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FIX ensure y_init is C-contiguous #164

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Merged
merged 1 commit into from
Sep 4, 2025
Merged

FIX ensure y_init is C-contiguous #164

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3 changes: 2 additions & 1 deletion fastcan/narx/_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -205,7 +205,7 @@ def fit(self, X, y, sample_weight=None, coef_init=None, **params):
"""
none_inputs = False
if X is None: # Auto-regressive model
X = np.empty((1, 0), dtype=float) # Skip validation
X = np.empty((1, 0), dtype=float, order="C") # Skip validation
none_inputs = True
check_X_params = dict(
dtype=float, order="C", ensure_all_finite="allow-nan", ensure_min_features=0
Expand Down Expand Up @@ -572,6 +572,7 @@ def predict(self, X, y_init=None):
y_init,
ensure_2d=False,
dtype=float,
order="C",
ensure_min_samples=0,
ensure_all_finite="allow-nan",
)
Expand Down
116 changes: 62 additions & 54 deletions fastcan/narx/tests/test_narx.py
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Expand Up @@ -43,60 +43,68 @@ def test_time_ids():
@pytest.mark.parametrize("nan", [False, True])
def test_narx(nan, multi_output):
"""Test NARX"""
if multi_output:
rng = np.random.default_rng(12345)
n_samples = 1000
max_delay = 3
e0 = rng.normal(0, 0.1, n_samples)
e1 = rng.normal(0, 0.02, n_samples)
u0 = rng.uniform(0, 1, n_samples + max_delay)
u1 = rng.normal(0, 0.1, n_samples + max_delay)
y0 = np.zeros(n_samples + max_delay)
y1 = np.zeros(n_samples + max_delay)
for i in range(max_delay, n_samples + max_delay):
y0[i] = (
0.5 * y0[i - 1]
+ 0.8 * y1[i - 1]
+ 0.3 * u0[i] ** 2
+ 2 * u0[i - 1] * u0[i - 3]
+ 1.5 * u0[i - 2] * u1[i - 3]
+ 1
)
y1[i] = (
0.6 * y1[i - 1]
- 0.2 * y0[i - 1] * y1[i - 2]
+ 0.3 * u1[i] ** 2
+ 1.5 * u1[i - 2] * u0[i - 3]
+ 0.5
)
y = np.c_[y0[max_delay:] + e0, y1[max_delay:] + e1]
X = np.c_[u0[max_delay:], u1[max_delay:]]
n_outputs = 2
else:
rng = np.random.default_rng(12345)
n_samples = 1000
max_delay = 3
e = rng.normal(0, 0.1, n_samples)
u0 = rng.uniform(0, 1, n_samples + max_delay)
u1 = rng.normal(0, 0.1, n_samples)
y = np.zeros(n_samples + max_delay)
for i in range(max_delay, n_samples + max_delay):
y[i] = (
0.5 * y[i - 1]
+ 0.3 * u0[i] ** 2
+ 2 * u0[i - 1] * u0[i - 3]
+ 1.5 * u0[i - 2] * u1[i - max_delay]
+ 1
)
y = y[max_delay:] + e
X = np.c_[u0[max_delay:], u1]
n_outputs = 1

if nan:
X_nan_ids = rng.choice(n_samples, 20, replace=False)
y_nan_ids = rng.choice(n_samples, 10, replace=False)
X[X_nan_ids] = np.nan
y[y_nan_ids] = np.nan
def make_data(multi_output, nan, rng):
if multi_output:
n_samples = 1000
max_delay = 3
e0 = rng.normal(0, 0.1, n_samples)
e1 = rng.normal(0, 0.02, n_samples)
u0 = rng.uniform(0, 1, n_samples + max_delay)
u1 = rng.normal(0, 0.1, n_samples + max_delay)
y0 = np.zeros(n_samples + max_delay)
y1 = np.zeros(n_samples + max_delay)
for i in range(max_delay, n_samples + max_delay):
y0[i] = (
0.5 * y0[i - 1]
+ 0.8 * y1[i - 1]
+ 0.3 * u0[i] ** 2
+ 2 * u0[i - 1] * u0[i - 3]
+ 1.5 * u0[i - 2] * u1[i - 3]
+ 1
)
y1[i] = (
0.6 * y1[i - 1]
- 0.2 * y0[i - 1] * y1[i - 2]
+ 0.3 * u1[i] ** 2
+ 1.5 * u1[i - 2] * u0[i - 3]
+ 0.5
)
y = np.c_[y0[max_delay:] + e0, y1[max_delay:] + e1]
X = np.c_[u0[max_delay:], u1[max_delay:]]
n_outputs = 2
else:
rng = np.random.default_rng(12345)
n_samples = 1000
max_delay = 3
e = rng.normal(0, 0.1, n_samples)
u0 = rng.uniform(0, 1, n_samples + max_delay)
u1 = rng.normal(0, 0.1, n_samples)
y = np.zeros(n_samples + max_delay)
for i in range(max_delay, n_samples + max_delay):
y[i] = (
0.5 * y[i - 1]
+ 0.3 * u0[i] ** 2
+ 2 * u0[i - 1] * u0[i - 3]
+ 1.5 * u0[i - 2] * u1[i - max_delay]
+ 1
)
y = y[max_delay:] + e
X = np.c_[u0[max_delay:], u1]
n_outputs = 1

if nan:
X_nan_ids = rng.choice(n_samples, 20, replace=False)
y_nan_ids = rng.choice(n_samples, 10, replace=False)
X[X_nan_ids] = np.nan
y[y_nan_ids] = np.nan

X = np.asfortranarray(X)
y = np.asfortranarray(y)
return X, y, n_outputs

rng = np.random.default_rng(12345)
X, y, n_outputs = make_data(multi_output, nan, rng)

if multi_output:
narx_score = make_narx(
Expand Down Expand Up @@ -178,7 +186,7 @@ def test_narx(nan, multi_output):
assert np.any(narx_osa_msa_coef != narx_array_init_msa.coef_)

if multi_output:
y_init = np.ones((narx_array_init_msa.max_delay_, n_outputs))
y_init = np.ones((narx_array_init_msa.max_delay_, n_outputs), order="F")
else:
y_init = [1] * narx_array_init_msa.max_delay_
y_hat = narx_array_init_msa.predict(X, y_init=y_init)
Expand Down
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