85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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import numpy as np
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from typing import Optional
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import torch.utils.data as data
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class SynAdaptiveEnv(data.Dataset):
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"""The synethtic dataset for adaptive environment."""
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def __init__(
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self,
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max_num_phase: int = 100,
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interval: float = 0.1,
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max_scale: float = 4,
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offset_scale: float = 1.5,
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mode: Optional[str] = None,
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):
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self._max_num_phase = max_num_phase
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self._interval = interval
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self._times = np.arange(0, np.pi * self._max_num_phase, self._interval)
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xmin, xmax = self._times.min(), self._times.max()
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self._inputs = []
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self._total_num = len(self._times)
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for i in range(self._total_num):
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scale = (i + 1.0) / self._total_num * max_scale
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sin_scale = (i + 1.0) / self._total_num * 0.7
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sin_scale = -4 * (sin_scale - 0.5) ** 2 + 1
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# scale = -(self._times[i] - (xmin - xmax) / 2) + max_scale
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self._inputs.append(
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np.sin(self._times[i] * sin_scale) * (offset_scale - scale)
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)
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self._inputs = np.array(self._inputs)
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# Training Set 60%
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num_of_train = int(self._total_num * 0.6)
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# Validation Set 20%
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num_of_valid = int(self._total_num * 0.2)
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# Test Set 20%
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num_of_set = self._total_num - num_of_train - num_of_valid
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all_indexes = list(range(self._total_num))
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if mode is None:
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self._indexes = all_indexes
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elif mode.lower() in ("train", "training"):
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self._indexes = all_indexes[:num_of_train]
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elif mode.lower() in ("valid", "validation"):
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self._indexes = all_indexes[num_of_train : num_of_train + num_of_valid]
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elif mode.lower() in ("test", "testing"):
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self._indexes = all_indexes[num_of_train + num_of_valid :]
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else:
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raise ValueError("Unkonwn mode of {:}".format(mode))
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# transformation function
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self._transform = None
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def set_transform(self, fn):
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self._transform = fn
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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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value = float(self._inputs[index])
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if self._transform is not None:
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value = self._transform(value)
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return index, float(self._times[index]), value
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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)".format(
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name=self.__class__.__name__, cur_num=self._total_num, total=len(self)
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)
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