[SPARK-10757] [MLlib] Add test cases - Java friendly constructor for distributed matrices

Author: yzhliu

mllib/src/test/java/org/apache/spark/mllib/linalg/distributed/JavaBlockMatrixSuite.java

@@ -0,0 +1,53 @@
+package org.apache.spark.mllib.linalg.distributed;
+
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.DenseMatrix;
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import scala.Tuple2;
+
+import java.util.Arrays;
+import java.util.List;
+
+import static org.junit.Assert.assertEquals;
+
+public class JavaBlockMatrixSuite {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaBlockMatrixSuite");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+  }
+
+  @Test
+  public void blockMatrixConstruction() {
+    List<Tuple2<Tuple2<Integer, Integer>, DenseMatrix>> blocks = Arrays.asList(
+      new Tuple2<Tuple2<Integer, Integer>, DenseMatrix>(
+        new Tuple2<Integer, Integer>(0, 0), new DenseMatrix(2, 2, new double[]{1.0, 0.0, 0.0, 2.0})),
+      new Tuple2<Tuple2<Integer, Integer>, DenseMatrix>(
+        new Tuple2<Integer, Integer>(0, 1), new DenseMatrix(2, 2, new double[]{0.0, 1.0, 0.0, 0.0})),
+      new Tuple2<Tuple2<Integer, Integer>, DenseMatrix>(
+        new Tuple2<Integer, Integer>(1, 0), new DenseMatrix(2, 2, new double[]{3.0, 0.0, 1.0, 1.0})),
+      new Tuple2<Tuple2<Integer, Integer>, DenseMatrix>(
+        new Tuple2<Integer, Integer>(1, 1), new DenseMatrix(2, 2, new double[]{1.0, 2.0, 0.0, 1.0})),
+      new Tuple2<Tuple2<Integer, Integer>, DenseMatrix>(
+        new Tuple2<Integer, Integer>(2, 1), new DenseMatrix(1, 2, new double[]{1.0, 5.0})));
+    BlockMatrix blockMatrix = BlockMatrix.from(sc.parallelize(blocks), 2, 2);
+    final DenseMatrix expectedMatrix = new DenseMatrix(5, 4, new double[]{
+      1.0, 0.0, 0.0, 0.0,
+      0.0, 2.0, 1.0, 0.0,
+      3.0, 1.0, 1.0, 0.0,
+      0.0, 1.0, 2.0, 1.0,
+      0.0, 0.0, 1.0, 5.0}, true);
+    assertEquals(5L, blockMatrix.numRows());
+    assertEquals(4L, blockMatrix.numCols());
+    assertEquals(expectedMatrix, blockMatrix.toLocalMatrix());
+  }
+}

mllib/src/test/java/org/apache/spark/mllib/linalg/distributed/JavaRowMatrixSuite.java

@@ -0,0 +1,48 @@
+package org.apache.spark.mllib.linalg.distributed;
+
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.DenseMatrix;
+import org.apache.spark.mllib.linalg.DenseVector;
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+
+import java.util.Arrays;
+import java.util.List;
+
+import static org.junit.Assert.assertEquals;
+
+public class JavaRowMatrixSuite {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaRowMatrixSuite");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+  }
+
+  @Test
+  public void rowMatrixConstruction() {
+    List<DenseVector> rows = Arrays.asList(
+      new DenseVector(new double[]{1.0, 0.0, 0.0, 0.0}),
+      new DenseVector(new double[]{0.0, 2.0, 1.0, 0.0}),
+      new DenseVector(new double[]{3.0, 1.0, 1.0, 0.0}),
+      new DenseVector(new double[]{0.0, 1.0, 2.0, 1.0}),
+      new DenseVector(new double[]{0.0, 0.0, 1.0, 5.0}));
+    RowMatrix rowMatrix = RowMatrix.from(sc.parallelize(rows));
+    final DenseMatrix expectedMatrix = new DenseMatrix(5, 4, new double[]{
+      1.0, 0.0, 0.0, 0.0,
+      0.0, 2.0, 1.0, 0.0,
+      3.0, 1.0, 1.0, 0.0,
+      0.0, 1.0, 2.0, 1.0,
+      0.0, 0.0, 1.0, 5.0}, true);
+    assertEquals(5L, rowMatrix.numRows());
+    assertEquals(4L, rowMatrix.numCols());
+    assertEquals(expectedMatrix.toBreeze(), rowMatrix.toBreeze());
+  }
+}