[SPARK-11920][ML][DOC] ML LinearRegression should use correct dataset in examples and user guide doc

ML ```LinearRegression``` use ```data/mllib/sample_libsvm_data.txt``` as dataset in examples and user guide doc, but it's actually classification dataset rather than regression dataset. We should use ```data/mllib/sample_linear_regression_data.txt``` instead.
The deeper causes is that ```LinearRegression``` with "normal" solver can not solve this dataset correctly, may be due to the ill condition and unreasonable label. This issue has been reported at [SPARK-11918](https://issues.apache.org/jira/browse/SPARK-11918).
It will confuse users if they run the example code but get exception, so we should make this change which can clearly illustrate the usage of ```LinearRegression``` algorithm.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #9905 from yanboliang/spark-11920.

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

@@ -37,7 +37,7 @@ public class JavaLinearRegressionWithElasticNetExample {
     // $example on$
     // Load training data
     DataFrame training = sqlContext.read().format("libsvm")
-      .load("data/mllib/sample_libsvm_data.txt");
+      .load("data/mllib/sample_linear_regression_data.txt");
 
     LinearRegression lr = new LinearRegression()
       .setMaxIter(10)

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

@@ -29,7 +29,8 @@ if __name__ == "__main__":
 
     # $example on$
     # Load training data
-    training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
+    training = sqlContext.read.format("libsvm")\
+        .load("data/mllib/sample_linear_regression_data.txt")
 
     lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
 

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

@@ -33,7 +33,8 @@ object LinearRegressionWithElasticNetExample {
 
     // $example on$
     // Load training data
-    val training = sqlCtx.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
+    val training = sqlCtx.read.format("libsvm")
+      .load("data/mllib/sample_linear_regression_data.txt")
 
     val lr = new LinearRegression()
       .setMaxIter(10)