Added LinearRegression and Regressor back from ml-api branch

mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala

@@ -0,0 +1,72 @@
+package org.apache.spark.ml.regression
+
+import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.ml.LabeledPoint
+import org.apache.spark.ml.param.{ParamMap, HasMaxIter, HasRegParam}
+import org.apache.spark.mllib.linalg.{BLAS, Vector}
+import org.apache.spark.mllib.regression.LinearRegressionWithSGD
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * :: AlphaComponent ::
+ * Params for linear regression.
+ */
+@AlphaComponent
+private[regression] trait LinearRegressionParams extends RegressorParams
+  with HasRegParam with HasMaxIter
+
+
+/**
+ * Logistic regression.
+ */
+class LinearRegression extends Regressor[LinearRegression, LinearRegressionModel]
+  with LinearRegressionParams {
+
+  // TODO: Extend IterativeEstimator
+
+  setRegParam(0.1)
+  setMaxIter(100)
+
+  def setRegParam(value: Double): this.type = set(regParam, value)
+  def setMaxIter(value: Int): this.type = set(maxIter, value)
+
+  def train(dataset: RDD[LabeledPoint], paramMap: ParamMap): LinearRegressionModel = {
+    val oldDataset = dataset.map { case LabeledPoint(label: Double, features: Vector, weight) =>
+      org.apache.spark.mllib.regression.LabeledPoint(label, features)
+    }
+    val handlePersistence = oldDataset.getStorageLevel == StorageLevel.NONE
+    if (handlePersistence) {
+      oldDataset.persist(StorageLevel.MEMORY_AND_DISK)
+    }
+    val lr = new LinearRegressionWithSGD()
+    lr.optimizer
+      .setRegParam(paramMap(regParam))
+      .setNumIterations(paramMap(maxIter))
+    val model = lr.run(oldDataset)
+    val lrm = new LinearRegressionModel(this, paramMap, model.weights, model.intercept)
+    if (handlePersistence) {
+      oldDataset.unpersist()
+    }
+    lrm
+  }
+}
+
+
+/**
+ * :: AlphaComponent ::
+ * Model produced by [[LinearRegression]].
+ */
+@AlphaComponent
+class LinearRegressionModel private[ml] (
+    override val parent: LinearRegression,
+    override val fittingParamMap: ParamMap,
+    val weights: Vector,
+    val intercept: Double)
+  extends RegressionModel[LinearRegressionModel]
+  with LinearRegressionParams {
+
+  override def predict(features: Vector): Double = {
+    BLAS.dot(features, weights) + intercept
+  }
+}

mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala

@@ -0,0 +1,33 @@
+package org.apache.spark.ml.regression
+
+import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.ml.Evaluator
+import org.apache.spark.ml.evaluation.RegressionEvaluator
+import org.apache.spark.ml.impl.estimator.{PredictionModel, HasDefaultEvaluator, Predictor,
+  PredictorParams}
+import org.apache.spark.mllib.linalg.Vector
+
+@AlphaComponent
+private[regression] trait RegressorParams extends PredictorParams
+
+/**
+ * Single-label regression
+ */
+abstract class Regressor[Learner <: Regressor[Learner, M], M <: RegressionModel[M]]
+  extends Predictor[Learner, M]
+  with RegressorParams
+  with HasDefaultEvaluator {
+
+  override def defaultEvaluator: Evaluator = new RegressionEvaluator
+}
+
+
+private[ml] abstract class RegressionModel[M <: RegressionModel[M]]
+  extends PredictionModel[M] with RegressorParams {
+
+  /**
+   * Predict real-valued label for the given features.
+   */
+  def predict(features: Vector): Double
+
+}