rename SparkPCA/SVD to TallSkinnyPCA/SVD

examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala

@@ -0,0 +1,64 @@
+/*
+ * 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.mllib
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.mllib.linalg.rdd.RowRDDMatrix
+import org.apache.spark.mllib.linalg.Vectors
+
+/**
+ * Compute the principal components of a tall-and-skinny matrix, whose rows are observations.
+ *
+ * The input matrix must be stored in row-oriented dense format, one line per row with its entries
+ * separated by space. For example,
+ * {{{
+ * 0 1
+ * 2 3
+ * 4 5
+ * }}}
+ * represents a 3-by-2 matrix, whose first row is (0, 1).
+ */
+object TallSkinnyPCA {
+  def main(args: Array[String]) {
+    if (args.length != 2) {
+      System.err.println("Usage: TallSkinnyPCA <master> <file>")
+      System.exit(1)
+    }
+
+    val conf = new SparkConf()
+      .setMaster(args(0))
+      .setAppName("TallSkinnyPCA")
+      .setSparkHome(System.getenv("SPARK_HOME"))
+      .setJars(SparkContext.jarOfClass(this.getClass))
+    val sc = new SparkContext(conf)
+
+    // Load and parse the data file.
+    val rows = sc.textFile(args(1)).map { line =>
+      val values = line.split(' ').map(_.toDouble)
+      Vectors.dense(values)
+    }
+    val mat = new RowRDDMatrix(rows)
+
+    // Compute principal components.
+    val pc = mat.computePrincipalComponents(mat.numCols().toInt)
+
+    println("Principal components are:\n" + pc)
+
+    sc.stop()
+  }
+}

examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala

@@ -0,0 +1,64 @@
+/*
+ * 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.mllib
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.mllib.linalg.rdd.RowRDDMatrix
+import org.apache.spark.mllib.linalg.Vectors
+
+/**
+ * Compute the singular value decomposition (SVD) of a tall-and-skinny matrix.
+ *
+ * The input matrix must be stored in row-oriented dense format, one line per row with its entries
+ * separated by space. For example,
+ * {{{
+ * 0 1
+ * 2 3
+ * 4 5
+ * }}}
+ * represents a 3-by-2 matrix, whose first row is (0, 1).
+ */
+object TallSkinnySVD {
+  def main(args: Array[String]) {
+    if (args.length != 2) {
+      System.err.println("Usage: TallSkinnySVD <master> <file>")
+      System.exit(1)
+    }
+
+    val conf = new SparkConf()
+      .setMaster(args(0))
+      .setAppName("TallSkinnySVD")
+      .setSparkHome(System.getenv("SPARK_HOME"))
+      .setJars(SparkContext.jarOfClass(this.getClass))
+    val sc = new SparkContext(conf)
+
+    // Load and parse the data file.
+    val rows = sc.textFile(args(1)).map { line =>
+      val values = line.split(' ').map(_.toDouble)
+      Vectors.dense(values)
+    }
+    val mat = new RowRDDMatrix(rows)
+
+    // Compute SVD.
+    val svd = mat.computeSVD(mat.numCols().toInt)
+
+    println("Singular values are " + svd.s)
+
+    sc.stop()
+  }
+}