Add tests for bias keyword and usm_ndarray input

Author: antonwolfy

dpnp/dpnp_utils/dpnp_utils_statistics.py

@@ -128,34 +128,18 @@ def dpnp_cov(
 
     """
 
-    def _get_2dmin_array(x):
-        """
-        Transform an input array to a form required for building a covariance matrix.
-
-        If applicable, it reshapes the input array to have 2 dimensions or greater.
-        If applicable, it transposes the input array when 'rowvar' is False.
-        It casts to another dtype, if the input array differs from requested one.
-
-        """
-        if x.ndim == 0:
-            x = dpnp.reshape(x, (1, 1))
-        elif x.ndim == 1:
-            x = x[dpnp.newaxis, :]
-        elif not rowvar:
-            x = x.T
-
-        if x.dtype != dtype:
-            x = dpnp.astype(x, dtype)
-        return x
-
-    x = _get_2dmin_array(m)
+    # need to create a copy of input, since it will be modified in-place
+    x = dpnp.array(m, ndmin=2, dtype=dtype)
+    if not rowvar and m.ndim != 1:
+        x = x.T
+
     if x.shape[0] == 0:
         return dpnp.empty_like(
             x, shape=(0, 0), dtype=dpnp.default_float_type(m.sycl_queue)
         )
 
     if y is not None:
-        y = _get_2dmin_array(y)
+        y = dpnp.array(y, copy=None, ndmin=2, dtype=dtype)
         x = dpnp.concatenate((x, y), axis=0)
 
     # get the product of frequencies and weights

dpnp/tests/test_statistics.py

@@ -532,6 +532,45 @@ def test_dtype(self, dt):
         assert_allclose(result, expected)
         assert result.dtype == dt
 
+    @pytest.mark.parametrize("dt", get_float_complex_dtypes())
+    @pytest.mark.parametrize("bias", [True, False])
+    def test_bias(self, dt, bias):
+        a = generate_random_numpy_array((3, 4), dtype=dt)
+        ia = dpnp.array(a)
+
+        expected = numpy.cov(a, bias=bias)
+        result = dpnp.cov(ia, bias=bias)
+        assert_dtype_allclose(result, expected)
+
+        # with rowvar
+        expected = numpy.cov(a, rowvar=False, bias=bias)
+        result = dpnp.cov(ia, rowvar=False, bias=bias)
+        assert_dtype_allclose(result, expected)
+
+        freq = numpy.array([1, 4, 1, 7])
+        ifreq = dpnp.array(freq)
+
+        # with frequency
+        expected = numpy.cov(a, bias=bias, fweights=freq)
+        result = dpnp.cov(ia, bias=bias, fweights=ifreq)
+        assert_dtype_allclose(result, expected)
+
+        weights = numpy.array([1.2, 3.7, 5.0, 1.1])
+        iweights = dpnp.array(weights)
+
+        # with weights
+        expected = numpy.cov(a, bias=bias, aweights=weights)
+        result = dpnp.cov(ia, bias=bias, aweights=iweights)
+        assert_dtype_allclose(result, expected)
+
+    def test_usm_ndarray(self):
+        a = numpy.array([[0, 2], [1, 1], [2, 0]])
+        ia = dpt.asarray(a)
+
+        expected = numpy.cov(a.T)
+        result = dpnp.cov(ia.T)
+        assert_allclose(result, expected)
+
     # numpy 2.2 properly transposes 2d array when rowvar=False
     @with_requires("numpy>=2.2")
     @pytest.mark.filterwarnings("ignore::RuntimeWarning")