94 lines
2.9 KiB
Python
94 lines
2.9 KiB
Python
#####################################################
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
|
|
#####################################################
|
|
import math
|
|
import abc
|
|
import copy
|
|
import numpy as np
|
|
from typing import Optional
|
|
import torch
|
|
import torch.utils.data as data
|
|
|
|
from .math_base_funcs import FitFunc
|
|
|
|
|
|
class DynamicFunc(FitFunc):
|
|
"""The dynamic quadratic function, where each param is a function."""
|
|
|
|
def __init__(self, freedom: int, params=None):
|
|
super(DynamicFunc, self).__init__(freedom, None, params)
|
|
self._timestamp = None
|
|
|
|
def __call__(self, x, timestamp=None):
|
|
raise NotImplementedError
|
|
|
|
def _getitem(self, x, weights):
|
|
raise NotImplementedError
|
|
|
|
def set_timestamp(self, timestamp):
|
|
self._timestamp = timestamp
|
|
|
|
def noise_call(self, x, timestamp=None, std=0.1):
|
|
clean_y = self.__call__(x, timestamp)
|
|
if isinstance(clean_y, np.ndarray):
|
|
noise_y = clean_y + np.random.normal(scale=std, size=clean_y.shape)
|
|
else:
|
|
raise ValueError("Unkonwn type: {:}".format(type(clean_y)))
|
|
return noise_y
|
|
|
|
|
|
class DynamicLinearFunc(DynamicFunc):
|
|
"""The dynamic linear function that outputs f(x) = a * x + b.
|
|
The a and b is a function of timestamp.
|
|
"""
|
|
|
|
def __init__(self, params=None):
|
|
super(DynamicLinearFunc, self).__init__(3, params)
|
|
|
|
def __call__(self, x, timestamp=None):
|
|
self.check_valid()
|
|
if timestamp is None:
|
|
timestamp = self._timestamp
|
|
a = self._params[0](timestamp)
|
|
b = self._params[1](timestamp)
|
|
convert_fn = lambda x: x[-1] if isinstance(x, (tuple, list)) else x
|
|
a, b = convert_fn(a), convert_fn(b)
|
|
return a * x + b
|
|
|
|
def __repr__(self):
|
|
return "{name}({a} * x + {b}, timestamp={timestamp})".format(
|
|
name=self.__class__.__name__,
|
|
a=self._params[0],
|
|
b=self._params[1],
|
|
timestamp=self._timestamp,
|
|
)
|
|
|
|
|
|
class DynamicQuadraticFunc(DynamicFunc):
|
|
"""The dynamic quadratic function that outputs f(x) = a * x^2 + b * x + c.
|
|
The a, b, and c is a function of timestamp.
|
|
"""
|
|
|
|
def __init__(self, params=None):
|
|
super(DynamicQuadraticFunc, self).__init__(3, params)
|
|
|
|
def __call__(self, x, timestamp=None):
|
|
self.check_valid()
|
|
if timestamp is None:
|
|
timestamp = self._timestamp
|
|
a = self._params[0](timestamp)
|
|
b = self._params[1](timestamp)
|
|
c = self._params[2](timestamp)
|
|
convert_fn = lambda x: x[-1] if isinstance(x, (tuple, list)) else x
|
|
a, b, c = convert_fn(a), convert_fn(b), convert_fn(c)
|
|
return a * x * x + b * x + c
|
|
|
|
def __repr__(self):
|
|
return "{name}({a} * x^2 + {b} * x + {c}, timestamp={timestamp})".format(
|
|
name=self.__class__.__name__,
|
|
a=self._params[0],
|
|
b=self._params[1],
|
|
c=self._params[2],
|
|
timestamp=self._timestamp,
|
|
)
|