add new API: paddle.linalg.cov

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

@@ -0,0 +1,286 @@
+# Copyright (c) 2019 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 paddle.fluid as fluid
+import unittest
+import numpy as np
+import six
+import paddle
+
+
+def numpy_cov(np_arr, rowvar=True, ddof=1, fweights=None, aweights=None):
+    return np.cov(np_arr,
+                  rowvar=rowvar,
+                  ddof=int(ddof),
+                  fweights=fweights,
+                  aweights=aweights)
+
+
+class Cov_Test(unittest.TestCase):
+    def setUp(self):
+        self.shape = [20, 10]
+        self.weightshape = [10]
+
+    def test_tensor_cov_default(self):
+        typelist = ['float64']
+        places = [fluid.CPUPlace()]
+        if fluid.core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+
+        for idx, p in enumerate(places):
+            if idx == 0:
+                paddle.set_device('cpu')
+            else:
+                paddle.set_device('gpu')
+
+            for dtype in typelist:
+                np_arr = np.random.rand(*self.shape).astype(dtype)
+                tensor = paddle.to_tensor(np_arr, place=p)
+                cov = paddle.linalg.cov(tensor,
+                                        rowvar=True,
+                                        ddof=True,
+                                        fweights=None,
+                                        aweights=None)
+                np_cov = numpy_cov(
+                    np_arr, rowvar=True, ddof=1, fweights=None, aweights=None)
+                self.assertTrue(np.allclose(np_cov, cov.numpy()))
+
+    def test_tensor_cov_rowvar(self):
+        typelist = ['float64']
+        places = [fluid.CPUPlace()]
+        if fluid.core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+
+        for idx, p in enumerate(places):
+            if idx == 0:
+                paddle.set_device('cpu')
+            else:
+                paddle.set_device('gpu')
+
+            for dtype in typelist:
+                np_arr = np.random.rand(*self.shape).astype(dtype)
+                tensor = paddle.to_tensor(np_arr, place=p)
+                cov = paddle.linalg.cov(tensor,
+                                        rowvar=False,
+                                        ddof=True,
+                                        fweights=None,
+                                        aweights=None)
+                np_cov = numpy_cov(
+                    np_arr, rowvar=False, ddof=1, fweights=None, aweights=None)
+                self.assertTrue(np.allclose(np_cov, cov.numpy()))
+
+    def test_tensor_cov_ddof(self):
+        typelist = ['float64']
+        places = [fluid.CPUPlace()]
+        if fluid.core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+
+        for idx, p in enumerate(places):
+            if idx == 0:
+                paddle.set_device('cpu')
+            else:
+                paddle.set_device('gpu')
+
+            for dtype in typelist:
+                np_arr = np.random.rand(*self.shape).astype(dtype)
+                tensor = paddle.to_tensor(np_arr, place=p)
+                cov = paddle.linalg.cov(tensor,
+                                        rowvar=True,
+                                        ddof=False,
+                                        fweights=None,
+                                        aweights=None)
+                np_cov = numpy_cov(
+                    np_arr, rowvar=True, ddof=0, fweights=None, aweights=None)
+                self.assertTrue(np.allclose(np_cov, cov.numpy()))
+
+    def test_tensor_cov_fweights(self):
+        typelist = ['float64']
+        places = [fluid.CPUPlace()]
+        if fluid.core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+
+        for idx, p in enumerate(places):
+            if idx == 0:
+                paddle.set_device('cpu')
+            else:
+                paddle.set_device('gpu')
+
+            for dtype in typelist:
+                np_arr = np.random.rand(*self.shape).astype(dtype)
+                np_fw = np.random.randint(
+                    10, size=self.weightshape).astype('int32')
+                tensor = paddle.to_tensor(np_arr, place=p)
+                fweights = paddle.to_tensor(np_fw, place=p)
+                cov = paddle.linalg.cov(tensor,
+                                        rowvar=True,
+                                        ddof=True,
+                                        fweights=fweights,
+                                        aweights=None)
+                np_cov = numpy_cov(
+                    np_arr, rowvar=True, ddof=1, fweights=np_fw, aweights=None)
+                self.assertTrue(np.allclose(np_cov, cov.numpy()))
+
+    def test_tensor_cov_aweights(self):
+        typelist = ['float64']
+        places = [fluid.CPUPlace()]
+        if fluid.core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+
+        for idx, p in enumerate(places):
+            if idx == 0:
+                paddle.set_device('cpu')
+            else:
+                paddle.set_device('gpu')
+
+            for dtype in typelist:
+                np_arr = np.random.rand(*self.shape).astype(dtype)
+                np_aw = np.random.randint(
+                    10, size=self.weightshape).astype('int32')
+                tensor = paddle.to_tensor(np_arr, place=p)
+                aweights = paddle.to_tensor(np_aw, place=p)
+                cov = paddle.linalg.cov(tensor,
+                                        rowvar=True,
+                                        ddof=True,
+                                        fweights=None,
+                                        aweights=aweights)
+                np_cov = numpy_cov(
+                    np_arr, rowvar=True, ddof=1, fweights=None, aweights=np_aw)
+                self.assertTrue(np.allclose(np_cov, cov.numpy()))
+
+    def test_tensor_cov_weights(self):
+        typelist = ['float64']
+        places = [fluid.CPUPlace()]
+        if fluid.core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+
+        for idx, p in enumerate(places):
+            if idx == 0:
+                paddle.set_device('cpu')
+            else:
+                paddle.set_device('gpu')
+
+            for dtype in typelist:
+                np_arr = np.random.rand(*self.shape).astype(dtype)
+                np_fw = np.random.randint(
+                    10, size=self.weightshape).astype('int64')
+                np_aw = np.random.rand(*self.weightshape).astype('float64')
+                tensor = paddle.to_tensor(np_arr, place=p)
+                fweights = paddle.to_tensor(np_fw, place=p)
+                aweights = paddle.to_tensor(np_aw, place=p)
+                cov = paddle.linalg.cov(tensor,
+                                        rowvar=True,
+                                        ddof=True,
+                                        fweights=fweights,
+                                        aweights=aweights)
+                np_cov = numpy_cov(
+                    np_arr, rowvar=True, ddof=1, fweights=np_fw, aweights=np_aw)
+                self.assertTrue(np.allclose(np_cov, cov.numpy()))
+
+
+class Cov_Test2(Cov_Test):
+    def setUp(self):
+        self.shape = [10]
+        self.weightshape = [10]
+
+
+# Input(x) only support N-D (1<=N<=2) tensor
+class Cov_Test3(unittest.TestCase):
+    def setUp(self):
+        self.shape = [2, 5, 10]
+        self.fweightshape = [10]
+        self.aweightshape = [10]
+        self.fw_s = 1.
+        self.aw_s = 1.
+
+    def test_errors(self):
+        def test_err():
+            np_arr = np.random.rand(*self.shape).astype('float64')
+            np_fw = self.fw_s * np.random.rand(
+                *self.fweightshape).astype('int32')
+            np_aw = self.aw_s * np.random.rand(
+                *self.aweightshape).astype('float64')
+            tensor = paddle.to_tensor(np_arr)
+            fweights = paddle.to_tensor(np_fw)
+            aweights = paddle.to_tensor(np_aw)
+            cov = paddle.linalg.cov(tensor,
+                                    rowvar=True,
+                                    ddof=True,
+                                    fweights=fweights,
+                                    aweights=aweights)
+
+        self.assertRaises(ValueError, test_err)
+
+
+#Input(fweights) only support N-D (N<=1) tensor
+class Cov_Test4(Cov_Test3):
+    def setUp(self):
+        self.shape = [5, 10]
+        self.fweightshape = [2, 10]
+        self.aweightshape = [10]
+        self.fw_s = 1.
+        self.aw_s = 1.
+
+
+#The number of Input(fweights) should equal to x's dim[1]
+class Cov_Test5(Cov_Test3):
+    def setUp(self):
+        self.shape = [5, 10]
+        self.fweightshape = [5]
+        self.aweightshape = [10]
+        self.fw_s = 1.
+        self.aw_s = 1.
+
+
+#The value of Input(fweights) cannot be negtive
+class Cov_Test6(Cov_Test3):
+    def setUp(self):
+        self.shape = [5, 10]
+        self.fweightshape = [10]
+        self.aweightshape = [10]
+        self.fw_s = -1.
+        self.aw_s = 1.
+
+
+#Input(aweights) only support N-D (N<=1) tensor
+class Cov_Test7(Cov_Test3):
+    def setUp(self):
+        self.shape = [5, 10]
+        self.fweightshape = [10]
+        self.aweightshape = [2, 10]
+        self.fw_s = 1.
+        self.aw_s = 1.
+
+
+#The number of Input(aweights) should equal to x's dim[1]
+class Cov_Test8(Cov_Test3):
+    def setUp(self):
+        self.shape = [5, 10]
+        self.fweightshape = [10]
+        self.aweightshape = [5]
+        self.fw_s = 1.
+        self.aw_s = 1.
+
+
+#The value of Input(aweights) cannot be negtive
+class Cov_Test9(Cov_Test3):
+    def setUp(self):
+        self.shape = [5, 10]
+        self.fweightshape = [10]
+        self.aweightshape = [10]
+        self.fw_s = 1.
+        self.aw_s = -1.
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/linalg.py

@@ -15,6 +15,7 @@
 from .tensor.linalg import cholesky  # noqa: F401
 from .tensor.linalg import norm  # noqa: F401
 from .tensor.linalg import eig  # noqa: F401
+from .tensor.linalg import cov  # noqa: F401
 from .tensor.linalg import cond  # noqa: F401
 from .tensor.linalg import matrix_power  # noqa: F401
 from .tensor.linalg import solve  # noqa: F401
@@ -36,6 +37,7 @@
     'cholesky',  #noqa
     'norm',
     'cond',
+    'cov',
     'inv',
     'eig',
     'eigvals',

python/paddle/tensor/__init__.py

@@ -39,6 +39,7 @@
 from .creation import complex  # noqa: F401
 from .linalg import matmul  # noqa: F401
 from .linalg import dot  # noqa: F401
+from .linalg import cov  # noqa: F401
 from .linalg import norm  # noqa: F401
 from .linalg import cond  # noqa: F401
 from .linalg import transpose  # noqa: F401
@@ -256,6 +257,7 @@
 tensor_method_func  = [ #noqa
            'matmul',
            'dot',
+           'cov',
            'norm',
            'cond',
            'transpose',