# Harbinger Package
# version 1.0.777

source("https://raw.githubusercontent.com/cefet-rj-dal/harbinger/master/jupyter.R")

#loading Harbinger
load_library("daltoolbox") 
load_library("harbinger") 

#Creating dataset
n <- 100  # size of each segment
serie1 <- c(sin((1:n)/pi), 2*sin((1:n)/pi), 10 + sin((1:n)/pi),
           10-10/n*(1:n)+sin((1:n)/pi)/2, sin((1:n)/pi)/2)
serie2 <- 2*c(sin((1:n)/pi), 2*sin((1:n)/pi), 10 + sin((1:n)/pi),
           10-10/n*(1:n)+sin((1:n)/pi)/2, sin((1:n)/pi)/2)
event <- rep(FALSE, length(serie1))
event[c(100, 200, 300, 400)] <- TRUE
dataset <- data.frame(serie1, serie2, event)

#ploting the time series
plot_ts(x = 1:length(dataset$serie1), y = dataset$serie1)

#ploting serie #2
plot_ts(x = 1:length(dataset$serie2), y = dataset$serie2)

# establishing drift method 
model <- hcd_eddm()

# fitting the model
model <- fit(model, dataset)

# making detections
detection <- detect(model, dataset)

# filtering detected events
print(detection[(detection$event),])

# evaluating the detections
  evaluation <- evaluate(model, detection$event, dataset$event)
  print(evaluation$confMatrix)

# ploting the results
  grf <- har_plot(model, dataset$serie1, detection, dataset$event)
  plot(grf)