e02039940e6759ca4ce06cd49a4f6d54b5846069,arviz/tests/test_plots.py,TestPlots,setup_class,#Any#,12

Before Change

        cls.y = np.asarray([28., 8., -3., 7., -1., 1., 18., 12.])
        sigma = np.asarray([15., 10., 16., 11., 9., 11., 10., 18.])
        with pm.Model():
            mu = pm.Normal("mu", mu=0, sd=5)
            tau = pm.HalfCauchy("tau", beta=5)
            theta = pm.Normal("theta", mu=mu, sd=tau, shape=num_schools)
            pm.Normal("obs", mu=theta, sd=sigma, observed=cls.y)
            cls.short_trace = pm.sample(600, chains=2)

After Change

        model, cls.short_trace = models["pymc3"]
        with model:
            cls.sample_ppc = pm.sample_ppc(cls.short_trace, 100)
        cls.stan_model, cls.fit = models["pystan"]
        cls.df_trace = DataFrame({"a": np.random.poisson(2.3, 100)})

Instances