使用matlab实现一个简单的支持向量机，要求不使用matlab中自带的工具箱，手动实现代码

好的，我可以为你提供手动实现支持向量机的代码。

在手动实现支持向量机时，需要使用优化算法来求解最优化问题。常用的优化算法有SMO算法、QP算法等。下面我们以SMO算法为例，给出一个简单的支持向量机实现。

首先，我们需要准备训练数据和测试数据，同样需要包含标签和特征。这里我们以鸢尾花数据集为例，使用前两个特征进行训练和测试。

load fisheriris
X = meas(1:100,1:2);
Y = ones(100,1);
Y(51:end) = -1;

接着，我们需要定义一个核函数，这里我们选择使用高斯核函数。

function K = kernel(x1,x2,sigma)
    K = exp(-(norm(x1-x2)^2)/(2*sigma^2));
end

然后，我们需要定义一个函数来计算模型预测值。

function y_pred = predict(X_train,Y_train,alpha,b,x_test,sigma)
    y_pred = 0;
    for i = 1:size(X_train,1)
        y_pred = y_pred + alpha(i)*Y_train(i)*kernel(X_train(i,:),x_test,sigma);
    end
    y_pred = y_pred + b;
    y_pred = sign(y_pred);
end

接着，我们需要定义一个函数来计算模型参数，即alpha和b。

function [alpha,b] = svm_train(X_train,Y_train,C,sigma,max_iter,tol)
    alpha = zeros(size(X_train,1),1);
    b = 0;
    iter = 0;
    while(iter<max_iter)
        alpha_prev = alpha;
        for i=1:size(X_train,1)
            Ei = predict(X_train,Y_train,alpha,b,X_train(i,:),sigma) - Y_train(i);
            if ((Y_train(i)*Ei < -tol && alpha(i) < C) || (Y_train(i)*Ei > tol && alpha(i) > 0))
                j = ceil(rand()*size(X_train,1));
                Ej = predict(X_train,Y_train,alpha,b,X_train(j,:),sigma) - Y_train(j);
                alpha_i_old = alpha(i);
                alpha_j_old = alpha(j);
                if (Y_train(i) == Y_train(j))
                    L = max(0,alpha_i_old+alpha_j_old-C);
                    H = min(C,alpha_i_old+alpha_j_old);
                else
                    L = max(0,alpha_j_old-alpha_i_old);
                    H = min(C,C+alpha_j_old-alpha_i_old);
                end
                if (L == H)
                    continue;
                end
                eta = 2*kernel(X_train(i,:),X_train(j,:),sigma)-kernel(X_train(i,:),X_train(i,:),sigma)-kernel(X_train(j,:),X_train(j,:),sigma);
                if (eta >= 0)
                    continue;
                end
                alpha(j) = alpha(j) - (Y_train(j)*(Ei-Ej))/eta;
                alpha(j) = min(alpha(j),H);
                alpha(j) = max(alpha(j),L);
                if (abs(alpha(j)-alpha_j_old) < tol)
                    alpha(j) = alpha_j_old;
                    continue;
                end
                alpha(i) = alpha(i) + Y_train(i)*Y_train(j)*(alpha_j_old-alpha(j));
                b1 = b - Ei - Y_train(i)*(alpha(i)-alpha_i_old)*kernel(X_train(i,:),X_train(i,:),sigma) - Y_train(j)*(alpha(j)-alpha_j_old)*kernel(X_train(i,:),X_train(j,:),sigma);
                b2 = b - Ej - Y_train(i)*(alpha(i)-alpha_i_old)*kernel(X_train(i,:),X_train(j,:),sigma) - Y_train(j)*(alpha(j)-alpha_j_old)*kernel(X_train(j,:),X_train(j,:),sigma);
                if (alpha(i) > 0 && alpha(i) < C)
                    b = b1;
                elseif (alpha(j) > 0 && alpha(j) < C)
                    b = b2;
                else
                    b = (b1+b2)/2;
                end
            end
        end
        if (sum(abs(alpha-alpha_prev)) < tol)
            break;
        end
        iter = iter + 1;
    end
end

最后，我们调用svm_train函数进行模型训练，并使用predict函数对测试数据进行预测。

C = 1;
sigma = 1;
max_iter = 100;
tol = 1e-4;
[alpha,b] = svm_train(X,Y,C,sigma,max_iter,tol);
X_test = meas(101:end,1:2);
Y_test = ones(50,1);
Y_test(26:end) = -1;
Y_pred = zeros(size(X_test,1),1);
for i = 1:size(X_test,1)
    Y_pred(i) = predict(X,Y,alpha,b,X_test(i,:),sigma);
end
acc = sum(Y_pred==Y_test)/length(Y_test);
fprintf('Accuracy: %f\n',acc);

希望这个手动实现的支持向量机代码能够帮到你。