def test_model_histories_do_not_matter(self):
n1 = 5
n2 = 8
usage_seq = np.hstack((np.ones(n2, dtype=int), np.zeros(n1,
dtype=int)))
seqs = []
for i in range(2):
if i == 0:
ic1 = ([0.3], [-0.2, 0.2])
ic2 = ([0.2, 0.1], [-0.1, 0.2, 0.0])
else:
ic1 = None
ic2 = None
arma1 = Arma(
[0.9],
[0.1, -0.2],
default_source=sources.GaussianNoise(),
initial_conditions=ic1,
)
arma2 = Arma(
[0.1, -0.2],
[0.3, 0.4, 0.5],
default_source=sources.GaussianNoise(),
initial_conditions=ic2,
)
seq = sample_switching_models([arma1, arma2], usage_seq)
seqs.append(seq)
np.testing.assert_allclose(seqs[0], seqs[1])
def test_second_model_history_is_not_default(self):
n1 = 13
n2 = 23
# these will get overridden
seq = [0]
seq1_exp = [1]
seq2_exp = [2]
for i in range(2):
arma1 = Arma([0.9], [0.1, -0.2],
default_source=sources.GaussianNoise())
arma2 = Arma([0.1, -0.2], [0.3],
default_source=sources.GaussianNoise())
if i == 0:
seq1_exp = arma1.transform(n1)
seq2_exp = arma2.transform(n2)
else:
seq = sample_switching_models(
[arma1, arma2],
np.hstack((np.zeros(n1, dtype=int), np.ones(n2,
dtype=int))),
)
np.testing.assert_allclose(seq[:n1], seq1_exp)
self.assertGreater(np.max(np.abs(seq[n1:] - seq2_exp)), 0)
def test_default_initial_conditions_are_zero(self):
n1 = 5
n2 = 8
usage_seq = np.hstack((np.ones(n2, dtype=int), np.zeros(n1,
dtype=int)))
seqs = []
for i in range(2):
if i == 0:
ic = ([0.0], [0.0, 0.0])
else:
ic = None
arma1 = Arma(
[0.9],
[0.1, -0.2],
default_source=sources.GaussianNoise(),
)
arma2 = Arma(
[0.1, -0.2],
[0.3, 0.4, 0.5],
default_source=sources.GaussianNoise(),
)
seq = sample_switching_models([arma1, arma2],
usage_seq,
initial_conditions=ic)
seqs.append(seq)
np.testing.assert_allclose(seqs[0], seqs[1])
def test_transform_first_return_value_matches_sample_switching_models(
self):
arma_hsmm = ArmaHSMM(self.armas)
n = 15
y, x, usage_seq = arma_hsmm.transform(n,
return_input=True,
return_usage_seq=True)
y_exp = sample_switching_models(self.armas, usage_seq, X=x)
np.testing.assert_allclose(y, y_exp)
def test_single_model(self):
n = 20
a = [0.9]
b = [0.1, -0.2]
arma1 = Arma(a, b, default_source=sources.GaussianNoise())
seq_exp = arma1.transform(n)
arma2 = Arma(a, b, default_source=sources.GaussianNoise())
seq = sample_switching_models([arma2], np.zeros(n, dtype=int))
np.testing.assert_allclose(seq, seq_exp)
def test_single_model(self):
n = 20
rng = np.random.default_rng(1)
X = rng.normal(size=n)
a = [0.9]
b = [0.1, -0.2]
arma = Arma(a, b)
_, X_ret = sample_switching_models([arma],
np.zeros(n, dtype=int),
X=X,
return_input=True)
np.testing.assert_allclose(X, X_ret)
def test_initial_conditions_obeyed(self):
arma_hsmm = ArmaHSMM(self.armas)
n = 15
ic = ([-0.3, 0.2, 0.8], [0.5, 0.7, 0.1])
y, x, usage_seq = arma_hsmm.transform(n,
initial_conditions=ic,
return_input=True,
return_usage_seq=True)
y_exp = sample_switching_models(self.armas,
usage_seq,
X=x,
initial_conditions=ic)
np.testing.assert_allclose(y, y_exp)
def test_multiple_models(self):
n1 = 13
n2 = 23
rng = np.random.default_rng(2)
X = rng.normal(size=n1 + n2)
arma1 = Arma([0.9], [0.1, -0.2])
arma2 = Arma([0.1, -0.2], [0.3])
_, X_ret = sample_switching_models(
[arma1, arma2],
np.hstack((np.zeros(n1, dtype=int), np.ones(n2, dtype=int))),
X=X,
return_input=True,
)
np.testing.assert_allclose(X, X_ret)
def test_multiple_models_callable_source(self):
n1 = 13
n2 = 23
seed = 12
src = sources.GaussianNoise(seed)
X_exp = src(size=n1 + n2)
arma1 = Arma([0.9], [0.1, -0.2])
arma2 = Arma([0.1, -0.2], [0.3])
_, X_ret = sample_switching_models(
[arma1, arma2],
np.hstack((np.zeros(n1, dtype=int), np.ones(n2, dtype=int))),
X=sources.GaussianNoise(seed),
return_input=True,
)
np.testing.assert_allclose(X_exp, X_ret)
def test_initial_conditions_parameter_is_obeyed(self):
a = [0.8]
b = [0.1, 0.2]
n = 32
ic = ([-0.5], [-0.5, 0.3])
arma = Arma(a,
b,
default_source=sources.GaussianNoise(),
initial_conditions=ic)
seq_exp = arma.transform(n)
arma = Arma(a, b, default_source=sources.GaussianNoise())
seq = sample_switching_models([arma],
np.zeros(n, dtype=int),
initial_conditions=ic)
np.testing.assert_allclose(seq, seq_exp)
def test_correct_history_used_at_switch_point(self):
n1 = 13
n2 = 5
arma1 = Arma([0.9], [0.1, -0.2],
default_source=sources.GaussianNoise())
a2 = [0.1, -0.2]
b2 = [0.3]
arma2 = Arma(a2, b2, default_source=sources.GaussianNoise())
seq, X = sample_switching_models(
[arma1, arma2],
np.hstack((np.zeros(n1, dtype=int), np.ones(n2, dtype=int))),
return_input=True,
)
exp_ar = np.dot(np.flip(a2), seq[n1 - len(a2):n1])
exp_ma = np.dot(np.flip(b2), X[n1 - len(b2):n1]) + X[n1]
self.assertAlmostEqual(exp_ar + exp_ma, seq[n1])
arma2=Arma([0.6], [],
default_source=sources.GaussianNoise(scale=0.802, rng=rng)),
)
two_ar_short.armas = [two_ar_short.arma1, two_ar_short.arma2]
crt_samples1 = two_ar_short.arma1.transform(100000)
crt_samples2 = two_ar_short.arma2.transform(100000)
print("should be almost zero:",
np.std(crt_samples1) - 1,
np.std(crt_samples2) - 1)
two_ar_short.n_samples = 500
two_ar_short.usage_seq = np.zeros(two_ar_short.n_samples, dtype=int)
two_ar_short.usage_seq[150:390] = 1
two_ar_short.sig = sample_switching_models(two_ar_short.armas,
two_ar_short.usage_seq)
two_ar_short.transitions = np.cumsum(
[_[1] for _ in rle_encode(two_ar_short.usage_seq)])
# %%
with FigureManager(figsize=(8, 2), sharex=True, despine_kws={"left":
True}) as (
fig1,
ax1,
):
ax1.plot(two_ar_short.sig)
ax1.set_xlabel("sample")
ax1.set_xlim(0, two_ar_short.n_samples)
ax1.set_yticks([])
def test_raises_if_length_x_shorter_than_usage_seq(self):
with self.assertRaises(ValueError):
sample_switching_models([object()], [0, 0, 0], X=[0.0, 0.3])
def test_raises_if_invalid_model_indices(self):
with self.assertRaises(ValueError):
sample_switching_models([object()], [0, 1, 1])
def test_raises_if_no_models(self):
with self.assertRaises(ValueError):
sample_switching_models([], [])
