##############################
# Markov source config example
# (cloud-like data model)
#

src = CloudMarkovChainSource(sleep=0.01, min_event_per_burst=500)

src.transitions.web_service = {
  "run=>slow": {
    "0":  0.001,
    "8":  0.02,
    "12": 0.07,
    "14": 0.07,
    "22": 0.03,
    "24": 0.00
  },
  "slow=>run": {
    "0": 0.99,
    "8": 0.7,
    "12": 0.1,
    "14": 0.1,
    "22": 0.8,
    "24": 0.9
  },
  "stop=>run": 0.7
}

src.transitions.host = {
  "on=>off": 0.005,
  "off=>on": 0.5
}

src.transitions.switch = {
  "on=>off": 0.01,
  "off=>on": 0.7
}

src.triggers.support = {
  "get_called" : {
    "0": 0.1,
    "8": 0.2,
    "12": 0.8,
    "14": 0.8,
    "22": 0.5,
    "24": 0.0
  }
}

ing1 = CloudIngestor()
ling = LiNGAM(threshold=0.5)
voter = PickIndexVoter(0)
sink = KafkaSink(host="localhost", port=9092, topic="transformed_alerts")
ldp = CloudCausalityLDP()

# Connections
src -> [ing1 -> ling, ldp]
ling -> voter -> ldp -> sink