Source code for gluonts.mx.distribution.deterministic

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# Licensed under the Apache License, Version 2.0 (the "License").
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#     http://www.apache.org/licenses/LICENSE-2.0
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# or in the "license" file accompanying this file. This file is distributed
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from typing import Dict, List, Optional, Tuple

import mxnet as mx
import numpy as np

from gluonts.core.component import DType, validated
from gluonts.mx 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 ()