def __init__(self, controller):
# c is the constant that is added if an obs is 0
self._emission_constant = 0.00001
self._transition_constant = 0.000001
self._pi_constant = 0.000001
ModelHMM_log_scaled.__init__(self, controller)
def test_pred_next_obs_single(self):
self.ctrl.load_dataset()
hmm_model = ModelHMM_log_scaled(self.ctrl)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
hmm_model._hmm.set_format_full(True)
#print(hmm_model)
print('#' * 100)
obs_seq = [('binary_sensor.motion_bed', 0),
('binary_sensor.motion_mirror', 1),
('binary_sensor.motion_bed', 0)]
tupel = hmm_model.predict_next_obs(obs_seq)
def test_classify(self):
self.ctrl.load_dataset()
hmm_model = ModelHMM_log_scaled(self.ctrl)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
hmm_model._hmm.set_format_full(True)
print(hmm_model)
print('-' * 10)
obs_seq = [('binary_sensor.motion_bed', 0),
('binary_sensor.motion_mirror', 1),
('binary_sensor.motion_bed', 0)]
pred_state = hmm_model.classify(obs_seq)
print('#' * 100)
print(pred_state)
def test_classify_multi(self):
"""
used to test for classification of multiple labels
"""
self.ctrl.load_dataset()
hmm_model = ModelHMM_log_scaled(self.ctrl)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
hmm_model._hmm.set_format_full(True)
print(hmm_model)
print('-' * 10)
obs_seq = [('binary_sensor.motion_bed', 0),
('binary_sensor.motion_mirror', 1)
] #, ('binary_sensor.motion_bed', 0)]
act_state_dict = hmm_model.classify_multi(obs_seq)
print('#' * 100)
def test_load_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = ModelHMM_log_scaled(self.ctrl)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
print(self.hass_obj.get_obs_lbl_hashmap())
print(self.hass_obj.get_state_lbl_hashmap())
def test_train_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = ModelHMM_log_scaled(self.ctrl)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
self.ctrl.train_model()
print(self.ctrl._model)
def test_pred_next_obs_multi(self):
self.ctrl.load_dataset()
hmm_model = ModelHMM_log_scaled(self.ctrl)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.train_model()
hmm_model._hmm.set_format_full(True)
#print(hmm_model)
print('#' * 100)
obs_seq = [('binary_sensor.motion_bed', 0),
('binary_sensor.motion_mirror', 1),
('binary_sensor.motion_bed', 0)]
#arr = hmm_model.predict_next_obs_arr(obs_seq)
print(hmm_model._obs_lbl_hashmap)
print(hmm_model._obs_lbl_rev_hashmap)
res_dict = hmm_model.predict_prob_xnp1(obs_seq)
print(hmm_model._obs_lbl_hashmap)
print(hmm_model._obs_lbl_rev_hashmap)
res_dict = hmm_model.predict_prob_xnp1(obs_seq)
print(hmm_model._obs_lbl_hashmap)
print(hmm_model._obs_lbl_rev_hashmap)
print('#' * 100)
print(res_dict)
def test_load_custom_lists_modelHMM(self):
custom_state_list = ['sleeping', 'cooking']
custom_obs_list = [
'binary_sensor.motion_bed', 'binary_sensor.motion_mirror',
'binary_sensor.motion_pc', 'switch.test_switch_1',
'light.test_light'
]
self.ctrl.set_custom_state_list(custom_state_list)
self.ctrl.set_custom_obs_list(custom_obs_list)
self.ctrl.load_dataset()
hmm_model = ModelHMM_log_scaled(self.ctrl)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
def test_bench_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = ModelHMM_log_scaled(self.ctrl)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print(self.ctrl._model)
self.ctrl.register_benchmark()
self.ctrl.train_model()
print(self.ctrl._model)
report = self.ctrl.create_report(conf_matrix=True,
accuracy=True,
precision=True,
recall=True,
f1=True)
print(report)
def setUp(self):
# set of observations
self.ctrl = Controller()
self.ctrl.set_dataset(Dataset.HASS_TESTING)
self.hass_obj = self.ctrl._dataset #type: DatasetHomeassistant
self.hmm_model = ModelHMM_log_scaled(self.ctrl)