Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/88#discussion_r10647325
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/PCA.scala ---
@@ -0,0 +1,153 @@
+/*
+ * 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.linalg
+
+import org.apache.spark.SparkContext
+import org.apache.spark.SparkContext._
+import org.apache.spark.rdd.RDD
+
+import org.apache.spark.mllib.util._
+
+
+/**
+ * Class used to obtain principal components
+ */
+class PCA {
+ private var k: Int = 1
+
+ /**
+ * Set the number of top-k principle components to return
+ */
+ def setK(k: Int): PCA = {
+ this.k = k
+ this
+ }
+
+ /**
+ * Compute PCA using the current set parameters
+ */
+ def compute(matrix: DenseMatrix): DenseMatrix = {
+ computePCA(matrix, k)
+ }
+
+ /**
+ * Principal Component Analysis.
+ * Computes the top k principal component coefficients for the m-by-n
data matrix X.
+ * Rows of X correspond to observations and columns correspond to
variables.
+ * The coefficient matrix is n-by-k. Each column of coeff contains
coefficients
+ * for one principal component, and the columns are in descending
+ * order of component variance.
+ * This function centers the data and uses the
+ * singular value decomposition (SVD) algorithm.
+ *
+ * All input and output is expected in DenseMatrix format
+ *
+ * @param matrix dense matrix to perform pca on
+ * @param k Recover k principal components
+ * @return An nxk matrix of principal components
+ */
+ def computePCA(matrix: DenseMatrix, k: Int): DenseMatrix = {
+ val m = matrix.m
+ val n = matrix.n
+ val sc = matrix.rows.sparkContext
+
+ if (m <= 0 || n <= 0) {
+ throw new IllegalArgumentException("Expecting a well-formed matrix")
+ }
+
+ val v = computePCA(matrix.rows.map(_.data), k)
+ val retV = DenseMatrix(sc.makeRDD(Array.tabulate(n)(i => MatrixRow(i,
v(i)))), n, k)
+ retV
+ }
+
+
+ /**
+ * Principal Component Analysis.
+ * Computes the top k principal component coefficients for the m-by-n
data matrix X.
+ * Rows of X correspond to observations and columns correspond to
variables.
+ * The coefficient matrix is n-by-k. Each column of coeff contains
coefficients
+ * for one principal component, and the columns are in descending
+ * order of component variance.
+ * This function centers the data and uses the
+ * singular value decomposition (SVD) algorithm.
+ *
+ * @param matrix dense matrix to perform pca on
+ * @param k Recover k principal components
+ * @return An nxk matrix of principal components
+ */
+ def computePCA(matrix: RDD[Array[Double]], k: Int): Array[Array[Double]]
= {
+ val n = matrix.first.size
+ val sc = matrix.sparkContext
+ val m = matrix.count
+
+ // compute column sums and normalize matrix
+ val colSums = sc.broadcast(matrix.fold(new Array[Double](n)){
+ (a, b) => for(i <- 0 until n) {
+ a(i) += b(i)
+ }
+ a
+ }).value
+
+ val data = matrix.map{
+ x =>
+ val row = Array.ofDim[Double](n)
+ for(i <- 0 until n) {
+ row(i) = (x(i) - colSums(i) / m) / Math.sqrt(n - 1)
--- End diff --
`colSums(i) / m` can be pre-computed. The corner case `n <= 1` should be
handled somewhere.
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