67 lines
2.1 KiB
Python
67 lines
2.1 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 math
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import abc
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import copy
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import numpy as np
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from typing import Optional
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import torch
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import torch.utils.data as data
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from .math_base_funcs import FitFunc
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class DynamicFunc(FitFunc):
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"""The dynamic quadratic function, where each param is a function."""
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def __init__(self, freedom: int, params=None):
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super(DynamicFunc, self).__init__(freedom, None, params)
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self._timestamp = None
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def __call__(self, x, timestamp=None):
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raise NotImplementedError
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def _getitem(self, x, weights):
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raise NotImplementedError
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def set_timestamp(self, timestamp):
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self._timestamp = timestamp
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def noise_call(self, x, timestamp=None, std=0.1):
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clean_y = self.__call__(x, timestamp)
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if isinstance(clean_y, np.ndarray):
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noise_y = clean_y + np.random.normal(scale=std, size=clean_y.shape)
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else:
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raise ValueError("Unkonwn type: {:}".format(type(clean_y)))
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return noise_y
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class DynamicQuadraticFunc(DynamicFunc):
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"""The dynamic quadratic function that outputs f(x) = a * x^2 + b * x + c.
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The a, b, and c is a function of timestamp.
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"""
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def __init__(self, params=None):
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super(DynamicQuadraticFunc, self).__init__(3, params)
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def __call__(self, x, timestamp=None):
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self.check_valid()
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if timestamp is None:
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timestamp = self._timestamp
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a = self._params[0](timestamp)
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b = self._params[1](timestamp)
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c = self._params[2](timestamp)
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convert_fn = lambda x: x[-1] if isinstance(x, (tuple, list)) else x
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a, b, c = convert_fn(a), convert_fn(b), convert_fn(c)
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return a * x * x + b * x + c
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def __repr__(self):
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return "{name}({a} * x^2 + {b} * x + {c}, timestamp={timestamp})".format(
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name=self.__class__.__name__,
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a=self._params[0],
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b=self._params[1],
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c=self._params[2],
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timestamp=self._timestamp,
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)
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