Repository: spark Updated Branches: refs/heads/master 9fd13d561 -> 4e4f74b5e
[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 <astera...@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 Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/4e4f74b5 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/4e4f74b5 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/4e4f74b5 Branch: refs/heads/master Commit: 4e4f74b5e1267d1ada4a8f57b86aee0d9c17d90a Parents: 9fd13d5 Author: Rosstin <astera...@gmail.com> Authored: Wed Jul 1 21:42:06 2015 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Wed Jul 1 21:42:06 2015 -0700 ---------------------------------------------------------------------- .../LogisticRegressionSuite.scala | 117 ++++++++++--------- 1 file changed, 63 insertions(+), 54 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/4e4f74b5/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala ---------------------------------------------------------------------- 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 bc6eeac..ba8fbee 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 --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
