fisher score代码matlab
时间: 2023-09-03 11:04:15 浏览: 112
Fisher Score是一种特征选择算法,在Matlab中可以通过以下代码实现:
```matlab
function fisher_score(X, y)
% 计算每个特征的Fisher Score值
% X为特征矩阵(每行为一个样本,每列为一个特征)
% y为标签向量(对应每个样本的类别标签)
class_labels = unique(y); % 获取所有类别标签
num_features = size(X, 2); % 特征的数量
fisher_scores = zeros(1, num_features); % 用于存储每个特征的Fisher Score
for i = 1:num_features
feature = X(:, i); % 获取当前特征
between_class_variance = 0; % 类间方差
within_class_variance = 0; % 类内方差
for j = 1:length(class_labels)
class_label = class_labels(j);
class_indices = find(y == class_label); % 当前类别的样本索引
class_samples = feature(class_indices); % 当前类别的特征值
class_mean = mean(class_samples); % 当前类别的均值
class_variance = var(class_samples); % 当前类别的方差
between_class_variance = between_class_variance + length(class_indices) * (class_mean - mean(feature))^2; % 更新类间方差
within_class_variance = within_class_variance + class_variance; % 更新类内方差
end
fisher_scores(i) = between_class_variance / within_class_variance; % 计算当前特征的Fisher Score
end
% 输出每个特征的Fisher Score值
disp(fisher_scores);
end
```
你可以将特征矩阵和标签向量作为输入参数传递给该函数,然后它将计算每个特征的Fisher Score,并将结果打印出来。请注意,上述代码假设每个特征都是连续值,如果你的特征是离散的或者有缺失值,你可能需要进行一些额外的处理。