Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/1093#discussion_r13829729
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala ---
@@ -0,0 +1,66 @@
+/*
+ * 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.stat
+
+import org.apache.spark.rdd.RDD
+import org.apache.commons.math3.util.FastMath
+
+object KernelDensity {
+ /**
+ * Given a set of samples form a distribution, estimates its density at
the set of given points.
+ * Uses a Gaussian kernel with the given standard deviation.
+ */
+ def estimate(samples: RDD[Double], standardDeviation: Double,
+ evaluationPoints: Array[Double]): Array[Double] = {
+ val logStandardDeviationPlusHalfLog2Pi =
+ FastMath.log(standardDeviation) + 0.5 * FastMath.log(2 * FastMath.PI)
+
+ val (points, count) = samples.aggregate((new
Array[Double](evaluationPoints.length), 0))(
+ (x, y) => {
+ var i = 0
+ while (i < evaluationPoints.length) {
+ x._1(i) += normPdf(y, standardDeviation,
logStandardDeviationPlusHalfLog2Pi,
+ evaluationPoints(i))
+ i += 1
+ }
+ (x._1, i)
+ },
+ (x, y) => {
+ var i = 0
+ while (i < evaluationPoints.length) {
+ x._1(i) += y._1(i)
+ i += 1
+ }
+ (x._1, x._2 + y._2)
+ })
+
+ var i = 0
+ while (i < points.length) {
+ points(i) /= count
+ i += 1
+ }
+ points
+ }
+
+ private def normPdf(mean: Double, standardDeviation: Double,
+ logStandardDeviationPlusHalfLog2Pi: Double, x: Double): Double = {
+ val x0 = x - mean
+ val x1 = x0 / standardDeviation
+ FastMath.exp(-0.5 * x1 * x1 - logStandardDeviationPlusHalfLog2Pi)
--- End diff --
Yeah it's doing this part in log space, but I don't see why these values
are expected to be exceptionally large or small. It looks like the original
code exposes a density and logDensity method, so one's written in terms of the
other to avoid duplication -- not sure it is necessarily because it's better in
that form. Anyone else have ideas?
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