Python torch 模块，pstrf() 实例源码
我们从Python开源项目中，提取了以下5个代码示例，用于说明如何使用torch.pstrf()。
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)