# 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") 

#loading the example database
data(examples_anomalies)

#Using the tt time series
dataset <- examples_anomalies$tt
dataset$event <- factor(dataset$event, labels=c("FALSE", "TRUE"))
head(dataset)

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

# data preprocessing
slevels <- levels(dataset$event)

train <- dataset[1:80,]
test <- dataset[-(1:80),]

norm <- minmax()
norm <- fit(norm, train)
train_n <- transform(norm, train)
summary(train_n)

# establishing class majority method 
model <- hanc_ml(cla_majority("event", slevels))

# fitting the model
model <- fit(model, train_n)
detection <- detect(model, train_n)
print(detection |> dplyr::filter(event==TRUE))
# evaluating the training
evaluation <- evaluate(model, detection$event, as.logical(train_n$event))
print(evaluation$confMatrix)

# ploting training results
  grf <- har_plot(model, train_n$serie, detection, as.logical(train_n$event))
  plot(grf)

# preparing for testing
  test_n <- transform(norm, test)

# evaluating the detections during testing
  detection <- detect(model, test_n)

  print(detection |> dplyr::filter(event==TRUE))

  evaluation <- evaluate(model, detection$event, as.logical(test_n$event))
  print(evaluation$confMatrix)

# ploting the results during testing
  grf <- har_plot(model, test_n$serie, detection, as.logical(test_n$event))
  plot(grf)