错误使用 costFunction 输出参数太多。
时间: 2024-04-12 08:30:03 浏览: 14
抱歉,我的错误。确实在上述代码中,我错误地将输出参数设置为一个变量,而实际上成本函数应该只返回一个值。以下是修正后的代码:
```matlab
function cost = costFunction(x, y, theta)
m = length(y); % 样本数量
predictions = x * theta; % 预测值
sqrErrors = (predictions - y).^2; % 平方误差
cost = 1/(2*m) * sum(sqrErrors); % 成本函数
end
```
现在,`costFunction` 函数将只返回一个值,即成本函数的计算结果。非常抱歉给你带来的困扰。谢谢你的指正!
相关问题
2016a版本的matlab怎么使用costfunction
在 MATLAB 2016a 中,可以使用 `fminunc` 函数来最小化代价函数。具体步骤如下:
1. 定义代价函数 `costFunction`,并将其作为输入参数传递给 `fminunc` 函数。代价函数应该有两个输入参数:参数向量 `theta` 和训练数据矩阵 `X`,以及一个输出参数 `J`,用于返回代价函数的值。
例如,如果你正在实现逻辑回归算法,则代价函数可以如下所示:
```matlab
function [J, grad] = costFunction(theta, X, y)
m = length(y); % 训练样本数
J = 0;
grad = zeros(size(theta));
h = sigmoid(X * theta); % 假设函数
J = (-1 / m) * sum(y .* log(h) + (1 - y) .* log(1 - h)); % 代价函数
grad = (1 / m) * X' * (h - y); % 梯度
end
```
2. 为 `fminunc` 函数提供初始参数向量 `theta`、训练数据矩阵 `X` 和目标值向量 `y`。
例如,如果你有一个包含两个特征的训练集,其中有 100 个训练样本,你可以这样做:
```matlab
% 初始化训练数据和目标值
X = [ones(100, 1) randn(100, 1) randn(100, 1)]; % 100x3 矩阵,第一列是全 1 向量
y = randn(100, 1) > 0.5; % 100x1 逻辑向量
% 初始化参数
initial_theta = zeros(size(X, 2), 1); % 3x1 列向量
```
3. 调用 `fminunc` 函数,并传递代价函数 `costFunction` 作为输入参数。`fminunc` 函数将返回最优的参数向量 `theta` 和代价函数的最小值。
例如,你可以这样做:
```matlab
% 最小化代价函数
options = optimset('GradObj', 'on', 'MaxIter', 400); % 设置选项
[theta, cost] = fminunc(@(t)(costFunction(t, X, y)), initial_theta, options); % 最小化代价函数
```
在这个例子中,我们将代价函数 `costFunction` 作为一个匿名函数传递给 `fminunc` 函数,这可以让我们在调用 `fminunc` 函数时方便地传递额外的参数 `X` 和 `y`。
robust cost function
In machine learning, a robust cost function is a way to measure the difference between the predicted output and the true output in a way that is less sensitive to outliers or errors in the data. This is particularly important when dealing with noisy or inconsistent data, where traditional cost functions like mean squared error may not be effective.
One example of a robust cost function is the Huber loss function. This function combines the advantages of both mean squared error and absolute error, by using a quadratic loss for small errors and a linear loss for larger errors. This makes it less sensitive to outliers than mean squared error alone, while still being differentiable and suitable for optimization algorithms like gradient descent.
Another example is the Tukey's biweight loss function, which is a type of M-estimator. This function is defined as a truncated parabolic function that gives zero weight to outliers beyond a certain threshold. This makes it highly robust to outliers while still being differentiable and computationally efficient.
Robust cost functions are particularly useful in applications like regression, where the goal is to predict a continuous value. By using a more robust cost function, the model can better handle noisy or inconsistent data, leading to more accurate predictions and better performance overall.