Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3519#discussion_r23594862
--- 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) {
--- End diff --
There are 4 cases:
1. hit a boundary -> return the corresponding prediction directly
2. fall between boundaries -> linear interpolation (Note that a special
case is singularity, where two boundaries are the same but their predictions
are different. We can set manual rules for this case and document the behavior.)
3. smaller than the smallest boundary -> return predictions(0)
4. larger than the largest boundary -> return predictions.last
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