"train": File.open_with_label("Beef_TRAIN"),
"test": File.open_with_label("Beef_TEST"),
"fn": two_sym_fn,
},
{
"title": "Beef.P=2.asym",
"train": File.open_with_label("Beef_TRAIN"),
"test": File.open_with_label("Beef_TEST"),
"fn": two_asym_fn,
},
]
for i, job in enumerate(jobs):
print("job:", i, "of", len(jobs))
print("job:", job["title"])
result = {"title": job["title"]}
start_time = time.process_time()
accuracy, correctness = DTW.predict_list(job["train"], job["test"], job["fn"])
end_time = time.process_time()
result["accuracy"] = accuracy
result["correctness"] = correctness
result["total"] = len(job["test"])
result["time_elapsed"] = end_time - start_time
File.write_json(job["title"] + ".json", result)
abc = [a, b, c]
zeros = abc.count(0)
if zeros == 3:
continue
if zeros == 2:
if b == 0:
continue
print('c:', c)
start_time = time.process_time()
accuracy, correctness = DTW.predict_list(job['train'], job['test'], [a, b, c], [ [-1, 0], [0, -1], [-1, -1] ])
print('[', a, b, c, ']', 'accuracy:', accuracy, 'correctness:', correctness)
end_time = time.process_time()
time_elasped = end_time - start_time
print('time_elapsed:', time_elasped)
result = {
'a': a,
'b': b,
'c': c,
'accuracy': accuracy,
'correctness': correctness,
'total': len(job['test']),
'time_elapsed': time_elasped
