def generate(m, n, dsizes, tsize, file_tager):
start_time = time.time()
# Create Hidden Markov Model randomly.
# m --- The number of hidden states
# n --- The number of observation states
model = HMM(m, n)
# Generate training set
for dsize in dsizes:
training_seq = model.generate_train_data(dsize)
training_filename = "train_" + file_tager + ("_%d.data" % dsize)
np.savetxt(training_filename, [training_seq], delimiter=",", fmt="%d")
# Generate test set
test_seqs_F = model.generate_test_data(tsize, min_seq_len=4, max_seq_len=5)
test_seqs_V = model.generate_test_data(tsize, min_seq_len=3, max_seq_len=50)
test_seqs_F_filename = "test_" + file_tager + "_F.data"
test_seqs_V_filename = "test_" + file_tager + "_V.data"
with file(test_seqs_F_filename, "wb") as fout:
writer = csv.writer(fout)
for seq in test_seqs_F:
writer.writerow(seq)
with file(test_seqs_V_filename, "wb") as fout:
writer = csv.writer(fout)
for seq in test_seqs_V:
writer.writerow(seq)
end_time = time.time()
pprint("Time used: %f seconds" % (end_time-start_time))
# Save model to file
model_filename = "model_" + file_tager + ".npy"
HMM.to_file(model_filename, model)
def generate(m, n, dsizes, tsize, file_tager):
start_time = time.time()
# Create Hidden Markov Model randomly.
# m --- The number of hidden states
# n --- The number of observation states
model = HMM(m, n)
# Generate training set
for dsize in dsizes:
training_seq = model.generate_train_data(dsize)
training_filename = "train_" + file_tager + ("_%d.data" % dsize)
np.savetxt(training_filename, [training_seq], delimiter=",", fmt="%d")
# Generate test set
test_seqs_F = model.generate_test_data(tsize, min_seq_len=4, max_seq_len=5)
test_seqs_V = model.generate_test_data(tsize,
min_seq_len=3,
max_seq_len=50)
test_seqs_F_filename = "test_" + file_tager + "_F.data"
test_seqs_V_filename = "test_" + file_tager + "_V.data"
with file(test_seqs_F_filename, "wb") as fout:
writer = csv.writer(fout)
for seq in test_seqs_F:
writer.writerow(seq)
with file(test_seqs_V_filename, "wb") as fout:
writer = csv.writer(fout)
for seq in test_seqs_V:
writer.writerow(seq)
end_time = time.time()
pprint("Time used: %f seconds" % (end_time - start_time))
# Save model to file
model_filename = "model_" + file_tager + ".npy"
HMM.to_file(model_filename, model)
