
-   * @param useFeatureScaling Set if the algorithm should use feature scaling to improve the 
-   *        convergence during optimization.
-   */
-  def train(
-      input: RDD[LabeledPoint],
-      numIterations: Int,
-      stepSize: Double,
-      regParam: Double,
-      miniBatchFraction: Double,
-      initialWeights: Vector,
-      useFeatureScaling: Boolean): SVMModel = {
-    new SVMWithSGD(stepSize, numIterations, regParam, miniBatchFraction)
-      .setFeatureScaling(useFeatureScaling)
-      .run(input, initialWeights)
-  }
-
-
   /**
    * Train a SVM model given an RDD of (label, features) pairs. We run a fixed number
    * of iterations of gradient descent using the specified step size. Each iteration uses