c0b94f051b458fdf27e41b2b4631421180b8883c,GPy/likelihoods/noise_models/noise_distributions.py,NoiseDistribution,_predictive_mean_numerical,#NoiseDistribution#Any#Any#,298

Before Change

        
        maximum = sp.optimize.fmin_ncg(self._nlog_conditional_mean_scaled,x0=self._mean(mu),fprime=self._dnlog_conditional_mean_dgp,fhess=self._d2nlog_conditional_mean_dgp2,args=(mu,sigma),disp=False)
        mean = np.exp(-self._nlog_conditional_mean_scaled(maximum,mu,sigma))/(np.sqrt(self._d2nlog_conditional_mean_dgp2(maximum,mu,sigma))*sigma)
        

        pb.figure()
        x = np.array([mu + step*sigma for step in np.linspace(-7,7,100)])

After Change

        //Compute first moment
        def int_mean(f):
            return self._mean(f)*np.exp(-(0.5/sigma2)*np.square(f - mu))
        scaled_mean, accuracy = quad(int_mean, -np.inf, np.inf)
        mean = scaled_mean / np.sqrt(2*np.pi*(sigma2))

        return mean

Instances