[SPARK-8661][ML] for LinearRegressionSuite.scala, changed javadoc-style comments to regular multiline comments, to make copy-pasting R code more simple

for mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala, changed javadoc-style comments to regular multiline comments, to make copy-pasting R code more simple

Author: Rosstin <asterazul@gmail.com>

Closes #7098 from Rosstin/SPARK-8661 and squashes the following commits:

5a05dee [Rosstin] SPARK-8661 for LinearRegressionSuite.scala, changed javadoc-style comments to regular multiline comments to make it easier to copy-paste the R code.
bb9a4b1 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8660
242aedd [Rosstin] SPARK-8660, changed comment style from JavaDoc style to normal multiline comment in order to make copypaste into R easier, in file classification/LogisticRegressionSuite.scala
2cd2985 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8639
21ac1e5 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8639
6c18058 [Rosstin] fixed minor typos in docs/README.md and docs/api.md

1 files changed, 96 insertions, 96 deletions

diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index ad1e9da692..5f39d44f37 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -28,26 +28,26 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
   @transient var dataset: DataFrame = _
   @transient var datasetWithoutIntercept: DataFrame = _
 
-  /**
-   * In `LinearRegressionSuite`, we will make sure that the model trained by SparkML
-   * is the same as the one trained by R's glmnet package. The following instruction
-   * describes how to reproduce the data in R.
-   *
-   * import org.apache.spark.mllib.util.LinearDataGenerator
-   * val data =
-   *   sc.parallelize(LinearDataGenerator.generateLinearInput(6.3, Array(4.7, 7.2),
-   *     Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2)
-   * data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1)).coalesce(1)
-   *   .saveAsTextFile("path")
+  /*
+     In `LinearRegressionSuite`, we will make sure that the model trained by SparkML
+     is the same as the one trained by R's glmnet package. The following instruction
+     describes how to reproduce the data in R.
+
+     import org.apache.spark.mllib.util.LinearDataGenerator
+     val data =
+       sc.parallelize(LinearDataGenerator.generateLinearInput(6.3, Array(4.7, 7.2),
+         Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2)
+     data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1)).coalesce(1)
+       .saveAsTextFile("path")
    */
   override def beforeAll(): Unit = {
     super.beforeAll()
     dataset = sqlContext.createDataFrame(
       sc.parallelize(LinearDataGenerator.generateLinearInput(
         6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2))
-    /**
-     * datasetWithoutIntercept is not needed for correctness testing but is useful for illustrating
-     * training model without intercept
+    /*
+       datasetWithoutIntercept is not needed for correctness testing but is useful for illustrating
+       training model without intercept
      */
     datasetWithoutIntercept = sqlContext.createDataFrame(
       sc.parallelize(LinearDataGenerator.generateLinearInput(
@@ -59,20 +59,20 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val trainer = new LinearRegression
     val model = trainer.fit(dataset)
 
-    /**
-     * Using the following R code to load the data and train the model using glmnet package.
-     *
-     * library("glmnet")
-     * data <- read.csv("path", header=FALSE, stringsAsFactors=FALSE)
-     * features <- as.matrix(data.frame(as.numeric(data$V2), as.numeric(data$V3)))
-     * label <- as.numeric(data$V1)
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0))
-     * > weights
-     *  3 x 1 sparse Matrix of class "dgCMatrix"
-     *                           s0
-     * (Intercept)         6.300528
-     * as.numeric.data.V2. 4.701024
-     * as.numeric.data.V3. 7.198257
+    /*
+       Using the following R code to load the data and train the model using glmnet package.
+
+       library("glmnet")
+       data <- read.csv("path", header=FALSE, stringsAsFactors=FALSE)
+       features <- as.matrix(data.frame(as.numeric(data$V2), as.numeric(data$V3)))
+       label <- as.numeric(data$V1)
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         6.300528
+       as.numeric.data.V2. 4.701024
+       as.numeric.data.V3. 7.198257
      */
     val interceptR = 6.298698
     val weightsR = Array(4.700706, 7.199082)
@@ -94,29 +94,29 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val model = trainer.fit(dataset)
     val modelWithoutIntercept = trainer.fit(datasetWithoutIntercept)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0,
-     *   intercept = FALSE))
-     * > weights
-     *  3 x 1 sparse Matrix of class "dgCMatrix"
-     *                           s0
-     * (Intercept)         .
-     * as.numeric.data.V2. 6.995908
-     * as.numeric.data.V3. 5.275131
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0,
+         intercept = FALSE))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         .
+       as.numeric.data.V2. 6.995908
+       as.numeric.data.V3. 5.275131
      */
     val weightsR = Array(6.995908, 5.275131)
 
     assert(model.intercept ~== 0 relTol 1E-3)
     assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
     assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
-    /**
-     * Then again with the data with no intercept:
-     * > weightsWithoutIntercept
-     * 3 x 1 sparse Matrix of class "dgCMatrix"
-     *                             s0
-     * (Intercept)           .
-     * as.numeric.data3.V2. 4.70011
-     * as.numeric.data3.V3. 7.19943
+    /*
+       Then again with the data with no intercept:
+       > weightsWithoutIntercept
+       3 x 1 sparse Matrix of class "dgCMatrix"
+                                   s0
+       (Intercept)           .
+       as.numeric.data3.V2. 4.70011
+       as.numeric.data3.V3. 7.19943
      */
     val weightsWithoutInterceptR = Array(4.70011, 7.19943)
 
@@ -129,14 +129,14 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val trainer = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
     val model = trainer.fit(dataset)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57))
-     * > weights
-     *  3 x 1 sparse Matrix of class "dgCMatrix"
-     *                           s0
-     * (Intercept)         6.24300
-     * as.numeric.data.V2. 4.024821
-     * as.numeric.data.V3. 6.679841
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         6.24300
+       as.numeric.data.V2. 4.024821
+       as.numeric.data.V3. 6.679841
      */
     val interceptR = 6.24300
     val weightsR = Array(4.024821, 6.679841)
@@ -158,15 +158,15 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setFitIntercept(false)
     val model = trainer.fit(dataset)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57,
-     *   intercept=FALSE))
-     * > weights
-     *  3 x 1 sparse Matrix of class "dgCMatrix"
-     *                           s0
-     * (Intercept)          .
-     * as.numeric.data.V2. 6.299752
-     * as.numeric.data.V3. 4.772913
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57,
+         intercept=FALSE))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)          .
+       as.numeric.data.V2. 6.299752
+       as.numeric.data.V3. 4.772913
      */
     val interceptR = 0.0
     val weightsR = Array(6.299752, 4.772913)
@@ -187,14 +187,14 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val trainer = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
     val model = trainer.fit(dataset)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3))
-     * > weights
-     *  3 x 1 sparse Matrix of class "dgCMatrix"
-     *                           s0
-     * (Intercept)         6.328062
-     * as.numeric.data.V2. 3.222034
-     * as.numeric.data.V3. 4.926260
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         6.328062
+       as.numeric.data.V2. 3.222034
+       as.numeric.data.V3. 4.926260
      */
     val interceptR = 5.269376
     val weightsR = Array(3.736216, 5.712356)
@@ -216,15 +216,15 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setFitIntercept(false)
     val model = trainer.fit(dataset)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3,
-     *   intercept = FALSE))
-     * > weights
-     *  3 x 1 sparse Matrix of class "dgCMatrix"
-     *                           s0
-     * (Intercept)         .
-     * as.numeric.data.V2. 5.522875
-     * as.numeric.data.V3. 4.214502
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3,
+         intercept = FALSE))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         .
+       as.numeric.data.V2. 5.522875
+       as.numeric.data.V3. 4.214502
      */
     val interceptR = 0.0
     val weightsR = Array(5.522875, 4.214502)
@@ -245,14 +245,14 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val trainer = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
     val model = trainer.fit(dataset)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6))
-     * > weights
-     * 3 x 1 sparse Matrix of class "dgCMatrix"
-     * s0
-     * (Intercept)         6.324108
-     * as.numeric.data.V2. 3.168435
-     * as.numeric.data.V3. 5.200403
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6))
+       > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+       s0
+       (Intercept)         6.324108
+       as.numeric.data.V2. 3.168435
+       as.numeric.data.V3. 5.200403
      */
     val interceptR = 5.696056
     val weightsR = Array(3.670489, 6.001122)
@@ -274,15 +274,15 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setFitIntercept(false)
     val model = trainer.fit(dataset)
 
-    /**
-     * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6,
-     *   intercept=FALSE))
-     * > weights
-     * 3 x 1 sparse Matrix of class "dgCMatrix"
-     * s0
-     * (Intercept)         .
-     * as.numeric.dataM.V2. 5.673348
-     * as.numeric.dataM.V3. 4.322251
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6,
+         intercept=FALSE))
+       > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+       s0
+       (Intercept)         .
+       as.numeric.dataM.V2. 5.673348
+       as.numeric.dataM.V3. 4.322251
      */
     val interceptR = 0.0
     val weightsR = Array(5.673348, 4.322251)