SPARK-8660 > symbols removed from LogisticRegressionSuite.scala for e…

…asy of copypaste
maropu · Jul 1, 2015 · fe6b112 · fe6b112
1 parent 39ddd50
commit fe6b112
Showing 1 changed file with 66 additions and 62 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
@@ -45,10 +45,9 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        val weights = Array(-0.57997, 0.912083, -0.371077, -0.819866, 2.688191)
        val xMean = Array(5.843, 3.057, 3.758, 1.199)
        val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)
-       val data = sc.parallelize(LogisticRegressionSuite.generateMultinomialLogisticInput(
-         weights, xMean, xVariance, true, nPoints, 42), 1)
-       data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1) + ", "
-         + x.features(2) + ", " + x.features(3)).saveAsTextFile("path")
+       val data = sc.parallelize(LogisticRegressionSuite.generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42), 1)
+       data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1) + ", " + x.features(2) + ", " + x.features(3)).saveAsTextFile("path")
+
      */
     binaryDataset = {
       val nPoints = 10000
@@ -214,12 +213,13 @@ 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 +245,13 @@ 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$V5))
-       > weights =
-           coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 0, intercept=FALSE))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$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
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                            s0
        (Intercept)   .
@@ -278,12 +278,13 @@ 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 +311,13 @@ 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$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 1, lambda = 0.12,
-           intercept=FALSE))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$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, intercept=FALSE))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)   .
@@ -343,12 +344,13 @@ 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 +377,13 @@ 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$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 0, lambda = 1.37,
-           intercept=FALSE))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$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, intercept=FALSE))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)   .
@@ -408,12 +410,13 @@ 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 +443,13 @@ 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$V5))
-       > weights = coef(glmnet(features,label, family="binomial", alpha = 0.38, lambda = 0.21,
-           intercept=FALSE))
-       > weights
+       library("glmnet")
+       data <- read.csv("path", header=FALSE)
+       label = factor(data$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, intercept=FALSE))
+       weights
+
        5 x 1 sparse Matrix of class "dgCMatrix"
                             s0
        (Intercept)   .
@@ -503,12 +506,13 @@ 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