| ams_ks <- function(train_data, test_data, p_value) { |
| |
| # res <- get_data(url) |
| # data <- data.frame(as.numeric(names(res$metrics[[1]]$metrics)), as.numeric(res$metrics[[1]]$metrics)) |
| # names(data) <- c("TS", res$metrics[[1]]$metricname) |
| # train_data <- data[which(data$TS >= train_start & data$TS <= train_end), 2] |
| # test_data <- data[which(data$TS >= test_start & data$TS <= test_end), 2] |
| |
| anomalies <- data.frame() |
| res <- ks.test(train_data, test_data[,2]) |
| |
| if (res[2] < p_value) { |
| anomaly <- c(test_data[1,1], test_data[length(test_data),1], res[1], res[2]) |
| anomalies <- rbind(anomalies, anomaly) |
| } |
| |
| if(length(anomalies) > 0) { |
| names(anomalies) <- c("TS Start", "TS End", "D", "p-value") |
| } |
| return (anomalies) |
| } |
