BayesianNeuralNetwork.py 文件源码

def get_error_and_ll(self, X, y, location, scale):
        v_noise = np.exp(self.parser.get(self.weights, 'log_v_noise')[0,0]) * scale**2
        q = self.__get_parameters_q__()
        samples_q = self.__draw_samples__(q)
        outputs = self.__predict__(samples_q, X)
        log_factor = -0.5*np.log(2*math.pi*v_noise) - 0.5*(np.tile(np.expand_dims(y,axis=0), (self.num_weight_samples,1,1))-np.array(outputs))**2/v_noise
        ll = np.mean(logsumexp(np.sum(log_factor,2)-np.log(self.num_weight_samples), 0))
        error = np.sqrt(np.mean((y-np.mean(outputs, axis=0))**2))
        return error, ll