bounds=bounds,
options=args['options'])
self.baseline, self.magnitude, self.decay = map(float, opt_res.x)
return opt_res
if __name__ == "__main__":
process = ExpHawkes(0.5, 0.8, 1.0)
raw_data = process.generate_sequences(30, 100)
data = []
for idx in range(raw_data.shape[0]):
data.append(raw_data[idx])
dataset = GeneralDataset(data)
batch = collate_no_marks(dataset)
fit_process = ExpHawkes(2, 2, 2)
args = {
'x0': [0.3, 0.3, 0.5],
'method': 'L-BFGS-B',
'options': {
'disp': True,
'ftol': 1e-05,
'gtol': 1e-04
}
}
res = fit_process.fit(batch, args)
print(res)
model = model_gen(True)
num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('[Info] Number of parameters: {}'.format(num_params))
save_name = name + '_' + model_name
print('Start Processing:', save_name)
data = []
with open(save_file, 'r') as f:
reader = csv.reader(f)
for row in reader:
data.append(torch.Tensor(list(map(float, row))))
whole_dataset = GeneralDataset(data)
whole_dataset.log_transform_rnn()
train_dataset, validation_dataset, test_dataset = whole_dataset.train_val_test_split(
0.6, 0.2, 0.2, seed=1)
rnn_mean, rnn_std = train_dataset.rnn_statistics()
train_dataset.normalise_data(rnn_mean=rnn_mean, rnn_std=rnn_std)
validation_dataset.normalise_data(rnn_mean=rnn_mean, rnn_std=rnn_std)
test_dataset.normalise_data(rnn_mean=rnn_mean, rnn_std=rnn_std)
opt = torch.optim.Adam(model.parameters(), lr=LR, weight_decay=L2REG)
save_path = os.path.join(ROOT_SAVE_FOLDER, save_name)
valid_hist = model.train_model(train_dataset,
EPOCHS,
def intensity(self, events):
dataset = GeneralDataset([events])
batch = collate_no_marks(dataset)
return self.forward(batch)[0]