diff --git a/.gitignore b/.gitignore
index 6d11f9400b..4d56e42f75 100644
--- a/.gitignore
+++ b/.gitignore
@@ -78,4 +78,6 @@ examples/models/mean_classifier/build/
 notebooks/my-ml-deployment/
 
 wrappers/s2i/python/Dockerfile
-wrappers/s2i/python/_wrappers
\ No newline at end of file
+wrappers/s2i/python/_wrappers
+
+.Rhistory
diff --git a/examples/models/r_mnist/.Rhistory b/examples/models/r_mnist/.Rhistory
deleted file mode 100644
index f05ad50259..0000000000
--- a/examples/models/r_mnist/.Rhistory
+++ /dev/null
@@ -1,174 +0,0 @@
-library(caret)
-install.packages("caret")
-install.packages("doParallel")
-install.packages("e1071")
-library(caret)
-library(doParallel)
-# Enable parallel processing.
-cl <- makeCluster(detectCores())
-registerDoParallel(cl)
-# Load the MNIST digit recognition dataset into R
-# http://yann.lecun.com/exdb/mnist/
-# assume you have all 4 files and gunzip'd them
-# creates train$n, train$x, train$y  and test$n, test$x, test$y
-# e.g. train$x is a 60000 x 784 matrix, each row is one digit (28x28)
-# call:  show_digit(train$x[5,])   to see a digit.
-# brendan o'connor - gist.github.com/39760 - anyall.org
-load_mnist <- function() {
-load_image_file <- function(filename) {
-ret = list()
-f = file(filename,'rb')
-readBin(f,'integer',n=1,size=4,endian='big')
-ret$n = readBin(f,'integer',n=1,size=4,endian='big')
-nrow = readBin(f,'integer',n=1,size=4,endian='big')
-ncol = readBin(f,'integer',n=1,size=4,endian='big')
-x = readBin(f,'integer',n=ret$n*nrow*ncol,size=1,signed=F)
-ret$x = matrix(x, ncol=nrow*ncol, byrow=T)
-close(f)
-ret
-}
-load_label_file <- function(filename) {
-f = file(filename,'rb')
-readBin(f,'integer',n=1,size=4,endian='big')
-n = readBin(f,'integer',n=1,size=4,endian='big')
-y = readBin(f,'integer',n=n,size=1,signed=F)
-close(f)
-y
-}
-train <<- load_image_file('train-images-idx3-ubyte')
-test <<- load_image_file('t10k-images-idx3-ubyte')
-train$y <<- load_label_file('train-labels-idx1-ubyte')
-test$y <<- load_label_file('t10k-labels-idx1-ubyte')
-}
-show_digit <- function(arr784, col=gray(12:1/12), ...) {
-image(matrix(arr784, nrow=28)[,28:1], col=col, ...)
-}
-train <- data.frame()
-test <- data.frame()
-# Load data.
-load_mnist()
-# Normalize: X = (X - min) / (max - min) => X = (X - 0) / (255 - 0) => X = X / 255.
-train$x <- train$x / 255
-# Setup training data with digit and pixel values with 60/40 split for train/cv.
-inTrain = data.frame(y=train$y, train$x)
-inTrain$y <- as.factor(inTrain$y)
-trainIndex = createDataPartition(inTrain$y, p = 0.60,list=FALSE)
-training = inTrain[trainIndex,]
-cv = inTrain[-trainIndex,]
-# SVM. 95/94.
-fit <- train(y ~ ., data = head(training, 1000), method = 'svmRadial', tuneGrid = data.frame(sigma=0.0107249, C=1))
-results <- predict(fit, newdata = head(cv, 1000))
-confusionMatrix(results, head(cv$y, 1000))
-saveRDS(fit, file = "model.Rds", compress = TRUE)
-View(fit)
-View(training)
-results <- predict(fit, newdata = head(cv, 1000))
-results
-confusionMatrix(results, head(cv$y, 1000))
-results <- predict(fit, newdata = head(cv, 1000), type='response')
-results <- predict(fit, newdata = head(cv, 1000), type='prob')
-results
-# SVM. 95/94.
-fit <- train(y ~ ., data = head(training, 1000), method = 'naiveBayes', tuneGrid = data.frame(sigma=0.0107249, C=1))
-# SVM. 95/94.
-fit <- train(y ~ ., data = head(training, 1000), method = 'xgbTree', tuneGrid = data.frame(sigma=0.0107249, C=1))
-# SVM. 95/94.
-fit <- train(y ~ ., data = head(training, 1000), method = 'randomGLM', tuneGrid = data.frame(sigma=0.0107249, C=1))
-# SVM. 95/94.
-#fit <- train(y ~ ., data = head(training, 1000), method = 'svmRadial', tuneGrid = data.frame(sigma=0.0107249, C=1))
-fit <- train(y ~ ., data = head(training, 1000), method = 'randomGLM')
-# SVM. 95/94.
-#fit <- train(y ~ ., data = head(training, 1000), method = 'svmRadial', tuneGrid = data.frame(sigma=0.0107249, C=1))
-fit <- train(y ~ ., data = head(training, 1000), method = 'bayesGLM')
-library(caret)
-library(doParallel)
-# Enable parallel processing.
-cl <- makeCluster(detectCores())
-registerDoParallel(cl)
-# Load the MNIST digit recognition dataset into R
-# http://yann.lecun.com/exdb/mnist/
-# assume you have all 4 files and gunzip'd them
-# creates train$n, train$x, train$y  and test$n, test$x, test$y
-# e.g. train$x is a 60000 x 784 matrix, each row is one digit (28x28)
-# call:  show_digit(train$x[5,])   to see a digit.
-# brendan o'connor - gist.github.com/39760 - anyall.org
-load_mnist <- function() {
-load_image_file <- function(filename) {
-ret = list()
-f = file(filename,'rb')
-readBin(f,'integer',n=1,size=4,endian='big')
-ret$n = readBin(f,'integer',n=1,size=4,endian='big')
-nrow = readBin(f,'integer',n=1,size=4,endian='big')
-ncol = readBin(f,'integer',n=1,size=4,endian='big')
-x = readBin(f,'integer',n=ret$n*nrow*ncol,size=1,signed=F)
-ret$x = matrix(x, ncol=nrow*ncol, byrow=T)
-close(f)
-ret
-}
-load_label_file <- function(filename) {
-f = file(filename,'rb')
-readBin(f,'integer',n=1,size=4,endian='big')
-n = readBin(f,'integer',n=1,size=4,endian='big')
-y = readBin(f,'integer',n=n,size=1,signed=F)
-close(f)
-y
-}
-train <<- load_image_file('train-images-idx3-ubyte')
-test <<- load_image_file('t10k-images-idx3-ubyte')
-train$y <<- load_label_file('train-labels-idx1-ubyte')
-test$y <<- load_label_file('t10k-labels-idx1-ubyte')
-}
-show_digit <- function(arr784, col=gray(12:1/12), ...) {
-image(matrix(arr784, nrow=28)[,28:1], col=col, ...)
-}
-train <- data.frame()
-test <- data.frame()
-# Load data.
-load_mnist()
-# Normalize: X = (X - min) / (max - min) => X = (X - 0) / (255 - 0) => X = X / 255.
-train$x <- train$x / 255
-# Setup training data with digit and pixel values with 60/40 split for train/cv.
-inTrain = data.frame(y=train$y, train$x)
-inTrain$y <- as.factor(inTrain$y)
-trainIndex = createDataPartition(inTrain$y, p = 0.60,list=FALSE)
-training = inTrain[trainIndex,]
-cv = inTrain[-trainIndex,]
-# SVM. 95/94.
-#fit <- train(y ~ ., data = head(training, 1000), method = 'svmRadial', tuneGrid = data.frame(sigma=0.0107249, C=1))
-fit <- train(y ~ ., data = head(training, 1000), method = 'bayesGLM')
-results <- predict(fit, newdata = head(cv, 1000), type='prob')
-confusionMatrix(results, head(cv$y, 1000))
-saveRDS(fit, file = "model.Rds", compress = TRUE)
-# SVM. 95/94.
-#fit <- train(y ~ ., data = head(training, 1000), method = 'svmRadial', tuneGrid = data.frame(sigma=0.0107249, C=1))
-fit <- train(y ~ ., data = head(training, 1000), method = 'bayesGLM')
-# SVM. 95/94.
-#fit <- train(y ~ ., data = head(training, 1000), method = 'svmRadial', tuneGrid = data.frame(sigma=0.0107249, C=1))
-fit <- train(y ~ ., data = head(training, 1000), method = 'rpart2')
-results <- predict(fit, newdata = head(cv, 1000), type='prob')
-View(results)
-install.packages("klaR")
-install.packages("gbm")
-install.packages("pls")
-d = data.frame(c(1,2,3))
-View(d)
-View(d)
-View(d)
-delete d
-d <- data.frame((1,2,3))
-d <- data.frame(1,2,3)
-ids <- 1:3
-cn <- Character(length(d))
-cn <- Character(length=length(ids))
-cat("X",1)
-"X"+1
-"X".1
-paste("x",1)
-paste("x",1,collapse = TRUE)
-paste("x",1,collapse = True)
-paste("x",1,sep="")
-for (i in seq_along(idx)){idx[i] <- paste("X",idx[i],sep = "")}
-for (i in seq_along(ids)){ids[i] <- paste("X",ids[i],sep = "")}
-colnames(d) <- ids
-for (i in seq_along(ids)){ids[i] <- paste("T",ids[i],sep = "")}
-colnames(d) <- ids