def simulate_dataset(model_params, T, dim, num_obs_samples):
transition_log_scale = model_params['transition_log_scale']
init_prior = ([0.0] * dim, [math.log(1.0)] * dim)
model = LearningDynamicsModel(init_prior, transition_log_scale, dim=dim)
num_obs = 50
y, x, z_true = model.sample(T=T, num_obs_samples=num_obs_samples)
return (y, x, z_true)
def simulate_datasets(model_params, model_params_grad, dim, num_obs_samples,
num_datasets):
# instantiate model
model = LearningDynamicsModel(model_params, model_params_grad, dim=dim)
num_obs = 50
datasets = []
for i in range(num_datasets):
y, x, z_true = model.sample(T=T,
num_obs_samples=num_obs_samples,
dim=dim)
datasets.append((y, x, z_true))
return datasets
inference_types = ['map', 'mfvi', 'is', 'smc', 'vsmc']
inference_type = inference_types[4]
# T = 200 # 100
T = 100
num_particles = 20# 200
# time-series model
# sim model parameters
dim = 3
init_prior = ([0.0]*dim, [math.log(0.1)]*dim)
transition_scale = [math.log(0.1)] * dim
beta = 4. # sigmoid(4.) = .9820
log_alpha = -1.
model = LearningDynamicsModel(init_prior, transition_scale, beta, log_alpha, dim=3)
#model = LogReg_LDS(init_prior=(0.0, 0.02), transition_scale=1e-3)
num_obs_samples = 2
y, x, z_true = model.sample(T=T, num_obs_samples=num_obs_samples)
plt.plot(to_numpy(z_true))
# plt.show()
# model params
init_prior = ([0.0]*dim, [math.log(0.1)]*dim)
transition_scale = [math.log(0.1)] * dim
model = LearningDynamicsModel(init_prior, transition_scale, beta, dim=3)
# proposal params
smc_init_prior = ([0.0]*dim, [math.log(0.1)]*dim)
smc_transition_scale = [math.log(0.1)] * dim
q_init_latent_loc = torch.tensor([smc_init_prior[0]],
requires_grad=False, device=device)
q_init_latent_log_scale = torch.tensor([smc_init_prior[1]],
requires_grad=False, device=device)
q_transition_log_scale = torch.tensor([smc_transition_scale],
-5 * torch.ones(T - 1, dtype=dtype, device=device),
requires_grad=True,
dtype=dtype,
device=device)
torch.save(model_params,
output_file + '/model_structs/true_model_params.pth')
plt.cla()
plt.plot(np.exp(to_numpy(log_alpha)))
plt.savefig(output_file + '/plots/sim_alpha.png')
# plt.show()
# embed()
y, x, z_true = model.sample(T=T,
model_params=model_params,
num_obs_samples=num_obs_samples,
dim=dim,
x=None,
switching=False,
alpha_time_dep=True)
rw = torch.mean(model.rat_reward_vec(y, x), dim=1)
y = y.detach().cpu().numpy()
x = x.detach().cpu().numpy()
z_true = z_true.detach().cpu().numpy()
plt.cla()
plt.plot(z_true, alpha=1.)
plt.savefig(output_file + '/plots/sim_z.png')
else:
num_obs_samples = 1
dtype=dtype,
device=device,
requires_grad=model_params_grad['beta']),
'log_alpha':
torch.tensor([math.log(.05)],
dtype=dtype,
device=device,
requires_grad=model_params_grad['log_alpha'])
}
torch.save(model_params,
output_file + '/model_structs/true_model_params.pth')
model = LearningDynamicsModel(dim=dim)
num_obs_samples = 1
y, x, z_true = model.sample(T=T,
model_params=model_params,
num_obs_samples=num_obs_samples,
dim=dim)
rw = torch.mean(model.rat_reward_vec(y, x), dim=1)
y = y.detach().cpu().numpy()
x = x.detach().cpu().numpy()
z_true = z_true.detach().cpu().numpy()
plt.cla()
plt.plot(z_true, alpha=1.)
plt.savefig(output_file + '/plots/sim_z.png')
else:
num_obs_samples = 1
#f = '/tigress/fdamani/neuro_data/data/clean/LearningData_W066_minmaxnorm.txt'