逻辑回归图像分类matlab代码
时间: 2023-11-21 08:53:41 浏览: 35
根据提供的引用内容,可以得知逻辑回归分类的matlab代码如下:
```
% 加载数据
data = load('data.txt');
X = data(:, 1:2);
y = data(:, 3);
% 设计模型
m = size(X, 1);
n = size(X, 2);
X = [ones(m, 1) X];
initial_theta = zeros(n + 1, 1);
% 训练模型
options = optimset('GradObj', 'on', 'MaxIter', 400);
[theta, cost] = fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
% 预测结果
prob = sigmoid(X * theta);
p = round(prob);
% 绘制决策边界
plotDecisionBoundary(theta, X, y);
% 定义代价函数
function [J, grad] = costFunction(theta, X, y)
m = length(y);
J = (-1/m) * sum(y .* log(sigmoid(X * theta)) + (1 - y) .* log(1 - sigmoid(X * theta)));
grad = (1/m) * X' * (sigmoid(X * theta) - y);
end
% 定义sigmoid函数
function g = sigmoid(z)
g = 1 ./ (1 + exp(-z));
end
% 绘制决策边界
function plotDecisionBoundary(theta, X, y)
plotData(X(:,2:3), y);
hold on;
if size(X, 2) <= 3
plot_x = [min(X(:,2))-2, max(X(:,2))+2];
plot_y = (-1./theta(3)).*(theta(2).*plot_x + theta(1));
plot(plot_x, plot_y)
legend('Admitted', 'Not admitted', 'Decision Boundary')
axis([30, 100, 30, 100])
else
u = linspace(-1, 1.5, 50);
v = linspace(-1, 1.5, 50);
z = zeros(length(u), length(v));
for i = 1:length(u)
for j = 1:length(v)
z(i,j) = mapFeature(u(i), v(j))*theta;
end
end
z = z';
contour(u, v, z, [0, 0], 'LineWidth', 2)
end
hold off
end
% 定义特征映射函数
function out = mapFeature(X1, X2)
degree = 6;
out = ones(size(X1(:,1)));
for i = 1:degree
for j = 0:i
out(:, end+1) = (X1.^(i-j)).*(X2.^j);
end
end
end
```
其中,`data.txt`是数据文件,包含两个特征和一个标签。代码中使用了梯度下降算法来训练模型,并使用sigmoid函数作为激活函数。最后,绘制了决策边界来可视化分类结果。