remove pytest from gaussian.py

.gitignore

@@ -130,3 +130,6 @@ benchmarks/.asv
 
 # Cargo file
 Cargo.lock
+
+# WASM
+/*.html

river/proba/gaussian.py

@@ -4,7 +4,6 @@
 
 import numpy as np
 import pandas as pd
-import pytest
 from scipy.stats import multivariate_normal
 
 from river import covariance, stats
@@ -313,29 +312,3 @@ def sample(self) -> dict[str, float]:
     @property
     def mode(self) -> dict:
         return self.mu
-
-
-@pytest.mark.parametrize(
-    "p",
-    [
-        pytest.param(
-            p,
-            id=f"{p=}",
-        )
-        for p in [1, 3, 5]
-    ],
-)
-def test_univariate_multivariate_consistency(p):
-    X = pd.DataFrame(np.random.random((30, p)), columns=range(p))
-
-    multi = MultivariateGaussian()
-    single = {c: Gaussian() for c in X.columns}
-
-    for x in X.to_dict(orient="records"):
-        multi = multi.update(x)
-        for c, s in single.items():
-            s.update(x[c])
-
-    for c in X.columns:
-        assert math.isclose(multi.mu[c], single[c].mu)
-        assert math.isclose(multi.sigma[c][c], single[c].sigma)

river/proba/test_gaussian.py

@@ -0,0 +1,31 @@
+import pytest
+import pandas as pd
+import numpy as np
+import math
+from river import proba
+
+
+@pytest.mark.parametrize(
+    "p",
+    [
+        pytest.param(
+            p,
+            id=f"{p=}",
+        )
+        for p in [1, 3, 5]
+    ],
+)
+def test_univariate_multivariate_consistency(p):
+    X = pd.DataFrame(np.random.random((30, p)), columns=range(p))
+
+    multi = proba.MultivariateGaussian()
+    single = {c: proba.Gaussian() for c in X.columns}
+
+    for x in X.to_dict(orient="records"):
+        multi = multi.update(x)
+        for c, s in single.items():
+            s.update(x[c])
+
+    for c in X.columns:
+        assert math.isclose(multi.mu[c], single[c].mu)
+        assert math.isclose(multi.sigma[c][c], single[c].sigma)