Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/88#discussion_r10727949
--- Diff: mllib/src/test/scala/org/apache/spark/mllib/linalg/PCASuite.scala
---
@@ -0,0 +1,122 @@
+/*
+ * 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 scala.util.Random
+
+import org.scalatest.BeforeAndAfterAll
+import org.scalatest.FunSuite
+
+import org.jblas.{DoubleMatrix, Singular, MatrixFunctions}
+
+import org.apache.spark.SparkContext
+import org.apache.spark.SparkContext._
+import org.apache.spark.rdd.RDD
+
+import org.apache.spark.mllib.util._
+
+import org.jblas._
+
+class PCASuite extends FunSuite with BeforeAndAfterAll {
+ @transient private var sc: SparkContext = _
+
+ override def beforeAll() {
+ sc = new SparkContext("local", "test")
+ }
+
+ override def afterAll() {
+ sc.stop()
+ System.clearProperty("spark.driver.port")
+ }
+
+ val EPSILON = 1e-3
+
+ // Return jblas matrix from sparse matrix RDD
+ def getDenseMatrix(matrix: SparseMatrix) : DoubleMatrix = {
+ val data = matrix.data
+ val m = matrix.m
+ val n = matrix.n
+ val ret = DoubleMatrix.zeros(m, n)
+ matrix.data.toArray.map(x => ret.put(x.i, x.j, x.mval))
+ ret
+ }
+
+ def assertMatrixEquals(a: DoubleMatrix, b: DoubleMatrix) {
+ assert(a.rows == b.rows && a.columns == b.columns, "dimension
mismatch")
+ val diff = DoubleMatrix.zeros(a.rows, a.columns)
+ Array.tabulate(a.rows, a.columns){(i, j) =>
+ diff.put(i, j,
+ Math.min(Math.abs(a.get(i, j) - b.get(i, j)),
+ Math.abs(a.get(i, j) + b.get(i, j)))) }
+ assert(diff.norm1 < EPSILON, "matrix mismatch: " + diff.norm1)
+ }
+
+ test("full rank matrix pca") {
+ val m = 5
+ val n = 3
+ val data = sc.makeRDD(Array.tabulate(m,n){ (a, b) =>
+ MatrixEntry(a, b, Math.sin(a+b+a*b)) }.flatten )
+ val a = LAUtils.spToDense(SparseMatrix(data, m, n))
+
+ val realPCAArray = Array((0,0,-0.2579), (0,1,-0.6602), (0,2,0.7054),
+ (1,0,-0.1448), (1,1,0.7483), (1,2,0.6474),
+ (2,0,0.9553), (2,1,-0.0649), (2,2,0.2886))
+ val realPCA = sc.makeRDD(realPCAArray.map(x => MatrixEntry(x._1, x._2,
x._3)))
+
+ val coeffs = new DoubleMatrix(new PCA().computePCA(a, n))
+
+ assertMatrixEquals(getDenseMatrix(SparseMatrix(realPCA,n,n)), coeffs)
+ }
+
+ test("sparse matrix full rank matrix pca") {
+ val m = 5
+ val n = 3
+ val data = sc.makeRDD(Array.tabulate(m,n){ (a, b) =>
+ MatrixEntry(a, b, Math.sin(a+b+a*b)) }.flatten.drop(1) )
--- End diff --
Need a comment here saying that the one gets dropped is zero.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
