122 lines
4.0 KiB
Python
122 lines
4.0 KiB
Python
#####################################################
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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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from .math_base_funcs import QuadraticFunc
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from .math_base_funcs import QuarticFunc
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class DynamicQuadraticFunc(FitFunc):
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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, list_of_points=None):
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super(DynamicQuadraticFunc, self).__init__(3, list_of_points)
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self._timestamp = None
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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 _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 __repr__(self):
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return "{name}({a} * x^2 + {b} * x + {c})".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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)
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class ConstantFunc(FitFunc):
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"""The constant function: f(x) = c."""
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def __init__(self, constant=None):
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param = dict()
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param[0] = constant
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super(ConstantFunc, self).__init__(0, None, param)
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def __call__(self, x):
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self.check_valid()
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return self._params[0]
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def fit(self, **kwargs):
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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 __repr__(self):
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return "{name}({a})".format(name=self.__class__.__name__, a=self._params[0])
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class ComposedSinFunc(FitFunc):
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"""The composed sin function that outputs:
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f(x) = amplitude-scale-of(x) * sin( period-phase-shift-of(x) )
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- the amplitude scale is a quadratic function of x
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- the period-phase-shift is another quadratic function of x
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"""
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def __init__(self, **kwargs):
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super(ComposedSinFunc, self).__init__(0, None)
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self.fit(**kwargs)
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def __call__(self, x):
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self.check_valid()
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scale = self._params["amplitude_scale"](x)
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period_phase = self._params["period_phase_shift"](x)
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return scale * math.sin(period_phase)
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def fit(self, **kwargs):
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num_sin_phase = kwargs.get("num_sin_phase", 7)
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min_amplitude = kwargs.get("min_amplitude", 1)
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max_amplitude = kwargs.get("max_amplitude", 4)
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phase_shift = kwargs.get("phase_shift", 0.0)
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# create parameters
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amplitude_scale = QuadraticFunc(
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[(0, min_amplitude), (0.5, max_amplitude), (1, min_amplitude)]
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)
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fitting_data = []
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temp_max_scalar = 2 ** (num_sin_phase - 1)
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for i in range(num_sin_phase):
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value = (2 ** i) / temp_max_scalar
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next_value = (2 ** (i + 1)) / temp_max_scalar
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for _phase in (0, 0.25, 0.5, 0.75):
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inter_value = value + (next_value - value) * _phase
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fitting_data.append((inter_value, math.pi * (2 * i + _phase)))
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period_phase_shift = QuarticFunc(fitting_data)
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self.set(
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dict(amplitude_scale=amplitude_scale, period_phase_shift=period_phase_shift)
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)
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def _getitem(self, x, weights):
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raise NotImplementedError
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def __repr__(self):
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return "{name}({amplitude_scale} * sin({period_phase_shift}))".format(
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name=self.__class__.__name__,
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amplitude_scale=self._params["amplitude_scale"],
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period_phase_shift=self._params["period_phase_shift"],
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)
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