python类pstrf()的实例源码

def test_pstrf(self):
        def checkPsdCholesky(a, uplo, inplace):
            if inplace:
                u = torch.Tensor(a.size())
                piv = torch.IntTensor(a.size(0))
                args = [u, piv, a]
            else:
                args = [a]

            if uplo is not None:
                args += [uplo]

            u, piv = torch.pstrf(*args)

            if uplo is False:
                a_reconstructed = torch.mm(u, u.t())
            else:
                a_reconstructed = torch.mm(u.t(), u)

            piv = piv.long()
            a_permuted = a.index_select(0, piv).index_select(1, piv)
            self.assertEqual(a_permuted, a_reconstructed, 1e-14)

        dimensions = ((5, 1), (5, 3), (5, 5), (10, 10))
        for dim in dimensions:
            m = torch.Tensor(*dim).uniform_()
            a = torch.mm(m, m.t())
            # add a small number to the diagonal to make the matrix numerically positive semidefinite
            for i in range(m.size(0)):
                a[i][i] = a[i][i] + 1e-7
            for inplace in (True, False):
                for uplo in (None, True, False):
                    checkPsdCholesky(a, uplo, inplace)

def test_pstrf(self):
        def checkPsdCholesky(a, uplo, inplace):
            if inplace:
                u = torch.Tensor(a.size())
                piv = torch.IntTensor(a.size(0))
                kwargs = {'out': (u, piv)}
            else:
                kwargs = {}
            args = [a]

            if uplo is not None:
                args += [uplo]

            u, piv = torch.pstrf(*args, **kwargs)

            if uplo is False:
                a_reconstructed = torch.mm(u, u.t())
            else:
                a_reconstructed = torch.mm(u.t(), u)

            piv = piv.long()
            a_permuted = a.index_select(0, piv).index_select(1, piv)
            self.assertEqual(a_permuted, a_reconstructed, 1e-14)

        dimensions = ((5, 1), (5, 3), (5, 5), (10, 10))
        for dim in dimensions:
            m = torch.Tensor(*dim).uniform_()
            a = torch.mm(m, m.t())
            # add a small number to the diagonal to make the matrix numerically positive semidefinite
            for i in range(m.size(0)):
                a[i][i] = a[i][i] + 1e-7
            for inplace in (True, False):
                for uplo in (None, True, False):
                    checkPsdCholesky(a, uplo, inplace)

def test_pstrf(self):
        def checkPsdCholesky(a, uplo, inplace):
            if inplace:
                u = torch.Tensor(a.size())
                piv = torch.IntTensor(a.size(0))
                kwargs = {'out': (u, piv)}
            else:
                kwargs = {}
            args = [a]

            if uplo is not None:
                args += [uplo]

            u, piv = torch.pstrf(*args, **kwargs)

            if uplo is False:
                a_reconstructed = torch.mm(u, u.t())
            else:
                a_reconstructed = torch.mm(u.t(), u)

            piv = piv.long()
            a_permuted = a.index_select(0, piv).index_select(1, piv)
            self.assertEqual(a_permuted, a_reconstructed, 1e-14)

        dimensions = ((5, 1), (5, 3), (5, 5), (10, 10))
        for dim in dimensions:
            m = torch.Tensor(*dim).uniform_()
            a = torch.mm(m, m.t())
            # add a small number to the diagonal to make the matrix numerically positive semidefinite
            for i in range(m.size(0)):
                a[i][i] = a[i][i] + 1e-7
            for inplace in (True, False):
                for uplo in (None, True, False):
                    checkPsdCholesky(a, uplo, inplace)

def test_pstrf(self):
        def checkPsdCholesky(a, uplo, inplace):
            if inplace:
                u = torch.Tensor(a.size())
                piv = torch.IntTensor(a.size(0))
                kwargs = {'out': (u, piv)}
            else:
                kwargs = {}
            args = [a]

            if uplo is not None:
                args += [uplo]

            u, piv = torch.pstrf(*args, **kwargs)

            if uplo is False:
                a_reconstructed = torch.mm(u, u.t())
            else:
                a_reconstructed = torch.mm(u.t(), u)

            piv = piv.long()
            a_permuted = a.index_select(0, piv).index_select(1, piv)
            self.assertEqual(a_permuted, a_reconstructed, 1e-14)

        dimensions = ((5, 1), (5, 3), (5, 5), (10, 10))
        for dim in dimensions:
            m = torch.Tensor(*dim).uniform_()
            a = torch.mm(m, m.t())
            # add a small number to the diagonal to make the matrix numerically positive semidefinite
            for i in range(m.size(0)):
                a[i][i] = a[i][i] + 1e-7
            for inplace in (True, False):
                for uplo in (None, True, False):
                    checkPsdCholesky(a, uplo, inplace)

def test_pstrf(self):
        def checkPsdCholesky(a, uplo, inplace):
            if inplace:
                u = torch.Tensor(a.size())
                piv = torch.IntTensor(a.size(0))
                kwargs = {'out': (u, piv)}
            else:
                kwargs = {}
            args = [a]

            if uplo is not None:
                args += [uplo]

            u, piv = torch.pstrf(*args, **kwargs)

            if uplo is False:
                a_reconstructed = torch.mm(u, u.t())
            else:
                a_reconstructed = torch.mm(u.t(), u)

            piv = piv.long()
            a_permuted = a.index_select(0, piv).index_select(1, piv)
            self.assertEqual(a_permuted, a_reconstructed, 1e-14)

        dimensions = ((5, 1), (5, 3), (5, 5), (10, 10))
        for dim in dimensions:
            m = torch.Tensor(*dim).uniform_()
            a = torch.mm(m, m.t())
            # add a small number to the diagonal to make the matrix numerically positive semidefinite
            for i in range(m.size(0)):
                a[i][i] = a[i][i] + 1e-7
            for inplace in (True, False):
                for uplo in (None, True, False):
                    checkPsdCholesky(a, uplo, inplace)