% selection (Parent Pt of 'N' pop size) ind1=randperm(popsize)'; ind2=randperm(popsize)'; popp1=population(ind1,:); popp2=population(ind2,:); popp=[popp1;popp2]; for i=1:popsize if popp(2*i-1,N+M+2)~=popp(2*i,N+M+2) if popp(2*i-1,N+M+2)<popp(2*i,N+M+2) parent_selected(i,:)=popp(2*i-1,:); else parent_selected(i,:)=popp(2*i,:); end else if popp(2*i-1,N+M+3)~=popp(2*i,N+M+3) if popp(2*i-1,N+M+3)>popp(2*i,N+M+3) parent_selected(i,:)=popp(2*i-1,:); else parent_selected(i,:)=popp(2*i,:); end else if rand()<0.5 parent_selected(i,:)=popp(2*i-1,:); else parent_selected(i,:)=popp(2*i,:); end end end end ind_rank1=find(parent_selected(:,N+M+2)==1);lrank1=length(ind_rank1);%非支配排序为1的解及个数 BestParent_pool=parent_selected(ind_rank1,:); if lrank1~=1 fmaxM=max(BestParent_pool(:,N+1:N+M)); fminM=min(BestParent_pool(:,N+1:N+M)); if fmaxM~=fminM fnsum=sqrt(((BestParent_pool(:,N+1)-fminM(1))/(fmaxM(1)-fminM(1))).^2+((BestParent_pool(:,N+2)-fminM(2))/(fmaxM(2)-fminM(2))).^2); indb1=find(fnsum==min(fnsum));indb=indb1(1); BS=parent_selected(indb,:); else BS=BestParent_pool(1,:); end else BS=parent_selected(ind_rank1,:); end

function [selectpop,selectfit] = myselect(allpop,allfit,popnum) [sfit,ind]=sort(allfit); %根据精英保留法保留种群数80%的部分 pop1 = allpop(ind(1:popnum0.8),:); fit1 = sfit(1:popnum0.8); %剩余的部分 restpop = allpop(ind(popnum0.8+1:end),:); restfit = sfit(popnum0.8+1:end); %再从剩余的部分用轮盘赌选择20%种群 f=restfit+1; f=1./f; f=f./sum(f); f=cumsum(f); % 轮盘赌的准备 t = 1; temp = zeros(length(f),1);的伪代码

pop1 = allpop(ind(1:popnum*0.8),:); %选择适应度最好的前80%个体 fit1 = sfit(1:popnum*0.8); %对应的适应度值 2. 接着，将剩余80%的个体作为备选个体，按照适应度大小转换为概率分布，使用轮盘赌算法选择20%...

% 生成随机网络结构和节点状态 n1 = 100; % 第一层网络节点数 n2 = 100; % 第二层网络节点数 a1 = rand(n1) < 0.1; % 第一层网络邻接矩阵 a2 = rand(n2) < 0.1; % 第二层网络邻接矩阵 file = fopen('liangcengjiedian.txt', 'r'); % 去掉空格 if file ~= -1 % 确认文件已经被正确打开 data = textscan(file, '(%f,%f,%f) to (%f,%f,%f)\n'); n_gateway = length(data{1}); gateway1 = []; gateway2 = []; for i = 1:size(data{1}, 1) x1 = data{1}(i); y1 = data{2}(i); z1 = data{3}(i); x2 = data{4}(i); y2 = data{5}(i); z2 = data{6}(i); gateway1 = [gateway1; x1, y1, z1]; gateway2 = [gateway2; x2, y2, z2]; end fclose(file); % 记得关闭文件 else disp('Error: file not found or could not be opened.'); end % 使用randperm函数随机选择n_gateway个网关 n_gateway = min(n_gateway, size(gateway1, 1)); idx = randperm(size(gateway1, 1), n_gateway); gateway1 = gateway1(idx, :); gateway2 = gateway2(idx, :); state1(gateway1) = randi([0, 3], n_gateway, 1); state2(gateway2) = randi([0, 3], n_gateway, 1); state1 = zeros(n1, 1); % 第一层节点状态 state2 = zeros(n2, 1); % 第二层节点状态数组索引必须为正整数或逻辑值。出错 oooo (line 716) state1(gateway1) = randi([0, 3], n_gateway, 1);如何修改

idx = randperm(size(gateway1, 1), n_gateway); gateway1 = gateway1(idx, :); gateway2 = gateway2(idx, :); state1 = zeros(n1, 1); % 第一层节点状态 state2 = zeros(n2, 1); % 第二层节点状态 % 将网关节点的...

function Im2=pParam(Im,perct) bestDelta=Im; BestThrd=0; [m,n]=size(Im); for Thrd=0:255 ind1=find(Im<=Thrd); ind2=find(Im>Thrd); if(~isempty(ind1)&&~isempty(ind2)) p1=length(ind1)/(mn); p2=length(ind2)/(mn); Delta=abs(p2-perct); if(Delta<bestDelta) BestThrd=Thrd; bestDelta=Delta; end end end Im2=zeros(m,n); Im2(find(Im>BestThrd))=1; Im2=logical(Im2); end给每段代码加上注释

ind2=find(Im>Thrd); % 找到大于当前阈值的像素的下标 if(~isempty(ind1)&&~isempty(ind2)) % 如果两个下标集合都不为空 p1=length(ind1)/(m*n); % 计算小于等于当前阈值的像素的占比 p2=length(ind2)/(m*n); % ...

data_origin = wdata; index_list = randperm(size(wdata, 1)); ind = round(0.8*length(index_list)); train_index = index_list(1:ind); test_index = index_list(ind+1:end); train_index = sort(train_index); test_index = sort(test_index); dataTrain = wdata(train_index, :); dataTest = wdata(test_index, :); XTrain = dataTrain(:, 1:end-1)'; YTrain = dataTrain(:, end)'; XTest = dataTest(:, 1:end-1)'; YTest = dataTest(:, end)'; layers = get_lstm_net(wd);

这段代码的作用是将构造出来的新数据集 wdata 划分为训练集和测试集，并将它们分别存储在 dataTrain 和 dataTest 中。然后将训练集和测试集的输入特征和输出标签分别存储在 XTrain、YTrain、XTest 和 YTest 中。...

详细解析如下代码：population = toolbox.population(n=POPULATION_SIZE) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", np.mean) stats.register("min", np.min) stats.register("max", np.max) population, logbook = algorithms.eaSimple(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION, ngen=MAX_GENERATIONS, stats=stats, halloffame=hall_of_fame)

首先定义了一个种群大小为POPULATION_SIZE的种群，并使用遗传算法的工具箱（toolbox）创建了这个种群。然后定义了一个函数来统计每个个体的适应度值，使用deap提供的统计模块对适应度值进行统计，包括统计平均值、...

%% pre-settig: [N, d] = size(X); labels = unique(Y); C = length(labels); ind = zeros(N, C); y_loc = zeros(N, 1); for i = 1 : C ind(Y == labels(i), i) = 1; y_loc(Y == labels(i), 1) = i; end

首先，通过调用 size(X) 获取矩阵 X 的大小，其中 N 是行数，d 是列数。然后，调用 unique(Y) 获取标签集合 labels，其中 Y 是标签向量。接下来，创建一个大小为 N × C 的全零矩阵 ind，其中 N 是样本数量，C ...

rowsindex=0; for i=1:rank-1 l_f=length(front(i).fr); if rowsindex+l_f>popsize Sol=chromosome_sorted(1:rowsindex+l_f,:); %normalization fobjmin=min(Sol(:,N+1:N+M)); fobjmax=max(Sol(:,N+1:N+M)); for lr=1:rowsindex+l_f Sol(lr,N+M+2:N+M+3)=(Sol(lr,N+1:N+2)-fobjmin)./(fobjmax-fobjmin); end ind_mind=zeros(1,rowsindex+l_f); disM=Inf*ones(rowsindex+l_f,rowsindex+l_f); for lp=1:rowsindex+l_f-1 for lq=lp+1:rowsindex+l_f disM(lp,lq)=norm(Sol(lp,N+M+2:N+M+3)-Sol(lq,N+M+2:N+M+3)); disM(lq,lp)=disM(lp,lq); end end for lr=1:rowsindex+l_f indlr1=find(disM(lr,:)==min(disM(lr,:)));indlr=indlr1(1); ind_mind(lr)=indlr;%与个体lr距离最小的个体为indlr chromosome_sorted(lr,N+M+4)=min(disM(lr,:)); end indb1=find(Sol(:,N+1)==min(Sol(:,N+1))); indb2=find(Sol(:,N+2)==min(Sol(:,N+2))); chromosome_sorted(indb1,N+M+4)=Inf; chromosome_sorted(indb2,N+M+4)=Inf; break; end rowsindex=rowsindex+l_f; end % chromosome_sorted(:,N+M+4)=sum(chromosome_sorted(:,N+M+2:N+M+3),2); % chromosome_sorted(:,N+M+4)=sqrt(chromosome_sorted(:,N+M+2).^2+chromosome_sorted(:,N+M+3).^2); chromosome_NDS_CD1=[chromosome_sorted(:,1:N+M) zeros(popsize1,1) chromosome_sorted(:,N+M+1) chromosome_sorted(:,N+M+4)]; % Final Output Variable end

- 提取已处理的染色体Sol，包括前N列（表示基因）和第N+1到N+M列（表示目标函数值）。 - 对Sol中的目标函数值进行归一化，即将目标函数值映射到[0, 1]的范围内。首先找出Sol中目标函数值的最小值和最大值，并分别...

if markk=='aa**': im = Image.open('C:/Users/renzi/Desktop/robot_chat_app/client/emoji/weixiao.gif') # 获取 GIF 中的所有帧 frames = [] for frame in ImageSequence.Iterator(im): frames.append(frame.copy()) # 显示 GIF 动图 def update(ind): frame = frames[ind] photo = ImageTk.PhotoImage(frame) label.config(image=photo) label.image = photo ind += 1 if ind == len(frames): ind = 0 chatbox.after(100, update, ind) label = chatbox.image_create(tkinter.END, label) label.pack() chatbox.after(0, update, 0)怎么让label显示在滚动的聊天框里

你可以将label添加到Text窗口中，然后使用窗口的yview_moveto()方法将滚动条移动到最下方。可以像这样修改代码： label = tkinter.Label(chatbox, image=photo) chatbox.window_create(tkinter.END, window=...

def fun(t,n): m=[0 for i in range(len(t))] b=list(enumerate(t)) for ind,money in b[::-1] : m[ind]=n//money n=n%money print("需要五十块的{},需要十块的{},需要一块的{}".format(m[-1],m[-2,],m[-3])

这段代码的作用是计算给定总金额n，需要多少张面值为50、10和1的钞票才能凑够总金额。但是代码存在一些语法错误，应该修改为： python def fun(t, n): m = [0 for i in range(len(t))] b = list(enumerate(t))...

未定义与 'double' 类型的输入参数相对应的函数 'indices'。 conn = database('','root','root','com.mysql.cj.jdbc.Driver','jdbc:mysql://localhost:3306/pca?serverTimezone=CST&useSSL=false'); app.mouse_ind = indices(1); %鼠标选择行 app.mouse_ind2 = indices(2); %鼠标选择列 curs = exec(conn,'delete from user where ID = ''5''') whereclause=strcat('where no= ',title_v); update(conn,app.tablename,{'name'},answer, whereclause);

[app.mouse_ind, app.mouse_ind2] = ind2sub(size(your_array), hObject.Parent.CurrentPoint(1,1:2)); 其中，"your_array" 表示你用于显示的数组，"hObject" 表示当前的图形句柄。这样，就可以获取鼠标选择的...

NormGrad = sum(abs(GradNew)); GradNew=GradNew/NormGrad; CostOld=CostNew; switch option.firstbasevariable case 'first' [val,coord] = max(SigmaNew) ; case 'random' [val,coord] = max(SigmaNew) ; coord=find(SigmaNew==val); indperm=randperm(length(coord)); coord=coord(indperm(1)); case 'fullrandom' indzero=find(SigmaNew~=0); if ~isempty(indzero) [mini,coord]=min(GradNew(indzero)); coord=indzero(coord); else [val,coord] = max(SigmaNew) ; end end GradNew = GradNew - GradNew(coord); desc = - GradNew.* ( (SigmaNew>0) | (GradNew<0) ); desc(coord) = - sum(desc); stepmin = 0; costmin = CostOld; costmax = 0; ind = find(desc<0); stepmax = min(-(SigmaNew(ind))./desc(ind)); deltmax = stepmax; if isempty(stepmax) || stepmax==0 Sigma = SigmaNew; return end if stepmax > 0.1 stepmax=0.1; end while costmax<costmin [costmax, S] = costgraph(KH,stepmax,desc,SigmaNew); if costmax<costmin costmin = costmax; SigmaNew = SigmaNew + stepmax * desc; desc = desc .* ( (SigmaNew>option.numericalprecision)|(desc>0)); desc(coord) = - sum(desc([[1:coord-1] [coord+1:end]])); ind = find(desc<0); if ~isempty(ind) stepmax = min(-(SigmaNew(ind))./desc(ind)); deltmax = stepmax; costmax = 0; else stepmax = 0; deltmax = 0; end end end Step = [stepmin stepmax]; Cost = [costmin costmax]; [val,coord] = min(Cost); while (stepmax-stepmin)>option.goldensearch_deltmax*(abs(deltmax)) && stepmax > eps stepmedr = stepmin+(stepmax-stepmin)/gold; stepmedl = stepmin+(stepmedr-stepmin)/gold; [costmedr, S1] = costgraph(KH,stepmedr,desc,SigmaNew); [costmedl, S2] = costgraph(KH,stepmedl,desc,SigmaNew); Step = [stepmin stepmedl stepmedr stepmax]; Cost = [costmin costmedl costmedr costmax]; [val,coord] = min(Cost); switch coord case 1 stepmax = stepmedl; costmax = costmedl; S = S2; case 2 stepmax = stepmedr; costmax = costmedr; S = S2; case 3 stepmin = stepmedl; costmin = costmedl; S = S2; case 4 stepmin = stepmedr; costmin = costmedr; S = S1; end end

这段代码看起来像是一个优化算法的实现，但是没有足够的上下文信息我无法确定具体是哪种优化算法。它似乎使用了一个金分割搜索的方法来找到最小化目标函数的步长，并且在每次迭代中更新参数向量 SigmaNew。...

如何在下面的代码中给nv值限制在nu <= 0 or nu > 1：from sklearn.svm import OneClassSVM from sklearn.model_selection import train_test_split import numpy as np from deap import creator, base, tools, algorithms # 创建OneClassSVM分类器 clf = OneClassSVM() # 定义优化目标，这里使用评估分类器的准确率 creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) # 定义一些算法参数 POPULATION_SIZE = 10 P_CROSSOVER = 0.9 P_MUTATION = 0.1 MAX_GENERATIONS = 50 HALL_OF_FAME_SIZE = 3 N_PARAMETER = 4 MIN_PARAM = 0.01 MAX_PARAM = 10.0 # 定义适应度评价函数，使用交叉验证计算准确率 def evaluate(individual): clf.set_params(kernel='rbf', gamma=individual[0], nu=individual[1]) accuracy = 0 for i in range(5): X_train, X_test = train_test_split(X_TRAIN, test_size=0.2) clf.fit(X_train) accuracy += clf.score(X_test) return accuracy / 5, # 定义遗传算法工具箱 toolbox = base.Toolbox() toolbox.register("attr_float", lambda: np.random.uniform(MIN_PARAM, MAX_PARAM)) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=N_PARAMETER) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evaluate) toolbox.register("mate", tools.cxBlend, alpha=0.5) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1) toolbox.register("select", tools.selTournament, tournsize=3) # 定义精英机制 hall_of_fame = tools.HallOfFame(HALL_OF_FAME_SIZE) # 运行遗传算法 population = toolbox.population(n=POPULATION_SIZE) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", np.mean) stats.register("min", np.min) stats.register("max", np.max) population, logbook = algorithms.eaSimple(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION, ngen=MAX_GENERATIONS, stats=stats, halloffame=hall_of_fame) # 输出优化结果 best_individual = tools.selBest(population, k=1)[0] best_parameters = [] for param in best_individual: best_parameters.append(round(param, 2)) print("OneClassSVM params: gamma={}, nu={}".format(*best_parameters))

可以在创建评估函数时对nv进行限制，如果nv不符合要求，则将其调整为0或1。具体实现方法为，先定义一个函数，在函数中使用if语句判断nu的取值，并将nv调整为0或1。然后在创建评估函数时，使用这个函数对nv进行限制。...

function [loc_C, loc_nu, loc_gamma, loc_pca_d] = find_max(acc_mat, direct_test) if length(direct_test) == 1 loc_pca_d = direct_test; acc_mat = acc_mat(:, :, :, loc_pca_d); [~, max_ind] = max(acc_mat(:)); [loc_C, loc_nu, loc_gamma] = ind2sub(size(acc_mat), max_ind); else [~, max_ind] = max(acc_mat(:)); [loc_C, loc_nu, loc_gamma, loc_pca_d] = ind2sub(size(acc_mat), max_ind); end end

如果只有一个参数需要测试，函数使用ind2sub函数从单个准确率子矩阵中提取出对应的C、nu和gamma值。如果有多个参数需要测试，函数使用ind2sub函数从整个准确率矩阵中提取出对应的C、nu、gamma和pca_d值。这个...

class UNetEx(nn.Layer): def init(self, in_channels, out_channels, kernel_size=3, filters=[16, 32, 64], layers=3, weight_norm=True, batch_norm=True, activation=nn.ReLU, final_activation=None): super().init() assert len(filters) > 0 self.final_activation = final_activation self.encoder = create_encoder(in_channels, filters, kernel_size, weight_norm, batch_norm, activation, layers) decoders = [] for i in range(out_channels): decoders.append(create_decoder(1, filters, kernel_size, weight_norm, batch_norm, activation, layers)) self.decoders = nn.Sequential(*decoders) def encode(self, x): tensors = [] indices = [] sizes = [] for encoder in self.encoder: x = encoder(x) sizes.append(x.shape) tensors.append(x) x, ind = F.max_pool2d(x, 2, 2, return_mask=True) indices.append(ind) return x, tensors, indices, sizes def decode(self, _x, _tensors, _indices, _sizes): y = [] for _decoder in self.decoders: x = _x tensors = _tensors[:] indices = _indices[:] sizes = _sizes[:] for decoder in _decoder: tensor = tensors.pop() size = sizes.pop() ind = indices.pop() # 反池化操作，为上采样 x = F.max_unpool2d(x, ind, 2, 2, output_size=size) x = paddle.concat([tensor, x], axis=1) x = decoder(x) y.append(x) return paddle.concat(y, axis=1) def forward(self, x): x, tensors, indices, sizes = self.encode(x) x = self.decode(x, tensors, indices, sizes) if self.final_activation is not None: x = self.final_activation(x) return x 不修改上述神经网络的encoder和decoder的生成方式，用嘴少量的代码实现attention机制，在上述代码里修改。

x = F.max_unpool2d(x, ind, 2, 2, output_size=size) x = self.attention(tensor, x) # 使用Attention模块 x = decoder(x) y.append(x) return paddle.concat(y, axis=1) def forward(self, x): x, tensors...

为什么这段python代码用不了？它报错的是AttributeError: 'OneClassSVM' object has no attribute 'score' 错误代码为population, logbook = algorithms.eaSimple(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION, ngen=MAX_GENERATIONS, stats=stats, halloffame=hall_of_fame)完整代码如下：from sklearn.svm import OneClassSVM from sklearn.model_selection import train_test_split import numpy as np from deap import creator, base, tools, algorithms # 生成随机数据作为样本 X = np.random.rand(100, 5) # 创建OneClassSVM分类器 clf = OneClassSVM() # 定义优化目标，这里使用评估分类器的准确率 creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) # 定义一些算法参数 POPULATION_SIZE = 10 P_CROSSOVER = 0.9 P_MUTATION = 0.1 MAX_GENERATIONS = 50 HALL_OF_FAME_SIZE = 3 N_PARAMETER = 4 MIN_PARAM = 0.01 MAX_PARAM = 10.0 # 定义适应度评价函数，使用交叉验证计算准确率 def evaluate(individual): clf.set_params(kernel='rbf', gamma=individual[0], nu=individual[1]) accuracy = 0 for i in range(5): X_train, X_test = train_test_split(X, test_size=0.3) clf.fit(X_train) accuracy += clf.score(X_test) return accuracy / 5, # 定义遗传算法工具箱 toolbox = base.Toolbox() toolbox.register("attr_float", lambda: np.random.uniform(MIN_PARAM, MAX_PARAM)) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=N_PARAMETER) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evaluate) toolbox.register("mate", tools.cxBlend, alpha=0.5) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1) toolbox.register("select", tools.selTournament, tournsize=3) # 定义精英机制 hall_of_fame = tools.HallOfFame(HALL_OF_FAME_SIZE) # 运行遗传算法 population = toolbox.population(n=POPULATION_SIZE) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", np.mean) stats.register("min", np.min) stats.register("max", np.max) population, logbook = algorithms.eaSimple(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION, ngen=MAX_GENERATIONS, stats=stats, halloffame=hall_of_fame) # 输出优化结果 best_individual = tools.selBest(population, k=1)[0] best_parameters = [] for param in best_individual: best_parameters.append(round(param, 2)) print("OneClassSVM params: gamma={}, nu={}".format(*best_parameters))

这段Python代码中可能使用了OneClassSVM模块的score()函数，但OneClassSVM模块并没有该属性（AttributeError: 'OneClassSVM' object has no attribute 'score'）。需要检查代码中使用的函数是否正确，并检查模块是否...

修改NumofSXH = [] for ind in range(100,1000): A = ind%100 B = (ind//10%10) C = ind%10 if pow(A,3) + pow(B,3) + pow(C,3) = ind: NumofSXH.append(ind) while: continue for ind1 in N: print(ind1,end=' ')

这段代码中有一些语法错误和逻辑问题。...for ind1 in NumofSXH: print(ind1, end=' ') 这段代码的作用是找出三位数中的所有水仙花数，即各位数字的立方和等于该数本身。修改后的代码会输出前四个水仙花数。

Kotlin开发的播放器（默认支持MediaPlayer播放器，可扩展VLC播放器、IJK播放器、EXO播放器、阿里云播放器）

基于Kotlin开发的播放器，默认支持MediaPlayer播放器，可扩展VLC播放器、IJK播放器、EXO播放器、阿里云播放器、以及任何使用TextureView的播放器, 开箱即用，欢迎提 issue 和 pull request

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ex15.zip_Contrast Map_equalization_ind2rgb_shadow

%% pre-settig: [N, d] = size(X); labels = unique(Y); C = length(labels); ind = zeros(N, C); y_loc = zeros(N, 1); for i = 1 : C ind(Y == labels(i), i) = 1; y_loc(Y == labels(i), 1) = i; end

def fun(t,n): m=[0 for i in range(len(t))] b=list(enumerate(t)) for ind,money in b[::-1] : m[ind]=n//money n=n%money print("需要五十块的{},需要十块的{},需要一块的{}".format(m[-1],m[-2,],m[-3])

修改NumofSXH = [] for ind in range(100,1000): A = ind%100 B = (ind//10%10) C = ind%10 if pow(A,3) + pow(B,3) + pow(C,3) = ind: NumofSXH.append(ind) while: continue for ind1 in N: print(ind1,end=' ')

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