removed randomness in model based initialisation, set all to zero

Author: davidsebfischer

batchglm/train/tf/glm_nb/estimator.py

@@ -190,11 +190,7 @@ def init_par(
                 my_loc_names = set(self.input_data.design_loc_names.values)
                 my_loc_names = my_loc_names.intersection(init_model.input_data.design_loc_names.values)
 
-                init_loc = np.random.uniform(
-                    low=np.nextafter(0, 1, dtype=self.input_data.X.dtype),
-                    high=np.sqrt(np.nextafter(0, 1, dtype=self.input_data.X.dtype)),
-                    size=(self.input_data.num_design_loc_params, self.input_data.num_features)
-                )
+                init_loc = np.zeros([self.input_data.num_loc_params, self.input_data.num_features])
                 for parm in my_loc_names:
                     init_idx = np.where(init_model.input_data.design_loc_names == parm)
                     my_idx = np.where(input_data.design_loc_names == parm)
@@ -208,11 +204,7 @@ def init_par(
                 my_scale_names = set(input_data.design_scale_names.values)
                 my_scale_names = my_scale_names.intersection(init_model.input_data.design_scale_names.values)
 
-                init_scale = np.random.uniform(
-                    low=np.nextafter(0, 1, dtype=self.input_data.X.dtype),
-                    high=np.sqrt(np.nextafter(0, 1, dtype=self.input_data.X.dtype)),
-                    size=(self.input_data.num_design_scale_params, self.input_data.num_features)
-                )
+                init_scale = np.zeros([self.input_data.num_scale_params, self.input_data.num_features])
                 for parm in my_scale_names:
                     init_idx = np.where(init_model.input_data.design_scale_names == parm)
                     my_idx = np.where(input_data.design_scale_names == parm)