empirical_covariance implementation (sklearn)

ivy/functional/frontends/sklearn/covariance/__init__.py

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+from . import _empirical_covariance
+from ._empirical_covariance import *

ivy/functional/frontends/sklearn/covariance/_empirical_covariance.py

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+import ivy
+from ivy.functional.frontends.numpy import to_ivy_arrays_and_back
+
+@to_ivy_arrays_and_back
+def empirical_covariance(X, *, assume_centered = False):
+    if X.ndim == 1:
+        X = ivy.reshape(X, (1, -1))
+
+    if assume_centered:
+        covariance = ivy.dot(X.T, X) / X.shape[0]
+    else:
+        covariance = ivy.cov(X.T, bias = 1)
+
+    if covariance.ndim == 0:
+        covariance = ivy.array([[covariance]])
+
+    if ivy.is_complex_dtype(X):
+        return covariance.astype(ivy.complex128)
+
+    return covariance.astype(ivy.float64)

ivy_tests/test_ivy/test_frontends/test_sklearn/test_covariance/test_empirical_covariance.py

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+import ivy_tests.test_ivy.helpers as helpers
+from ivy_tests.test_ivy.helpers import handle_frontend_test
+from hypothesis import strategies as st
+
+@handle_frontend_test(
+    fn_tree="sklearn.covariance.empirical_covariance",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes= helpers.get_dtypes("valid"),
+        min_num_dims=1,
+        max_num_dims=2,
+        min_dim_size=1,
+        max_dim_size=3,
+    ),
+    assume_centered = st.booleans()
+)
+def test_sklearn_empirical_covariance(
+    dtype_and_x,
+    on_device,
+    assume_centered,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    dtypes, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=dtypes,
+        backend_to_test=backend_fw,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        frontend=frontend,
+        on_device=on_device,
+        X=x[0],
+        assume_centered=assume_centered
+    )