Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1311#discussion_r14863023
--- Diff: docs/mllib-optimization.md ---
@@ -263,7 +267,110 @@ println("Loss of each step in training process")
loss.foreach(println)
println("Area under ROC = " + auROC)
{% endhighlight %}
-
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+import java.util.Random;
+import java.util.Arrays;
+
+import scala.Product2;
+import scala.Tuple2;
+
+import org.apache.spark.api.java.*;
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.optimization.*;
+import org.apache.spark.mllib.classification.LogisticRegressionModel;
+import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics;
+
+public class LBFGSExample {
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf().setAppName("L-BFGS Example");
+ SparkContext sc = new SparkContext(conf);
+ String path = "{SPARK_HOME}/mllib/data/sample_libsvm_data.txt";
+ JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc,
path).toJavaRDD();
+ int numFeatures = data.take(1).get(0).features().size();
+
+ // Split initial RDD into two... [60% training data, 40% testing data].
+ JavaRDD<LabeledPoint> trainingInit = data.filter(
+ new Function<LabeledPoint, Boolean>() {
+ public final Random random = new Random(11L);
+ public Boolean call(LabeledPoint p) {
+ if (random.nextDouble() <= 0.6)
+ return true;
+ else
+ return false;
+ }
+ }
+ );
+ JavaRDD<LabeledPoint> test = data.subtract(trainingInit);
+
+ // Append 1 into the training data as intercept.
+ JavaRDD<Tuple2<Object, Vector>> training = data.map(
+ new Function<LabeledPoint, Tuple2<Object, Vector>>() {
+ public Tuple2<Object, Vector> call(LabeledPoint p) {
+ return new Tuple2<Object, Vector>(p.label(),
MLUtils.appendBias(p.features()));
+ }
+ });
+ training.cache();
+
+ // Run training algorithm to build the model.
+ int numCorrections = 10;
+ double convergenceTol = 1e-4;
+ int maxNumIterations = 20;
+ double regParam = 0.1;
+ Vector initialWeightsWithIntercept = Vectors.dense(new
double[numFeatures + 1]);
+
+ Tuple2<Vector, double[]> result = LBFGS.runLBFGS(
+ JavaRDD.toRDD(training),
--- End diff --
use 2 spaces for continuous indentation
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