For our use case, we are using the actigraphy data from smartphones we have previously examined. These data include actimetry on a number of individuals while sitting, standing, lying, walking, walking downstairs, and walking upstairs. Our goal is to identify any anomalous values or values that are aberrant or otherwise unusual.
To start with, we will load the training and testing data into R and then convert it over to H2O for analysis:
use.train.x <- read.table("UCI HAR Dataset/train/X_train.txt") use.test.x <- read.table("UCI HAR Dataset/test/X_test.txt") use.train.y <- read.table("UCI HAR Dataset/train/y_train.txt")[[1]] use.test.y <- read.table("UCI HAR Dataset/test/y_test.txt")[[1]] use.labels <- read.table("UCI HAR Dataset/activity_labels.txt") h2oactivity.train <- as.h2o( use.train.x, destination_frame = "h2oactivitytrain") h2oactivity.test <- as.h2o( use.test.x, destination_frame = "h2oactivitytest...