new example file

1 files changed, 58 insertions, 0 deletions

diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkSVD.scala b/examples/src/main/scala/org/apache/spark/examples/SparkSVD.scala
new file mode 100644
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+++ b/examples/src/main/scala/org/apache/spark/examples/SparkSVD.scala
@@ -0,0 +1,58 @@
+/*
+ * 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.examples
+      
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.linalg.SVD
+import org.apache.spark.mllib.linalg.MatrixEntry
+
+/**
+ * Compute SVD of an example matrix
+ * Input file should be comma separated, 1 indexed of the form
+ * i,j,value
+ * Where i is the column, j the row, and value is the matrix entry
+ * 
+ * For example input file, see:
+ * mllib/data/als/test.data
+ */
+object SparkSVD {
+  def main(args: Array[String]) {
+   if (args.length < 3) {
+      System.err.println("Usage: SVD <master> <file>")
+      System.exit(1)
+    }
+    val sc = new SparkContext(args(0), "SVD",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
+
+    // Load and parse the data file
+    val data = sc.textFile(args(1)).map { line =>
+      val parts = line.split(',')
+      MatrixEntry(parts(0).toInt, parts(1).toInt, parts(2).toDouble)
+    }
+    val m = 4
+    val n = 4
+
+    // recover largest singular vector
+    val decomposed = SVD.sparseSVD(data, m, n, 1)
+    val u = decomposed.U
+    val s = decomposed.S
+    val v = decomposed.V
+
+    println("singular values = " + s.toArray.mkString)
+  }
+}