b96598a16031c8b940e991f7c1bf80d702fea07f,gpflowopt/acquisition/mes.py,MinValueEntropySearch,_setup,#MinValueEntropySearch#,64

Before Change

        // Work with feasible data
        X = self.data[0][valid, :]
        Y = self.data[1][valid, :]
        Xrand = RandomDesign(self.gridsize, self._domain).generate()
        fmean, fvar = m.predict_f(np.vstack((Xrand, X)))
        right = np.min(Y)
        left = right
        probf = lambda x: np.exp(np.sum(norm.logcdf(-(x - fmean) / np.sqrt(fvar)), axis=0))

        i = 0

After Change

        Xrand = RandomDesign(self.gridsize, self._domain).generate()
        fmean, fvar = m.predict_f(np.vstack((X, Xrand)))
        idx = np.argmin(fmean[:N])
        right = fmean[idx].flatten() + 2*np.sqrt(fvar[idx]).flatten()
        left = right
        probf = lambda x: np.exp(np.sum(norm.logcdf(-(x - fmean) / np.sqrt(fvar)), axis=0))

        i = 0

Instances