tic clear clc k=1000; n=448; M=0;w=0; a=[];b=[];d=1; for q=1:10000 x=100*rand(1,n); y=100*rand(1,n); X=100*rand(1,k); Y=100*rand(1,k); w=0; for m=1:k for p=1:n(d) b=sqrt((x(p)-X(m))^2+(y(p)-Y(m))^2); if b<10 w=w+1; break; end end if w==1000 M=M+1; break; end end end result=M/10000什么意思

时间: 2023-10-24 17:05:47 浏览: 53
这段代码是一段MATLAB或Octave的程序,它用于模拟一个实验,并计算一个特定条件下的结果。 代码中的变量含义如下: - `k`:表示一个常数,用于定义X和Y的长度。 - `n`:表示一个常数,用于定义x和y的长度。 - `M`:表示一个计数器,用于记录满足特定条件的次数。 - `w`:表示一个计数器,用于记录某个内部循环中满足特定条件的次数。 - `a`和`b`:分别表示空数组。 - `d`:表示一个常量,初始化为1。 以下是代码的主要逻辑: 1. 通过循环进行10000次实验。 2. 在每次实验中,生成长度为n的随机数组x和y,以及长度为k的随机数组X和Y。 3. 通过嵌套循环遍历X和Y,检查每个元素与x和y之间的距离是否小于10。 4. 如果找到了1000个满足条件的点,则将M计数器加1,并跳出内部循环。 5. 最后,计算result为M除以10000,得到满足条件的比例。 因此,`result`的含义是经过10000次实验后,满足特定条件的比例。具体条件是在X和Y数组中找到至少1000个距离x和y小于10的点。
相关问题

clc;clear; tic syms x y=300/pi*log(abs(sec(pi*x/300)));%悬链线方程 dy=diff(y,1);%求导 x=linspace(-112.6,112.6,564); b=300/pi*log(abs(sec(pi*x/300))); k1 = eval(dy); x0 = x; y0 = b; k2 = -1./k1; syms x y y=k2.*(x-x0)+y0;%法线方程 m = x0.'; n = y0.'; k = k2.'; [x, y] = solve(k.*(x-m)-y+n, 300/pi*log(abs(sec(pi*x/300)))+30-y); p = sqrt((x-m).^2 + (y-n).^2); toc

这是一段 MATLAB 代码,用于计算悬链线的方程和法线方程,并求解其与一条给定线段的交点。其中,syms x y 声明了 x 和 y 为符号变量,y=300/pi*log(abs(sec(pi*x/300))) 计算了悬链线的方程,dy=diff(y,1) 对 y 求导,x=linspace(-112.6,112.6,564) 生成了一系列 x 值,b=300/pi*log(abs(sec(pi*x/300))) 计算了对应的 b 值,k1 = eval(dy) 计算了斜率 k1,x0 = x,y0 = b,k2 = -1./k1 计算了法线的斜率 k2,y=k2.*(x-x0)+y0 计算了法线方程。接下来,m = x0.'; n = y0.'; k = k2.'; 将 x0、y0 和 k2 转置,[x, y] = solve(k.*(x-m)-y+n, 300/pi*log(abs(sec(pi*x/300)))+30-y) 求解法线方程与给定的线段的交点,p = sqrt((x-m).^2 + (y-n).^2) 计算了交点与给定点的距离。最后,用 tic 和 toc 记录了代码的运行时间。

clc; clear; close all; tic; N=128; M=[4 16 32 64]; D=5; c=0.15; nt=0.1289; nr=0.9500; N_ofdm=1000; snr_dB=1:18; SNR=10.^(snr_dB./10); for kk=1:length(snr_dB) N_fft=N*2+2; for jj=1:length(M) base_data=randi([0 1],1,N*N_ofdm*log2(M(jj))); data_temp1= reshape(base_data,log2(M(jj)),[])'; data_temp2= bi2de(data_temp1); mod_data = qammod(data_temp2,M(jj)); data=reshape(mod_data,N,[])'; H_data=zeros(N_ofdm,N_fft); H_data(:,2:N_fft/2)= data; H_data(:,N_fft/2+2:N_fft)= conj(fliplr(data)); ifft_data=ifft(H_data,[],2); ifft_data=ifft_data+0.02*ones(size(ifft_data)); Noise=awgn(ifft_data,SNR(kk),'measured')-ifft_data; Rx_data=ifft_data*nt*nr*exp(-c*D)+Noise; Rx_data=Rx_data/(nt*nr*exp(-c*D)) fft_data=fft(Rx_data,[],2); Rx_psk_data=fft_data(:,2:N_fft/2); demodulation_data = qamdemod(Rx_psk_data',M(jj)); demodulation_data= reshape(demodulation_data,[],1); temp1=de2bi(demodulation_data); err(kk,jj)=sum(sum((temp1~=data_temp1))); end BER(kk,:)=err(kk,:)./(N*N_ofdm*log2(M(jj))); end figure(); for a=1:length(M) semilogy(snr_dB,BER(:,a),'*-','LineWidth',1.5);hold on; end代码翻译

clc; clear; close all; tic; % 清空变量和图形窗口,记录程序开始时间 N=128; % 子载波数 M=[4 16 32 64]; % 调制阶数 D=5; % 距离 c=0.15; % 衰落系数 nt=0.1289; % 发射天线数 nr=0.9500; % 接收天线数 N_ofdm=1000; % OFDM符号数 snr_dB=1:18; % 信噪比范围(单位:dB) SNR=10.^(snr_dB./10); % 信噪比(单位:线性) for kk=1:length(snr_dB) % 循环信噪比 N_fft=N*2+2; % FFT点数 for jj=1:length(M) % 循环调制阶数 base_data=randi([0 1],1,N*N_ofdm*log2(M(jj))); % 生成随机二进制数据 data_temp1= reshape(base_data,log2(M(jj)),[])'; % 二进制数据转换成M进制数据 data_temp2= bi2de(data_temp1); % 将M进制数据转换为十进制数据 mod_data = qammod(data_temp2,M(jj)); % QAM调制 data=reshape(mod_data,N,[])'; % 按子载波进行分组 H_data=zeros(N_ofdm,N_fft); % 初始化频域数据 H_data(:,2:N_fft/2)= data; % 将调制后的数据存放在频域数据中 H_data(:,N_fft/2+2:N_fft)= conj(fliplr(data)); % 利用对称性将数据存放在频域数据中 ifft_data=ifft(H_data,[],2); % IFFT变换 ifft_data=ifft_data+0.02*ones(size(ifft_data)); % 加上直流偏置 Noise=awgn(ifft_data,SNR(kk),'measured')-ifft_data; % 加入高斯白噪声 Rx_data=ifft_data*nt*nr*exp(-c*D)+Noise; % 接收信号经过信道影响和加性高斯白噪声干扰,得到接收信号 Rx_data=Rx_data/(nt*nr*exp(-c*D)); % 接收信号归一化 fft_data=fft(Rx_data,[],2); % FFT变换 Rx_psk_data=fft_data(:,2:N_fft/2); % 从频域数据中提取QAM调制后的数据 demodulation_data = qamdemod(Rx_psk_data',M(jj)); % QAM解调 demodulation_data= reshape(demodulation_data,[],1); % 将解调后的十进制数据转换为一维向量 temp1=de2bi(demodulation_data); % 将十进制数据转换为二进制数据 err(kk,jj)=sum(sum((temp1~=data_temp1))); % 统计误码数 end BER(kk,:)=err(kk,:)./(N*N_ofdm*log2(M(jj))); % 计算误码率 end figure(); % 新建图形窗口 for a=1:length(M) % 循环调制阶数 semilogy(snr_dB,BER(:,a),'*-','LineWidth',1.5);hold on; % 绘制误码率曲线 end

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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

clear all; close all; clc; tic bits_options = [0,1,2]; noise_option = 1; b = 4; NT = 2; SNRdBs =[0:2:20]; sq05=sqrt(0.5); nobe_target = 500; BER_target = 1e-3; raw_bit_len = 2592-6; interleaving_num = 72; deinterleaving_num = 72; N_frame = 1e8; for i_bits=1:length(bits_options) bits_option=bits_options(i_bits); BER=zeros(size(SNRdBs)); for i_SNR=1:length(SNRdBs) sig_power=NT; SNRdB=SNRdBs(i_SNR); sigma2=sig_power10^(-SNRdB/10)noise_option; sigma1=sqrt(sigma2/2); nobe = 0; Viterbi_init for i_frame=1:1:N_frame switch (bits_option) case {0}, bits=zeros(1,raw_bit_len); case {1}, bits=ones(1,raw_bit_len); case {2}, bits=randi(1,raw_bit_len,[0,1]); end encoding_bits = convolution_encoder(bits); interleaved=[]; for i=1:interleaving_num interleaved=[interleaved encoding_bits([i:interleaving_num:end])]; end temp_bit =[]; for tx_time=1:648 tx_bits=interleaved(1:8); interleaved(1:8)=[]; QAM16_symbol = QAM16_mod(tx_bits, 2); x(1,1) = QAM16_symbol(1); x(2,1) = QAM16_symbol(2); if rem(tx_time-1,81)==0 H = sq05(randn(2,2)+jrandn(2,2)); end y = Hx; if noise_option==1 noise = sqrt(sigma2/2)(randn(2,1)+j*randn(2,1)); y = y + noise; end W = inv(H'H+sigma2diag(ones(1,2)))H'; X_tilde = Wy; X_hat = QAM16_slicer(X_tilde, 2); temp_bit = [temp_bit QAM16_demapper(X_hat, 2)]; end deinterleaved=[]; for i=1:deinterleaving_num deinterleaved=[deinterleaved temp_bit([i:deinterleaving_num:end])]; end received_bit=Viterbi_decode(deinterleaved); for EC_dummy=1:1:raw_bit_len, if bits(EC_dummy)~=received_bit(EC_dummy), nobe=nobe+1; end if nobe>=nobe_target, break; end end if (nobe>=nobe_target) break; end end = BER(i_SNR) = nobe/((i_frame-1)*raw_bit_len+EC_dummy); fprintf('bits_option:%d,SNR:%d dB,BER:%1.4f\n',bits_option,SNRdB,BER(i_SNR)); end figure; semilogy(SNRdBs,BER); xlabel('SNR(dB)'); ylabel('BER'); title(['Bits_option:',num2str(bits_option)]); grid on; end将这段代码改为有噪声的情况

请解释下面这段程序每一部分所起的作用:%%%%%%配电网潮流优化%%%%%%%% %%%%%%标幺值SB=100MVA,UB=12.66kV,二阶锥松弛%%%%%% %%%%%%MISOCP模型,分时段优化,并行计算%%%%%%%%%%%% clear clc tic; %%%%%IEEE33配电网数据%%%%%%%%%%%; Pload=[0.0004666666667,0.0005,0.0005666666667,0.0006333333333,0.0006666666667,0.0007333333333,0.0007666666667,0.0008,0.0008666666667,0.0009333333333,0.0009666666667,0.001,0.0009333333333,0.0008666666667,0.0008,0.0007,0.0006666666667,0.0007333333333,0.0008,0.0009333333333,0.0008666666667,0.0007333333333,0.0006,0.0005333333333;0.00042,0.00045,0.00051,0.00057,0.0006,0.00066,0.00069,0.00072,0.00078,0.00084,0.00087,0.0009,0.00084,0.00078,0.00072,0.00063,0.0006,0.00066,0.00072,0.00084,0.00078,0.00066,0.00054,0.00048;0.00056,0.0006,0.00068,0.00076,0.0008,0.00088,0.00092,0.00096,0.00104,0.00112,0.00116,0.0012,0.00112,0.00104,0.00096,0.00084,0.0008,0.00088,0.00096,0.00112,0.00104,0.00088,0.00072,0.00064;0.00028,0.0003,0.00034,0.00038,0.0004,0.00044,0.00046,0.00048,0.00052,0.00056,0.00058,0.0006,0.00056,0.00052,0.00048,0.00042,0.0004,0.00044,0.00048,0.00056,0.00052,0.00044,0.00036,0.00032;0.00028,0.0003,0.00034,0.00038,0.0004,0.00044,0.00046,0.00048,0.00052,0.00056,0.00058,0.0006,0.00056,0.00052,0.00048,0.00042,0.0004,0.00044,0.00048,0.00056,0.00052,0.00044,0.00036,0.00032;0.0009333333333,0.001,0.001133333333,0.001266666667,0.001333333333,0.001466666667,0.001533333333,0.0016,0.001733333333,0.001866666667,0.001933333333,0.002,0.001866666667,0.001733333333,0.0016,0.0014,0.001333333333,0.001466666667,0.0016,0.001866666667,0.001733333333,0.001466666667,0.0012,0.001066666667;0.0009333333333,0.001,0.001133333333,0.001266666667,0.001333333333,0.001466666667,0.001533333333,0.0016,0.001733333333,0.001866666667,0.001933333333,0.002,0.001866666667,0.001733333333,0.0016,0.0014,0.001333333333,0.001466666667,0.0016,0.001866666667,0.001733333333,0.001466666667,0.0012,0.001066666667;0.00028,0.0003,0.00034,0.00038,0.0004,0.00044,0.00046,0.00048,0.00052,0.00056,0.00058,0.0006,0.00056,0.00052,0.00048,0.00042,0.0004,0.00044,0.00048,0.00056,0.00052,0.00044,0.00036,0.00032;0.00028,0.0003,0.0003

代码解释:format long; close all; clear ; clc tic global B0 bh B1 B2 M N pd=8; %问题维度(决策变量的数量) N=100; % 群 (鲸鱼) 规模 readfile HPpos=chushihua; tmax=300; % 最大迭代次数 (tmax) Wzj=fdifference(HPpos); Convergence_curve = zeros(1,tmax); B = 0.1; for t=1:tmax for i=1:size(HPpos,1)%对每一个个体地多维度进行循环运算 % 更新位置和记忆 % j1=(HPpos(i,:)>=B1);j2=(HPpos(i,:)<=B2); % if (j1+j2)==16 % HPpos(i,:)=HPpos(i,:); %%%%有问题,原算法改正&改进算法映射规则 % else % %HPpos(i,:)=B0+bh.(ones(1,8)(-1)+rand(1,8)2);%产生范围内的随机数更新鲸鱼位置 % HPpos(i,:)=rand(1,8).(B2-B1)+B1; % end HPposFitness=Wzj(:,2M+1); end [~,indx] = min(HPposFitness); Target = HPpos(indx,:); % Target HPO TargetScore =HPposFitness(indx); % Convergence_curve(1)=TargetScore; % Convergence_curve(1)=TargetScore; %nfe = zeros(1,MaxIt); %end % for t=2:tmax c = 1 - t((0.98)/tmax); % Update C Parameter kbest=round(Nc); % Update kbest一种递减机制 % for i = 1:N r1=rand(1,pd)<c; r2=rand; r3=rand(1,pd); idx=(r1==0); z=r2.idx+r3.~idx; % r11=rand(1,dim)<c; % r22=rand; % r33=rand(1,dim); % idx=(r11==0); % z2=r22.idx+r33.~idx; if rand<B xi=mean(HPpos); dist = pdist2(xi,HPpos);%欧几里得距离 [~,idxsortdist]=sort(dist); SI=HPpos(idxsortdist(kbest),:);%距离位置平均值最大的搜索代理被视为猎物 HPpos(i,:) =HPpos(i,:)+0.5((2*(c)z.SI-HPpos(i,:))+(2(1-c)z.xi-HPpos(i,:))); else for j=1:pd rr=-1+2z(j); HPpos(i,j)= 2z(j)cos(2pirr)(Target(j)-HPpos(i,j))+Target(j); end end HPposFitness=Wzj(:,2M+1); % % Update Target if HPposFitness(i)<TargetScore Target = HPpos(i,:); TargetScore = HPposFitness(i); end Convergence_curve(t)=TargetScore; disp(['Iteration: ',num2str(t),' Best Fitness = ',num2str(TargetScore)]); end

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