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/usr/local/lib64/python3.6/site-packages/torch/distributions/

/usr/local/lib64/python3.6/site-packages/torch/distributions

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
bernoulli.py	3904	0644	edit dl rm
beta.py	3406	0644	edit dl rm
binomial.py	5179	0644	edit dl rm
categorical.py	5488	0644	edit dl rm
cauchy.py	2714	0644	edit dl rm
chi2.py	909	0644	edit dl rm
constraints.py	17288	0644	edit dl rm
constraint_registry.py	10234	0644	edit dl rm
continuous_bernoulli.py	8532	0644	edit dl rm
dirichlet.py	3584	0644	edit dl rm
distribution.py	11735	0644	edit dl rm
exponential.py	2525	0644	edit dl rm
exp_family.py	2275	0644	edit dl rm
fishersnedecor.py	3152	0644	edit dl rm
gamma.py	3121	0644	edit dl rm
geometric.py	4266	0644	edit dl rm
gumbel.py	2528	0644	edit dl rm
half_cauchy.py	2257	0644	edit dl rm
half_normal.py	2058	0644	edit dl rm
independent.py	4361	0644	edit dl rm
kl.py	29998	0644	edit dl rm
kumaraswamy.py	2927	0644	edit dl rm
laplace.py	3054	0644	edit dl rm
lkj_cholesky.py	6124	0644	edit dl rm
logistic_normal.py	1983	0644	edit dl rm
log_normal.py	1772	0644	edit dl rm
lowrank_multivariate_normal.py	9930	0644	edit dl rm
mixture_same_family.py	8636	0644	edit dl rm
multinomial.py	4776	0644	edit dl rm
multivariate_normal.py	10548	0644	edit dl rm
negative_binomial.py	4091	0644	edit dl rm
normal.py	3351	0644	edit dl rm
one_hot_categorical.py	4375	0644	edit dl rm
pareto.py	2057	0644	edit dl rm
poisson.py	2066	0644	edit dl rm
relaxed_bernoulli.py	5360	0644	edit dl rm
relaxed_categorical.py	5202	0644	edit dl rm
studentT.py	3550	0644	edit dl rm
transformed_distribution.py	8270	0644	edit dl rm
transforms.py	38408	0644	edit dl rm
uniform.py	3112	0644	edit dl rm
utils.py	6196	0644	edit dl rm
von_mises.py	5091	0644	edit dl rm
weibull.py	2854	0644	edit dl rm
__init__.py	5884	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/distributions/constraints.py (17288B)

r"""
The following constraints are implemented:

- ``constraints.boolean``
- ``constraints.cat``
- ``constraints.corr_cholesky``
- ``constraints.dependent``
- ``constraints.greater_than(lower_bound)``
- ``constraints.greater_than_eq(lower_bound)``
- ``constraints.independent(constraint, reinterpreted_batch_ndims)``
- ``constraints.integer_interval(lower_bound, upper_bound)``
- ``constraints.interval(lower_bound, upper_bound)``
- ``constraints.less_than(upper_bound)``
- ``constraints.lower_cholesky``
- ``constraints.lower_triangular``
- ``constraints.multinomial``
- ``constraints.nonnegative_integer``
- ``constraints.one_hot``
- ``constraints.positive_definite``
- ``constraints.positive_integer``
- ``constraints.positive``
- ``constraints.real_vector``
- ``constraints.real``
- ``constraints.simplex``
- ``constraints.stack``
- ``constraints.unit_interval``
"""

import torch

__all__ = [
    'Constraint',
    'boolean',
    'cat',
    'corr_cholesky',
    'dependent',
    'dependent_property',
    'greater_than',
    'greater_than_eq',
    'independent',
    'integer_interval',
    'interval',
    'half_open_interval',
    'is_dependent',
    'less_than',
    'lower_cholesky',
    'lower_triangular',
    'multinomial',
    'nonnegative_integer',
    'positive',
    'positive_definite',
    'positive_integer',
    'real',
    'real_vector',
    'simplex',
    'stack',
    'unit_interval',
]

class Constraint(object):
    """
    Abstract base class for constraints.

A constraint object represents a region over which a variable is valid,
    e.g. within which a variable can be optimized.

Attributes:
        is_discrete (bool): Whether constrained space is discrete.
            Defaults to False.
        event_dim (int): Number of rightmost dimensions that together define
            an event. The :meth:`check` method will remove this many dimensions
            when computing validity.
    """
    is_discrete = False  # Default to continuous.
    event_dim = 0  # Default to univariate.

def check(self, value):
        """
        Returns a byte tensor of ``sample_shape + batch_shape`` indicating
        whether each event in value satisfies this constraint.
        """
        raise NotImplementedError

def __repr__(self):
        return self.__class__.__name__[1:] + '()'

class _Dependent(Constraint):
    """
    Placeholder for variables whose support depends on other variables.
    These variables obey no simple coordinate-wise constraints.

Args:
        is_discrete (bool): Optional value of ``.is_discrete`` in case this
            can be computed statically. If not provided, access to the
            ``.is_discrete`` attribute will raise a NotImplementedError.
        event_dim (int): Optional value of ``.event_dim`` in case this
            can be computed statically. If not provided, access to the
            ``.event_dim`` attribute will raise a NotImplementedError.
    """
    def __init__(self, *, is_discrete=NotImplemented, event_dim=NotImplemented):
        self._is_discrete = is_discrete
        self._event_dim = event_dim
        super().__init__()

@property
    def is_discrete(self):
        if self._is_discrete is NotImplemented:
            raise NotImplementedError(".is_discrete cannot be determined statically")
        return self._is_discrete

@property
    def event_dim(self):
        if self._event_dim is NotImplemented:
            raise NotImplementedError(".event_dim cannot be determined statically")
        return self._event_dim

def __call__(self, *, is_discrete=NotImplemented, event_dim=NotImplemented):
        """
        Support for syntax to customize static attributes::

constraints.dependent(is_discrete=True, event_dim=1)
        """
        if is_discrete is NotImplemented:
            is_discrete = self._is_discrete
        if event_dim is NotImplemented:
            event_dim = self._event_dim
        return _Dependent(is_discrete=is_discrete, event_dim=event_dim)

def check(self, x):
        raise ValueError('Cannot determine validity of dependent constraint')

def is_dependent(constraint):
    return isinstance(constraint, _Dependent)

class _DependentProperty(property, _Dependent):
    """
    Decorator that extends @property to act like a `Dependent` constraint when
    called on a class and act like a property when called on an object.

Example::

class Uniform(Distribution):
            def __init__(self, low, high):
                self.low = low
                self.high = high
            @constraints.dependent_property(is_discrete=False, event_dim=0)
            def support(self):
                return constraints.interval(self.low, self.high)

Args:
        fn (callable): The function to be decorated.
        is_discrete (bool): Optional value of ``.is_discrete`` in case this
            can be computed statically. If not provided, access to the
            ``.is_discrete`` attribute will raise a NotImplementedError.
        event_dim (int): Optional value of ``.event_dim`` in case this
            can be computed statically. If not provided, access to the
            ``.event_dim`` attribute will raise a NotImplementedError.
    """
    def __init__(self, fn=None, *, is_discrete=NotImplemented, event_dim=NotImplemented):
        super().__init__(fn)
        self._is_discrete = is_discrete
        self._event_dim = event_dim

def __call__(self, fn):
        """
        Support for syntax to customize static attributes::

@constraints.dependent_property(is_discrete=True, event_dim=1)
            def support(self):
                ...
        """
        return _DependentProperty(fn, is_discrete=self._is_discrete, event_dim=self._event_dim)

class _IndependentConstraint(Constraint):
    """
    Wraps a constraint by aggregating over ``reinterpreted_batch_ndims``-many
    dims in :meth:`check`, so that an event is valid only if all its
    independent entries are valid.
    """
    def __init__(self, base_constraint, reinterpreted_batch_ndims):
        assert isinstance(base_constraint, Constraint)
        assert isinstance(reinterpreted_batch_ndims, int)
        assert reinterpreted_batch_ndims >= 0
        self.base_constraint = base_constraint
        self.reinterpreted_batch_ndims = reinterpreted_batch_ndims
        super().__init__()

@property
    def is_discrete(self):
        return self.base_constraint.is_discrete

@property
    def event_dim(self):
        return self.base_constraint.event_dim + self.reinterpreted_batch_ndims

def check(self, value):
        result = self.base_constraint.check(value)
        if result.dim() < self.reinterpreted_batch_ndims:
            expected = self.base_constraint.event_dim + self.reinterpreted_batch_ndims
            raise ValueError(f"Expected value.dim() >= {expected} but got {value.dim()}")
        result = result.reshape(result.shape[:result.dim() - self.reinterpreted_batch_ndims] + (-1,))
        result = result.all(-1)
        return result

def __repr__(self):
        return "{}({}, {})".format(self.__class__.__name__[1:], repr(self.base_constraint),
                                   self.reinterpreted_batch_ndims)

class _Boolean(Constraint):
    """
    Constrain to the two values `{0, 1}`.
    """
    is_discrete = True

def check(self, value):
        return (value == 0) | (value == 1)

class _OneHot(Constraint):
    """
    Constrain to one-hot vectors.
    """
    is_discrete = True
    event_dim = 1

def check(self, value):
        is_boolean = (value == 0) | (value == 1)
        is_normalized = value.sum(-1).eq(1)
        return is_boolean.all(-1) & is_normalized

class _IntegerInterval(Constraint):
    """
    Constrain to an integer interval `[lower_bound, upper_bound]`.
    """
    is_discrete = True

def __init__(self, lower_bound, upper_bound):
        self.lower_bound = lower_bound
        self.upper_bound = upper_bound
        super().__init__()

def check(self, value):
        return (value % 1 == 0) & (self.lower_bound <= value) & (value <= self.upper_bound)

def __repr__(self):
        fmt_string = self.__class__.__name__[1:]
        fmt_string += '(lower_bound={}, upper_bound={})'.format(self.lower_bound, self.upper_bound)
        return fmt_string

class _IntegerLessThan(Constraint):
    """
    Constrain to an integer interval `(-inf, upper_bound]`.
    """
    is_discrete = True

def __init__(self, upper_bound):
        self.upper_bound = upper_bound
        super().__init__()

def check(self, value):
        return (value % 1 == 0) & (value <= self.upper_bound)

def __repr__(self):
        fmt_string = self.__class__.__name__[1:]
        fmt_string += '(upper_bound={})'.format(self.upper_bound)
        return fmt_string

class _IntegerGreaterThan(Constraint):
    """
    Constrain to an integer interval `[lower_bound, inf)`.
    """
    is_discrete = True

def __init__(self, lower_bound):
        self.lower_bound = lower_bound
        super().__init__()

def check(self, value):
        return (value % 1 == 0) & (value >= self.lower_bound)

def __repr__(self):
        fmt_string = self.__class__.__name__[1:]
        fmt_string += '(lower_bound={})'.format(self.lower_bound)
        return fmt_string

class _Real(Constraint):
    """
    Trivially constrain to the extended real line `[-inf, inf]`.
    """
    def check(self, value):
        return value == value  # False for NANs.

class _GreaterThan(Constraint):
    """
    Constrain to a real half line `(lower_bound, inf]`.
    """
    def __init__(self, lower_bound):
        self.lower_bound = lower_bound
        super().__init__()

def check(self, value):
        return self.lower_bound < value

def __repr__(self):
        fmt_string = self.__class__.__name__[1:]
        fmt_string += '(lower_bound={})'.format(self.lower_bound)
        return fmt_string

class _GreaterThanEq(Constraint):
    """
    Constrain to a real half line `[lower_bound, inf)`.
    """
    def __init__(self, lower_bound):
        self.lower_bound = lower_bound
        super().__init__()

def check(self, value):
        return self.lower_bound <= value

def __repr__(self):
        fmt_string = self.__class__.__name__[1:]
        fmt_string += '(lower_bound={})'.format(self.lower_bound)
        return fmt_string

class _LessThan(Constraint):
    """
    Constrain to a real half line `[-inf, upper_bound)`.
    """
    def __init__(self, upper_bound):
        self.upper_bound = upper_bound
        super().__init__()

def check(self, value):
        return value < self.upper_bound

def __repr__(self):
        fmt_string = self.__class__.__name__[1:]
        fmt_string += '(upper_bound={})'.format(self.upper_bound)
        return fmt_string

class _Interval(Constraint):
    """
    Constrain to a real interval `[lower_bound, upper_bound]`.
    """
    def __init__(self, lower_bound, upper_bound):
        self.lower_bound = lower_bound
        self.upper_bound = upper_bound
        super().__init__()

def check(self, value):
        return (self.lower_bound <= value) & (value <= self.upper_bound)

def __repr__(self):
        fmt_string = self.__class__.__name__[1:]
        fmt_string += '(lower_bound={}, upper_bound={})'.format(self.lower_bound, self.upper_bound)
        return fmt_string

class _HalfOpenInterval(Constraint):
    """
    Constrain to a real interval `[lower_bound, upper_bound)`.
    """
    def __init__(self, lower_bound, upper_bound):
        self.lower_bound = lower_bound
        self.upper_bound = upper_bound
        super().__init__()

def check(self, value):
        return (self.lower_bound <= value) & (value < self.upper_bound)

def __repr__(self):
        fmt_string = self.__class__.__name__[1:]
        fmt_string += '(lower_bound={}, upper_bound={})'.format(self.lower_bound, self.upper_bound)
        return fmt_string

class _Simplex(Constraint):
    """
    Constrain to the unit simplex in the innermost (rightmost) dimension.
    Specifically: `x >= 0` and `x.sum(-1) == 1`.
    """
    event_dim = 1

def check(self, value):
        return torch.all(value >= 0, dim=-1) & ((value.sum(-1) - 1).abs() < 1e-6)

class _Multinomial(Constraint):
    """
    Constrain to nonnegative integer values summing to at most an upper bound.

Note due to limitations of the Multinomial distribution, this currently
    checks the weaker condition ``value.sum(-1) <= upper_bound``. In the future
    this may be strengthened to ``value.sum(-1) == upper_bound``.
    """
    is_discrete = True
    event_dim = 1

def __init__(self, upper_bound):
        self.upper_bound = upper_bound

def check(self, x):
        return (x >= 0).all(dim=-1) & (x.sum(dim=-1) <= self.upper_bound)

class _LowerTriangular(Constraint):
    """
    Constrain to lower-triangular square matrices.
    """
    event_dim = 2

def check(self, value):
        value_tril = value.tril()
        return (value_tril == value).view(value.shape[:-2] + (-1,)).min(-1)[0]

class _LowerCholesky(Constraint):
    """
    Constrain to lower-triangular square matrices with positive diagonals.
    """
    event_dim = 2

def check(self, value):
        value_tril = value.tril()
        lower_triangular = (value_tril == value).view(value.shape[:-2] + (-1,)).min(-1)[0]

positive_diagonal = (value.diagonal(dim1=-2, dim2=-1) > 0).min(-1)[0]
        return lower_triangular & positive_diagonal

class _CorrCholesky(Constraint):
    """
    Constrain to lower-triangular square matrices with positive diagonals and each
    row vector being of unit length.
    """
    event_dim = 2

def check(self, value):
        tol = torch.finfo(value.dtype).eps * value.size(-1) * 10  # 10 is an adjustable fudge factor
        row_norm = torch.linalg.norm(value.detach(), dim=-1)
        unit_row_norm = (row_norm - 1.).abs().le(tol).all(dim=-1)
        return _LowerCholesky().check(value) & unit_row_norm

class _PositiveDefinite(Constraint):
    """
    Constrain to positive-definite matrices.
    """
    event_dim = 2

def check(self, value):
        # Assumes that the matrix or batch of matrices in value are symmetric
        # info == 0 means no error, that is, it's SPD
        return torch.linalg.cholesky_ex(value).info.eq(0).unsqueeze(0)

class _Cat(Constraint):
    """
    Constraint functor that applies a sequence of constraints
    `cseq` at the submatrices at dimension `dim`,
    each of size `lengths[dim]`, in a way compatible with :func:`torch.cat`.
    """
    def __init__(self, cseq, dim=0, lengths=None):
        assert all(isinstance(c, Constraint) for c in cseq)
        self.cseq = list(cseq)
        if lengths is None:
            lengths = [1] * len(self.cseq)
        self.lengths = list(lengths)
        assert len(self.lengths) == len(self.cseq)
        self.dim = dim
        super().__init__()

@property
    def is_discrete(self):
        return any(c.is_discrete for c in self.cseq)

@property
    def event_dim(self):
        return max(c.event_dim for c in self.cseq)

def check(self, value):
        assert -value.dim() <= self.dim < value.dim()
        checks = []
        start = 0
        for constr, length in zip(self.cseq, self.lengths):
            v = value.narrow(self.dim, start, length)
            checks.append(constr.check(v))
            start = start + length  # avoid += for jit compat
        return torch.cat(checks, self.dim)

class _Stack(Constraint):
    """
    Constraint functor that applies a sequence of constraints
    `cseq` at the submatrices at dimension `dim`,
    in a way compatible with :func:`torch.stack`.
    """
    def __init__(self, cseq, dim=0):
        assert all(isinstance(c, Constraint) for c in cseq)
        self.cseq = list(cseq)
        self.dim = dim
        super().__init__()

@property
    def is_discrete(self):
        return any(c.is_discrete for c in self.cseq)

@property
    def event_dim(self):
        dim = max(c.event_dim for c in self.cseq)
        if self.dim + dim < 0:
            dim += 1
        return dim

def check(self, value):
        assert -value.dim() <= self.dim < value.dim()
        vs = [value.select(self.dim, i) for i in range(value.size(self.dim))]
        return torch.stack([constr.check(v)
                            for v, constr in zip(vs, self.cseq)], self.dim)

# Public interface.
dependent = _Dependent()
dependent_property = _DependentProperty
independent = _IndependentConstraint
boolean = _Boolean()
one_hot = _OneHot()
nonnegative_integer = _IntegerGreaterThan(0)
positive_integer = _IntegerGreaterThan(1)
integer_interval = _IntegerInterval
real = _Real()
real_vector = independent(real, 1)
positive = _GreaterThan(0.)
nonnegative = _GreaterThanEq(0.)
greater_than = _GreaterThan
greater_than_eq = _GreaterThanEq
less_than = _LessThan
multinomial = _Multinomial
unit_interval = _Interval(0., 1.)
interval = _Interval
half_open_interval = _HalfOpenInterval
simplex = _Simplex()
lower_triangular = _LowerTriangular()
lower_cholesky = _LowerCholesky()
corr_cholesky = _CorrCholesky()
positive_definite = _PositiveDefinite()
cat = _Cat
stack = _Stack

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