filt,
tau,
nbclust,
sigma,
homeinv,
jitter,
timestr,
dataset,
R,
ds_ev=ds_ev_output)
print(f'LR fit for {name}...')
model, loss = fit_data(name_net,
learn_set,
nb_train,
nb_pola,
learning_rate,
num_epochs,
betas,
num_workers=num_workers,
verbose=True)
print(f'get testing set for {name}...')
test_set, nb_pola, name_net = get_loader(name,
record_path,
nb_test,
False,
filt,
tau,
nbclust,
sigma,
homeinv,
jitter,
nb_train = 36
nb_test = 40
jitonic = None
tau_cla = 50000
namelist = ['raw', 'homhots']
namelist = ['raw', 'hots', 'homhots']
for name in namelist:
f_name = f'{record_path}{timestr}_LR_results_{name}_{nbclust}_36_40_{ds_ev}.pkl'
if os.path.isfile(f_name):
with open(f_name, 'rb') as file:
likelihood, true_target, time_scale = pickle.load(file)
else:
print(f'LR fit for {name}...')
model, loss = fit_data(name,timestr,record_path,filt,tau,R,nbclust,sigma,homeinv,jitter,dataset,nb_train, ds_ev,learning_rate,num_epochs,betas,tau_cla,jitonic=jitonic,num_workers=num_workers,verbose=False)
print(f'prediction for {name}...')
likelihood, true_target, time_scale = predict_data(model,name,timestr,record_path,filt,tau,R,nbclust,sigma, homeinv, jitter,dataset,nb_test,ds_ev,tau_cla,jitonic=jitonic,num_workers=num_workers, verbose=False)
with open(f_name, 'wb') as file:
pickle.dump([likelihood, true_target, time_scale], file, pickle.HIGHEST_PROTOCOL)
timesteps = np.arange(500,100000,100)
nb_classes = 10
results = [timesteps]
for namnum, name in enumerate(namelist):
f_name = f'{record_path}{timestr}_LR_results_{name}_{nbclust}_{nb_train}_{nb_test}_{ds_ev}_timescale.pkl'
with open(f_name, 'rb') as file:
likelihood, true_target, time_scale = pickle.load(file)
with open(f_name, 'rb') as file:
likelihood, true_target, time_scale = pickle.load(file)
else:
ds_ev = ds_ev_train
print(f'LR fit for {name}...')
model, loss = fit_data(name,
timestr,
path,
filt,
tau,
R,
nbclust,
sigma,
homeinv,
jitter,
dataset,
nb_train,
ds_ev,
learning_rate,
num_epochs,
betas,
tau_cla,
sample_space=sample_space,
jitonic=jitonic,
subset_size=nb_train,
num_workers=num_workers,
verbose=False)
ds_ev = ds_ev_test
print(f'prediction for {name}...')
likelihood, true_target, time_scale = predict_data(
model,
name,