avg = mean(predLabels); if abs(avg - 0.5) < 1 anss=1; elseif abs(avg) >= 1.5 anss=0; end，请帮我把这串代码改编得越复杂越好，但不要影响其功能

以下是一个更复杂的版本：

function anss = complexFunction(predLabels)
% This function computes a binary classification result based on the mean of
% the input labels.

% Compute the mean of the input labels.
avg = mean(predLabels);

% Compute the standard deviation of the input labels.
std_dev = std(predLabels);

% Compute the skewness of the input labels.
skewness = skewness(predLabels);

% Apply a sigmoid function to the mean of the input labels.
sigmoid_avg = 1 / (1 + exp(-avg));

% Apply a Gaussian function to the standard deviation of the input labels.
gaussian_std_dev = exp(-(std_dev^2));

% Apply a hyperbolic tangent function to the skewness of the input labels.
tanh_skewness = tanh(skewness);

% Compute a weighted sum of the transformed input values.
weighted_sum = 0.5*sigmoid_avg + 0.3*gaussian_std_dev + 0.2*tanh_skewness;

% Apply a threshold to the weighted sum to generate the final classification result.
if weighted_sum >= 0.5
    anss = 1;
else
    anss = 0;
end
end

此版本包括计算标准差和偏度的步骤，并应用了不同的函数来转换输入值。最后，它将转换后的值进行加权求和，并应用阈值来生成最终分类结果。