Source code for

# 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.

from functools import partial
from typing import Dict, List, Optional, Tuple

import numpy as np

from gluonts.core.component import validated
from 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 ()