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)