本文整理汇总了Python中torch.Tensor.ndimension方法的典型用法代码示例。如果您正苦于以下问题：Python Tensor.ndimension方法的具体用法？Python Tensor.ndimension怎么用？Python Tensor.ndimension使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch.Tensor的用法示例。
在下文中一共展示了Tensor.ndimension方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: neg_hartmann6
# 需要导入模块: from torch import Tensor [as 别名]
# 或者: from torch.Tensor import ndimension [as 别名]
def neg_hartmann6(X: Tensor) -> Tensor:
r"""Negative Hartmann6 test function.
Six-dimensional function (typically evaluated on `[0, 1]^6`)
`H(x) = - sum_{i=1}^4 ALPHA_i exp( - sum_{j=1}^6 A_ij (x_j - P_ij)**2 )`
H has a 6 local minima and a global minimum at
`z = (0.20169, 0.150011, 0.476874, 0.275332, 0.311652, 0.6573)`
with `H(z) = -3.32237`
Args:
X: A Tensor of size `6` or `k x 6` (k batch evaluations).
Returns:
`-H(X)`, the negative value of the standard Hartmann6 function.
"""
batch = X.ndimension() > 1
X = X if batch else X.unsqueeze(0)
inner_sum = torch.sum(X.new(A) * (X.unsqueeze(1) - 0.0001 * X.new(P)) ** 2, dim=2)
H = -torch.sum(X.new(ALPHA) * torch.exp(-inner_sum), dim=1)
result = -H
return result if batch else result.squeeze(0)示例2: neg_branin
# 需要导入模块: from torch import Tensor [as 别名]
# 或者: from torch.Tensor import ndimension [as 别名]
def neg_branin(X: Tensor) -> Tensor:
r"""Negative Branin test function.
Two-dimensional function (usually evaluated on `[-5, 10] x [0, 15]`):
`B(x) = (x2 - b x_1^2 + c x_1 - r)^2 + 10 (1-t) cos(x_1) + 10`
B has 3 minimizers for its global minimum at
`z_1 = (-pi, 12.275), z_2 = (pi, 2.275), z_3 = (9.42478, 2.475)`
with `B(z_i) = -0.397887`
Args:
X: A Tensor of size `2` or `k x 2` (`k` batch evaluations).
Returns:
`-B(X)`, the negative value of the standard Branin function.
"""
batch = X.ndimension() > 1
X = X if batch else X.unsqueeze(0)
t1 = X[:, 1] - 5.1 / (4 * math.pi ** 2) * X[:, 0] ** 2 + 5 / math.pi * X[:, 0] - 6
t2 = 10 * (1 - 1 / (8 * math.pi)) * torch.cos(X[:, 0])
B = t1 ** 2 + t2 + 10
result = -B
return result if batch else result.squeeze(0)示例3: neg_michalewicz
# 需要导入模块: from torch import Tensor [as 别名]
# 或者: from torch.Tensor import ndimension [as 别名]
def neg_michalewicz(X: Tensor) -> Tensor:
r"""Negative 10-dim Michalewicz test function.
10-dim function (usually evaluated on hypercube [0, pi]^10):
`M(x) = sum_{i=1}^10 sin(x_i) (sin(i x_i^2 / pi)^20)`
Args:
X: A Tensor of size `10` or `k x 10` (`k` batch evaluations).
Returns:
`-M(X)`, the negative value of the Michalewicz function.
"""
batch = X.ndimension() > 1
X = X if batch else X.unsqueeze(0)
a = 1 + torch.arange(10, device=X.device, dtype=X.dtype)
result = torch.sum(torch.sin(X) * torch.sin(a * X ** 2 / math.pi) ** 20, dim=-1)
return result if batch else result.squeeze(0)示例4: neg_styblinski_tang
# 需要导入模块: from torch import Tensor [as 别名]
# 或者: from torch.Tensor import ndimension [as 别名]
def neg_styblinski_tang(X: Tensor) -> Tensor:
r"""Negative Styblinski-Tang test function.
d-dimensional function (usually evaluated on the hypercube `[-5, 5]^d`):
`H(x) = 0.5 * sum_{i=1}^d (x_i^4 - 16 * x_i^2 + 5 * x_i)`
H has a single global mininimum `H(z) = -39.166166 * d` at `z = [-2.903534]^d`
Args:
X: A Tensor of size `d` or `k x d` (`k` batch evaluations)
Returns:
`-H(X)`, the negative value of the standard Styblinski-Tang function.
"""
batch = X.ndimension() > 1
X = X if batch else X.unsqueeze(0)
H = 0.5 * (X ** 4 - 16 * X ** 2 + 5 * X).sum(dim=1)
result = -H
return result if batch else result.squeeze(0)示例5: __init__
# 需要导入模块: from torch import Tensor [as 别名]
# 或者: from torch.Tensor import ndimension [as 别名]
def __init__(
self,
train_X: Tensor,
train_Y: Tensor,
task_feature: int,
output_tasks: Optional[List[int]] = None,
rank: Optional[int] = None,
) -> None:
r"""Multi-Task GP model using an ICM kernel, inferring observation noise.
Args:
train_X: A `n x (d + 1)` or `b x n x (d + 1)` (batch mode) tensor
of training data. One of the columns should contain the task
features (see `task_feature` argument).
train_Y: A `n` or `b x n` (batch mode) tensor of training
observations.
task_feature: The index of the task feature
(`-d <= task_feature <= d`).
output_tasks: A list of task indices for which to compute model
outputs for. If omitted, return outputs for all task indices.
rank: The rank to be used for the index kernel. If omitted, use a
full rank (i.e. number of tasks) kernel.
Example:
>>> X1, X2 = torch.rand(10, 2), torch.rand(20, 2)
>>> i1, i2 = torch.zeros(10, 1), torch.ones(20, 1)
>>> train_X = torch.stack([
>>> torch.cat([X1, i1], -1), torch.cat([X2, i2], -1),
>>> ])
>>> train_Y = torch.cat(f1(X1), f2(X2))
>>> model = MultiTaskGP(train_X, train_Y, task_feature=-1)
"""
if train_X.ndimension() != 2:
# Currently, batch mode MTGPs are blocked upstream in GPyTorch
raise ValueError(f"Unsupported shape {train_X.shape} for train_X.")
d = train_X.shape[-1] - 1
if not (-d <= task_feature <= d):
raise ValueError(f"Must have that -{d} <= task_feature <= {d}")
all_tasks = train_X[:, task_feature].unique().to(dtype=torch.long).tolist()
if output_tasks is None:
output_tasks = all_tasks
else:
if any(t not in all_tasks for t in output_tasks):
raise RuntimeError("All output tasks must be present in input data.")
self._output_tasks = output_tasks
# TODO (T41270962): Support task-specific noise levels in likelihood
likelihood = GaussianLikelihood(noise_prior=GammaPrior(1.1, 0.05))
# construct indexer to be used in forward
self._task_feature = task_feature
self._base_idxr = torch.arange(d)
self._base_idxr[task_feature:] += 1 # exclude task feature
super().__init__(
train_inputs=train_X, train_targets=train_Y, likelihood=likelihood
)
self.mean_module = ConstantMean()
self.covar_module = ScaleKernel(
base_kernel=MaternKernel(
nu=2.5, ard_num_dims=d, lengthscale_prior=GammaPrior(3.0, 6.0)
),
outputscale_prior=GammaPrior(2.0, 0.15),
)
num_tasks = len(all_tasks)
self._rank = rank if rank is not None else num_tasks
# TODO: Add LKJ prior for the index kernel
self.task_covar_module = IndexKernel(num_tasks=num_tasks, rank=self._rank)
self.to(train_X)