code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
import torch
from .Criterion import Criterion
class CosineEmbeddingCriterion(Criterion):
def __init__(self, margin=0, sizeAverage=True):
super(CosineEmbeddingCriterion, self).__init__()
self.margin = margin
self.sizeAverage = sizeAverage
self.gradInput = [torch.Tensor(), torch.Ten... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/CosineEmbeddingCriterion.py | 0.656108 | 0.267244 | CosineEmbeddingCriterion.py | pypi |
import torch
from .Module import Module
from .utils import clear
class MaskedSelect(Module):
def __init__(self):
super(MaskedSelect, self).__init__()
self._maskIndices = torch.LongTensor()
self._maskIndexBuffer = torch.LongTensor()
self._maskIndexBufferCPU = torch.FloatTensor()
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/MaskedSelect.py | 0.82029 | 0.357539 | MaskedSelect.py | pypi |
import math
import torch
from .Module import Module
class VolumetricFullConvolution(Module):
def __init__(self, nInputPlane, nOutputPlane,
kT, kW, kH, # kernel size
dT=1, dW=1, dH=1, # stride
padT=0, padW=0, padH=0, # padding
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/VolumetricFullConvolution.py | 0.859029 | 0.407628 | VolumetricFullConvolution.py | pypi |
import math
import torch
from .MSECriterion import MSECriterion
"""
This file implements a criterion for multi-class classification.
It learns an embedding per class, where each class' embedding
is a point on an (N-1)-dimensional simplex, where N is
the number of classes.
F... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/ClassSimplexCriterion.py | 0.905771 | 0.62088 | ClassSimplexCriterion.py | pypi |
import torch
from .Module import Module
from .utils import clear
class SpatialMaxPooling(Module):
def __init__(self, kW, kH, dW=None, dH=None, padW=0, padH=0):
super(SpatialMaxPooling, self).__init__()
dW = dW or kW
dH = dH or kH
self.kW = kW
self.kH = kH
self.dW... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/SpatialMaxPooling.py | 0.773858 | 0.158891 | SpatialMaxPooling.py | pypi |
import math
import torch
from .Module import Module
from .utils import clear
class Linear(Module):
def __init__(self, inputSize, outputSize, bias=True):
super(Linear, self).__init__()
self.weight = torch.Tensor(outputSize, inputSize)
self.gradWeight = torch.Tensor(outputSize, inputSize)
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/Linear.py | 0.842183 | 0.452234 | Linear.py | pypi |
import torch
from .SpatialConvolution import SpatialConvolution
class SpatialDilatedConvolution(SpatialConvolution):
def __init__(self, nInputPlane, nOutputPlane, kW, kH, dW=1, dH=1, padW=0, padH=None, dilationH=1, dilationW=None):
super(SpatialDilatedConvolution, self).__init__(nInputPlane, nOutputPlane... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/SpatialDilatedConvolution.py | 0.750278 | 0.252131 | SpatialDilatedConvolution.py | pypi |
import math
import torch
from .Module import Module
from .utils import clear
class VolumetricConvolution(Module):
def __init__(self, nInputPlane, nOutputPlane, kT, kW, kH, dT=1, dW=1, dH=1, padT=0, padW=None, padH=None):
super(VolumetricConvolution, self).__init__()
self.nInputPlane = nInputPlan... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/VolumetricConvolution.py | 0.729134 | 0.19199 | VolumetricConvolution.py | pypi |
import math
import torch
from .Module import Module
from .utils import clear, contiguousView
class CMul(Module):
def __init__(self, *args):
super(CMul, self).__init__()
if len(args) == 1 and isinstance(args[0], torch.Size):
self.size = args[0]
else:
self.size = t... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/CMul.py | 0.651133 | 0.230065 | CMul.py | pypi |
import torch
from .Container import Container
class Parallel(Container):
def __init__(self, inputDimension, outputDimension):
super(Parallel, self).__init__()
self.inputDimension = inputDimension
self.outputDimension = outputDimension
self.totalOutputSize = None
def updateOut... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/Parallel.py | 0.656768 | 0.176867 | Parallel.py | pypi |
import torch
from .Module import Module
from .utils import clear
class CosineDistance(Module):
def __init__(self, ):
super(CosineDistance, self).__init__()
self.gradInput = [torch.Tensor(), torch.Tensor()]
self._input1 = None
self._input2 = None
self.buffer = None
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/CosineDistance.py | 0.756088 | 0.309206 | CosineDistance.py | pypi |
import torch
from .Container import Container
class Sequential(Container):
def __len__(self):
return len(self.modules)
def add(self, module):
if len(self.modules) == 0:
self.gradInput = module.gradInput
self.modules.append(module)
self.output = module.output
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/Sequential.py | 0.586878 | 0.167117 | Sequential.py | pypi |
import torch
from .Module import Module
from .utils import clear
class LookupTable(Module):
def __init__(self, nIndex, nOutput, paddingValue=-1, maxNorm=None, normType=None):
super(LookupTable, self).__init__()
self.weight = torch.Tensor(nIndex, nOutput)
self.gradWeight = torch.Tensor(nIn... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/LookupTable.py | 0.837387 | 0.407687 | LookupTable.py | pypi |
import math
import torch
from .Module import Module
from .utils import clear
class Bilinear(Module):
def _assertInput(self, input):
if len(input) != 2 or not isinstance(input[0], torch.Tensor) or not isinstance(input[1], torch.Tensor):
raise RuntimeError('input should be a table containing tw... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/Bilinear.py | 0.761272 | 0.582194 | Bilinear.py | pypi |
import torch
from .Module import Module
class Padding(Module):
# pad puts in [pad] amount of [value] over dimension [dim], starting at
# index [index] in that dimension. If pad<0, index counts from the left.
# If pad>0 index counts from the right index = 1 pads before index 1.
# index = 2 pads startin... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/Padding.py | 0.751192 | 0.697016 | Padding.py | pypi |
import math
import torch
from .Module import Module
from .Sequential import Sequential
from .SpatialZeroPadding import SpatialZeroPadding
from .SpatialConvolution import SpatialConvolution
from .SpatialConvolutionMap import SpatialConvolutionMap
from .Replicate import Replicate
from .Square import Square
from .Sqrt imp... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/SpatialDivisiveNormalization.py | 0.775817 | 0.39321 | SpatialDivisiveNormalization.py | pypi |
import torch
from .Container import Container
class ConcatTable(Container):
def __init__(self, ):
super(ConcatTable, self).__init__()
self.modules = []
self.output = []
def updateOutput(self, input):
self.output = [module.updateOutput(input) for module in self.modules]
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/ConcatTable.py | 0.479747 | 0.216239 | ConcatTable.py | pypi |
import torch
from .Module import Module
from .utils import clear, addSingletondimension
class Min(Module):
def __init__(self, dimension=0):
super(Min, self).__init__()
self.dimension = dimension
self._output = None
self._indices = None
def _getPositiveDimension(self, input):
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/Min.py | 0.812942 | 0.247416 | Min.py | pypi |
import math
import torch
from .Module import Module
class WeightedEuclidean(Module):
def __init__(self, inputSize, outputSize):
super(WeightedEuclidean, self).__init__()
self.weight = torch.Tensor(inputSize, outputSize)
self.gradWeight = torch.Tensor(inputSize, outputSize)
# eac... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/WeightedEuclidean.py | 0.543833 | 0.262121 | WeightedEuclidean.py | pypi |
import torch
from .Criterion import Criterion
class MarginRankingCriterion(Criterion):
def __init__(self, margin=0, sizeAverage=True):
super(MarginRankingCriterion, self).__init__()
self.margin = margin
self.sizeAverage = sizeAverage
self.gradInput = [torch.Tensor(), torch.Tensor(... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/MarginRankingCriterion.py | 0.705481 | 0.263676 | MarginRankingCriterion.py | pypi |
import math
INFINITY = float('inf')
def sqrt_nothrow(x):
return math.sqrt(x) if x >= 0 else float('nan')
def cg(opfunc, x, config, state=None):
"""
This cg implementation is a rewrite of minimize.m written by Carl
E. Rasmussen. It is supposed to produce exactly same results (give
or take numer... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/optim/cg.py | 0.772788 | 0.643861 | cg.py | pypi |
import torch
from .optimizer import Optimizer
class RMSprop(Optimizer):
"""Implements RMSprop algorithm.
Proposed by G. Hinton in his
`course <http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf>`_.
The centered version first appears in `Generating Sequences
With Recurrent Ne... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/optim/rmsprop.py | 0.931633 | 0.592107 | rmsprop.py | pypi |
import torch
from .optimizer import Optimizer
class Adagrad(Optimizer):
"""Implements Adagrad algorithm.
It has been proposed in `Adaptive Subgradient Methods for Online Learning
and Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/optim/adagrad.py | 0.923301 | 0.490907 | adagrad.py | pypi |
import math
from bisect import bisect_right
from functools import partial
from .optimizer import Optimizer
class _LRScheduler(object):
def __init__(self, optimizer, last_epoch=-1):
if not isinstance(optimizer, Optimizer):
raise TypeError('{} is not an Optimizer'.format(
type(op... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/optim/lr_scheduler.py | 0.892533 | 0.324342 | lr_scheduler.py | pypi |
import math
import torch
from .optimizer import Optimizer
class Adam(Optimizer):
"""Implements Adam algorithm.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/optim/adam.py | 0.909365 | 0.591458 | adam.py | pypi |
from collections import defaultdict, Iterable
import torch
from copy import deepcopy
from itertools import chain
required = object()
class Optimizer(object):
r"""Base class for all optimizers.
.. warning::
Parameters need to be specified as collections that have a deterministic
ordering tha... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/optim/optimizer.py | 0.938864 | 0.358129 | optimizer.py | pypi |
import torch
from .optimizer import Optimizer
class Adadelta(Optimizer):
"""Implements Adadelta algorithm.
It has been proposed in `ADADELTA: An Adaptive Learning Rate Method`__.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/optim/adadelta.py | 0.932891 | 0.512937 | adadelta.py | pypi |
import torch
from .optimizer import Optimizer, required
class SGD(Optimizer):
r"""Implements stochastic gradient descent (optionally with momentum).
Nesterov momentum is based on the formula from
`On the importance of initialization and momentum in deep learning`__.
Args:
params (iterable): ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/optim/sgd.py | 0.952668 | 0.636777 | sgd.py | pypi |
import math
import torch
from .optimizer import Optimizer
class ASGD(Optimizer):
"""Implements Averaged Stochastic Gradient Descent.
It has been proposed in `Acceleration of stochastic approximation by
averaging`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts def... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/optim/asgd.py | 0.867766 | 0.417331 | asgd.py | pypi |
import torch
from .optimizer import Optimizer
class Adamax(Optimizer):
"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
It has been proposed in `Adam: A Method for Stochastic Optimization`__.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defi... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/optim/adamax.py | 0.914739 | 0.568955 | adamax.py | pypi |
import math
import torch
from .optimizer import Optimizer
class SparseAdam(Optimizer):
"""Implements lazy version of Adam algorithm suitable for sparse tensors.
In this variant, only moments that show up in the gradient get updated, and
only those portions of the gradient get applied to the parameters.
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/optim/sparse_adam.py | 0.88906 | 0.608449 | sparse_adam.py | pypi |
import math
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all
class Cauchy(Distribution):
r"""
Samples from a Cauchy (Lorentz) distribution. The distribution of the ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/cauchy.py | 0.946138 | 0.581303 | cauchy.py | pypi |
from numbers import Number
import math
import torch
from torch.distributions import constraints
from torch.distributions.uniform import Uniform
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import AffineTransform, ExpTransform
from torch.distributio... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/gumbel.py | 0.936619 | 0.456228 | gumbel.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all, probs_to_logits, lazy_property, logits_to_probs
class Binomial(Distribution):
r"""
Creates a Binomial distribution ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/binomial.py | 0.936663 | 0.47317 | binomial.py | pypi |
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.transforms import Transform
from torch.distributions.utils import _sum_rightmost
class TransformedDistribution(Distribution):
r"""
Extension of the Distribution class, wh... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/transformed_distribution.py | 0.938871 | 0.725393 | transformed_distribution.py | pypi |
import torch
import warnings
from torch.distributions import constraints
from torch.distributions.utils import lazy_property
class Distribution(object):
r"""
Distribution is the abstract base class for probability distributions.
"""
has_rsample = False
has_enumerate_support = False
_validate_... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/distribution.py | 0.936872 | 0.537102 | distribution.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all, probs_to_logits, logits_to_probs, lazy_property
from torch.nn.functional import binary_cross_entropy_with_logits
class ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/bernoulli.py | 0.946163 | 0.55646 | bernoulli.py | pypi |
import torch
from torch.distributions.distribution import Distribution
from torch.autograd import Variable
class ExponentialFamily(Distribution):
r"""
ExponentialFamily is the abstract base class for probability distributions belonging to an
exponential family, whose probability mass/density function has ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/exp_family.py | 0.922822 | 0.882833 | exp_family.py | pypi |
import math
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all
class Uniform(Distribution):
r"""
Generates uniformly distributed random samples from the half-open int... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/uniform.py | 0.865196 | 0.563438 | uniform.py | pypi |
import math
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import lazy_property
def _get_batch_shape(bmat, bvec):
r"""
Given a batch of matrices and a batch of vectors, compute the co... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/multivariate_normal.py | 0.88996 | 0.795102 | multivariate_normal.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.dirichlet import Dirichlet
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all
class Beta(ExponentialFamily):
r"""
Beta distribution parame... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/beta.py | 0.965536 | 0.59749 | beta.py | pypi |
from numbers import Number
import torch
import math
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.gamma import Gamma
from torch.distributions.utils import broadcast_all, _finfo
class FisherSnedecor(Distribution):
r"""
Creates a F... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/fishersnedecor.py | 0.943996 | 0.649718 | fishersnedecor.py | pypi |
import torch
from torch.distributions import constraints
from torch.distributions.categorical import Categorical
from torch.distributions.utils import clamp_probs, broadcast_all, log_sum_exp
from torch.distributions.distribution import Distribution
from torch.distributions.transformed_distribution import TransformedDis... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/relaxed_categorical.py | 0.956887 | 0.74934 | relaxed_categorical.py | pypi |
import math
import numbers
import weakref
import torch
from torch.distributions import constraints
from torch.distributions.utils import (_sum_rightmost, broadcast_all,
lazy_property)
from torch.nn.functional import pad, sigmoid
__all__ = [
'AbsTransform',
'AffineTransfo... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/transforms.py | 0.928051 | 0.637369 | transforms.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import _finfo, broadcast_all
class Laplace(Distribution):
r"""
Creates a Laplace distribution parameterized by `loc` and 'scale'.
Ex... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/laplace.py | 0.897891 | 0.494019 | laplace.py | pypi |
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import _sum_rightmost
class Independent(Distribution):
r"""
Reinterprets some of the batch dims of a distribution as event dims.
This is mainly useful for chan... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/independent.py | 0.945588 | 0.67818 | independent.py | pypi |
import torch
from torch.distributions.distribution import Distribution
from torch.distributions import Categorical
from numbers import Number
from torch.distributions import constraints
from torch.distributions.utils import broadcast_all
class Multinomial(Distribution):
r"""
Creates a Multinomial distribution... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/multinomial.py | 0.946584 | 0.752649 | multinomial.py | pypi |
from numbers import Number
import torch
import math
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions import Chi2
from torch.distributions.utils import broadcast_all
class StudentT(Distribution):
r"""
Creates a Student's t-distributi... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/studentT.py | 0.953232 | 0.473049 | studentT.py | pypi |
from collections import namedtuple
from functools import update_wrapper
from numbers import Number
import math
import torch
import torch.nn.functional as F
# This follows semantics of numpy.finfo.
_Finfo = namedtuple('_Finfo', ['eps', 'tiny'])
_FINFO = {
torch.HalfStorage: _Finfo(eps=0.00097656, tiny=6.1035e-05),
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/utils.py | 0.940993 | 0.583856 | utils.py | pypi |
from numbers import Number
import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import _finfo, broadcast_all
def _dirichlet_sampl... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/dirichlet.py | 0.963109 | 0.506652 | dirichlet.py | pypi |
import torch
from torch.distributions import constraints
from torch.distributions.categorical import Categorical
from torch.distributions.distribution import Distribution
class OneHotCategorical(Distribution):
r"""
Creates a one-hot categorical distribution parameterized by :attr:`probs` or
:attr:`logits`... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/one_hot_categorical.py | 0.957755 | 0.647422 | one_hot_categorical.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all
class Exponential(ExponentialFamily):
r"""
Creates a Exponential distribution parameterized by `rate`.
Exam... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/exponential.py | 0.952164 | 0.475544 | exponential.py | pypi |
r"""
The following constraints are implemented:
- ``constraints.boolean``
- ``constraints.dependent``
- ``constraints.greater_than(lower_bound)``
- ``constraints.integer_interval(lower_bound, upper_bound)``
- ``constraints.interval(lower_bound, upper_bound)``
- ``constraints.lower_cholesky``
- ``constraints.lower_tria... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/constraints.py | 0.939927 | 0.674057 | constraints.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all, probs_to_logits, logits_to_probs, lazy_property, _finfo
from torch.nn.functional import binary_cross_entropy_with_logits
c... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/geometric.py | 0.940237 | 0.624036 | geometric.py | pypi |
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import probs_to_logits, logits_to_probs, log_sum_exp, lazy_property, broadcast_all
class Categorical(Distribution):
r"""
Creates a categorical distribution paramete... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/categorical.py | 0.941506 | 0.689005 | categorical.py | pypi |
import math
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all
class Normal(ExponentialFamily):
r"""
Creates a normal (also called Gaussian) distribution parameter... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/normal.py | 0.947745 | 0.517083 | normal.py | pypi |
r"""
The ``distributions`` package contains parameterizable probability distributions
and sampling functions. This allows the construction of stochastic computation
graphs and stochastic gradient estimators for optimization. This package
generally follows the design of the `TensorFlow Distributions`_ package.
.. _`Ten... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/__init__.py | 0.962848 | 0.902738 | __init__.py | pypi |
from numbers import Number
import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import _finfo, broadcast_all, lazy_property
def _... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/gamma.py | 0.956176 | 0.625295 | gamma.py | pypi |
import torch
from numbers import Number
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import SigmoidTransform
from torch.distributions.utils import... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributions/relaxed_bernoulli.py | 0.941088 | 0.694027 | relaxed_bernoulli.py | pypi |
import torch
import sys
import ast
import inspect
import string
from textwrap import dedent
from functools import partial
from collections import namedtuple
from torch._C._jit_tree_views import *
PY2 = sys.version_info[0] == 2
_reserved_prefix = '__jit'
_reserved_names = {'print'}
_identifier_chars = set(string.ascii_... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/jit/frontend.py | 0.480966 | 0.385143 | frontend.py | pypi |
import re
import sys
import ast
import inspect
import torch
from torch._C import DynamicType, TupleType
from textwrap import dedent
PY35 = sys.version_info >= (3, 5)
try:
import typing
from typing import Tuple
def is_tuple(ann):
# For some reason Python 3.7 violates the Type[A, B].__origin__ ==... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/jit/annotations.py | 0.655557 | 0.295459 | annotations.py | pypi |
import torch
import hashlib
import os
import re
import shutil
import sys
import tempfile
try:
from requests.utils import urlparse
from requests import get as urlopen
requests_available = True
except ImportError:
requests_available = False
if sys.version_info[0] == 2:
from urlparse import u... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/utils/model_zoo.py | 0.50708 | 0.163813 | model_zoo.py | pypi |
import torch
import warnings
def detach_variable(inputs):
if isinstance(inputs, tuple):
out = []
for inp in inputs:
x = inp.detach()
x.requires_grad = inp.requires_grad
out.append(x)
return tuple(out)
else:
raise RuntimeError(
"On... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/utils/checkpoint.py | 0.894467 | 0.446434 | checkpoint.py | pypi |
import heapq
class Trainer(object):
def __init__(self, model=None, criterion=None, optimizer=None, dataset=None):
self.model = model
self.criterion = criterion
self.optimizer = optimizer
self.dataset = dataset
self.iterations = 0
self.stats = {}
self.plugin... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/utils/trainer/trainer.py | 0.747984 | 0.185007 | trainer.py | pypi |
from .plugin import Plugin
class Monitor(Plugin):
def __init__(self, running_average=True, epoch_average=True, smoothing=0.7,
precision=None, number_format=None, unit=''):
if precision is None:
precision = 4
if number_format is None:
number_format = '.{}f'... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/utils/trainer/plugins/monitor.py | 0.599016 | 0.205974 | monitor.py | pypi |
import bisect
import warnings
from torch._utils import _accumulate
from torch import randperm
class Dataset(object):
"""An abstract class representing a Dataset.
All other datasets should subclass it. All subclasses should override
``__len__``, that provides the size of the dataset, and ``__getitem__``,... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/utils/data/dataset.py | 0.859147 | 0.749018 | dataset.py | pypi |
import torch
from torch._six import int_classes as _int_classes
class Sampler(object):
r"""Base class for all Samplers.
Every Sampler subclass has to provide an __iter__ method, providing a way
to iterate over indices of dataset elements, and a __len__ method that
returns the length of the returned i... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/utils/data/sampler.py | 0.920727 | 0.624021 | sampler.py | pypi |
import torch
import atexit
import warnings
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
class dist_backend:
UNDEFINED = -1
TCP = 0
MPI = 1
GLOO = 2
NCCL = 3
_INITIALIZED_PG = 1
_INITIALIZED_MW = 2
_initialized = 0
_backend = dist_backend.UNDEFINED
_scope = locals()
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/distributed/__init__.py | 0.76533 | 0.388966 | __init__.py | pypi |
import torch
class no_grad(object):
r"""Context-manager that disabled gradient calculation.
Disabling gradient calculation is useful for inference, when you are sure
that you will not call :meth:`Tensor.backward()`. It will reduce memory
consumption for computations that would otherwise have `require... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/autograd/grad_mode.py | 0.911112 | 0.641142 | grad_mode.py | pypi |
import torch
from collections import Iterable
import torch.testing
import sys
def zero_gradients(x):
if isinstance(x, torch.Tensor):
if x.grad is not None:
x.grad.detach_()
x.grad.data.zero_()
elif isinstance(x, Iterable):
for elem in x:
zero_gradients(elem)... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/autograd/gradcheck.py | 0.608594 | 0.587766 | gradcheck.py | pypi |
import torch
from functools import reduce
def maybe_view(tensor, size, check_same_size=True):
if check_same_size and tensor.size() == size:
return tensor
return tensor.contiguous().view(size)
def maybe_unexpand(tensor, old_size, check_same_size=True):
if check_same_size and tensor.size() == old_... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/autograd/_functions/utils.py | 0.843638 | 0.722111 | utils.py | pypi |
class WeightModel:
def __init__(self,ReceiveID='',MessageID='', ReceiveType=20, BatchID='', FactoryID=0, FactoryName='',
FarmID=0, FarmName='', QRCode='', SpecificationID=0, CartonWeight=0, RealWeight=0, GrossWeight=0, StandardWeight=0,
ReceiveCount=1, ReceiveTime=''):
self.ReceiveID = ReceiveID
... | /rpicommon-0.0.1-py3-none-any.whl/common/data/weightmodel.py | 0.613237 | 0.209793 | weightmodel.py | pypi |
import RPi.GPIO as GPIO
import time
import sys
class HX711:
def __init__(self, dout, pd_sck, gain=128):
"""
Set GPIO Mode, and pin for communication with HX711
:param dout: Serial Data Output pin
:param pd_sck: Power Down and Serial Clock Input pin
:param gain: set gain 12... | /rpicommon-0.0.1-py3-none-any.whl/common/fhx711/hx711.py | 0.55254 | 0.482917 | hx711.py | pypi |
"""A python 3 library for various
motors and servos to connect to a raspberry pi"""
# ========================= HEADER ===================================
# title :rpiMotorlib.py
# description :A python 3 library for various motors
# and servos to connect to a raspberry pi
# This file is for stepper ... | /rpimotorlib-3.2.tar.gz/rpimotorlib-3.2/RpiMotorLib/RpiMotorLib.py | 0.407687 | 0.423041 | RpiMotorLib.py | pypi |
# ========================== IMPORTS ======================
# Import the system modules needed to run rpiMotorlib.py
import time
import RPi.GPIO as GPIO
# ==================== CLASS SECTION ===============================
class L298NMDc():
""" Class to control DC motor via L298n motor controller
6 methods 1.... | /rpimotorlib-3.2.tar.gz/rpimotorlib-3.2/RpiMotorLib/rpi_dc_lib.py | 0.549882 | 0.206834 | rpi_dc_lib.py | pypi |
import fnmatch
import os
class GitIgnore(object):
"""A class to manage a .gitignore file"""
def __init__(self, path):
"""Constructor
:param str path: The full path to the .gitignore file. If it does not
exist, the file will be created when running :py:meth:GitIgnore.write for
... | /rpkg-1.66-py3-none-any.whl/pyrpkg/gitignore.py | 0.535827 | 0.205874 | gitignore.py | pypi |
"""rpkincant conjure plugins for RPKI ASPA objects."""
from __future__ import annotations
import logging
import typing
from rpkimancer.cli import Args
from rpkimancer.cli.conjure import (ConjurePlugin,
DEFAULT_CA_AS_RESOURCES,
META_AS,
... | /rpkimancer-aspa-0.9.2.tar.gz/rpkimancer-aspa-0.9.2/rpkimancer_aspa/conjure.py | 0.788176 | 0.253151 | conjure.py | pypi |
"""RPKI DOA implementation - draft-spaghetti-sidrops-rpki-doa."""
from __future__ import annotations
import logging
from typing import Any, Dict, Iterable, Optional, Tuple
from rpkimancer.asn1 import Content
from rpkimancer.asn1.mod import RpkiDiscardOriginAuthorization_2021
from rpkimancer.resources import (AFI, IP... | /rpkimancer-doa-0.0.2.tar.gz/rpkimancer-doa-0.0.2/rpkimancer_doa/sigobj.py | 0.874091 | 0.205256 | sigobj.py | pypi |
"""RPKI Signed Checklist implementation - draft-ietf-sidrops-rpki-rsc."""
from __future__ import annotations
import copy
import ipaddress
import json
import logging
import os
import typing
from rpkimancer.algorithms import DIGEST_ALGORITHMS, SHA256
from rpkimancer.asn1 import Interface
from rpkimancer.asn1.mod impor... | /rpkimancer_sig-0.11.0-py3-none-any.whl/rpkimancer_sig/sigobj.py | 0.804021 | 0.161949 | sigobj.py | pypi |
import time
import json
from typing import Any, Callable
import numpy as np
class NumpyEncoder(json.JSONEncoder):
"""Subclass of json.JSONEncoder used for encoding numpy arrays as json
objects.
"""
def default(self, obj: Any) -> Any:
"""Encode with numpy.ndarray.tolist() if numpy array, other... | /rpl_pack-0.1.1-py3-none-any.whl/rpl_pack/utils.py | 0.780244 | 0.407216 | utils.py | pypi |
import logging
import sys
import time
import codecs
import serial
import struct
from collections import namedtuple
SYNC_BYTE = b'\xA5'
SYNC_BYTE2 = b'\x5A'
GET_INFO_BYTE = b'\x50'
GET_HEALTH_BYTE = b'\x52'
STOP_BYTE = b'\x25'
RESET_BYTE = b'\x40'
_SCAN_TYPE = {
'normal': {'byte': b'\x20', 'response': 129, 'siz... | /rplidar_mrumel-1.1.2-py3-none-any.whl/rplidar.py | 0.499023 | 0.200049 | rplidar.py | pypi |
import logging
import sys
import time
import codecs
import serial
import struct
from collections import namedtuple
SYNC_BYTE = b'\xA5'
SYNC_BYTE2 = b'\x5A'
GET_INFO_BYTE = b'\x50'
GET_HEALTH_BYTE = b'\x52'
STOP_BYTE = b'\x25'
RESET_BYTE = b'\x40'
_SCAN_TYPE = {
'normal': {'byte': b'\x20', 'response': 129, 'siz... | /rplidar-roboticia-0.9.5.tar.gz/rplidar-roboticia-0.9.5/rplidar.py | 0.514156 | 0.190856 | rplidar.py | pypi |
import logging
import sys
import time
import codecs
from typing import Tuple, List
import serial
import struct
from collections import namedtuple
SYNC_BYTE = b"\xA5"
SYNC_BYTE2 = b"\x5A"
GET_INFO_BYTE = b"\x50"
GET_HEALTH_BYTE = b"\x52"
STOP_BYTE = b"\x25"
RESET_BYTE = b"\x40"
_SCAN_TYPE = {
"normal": {"byte":... | /rplidar-sharpattack-0.9.6.tar.gz/rplidar-sharpattack-0.9.6/rplidar.py | 0.584983 | 0.292368 | rplidar.py | pypi |
import logging
import sys
import time
import codecs
import serial
import struct
SYNC_BYTE = b'\xA5'
SYNC_BYTE2 = b'\x5A'
GET_INFO_BYTE = b'\x50'
GET_HEALTH_BYTE = b'\x52'
STOP_BYTE = b'\x25'
RESET_BYTE = b'\x40'
SCAN_BYTE = b'\x20'
FORCE_SCAN_BYTE = b'\x21'
DESCRIPTOR_LEN = 7
INFO_LEN = 20
HEALTH_LEN = 3
INFO_TYP... | /rplidar-0.9.2.tar.gz/rplidar-0.9.2/rplidar.py | 0.486575 | 0.151938 | rplidar.py | pypi |
from datetime import datetime, timedelta
from typing import Dict, Any, Tuple
import jwt
class JWTToken:
def __init__(
self,
jwt_algorithm: str,
jwt_secret: str,
jwt_lifetime_hour: int,
):
self.algorithm = jwt_algorithm
self.secret = jwt_secret
self.life... | /rplus_utils_module-0.0.3-py3-none-any.whl/rplus_utils/jwt/jwt.py | 0.884825 | 0.203866 | jwt.py | pypi |
import io
from typing import ClassVar, Dict, Any, List, Set
import boto3
import pandas as pd
from botocore.exceptions import ClientError
from tinydb import Storage, TinyDB
from tinydb.storages import MemoryStorage
from rplus_constants.rplus_utils_module import AWS_ACCESS_KEY, AWS_SECRET_KEY, REGION_NAME, BUCKET_NAME
... | /rplus_utils_module-0.0.3-py3-none-any.whl/rplus_utils/services/s3_db_services.py | 0.690037 | 0.176743 | s3_db_services.py | pypi |
import re
from rply.分词器 import 分词器
class 词模式(object):
_attrs_ = ['词名', '匹配参数', '_模式']
def __init__(自身, 词名, 模式, 匹配参数=0):
自身.词名 = 词名
自身.正则 = re.compile(模式, flags=匹配参数)
def 匹配(自身, 源码, 起点, 终点 = None):
m = 自身.正则.match(源码, 起点, 终点) if 终点 else 自身.正则.match(源码, 起点)
return 范围(*m.sp... | /rply-ulang-0.8.3.tar.gz/rply-ulang-0.8.3/rply/分词器母机.py | 0.514156 | 0.36591 | 分词器母机.py | pypi |
class BaseBox(object):
"""
A base class for polymorphic boxes that wrap parser results. Simply use
this as a base class for anything you return in a production function of a
parser. This is necessary because RPython unlike Python expects functions
to always return objects of the same type.
既然现在不... | /rply-ulang-0.8.3.tar.gz/rply-ulang-0.8.3/rply/词.py | 0.78374 | 0.484807 | 词.py | pypi |
import binascii
import struct
from cryptography.hazmat.primitives.asymmetric.utils import Prehashed
import cryptography.hazmat.primitives.hashes as crypto_hashes
import cryptography.hazmat.primitives.asymmetric.ec as crypto_ec
from .extract_rpm_with_filesigs import _extract_filesigs
def parse_ima_signature(sig):
... | /rpm_head_signing-1.7.1.tar.gz/rpm_head_signing-1.7.1/rpm_head_signing/extract_signature_and_ima_info.py | 0.461259 | 0.250715 | extract_signature_and_ima_info.py | pypi |
import base64
import binascii
import struct
from .insertlib import insert_signatures as insertlib_insert_signatures
def _fix_sig_size_byteorder(signature):
sig_size_orig = struct.unpack(">H", signature[6:8])[0]
sig_size_reversed = struct.unpack("<H", signature[6:8])[0]
if sig_size_orig == (len(signature)... | /rpm_head_signing-1.7.1.tar.gz/rpm_head_signing-1.7.1/rpm_head_signing/insert_signature.py | 0.418222 | 0.178597 | insert_signature.py | pypi |
import re, os
from abc import (ABCMeta, abstractmethod)
#__all__ = ['Spec', 'replace_macros', 'Package']
class _Tag(metaclass=ABCMeta):
def __init__(self, name, pattern_obj, attr_type):
self.name = name
self.pattern_obj = pattern_obj
self.attr_type = attr_type
def test(self, line):
... | /rpm-spec-dependency-analyzer-0.5.tar.gz/rpm-spec-dependency-analyzer-0.5/rpm_spec_dependency_analyzer/pyrpm_spec.py | 0.724968 | 0.194904 | pyrpm_spec.py | pypi |
from __future__ import print_function
from __future__ import unicode_literals
import re
class Vercmp(object):
R_NONALNUMTILDE = re.compile(br"^([^a-zA-Z0-9~]*)(.*)$")
R_NUM = re.compile(br"^([\d]+)(.*)$")
R_ALPHA = re.compile(br"^([a-zA-Z]+)(.*)$")
@classmethod
def compare(cls, first, second):
... | /rpm_vercmp-0.1.2.tar.gz/rpm_vercmp-0.1.2/rpm_vercmp/vercmp.py | 0.514644 | 0.278783 | vercmp.py | pypi |
import math
from fractions import Fraction
from decimal import Decimal
import yaml
def get_number(num):
"""If possible return a number, else return num as a string"""
for cast in (int, float):
try:
num = cast(num)
return num
except ValueError:
pass
if n... | /rpn_calc-0.2.3-py3-none-any.whl/rpn_calc/calculator.py | 0.479504 | 0.329351 | calculator.py | pypi |
from math import sqrt, log2, ceil, floor
import random
import sys
if sys.version_info[ 0 ] == 3 and sys.version_info[ 1 ] >= 9:
from math import gcd
else:
from fractions import gcd
import sys
from builtins import ValueError
"""This script factorises a natural number given as a command line
parameter into ... | /rpnChilada-8.5.6-py3-none-any.whl/rpn/factorise.py | 0.693577 | 0.571468 | factorise.py | pypi |
__all__ = ()
#-----------------------------------------------------------------------------
# IMPORTANT NOTE: DO NOT import the clnum module into the global namespace of
# this module. This module is imported during the initialization of clnum and
# would create a circular import.
#-----------------------------------... | /rpncalc-2.7.tar.gz/rpncalc-2.7/clnum/_clnum_str.py | 0.578567 | 0.2174 | _clnum_str.py | pypi |
from __future__ import print_function
import sys
import os
import readline
import argparse
from rpnpy import Calculator
def setupReadline():
"""Initialize the readline library and command history.
@return: A C{bool} to indicate whether standard input is a terminal
(and therefore interactive).
... | /rpnpy-1.0.31.tar.gz/rpnpy-1.0.31/rpn.py | 0.45423 | 0.158858 | rpn.py | pypi |
# rportion - data structure and operations for rectilinear polygons
[](https://test.pypi.org/project/rportion/)
[](http... | /rportion-0.1.0.tar.gz/rportion-0.1.0/README.md | 0.888517 | 0.992192 | README.md | pypi |
from .RpqLua import RpqLua
class RpqQueue:
def __init__(self, redis, queueName):
"""
Registers RpqLua queue from a Redis connection
"""
# RpqLua instance
self.queue = RpqLua(redis)
# Set queue name
self.setqueueName(queueName)
def eval(self, arg... | /rpq-2.2.tar.gz/rpq-2.2/clients/python/lib/RpqQueue.py | 0.674694 | 0.176707 | RpqQueue.py | pypi |
import hashlib
class RpqLua:
def __init__(self, redisConnection):
'''Sets Redis connection, load LUA from path'''
# Set Redis connection
self.setRedisConnection(redisConnection)
# Load LUA source
self.loadSource(self.getLuaPath())
# Register LUA script
s... | /rpq-2.2.tar.gz/rpq-2.2/clients/python/lib/RpqLua.py | 0.739705 | 0.18939 | RpqLua.py | pypi |
import numpy as np
import typing as ty
import rpy2.robjects.packages as rpackages
from rpy2.robjects.vectors import FloatVector
def symmetry_test(
x: ty.Union[tuple, list, np.ndarray],
test_statistic: str = 'MI',
test_k: int = 1,
_module: str = 'symmetry',
_method: str = 'symmetry_test',
**kw,... | /rpy_symmetry-0.1.0.tar.gz/rpy_symmetry-0.1.0/rpy_symmetry/rpy_symmetry.py | 0.703651 | 0.646125 | rpy_symmetry.py | pypi |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.