Github user miccagiann commented on a diff in the pull request:
https://github.com/apache/spark/pull/1311#discussion_r14800873
--- Diff: docs/mllib-linear-methods.md ---
@@ -338,7 +429,76 @@ and
[`LassoWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.Lass
All of MLlib's methods use Java-friendly types, so you can import and call
them there the same
way you do in Scala. The only caveat is that the methods take Scala RDD
objects, while the
Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD
to a Scala one by
-calling `.rdd()` on your `JavaRDD` object.
+calling `.rdd()` on your `JavaRDD` object. The corresponding Java example
to
+the Scala snippet provided, is presented bellow:
+
+{% highlight java %}
+import org.apache.spark.api.java.*;
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.regression.LinearRegressionWithSGD;
+import org.apache.spark.mllib.regression.LinearRegressionModel;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.linalg.Vector;
+
+import scala.Product2;
+
+public class LinearRegression {
+ public static void main( String[] args ) {
+ SparkConf conf = new SparkConf().setAppName("Linear Regression
Example");
+ JavaSparkContext sc = new JavaSparkContext(conf);
+
+ // Load and parse the data
+ String path = "{SPARK_HOME}/mllib/data/ridge-data/lpsa.data";
+ JavaRDD<String> data = sc.textFile(path);
+ JavaRDD<LabeledPoint> parsedData = data.map(
+ new Function<String, LabeledPoint>() {
+ public LabeledPoint call(String line) {
+ String[] parts = line.split(",");
+ String[] features = parts[1].split(" ");
+ double[] v = new double[features.length];
+ for (int i = 0; i < features.length - 1; i++)
+ v[i] = Double.parseDouble(features[i]);
+ return new LabeledPoint(Double.parseDouble(parts[0]),
Vectors.dense(v));
+ }
+ }
+ );
+
+ // Building the model
+ int numIterations = 100;
+ final LinearRegressionModel model =
LinearRegressionWithSGD.train(JavaRDD.toRDD(parsedData), numIterations);
+
+ // Evaluate model on training examples and compute training error
+ JavaRDD<double[]> valuesAndPreds = parsedData.map(
--- End diff --
From my understanding the LabeledPoint instances consist of two fields:
a) The first field is the label of the point.
b) The second field is a double[] array (Vector[Double]) that consists of
all the attributes' values.
I assume that I should switch the values in 'valuesAndPreds' variable so as
prediction to proceed the actual label of each datapoint included in ths
JavaRDD<double[]>.
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