Github user rezazadeh commented on a diff in the pull request:
https://github.com/apache/spark/pull/296#discussion_r11415360
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala
---
@@ -0,0 +1,120 @@
+/*
+ * 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.distributed
+
+import org.scalatest.FunSuite
+
+import breeze.linalg.{diag => brzDiag, DenseMatrix => BDM, DenseVector =>
BDV}
+
+import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Matrices, Vectors}
+
+class IndexedRowMatrixSuite extends FunSuite with LocalSparkContext {
+
+ val m = 4
+ val n = 3
+ val data = Seq(
+ (0L, Vectors.dense(0.0, 1.0, 2.0)),
+ (1L, Vectors.dense(3.0, 4.0, 5.0)),
+ (3L, Vectors.dense(9.0, 0.0, 1.0))
+ ).map(x => IndexedRow(x._1, x._2))
+ var indexedRows: RDD[IndexedRow] = _
+
+ override def beforeAll() {
+ super.beforeAll()
+ indexedRows = sc.parallelize(data, 2)
+ }
+
+ test("size") {
+ val mat1 = new IndexedRowMatrix(indexedRows)
+ assert(mat1.numRows() === m)
+ assert(mat1.numCols() === n)
+
+ val mat2 = new IndexedRowMatrix(indexedRows, 5, 0)
+ assert(mat2.numRows() === 5)
+ assert(mat2.numCols() === n)
+ }
+
+ test("empty rows") {
+ val rows = sc.parallelize(Seq[IndexedRow](), 1)
+ val mat = new IndexedRowMatrix(rows)
+ intercept[RuntimeException] {
+ mat.numRows()
+ }
+ intercept[RuntimeException] {
+ mat.numCols()
+ }
+ }
+
+ test("toBreeze") {
+ val mat = new IndexedRowMatrix(indexedRows)
+ val expected = BDM(
+ (0.0, 1.0, 2.0),
+ (3.0, 4.0, 5.0),
+ (0.0, 0.0, 0.0),
+ (9.0, 0.0, 1.0))
+ assert(mat.toBreeze() === expected)
+ }
+
+ test("toRowMatrix") {
+ val idxRowMat = new IndexedRowMatrix(indexedRows)
+ val rowMat = idxRowMat.toRowMatrix()
+ assert(rowMat.numCols() === n)
+ assert(rowMat.numRows() === 3, "should drop empty rows")
+ assert(rowMat.rows.collect().toSeq === data.map(_.vector).toSeq)
+ }
+
+ test("multiply a local matrix") {
+ val A = new IndexedRowMatrix(indexedRows)
+ val B = Matrices.dense(3, 2, Array(0.0, 1.0, 2.0, 3.0, 4.0, 5.0))
+ val C = A.multiply(B)
+ val localA = A.toBreeze()
+ val localC = C.toBreeze()
+ val expected = localA * B.toBreeze.asInstanceOf[BDM[Double]]
+ assert(localC === expected)
+ }
+
+ test("gram") {
+ val A = new IndexedRowMatrix(indexedRows)
+ val G = A.computeGramianMatrix()
+ val expected = BDM(
+ (90.0, 12.0, 24.0),
+ (12.0, 17.0, 22.0),
+ (24.0, 22.0, 30.0))
+ assert(G.toBreeze === expected)
+ }
+
+ test("svd") {
+ val A = new IndexedRowMatrix(indexedRows)
+ val svd = A.computeSVD(n, computeU = true)
--- End diff --
Consider adding a test for non full rank matrix: e.g. take the svd of an
outer product of a vector and see that computeSVD(1) recovers the vector
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