07 2022 档案
摘要：import numpy as np inf = 99999 # 不连通值 mtx_graph = [[0, 1, inf, 3, inf, inf, inf, inf, inf], [1, 0, 5, inf, 2, inf, inf, inf, inf], [inf, inf, 0, 1, in
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摘要：from collections import defaultdict from heapq import * # 堆--先进后出 inf = 99999 # 不连通值 mtx_graph = [[0, 1, inf, 3, inf, inf, inf, inf, inf], [1, 0, 5, i
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摘要：六个模型的区别 SI-Model import scipy.integrate as spi import numpy as np import matplotlib.pyplot as plt # N为人群总数 N = 10000 # β为传染率系数 beta = 0.25 # gamma为恢复率
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摘要：import numpy as np from scipy.integrate import odeint from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt def dmove(Point, t, sets
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摘要：import numpy as np from scipy import integrate import matplotlib.pyplot as plt import sympy def plot_direction_field(x, y_x, f_xy, x_lim=(-5, 5), y_li
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摘要：import numpy as np import matplotlib.pyplot as plt from scipy.optimize import leastsq from matplotlib.pylab import mpl mpl.rcParams['font.sans-serif']
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摘要：import numpy as np from mpl_toolkits.mplot3d import Axes3D import matplotlib as mpl from scipy import interpolate import matplotlib.cm as cm import ma
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摘要：为了满足对函数光滑性的需要，我们可以使用一种有弹性的长条（称之为样条），强迫它弯曲通过样本点。 import numpy as npimport matplotlib.pylab as plfrom scipy import interpolatex = np.linspace(0, 10, 11)
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摘要：凸函数的非线性规划 minimize 求解的是局部最优解 简单的函数，无所谓 复杂的函数 初始值的设定很重要 scipy.optimize.minimize(fun,x0,args=(),method=None,jac=None,hess=None,hessp=None,bounds= None,c
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摘要：zip函数 a = [1,2,3,4]b = [5,6,7,8]i = sum(x * y for x, y in zip(a, b))print(i)70 floor and ceil 函数 import matha = 34.3print(math.floor(a))print(math.cei
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摘要：代码 import pulp import numpy as np from pprint import pprint def transport_problem(costs, x_max, y_max): row = len(costs) col = len(costs[0]) prob = pu
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摘要：instance 代码： import pulp z = [2, 3, 1] a = [[1, 4, 2], [3, 2, 0]] b = [8, 6] aeq = [[1,2,4]] beq = [101] #确定最大化最小化问题，最大化只需将Min改成Max即可 m = pulp.LpProbl
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摘要：instance1： 求解下列线性规划问题 s.t. 代码： from scipy import optimizeimport numpy as npc = np.array([2,3,-5])A = np.array([[-2,5,-1],[1,3,1]])B = np.array([-10,12
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摘要：线程安全性问题# 多线程环境下 肯定不可以变为单线程# 共享变量 不可以# 并发对共享变量进行修改 可以 import threadingclass MyThread(threading.Thread): sum = 0 # 锁 lock = threading.Lock() def run(sel
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摘要：import threadingclass MyThreading(threading.Thread): def run(self): print("Hello,world!")my_threading = MyThreading()my_threading2 = MyThreading()my_t
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