【PaddlePaddle Hackathon 4 NO.23】为 Paddle 新增 vander API

python/paddle/__init__.py

@@ -294,6 +294,7 @@
 from .tensor.math import frexp  # noqa: F401
 from .tensor.math import trapezoid  # noqa: F401
 from .tensor.math import cumulative_trapezoid  # noqa: F401
+from .tensor.math import vander  # noqa: F401
 
 from .tensor.random import bernoulli  # noqa: F401
 from .tensor.random import poisson  # noqa: F401
@@ -687,4 +688,5 @@
     'trapezoid',
     'cumulative_trapezoid',
     'polar',
+    'vander',
 ]

python/paddle/fluid/tests/unittests/test_vander.py

@@ -0,0 +1,100 @@
+#   Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+import paddle
+from paddle.fluid import core
+
+np.random.seed(10)
+
+
+def ref_vander(x, N=None, increasing=False):
+    return np.vander(x, N, increasing)
+
+
+class TestVanderAPI(unittest.TestCase):
+    # test paddle.tensor.math.vander
+
+    def setUp(self):
+        self.shape = [5]
+        self.x = np.random.uniform(-1, 1, self.shape).astype(np.float32)
+        self.place = (
+            paddle.CUDAPlace(0)
+            if core.is_compiled_with_cuda()
+            else paddle.CPUPlace()
+        )
+
+    def api_case(self, N=None, increasing=False):
+        paddle.enable_static()
+        out_ref = ref_vander(self.x, N, increasing)
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.static.data('X', self.shape)
+            out = paddle.vander(x, N, increasing)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={'X': self.x}, fetch_list=[out])
+        if N != 0:
+            np.testing.assert_allclose(res[0], out_ref, rtol=1e-05)
+        else:
+            np.testing.assert_allclose(res[0].size, out_ref.size, rtol=1e-05)
+
+        paddle.disable_static(self.place)
+        x = paddle.to_tensor(self.x)
+        out = paddle.vander(x, N, increasing)
+        np.testing.assert_allclose(out.numpy(), out_ref, rtol=1e-05)
+        paddle.enable_static()
+
+    def test_api(self):
+        self.api_case()
+        N = list(range(9))
+        for n in N:
+            self.api_case(n)
+            self.api_case(n, increasing=True)
+
+    def test_complex(self):
+        paddle.disable_static(self.place)
+        real = np.random.rand(5)
+        imag = np.random.rand(5)
+        complex_np = real + 1j * imag
+        complex_paddle = paddle.complex(
+            paddle.to_tensor(real), paddle.to_tensor(imag)
+        )
+
+        def test_api_case(N, increasing=False):
+            for n in N:
+                res_np = np.vander(complex_np, n, increasing)
+                res_paddle = paddle.vander(complex_paddle, n, increasing)
+                np.testing.assert_allclose(
+                    res_paddle.numpy(), res_np, rtol=1e-05
+                )
+
+        N = [0, 1, 2, 3, 4]
+        test_api_case(N)
+        test_api_case(N, increasing=True)
+        paddle.enable_static()
+
+    def test_errors(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program()):
+            self.assertRaises(TypeError, paddle.vander, 1)
+            x = paddle.static.data('X', [10, 12], 'int32')
+            self.assertRaises(ValueError, paddle.vander, x)
+            x1 = paddle.static.data('X1', [10], 'int32')
+            self.assertRaises(ValueError, paddle.vander, x1, n=-1)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -250,6 +250,7 @@
 from .math import cumulative_trapezoid  # noqa: F401
 from .math import sigmoid  # noqa: F401
 from .math import sigmoid_  # noqa: F401
+from .math import vander  # noqa: F401
 
 from .random import multinomial  # noqa: F401
 from .random import standard_normal  # noqa: F401
@@ -538,6 +539,7 @@
     'polar',
     'sigmoid',
     'sigmoid_',
+    'vander',
 ]
 
 # this list used in math_op_patch.py for magic_method bind

python/paddle/tensor/math.py

@@ -5333,3 +5333,79 @@ def cumulative_trapezoid(y, x=None, dx=None, axis=-1, name=None):
             #         [3.50000000, 8.        ]])
     """
     return _trapezoid(y, x, dx, axis, mode='cumsum')
+
+
+def vander(x, n=None, increasing=False, name=None):
+    """
+    Generate a Vandermonde matrix.
+
+    The columns of the output matrix are powers of the input vector. Order of the powers is
+    determined by the increasing Boolean parameter. Specifically, when the increment is
+    "false", the ith output column is a step-up in the order of the elements of the input
+    vector to the N - i - 1 power. Such a matrix with a geometric progression in each row
+    is named after Alexandre-Theophile Vandermonde.
+
+    Args:
+        x (Tensor): The input tensor, it must be 1-D Tensor, and it's data type should be ['complex64', 'complex128', 'float32', 'float64', 'int32', 'int64'].
+        n (int): Number of columns in the output. If n is not specified, a square array is returned (n = len(x)).
+        increasing(bool): Order of the powers of the columns. If True, the powers increase from left to right, if False (the default) they are reversed.
+        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
+    Returns:
+        Tensor, A vandermonde matrix with shape (len(x), N). If increasing is False, the first column is :math:`x^{(N-1)}`, the second :math:`x^{(N-2)}` and so forth.
+        If increasing is True, the columns are :math:`x^0`, :math:`x^1`, ..., :math:`x^{(N-1)}`.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            x = paddle.to_tensor([1., 2., 3.], dtype="float32")
+            out = paddle.vander(x)
+            print(out.numpy())
+            # [[1., 1., 1.],
+            #  [4., 2., 1.],
+            #  [9., 3., 1.]]
+            out1 = paddle.vander(x,2)
+            print(out1.numpy())
+            # [[1., 1.],
+            #  [2., 1.],
+            #  [3., 1.]]
+            out2 = paddle.vander(x, increasing = True)
+            print(out2.numpy())
+            # [[1., 1., 1.],
+            #  [1., 2., 4.],
+            #  [1., 3., 9.]]
+            real = paddle.to_tensor([2., 4.])
+            imag = paddle.to_tensor([1., 3.])
+            complex = paddle.complex(real, imag)
+            out3 = paddle.vander(complex)
+            print(out3.numpy())
+            # [[2.+1.j, 1.+0.j],
+            #  [4.+3.j, 1.+0.j]]
+    """
+    check_variable_and_dtype(
+        x,
+        'x',
+        ['complex64', 'complex128', 'float32', 'float64', 'int32', 'int64'],
+        'vander',
+    )
+    if x.dim() != 1:
+        raise ValueError(
+            "The input of x is expected to be a 1-D Tensor."
+            "But now the dims of Input(X) is %d." % x.dim()
+        )
+
+    if n is None:
+        n = x.shape[0]
+
+    if n < 0:
+        raise ValueError("N must be non-negative.")
+
+    res = paddle.empty([x.shape[0], n], dtype=x.dtype)
+
+    if n > 0:
+        res[:, 0] = paddle.to_tensor([1], dtype=x.dtype)
+    if n > 1:
+        res[:, 1:] = x[:, None]
+        res[:, 1:] = paddle.cumprod(res[:, 1:], dim=-1)
+    res = res[:, ::-1] if not increasing else res
+    return res