class TestHomeassistant(unittest.TestCase):
def setUp(self):
# set of observations
self.ctrl = Controller()
self.ctrl.set_dataset(Dataset.HASS)
self.ctrl.load_dataset()
self.hass_obj = self.ctrl._dataset #type: DatasetHomeassistant
# algo = self.get_sel_algorithm()
# dataset = self.get_dataset_by_name(dataset_name)
# ctrl, dk, hmm_model = self._init_model_on_dataset(algo, dataset)
# ctrl.set_dataset(dk)
#
# if dataset_name == DATASET_NAME_HASS:
# # get activities
# act_list = []
# for act in Activity.objects.all():
# act_list.append(act.name)
# dev_list = []
# for dev in Device.objects.all():
# if dev.location is not None:
# dev_list.append(dev.component.name + "." + dev.name)
# print('*'*100)
# print(act_list)
# print(dev_list)
# print('*'*100)
# ctrl.set_custom_state_list(act_list)
# ctrl.set_custom_obs_list(dev_list)
#
# ctrl.load_dataset()
# ctrl.register_model(hmm_model)
def tearDown(self):
pass
def test_load_data(self):
pass
def test_print_hass_df(self):
df = self.hass_obj._df
print(DatasetHomeassistant.format_mat_full(df))
def test_hashmaps(self):
print(self.hass_obj.get_state_lbl_hashmap())
print(self.hass_obj.get_obs_lbl_hashmap())
def test_get_train_seqs(self):
tr_seqs = self.hass_obj.get_train_seq()
print(tr_seqs)
print(self.hass_obj.decode_obs_seq(tr_seqs))
def test_get_test_seq(self):
test_seqs = self.hass_obj._test_seqs
print(test_seqs)
lbl_seqs, obs_seqs = self.hass_obj.get_test_labels_and_seq()
print('-' * 20)
print('lbl_seqs: ', lbl_seqs)
print('obs_seqs: ', obs_seqs)
def _create_ctrl_for_normal_dataset(self, algorithm, dataset):
"""
generates an instance of controller and model
:param algorithm:
:param dataset:
:return:
"""
from hassbrain_algorithm.controller import Controller
ctrl = Controller(settings.HASSBRAIN_ALGO_CONFIG) # type: Controller
dk = self.train_get_ds_type_by_name(dataset.class_name)
ctrl.set_dataset(dk)
model_object = self._create_class_from_name(ctrl, algorithm)
ctrl.load_dataset()
ctrl.register_model(model_object)
return ctrl
def main():
dk = DK
data_name = DATA_NAME
# set of observations
ctrl_config = {
'datasets': {
'kasteren': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/kasteren/config.yaml'
},
'hass_testing': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_testing/config.yaml'
},
'hass_testing2': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_testing2/config.yaml'
},
'hass_chris': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_chris/config.yaml'
},
'hass_chris_final': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_chris_final/config.yaml'
}
}
}
ctrl = Controller(config=ctrl_config)
params = {
'repr': DATA_TYPE,
'data_format': 'bernoulli',
'test_selection': TEST_SEL,
'test_day': TEST_DAY,
'freq': TIME_DIFF,
'include_idle': False
}
ctrl.set_dataset(data_name=data_name, data_type=dk, params=params)
dataset = ctrl._dataset # type: _Dataset
join_two_seperate_databses(dataset)
load_rest(dataset)
#act_stats = dataset.get_act_stats()
#dev_stats = dataset.get_dev_stats()
ctrl.save_dataset(DATASET_FILE_PATH)
#print('filepath: ', DATASET_FILE_PATH)
print('*' * 10)
def _create_dataset(self, datainstance):
db_path = self._create_hass_db_path()
activity_file_path = self._create_activity_file_path(person)
ctrl = Controller(path_to_config=settings.HASSBRAIN_ALGO_CONFIG,
config={'datasets': {
datainstance.name: db_path
}})
params = {
'repr': DataRep(datainstance.data_rep),
'data_format': 'bernoulli',
'test_selection': datainstance.test_sel,
'freq': datainstance.timeslicelength
}
ctrl.set_dataset(data_name=datainstance.name,
data_type=Datasettype.HASS,
params=params)
def main():
dk = DK
data_name = DATA_NAME
# set of observations
ctrl_config = {
'datasets': {
'kasteren': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/kasteren/config.yaml'
},
'hass_testing': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_testing/config.yaml'
},
'hass_testing2': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_testing2/config.yaml'
},
'hass_chris': {
'path_to_config':
'/home/cmeier/code/data/hassbrain/datasets/hass_chris/config.yaml'
}
}
}
ctrl = Controller(config=ctrl_config)
params = {
'repr': DATA_TYPE,
'data_format': 'bernoulli',
'test_selection': TEST_SEL,
'test_day': TEST_DAY,
'freq': TIME_DIFF
}
ctrl.set_dataset(data_name=data_name, data_type=dk, params=params)
ctrl.load_dataset()
tmp1 = ctrl._dataset.get_dev_stats()
tmp2 = ctrl._dataset.get_act_stats()
#ctrl.save_dataset(DATASET_FILE_PATH)
#print('filepath: ', DATASET_FILE_PATH)
print('*' * 10)
class TestHomeassistantModelHMMLogScaled(unittest.TestCase):
# Model part
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 = HMMForward(self.ctrl)
def tearDown(self):
pass
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'
]
hmm_model = self.hmm_model
self.ctrl.set_custom_state_list(custom_state_list)
self.ctrl.set_custom_obs_list(custom_obs_list)
self.ctrl.load_dataset()
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_load_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
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 = self.hmm_model
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_bench_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
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 test_classify(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
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 = self.hmm_model
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)
print(act_state_dict)
#print(act_state_dict)
#print(hmm_model.get_state_label_list())
def test_pred_next_obs_single(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
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)
print(tupel)
def test_pred_next_obs_multi(self):
hmm_model = self.hmm_model
self.ctrl.load_dataset()
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)
class TestController(unittest.TestCase):
def setUp(self):
# set of observations
self.ctrl = Controller() # type: Controller
#self.hmm_model = ModelHMM(self.ctrl)
#self.hmm_model = ModelHMM_scaled(self.ctrl)
#self.hmm_model = ModelHMM_log(self.ctrl)
self.hmm_model = ModelHMM_log_scaled(self.ctrl)
def tearDown(self):
pass
def test_ctrl_get_bench_metrics(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
self.ctrl.register
# render
#dot = self.ctrl.render_model()
#dot.render('test.gv', view=True)
self.ctrl.train_model(True)
print(self.ctrl.get_bench_metrics())
def test_ctrl_train_seqs(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.set_dataset(dk)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
# render
#dot = self.ctrl.render_model()
#dot.render('test.gv', view=True)
self.ctrl.train_model()
report = self.ctrl.create_report(conf_matrix=True,
accuracy=True,
precision=True,
recall=True,
f1=True)
print(self.ctrl._model)
print(report)
#self.ctrl.show_plot()
def test_ctrl_presentation(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
# render
#dot = self.ctrl.render_model()
#dot.render('test.gv', view=True)
self.ctrl.train_model(True)
report = self.ctrl.create_report(conf_matrix=True,
accuracy=True,
precision=True,
recall=True,
f1=True)
print(self.ctrl._model)
print(report)
#self.ctrl.show_plot()
# render
#dot = self.ctrl.render_model()
#dot.render('test.gv', view=True)
def test_generate_visualization(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.set_dataset(dk)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
print('state_label_hm: ', hmm_model._state_lbl_hashmap)
print('state_label_rev_hm: ', hmm_model._state_lbl_rev_hashmap)
self.ctrl.save_visualization_to_file(
'/home/cmeier/code/tmp/visualization.png')
def test_bench_train_loss(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.set_dataset(dk)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
self.ctrl.register_loss_file_path(
'/home/cmeier/code/tmp/kasteren/train_loss.log')
self.ctrl.train_model()
self.ctrl.save_model(
'/home/cmeier/code/tmp/kasteren/kasteren_model.joblib')
def test_bench_reports_conf_matrix(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
self.ctrl.train_model()
report = self.ctrl.create_report(conf_matrix=True)
print(report)
def test_bench_reports(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
self.ctrl.train_model()
report = self.ctrl.create_report(conf_matrix=True,
accuracy=True,
precision=True,
recall=True,
f1=True)
print(report)
#self.ctrl.show_plot()
def test_bench_q_fct(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
self.ctrl.register_benchmark()
print(self.ctrl._model)
# use dataset Kasteren and q_fct
self.ctrl.train_model(True)
report = self.ctrl.create_report()
print(self.ctrl._model)
print(report)
#self.ctrl.show_plot()
def test_om(self):
#plt.figure(figsize=(10,6))
#self.pom.plot()
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
dot = self.ctrl.render_model()
dot.render('test.gv', view=True)
def test_train_model(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.set_dataset(dk)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
#print(hmm_model._hmm)
self.ctrl.train_model()
print('#' * 200)
print(hmm_model)
hmm_model._hmm.set_str_exp(True)
hmm_model._hmm.set_format_full(True)
print(hmm_model)
#print(hmm_model._hmm.verify_transition_matrix())
#print(hmm_model._hmm.verify_emission_matrix())
#self._bench._model.draw()
#self.ctrl.register_benchmark()
#report = self.ctrl.create_report(True, True, True, True, True)
#print(report)
#self._bench.show_plot()
def test_save_model(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.load_dataset(dk)
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
self.ctrl.train_model()
self.ctrl.save_model()
def test_init_model(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.set_dataset(dk)
self.ctrl.load_dataset()
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_load_model(self):
hmm_model = self.hmm_model
dk = Dataset.KASTEREN
self.ctrl.set_dataset(dk)
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.load_model()
print(self.ctrl._model)
class TestHomeassistantModelHMMLogScaled(unittest.TestCase):
# Model part
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 = PreConfHMM(self.ctrl)
def tearDown(self):
pass
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'
]
hmm_model = self.hmm_model
self.ctrl.set_custom_state_list(custom_state_list)
self.ctrl.set_custom_obs_list(custom_obs_list)
self.ctrl.load_dataset()
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_load_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
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 = self.hmm_model
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_bench_modelHMM(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
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 test_classify(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
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 = self.hmm_model
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)
print(act_state_dict)
#print(act_state_dict)
#print(hmm_model.get_state_label_list())
def test_pred_next_obs_single(self):
self.ctrl.load_dataset()
hmm_model = self.hmm_model
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)
print(tupel)
def test_pred_next_obs_multi(self):
hmm_model = self.hmm_model
self.ctrl.load_dataset()
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_encode_loc_data(self):
loc_data = [ {
"name" : "loc1",
"activities" : ['cooking'],
"devices" : ['binary_sensor.motion_hallway', 'binary_sensor.motion_mirror'],
},
{"name" : "loc2",
"activities" : ['cooking', 'eating'],
"devices" : [],
},
{"name" : "loc3",
"activities" : ['sleeping'],
"devices" : ['binary_sensor.motion_bed'],
},
]
hmm_model = self.hmm_model
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
hmm_model._hmm.set_format_full(True)
#print('state_hm: ', hmm_model._state_lbl_hashmap)
#print('obs_hm: ', hmm_model._obs_lbl_hashmap)
#print('raw_loc_data: \t' + str(loc_data))
enc_loc_data = hmm_model._encode_location_data(loc_data)
#print('#'*100)
#print('enc_loc_data: \t' + str(enc_loc_data))
def test_encode_act_data(self):
act_data = [
{"name" : "cooking",
"day_of_week" : 2,
"start" : datetime.time.fromisoformat("06:15:00"),
"end" : datetime.time.fromisoformat("08:45:00")
},
{"name" : "eating",
"day_of_week" : 1,
"start" : datetime.time.fromisoformat("06:15:00"),
"end" : datetime.time.fromisoformat("08:45:00")
},
{"name" : "eating",
"day_of_week" : 1,
"start" : datetime.time.fromisoformat("08:46:00"),
"end" : datetime.time.fromisoformat("10:00:00")
},
]
hmm_model = self.hmm_model
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.init_model_on_dataset()
print('raw_act_data: \t' + str(act_data))
print('state_hm: ', hmm_model._state_lbl_hashmap)
print('obs_hm: ', hmm_model._obs_lbl_hashmap)
print('#'*100)
enc_act_data = hmm_model._encode_act_data(act_data)
print('enc_act_data: \t' + str(enc_act_data))
def test_init(self):
loc_data = [ {
"name" : "loc1",
"activities" : ['cooking'],
"devices" : ['binary_sensor.motion_hallway', 'binary_sensor.motion_mirror'],
},
{"name" : "loc2",
"activities" : ['cooking', 'eating'],
"devices" : [],
},
{"name" : "loc3",
"activities" : ['sleeping'],
"devices" : ['binary_sensor.motion_bed'],
},
]
act_data = [
{"name" : "sleeping",
"day_of_week" : 0,
"start" : datetime.time.fromisoformat("04:00:00"),
"end" : datetime.time.fromisoformat("06:15:00")
},
{"name" : "cooking",
"day_of_week" : 0,
"start" : datetime.time.fromisoformat("06:15:00"),
"end" : datetime.time.fromisoformat("08:45:00")
},
{"name" : "eating",
"day_of_week" : 0,
"start" : datetime.time.fromisoformat("08:46:00"),
"end" : datetime.time.fromisoformat("10:00:00")
},
{"name" : "sleeping",
"day_of_week" : 0,
"start" : datetime.time.fromisoformat("12:00:00"),
"end" : datetime.time.fromisoformat("13:00:00")
},
{"name" : "sleeping",
"day_of_week" : 1,
"start" : datetime.time.fromisoformat("02:00:00"),
"end" : datetime.time.fromisoformat("06:30:00")
},
{"name" : "cooking",
"day_of_week" : 1,
"start" : datetime.time.fromisoformat("12:00:00"),
"end" : datetime.time.fromisoformat("13:00:00")
},
{"name" : "cooking",
"day_of_week" : 2,
"start" : datetime.time.fromisoformat("19:00:00"),
"end" : datetime.time.fromisoformat("00:00:00")
},
{"name" : "cooking",
"day_of_week" : 2,
"start" : datetime.time.fromisoformat("23:00:00"),
"end" : datetime.time.fromisoformat("00:00:00")
},
{"name" : "sleeping",
"day_of_week" : 2,
"start" : datetime.time.fromisoformat("00:00:00"),
"end" : datetime.time.fromisoformat("03:00:00")
},
]
hmm_model = self.hmm_model
self.ctrl.load_dataset()
self.ctrl.register_model(hmm_model)
self.ctrl.register_location_info(loc_data)
print('raw_act_data: \t' + str(act_data))
print('state_hm: ', hmm_model._state_lbl_hashmap)
#print('obs_hm: ', hmm_model._obs_lbl_hashmap)
self.ctrl.register_activity_info(act_data)
#print('#'*100)
#enc_act_data = hmm_model._encode_act_data(act_data)
#print('enc_act_data: \t' + str(enc_act_data))
self.ctrl.init_model_on_dataset()
hmm = hmm_model._hmm
hmm.set_format_full(True)
self.assertAlmostEqual(1.0, Probs.prob_to_norm(hmm._pi.sum()), 6)
self.assertTrue(hmm.verify_emission_matrix())
self.assertTrue(hmm.verify_transition_matrix())