94ba2ce2cbad284c48d1a5f6040699675410d559,pymc3/distributions/continuous.py,ExGaussian,random,#ExGaussian#Any#Any#Any#,969

Before Change

    def random(self, point=None, size=None, repeat=None):
        mu, sigma, nu = draw_values([self.mu, self.sigma, self.nu],
                                    point=point)
        return generate_samples(lambda mu, sigma, nu, size=None: nr.normal(mu, sigma, size=size) +
                                    nr.exponential(scale=nu, size=size),
                                mu, sigma, nu,
                                dist_shape=self.shape,
                                size=size)

    def logp(self, value):
        mu = self.mu
        sigma = self.sigma

After Change

            return (np.random.normal(mu, sigma, size=size)
                    + np.random.exponential(scale=nu, size=size))

        return generate_samples(_random, mu, sigma, nu,
                                dist_shape=self.shape,
                                size=size)

    def logp(self, value):
        mu = self.mu
        sigma = self.sigma

Instances