def main_ACTION():
st.SESSION_CUT = 2
print("***Computing training features")
st.CASE = 'training'
rd.process_files(st.CASE)
print('***Evaluating on the test set')
st.CASE = 'test'
rd.process_files(st.CASE)
return
def main_ACTION_SEQUENCE():
st.SESSION_CUT = 1
st.CASE = 'training'
print("***Computing training features")
rd.process_files(st.CASE)
print('***Evaluating on the test set')
st.CASE = 'test'
rd.process_files(st.CASE)
tc.NUM_NEGATIVE_SAMPLES_PER_CLASS = 70
tc.NUM_POSITIVE_SAMPLES = 630
# in case of this modeling unit (sequence of actions), from each test session we extract exactly one feature vector
tc.evaluate_test_actions2(st.TRAINING_FEATURE_FILENAME, st.TEST_FEATURE_FILENAME)
return
def main_ACTION():
st.SESSION_CUT = 2
print("***Computing training features")
st.CASE = 'training'
rd.process_files( st.CASE )
print('***Evaluating on the test set')
st.CASE = 'test'
rd.process_files(st.CASE)
print('EVAL_TEST_UNIT: '+str(st.EVAL_TEST_UNIT))
tc.NUM_NEGATIVE_SAMPLES_PER_CLASS = 200
tc.NUM_POSITIVE_SAMPLES = 1800
if st.EVAL_TEST_UNIT == 0:
# evaluation: all actions from a session
tc.evaluate_test_session(st.TRAINING_FEATURE_FILENAME, st.TEST_FEATURE_FILENAME)
else:
# evaluation: action by action
tc.evaluate_test_actions(st.TRAINING_FEATURE_FILENAME, st.TEST_FEATURE_FILENAME,
st.NUM_EVAL_ACTIONS)
return
