c0b94f051b458fdf27e41b2b4631421180b8883c,GPy/likelihoods/noise_models/noise_distributions.py,NoiseDistribution,_predictive_variance_numerical,#NoiseDistribution#Any#Any#Any#,335

Before Change

        
        // E( V(Y_star|f_star) )
        maximum = sp.optimize.fmin_ncg(self._nlog_exp_conditional_variance_scaled,x0=self._variance(mu),fprime=self._dnlog_exp_conditional_variance_dgp,fhess=self._d2nlog_exp_conditional_variance_dgp2,args=(mu,sigma),disp=False)
        exp_var = np.exp(-self._nlog_exp_conditional_variance_scaled(maximum,mu,sigma))/(np.sqrt(self._d2nlog_exp_conditional_variance_dgp2(maximum,mu,sigma))*sigma)

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

After Change

        
        sigma2 = sigma**2
        normalizer = np.sqrt(2*np.pi*sigma2)

        // E( V(Y_star|f_star) )
        //Compute expected value of variance
        def int_var(f):
            return self._variance(f)*np.exp(-(0.5/sigma2)*np.square(f - mu))
        scaled_exp_variance, accuracy = quad(int_var, -np.inf, np.inf)
        exp_var = scaled_exp_variance / normalizer

        //V( E(Y_star|f_star) ) =  E( E(Y_star|f_star)**2 ) - E( E(Y_star|f_star) )**2
        if predictive_mean is None:
            predictive_mean = self.predictive_mean(mu,sigma)

        predictive_mean_sq = predictive_mean**2
        def int_pred_mean_sq(f):
            return predictive_mean_sq*np.exp(-(0.5/(sigma2))*np.square(f - mu))

        scaled_exp_exp2, accuracy = quad(int_pred_mean_sq, -np.inf, np.inf)
        exp_exp2 = scaled_exp_exp2 / normalizer

        var_exp = exp_exp2 - predictive_mean**2
        // V(Y_star | f_star) = E( V(Y_star|f_star) ) + V( E(Y_star|f_star) )
        return exp_var + var_exp

Instances