wehub-resource-sync
21 lines
561 B
Matlab
Executable File
21 lines
561 B
Matlab
Executable File
function model = nbGauss(X, t)
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% Naive bayes classifier with indepenet Gaussian, each dimension of data is
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% assumed from a 1d Gaussian distribution with independent mean and variance.
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% Input:
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% X: d x n data matrix
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% t: 1 x n label (1~k)
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% Output:
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% model: trained model structure
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% Written by Mo Chen (sth4nth@gmail.com).
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n = size(X,2);
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k = max(t);
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E = sparse(t,1:n,1,k,n,n);
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nk = full(sum(E,2));
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w = nk/n;
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R = E'*spdiags(1./nk,0,k,k);
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mu = X*R;
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var = X.^2*R-mu.^2;
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model.mu = mu; % d x k means
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model.var = var; % d x k variances
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model.w = w; |