Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/268#discussion_r11236175
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/rdd/VectorRDDFunctions.scala ---
@@ -0,0 +1,179 @@
+/*
+ * 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.rdd
+
+import breeze.linalg.{axpy, Vector => BV}
+
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+import org.apache.spark.rdd.RDD
+
+/**
+ * Trait of the summary statistics, including mean, variance, count, max,
min, and non-zero elements
+ * count.
+ */
+trait VectorRDDStatisticalSummary {
+ def mean: Vector
+ def variance: Vector
+ def totalCount: Long
+ def numNonZeros: Vector
+ def max: Vector
+ def min: Vector
+}
+
+/**
+ * Aggregates [[org.apache.spark.mllib.rdd.VectorRDDStatisticalSummary
VectorRDDStatisticalSummary]]
+ * together with add() and merge() function.
+ */
+private class Aggregator(
+ val currMean: BV[Double],
+ val currM2n: BV[Double],
+ var totalCnt: Double,
+ val nnz: BV[Double],
+ val currMax: BV[Double],
+ val currMin: BV[Double]) extends VectorRDDStatisticalSummary with
Serializable {
+
+ // lazy val is used for computing only once time. Same below.
+ override lazy val mean = Vectors.fromBreeze(currMean :* nnz :/ totalCnt)
+
+ // Online variance solution used in add() function, while parallel
variance solution used in
+ // merge() function. Reference here:
+ // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+ // Solution here ignoring the zero elements when calling add() and
merge(), for decreasing the
+ // O(n) algorithm to O(nnz). Real variance is computed here after we get
other statistics, simply
+ // by another parallel combination process.
+ override lazy val variance = {
+ val deltaMean = currMean
+ var i = 0
+ while(i < currM2n.size) {
+ currM2n(i) += deltaMean(i) * deltaMean(i) * nnz(i) *
(totalCnt-nnz(i)) / totalCnt
+ currM2n(i) /= totalCnt
+ i += 1
+ }
+ Vectors.fromBreeze(currM2n)
+ }
+
+ override lazy val totalCount: Long = totalCnt.toLong
+
+ override lazy val numNonZeros: Vector = Vectors.fromBreeze(nnz)
+
+ override lazy val max: Vector = {
+ nnz.iterator.foreach {
+ case (id, count) =>
+ if ((count == 0.0) || ((count < totalCnt) && (currMax(id) < 0.0)))
currMax(id) = 0.0
+ }
+ Vectors.fromBreeze(currMax)
+ }
+
+ override lazy val min: Vector = {
+ nnz.iterator.foreach {
+ case (id, count) =>
+ if ((count == 0.0) || ((count < totalCnt) && (currMin(id) > 0.0)))
currMin(id) = 0.0
+ }
+ Vectors.fromBreeze(currMin)
+ }
+
+ /**
+ * Aggregate function used for aggregating elements in a worker together.
+ */
+ def add(currData: BV[Double]): this.type = {
+ currData.activeIterator.foreach {
+ // this case is used for filtering the zero elements if the vector
is a dense one.
+ case (id, 0.0) =>
+ case (id, value) =>
+ if (currMax(id) < value) currMax(id) = value
+ if (currMin(id) > value) currMin(id) = value
+
+ val tmpPrevMean = currMean(id)
+ currMean(id) = (currMean(id) * nnz(id) + value) / (nnz(id) + 1.0)
+ currM2n(id) += (value - currMean(id)) * (value - tmpPrevMean)
+
+ nnz(id) += 1.0
+ }
+
+ totalCnt += 1.0
+ this
+ }
+
+ /**
+ * Combine function used for combining intermediate results together
from every worker.
+ */
+ def merge(other: Aggregator): this.type = {
+
+ totalCnt += other.totalCnt
+
+ val deltaMean = currMean - other.currMean
+
+ other.currMean.activeIterator.foreach {
+ case (id, 0.0) =>
+ case (id, value) =>
+ currMean(id) =
+ (currMean(id) * nnz(id) + other.currMean(id) * other.nnz(id)) /
(nnz(id) + other.nnz(id))
+ }
+
+ var i = 0
+ while(i < currM2n.size) {
+ (nnz(i), other.nnz(i)) match {
+ case (0.0, 0.0) =>
+ case _ => currM2n(i) +=
+ other.currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) *
other.nnz(i) / (nnz(i)+other.nnz(i))
+ }
+ i += 1
+ }
+
+ other.currMax.activeIterator.foreach {
+ case (id, value) =>
+ if (currMax(id) < value) currMax(id) = value
+ }
+
+ other.currMin.activeIterator.foreach {
+ case (id, value) =>
+ if (currMin(id) > value) currMin(id) = value
+ }
+
+ axpy(1.0, other.nnz, nnz)
+ this
+ }
+}
+
+/**
+ * Extra functions available on RDDs of
[[org.apache.spark.mllib.linalg.Vector Vector]] through an
+ * implicit conversion. Import `org.apache.spark.MLContext._` at the top
of your program to use
+ * these functions.
+ */
+class VectorRDDFunctions(self: RDD[Vector]) extends Serializable {
+
+ /**
+ * Compute full column-wise statistics for the RDD with the size of
Vector as input parameter.
+ */
+ def summarizeStatistics(): VectorRDDStatisticalSummary = {
+ val size = self.take(1).head.size
--- End diff --
`take(1).head` = `first()`
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