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[SPARK-8660] [MLLib] removed > symbols from comments in LogisticRegressionSuite.scala for ease of copypaste #7167

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[SPARK-8660] [MLLib] removed > symbols from comments in LogisticRegressionSuite.scala for ease of copypaste #7167

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6c18058
fixed minor typos in docs/README.md and docs/api.md
Rosstin Jun 26, 2015
21ac1e5
Merge branch 'master' of github.com:apache/spark into SPARK-8639
Rosstin Jun 29, 2015
2cd2985
Merge branch 'master' of github.com:apache/spark into SPARK-8639
Rosstin Jun 29, 2015
242aedd
SPARK-8660, changed comment style from JavaDoc style to normal multil…
Rosstin Jun 29, 2015
bb9a4b1
Merge branch 'master' of github.com:apache/spark into SPARK-8660
Rosstin Jun 29, 2015
5a05dee
SPARK-8661 for LinearRegressionSuite.scala, changed javadoc-style com…
Rosstin Jun 29, 2015
39ddd50
Merge branch 'master' of github.com:apache/spark into SPARK-8661
Rosstin Jul 1, 2015
fe6b112
SPARK-8660 > symbols removed from LogisticRegressionSuite.scala for e…
Rosstin Jul 1, 2015
f4b9bc8
SPARK-8660 restored character limit on multiline comments in Logistic…
Rosstin Jul 1, 2015
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117 changes: 63 additions & 54 deletions mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -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) .
Expand All @@ -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
Expand All @@ -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) .
Expand All @@ -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
Expand All @@ -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) .
Expand All @@ -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
Expand All @@ -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) .
Expand Down Expand Up @@ -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
Expand Down