[SPARK-7568] [ML] ml.LogisticRegression doesn't output the right prediction

The difference is because we previously don't fit the intercept in Spark 1.3. Here, we change the input `String` so that the probability of instance 6 can be classified as `1.0` without any ambiguity.

with lambda = 0.001 in current LOR implementation, the prediction is
```
(4, spark i j k) --> prob=[0.1596407738787411,0.8403592261212589], prediction=1.0
(5, l m n) --> prob=[0.8378325685476612,0.16216743145233883], prediction=0.0
(6, spark hadoop spark) --> prob=[0.0692663313297627,0.9307336686702373], prediction=1.0
(7, apache hadoop) --> prob=[0.9821575333444208,0.01784246665557917], prediction=0.0
```
and the training accuracy is
```
(0, a b c d e spark) --> prob=[0.0021342419881406746,0.9978657580118594], prediction=1.0
(1, b d) --> prob=[0.9959176174854043,0.004082382514595685], prediction=0.0
(2, spark f g h) --> prob=[0.0014541569986711233,0.9985458430013289], prediction=1.0
(3, hadoop mapreduce) --> prob=[0.9982978367343561,0.0017021632656438518], prediction=0.0
```

Author: DB Tsai <dbt@netflix.com>

Closes #6109 from dbtsai/lor-example and squashes the following commits:

ac63ce4 [DB Tsai] first commit

examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java

@@ -66,7 +66,7 @@ public class JavaSimpleTextClassificationPipeline {
       .setOutputCol("features");
     LogisticRegression lr = new LogisticRegression()
       .setMaxIter(10)
-      .setRegParam(0.01);
+      .setRegParam(0.001);
     Pipeline pipeline = new Pipeline()
       .setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
 
@@ -77,7 +77,7 @@ public class JavaSimpleTextClassificationPipeline {
     List<Document> localTest = Lists.newArrayList(
       new Document(4L, "spark i j k"),
       new Document(5L, "l m n"),
-      new Document(6L, "mapreduce spark"),
+      new Document(6L, "spark hadoop spark"),
       new Document(7L, "apache hadoop"));
     DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class);
 

examples/src/main/python/ml/simple_text_classification_pipeline.py

@@ -48,7 +48,7 @@ if __name__ == "__main__":
     # Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr.
     tokenizer = Tokenizer(inputCol="text", outputCol="words")
     hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
-    lr = LogisticRegression(maxIter=10, regParam=0.01)
+    lr = LogisticRegression(maxIter=10, regParam=0.001)
     pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
 
     # Fit the pipeline to training documents.
@@ -58,7 +58,7 @@ if __name__ == "__main__":
     Document = Row("id", "text")
     test = sc.parallelize([(4, "spark i j k"),
                            (5, "l m n"),
-                           (6, "mapreduce spark"),
+                           (6, "spark hadoop spark"),
                            (7, "apache hadoop")]) \
         .map(lambda x: Document(*x)).toDF()
 

examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala

@@ -64,7 +64,7 @@ object SimpleTextClassificationPipeline {
       .setOutputCol("features")
     val lr = new LogisticRegression()
       .setMaxIter(10)
-      .setRegParam(0.01)
+      .setRegParam(0.001)
     val pipeline = new Pipeline()
       .setStages(Array(tokenizer, hashingTF, lr))
 
@@ -75,7 +75,7 @@ object SimpleTextClassificationPipeline {
     val test = sc.parallelize(Seq(
       Document(4L, "spark i j k"),
       Document(5L, "l m n"),
-      Document(6L, "mapreduce spark"),
+      Document(6L, "spark hadoop spark"),
       Document(7L, "apache hadoop")))
 
     // Make predictions on test documents.