randIndex = randi([1,2],1);

这段代码的作用是生成一个随机数，范围在1到2之间（包括1和2），并将结果赋值给变量randIndex。具体来说，randi函数用来生成整数随机数，函数的第一个参数是一个包含两个元素的向量，表示生成随机数的范围，第二个参数表示生成的随机数的个数。因此，randi([1,2],1)表示生成一个1到2之间的整数随机数。

相关问题

将以下代码转换为python：function newpop=zmutate(pop,popsize,pm1,pm2,fitness1,M,N,Tn0,Tn1,Q,ST0,maxT,t,maxgen,LCR,ECR,MCR,FC,ICR) %M为辅助坑道数量；N为单元数 x=pop(:,1:2*M+1);%分段点位置 y=pop(:,2*M+2:4*M+2);%是否选择该分段点 z=pop(:,4*M+3:6*M+4);%开挖方向 W=pop(:,6*M+5:8*M+6);%作业班次 lenx=length(x(1,:)); leny=length(y(1,:)); lenz=length(z(1,:)); lenW=length(W(1,:)); avefit=sum(fitness1)/popsize; worstfit=min(fitness1); % sumy=sum(y); % lenz=sumy+1; % lenW=sumy+1; for i=1:popsize %选择popsize次，每次选择一个，输出一个 %随机选择一个染色体 pick=rand; while pick==0 pick=rand; end index=ceil(pick*popsize); f1=fitness1(index); if f1<=avefit % pm=(exp(-t/maxgen))*(pm1-(pm1-pm2)*(f1-avefit)/max(fitness1)-avefit); pm=1/(1+exp(t/maxgen))*(pm1-(pm1-pm2)*(f1-avefit)/max(fitness1)-avefit); else % pm=(exp(-t/maxgen))*pm1; pm=1/(1+exp(t/maxgen))*pm1; end pick=rand; while pick==0 pick=rand; end if pick>pm continue; end % flag0=0; % while(flag0==0) %随机选择变异位置 pick1=rand; pick2=rand; pick3=rand; pick4=rand; while pick1*pick2*pick3*pick4==0 pick1=rand; pick2=rand; pick3=rand; pick4=rand; end posx=ceil(pick1*lenx); posy=ceil(pick2*leny); %x,y变异 randx=randi([1,N-1]); while ismember(randx,x(index,:)) randx=randi([1,N-1]); end b=x(index,posx); x(index,posx)=randx; a=[0 1]; c=y(index,posy); y(index,posy)=setxor(y(index,posy),a); %z,W变异 posz=ceil(pick3*lenz); posW=ceil(pick4*lenW); d=z(index,posz); z(index,posz)=setxor(z(index,posz),a); randW=randi([1,3]); while randW==W(index,posW) randW=randi([1,3]); end e=W(index,posW); W(index,posW)=randW; mpop=[x(index,:),y(index,:),z(index,:),W(index,:)]; mtime=ztime(mpop,M,N,Tn0,Tn1,Q,ST0); mutfit=zcost(mpop,M,N,mtime(:,1),mtime(:,2:2*M+3),mtime(:,2*M+4:2*M+2+N),LCR,ECR,MCR,FC,ICR,Q); if mtime(:,1)>maxT||mutfit<=worstfit x(index,posx)=b; y(index,posy)=c; z(index,posz)=d; W(index,posW)=e; end end newpop=[x,y,z,W]; end

以下是Python代码：

import numpy as np

def zmutate(pop, popsize, pm1, pm2, fitness1, M, N, Tn0, Tn1, Q, ST0, maxT, t, maxgen, LCR, ECR, MCR, FC, ICR):
    # M为辅助坑道数量；N为单元数
    x = pop[:, :2*M+1] # 分段点位置
    y = pop[:, 2*M+2:4*M+2] # 是否选择该分段点
    z = pop[:, 4*M+3:6*M+4] # 开挖方向
    W = pop[:, 6*M+5:8*M+6] # 作业班次

    lenx = x.shape[1]
    leny = y.shape[1]
    lenz = z.shape[1]
    lenW = W.shape[1]

    avefit = np.sum(fitness1) / popsize
    worstfit = np.min(fitness1)

    newpop = np.zeros(pop.shape)

    for i in range(popsize):
        # 随机选择一个染色体
        pick = np.random.rand()
        while pick == 0:
            pick = np.random.rand()
        index = int(np.ceil(pick * popsize))
        f1 = fitness1[index]

        if f1 <= avefit:
            pm = (np.exp(-t / maxgen)) * (pm1 - (pm1 - pm2) * (f1 - avefit) / np.max(fitness1) - avefit)
            pm = 1 / (1 + np.exp(t / maxgen)) * (pm1 - (pm1 - pm2) * (f1 - avefit) / np.max(fitness1) - avefit)
        else:
            pm = (np.exp(-t / maxgen)) * pm1
            pm = 1 / (1 + np.exp(t / maxgen)) * pm1

        pick = np.random.rand()
        while pick == 0:
            pick = np.random.rand()

        if pick > pm:
            newpop[i, :] = pop[index, :]
            continue

        # 随机选择变异位置
        pick1 = np.random.rand()
        pick2 = np.random.rand()
        pick3 = np.random.rand()
        pick4 = np.random.rand()

        while pick1 * pick2 * pick3 * pick4 == 0:
            pick1 = np.random.rand()
            pick2 = np.random.rand()
            pick3 = np.random.rand()
            pick4 = np.random.rand()

        posx = int(np.ceil(pick1 * lenx))
        posy = int(np.ceil(pick2 * leny))

        # x,y变异
        randx = np.random.randint(1, N)
        while randx in x[index, :]:
            randx = np.random.randint(1, N)

        b = x[index, posx]
        x[index, posx] = randx

        c = y[index, posy]
        y[index, posy] = np.setxor1d(y[index, posy], [0, 1])

        # z,W变异
        posz = int(np.ceil(pick3 * lenz))
        posW = int(np.ceil(pick4 * lenW))

        d = z[index, posz]
        z[index, posz] = np.setxor1d(z[index, posz], [0, 1])

        randW = np.random.randint(1, 4)
        while randW == W[index, posW]:
            randW = np.random.randint(1, 4)

        e = W[index, posW]
        W[index, posW] = randW

        mpop = np.concatenate((x[index, :], y[index, :], z[index, :], W[index, :]))
        mtime = ztime(mpop, M, N, Tn0, Tn1, Q, ST0)
        mutfit = zcost(mpop, M, N, mtime[:, 0], mtime[:, 1:2*M+3], mtime[:, 2*M+4:2*M+2+N], LCR, ECR, MCR, FC, ICR, Q)

        if mtime[:, 0] > maxT or mutfit <= worstfit:
            x[index, posx] = b
            y[index, posy] = c
            z[index, posz] = d
            W[index, posW] = e

        newpop[i, :] = np.concatenate((x[index, :], y[index, :], z[index, :], W[index, :]))

    return newpop

注意，Python的数组下标是从0开始的，而MATLAB是从1开始的。此外，Python中的setxor函数的用法与MATLAB有些不同，需要将第二个参数改为一个数组。

clc clf clear all; tic Nt = 1; G = 4; N = 20; %number of RIS Ng = N/G; Nr = 3; %number of receive antenna It = 80000; M = 4; B = log2(G) + log2(M); W = 8; snr = -10:2:12; %signal-to-noise rate sigma = sqrt(1./(10 .^ (snr / 10 )) ); %sigma MPSK = pskmod(0:M-1,M); %Q = diag([chirp_table{1,chirp_nck(randi(size(chirp_nck,1)),:)}]) %Q=blkdiag(Fi_table{1},Fi_table{4},Fi_table{9},Fi_table{11}); %Q=diag(reshape(hadamard_code,1,K*N));%blkdiag(Fi_table{1},Fi_table{1},Fi_table{1}); diag([1 -1 1 -1 1 1 -1 -1]) for ii = 1:size(sigma,2) %parallel computing errorBits = 0; snr(ii) tic parfor jj = 1 : It h1=(randn(N,Nt)+1j*randn(N,Nt))/sqrt(2); h2=(randn(Nr,N)+1j*randn(Nr,N))/sqrt(2); hd=(randn(Nr,Nt)+1j*randn(Nr,Nt))/sqrt(2); Q = zeros(N,N,G); for kk = 1:G Q((kk-1)*Ng+1:kk*Ng,(kk-1)*Ng+1:kk*Ng,kk)=diag(exp(1j*2*pi*rand(1,Ng))); end for uu = 1:W inputIndex_group = randi(G); inputIndex_psk = randi(M); Q_choose = Q(:,:,inputIndex_group); St = MPSK(inputIndex_psk); V = (randn(Nr,1 ) + 1j*randn(Nr,1) ) ./sqrt(2) .*sigma(ii); %noise matrix Yt = (h2*Q_choose*h1+hd) * St + V; dis = zeros(G,M); for mm = 1:G for nn = 1:M dis(mm,nn) = norm(Yt-(h2*Q(:,:,mm)*h1+hd)*MPSK(nn),"fro"); end end [outputIndex_group,outputIndex_psk] = find(dis== min(min(dis))); %output the decode index errorBits = errorBits + sum( de2bi( inputIndex_group - 1 , log2(G)) ~= de2bi( outputIndex_group -1 , log2(G)) ); %sum of error Bits errorBits = errorBits + sum( de2bi( inputIndex_psk - 1 , log2(M)) ~= de2bi( outputIndex_psk -1 , log2(M)) ); end end toc bers(ii) = errorBits / (It*(W)* B); end toc figure('name','result'); semilogy(snr,bers,color='k',Marker='square',LineStyle='-',LineWidth=2) grid on set(gca, 'LineWidth',1) legend('RM,K=4,N=20,Nr=3,M=4') xlabel("SNR [dB]"); ylabel("BER") set(gcf,'color','w');都用到了什么算法

这段代码主要实现了基于反射智能表面（RIS）的通信系统的仿真。其中用到了MPSK调制、并行计算、随机矩阵生成、最小距离解码等算法。具体来说，这段代码实现了以下步骤：

1. 初始化参数，包括RIS数量、接收天线数量、调制方式、信噪比范围等等。

2. 进行多次仿真，对于每次仿真，生成随机信道矩阵和反射矩阵，并进行多次发送和接收，每次发送时随机选择一个反射矩阵和一个调制符号，接收时计算接收信号与所有可能的发送信号之间的距离，选取距离最小的作为解码结果，最后统计误比特率并记录。

3. 画出误比特率随信噪比的变化曲线。

总的来说，这段代码实现了一个基于RIS的通信系统的仿真，其中用到了多种算法来实现信号的生成、传输和解码等功能。