mes.py 文件源码

def _setup(self):
        super(MinValueEntropySearch, self)._setup()

        # Apply Gumbel sampling
        m = self.models[0]
        valid = self.feasible_data_index()

        # Work with feasible data
        X = self.data[0][valid, :]
        N = np.shape(X)[0]
        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
        while probf(left) < 0.75:
            left = 2. ** i * np.min(fmean - 5. * np.sqrt(fvar)) + (1. - 2. ** i) * right
            i += 1

        # Binary search for 3 percentiles
        q1, med, q2 = map(lambda val: bisect(lambda x: probf(x) - val, left, right, maxiter=10000, xtol=0.01),
                          [0.25, 0.5, 0.75])
        beta = (q1 - q2) / (np.log(np.log(4. / 3.)) - np.log(np.log(4.)))
        alpha = med + beta * np.log(np.log(2.))

        # obtain samples from y*
        mins = -np.log(-np.log(np.random.rand(self.num_samples).astype(np_float_type))) * beta + alpha
        self.samples.set_data(mins)