def save_to_ml_json(write_path):
# dictionary to store
data = {"zcr": [], "ste": [], "classification": []}
# load data
X, y, mapping = load_data(DATA_READ)
with open(DATA_WRITE, 'w', newline='') as f:
fieldnames = ['ste', 'zcr', 'classification']
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
for (samples, c) in zip(X, y):
# create object
f = Frame(samples, c)
f.calculate_frame_parameters()
# write to csv
writer.writerow({'ste': f.ste, 'zcr': f.zcr, 'classification': c})
print("process...")
def calibrate_param(data_path):
# load data
X, y, mapping = load_data(data_path)
ste = 0
zcr = 0
# calculate ste and zcr
for (samples, c) in zip(X, y):
f = Frame(samples, c)
f.calculate_frame_parameters()
ste += f.ste
zcr += f.zcr
# avarage results to return
ste_avarage = ste / len(X)
zcr_avarage = zcr / len(X)
return ste_avarage, zcr_avarage
