Cov

ivy/functional/frontends/paddle/tensor/tensor.py

@@ -753,6 +753,17 @@ def trace(self, offset=0, axis1=0, axis2=1, name=None):
             ivy.trace(self._ivy_array, offset=offset, axis1=axis1, axis2=axis2)
         )
 
+    @with_supported_dtypes({"2.5.1 and below": ("float64", "float32")}, "paddle")
+    def cov(self, rowvar=True, ddof=True, fweights=None, aweights=None):
+        return paddle_frontend.Tensor(
+            ivy.cov(
+                self._ivy_array,
+                rowVar=rowvar,
+                ddof=int(ddof),
+                fweights=fweights,
+                aweights=aweights,
+            )
+
     @with_supported_dtypes(
         {"2.5.1 and below": ("float32", "float64", "int16", "int32", "int64", "uint8")},
         "paddle",

ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py

@@ -198,6 +198,94 @@ def _get_dtype_and_values_for_lerp(draw):
         return input_dtype, x[0], x[1], weight
 
 
+@st.composite
+def _get_dtype_value1_value2_cov(
+    draw,
+    available_dtypes,
+    min_num_dims,
+    max_num_dims,
+    min_dim_size,
+    max_dim_size,
+    abs_smallest_val=None,
+    min_value=None,
+    max_value=None,
+    allow_inf=False,
+    exclude_min=False,
+    exclude_max=False,
+    large_abs_safety_factor=4,
+    small_abs_safety_factor=4,
+    safety_factor_scale="log",
+):
+    shape = draw(
+        helpers.get_shape(
+            allow_none=False,
+            min_num_dims=min_num_dims,
+            max_num_dims=max_num_dims,
+            min_dim_size=min_dim_size,
+            max_dim_size=max_dim_size,
+        )
+    )
+
+    dtype = draw(st.sampled_from(draw(available_dtypes)))
+
+    values = []
+    for i in range(2):
+        values.append(
+            draw(
+                helpers.array_values(
+                    dtype=dtype,
+                    shape=shape,
+                    abs_smallest_val=abs_smallest_val,
+                    min_value=min_value,
+                    max_value=max_value,
+                    allow_inf=allow_inf,
+                    exclude_min=exclude_min,
+                    exclude_max=exclude_max,
+                    large_abs_safety_factor=large_abs_safety_factor,
+                    small_abs_safety_factor=small_abs_safety_factor,
+                    safety_factor_scale=safety_factor_scale,
+                )
+            )
+        )
+
+    value = values[0]
+
+    # modifiers: rowVar, bias, ddof
+    rowVar = draw(st.booleans())
+    ddof = draw(st.booleans())
+
+    numVals = None
+    if rowVar is False:
+        numVals = -1 if numVals == 0 else 0
+    else:
+        numVals = 0 if len(shape) == 1 else -1
+
+    fweights = draw(
+        helpers.array_values(
+            dtype="int64",
+            shape=shape[numVals],
+            abs_smallest_val=1,
+            min_value=1,
+            max_value=10,
+            allow_inf=False,
+        )
+    )
+
+    aweights = draw(
+        helpers.array_values(
+            dtype="float64",
+            shape=shape[numVals],
+            abs_smallest_val=1,
+            min_value=1,
+            max_value=10,
+            allow_inf=False,
+            small_abs_safety_factor=1,
+        )
+    )
+
+    return [dtype], value, rowVar, ddof, fweights, aweights
+
+
 @st.composite
 def _reshape_helper(draw):
     # generate a shape s.t len(shape) > 0
@@ -1609,6 +1697,55 @@ def test_paddle_cosh(
     )
 
 
+# cov
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="paddle.to_tensor",
+    method_name="cov",
+    dtype_x1_corr_cov=_get_dtype_value1_value2_cov(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_num_dims=2,
+        max_num_dims=2,
+        min_dim_size=2,
+        max_dim_size=5,
+        min_value=1,
+        max_value=1e10,
+        abs_smallest_val=0.01,
+        large_abs_safety_factor=2,
+        safety_factor_scale="log",
+    ),
+)
+def test_paddle_tensor_cov(
+    dtype_x1_corr_cov,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    backend_fw,
+    on_device,
+):
+    dtype, x, rowvar, ddof, fweights, aweights = dtype_x1_corr_cov
+    helpers.test_frontend_method(
+        init_input_dtypes=["float64", "int64", "float64"],
+        init_all_as_kwargs_np={
+            "data": x[0],
+        },
+        method_input_dtypes=["float64", "int64", "float64"],
+        backend_to_test=backend_fw,
+        method_all_as_kwargs_np={
+            "rowvar": rowvar,
+            "ddof": ddof,
+            "fweights": fweights,
+            "aweights": aweights,
+        },
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+    )
+
+
 @handle_frontend_method(
     class_tree=CLASS_TREE,
     init_tree="paddle.to_tensor",