WebFor logistic regression, the C o s t function is defined as: C o s t ( h θ ( x), y) = { − log ( h θ ( x)) if y = 1 − log ( 1 − h θ ( x)) if y = 0. The i indexes have been removed for clarity. In words this is the cost the algorithm pays if it predicts a value h θ ( x) while the actual cost label turns out to be y. Web25 lines (16 sloc) 791 Bytes. Raw Blame. function J = computeCost (X, y, theta) %COMPUTECOST Compute cost for linear regression. % J = COMPUTECOST (X, y, theta) computes the cost of using theta as the. % parameter for linear regression to fit the data points in X and y.
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Webdef computeCost(X, y, theta): #COMPUTECOST Compute cost for linear regression # J = COMPUTECOST(X, y, theta) computes the cost of using theta as the # parameter for … WebAs a partial answer I can show the first equation is valid: On the upper surface of the sphere, \vec r=\langle x,y,z\rangle=\langle x,y,\sqrt{r^2-x^2-y^2}\rangle So d\vec r=\langle 1,0,\frac{-x}{\sqrt{r^2-x^2-y^2}}\rangle dx+\langle 0,1,\frac{-y}{\sqrt{r^2-x^2-y^2}}\rangle dx ... Webfunction [J, grad] = costFunction(theta, X, y) %COSTFUNCTION Compute cost and gradient for logistic regression % J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the % parameter for logistic regression and the gradient of the cost % w.r.t. to the parameters. % Initialize some useful values m = length(y); % number of training ... diy gifts people actually want