Source code for gluonts.mx.distribution.uniform

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# Licensed under the Apache License, Version 2.0 (the "License").
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from functools import partial
from typing import Dict, List, Optional, Tuple

import numpy as np

from gluonts.core.component import validated
from gluonts.mx import Tensor

from .distribution import Distribution, _sample_multiple, getF, softplus
from .distribution_output import DistributionOutput


[docs]class Uniform(Distribution): r""" Uniform distribution. Parameters ---------- low Tensor containing the lower bound of the distribution domain. high Tensor containing the higher bound of the distribution domain. F """ is_reparameterizable = True @validated() def __init__(self, low: Tensor, high: Tensor) -> None: self.low = low self.high = high @property def F(self): return getF(self.low) @property def batch_shape(self) -> Tuple: return self.low.shape @property def event_shape(self) -> Tuple: return () @property def event_dim(self) -> int: return 0
[docs] def log_prob(self, x: Tensor) -> Tensor: F = self.F is_in_range = F.broadcast_greater_equal( x, self.low ) * F.broadcast_lesser(x, self.high) return F.log(is_in_range) - F.log(self.high - self.low)
@property def mean(self) -> Tensor: return (self.high + self.low) / 2 @property def stddev(self) -> Tensor: return (self.high - self.low) / (12 ** 0.5)
[docs] def sample( self, num_samples: Optional[int] = None, dtype=np.float32 ) -> Tensor: return _sample_multiple( partial(self.F.sample_uniform, dtype=dtype), low=self.low, high=self.high, num_samples=num_samples, )
[docs] def sample_rep( self, num_samples: Optional[int] = None, dtype=np.float32 ) -> Tensor: def s(low: Tensor, high: Tensor) -> Tensor: raw_samples = self.F.sample_uniform( low=low.zeros_like(), high=high.ones_like(), dtype=dtype ) return low + raw_samples * (high - low) return _sample_multiple( s, low=self.low, high=self.high, num_samples=num_samples )
[docs] def cdf(self, x: Tensor) -> Tensor: return self.F.broadcast_div(x - self.low, self.high - self.low)
[docs] def quantile(self, level: Tensor) -> Tensor: F = self.F for _ in range(self.all_dim): level = level.expand_dims(axis=-1) return F.broadcast_add( F.broadcast_mul(self.high - self.low, level), self.low )
@property def args(self) -> List: return [self.low, self.high]
[docs]class UniformOutput(DistributionOutput): args_dim: Dict[str, int] = {"low": 1, "width": 1} distr_cls: type = Uniform
[docs] @classmethod def domain_map(cls, F, low, width): high = low + softplus(F, width) return low.squeeze(axis=-1), high.squeeze(axis=-1)
@property def event_shape(self) -> Tuple: return ()