Table Of Contents
Table Of Contents

Source code for gluonts.distribution.uniform

# Copyright 2018, Inc. or its affiliates. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
# or in the "license" file accompanying this file. This file is distributed
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.

# Standard library imports
from typing import Dict, Optional, Tuple

# First-party imports
from gluonts.model.common import Tensor

# Relative imports
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 def __init__(self, low: Tensor, high: Tensor, F=None) -> None: self.low = low self.high = high self.F = F if F else getF(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: is_in_range = self.F.broadcast_greater_equal( x, self.low ) * self.F.broadcast_lesser(x, self.high) return self.F.log(is_in_range) - self.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) -> Tensor: return _sample_multiple( self.F.sample_uniform, low=self.low, high=self.high, num_samples=num_samples, )
[docs] def sample_rep(self, num_samples: Optional[int] = None) -> Tensor: def s(low: Tensor, high: Tensor) -> Tensor: raw_samples = self.F.sample_uniform( low=low.zeros_like(), high=high.ones_like() ) 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]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 ()