Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1311#discussion_r14862947
--- Diff: docs/mllib-linear-methods.md ---
@@ -338,7 +427,74 @@ 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;
+
+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(
+ new Function<LabeledPoint, double[]>() {
+ public final LinearRegressionModel m = model;
+ public double[] call(LabeledPoint point) {
+ double prediction = m.predict(point.features());
+ return new double[] {prediction, point.label()};
+ }
+ }
+ );
+ JavaRDD<Object> MSERdd = valuesAndPreds.map(
+ new Function<double[], Object>() {
+ public Object call(double[] lp) {
+ return Math.pow(lp[0] - lp[1], 2.0);
+ }
+ }
+ );
+ JavaDoubleRDD MSEDoubleRdd = new JavaDoubleRDD(JavaRDD.toRDD(MSERdd));
+ double MSE = MSEDoubleRdd.mean();
--- End diff --
Could you chain the operations from line 480 and get `MSE` directly?
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