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 typing import Dict, List, Optional, Tuple

import mxnet as mx
import numpy as np

from gluonts.core.component import DType, validated
from import Tensor

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

[docs]class Deterministic(Distribution): r""" Deterministic/Degenerate distribution. Parameters ---------- value Tensor containing the values, of shape `(*batch_shape, *event_shape)`. F """ is_reparameterizable = True @validated() def __init__(self, value: Tensor) -> None: self.value = value @property def F(self): return getF(self.value) @property def batch_shape(self) -> Tuple: return self.value.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 value = self.value is_both_nan = F.broadcast_logical_and(x != x, value != value) is_equal_or_both_nan = F.broadcast_logical_or( (x == value), is_both_nan ) return F.log(is_equal_or_both_nan)
@property def mean(self) -> Tensor: return self.value @property def stddev(self) -> Tensor: return self.value.zeros_like()
[docs] def cdf(self, x): F = self.F value = self.value is_both_nan = F.broadcast_logical_and( F.contrib.isnan(x), F.contrib.isnan(value) ) is_greater_equal_or_both_nan = F.broadcast_logical_or( (x >= value), is_both_nan ) return is_greater_equal_or_both_nan
[docs] def sample( self, num_samples: Optional[int] = None, dtype=np.float32 ) -> Tensor: return _sample_multiple( lambda value: value, value=self.value, num_samples=num_samples ).astype(dtype=dtype)
[docs] def quantile(self, level: Tensor) -> Tensor: F = self.F # we consider level to be an independent axis and so expand it # to shape (num_levels, 1, 1, ...) for _ in range(self.all_dim): level = level.expand_dims(axis=-1) quantiles = F.broadcast_mul(self.value, level.ones_like()) level = F.broadcast_mul(quantiles.ones_like(), level) minus_inf = -quantiles.ones_like() / 0.0 quantiles = F.where( F.broadcast_logical_or(level != 0, F.contrib.isnan(quantiles)), quantiles, minus_inf, ) nans = level.zeros_like() / 0.0 quantiles = F.where(level != level, nans, quantiles) return quantiles
@property def args(self) -> List: return [self.value]
[docs]class DeterministicArgProj(mx.gluon.HybridBlock): def __init__( self, value: float, args_dim: Dict[str, int], dtype: DType = np.float32, **kwargs, ) -> None: super().__init__(**kwargs) self.value = value self.args_dim = args_dim self.dtype = dtype # noinspection PyMethodOverriding,PyPep8Naming
[docs] def hybrid_forward(self, F, x: Tensor) -> Tuple[Tensor]: return (self.value * F.ones_like(x.sum(axis=-1)),)
[docs]class DeterministicOutput(DistributionOutput): args_dim: Dict[str, int] = {"value": 1} distr_cls: type = Deterministic @validated() def __init__(self, value: float): super().__init__() self.value = value
[docs] def get_args_proj( self, prefix: Optional[str] = None ) -> mx.gluon.HybridBlock: return DeterministicArgProj( value=self.value, args_dim=self.args_dim, dtype=self.dtype )
@property def event_shape(self) -> Tuple: return ()