def ex_310():
y, x = datasets.table_33()
discount_factors = np.arange(0.60, 1.01, 0.05)
rows, columns = 3, 3
rmse = []
mad = []
like = []
var_est = []
pred_var = []
fig, axes = plt.subplots(3, 3, sharey=True, figsize=(12, 12))
for i, disc in enumerate(discount_factors):
model = dlm.DLM(y, x, m0=m0, C0=C0, n0=n0, s0=s0,
state_discount=disc)
rmse.append(model.rmse)
mad.append(model.mad)
like.append(model.pred_like)
var_est.append(model.var_est[-1])
# model.var_est[-1] + model.mu_scale[-1] / disc
pred_var.append(model.forc_var[-1])
ax = axes[i / rows][i % rows]
# plot posterior
model.plot_mu(prior=False, ax=ax)
ax.set_title(str(disc))
ax.set_ylim([0.4, 0.48])
like = np.array(like).prod(axis=1)
llr = np.log(like / like[-1])
fig = plt.figure(figsize=(12, 12))
ax1 = fig.add_subplot(311)
ax1.plot(discount_factors, rmse, label='RMSE')
ax1.plot(discount_factors, mad, label='MAD')
ax2 = fig.add_subplot(312)
ax2.plot(discount_factors, llr, label='LLR')
ax1.legend()
ax2.legend()
# plot s_42 and q_42
ax3 = fig.add_subplot(313)
ax3.plot(discount_factors, var_est, label='S')
ax3.plot(discount_factors, pred_var, label='Q')
ax3.legend()
import numpy as np
import matplotlib.pyplot as plt
import statlib.dlm as dlm
reload(dlm)
from statlib.dlm import DLM, st
import statlib.datasets as datasets
lam = 1.
phis = np.arange(0.95, 1.05, .001)
disc = 0.8
y, x = datasets.table_33()
X = np.vstack((np.ones(len(x)),x )).T
m0, C0 = ([0, 0.45], [[0.005, 0],
[0, 0.0025]])
n0, s0 = (1, 1)
G = np.diag([lam, phis[0]])
like = []
for phi in phis:
G = np.diag([lam, phi])
model = DLM(y, X, G=G, m0=m0, C0=C0, n0=n0, s0=s0,
state_discount=disc)
like.append(model.pred_like)
like = np.array(like).prod(axis=1)
