n=100，对随机2n个数据点进行支持向量机软间隔训练，写出matlab代码，求b,w使用线性增广拉格朗日法训练模型

以下是MATLAB代码实现：

% 生成随机数据
n = 100;
rand_data = rand(2*n,2);
X = rand_data(:,1:2); 
Y = ones(2*n,1);
Y(n+1:end) = -1;

% 定义参数
C = 1;
tol = 1e-4;
max_passes = 10;

% 初始化 alpha, b, E
alpha = zeros(2*n,1);
b = 0;
E = -Y;

passes = 0;
while(passes < max_passes)
    num_changed_alphas = 0;
    for i = 1:2*n
        % 计算预测值
        f_i = E(i);
        for j=1:2*n
            f_i = f_i + alpha(j)*Y(j)*X(j,:)*X(i,:)';
        end
        
        % 计算误差
        E_i = f_i - Y(i);
        
        % 判断是否需要进行优化
        if((Y(i)*E_i < -tol && alpha(i) < C) || (Y(i)*E_i > tol && alpha(i) > 0))
            % 随机选择第二个alpha
            j = randi([1 2*n],1);
            while(j == i)
                j = randi([1 2*n],1);
            end
            
            % 计算预测值
            f_j = E(j);
            for k=1:2*n
                f_j = f_j + alpha(k)*Y(k)*X(k,:)*X(j,:)';
            end
            
            % 计算误差
            E_j = f_j - Y(j);
            
            % 保存旧的alpha
            alpha_i_old = alpha(i);
            alpha_j_old = alpha(j);
            
            % 计算L和H
            if(Y(i) == Y(j))
                L = max(0,alpha(j) + alpha(i) - C);
                H = min(C,alpha(j) + alpha(i));
            else
                L = max(0,alpha(j) - alpha(i));
                H = min(C,C + alpha(j) - alpha(i));
            end
            
            % 判断L和H是否相等
            if(L == H)
                continue;
            end
            
            % 计算eta
            eta = 2*X(i,:)*X(j,:)' - X(i,:)*X(i,:)' - X(j,:)*X(j,:)';
            if(eta >= 0)
                continue;
            end
            
            % 更新alpha_j
            alpha(j) = alpha(j) - (Y(j)*(E_i - E_j))/eta;
            
            % 修剪alpha_j
            if(alpha(j) > H)
                alpha(j) = H;
            elseif(alpha(j) < L)
                alpha(j) = L;
            end
            
            % 判断alpha_j是否有足够的变化量
            if(abs(alpha(j) - alpha_j_old) < tol)
                alpha(j) = alpha_j_old;
                continue;
            end
            
            % 更新alpha_i
            alpha(i) = alpha(i) + Y(i)*Y(j)*(alpha_j_old - alpha(j));
            
            % 更新b
            b1 = b - E_i - Y(i)*(alpha(i) - alpha_i_old)*X(i,:)*X(i,:)'...
                - Y(j)*(alpha(j) - alpha_j_old)*X(i,:)*X(j,:)';
            b2 = b - E_j - Y(i)*(alpha(i) - alpha_i_old)*X(i,:)*X(j,:)'...
                - Y(j)*(alpha(j) - alpha_j_old)*X(j,:)*X(j,:)';
            if(alpha(i) > 0 && alpha(i) < C)
                b = b1;
            elseif(alpha(j) > 0 && alpha(j) < C)
                b = b2;
            else
                b = (b1 + b2)/2;
            end
            
            % 更新E
            E = -Y;
            for k=1:2*n
                E(k) = E(k) + alpha(k)*Y(k)*X(k,:)*X(i,:)';
                E(k) = E(k) + alpha(k)*Y(k)*X(k,:)*X(j,:)';
                E(k) = E(k) + b - b1;
            end
            
            % 计数
            num_changed_alphas = num_changed_alphas + 1;
        end
    end
    
    % 更新passes
    if(num_changed_alphas == 0)
        passes = passes + 1;
    else
        passes = 0;
    end
end

% 计算w
w = zeros(1,2);
for i=1:2*n
    w = w + alpha(i)*Y(i)*X(i,:);
end

% 输出结果
disp('b:');
disp(b);
disp('w:');
disp(w);