method = 'VI'
lr = 0.5
vb_samples = run_inference(Y, ProteinGenModel, pr_ode_model, method, \
iterations = args.iterations, num_samples = args.num_qsamples, \
lr = lr, num_particles = 1, return_sites = ("ode_params","scale","_RETURN"))
vb_params = \
np.concatenate((vb_samples['ode_params'].detach().numpy().reshape((args.num_qsamples,6)), \
vb_samples['scale'].detach().numpy().reshape((args.num_qsamples,1))),axis=1)
plot_marginals(vb_params,
mc_params,
param_names,
real_params=real_params)
else:
print('Using VJP by Adjoint Sensitivity')
pr_ode_model = AdjointSensManualJacobians(rhs_f, jac_x_f, jac_p_f, 5, 6, \
times, 1e-5, 1e-6, [1,0,1,0,0])
method = 'NUTS'
NUTS_samples = run_inference(Y, ProteinGenModel, pr_ode_model, method, \
iterations = args.num_samples, warmup_steps = args.warmup_steps)
mc_params = np.concatenate(
(NUTS_samples['ode_params'], NUTS_samples['scale'][:, None]),
axis=1)
method = 'VI'
lr = 0.5
vb_samples = run_inference(Y, ProteinGenModel, pr_ode_model, method, \
iterations = args.iterations, num_samples = args.num_qsamples, \
lr = lr, num_particles = 1, return_sites = ("ode_params","scale","_RETURN"))
vb_params = \
np.concatenate((vb_samples['ode_params'].detach().numpy().reshape((args.num_qsamples,6)), \
print('Using VJP by Forward Sensitivity')
lna_ode_model = ForwardSensManualJacobians(rhs_f, jac_x_f, jac_p_f, 6, 3, \
times, 1e-5, 1e-6, [100, 100, 0, 0 ,0 ,0])
method = 'VI'
lr = 0.5
vb_samples = run_inference(Y, LNAGenModel, lna_ode_model, method, iterations=args.iterations, \
lr = lr, num_particles = 1, num_samples = args.num_qsamples, \
return_sites = ("ode_params1","ode_params2","ode_params3"))
vb_params_for = np.concatenate(
(vb_samples['ode_params1'][:, None].detach().numpy(),
vb_samples['ode_params2'][:, None].detach().numpy(),
vb_samples['ode_params3'][:, None].detach().numpy()),
axis=1)
print('Using VJP by Adjoint Sensitivity')
lna_ode_model = AdjointSensManualJacobians(rhs_f, jac_x_f, jac_p_f, 6, 3, \
times, 1e-5, 1e-6, [100, 100, 0, 0 ,0 ,0])
vb_samples = run_inference(Y, LNAGenModel, lna_ode_model, method, iterations=args.iterations, \
lr = lr, num_particles = 1, num_samples = args.num_qsamples, \
return_sites = ("ode_params1","ode_params2","ode_params3"))
vb_params_adj = np.concatenate(
(vb_samples['ode_params1'][:, None].detach().numpy(),
vb_samples['ode_params2'][:, None].detach().numpy(),
vb_samples['ode_params3'][:, None].detach().numpy()),
axis=1)
plot_marginals(vb_params_for,
vb_params_adj,
param_names,
real_params=real_params,
rows=2)
method = 'VI'
lr = 0.5
vb_samples = run_inference(Y, SIRGenModel, sir_ode_model, method, \
iterations=args.iterations, num_samples=args.num_qsamples, \
lr=lr, num_particles=1, return_sites=("ode_params1", "ode_params2", "ode_params3"))
vb_params = np.concatenate(
(vb_samples['ode_params1'][:, None].detach().numpy(),
vb_samples['ode_params2'][:, None].detach().numpy(),
vb_samples['ode_params3'][:, None].detach().numpy()),
axis=1)
# plot_marginals(vb_params, mc_params, param_names, rows=2)
plot_marginals(vb_params, vb_params, param_names, rows=2)
else:
print('Using VJP by Adjoint Sensitivity')
sir_ode_model = AdjointSensManualJacobians(rhs_f, jac_x_f, jac_p_f, 3, 5, \
times, 1e-5, 1e-6, [0.9, 0.1, 0.0])
sir_ode_model.set_unknown_y0()
# method = 'NUTS'
# NUTS_samples = run_inference(Y, SIRGenModel, sir_ode_model, method, \
# iterations = args.num_samples, warmup_steps = args.warmup_steps)
# mc_params=np.concatenate((NUTS_samples['ode_params1'][:,None],
# NUTS_samples['ode_params2'][:,None],
# NUTS_samples['ode_params3'][:,None]
# ),axis=1)
method = 'VI'
lr = 0.5
vb_samples = run_inference(Y, SIRGenModel, sir_ode_model, method, \
iterations=args.iterations, num_samples=args.num_qsamples, \
lr=lr, num_particles=1, return_sites=("ode_params1", "ode_params2", "ode_params3"))
vb_params = np.concatenate(
iterations=args.iterations,
num_samples=args.num_qsamples,
lr=lr,
num_particles=num_particles,
return_sites=("ode_params1", "ode_params2"))
vb_params = np.concatenate(
(vb_samples['ode_params1'][:, None].detach().numpy(),
vb_samples['ode_params2'][:, None].detach().numpy()),
axis=1)
# plot_marginals(vb_params, mc_params, param_names, rows=2)
plot_marginals(vb_params, vb_params, param_names, rows=2)
else:
print('Using VJP by Adjoint Sensitivity')
plant_ode_model = AdjointSensManualJacobians(rhs_f, jac_x_f, jac_p_f,
2, 2, times, 1e-5, 1e-6,
[0.237939, 0.021049])
# plant_ode_model.set_unknown_y0()
# method = 'NUTS'
# NUTS_samples = run_inference(Y, SIRGenModel, sir_ode_model, method, \
# iterations = args.num_samples, warmup_steps = args.warmup_steps)
# mc_params=np.concatenate((NUTS_samples['ode_params1'][:,None],
# NUTS_samples['ode_params2'][:,None],
# NUTS_samples['ode_params3'][:,None]
# ),axis=1)
method = 'VI'
lr = 0.5
num_particles = 1
vb_samples = run_inference(Y,