importnumpyasnpdistances=np.array([[0,2,7,np.inf,np.inf,np.inf],[2,0,4,6,8,np.inf],[7,4,0,1,3,np.inf],[np.inf,6,1,0,1,6],[np.inf,8,3,1,0,3],[np.inf,np.inf,np.inf,6,3,0]],dtype=float)students=np.array([50,40,

importnumpyasnpmatches=np.array([[0,1,0,1,1,1],#1队[0,0,0,1,1,1],#2队[1,1,0,1,0,0],#3队[0,0,0,0,1,1],#4队[0,0,1,0,0,1],#5队[0,0,1,0,0,0]#6队],dtype=int)n=matches.shape[0]closure=matches.copy

importnumpyasnpdeff(x):return(abs(x+1)-abs(x-1))/2+np.sin(x)defg(x):return(abs(x+3)-abs(x-3))/2+np.cos(x)假设我们有一些初始猜测值（这里只是随机选择的）x1_guess=0.5x2_guess=1.0y1_guess=0.2y2_guess=0.3定义方程组矩阵A和向

importmatplotlib.pyplotaspltimportnumpyasnpimportcvxpyascpx=cp.Variable(6,pos=True)obj=cp.Minimize(x[5])a1=np.array([0.025,0.015,0.055,0.026])a2=np.array([0.05,0.27,0.19,0.185,0.185])a3=np.array([1,1.01,1.02,1.045,1.065])k=0.05

MAX_A=15MAX_B=24MAX_DEBUG=5products=[{"name":"Ⅰ","A_hours":1,"B_hours":6,"debug_hours":1,"profit":2},#假设产品Ⅰ至少使用1小时设备A{"name":"Ⅱ","A_hours"

importnumpyasnp初始化系数矩阵A和常数项向量bn=1000A=np.zeros((n,n))b=np.arange(1,n+1)填充系数矩阵Aforiinrange(n):A[i,i]=4#对角线元素为4ifi<n-1:A[i,i+1]=1#每一行的下一个元素为1ifi>0:A[i,i-1]=1#每一行的上一个元素

importsympyassp定义变量x,y=sp.symbols('xy')定义方程组equation1=sp.Eq(x**2-y-x,3)equation2=sp.Eq(x+3*y,2)解方程组solutions=sp.solve((equation1,equation2),(x,y),dict=True)print("符号解：")forsolinsolutions:print(sol)

fromscipy.integrateimportquadimportnumpyasnp第一部分：抛物线旋转体（修正后）defV1_quad(y):returnnp.pi*(4*y-y**2)V1_corrected,_=quad(V1_quad,1,3)第二部分保持不变V2=0.5*(4/3)*np.pi*23-(1/3)*np.pi*22*1计算总体积total_volume_co

importnumpyasnpfromscipy.linalgimporteig定义矩阵A=np.array([[-1,1,0],[-4,3,0],[1,0,2]])计算特征值和特征向量eigenvalues,eigenvectors=eig(A)打印特征值print("特征值:")print(eigenvalues)打印特征向量print("特征向量:")foriinrange(ei

importnumpyasnpdeff(x):return(abs(x+1)-abs(x-1))/2+np.sin(x)defg(x):return(abs(x+3)-abs(x-3))/2+np.cos(x)fromscipy.optimizeimportfsolvedefequation_system(vars):x1,x2,y1,y2=varseq1=2x1-3f(y1)-4g(y2)+1eq2

importnumpyasnpimportmatplotlib.pyplotasplt定义x的范围x=np.linspace(-10,10,400)创建一个2行3列的子图布局fig,axs=plt.subplots(2,3,figsize=(12,8))遍历每个子图fork,axinenumerate(axs.flat,start=1):y=k*x**2+2*kax.plot(x,y,label

importnumpyasnpimportmatplotlib.pyplotaspltfrommpl_toolkits.mplot3dimportAxes3D定义参数u和vu=np.linspace(-2,2,400)v=np.linspace(0,2*np.pi,400)U,V=np.meshgrid(u,v)根据参数方程计算x,y,zx=np.sqrt(1+U2+V2)*np.cos(V)y=np

importnumpyasnpimportmatplotlib.pyplotaspltfrommpl_toolkits.mplot3dimportAxes3D模拟高程数据（假设数据已经过某种方式插值或生成）这里我们创建一个简单的40x50网格，并填充随机高程值x=np.linspace(0,43.65,40)y=np.linspace(0,58.2,50)X,Y=np.meshgr

importnumpyasnp定义系数矩阵A和常数项向量bA=np.array([[4,2,-1],[3,-1,2],[11,3,0]])b=np.array([2,10,8])使用numpy的lstsq求解最小二乘解x,residuals,rank,s=np.linalg.lstsq(A,b,rcond=None)print("最小二乘解为：")print(x)打印残差和矩阵A的

importnumpyasnpimportmatplotlib.pyplotasplt定义x的范围x=np.linspace(-5,5,400)计算三个函数的值y_cosh=np.cosh(x)y_sinh=np.sinh(x)y_half_exp=0.5*np.exp(x)创建图形和坐标轴plt.figure(figsize=(10,6))ax=plt.gca()绘制函数ax.plot(x,

前言hello大家好，我是文宇。正文3021#include<iostream>usingnamespacestd;inta,b,c,d;intmain(){ cin>>a>>b>>c>>d; cout<<a+b+c+d; return0;}3022#include<bits/stdc++.h>usingnamespacestd;intmain(){ inta,b,c;