207 lines
6.2 KiB
Python
207 lines
6.2 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 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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class FitFunc(abc.ABC):
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"""The fit function that outputs f(x) = a * x^2 + b * x + c."""
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def __init__(self, freedom: int, list_of_points=None):
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self._params = dict()
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for i in range(freedom):
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self._params[i] = None
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self._freedom = freedom
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if list_of_points is not None:
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self.fit(list_of_points)
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def set(self, _params):
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self._params = copy.deepcopy(_params)
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def check_valid(self):
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for key, value in self._params.items():
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if value is None:
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raise ValueError("The {:} is None".format(key))
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@abc.abstractmethod
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def __getitem__(self, x):
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raise NotImplementedError
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@abc.abstractmethod
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def _getitem(self, x):
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raise NotImplementedError
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def fit(
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self,
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list_of_points,
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max_iter=900,
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lr_max=1.0,
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verbose=False,
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):
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with torch.no_grad():
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data = torch.Tensor(list_of_points).type(torch.float32)
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assert data.ndim == 2 and data.size(1) == 2, "Invalid shape : {:}".format(
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data.shape
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)
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x, y = data[:, 0], data[:, 1]
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weights = torch.nn.Parameter(torch.Tensor(self._freedom))
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torch.nn.init.normal_(weights, mean=0.0, std=1.0)
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optimizer = torch.optim.Adam([weights], lr=lr_max, amsgrad=True)
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lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
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optimizer,
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milestones=[
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int(max_iter * 0.25),
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int(max_iter * 0.5),
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int(max_iter * 0.75),
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],
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gamma=0.1,
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)
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if verbose:
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print("The optimizer: {:}".format(optimizer))
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best_loss = None
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for _iter in range(max_iter):
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y_hat = self._getitem(x, weights)
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loss = torch.mean(torch.abs(y - y_hat))
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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lr_scheduler.step()
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if verbose:
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print(
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"In the fit, loss at the {:02d}/{:02d}-th iter is {:}".format(
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_iter, max_iter, loss.item()
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)
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)
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# Update the params
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if best_loss is None or best_loss > loss.item():
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best_loss = loss.item()
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for i in range(self._freedom):
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self._params[i] = weights[i].item()
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def __repr__(self):
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return "{name}(freedom={freedom})".format(
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name=self.__class__.__name__, freedom=freedom
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)
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class QuadraticFunc(FitFunc):
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"""The quadratic function that outputs f(x) = a * x^2 + b * x + c."""
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def __init__(self, list_of_points=None):
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super(QuadraticFunc, self).__init__(3, list_of_points)
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def __getitem__(self, x):
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self.check_valid()
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return self._params[0] * x * x + self._params[1] * x + self._params[2]
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def _getitem(self, x, weights):
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return weights[0] * x * x + weights[1] * x + weights[2]
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def __repr__(self):
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return "{name}(y = {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 CubicFunc(FitFunc):
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"""The cubic function that outputs f(x) = a * x^3 + b * x^2 + c * x + d."""
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def __init__(self, list_of_points=None):
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super(CubicFunc, self).__init__(4, list_of_points)
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def __getitem__(self, x):
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self.check_valid()
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return (
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self._params[0] * x ** 3
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+ self._params[1] * x ** 2
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+ self._params[2] * x
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+ self._params[3]
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)
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def _getitem(self, x, weights):
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return weights[0] * x ** 3 + weights[1] * x ** 2 + weights[2] * x + weights[3]
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def __repr__(self):
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return "{name}(y = {a} * x^3 + {b} * x^2 + {c} * x + {d})".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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d=self._params[3],
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)
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class QuarticFunc(FitFunc):
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"""The quartic function that outputs f(x) = a * x^4 + b * x^3 + c * x^2 + d * x + e."""
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def __init__(self, list_of_points=None):
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super(QuarticFunc, self).__init__(5, list_of_points)
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def __getitem__(self, x):
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self.check_valid()
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return (
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self._params[0] * x ** 4
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+ self._params[1] * x ** 3
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+ self._params[2] * x ** 2
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+ self._params[3] * x
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+ self._params[4]
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)
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def _getitem(self, x, weights):
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return (
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weights[0] * x ** 4
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+ weights[1] * x ** 3
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+ weights[2] * x ** 2
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+ weights[3] * x
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+ weights[4]
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)
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def __repr__(self):
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return "{name}(y = {a} * x^4 + {b} * x^3 + {c} * x^2 + {d} * x + {e})".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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d=self._params[3],
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e=self._params[3],
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)
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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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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 __getitem__(self, x):
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self.check_valid()
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return (
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self._params[0][self._timestamp] * x * x
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+ self._params[1][self._timestamp] * x
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+ self._params[2][self._timestamp]
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)
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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}(y = {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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