from hidden_markov_model import HiddenMarkovModel
from hmm_trainer import HMMTrainer
#crazy coke machine example
Pi = {"cola_pref":1}
A = {}
A["cola_pref"] = {"cola_pref":0.7,"ice_t_pref":0.3}
A["ice_t_pref"] = {"cola_pref":0.5,"ice_t_pref":0.5}
emission_probs = {}
emission_probs["cola_pref"] = {"cola": 0.6,"ice_t": 0.1,"lem":0.3}
emission_probs["ice_t_pref"] = {"cola":0.1,"ice_t":0.7,"lem":0.2}
example_hmm = HiddenMarkovModel(Pi,A,emission_probs)
obs = ["lem","ice_t","cola"]
hmm_trainer = HMMTrainer(2)
r_hmm = hmm_trainer.random_hmm(["cola","ice_t","lem"])
training_hmm = HiddenMarkovModel(Pi,A,emission_probs)
"scissor_loving" : {
"rock_loving" : 0.3,
"paper_loving" : 0.1,
"scissor_loving" : 0.6
}
}
emission_probs = {
"rock_loving" : {
"rock": 0.6,
"paper": 0.2,
"scissor": 0.2
},
"paper_loving" : {
"rock" : 0.2,
"paper" : 0.7,
"scissor" : 0.1
},
"scissor_loving" : {
"rock": 0.1,
"paper": 0.1,
"scissor": 0.8
}
}
player = HiddenMarkovModel(start_probs,transition_probs,emission_probs)
trainer = HMMTrainer(3)
alphabet = ["rock","paper","scissor"]
random_player = trainer.random_hmm(alphabet)
