Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3519#discussion_r23594866
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
---
@@ -0,0 +1,238 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.regression
+
+import java.io.Serializable
+import java.util.Arrays.binarySearch
+
+import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD}
+import org.apache.spark.rdd.RDD
+
+/**
+ * Regression model for Isotonic regression
+ *
+ * @param features Array of features.
+ * @param labels Array of labels associated to the features at the same
index.
+ */
+class IsotonicRegressionModel (
+ features: Array[Double],
+ val labels: Array[Double])
+ extends Serializable {
+
+ /**
+ * Predict labels for provided features
+ * Using a piecewise constant function
+ *
+ * @param testData features to be labeled
+ * @return predicted labels
+ */
+ def predict(testData: RDD[Double]): RDD[Double] =
+ testData.map(predict)
+
+ /**
+ * Predict labels for provided features
+ * Using a piecewise constant function
+ *
+ * @param testData features to be labeled
+ * @return predicted labels
+ */
+ def predict(testData: JavaRDD[java.lang.Double]): JavaDoubleRDD =
+ JavaDoubleRDD.fromRDD(predict(testData.rdd.asInstanceOf[RDD[Double]]))
+
+ /**
+ * Predict a single label
+ * Using a piecewise constant function
+ *
+ * @param testData feature to be labeled
+ * @return predicted label
+ */
+ def predict(testData: Double): Double = {
+ val result = binarySearch(features, testData)
+
+ val index =
+ if (result == -1) {
+ 0
+ } else if (result < 0) {
+ -result - 2
+ } else {
+ result
+ }
+
+ labels(index)
+ }
+}
+
+/**
+ * Isotonic regression
+ * Currently implemented using parallel pool adjacent violators algorithm
+ */
+class IsotonicRegression
+ extends Serializable {
--- End diff --
merge this line with the one above
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