wehub-resource-sync
31 lines
711 B
Matlab
Executable File
31 lines
711 B
Matlab
Executable File
function model = knReg(X, t, lambda, kn)
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% Gaussian process (kernel) regression
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% Input:
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% X: d x n data
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% t: 1 x n response
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% lambda: regularization parameter
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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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if nargin < 4
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kn = @knGauss;
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end
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if nargin < 3
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lambda = 1e-2;
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end
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K = knCenter(kn,X);
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tbar = mean(t);
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U = chol(K+lambda*eye(size(X,2))); % 6.62
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a = U\(U'\(t(:)-tbar)); % 6.68
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model.kn = kn;
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model.a = a;
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model.X = X;
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model.tbar = tbar;
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%% for probability prediction
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y = a'*K+tbar;
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beta = 1/mean((t-y).^2); % 3.21
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alpha = lambda*beta; % lambda=a/b P.153 3.55
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model.alpha = alpha;
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model.beta = beta;
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model.U = U; |