wehub-resource-sync
38 lines
967 B
Matlab
Executable File
38 lines
967 B
Matlab
Executable File
function [model, llh] = logitBin(X, y, lambda, eta)
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% Logistic regression for binary classification optimized by Newton-Raphson method.
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% Input:
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% X: d x n data matrix
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% z: 1 x n label (0/1)
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% lambda: regularization parameter
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% eta: step size
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% Output:
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% model: trained model structure
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% llh: loglikelihood
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% Written by Mo Chen (sth4nth@gmail.com).
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if nargin < 4
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eta = 1e-1;
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end
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if nargin < 3
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lambda = 1e-4;
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end
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X = [X; ones(1,size(X,2))];
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[d,n] = size(X);
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tol = 1e-4;
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epoch = 200;
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llh = -inf(1,epoch);
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h = 2*y-1;
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w = rand(d,1);
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for t = 2:epoch
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a = w'*X;
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llh(t) = -(sum(log1pexp(-h.*a))+0.5*lambda*dot(w,w))/n; % 4.89
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if llh(t)-llh(t-1) < tol; break; end
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z = sigmoid(a); % 4.87
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g = X*(z-y)'+lambda*w; % 4.96
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r = z.*(1-z); % 4.98
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Xw = bsxfun(@times, X, sqrt(r));
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H = Xw*Xw'+lambda*eye(d); % 4.97
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w = w-eta*(H\g);
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end
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llh = llh(2:t);
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model.w = w;
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