E2,
models,
order,
prior_model_prob,
m0=m0,
C0=C0,
n0=n0,
s0=d0 / n0,
approx_steps=1)
return multi
if __name__ == '__main__':
y = datasets.table_22()
x = [[1]]
m0, C0 = (0, 1)
n0, s0 = (1, 0.01)
discounts = np.arange(0.7, 1.01, 0.1)
models = {}
for delta in discounts:
models['%.2f' % delta] = DLM(y,
x,
m0=m0,
C0=C0,
n0=n0,
s0=s0,
state_discount=delta)
# priors
m0 = np.array([600., 10.])
C0 = np.diag([10000., 25.])
n0, d0 = 10., 1440.
multi = MultiProcessDLM(cp6, E2, models, order,
prior_model_prob,
m0=m0, C0=C0, n0=n0, s0=d0 / n0,
approx_steps=1)
return multi
if __name__ == '__main__':
y = datasets.table_22()
x = [[1]]
m0, C0 = (0, 1)
n0, s0 = (1, 0.01)
discounts = np.arange(0.7, 1.01, 0.1)
models = {}
for delta in discounts:
models['%.2f' % delta] = DLM(y, x, m0=m0, C0=C0, n0=n0, s0=s0,
state_discount=delta)
# G = tools.jordan_form(2)
# cp6 = datasets.table_111()
# x = [1, 0]
# m0, C0 = ([0, 0.45], [[0.005, 0],
# [0, 0.0025]])
def ex_21():
y = datasets.table_22()
x = [1]
m0, C0 = (0, 1)
n0, s0 = (1, 0.01)
discount_factors = np.arange(0.6, 1.01, 0.05)
fig = plt.figure(figsize=(12, 12))
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312)
ax3 = fig.add_subplot(313)
rng = y.index
ax1.plot(rng, np.asarray(y), 'b')
rmse = []
mad = []
like = []
y_pred = []
V = []
mu = []
for disc in discount_factors:
model = dlm.DLM(y, x, m0=m0, C0=C0, n0=n0, s0=s0,
state_discount=disc)
ax1.plot(rng, model.forecast, '%.2f' % (1 - disc))
rmse.append(model.rmse)
mad.append(model.mad)
like.append(model.pred_like)
# level and error bars for y_116, mu_115, V
# mu_115
level, lower, upper = model.mu_ci(prior=False)
mu.append((level[-1], lower[-1], upper[-1]))
# var_est final
phi_a = model.df[-1] / 2.
phi_b = model.var_est[-1] * phi_a
sampler = lambda sz: 1 / np.random.gamma(phi_a, scale=1./phi_b, size=sz)
lower, upper = boot_ci(sampler)
V.append((model.var_est[-1], lower, upper))
# y_116
dist = stats.t(model.df[-1], loc=model.mu_mode[-1, 0],
scale=model.mu_scale[-1, 0] / disc + model.var_est[-1])
lower, upper = boot_ci(dist.rvs)
y_pred.append((model.mu_mode[-1], lower, upper))
like = np.array(like).prod(axis=1)
llr = np.log(like / like[-1])
ax2.plot(discount_factors, rmse, label='RMSE')
ax2.plot(discount_factors, mad, label='MAD')
ax3.plot(discount_factors, llr, label='LLR')
ax2.legend()
ax3.legend()
fig = plt.figure(figsize=(12, 12))
ax1 = fig.add_subplot(311)
level, lower, upper = zip(*mu)
ax1.plot(discount_factors, level, 'k', label=r'$\mu_{115}$')
ax1.plot(discount_factors, lower, 'k--')
ax1.plot(discount_factors, upper, 'k--')
ax1.legend()
ax2 = fig.add_subplot(312)
level, lower, upper = zip(*V)
ax2.plot(discount_factors, level, 'k', label=r'$V$')
ax2.plot(discount_factors, lower, 'k--')
ax2.plot(discount_factors, upper, 'k--')
ax2.legend()
ax3 = fig.add_subplot(313)
level, lower, upper = zip(*y_pred)
ax3.plot(discount_factors, level, 'k', label=r'$y_{116}$')
ax3.plot(discount_factors, lower, 'k--')
ax3.plot(discount_factors, upper, 'k--')
ax3.legend()
