def test_check_model(data, ss):
reg = Regressor(ss=ss)
summary, corr = reg.fit(df=data, outputs='T_int',
inputs=['T_ext', 'P_hea'])
assert not check_model(reg, summary, corr, verbose=True)
dict(name="sigw_w", value=0.1, transform="log"),
dict(name="sigw_i", value=1e-8, transform="fixed"),
dict(name="sigv", value=0.1, transform="log"),
dict(name="x0_w", value=0.28, transform="log"),
dict(name="x0_i", value=y0, transform="fixed"),
dict(name="sigx0_w", value=1.0, transform="fixed"),
dict(name="sigx0_i", value=1.0, transform="fixed")
]
reg = Regressor(TwTi_RoRiAwAi(parameters, hold_order='foh'))
inputs = [
'Ambient temperature', 'Solar radiation: global horizontal', 'south_H'
]
outputs = 'south_T'
out = reg.fit(df=df[start_fit:stop_fit], inputs=inputs, outputs=outputs)
# compute log-likelihood on validation dataset
x, P = reg.estimate_states(df=df[start_fit:stop_fit],
inputs=inputs,
outputs=outputs)
x0v = x[-1, :, :]
P0v = np.linalg.cholesky(P[-1, :, :]).T
reg.eval_log_likelihood(df=df[start_valid:stop_valid],
inputs=inputs,
outputs=outputs,
x0=x0v,
P0=P0v)
transform="lowup",
bounds=(1e-10, 1.0))
reg.ss.parameters.set_parameter("sigw_i", value=0.1,
transform="lowup",
bounds=(1e-10, 1.0))
reg.ss.parameters.set_parameter("sigv", value=0.1,
transform="lowup",
bounds=(1e-10, 1.0))
reg.ss.parameters.set_parameter("x0_w", value=0.28,
transform="lowup",
bounds=(1e-7, 0.6))
reg.ss.parameters.set_parameter("x0_i", value=y0, transform="fixed")
reg.ss.parameters.set_parameter("sigx0_w", value=1.0, transform="fixed")
reg.ss.parameters.set_parameter("sigx0_i", value=1.0, transform="fixed")
out = reg.fit(t, y, u, 'foh')
# compute log-likelihood on validation dataset
yp = df['south_T'][start_valid:stop_valid].values[np.newaxis, :]
y0p = y[0, -1] / 100
tp = np.arange(0, dt * yp.shape[1], dt)
reg.ss.parameters.set_parameter("x0_i", value=y0p, transform="fixed")
up = df[['Ambient temperature',
'north_T',
'Solar radiation: global horizontal',
'south_H',
'vent2_H']][start_valid:stop_valid].values.T
reg.eval_log_likelihood(tp, yp, up, 'foh')
reg.ss.parameters.set_parameter("x0_i", value=y0, transform="fixed")
parameters = [
dict(name="Ro", value=0.1, transform="log"),
dict(name="Ri", value=0.01, transform="log"),
dict(name="Cw", value=0.1, transform="log"),
dict(name="Ci", value=0.01, transform="log"),
dict(name="sigw_w", value=0.01, transform="log"),
dict(name="sigw_i", value=0.0, transform="fixed"),
dict(name="sigv", value=0.01, transform="log"),
dict(name="x0_w", value=0.25, transform="log"),
dict(name="x0_i", value=y0, transform="fixed"),
dict(name="sigx0_w", value=1.0, transform="fixed"),
dict(name="sigx0_i", value=1.0, transform="fixed")
]
reg = Regressor(TwTi_RoRi(parameters, hold_order='foh'))
out = reg.fit(df=df, inputs=['T_ext', 'P_hea'], outputs='T_int')
dt = 60
tnew = np.arange(df.index[0], df.index[-1] + dt, dt)
y_mean_f, y_std_f = reg.predict(df=df, inputs=['T_ext', 'P_hea'], tpred=tnew)
y_mean_s, y_std_s = reg.predict(df=df,
outputs='T_int',
inputs=['T_ext', 'P_hea'],
tpred=tnew,
smooth=True)
# plot filtered and smoothed output
plt.close("all")
fig, axes = plt.subplots(nrows=1, ncols=1, sharex=True, sharey=True)
fig.suptitle("filtered vs. smoothed state ")
df = pd.DataFrame(index=t, data=y, columns=['y'])
parameters = [
dict(name="mscale", value=0.5, transform="log"),
dict(name="lscale", value=0.5, transform="log"),
dict(name="sigv", value=0.1, transform="log")
]
reg12 = Regressor(Matern12(parameters))
reg32 = Regressor(Matern32(parameters))
reg52 = Regressor(Matern52(parameters))
# fit
print("\nMatérn(1/2)")
print("-" * 11)
reg12.fit(df=df, outputs='y')
print("\nMatérn(3/2)")
print("-" * 11)
reg32.fit(df=df, outputs='y')
print("\nMatérn(5/2)")
print("-" * 11)
reg52.fit(df=df, outputs='y')
# new data
Nnew = 500
tnew = np.linspace(-0.1, 1.1, Nnew)
# interpolate
ym12, yd12 = reg12.predict(df=df, outputs='y', tpred=tnew, smooth=True)
df = pd.read_csv('../data/armadillo/armadillo_data_H2.csv').set_index('Time')
y0 = 0.2670106194217502
parameters = [
dict(name="Ro", value=0.1, bounds=(1e-6, 0.3), prior=Normal(0.1, 0.04)),
dict(name="Ri", value=0.01, bounds=(1e-6, 0.3), prior=Normal(0.05, 0.02)),
dict(name="Cw", value=0.1, bounds=(1e-6, 0.3), prior=Normal(0.1, 0.04)),
dict(name="Ci", value=0.01, bounds=(1e-6, 0.3), prior=Normal(0.05, 0.02)),
dict(name="sigw_w",
value=0.01,
bounds=(1e-6, 0.3),
prior=Normal(0.05, 0.02)),
dict(name="sigw_i", value=0.0, transform="fixed"),
dict(name="sigv", value=0.01, bounds=(1e-6, 0.3), prior=Normal(0.05,
0.02)),
dict(name="x0_w", value=0.25, bounds=(0.1, 0.5), prior=Normal(0.25, 0.07)),
dict(name="x0_i", value=y0, transform="fixed"),
dict(name="sigx0_w", value=1.0, transform="fixed"),
dict(name="sigx0_i", value=1.0, transform="fixed")
]
reg = Regressor(TwTi_RoRi(parameters, hold_order='foh'))
mh = reg.fit(df=df,
inputs=['T_ext', 'P_hea'],
outputs='T_int',
options={
'method': 'sMMALA',
'n_chains': 6,
'n_samples': 2000
})
N = 20
t = np.sort(np.random.rand(1, N), axis=1).flatten()
y = np.sin(12 * t) + 0.66 * np.cos(25 * t) + np.random.randn(1, N) * 0.01
y = y.flatten()
df = pd.DataFrame(index=t, data=y, columns=['y'])
# Set Matern(3/2)
parameters = [
dict(name="mscale", value=0.5, transform="log"),
dict(name="lscale", value=0.5, transform="log"),
dict(name="sigv", value=0.1, transform="log")
]
reg = Regressor(Matern32(parameters))
results = reg.fit(df=df, outputs='y')
# new data
Nnew = 500
tnew = np.linspace(-0.1, 1.1, Nnew)
ym_f, ys_f = reg.predict(df=df, outputs='y', tpred=tnew, smooth=False)
ym_s, ys_s = reg.predict(df=df, outputs='y', tpred=tnew, smooth=True)
# plot filtered and smoothed output
plt.close("all")
fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True, sharey=True)
fig.suptitle("filtered vs. smoothed state ")
plt.xlim(-0.1, 1.1)
axes[0].set_title("filtered")
axes[0].plot(t, y, 'kx', mew=2)
dict(name="period", value=1.5, transform="log"),
dict(name="mscale", value=1.0, transform="log"),
dict(name="lscale", value=1.0, transform="log"),
dict(name="sigv", value=0.1, transform="log")
]
par_Matern12 = [
dict(name="mscale", value=1.0, transform="log"),
dict(name="lscale", value=10.0, transform="log"),
dict(name="sigv", value=0.1, transform="log")
]
quasi_periodic = GPProduct(Periodic(par_Periodic), Matern12(par_Matern12))
reg = Regressor(quasi_periodic)
results = reg.fit(df=data, outputs='y')
Nnew = 500
tnew = np.linspace(xlim[0], xlim[1] + 3, Nnew)
y_mean_f, y_std_f = reg.predict(df=data, outputs='y', tpred=tnew, smooth=False)
y_mean_s, y_std_s = reg.predict(df=data, outputs='y', tpred=tnew, smooth=True)
# plot filtered and smoothed output
plt.close("all")
fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True, sharey=False)
fig.suptitle("filtered vs. smoothed state ")
axes[0].set_title("filtered")
axes[0].plot(t, y, 'kx', mew=2)
axes[0].plot(tnew, y_mean_f, 'C0', lw=2)
axes[0].fill_between(tnew,