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author | Rosstin <asterazul@gmail.com> | 2015-07-01 21:42:06 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-07-01 21:42:06 -0700 |
commit | 4e4f74b5e1267d1ada4a8f57b86aee0d9c17d90a (patch) | |
tree | 27884fc8bae5a46e462429930716b5fe6a9c6a39 /mllib | |
parent | 9fd13d5613b6d16a78d97d4798f085b56107d343 (diff) | |
download | spark-4e4f74b5e1267d1ada4a8f57b86aee0d9c17d90a.tar.gz spark-4e4f74b5e1267d1ada4a8f57b86aee0d9c17d90a.tar.bz2 spark-4e4f74b5e1267d1ada4a8f57b86aee0d9c17d90a.zip |
[SPARK-8660] [MLLIB] removed > symbols from comments in LogisticRegressionSuite.scala for ease of copypaste
'>' symbols removed from comments in LogisticRegressionSuite.scala, for ease of copypaste
also single-lined the multiline commands (is this desirable, or does it violate style?)
Author: Rosstin <asterazul@gmail.com>
Closes #7167 from Rosstin/SPARK-8660-2 and squashes the following commits:
f4b9bc8 [Rosstin] SPARK-8660 restored character limit on multiline comments in LogisticRegressionSuite.scala
fe6b112 [Rosstin] SPARK-8660 > symbols removed from LogisticRegressionSuite.scala for easy of copypaste
39ddd50 [Rosstin] Merge branch 'master' of github.com:apache/spark into SPARK-8661
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
Diffstat (limited to 'mllib')
-rw-r--r-- | mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala | 117 |
1 files changed, 63 insertions, 54 deletions
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala index bc6eeac1db..ba8fbee841 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala @@ -214,12 +214,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { /* Using the following R code to load the data and train the model using glmnet package. - > library("glmnet") - > data <- read.csv("path", header=FALSE) - > label = factor(data$V1) - > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) - > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0)) - > weights + library("glmnet") + data <- read.csv("path", header=FALSE) + label = factor(data$V1) + features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0)) + weights + 5 x 1 sparse Matrix of class "dgCMatrix" s0 (Intercept) 2.8366423 @@ -245,13 +246,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { /* Using the following R code to load the data and train the model using glmnet package. - > library("glmnet") - > data <- read.csv("path", header=FALSE) - > label = factor(data$V1) - > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) - > weights = + library("glmnet") + data <- read.csv("path", header=FALSE) + label = factor(data$V1) + features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0, intercept=FALSE)) - > weights + weights + 5 x 1 sparse Matrix of class "dgCMatrix" s0 (Intercept) . @@ -278,12 +280,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { /* Using the following R code to load the data and train the model using glmnet package. - > library("glmnet") - > data <- read.csv("path", header=FALSE) - > label = factor(data$V1) - > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) - > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12)) - > weights + library("glmnet") + data <- read.csv("path", header=FALSE) + label = factor(data$V1) + features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12)) + weights + 5 x 1 sparse Matrix of class "dgCMatrix" s0 (Intercept) -0.05627428 @@ -310,13 +313,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { /* Using the following R code to load the data and train the model using glmnet package. - > library("glmnet") - > data <- read.csv("path", header=FALSE) - > label = factor(data$V1) - > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) - > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12, + library("glmnet") + data <- read.csv("path", header=FALSE) + label = factor(data$V1) + features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12, intercept=FALSE)) - > weights + weights + 5 x 1 sparse Matrix of class "dgCMatrix" s0 (Intercept) . @@ -343,12 +347,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { /* Using the following R code to load the data and train the model using glmnet package. - > library("glmnet") - > data <- read.csv("path", header=FALSE) - > label = factor(data$V1) - > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) - > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37)) - > weights + library("glmnet") + data <- read.csv("path", header=FALSE) + label = factor(data$V1) + features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37)) + weights + 5 x 1 sparse Matrix of class "dgCMatrix" s0 (Intercept) 0.15021751 @@ -375,13 +380,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { /* Using the following R code to load the data and train the model using glmnet package. - > library("glmnet") - > data <- read.csv("path", header=FALSE) - > label = factor(data$V1) - > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) - > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37, + library("glmnet") + data <- read.csv("path", header=FALSE) + label = factor(data$V1) + features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37, intercept=FALSE)) - > weights + weights + 5 x 1 sparse Matrix of class "dgCMatrix" s0 (Intercept) . @@ -408,12 +414,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { /* Using the following R code to load the data and train the model using glmnet package. - > library("glmnet") - > data <- read.csv("path", header=FALSE) - > label = factor(data$V1) - > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) - > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21)) - > weights + library("glmnet") + data <- read.csv("path", header=FALSE) + label = factor(data$V1) + features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21)) + weights + 5 x 1 sparse Matrix of class "dgCMatrix" s0 (Intercept) 0.57734851 @@ -440,13 +447,14 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { /* Using the following R code to load the data and train the model using glmnet package. - > library("glmnet") - > data <- read.csv("path", header=FALSE) - > label = factor(data$V1) - > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) - > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21, + library("glmnet") + data <- read.csv("path", header=FALSE) + label = factor(data$V1) + features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21, intercept=FALSE)) - > weights + weights + 5 x 1 sparse Matrix of class "dgCMatrix" s0 (Intercept) . @@ -503,12 +511,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { /* Using the following R code to load the data and train the model using glmnet package. - > library("glmnet") - > data <- read.csv("path", header=FALSE) - > label = factor(data$V1) - > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) - > weights = coef(glmnet(features,label, family="binomial", alpha = 1.0, lambda = 6.0)) - > weights + library("glmnet") + data <- read.csv("path", header=FALSE) + label = factor(data$V1) + features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5)) + weights = coef(glmnet(features,label, family="binomial", alpha = 1.0, lambda = 6.0)) + weights + 5 x 1 sparse Matrix of class "dgCMatrix" s0 (Intercept) -0.2480643 |