Added aesara.tensor.full_like equivalent to np.full_like (#567)

aesara/tensor/basic.py

@@ -1613,6 +1613,24 @@ def full(shape, fill_value, dtype=None):
     return alloc(fill_value, *shape)
 
 
+def full_like(
+    a: TensorVariable,
+    fill_value: Union[TensorVariable, int, float],
+    dtype: Union[str, np.generic, np.dtype] = None,
+) -> TensorVariable:
+    """Equivalent of `numpy.full_like`.
+
+    Returns
+    -------
+    tensor
+        tensor the shape of `a` containing `fill_value` of the type of dtype.
+    """
+    fill_value = as_tensor_variable(fill_value)
+    if dtype is not None:
+        fill_value = fill_value.astype(dtype)
+    return fill(a, fill_value)
+
+
 class MakeVector(COp):
     """Concatenate a number of scalars together into a vector.
 
@@ -4482,6 +4500,7 @@ def take_along_axis(arr, indices, axis=0):
     "as_tensor",
     "extract_diag",
     "full",
+    "full_like",
     "empty",
     "empty_like",
 ]

tests/tensor/test_basic.py

@@ -55,6 +55,7 @@
     fill,
     flatnonzero,
     flatten,
+    full_like,
     get_scalar_constant_value,
     get_vector_length,
     horizontal_stack,
@@ -4216,3 +4217,20 @@ def test_ndim_dtype_failures(self):
         indices = aet.tensor(np.float64, [False] * 2)
         with pytest.raises(IndexError):
             aet.take_along_axis(arr, indices)
+
+
+@pytest.mark.parametrize(
+    "inp, shape",
+    [(scalar, ()), (vector, 3), (matrix, (3, 4))],
+)
+def test_full_like(inp, shape):
+    fill_value = 5
+    dtype = config.floatX
+
+    x = inp("x")
+    y = full_like(x, fill_value, dtype=dtype)
+
+    np.testing.assert_array_equal(
+        y.eval({x: np.zeros(shape, dtype=dtype)}),
+        np.full(shape, fill_value, dtype=dtype),
+    )