doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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support = Real() | torch.distributions#torch.distributions.cauchy.Cauchy.support |
property variance | torch.distributions#torch.distributions.cauchy.Cauchy.variance |
class torch.distributions.chi2.Chi2(df, validate_args=None) [source]
Bases: torch.distributions.gamma.Gamma Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to Gamma(alpha=0.5*df, beta=0.5) Example: >>> m = Chi2(torch.tensor([1.0]))
>>> m.sample() # Chi2 distributed with sh... | torch.distributions#torch.distributions.chi2.Chi2 |
arg_constraints = {'df': GreaterThan(lower_bound=0.0)} | torch.distributions#torch.distributions.chi2.Chi2.arg_constraints |
property df | torch.distributions#torch.distributions.chi2.Chi2.df |
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.chi2.Chi2.expand |
torch.distributions.constraints.cat
alias of torch.distributions.constraints._Cat | torch.distributions#torch.distributions.constraints.cat |
class torch.distributions.constraints.Constraint [source]
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. Variables
~Constraint.is_discrete (bool) – Whether constrained space is discrete. Defaults to False.... | torch.distributions#torch.distributions.constraints.Constraint |
check(value) [source]
Returns a byte tensor of sample_shape + batch_shape indicating whether each event in value satisfies this constraint. | torch.distributions#torch.distributions.constraints.Constraint.check |
torch.distributions.constraints.dependent_property
alias of torch.distributions.constraints._DependentProperty | torch.distributions#torch.distributions.constraints.dependent_property |
torch.distributions.constraints.greater_than
alias of torch.distributions.constraints._GreaterThan | torch.distributions#torch.distributions.constraints.greater_than |
torch.distributions.constraints.greater_than_eq
alias of torch.distributions.constraints._GreaterThanEq | torch.distributions#torch.distributions.constraints.greater_than_eq |
torch.distributions.constraints.half_open_interval
alias of torch.distributions.constraints._HalfOpenInterval | torch.distributions#torch.distributions.constraints.half_open_interval |
torch.distributions.constraints.independent
alias of torch.distributions.constraints._IndependentConstraint | torch.distributions#torch.distributions.constraints.independent |
torch.distributions.constraints.integer_interval
alias of torch.distributions.constraints._IntegerInterval | torch.distributions#torch.distributions.constraints.integer_interval |
torch.distributions.constraints.interval
alias of torch.distributions.constraints._Interval | torch.distributions#torch.distributions.constraints.interval |
torch.distributions.constraints.less_than
alias of torch.distributions.constraints._LessThan | torch.distributions#torch.distributions.constraints.less_than |
torch.distributions.constraints.multinomial
alias of torch.distributions.constraints._Multinomial | torch.distributions#torch.distributions.constraints.multinomial |
torch.distributions.constraints.stack
alias of torch.distributions.constraints._Stack | torch.distributions#torch.distributions.constraints.stack |
class torch.distributions.constraint_registry.ConstraintRegistry [source]
Registry to link constraints to transforms.
register(constraint, factory=None) [source]
Registers a Constraint subclass in this registry. Usage: @my_registry.register(MyConstraintClass)
def construct_transform(constraint):
assert isinst... | torch.distributions#torch.distributions.constraint_registry.ConstraintRegistry |
register(constraint, factory=None) [source]
Registers a Constraint subclass in this registry. Usage: @my_registry.register(MyConstraintClass)
def construct_transform(constraint):
assert isinstance(constraint, MyConstraint)
return MyTransform(constraint.arg_constraints)
Parameters
constraint (subclass of ... | torch.distributions#torch.distributions.constraint_registry.ConstraintRegistry.register |
class torch.distributions.continuous_bernoulli.ContinuousBernoulli(probs=None, logits=None, lims=(0.499, 0.501), validate_args=None) [source]
Bases: torch.distributions.exp_family.ExponentialFamily Creates a continuous Bernoulli distribution parameterized by probs or logits (but not both). The distribution is support... | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli |
arg_constraints = {'logits': Real(), 'probs': Interval(lower_bound=0.0, upper_bound=1.0)} | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.arg_constraints |
cdf(value) [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.cdf |
entropy() [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.entropy |
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.expand |
has_rsample = True | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.has_rsample |
icdf(value) [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.icdf |
logits [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.logits |
log_prob(value) [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.log_prob |
property mean | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.mean |
property param_shape | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.param_shape |
probs [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.probs |
rsample(sample_shape=torch.Size([])) [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.rsample |
sample(sample_shape=torch.Size([])) [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.sample |
property stddev | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.stddev |
support = Interval(lower_bound=0.0, upper_bound=1.0) | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.support |
property variance | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.variance |
class torch.distributions.dirichlet.Dirichlet(concentration, validate_args=None) [source]
Bases: torch.distributions.exp_family.ExponentialFamily Creates a Dirichlet distribution parameterized by concentration concentration. Example: >>> m = Dirichlet(torch.tensor([0.5, 0.5]))
>>> m.sample() # Dirichlet distributed ... | torch.distributions#torch.distributions.dirichlet.Dirichlet |
arg_constraints = {'concentration': IndependentConstraint(GreaterThan(lower_bound=0.0), 1)} | torch.distributions#torch.distributions.dirichlet.Dirichlet.arg_constraints |
entropy() [source] | torch.distributions#torch.distributions.dirichlet.Dirichlet.entropy |
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.dirichlet.Dirichlet.expand |
has_rsample = True | torch.distributions#torch.distributions.dirichlet.Dirichlet.has_rsample |
log_prob(value) [source] | torch.distributions#torch.distributions.dirichlet.Dirichlet.log_prob |
property mean | torch.distributions#torch.distributions.dirichlet.Dirichlet.mean |
rsample(sample_shape=()) [source] | torch.distributions#torch.distributions.dirichlet.Dirichlet.rsample |
support = Simplex() | torch.distributions#torch.distributions.dirichlet.Dirichlet.support |
property variance | torch.distributions#torch.distributions.dirichlet.Dirichlet.variance |
class torch.distributions.distribution.Distribution(batch_shape=torch.Size([]), event_shape=torch.Size([]), validate_args=None) [source]
Bases: object Distribution is the abstract base class for probability distributions.
property arg_constraints
Returns a dictionary from argument names to Constraint objects that... | torch.distributions#torch.distributions.distribution.Distribution |
property arg_constraints
Returns a dictionary from argument names to Constraint objects that should be satisfied by each argument of this distribution. Args that are not tensors need not appear in this dict. | torch.distributions#torch.distributions.distribution.Distribution.arg_constraints |
property batch_shape
Returns the shape over which parameters are batched. | torch.distributions#torch.distributions.distribution.Distribution.batch_shape |
cdf(value) [source]
Returns the cumulative density/mass function evaluated at value. Parameters
value (Tensor) – | torch.distributions#torch.distributions.distribution.Distribution.cdf |
entropy() [source]
Returns entropy of distribution, batched over batch_shape. Returns
Tensor of shape batch_shape. | torch.distributions#torch.distributions.distribution.Distribution.entropy |
enumerate_support(expand=True) [source]
Returns tensor containing all values supported by a discrete distribution. The result will enumerate over dimension 0, so the shape of the result will be (cardinality,) + batch_shape + event_shape (where event_shape = () for univariate distributions). Note that this enumerates ... | torch.distributions#torch.distributions.distribution.Distribution.enumerate_support |
property event_shape
Returns the shape of a single sample (without batching). | torch.distributions#torch.distributions.distribution.Distribution.event_shape |
expand(batch_shape, _instance=None) [source]
Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape. This method calls expand on the distribution’s parameters. As such, this does not allocate new memory for the expanded distrib... | torch.distributions#torch.distributions.distribution.Distribution.expand |
icdf(value) [source]
Returns the inverse cumulative density/mass function evaluated at value. Parameters
value (Tensor) – | torch.distributions#torch.distributions.distribution.Distribution.icdf |
log_prob(value) [source]
Returns the log of the probability density/mass function evaluated at value. Parameters
value (Tensor) – | torch.distributions#torch.distributions.distribution.Distribution.log_prob |
property mean
Returns the mean of the distribution. | torch.distributions#torch.distributions.distribution.Distribution.mean |
perplexity() [source]
Returns perplexity of distribution, batched over batch_shape. Returns
Tensor of shape batch_shape. | torch.distributions#torch.distributions.distribution.Distribution.perplexity |
rsample(sample_shape=torch.Size([])) [source]
Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched. | torch.distributions#torch.distributions.distribution.Distribution.rsample |
sample(sample_shape=torch.Size([])) [source]
Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched. | torch.distributions#torch.distributions.distribution.Distribution.sample |
sample_n(n) [source]
Generates n samples or n batches of samples if the distribution parameters are batched. | torch.distributions#torch.distributions.distribution.Distribution.sample_n |
static set_default_validate_args(value) [source]
Sets whether validation is enabled or disabled. The default behavior mimics Python’s assert statement: validation is on by default, but is disabled if Python is run in optimized mode (via python -O). Validation may be expensive, so you may want to disable it once a mod... | torch.distributions#torch.distributions.distribution.Distribution.set_default_validate_args |
property stddev
Returns the standard deviation of the distribution. | torch.distributions#torch.distributions.distribution.Distribution.stddev |
property support
Returns a Constraint object representing this distribution’s support. | torch.distributions#torch.distributions.distribution.Distribution.support |
property variance
Returns the variance of the distribution. | torch.distributions#torch.distributions.distribution.Distribution.variance |
class torch.distributions.exponential.Exponential(rate, validate_args=None) [source]
Bases: torch.distributions.exp_family.ExponentialFamily Creates a Exponential distribution parameterized by rate. Example: >>> m = Exponential(torch.tensor([1.0]))
>>> m.sample() # Exponential distributed with rate=1
tensor([ 0.1046... | torch.distributions#torch.distributions.exponential.Exponential |
arg_constraints = {'rate': GreaterThan(lower_bound=0.0)} | torch.distributions#torch.distributions.exponential.Exponential.arg_constraints |
cdf(value) [source] | torch.distributions#torch.distributions.exponential.Exponential.cdf |
entropy() [source] | torch.distributions#torch.distributions.exponential.Exponential.entropy |
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.exponential.Exponential.expand |
has_rsample = True | torch.distributions#torch.distributions.exponential.Exponential.has_rsample |
icdf(value) [source] | torch.distributions#torch.distributions.exponential.Exponential.icdf |
log_prob(value) [source] | torch.distributions#torch.distributions.exponential.Exponential.log_prob |
property mean | torch.distributions#torch.distributions.exponential.Exponential.mean |
rsample(sample_shape=torch.Size([])) [source] | torch.distributions#torch.distributions.exponential.Exponential.rsample |
property stddev | torch.distributions#torch.distributions.exponential.Exponential.stddev |
support = GreaterThan(lower_bound=0.0) | torch.distributions#torch.distributions.exponential.Exponential.support |
property variance | torch.distributions#torch.distributions.exponential.Exponential.variance |
class torch.distributions.exp_family.ExponentialFamily(batch_shape=torch.Size([]), event_shape=torch.Size([]), validate_args=None) [source]
Bases: torch.distributions.distribution.Distribution ExponentialFamily is the abstract base class for probability distributions belonging to an exponential family, whose probabil... | torch.distributions#torch.distributions.exp_family.ExponentialFamily |
entropy() [source]
Method to compute the entropy using Bregman divergence of the log normalizer. | torch.distributions#torch.distributions.exp_family.ExponentialFamily.entropy |
class torch.distributions.fishersnedecor.FisherSnedecor(df1, df2, validate_args=None) [source]
Bases: torch.distributions.distribution.Distribution Creates a Fisher-Snedecor distribution parameterized by df1 and df2. Example: >>> m = FisherSnedecor(torch.tensor([1.0]), torch.tensor([2.0]))
>>> m.sample() # Fisher-Sn... | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor |
arg_constraints = {'df1': GreaterThan(lower_bound=0.0), 'df2': GreaterThan(lower_bound=0.0)} | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.arg_constraints |
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.expand |
has_rsample = True | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.has_rsample |
log_prob(value) [source] | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.log_prob |
property mean | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.mean |
rsample(sample_shape=torch.Size([])) [source] | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.rsample |
support = GreaterThan(lower_bound=0.0) | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.support |
property variance | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.variance |
class torch.distributions.gamma.Gamma(concentration, rate, validate_args=None) [source]
Bases: torch.distributions.exp_family.ExponentialFamily Creates a Gamma distribution parameterized by shape concentration and rate. Example: >>> m = Gamma(torch.tensor([1.0]), torch.tensor([1.0]))
>>> m.sample() # Gamma distribut... | torch.distributions#torch.distributions.gamma.Gamma |
arg_constraints = {'concentration': GreaterThan(lower_bound=0.0), 'rate': GreaterThan(lower_bound=0.0)} | torch.distributions#torch.distributions.gamma.Gamma.arg_constraints |
entropy() [source] | torch.distributions#torch.distributions.gamma.Gamma.entropy |
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.gamma.Gamma.expand |
has_rsample = True | torch.distributions#torch.distributions.gamma.Gamma.has_rsample |
log_prob(value) [source] | torch.distributions#torch.distributions.gamma.Gamma.log_prob |
property mean | torch.distributions#torch.distributions.gamma.Gamma.mean |
rsample(sample_shape=torch.Size([])) [source] | torch.distributions#torch.distributions.gamma.Gamma.rsample |
support = GreaterThan(lower_bound=0.0) | torch.distributions#torch.distributions.gamma.Gamma.support |
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