孤立森林matlab代码

孤立森林（Isolation Forest）是一种基于树结构的异常检测算法，具有高效、可扩展、易于实现等优点。以下是MATLAB实现孤立森林的代码示例：

function [scores, threshold] = isolationForest(X, nTrees, sampleSize)

[nSamples, nFeatures] = size(X);

if nargin < 3
    sampleSize = min(256, nSamples);
end

if nargin < 2
    nTrees = 100;
end

trees = cell(nTrees, 1);

for i = 1:nTrees
    trees{i} = buildTree(X, sampleSize);
end

pathLengths = zeros(nSamples, nTrees);

for i = 1:nTrees
    pathLengths(:, i) = treePathLengths(X, trees{i});
end

scores = 2.^(-mean(pathLengths, 2) / mean(pathLengths(:)));
threshold = quantile(scores, 0.99);

end

function tree = buildTree(X, sampleSize)

[nSamples, nFeatures] = size(X);

if nSamples <= sampleSize
    tree = struct('left', [], 'right', [], 'splitFeature', [], 'splitValue', [], 'size', nSamples);
    return;
end

i1 = randi(nSamples);
i2 = randi(nSamples);
while i2 == i1
    i2 = randi(nSamples);
end

splitFeature = randi(nFeatures);
splitValue = rand * (X(i1, splitFeature) - X(i2, splitFeature)) + min(X(i1, splitFeature), X(i2, splitFeature));

leftSamples = X(:, splitFeature) < splitValue;
leftSize = sum(leftSamples);
rightSize = nSamples - leftSize;

if leftSize == 0 || rightSize == 0
    tree = struct('left', [], 'right', [], 'splitFeature', [], 'splitValue', [], 'size', nSamples);
    return;
end

tree = struct('left', buildTree(X(leftSamples, :), sampleSize), ...
              'right', buildTree(X(~leftSamples, :), sampleSize), ...
              'splitFeature', splitFeature, 'splitValue', splitValue, 'size', nSamples);

end

function pathLengths = treePathLengths(X, tree)

nSamples = size(X, 1);
pathLengths = zeros(nSamples, 1);

for i = 1:nSamples
    node = tree;
    pathLength = 0;
    while ~isempty(node.left) && ~isempty(node.right)
        if X(i, node.splitFeature) < node.splitValue
            node = node.left;
        else
            node = node.right;
        end
        pathLength = pathLength + 1;
    end
    pathLengths(i) = pathLength + log2(node.size);
end

end

使用示例：

X = randn(1000, 10);
scores = isolationForest(X);

其中，X为数据矩阵，nTrees为树的数量（默认为100），sampleSize为每个树的样本数（默认为256）。函数会返回每个样本的异常分数和阈值（默认为99%分位数）。

需要注意的是，该代码仅供参考，实际使用中可能需要根据具体情况进行优化和修改。