82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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import math
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import abc
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import numpy as np
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from typing import List, Optional
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import torch
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import torch.utils.data as data
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from .synthetic_utils import UnifiedSplit
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class SyntheticDEnv(UnifiedSplit, data.Dataset):
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"""The synethtic dynamic environment."""
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def __init__(
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self,
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mean_generators: List[data.Dataset],
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cov_generators: List[List[data.Dataset]],
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num_per_task: int = 5000,
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mode: Optional[str] = None,
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):
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self._ndim = len(mean_generators)
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assert self._ndim == len(
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cov_generators
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), "length does not match {:} vs. {:}".format(self._ndim, len(cov_generators))
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for cov_generator in cov_generators:
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assert self._ndim == len(
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cov_generator
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), "length does not match {:} vs. {:}".format(
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self._ndim, len(cov_generator)
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)
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self._num_per_task = num_per_task
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self._total_num = len(mean_generators[0])
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for mean_generator in mean_generators:
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assert self._total_num == len(mean_generator)
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for cov_generator in cov_generators:
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for cov_g in cov_generator:
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assert self._total_num == len(cov_g)
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self._mean_generators = mean_generators
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self._cov_generators = cov_generators
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UnifiedSplit.__init__(self, self._total_num, mode)
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def __iter__(self):
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self._iter_num = 0
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return self
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def __next__(self):
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if self._iter_num >= len(self):
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raise StopIteration
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self._iter_num += 1
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return self.__getitem__(self._iter_num - 1)
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def __getitem__(self, index):
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assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
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index = self._indexes[index]
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mean_list = [generator[index][-1] for generator in self._mean_generators]
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cov_matrix = [
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[cov_gen[index][-1] for cov_gen in cov_generator]
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for cov_generator in self._cov_generators
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]
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dataset = np.random.multivariate_normal(
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mean_list, cov_matrix, size=self._num_per_task
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)
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return index, torch.Tensor(dataset)
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def __len__(self):
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return len(self._indexes)
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def __repr__(self):
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return "{name}({cur_num:}/{total} elements, ndim={ndim}, num_per_task={num_per_task})".format(
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name=self.__class__.__name__,
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cur_num=len(self),
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total=self._total_num,
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ndim=self._ndim,
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num_per_task=self._num_per_task,
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)
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