[SPARK-10070] [DOCS] Remove Guava dependencies in user guides

`Lists.newArrayList` -> `Arrays.asList`

CC jkbradley feynmanliang

Anybody into replacing usages of `Lists.newArrayList` in the examples / source code too? this method isn't useful in Java 7 and beyond.

Author: Sean Owen <sowen@cloudera.com>

Closes #8272 from srowen/SPARK-10070.

1 files changed, 12 insertions, 9 deletions

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index a03ab4356a..4fe0ea78bb 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -274,8 +274,9 @@ sc.stop()
 
 <div data-lang="java">
 {% highlight java %}
+import java.util.Arrays;
 import java.util.List;
-import com.google.common.collect.Lists;
+
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.classification.LogisticRegressionModel;
@@ -294,7 +295,7 @@ SQLContext jsql = new SQLContext(jsc);
 // Prepare training data.
 // We use LabeledPoint, which is a JavaBean.  Spark SQL can convert RDDs of JavaBeans
 // into DataFrames, where it uses the bean metadata to infer the schema.
-List<LabeledPoint> localTraining = Lists.newArrayList(
+List<LabeledPoint> localTraining = Arrays.asList(
   new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
   new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
   new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
@@ -335,7 +336,7 @@ LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
 System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());
 
 // Prepare test documents.
-List<LabeledPoint> localTest = Lists.newArrayList(
+List<LabeledPoint> localTest = Arrays.asList(
     new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
     new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
     new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)));
@@ -496,8 +497,9 @@ sc.stop()
 
 <div data-lang="java">
 {% highlight java %}
+import java.util.Arrays;
 import java.util.List;
-import com.google.common.collect.Lists;
+
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.Pipeline;
@@ -546,7 +548,7 @@ JavaSparkContext jsc = new JavaSparkContext(conf);
 SQLContext jsql = new SQLContext(jsc);
 
 // Prepare training documents, which are labeled.
-List<LabeledDocument> localTraining = Lists.newArrayList(
+List<LabeledDocument> localTraining = Arrays.asList(
   new LabeledDocument(0L, "a b c d e spark", 1.0),
   new LabeledDocument(1L, "b d", 0.0),
   new LabeledDocument(2L, "spark f g h", 1.0),
@@ -571,7 +573,7 @@ Pipeline pipeline = new Pipeline()
 PipelineModel model = pipeline.fit(training);
 
 // Prepare test documents, which are unlabeled.
-List<Document> localTest = Lists.newArrayList(
+List<Document> localTest = Arrays.asList(
   new Document(4L, "spark i j k"),
   new Document(5L, "l m n"),
   new Document(6L, "mapreduce spark"),
@@ -747,8 +749,9 @@ sc.stop()
 
 <div data-lang="java">
 {% highlight java %}
+import java.util.Arrays;
 import java.util.List;
-import com.google.common.collect.Lists;
+
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.Pipeline;
@@ -800,7 +803,7 @@ JavaSparkContext jsc = new JavaSparkContext(conf);
 SQLContext jsql = new SQLContext(jsc);
 
 // Prepare training documents, which are labeled.
-List<LabeledDocument> localTraining = Lists.newArrayList(
+List<LabeledDocument> localTraining = Arrays.asList(
   new LabeledDocument(0L, "a b c d e spark", 1.0),
   new LabeledDocument(1L, "b d", 0.0),
   new LabeledDocument(2L, "spark f g h", 1.0),
@@ -849,7 +852,7 @@ crossval.setNumFolds(2); // Use 3+ in practice
 CrossValidatorModel cvModel = crossval.fit(training);
 
 // Prepare test documents, which are unlabeled.
-List<Document> localTest = Lists.newArrayList(
+List<Document> localTest = Arrays.asList(
   new Document(4L, "spark i j k"),
   new Document(5L, "l m n"),
   new Document(6L, "mapreduce spark"),