undo the proposed fix in sdv-dev#169

ctgan/data_sampler.py

@@ -59,27 +59,21 @@ def is_discrete_column(column_info):
 
         st = 0
         current_id = 0
-        discrete_st = 0
         current_cond_st = 0
         for column_info in output_info:
             if is_discrete_column(column_info):
                 span_info = column_info[0]
                 ed = st + span_info.dim
-                discrete_ed = discrete_st + span_info.dim
                 category_freq = np.sum(data[:, st:ed], axis=0)
                 if log_frequency:
                     category_freq = np.log(category_freq + 1)
                 category_prob = category_freq / np.sum(category_freq)
                 self._discrete_column_category_prob[current_id, :span_info.dim] = category_prob
                 self._discrete_column_cond_st[current_id] = current_cond_st
                 self._discrete_column_n_category[current_id] = span_info.dim
-
-                self._discrete_column_matrix_st[current_id] = discrete_st
-
                 current_cond_st += span_info.dim
                 current_id += 1
                 st = ed
-                discrete_st = discrete_ed
             else:
                 st += sum([span_info.dim for span_info in column_info])
 
@@ -156,7 +150,7 @@ def dim_cond_vec(self):
     def generate_cond_from_condition_column_info(self, condition_info, batch):
         """Generate the condition vector."""
         vec = np.zeros((batch, self._n_categories), dtype='float32')
-        id_ = self._discrete_column_matrix_st[condition_info['discrete_column_id']]
+        id_ = self._discrete_column_cond_st[condition_info['discrete_column_id']]
         id_ += condition_info['value_id']
         vec[:, id_] = 1
         return vec