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pythonbook
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shao

This commit is contained in:
13002457275 2023-02-24 23:10:33 +08:00
parent 4433d57fed
commit 9eff845195
38 changed files with 2787 additions and 2077 deletions
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2
.idea/misc.xml
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@ -3,5 +3,5 @@
<component name="JavaScriptSettings">
<option name="languageLevel" value="ES6" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (venv)" project-jdk-type="Python SDK" />
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (regression)" project-jdk-type="Python SDK" />
</project>

2
.idea/pythonbook.iml
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@ -4,7 +4,7 @@
<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/venv" />
</content>
<orderEntry type="jdk" jdkName="Python 3.9 (venv)" jdkType="Python SDK" />
<orderEntry type="jdk" jdkName="Python 3.11 (regression)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>

80
Python高级编程/上下文管理器 读文本文件.py
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@ -1,45 +1,45 @@
class Resource():
def __enter__(self):
print('===connect to resource===')
return self
def __exit__(self, exc_type, exc_val, exc_tb):
print('===close resource connection===')
print(exc_type)
print(exc_val)
print(exc_tb)
return True
def operate(self):
1/0
with Resource() as res:
res.operate()
# class Resource():
# def __enter__(self):
# print('===connect to resource===')
# return self
#
# def __exit__(self, exc_type, exc_val, exc_tb):
# print('===close resource connection===')
# print(exc_type)
# print(exc_val)
# print(exc_tb)
# return True
#
# def operate(self):
# 1/0
#
# with Resource() as res:
# res.operate()
import contextlib
@contextlib.contextmanager
def open_func(file_name):
# __enter__方法
print('open file:', file_name, 'in __enter__')
file_handler = open(file_name, 'r')
try:
yield file_handler
except Exception as exc:
# deal with exception
print('the exception was thrown')
finally:
print('close file:', file_name, 'in __exit__')
file_handler.close()
return
with open_func('mytest.txt') as file_in:
for line in file_in:
1/0
print(line)
# import contextlib
#
# @contextlib.contextmanager
# def open_func(file_name):
# # __enter__方法
# print('open file:', file_name, 'in __enter__')
# file_handler = open(file_name, 'r')
#
# try:
# yield file_handler
# except Exception as exc:
# # deal with exception
# print('the exception was thrown')
# finally:
# print('close file:', file_name, 'in __exit__')
# file_handler.close()
#
# return
#
# with open_func('mytest.txt') as file_in:
# for line in file_in:
# 1/0
# print(line)
import contextlib

42
Python高级编程/单例模式.py Normal file
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@ -0,0 +1,42 @@
# https://www.cnblogs.com/wozijisun/p/16635365.html
def singleton(cls):
_instance_dict = {} # 采用字典，可以装饰多个类，控制多个类实现单例模式
def inner(*args, **kwargs):
if cls not in _instance_dict:
_instance_dict[cls] = cls(*args, **kwargs)
return _instance_dict.get(cls)
return inner
@singleton
class Teacher:
def __init__(self, name, age):
self.name = name
self.age = age
@singleton
class Student:
def __init__(self, name, age):
self.name = name
self.age = age
def get(self):
t1 = Teacher("aa",52)
return t1
# def __new__(cls, *args, **kwargs): # 将方法3的这部分代码搬到了函数装饰器中
# if not cls._instance:
# cls._instance = super().__new__(cls)
# return cls._instan
stu1 = Student('bb', 18)
stu2 = Student('jack', 18)
stu3 = Student('aaa', 19)
print(stu1 is stu3)
print(stu1.__dict__, stu3.__dict__)
t1 = Student('bb', 18).get()
t2 = Student('bb', 18).get()
print(t1 is t2)

109
pandasSQL例子/demo.py Normal file
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from sklearn.datasets import load_iris
import pandas as pd
from pandasql import sqldf
from pandasql import load_meat, load_births
import re
births = load_births()
meat = load_meat()
iris = load_iris()
iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
iris_df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)
iris_df.columns = [re.sub("[() ]", "", col) for col in iris_df.columns]
print(sqldf("SELECT * FROM iris_df LIMIT 10;", locals()))
print(sqldf("SELECT sepalwidthcm, species FROM iris_df LIMIT 10;", locals()))
q = """
select
species
, avg(sepalwidthcm)
, min(sepalwidthcm)
, max(sepalwidthcm)
from
iris_df
group by
species;
"""
print("*" * 80)
print("aggregation")
print("-" * 80)
print(q)
print(sqldf(q, locals()))
def pysqldf(q):
"add this to your script if you get tired of calling locals()"
return sqldf(q, globals())
print("*" * 80)
print("calling from a helper function")
print('''def pysqldf(q):)
"add this to your script if you get tired of calling locals()"
return sqldf(q, globals())''')
print("-" * 80)
print(q)
print(pysqldf(q))
q = """
select
a.*
from
iris_df a
inner join
iris_df b
on a.species = b.species
limit 10;
"""
print("*" * 80)
print("joins")
print("-" * 80)
print(q)
print(pysqldf(q))
q = """
select
*
from
iris_df
where
species = 'virginica'
and sepallengthcm > 7.7;
"""
print("*" * 80)
print("where clause")
print("-" * 80)
print(q)
print(pysqldf(q))
iris_df['id'] = range(len(iris_df))
q = """
select
*
from
iris_df
where
id in (select id from iris_df where sepalwidthcm*sepallengthcm > 25);
"""
print("*" * 80)
print("subqueries")
print("-" * 80)
print(q)
print(pysqldf(q))
q = """
SELECT
m.*
, b.births
FROM
meat m
INNER JOIN
births b
on m.date = b.date
ORDER BY
m.date;
"""
print(pysqldf(q).head())

77
优化算法/AA.py
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@ -1,77 +0,0 @@
#coding:utf-8
# https://mp.weixin.qq.com/s/ac-CgZj-avmPBraVvQUuBQ
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
class Function():
def __init__(self):
self.points_x = np.linspace(-20, 20, 1000)
self.points_y = self.f(self.points_x)
def f(self,x):
return (0.15*x)**2 + np.cos(x) + np.sin(3*x)/3 + np.cos(5*x)/5 + np.sin(7*x)/7
def df(self,x):
return (9/200)*x - np.sin(x) -np.sin(5*x) + np.cos(3*x) + np.cos(7*x)
def ddf(self,x):
return (9/200) - np.cos(x) -3*np.sin(x) - 5*np.cos(5*x) -7* np.sin(7*x)
# #
# #
# # # AdaGrad
# # for i in range(15):
# # # 绘制原来的函数
# # plt.plot(points_x, points_y, c="b", alpha=0.5, linestyle="-")
# # # 算法开始
# # lr = pow(1.5,-i)*32
# # delta = 1e-7
# # x = -20
# # r = 0
# # AdaGrad_x, AdaGrad_y = [], []
# # for it in range(1000):
# # AdaGrad_x.append(x), AdaGrad_y.append(f(x))
# # g = df(x)
# # r = r + g*g # 积累平方梯度
# # x = x - lr /(delta + np.sqrt(r)) * g
# #
# # plt.xlim(-20, 20)
# # plt.ylim(-2, 10)
# # plt.plot(AdaGrad_x, AdaGrad_y, c="r", linestyle="-")
# # plt.scatter(AdaGrad_x[-1],AdaGrad_y[-1],90,marker = "x",color="g")
# # plt.title("AdaGrad,lr=%f"%(lr))
# # plt.savefig("AdaGrad,lr=%f"%(lr) + ".png")
# # plt.clf()
# #
# #
# # # RMSProp
# # for i in range(15):
# # # 绘制原来的函数
# # plt.plot(points_x, points_y, c="b", alpha=0.5, linestyle="-")
# # # 算法开始
# # lr = pow(1.5,-i)*32
# # delta = 1e-6
# # rou = 0.8
# # x = -20
# # r = 0
# # RMSProp_x, RMSProp_y = [], []
# # for it in range(1000):
# # RMSProp_x.append(x), RMSProp_y.append(f(x))
# # g = df(x)
# # r = rou * r + (1-rou)*g*g # 积累平方梯度
# # x = x - lr /(delta + np.sqrt(r)) * g
# #
# # plt.xlim(-20, 20)
# # plt.ylim(-2, 10)
# # plt.plot(RMSProp_x, RMSProp_y, c="r", linestyle="-")
# # plt.scatter(RMSProp_x[-1],RMSProp_y[-1],90,marker = "x",color="g")
# # plt.title("RMSProp,lr=%f,rou=%f"%(lr,rou))
# # plt.savefig("RMSProp,lr=%f,rou=%f"%(lr,rou) + ".png")
# # plt.clf()
# #
# #

61
优化算法/AdaFactor.py Normal file
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@ -0,0 +1,61 @@
import numpy as np
from function import *
import math
z_history = []
x1_history = []
x2_history = []
# fName = 'semicircle'
# fName = 'rosenbrock'
# fName = 'semicircle2'
fName = 'himmelblau'
dfName = 'df_'+fName
def objective(trial):
x1 = 0.1
x2 = 0.1
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
z = eval(fName)(x1, x2)
z_history.append(z)
s1 ,s2 = 0,0
eps = 1e-5
deltax1,deltax1 = 0,0
for epoch in range(epochs):
dx1, dx2 = eval(dfName)(x1, x2)
s1 = beta*s1+(1-beta)* dx1 ** 2
s2 = beta*s2+(1-beta)* dx2 ** 2
dx1 =
g1 = math.sqrt(s2)
deltax1,deltax2 = beta*deltax1+(1-beta)* dx1 ** 2
lr1,lr2 = lr/ math.sqrt(s1),lr/ math.sqrt(s2)
x1 -= lr1 * dx1
x2 -= lr2 * dx2
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
return eval(fName)(x1, x2)
def main():
study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=1, timeout=600)
plot_optimization_history(study).show()
plotAll(fName,np.array(x1_history), np.array(x2_history))
print(study.best_value,study.best_trial)
if __name__ == '__main__':
main()

61
优化算法/Adadelta.py Normal file
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@ -0,0 +1,61 @@
import numpy as np
from function import *
import math
z_history = []
x1_history = []
x2_history = []
# fName = 'semicircle'
# fName = 'rosenbrock'
# fName = 'semicircle2'
fName = 'himmelblau'
dfName = 'df_'+fName
def objective(trial):
x1 = 0.1
x2 = 0.1
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
z = eval(fName)(x1, x2)
z_history.append(z)
s1 ,s2 = 0,0
eps = 1e-5
deltax1,deltax1 = 0,0
for epoch in range(epochs):
dx1, dx2 = eval(dfName)(x1, x2)
s1 = beta*s1+(1-beta)* dx1 ** 2
s2 = beta*s2+(1-beta)* dx2 ** 2
dx1 =
g1 = math.sqrt(s2)
deltax1,deltax2 = beta*deltax1+(1-beta)* dx1 ** 2
lr1,lr2 = lr/ math.sqrt(s1),lr/ math.sqrt(s2)
x1 -= lr1 * dx1
x2 -= lr2 * dx2
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
return eval(fName)(x1, x2)
def main():
study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=1, timeout=600)
plot_optimization_history(study).show()
plotAll(fName,np.array(x1_history), np.array(x2_history))
print(study.best_value,study.best_trial)
if __name__ == '__main__':
main()

53
优化算法/Adagrad abuslute.py Normal file
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import numpy as np
from function import *
import math
z_history = []
x1_history = []
x2_history = []
# fName = 'semicircle'
# fName = 'rosenbrock'
# fName = 'semicircle2'
fName = 'himmelblau'
dfName = 'df_'+fName
def objective(trial):
x1 = 0.1
x2 = 0.1
lr = 0.5
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
z = eval(fName)(x1, x2)
z_history.append(z)
s1 ,s2 = 0.0001,0.0001
for epoch in range(epochs):
dx1, dx2 = eval(dfName)(x1, x2)
s1 += math.fabs(dx1)/2
s2 += math.fabs(dx2)/2
lr1,lr2 = lr/s1,lr/ s2
x1 -= lr1 * dx1
x2 -= lr2 * dx2
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
return eval(fName)(x1, x2)
def main():
study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=1, timeout=600)
plot_optimization_history(study).show()
plotAll(fName,np.array(x1_history), np.array(x2_history))
print(study.best_value,study.best_trial)
if __name__ == '__main__':
main()

53
优化算法/Adagrad futrure.py Normal file
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import numpy as np
from function import *
import math
z_history = []
x1_history = []
x2_history = []
# fName = 'semicircle'
# fName = 'rosenbrock'
# fName = 'semicircle2'
fName = 'himmelblau'
dfName = 'df_'+fName
def objective(trial):
x1 = 0.1
x2 = 0.1
lr = 0.5
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
z = eval(fName)(x1, x2)
z_history.append(z)
s1 ,s2 = 0.0001,0.0001
for epoch in range(epochs):
dx1, dx2 = eval(dfName)(x1, x2)
s1 += math.fabs(dx1)/2
s2 += math.fabs(dx2)/2
lr1,lr2 = lr/s1,lr/ s2
x1 -= lr1 * dx1
x2 -= lr2 * dx2
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
return eval(fName)(x1, x2)
def main():
study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=1, timeout=600)
plot_optimization_history(study).show()
plotAll(fName,np.array(x1_history), np.array(x2_history))
print(study.best_value,study.best_trial)
if __name__ == '__main__':
main()

53
优化算法/Adagrad.py Normal file
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import numpy as np
from function import *
import math
z_history = []
x1_history = []
x2_history = []
# fName = 'semicircle'
# fName = 'rosenbrock'
# fName = 'semicircle2'
fName = 'himmelblau'
dfName = 'df_'+fName
def objective(trial):
x1 = 0.1
x2 = 0.1
lr = 0.5
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
z = eval(fName)(x1, x2)
z_history.append(z)
s1 ,s2 = 0,0
for epoch in range(epochs):
dx1, dx2 = eval(dfName)(x1, x2)
s1 += dx1 ** 2
s2 += dx2 ** 2
lr1,lr2 = lr/ math.sqrt(s1),lr/ math.sqrt(s2)
x1 -= lr1 * dx1
x2 -= lr2 * dx2
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
return eval(fName)(x1, x2)
def main():
study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=1, timeout=600)
plot_optimization_history(study).show()
plotAll(fName,np.array(x1_history), np.array(x2_history))
print(study.best_value,study.best_trial)
if __name__ == '__main__':
main()

36
优化算法/Adam.py
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@ -1,36 +0,0 @@
# Adam
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from AA import Function
ff = Function()
for i in range(48):
# 绘制原来的函数
plt.plot(ff.points_x, ff.points_y, c="b", alpha=0.5, linestyle="-")
# 算法开始
lr = pow(1.2,-i)*2
rou1,rou2 = 0.9,0.9 # 原来的算法中rou2=0.999，但是效果很差
delta = 1e-8
x = -20
s,r = 0,0
t = 0
Adam_x, Adam_y = [], []
for it in range(1000):
Adam_x.append(x), Adam_y.append(ff.f(x))
t += 1
g = ff.df(x)
s = rou1 * s + (1 - rou1)*g
r = rou2 * r + (1 - rou2)*g*g # 积累平方梯度
s = s/(1-pow(rou1,t))
r = r/(1-pow(rou2,t))
x = x - lr /(delta + np.sqrt(r)) * s
plt.xlim(-20, 20)
plt.ylim(-2, 10)
plt.plot(Adam_x, Adam_y, c="r", linestyle="-")
plt.scatter(Adam_x[-1],Adam_y[-1],90,marker = "X",color="g")
plt.title("Adam,lr=%f"%(lr))
# plt.savefig("Adam,lr=%f"%(lr) + ".png")
plt.show()
plt.clf()

59
优化算法/Adma.py Normal file
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@ -0,0 +1,59 @@
import numpy as np
from function import *
import math
z_history = []
x1_history = []
x2_history = []
# fName = 'semicircle'
# fName = 'rosenbrock'
# fName = 'semicircle2'
fName = 'himmelblau'
dfName = 'df_'+fName
def objective(trial):
x1 = 0.1
x2 = 0.1
lr = 0.2
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
beta2 = 0.5
z = eval(fName)(x1, x2)
z_history.append(z)
s1 ,s2 = 0,0
v1, v2 = 0, 0
for epoch in range(epochs):
dx1, dx2 = eval(dfName)(x1, x2)
v1 = beta * v1 + (1 - beta) * dx1
v2 = beta * v2 + (1 - beta) * dx2
s1 = beta2*s1+(1-beta2)* dx1 ** 2
s2 = beta2*s2+(1-beta2)* dx2 ** 2
lr1,lr2 = lr/ math.sqrt(s1),lr/ math.sqrt(s2)
x1 -= lr1 * v1
x2 -= lr2 * v2
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
return eval(fName)(x1, x2)
def main():
study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=1, timeout=600)
plot_optimization_history(study).show()
plotAll(fName,np.array(x1_history), np.array(x2_history))
print(study.best_value,study.best_trial)
if __name__ == '__main__':
main()

50
优化算法/Nesterov.py Normal file
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import numpy as np
from function import *
z_history = []
x1_history = []
x2_history = []
# fName = 'semicircle'
# fName = 'rosenbrock'
fName = 'semicircle2'
fName = 'himmelblau'
dfName = 'df_'+fName
def objective(trial):
x1 = 0.1
x2 = 0.1
lr = 0.01
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
z = eval(fName)(x1, x2)
z_history.append(z)
v1,v2 = 0,0
for epoch in range(epochs):
dx1, dx2 = eval(dfName)(x1, x2)
v1 = beta * v1 + (1-beta)* dx1
v2 = beta * v2 + (1-beta) * dx2
x1 -= lr * v1
x2 -= lr * v2
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
return eval(fName)(x1, x2)
def main():
study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=1, timeout=600)
plot_optimization_history(study).show()
plotAll(fName,np.array(x1_history), np.array(x2_history))
print(study.best_value,study.best_trial)
if __name__ == '__main__':
main()

44
优化算法/NewTon.py Normal file
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import numpy as np
from function import *
z_history = []
x1_history = []
x2_history = []
# fName = 'semicircle'
# fName = 'rosenbrock'
# fName = 'semicircle2'
fName = 'himmelblau'
dfName = 'df_'+fName
ddfName = 'ddf_'+fName
def objective(trial):
x1 = 0.1
x2 = 0.1
epochs = 15
z = eval(fName)(x1, x2)
z_history.append(z)
for epoch in range(epochs):
alpha = pow(1.2, -epoch) * 20
dx1, dx2 = eval(dfName)(x1, x2)
ddx1,ddx2 = eval(ddfName)(x1, x2)
x1 -= dx1/(alpha+ddx1)
x2 -= dx2/(alpha+ddx2)
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
return eval(fName)(x1, x2)
def main():
study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=1, timeout=600)
plot_optimization_history(study).show()
plotAll(fName,np.array(x1_history), np.array(x2_history))
print(study.best_value,study.best_trial)
if __name__ == '__main__':
main()

53
优化算法/RMSProp.py Normal file
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import numpy as np
from function import *
import math
z_history = []
x1_history = []
x2_history = []
# fName = 'semicircle'
# fName = 'rosenbrock'
# fName = 'semicircle2'
fName = 'himmelblau'
dfName = 'df_'+fName
def objective(trial):
x1 = 0.1
x2 = 0.1
lr = 0.2
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
z = eval(fName)(x1, x2)
z_history.append(z)
s1 ,s2 = 0,0
for epoch in range(epochs):
dx1, dx2 = eval(dfName)(x1, x2)
s1 = beta*s1+(1-beta)* dx1 ** 2
s2 = beta*s2+(1-beta)* dx2 ** 2
lr1,lr2 = lr/ math.sqrt(s1),lr/ math.sqrt(s2)
x1 -= lr1 * dx1
x2 -= lr2 * dx2
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
return eval(fName)(x1, x2)
def main():
study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=1, timeout=600)
plot_optimization_history(study).show()
plotAll(fName,np.array(x1_history), np.array(x2_history))
print(study.best_value,study.best_trial)
if __name__ == '__main__':
main()

0
优化算法/__init__.py
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83
优化算法/function.py Normal file
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import optuna
import plotly
from optuna.visualization import plot_optimization_history
import numpy as np
import plotly.graph_objects as go
def himmelblau(x1, x2):
# rosenbrock函数
return ((x1**2) + x1 - 11)**2 + (x1 + x2 ** 2 - 7) ** 2
def df_himmelblau(x1, x2):
return (x1 ** 2 + x1 - 11) *(4*x1+2) + (x1 + x2 ** 2 - 7) * 2, 2*(x1 + x2 **2 -7)*2*x2
def ddf_himmelblau(x1, x2):
return 12*x1**2+12*x1+2,4*x1+12*x2**2-28
def rosenbrock(x1, x2):
# rosenbrock函数
return (1 - x1) ** 2 + 10 * (x2 - x1 ** 2) ** 2
def df_rosenbrock(x1, x2):
return -2 + 2 * x1 - 40 * (x2 - x1 ** 2) * x1, 20 * (x2 - x1 ** 2)
def semicircle(x1, x2):
# 半圆函数
return x1 ** 2 + x2 ** 2
def df_semicircle(x1, x2):
return x1 * 2 , x2 * 2
def semicircle2(x1, x2):
return 0.1 * x1 ** 2 + 2 * x2 ** 2
def df_semicircle2(x1, x2):
return x1 * 0.2 , x2 * 4
def plotF(fName,x,y,grid):
x1 = np.arange(-x, x, grid)
x2 = np.arange(-y, y, grid)
x1, x2 = np.meshgrid(x1, x2)
fig = go.Figure(data = [
go.Surface(
contours = { "x": {"show": True, "start": -4, "end": 4, "size": 0.4, "color":"white"}, "y": {"show": True, "start": -4, "end": 4, "size": 0.4, "color":"white"}, "z": {"show": True, "start": 0.5, "end": 800, "size": 5} },
x = x1,
y = x2,
z = fName(x1,x2),
opacity=0.2),
])
fig.show()
def plotAll(fName,x1_history,x2_history):
x1 = np.arange(-5, 5, 0.5)
x2 = np.arange(-5, 5, 0.5)
x1, x2 = np.meshgrid(x1, x2)
z_history = eval(fName)(x1_history, x2_history)
fig = go.Figure(data=[
go.Surface(
contours={"x": {"show": True, "start": -4, "end": 4, "size": 0.4, "color": "white"},
"y": {"show": True, "start": -4, "end": 4, "size": 0.4, "color": "white"},
"z": {"show": True, "start": 0.5, "end": 800, "size": 5}},
x=x1,
y=x2,
z=eval(fName)(x1, x2),
opacity=0.2),
go.Scatter3d(
x=x1_history, y=x2_history, z=z_history,
mode='lines+markers',
marker=dict(size=4, colorscale='Viridis', opacity=1),
line=dict(width=2)
),
])
fig.show()
def main():
# plotF(rosenbrock,2,8,0.1)
plotF(himmelblau, 5, 5, 0.5)
# plotF(semicircle, 5, 5, 0.5)
if __name__ == '__main__':
main()

2
优化算法/test.py
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@ -1,2 +0,0 @@
lr = pow(2,-2)*16
print(lr)

29
优化算法/动量 + 梯度下降法.py
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@ -1,29 +0,0 @@
# 动量 + 梯度下降法
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from AA import Function
ff = Function()
for i in range(10):
# 绘制原来的函数
plt.plot(ff.points_x, ff.points_y, c="b", alpha=0.5, linestyle="-")
# 算法开始
lr = 0.002
m = 1 - pow(0.5,i)
x = -20
v = 1.0
GDM_x, GDM_y = [], []
for it in range(1000):
GDM_x.append(x), GDM_y.append(ff.f(x))
v = m * v - lr * ff.df(x)
x = x + v
plt.xlim(-20, 20)
plt.ylim(-2, 10)
plt.plot(GDM_x, GDM_y, c="r", linestyle="-")
plt.scatter(GDM_x[-1],GDM_y[-1],90,marker = "x",color="g")
plt.title("Gradient descent + momentum,lr=%f,m=%f"%(lr,m))
# plt.savefig("Gradient descent + momentum,lr=%f,m=%f"%(lr,m) + ".png")
plt.show()
plt.clf()

51
优化算法/动量法.py Normal file
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import numpy as np
from function import *
z_history = []
x1_history = []
x2_history = []
# fName = 'semicircle'
# fName = 'rosenbrock'
fName = 'semicircle2'
fName = 'himmelblau'
dfName = 'df_'+fName
def objective(trial):
x1 = 0.1
x2 = 0.1
lr = 0.01
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
z = eval(fName)(x1, x2)
z_history.append(z)
v1,v2 = 0,0
for epoch in range(epochs):
dx1, dx2 = eval(dfName)(x1, x2)
v1 = beta * v1 + (1-beta)* dx1
v2 = beta * v2 + (1-beta) * dx2
x1 -= lr * v1
x2 -= lr * v2
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
return eval(fName)(x1, x2)
def main():
study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=1, timeout=600)
plot_optimization_history(study).show()
plotAll(fName,np.array(x1_history), np.array(x2_history))
print(study.best_value,study.best_trial)
if __name__ == '__main__':
main()

89
优化算法/动量法wandb.py Normal file
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import numpy as np
from function import *
import wandb
import math
z_history = []
x1_history = []
x2_history = []
# fName = 'semicircle'
# fName = 'rosenbrock'
fName = 'semicircle2'
fName = 'himmelblau'
dfName = 'df_'+fName
def objective():
x1 = 0.1
x2 = 0.1
lr = 0.01
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
z = eval(fName)(x1, x2)
z_history.append(z)
v1,v2 = 0,0
for epoch in range(epochs):
dx1, dx2 = eval(dfName)(x1, x2)
v1 = beta * v1 + (1-beta)* dx1
v2 = beta * v2 + (1-beta) * dx2
x1 -= lr * v1
x2 -= lr * v2
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
wandb.log({
'loss': z,
})
return eval(fName)(x1, x2)
def RMS():
x1 = 0.1
x2 = 0.1
lr = 0.2
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
z = eval(fName)(x1, x2)
z_history.append(z)
s1 ,s2 = 0,0
for epoch in range(epochs):
dx1, dx2 = eval(dfName)(x1, x2)
s1 = beta*s1+(1-beta)* dx1 ** 2
s2 = beta*s2+(1-beta)* dx2 ** 2
lr1,lr2 = lr/ math.sqrt(s1),lr/ math.sqrt(s2)
x1 -= lr1 * dx1
x2 -= lr2 * dx2
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
wandb.log({
'loss': z,
})
return eval(fName)(x1, x2)
def main():
RMS()
plotAll(fName,np.array(x1_history), np.array(x2_history))
if __name__ == '__main__':
wandb.init(project="优化算法", name="RMS")
main()
wandb.finish()

1
优化算法/学习率调度器.py Normal file
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# http://zh.d2l.ai/chapter_optimization/lr-scheduler.html

46
优化算法/梯度下降.py Normal file
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import numpy as np
from function import *
z_history = []
x1_history = []
x2_history = []
# fName = 'semicircle'
# fName = 'rosenbrock'
# fName = 'semicircle2'
fName = 'himmelblau'
dfName = 'df_'+fName
def objective(trial):
x1 = 0.1
x2 = 0.1
lr = 0.002
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
z = eval(fName)(x1, x2)
z_history.append(z)
for epoch in range(epochs):
dx1, dx2 = eval(dfName)(x1, x2)
x1 -= lr * dx1
x2 -= lr * dx2
x1_history.append(x1)
x2_history.append(x2)
z = eval(fName)(x1, x2)
z_history.append(z)
return eval(fName)(x1, x2)
def main():
study = optuna.create_study(direction="minimize")
study.optimize(objective, n_trials=1, timeout=600)
plot_optimization_history(study).show()
plotAll(fName,np.array(x1_history), np.array(x2_history))
print(study.best_value,study.best_trial)
if __name__ == '__main__':
main()

29
优化算法/牛顿法.py
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@ -1,29 +0,0 @@
# # 牛顿法
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from AA import Function
ff = Function()
for i in range(72):
# 绘制原来的函数
plt.plot(ff.points_x, ff.points_y, c="b", alpha=0.5, linestyle="-")
# 算法开始
alpha= pow(1.2,-i)*20
x = -20.0
Newton_x, Newton_y = [], []
for it in range(1000):
Newton_x.append(x), Newton_y.append(ff.f(x))
g = ff.df(x)
gg = ff.ddf(x)
x = x - g/(gg+alpha)
plt.xlim(-20, 20)
plt.ylim(-2, 10)
plt.plot(Newton_x, Newton_y, c="r", linestyle="-")
plt.scatter(Newton_x[-1],Newton_y[-1],90,marker = "x",color="g")
plt.title("Newton,alpha=%f"%(alpha))
# plt.savefig("Newton,alpha=%f"%(alpha) + ".png")
plt.show()
plt.clf()

26
优化算法/纯粹的梯度下降法,GD.py
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@ -1,26 +0,0 @@
# 纯粹的梯度下降法,GD
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from AA import Function
ff = Function()
for i in range(10):
# 绘制原来的函数
plt.plot(ff.points_x, ff.points_y, c="b", alpha=0.5, linestyle="-")
# 算法开始
lr = pow(2,-i)*16
x = -20.0
GD_x, GD_y = [], []
for it in range(1000):
GD_x.append(x), GD_y.append(ff.f(x))
dx = ff.df(x)
x = x - lr * dx
plt.xlim(-20, 20)
plt.ylim(-2, 10)
plt.plot(GD_x, GD_y, c="r", linestyle="--")
plt.title("Gradient descent,lr=%f"%(lr))
# plt.savefig("Gradient descent,lr=%f"%(lr) + ".png")
plt.show()
plt.clf()

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实验/猪肉价格/bean.txt
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732
实验/猪肉价格/maizeprice.txt
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@ -1,366 +1,366 @@
1965.00
1990.00
1977.00
1991.00
1988.00
1995.00
2002.00
1982.00
1987.00
1982.00
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730
实验/猪肉价格/pig_in.txt
View File

@ -1,366 +1,366 @@
31.58
32.10
32.01
32.15
32.23
32.22
32.19
31.86
31.39
31.07
31.17
31.52
32.12
33.29
33.62
33.68
34.00
33.68
33.60
33.47
33.44
33.32
32.91
32.61
32.60
32.44
32.65
32.77
32.98
32.72
32.84
32.89
32.89
32.79
32.82
32.97
32.90
33.02
32.96
32.95
32.93
33.03
33.15
33.93
34.53
34.57
34.61
34.66
34.99
35.25
35.11
35.26
35.17
35.08
35.37
35.71
35.67
35.76
35.08
35.22
35.27
35.40
35.32
35.49
35.09
35.25
35.33
35.55
35.85
35.85
35.68
35.70
35.55
35.41
35.86
35.60
35.69
35.96
35.64
35.93
35.91
36.15
36.42
36.34
36.97
36.89
37.32
37.19
37.20
37.38
37.18
36.89
37.10
37.11
36.94
36.28
36.14
35.90
36.47
36.15
36.10
35.66
35.76
35.68
35.62
35.76
35.73
36.09
35.80
36.14
35.81
35.90
35.81
35.80
35.69
35.83
35.72
35.46
35.52
35.61
35.68
35.36
35.24
34.97
34.72
34.48
34.37
34.27
34.23
34.09
33.75
33.88
33.88
33.78
33.72
33.63
33.55
32.99
33.07
32.94
33.20
33.11
32.78
32.86
32.83
32.86
32.72
33.04
32.79
32.90
32.77
32.68
32.81
32.69
32.56
32.36
32.23
32.05
32.04
32.00
31.85
31.64
31.56
31.43
31.14
31.17
31.16
31.14
30.91
30.57
30.01
29.78
29.40
29.10
28.77
28.41
12.45
12.53
12.49
12.44
12.47
12.54
12.34
12.51
12.44
12.45
12.53
12.46
12.41
12.38
12.31
12.10
12.15
12.11
12.27
12.23
12.11
12.05
11.98
12.00
11.92
11.77
11.78
11.62
11.63
11.74
11.91
12.11
12.17
12.14
12.10
12.11
12.03
12.14
12.21
12.31
12.36
12.36
12.42
12.34
12.27
12.27
12.25
12.23
12.16
12.18
12.04
12.16
12.27
12.39
12.55
12.62
12.70
12.92
13.34
13.94
14.46
14.23
14.11
14.33
14.61
14.67
14.50
14.56
14.72
14.90
14.99
15.04
15.02
14.97
14.77
14.57
14.62
14.75
14.97
15.23
15.22
15.08
15.14
15.19
15.29
15.43
15.47
15.54
15.66
15.61
15.64
15.66
15.64
15.69
15.76
15.74
15.77
15.70
15.73
15.71
15.75
15.76
15.63
15.65
15.66
15.70
15.80
15.76
15.74
15.75
15.77
15.76
15.91
16.02
16.14
16.31
16.37
16.32
16.38
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16.48
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17.05
17.35
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25.87
26.15
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26.48
26.77
27.32
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27.94
27.68
27.59
27.86
27.03
25.70
25.57
26.07
26.44
26.66
27.00
27.37
27.90
28.35
28.58
28.42
28.47
28.39
28.60
28.57
28.72
28.74
28.95
29.75
30.46
30.72
30.65
30.66
30.90
31.06
31.17
31.30
31.33
31.55
31.73
31.79
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35.13
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35.45
35.58
35.63
35.93
36.52
37.11
37.38
37.35
37.43
37.55
37.73
37.65
37.45
37.21
37.07
37.05
37.03
36.93
36.97
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36.51
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36.30
36.43
36.69
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37.16
37.17
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37.13
36.75
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36.56
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36.30
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36.40
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36.20
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35.89
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34.76
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34.00
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32.81
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32.14
31.86
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31.11
30.81
30.50
30.28
29.84
29.39
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29.59
29.62
29.73
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29.38
29.14
29.02
28.48
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28.32
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28.12
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27.71
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26.76
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15.09

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730
实验/猪肉价格/pig_local.txt
View File

@ -1,366 +1,366 @@
30.99
31.25
31.31
31.39
31.40
31.60
31.43
31.23
30.78
30.59
30.58
30.94
31.45
32.41
32.87
33.06
33.34
33.18
32.91
32.78
32.85
32.68
32.40
32.18
31.91
32.00
31.93
32.15
32.45
32.23
32.36
32.43
32.42
32.37
32.33
32.54
32.37
32.33
32.40
32.39
32.38
32.45
32.59
33.53
34.00
34.10
34.06
34.14
34.37
34.72
34.62
34.66
34.56
34.56
34.84
35.14
35.24
35.16
34.56
34.66
34.77
34.93
34.94
34.88
34.52
34.70
34.85
34.98
35.25
35.31
35.07
35.03
34.92
34.83
35.32
34.89
35.11
35.09
34.88
35.20
35.30
35.58
35.78
35.88
36.45
36.31
36.81
36.56
36.73
36.73
36.30
36.19
36.40
36.33
36.24
35.64
35.57
35.30
35.70
35.53
35.48
35.09
35.15
35.07
35.29
35.13
35.09
35.52
35.21
35.61
35.25
35.37
35.43
35.25
35.10
35.26
35.19
35.00
35.00
35.06
35.06
34.96
34.66
34.48
34.15
33.87
33.81
33.73
33.65
33.52
33.31
33.40
33.36
33.30
33.26
33.07
33.04
32.52
32.37
32.26
32.53
32.47
32.25
32.25
32.26
32.27
32.21
32.40
32.18
32.30
32.21
32.15
32.25
32.31
32.08
31.80
31.69
31.67
31.54
31.45
31.42
31.13
31.12
30.86
30.53
30.59
30.69
30.54
30.38
29.99
29.62
29.36
28.86
28.57
28.19
27.86
27.41
26.48
25.17
24.99
25.60
12.11
12.16
12.16
12.01
12.16
12.13
11.89
12.07
11.98
12.09
12.15
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12.00
11.99
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11.69
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11.77
11.64
11.67
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11.62
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12.00
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12.23
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12.58
12.95
13.54
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13.84
13.70
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14.61
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14.55
14.38
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14.17
14.31
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14.75
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15.31
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15.66
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16.13
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16.67
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21.48
21.16
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20.74
21.14
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21.07
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21.18
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20.82
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20.61
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20.89
20.95
20.97
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20.93
21.22
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21.59
21.63
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23.13
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23.79
23.86
24.03
24.03
24.07
24.55
24.76
25.13
25.43
25.64
25.92
26.20
26.39
26.81
27.24
27.76
28.00
27.81
27.88
27.97
28.18
28.21
28.21
28.41
28.59
29.21
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30.09
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30.12
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30.50
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31.17
31.25
31.43
31.87
32.45
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32.97
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33.25
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36.50
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36.82
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36.66
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36.48
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36.13
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36.02
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36.27
36.35
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36.64
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36.26
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36.02
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36.44
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35.79
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31.83
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31.47
31.37
30.98
30.61
30.38
30.08
29.72
29.41
29.31
28.75
29.09
29.13
29.20
29.25
28.91
28.67
28.54
28.04
27.99
27.78
28.23
28.31
28.25
28.11
28.22
28.41
28.54
28.81
28.93
29.06
28.90
28.91
28.77
28.96
28.93
28.94
28.81
28.75
28.74
28.59
28.66
28.88
28.79
28.85
26.02
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26.34
26.78
27.28
27.37
27.22
27.07
27.30
27.65
27.60
27.47
27.46
27.56
27.50
27.15
27.02
26.86
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26.22
25.62
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25.64
25.59
25.41
24.39
23.96
24.17
24.43
24.74
24.76
24.45
24.21
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24.41
24.18
23.90
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22.30
22.45
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21.39
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18.94
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18.39
17.87
17.37
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16.08
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16.50
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13.85
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732
实验/猪肉价格/pigprice.txt
View File

@ -1,366 +1,366 @@
32.41
32.66
32.70
32.82
33.30
33.22
32.88
32.57
32.18
32.00
31.98
32.30
33.00
34.25
34.82
34.84
35.00
34.94
34.80
34.68
34.46
34.39
34.06
33.75
33.40
33.21
33.44
33.52
33.82
33.68
33.65
33.77
33.74
33.69
33.67
33.77
33.72
33.80
33.81
33.66
33.73
33.77
34.02
34.74
35.42
35.58
35.67
35.63
35.98
36.14
36.10
36.18
36.06
36.13
36.12
36.50
36.68
36.56
36.14
36.24
36.32
36.44
36.47
36.46
36.33
36.72
36.95
36.17
36.41
36.34
36.35
36.17
36.18
37.09
37.49
37.12
37.44
37.41
37.57
37.55
37.47
37.78
37.86
38.05
38.30
38.52
38.74
38.46
38.54
38.66
38.75
38.46
38.39
38.20
38.07
37.58
37.44
36.92
37.54
37.11
37.04
36.75
36.50
36.55
36.60
36.46
36.54
36.82
36.64
36.71
36.50
36.59
36.61
36.50
36.42
36.49
36.41
36.33
36.26
36.31
36.19
36.17
35.88
35.81
35.46
35.17
35.13
35.06
34.91
34.83
34.62
34.56
34.46
34.38
34.32
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34.20
33.97
33.77
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33.85
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33.78
33.78
33.64
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33.62
33.40
33.31
33.20
32.92
32.92
32.75
32.80
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32.56
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32.19
32.13
32.06
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32.04
31.82
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31.05
30.77
30.37
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29.63
29.35
28.74
28.04
26.94
26.81
27.32
27.40
27.51
27.60
27.87
28.42
28.91
29.05
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29.18
29.14
29.15
29.41
29.49
29.51
29.79
30.55
31.45
31.52
31.58
31.55
31.74
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35.00
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36.09
36.23
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38.34
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38.22
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38.45
38.40
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37.96
37.85
37.74
37.71
37.65
37.42
37.35
37.19
37.03
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36.93
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37.31
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37.54
37.57
37.81
37.76
37.82
37.86
37.81
37.72
37.59
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37.49
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37.35
37.11
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36.93
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36.97
37.18
37.21
37.29
37.51
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37.42
37.25
36.89
36.83
36.66
36.78
36.79
36.95
37.07
36.94
36.87
36.87
36.81
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36.43
36.15
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34.86
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23.65
23.68
23.57
23.59
23.72
23.94
24.02
24.04
24.07
24.17
24.29
24.31
24.52
24.53
24.39
24.29
24.38
24.54
24.59
24.82
24.92
25.01
25.34
25.61
25.93
26.28
26.57
26.68
26.67
26.82
27.16
27.73
28.24
28.40
28.18
27.81
28.17
28.59
28.46
28.26
28.54
28.52
28.15
27.80
27.55
27.44
27.50
27.10
26.59
26.67
26.83
26.44
26.55
26.40
26.49
26.44
26.45
26.16
25.42
25.21
25.13
25.31
25.62
25.69
25.35
25.11
25.35
25.22
25.01
24.85
24.63
24.54
24.16
23.36
22.93
23.04
22.92
23.01
23.22
23.23
23.13
23.17
23.48
23.73
23.56
23.57
23.25
22.54
21.87
21.71
21.00
20.27
20.40
20.32
20.25
20.17
19.40
18.69
18.43
17.51
17.09
16.98
17.48
17.53
17.64
18.19
18.70
18.66
18.18
17.64
16.84
16.94
16.45
15.98
15.54
15.20
15.27
15.76
15.70
15.46
15.51
15.08
14.71
14.56
14.38
14.40
14.80
15.17
15.38
15.54
15.54
15.60
16.55
15.72
15.76
15.80
15.65
15.74
15.53
15.10
14.67
14.54
14.61
14.47
14.25
14.27
14.33
14.42
14.53
14.51
14.58
14.53
14.44
14.52
14.79
15.01
14.97
14.91
14.96
15.08
15.49
15.63

8
实验/猪肉价格/time.txt
View File

@ -1,4 +1,4 @@
20201020
20200820
20200523
20191120
20230122
20221122
20220825
20220221

9
梯度下降/最简单函数求极值.py
View File

@ -1,3 +1,4 @@
# https://www.bilibili.com/video/BV1ar4y137GD/?spm_id_from=333.337.search-card.all.click&vd_source=311a862c74a77082f872d2e1ab5d1523
import numpy as np
import matplotlib.pyplot as plt
@ -16,15 +17,15 @@ def plotf(loss):
def main():
x = 15
lr = 0.1
lr = 1.1
steps = 400
loss = []
for i in range(steps):
x = x-lr*df(x)
loss.append(f(x))
print(loss[i])
# y = f(x)
# print(y)
# print(loss[i])
y = f(x)
print(y)
plotf(loss)