- estimateGaussian.m
- mu = 1 / m * sum(X);
- sigma2 = 1 / m * sum((X - repmat(mu, m, 1)). ^ 2);
- selectThreshold.m
- predictions = (pval < epsilon);
- fp = sum((predictions == 1) & (yval == 0));
- fn = sum((predictions == 0) & (yval == 1));
- tp = sum((predictions == 1) & (yval == 1));
- prec = tp / (tp + fp);
- rec = tp / (tp + fn);
- F1 = 2 * prec * rec / (prec + rec);
- cofiCostFunc.m
- temp = (X*Theta).*R;
- J = sum( sum( (temp - Y.*R).^2) )/2.0 + (lambda/2) * ( sum(sum( X.^2 )) + sum(sum( Theta.^2 )) );
- % J = sum( sum( (temp - Y.*R).^2) )/2.0 + (lambda/2) * ( sum(sum( X.^2 )) + sum(sum( Theta.^2 )) ) ;
- X_grad = (temp - Y.*R) * Theta + lambda * X;
- Theta_grad = (temp - Y.*R) * X + lambda * Theta;
来源: http://www.bubuko.com/infodetail-2493783.html