pplbench/ppls/pymc3/logistic_regression.py [20:29]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    def get_model(self, data: xr.Dataset) -> pm.Model:
        # transpose the dataset to ensure that it is the way we expect
        data = data.transpose("item", "feature")

        with pm.Model() as model:
            X = pm.Data("x_obs", data.X.values)
            Y = pm.Data("y_obs", data.Y.values)

            alpha = pm.Normal("alpha", mu=0, sd=self.alpha_scale)
            beta = pm.Normal("beta", mu=self.beta_loc, sd=self.beta_scale, shape=self.k)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



pplbench/ppls/pymc3/robust_regression.py [27:36]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    def get_model(self, data: xr.Dataset) -> pm.Model:
        # transpose the dataset to ensure that it is the way we expect
        data = data.transpose("item", "feature")

        with pm.Model() as model:
            X = pm.Data("x_obs", data.X.values)
            Y = pm.Data("y_obs", data.Y.values)

            alpha = pm.Normal("alpha", mu=0, sd=self.alpha_scale)
            beta = pm.Normal("beta", mu=self.beta_loc, sd=self.beta_scale, shape=self.k)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -