Transitions Improvement

PyCTBN/PyCTBN/structure_graph/trajectory_generator.py

@@ -46,7 +46,7 @@ class TrajectoryGenerator(object):
                     current_values = sigma.loc[len(sigma) - 1]
                     cim = self._cims[self._vnames[i]].filter_cims_with_mask(np.array([True for p in self._parents[self._vnames[i]]]), 
                         [current_values.at[p] for p in self._parents[self._vnames[i]]])[0].cim
-                    param = cim[current_values.at[self._vnames[i]]][1 - current_values.at[self._vnames[i]]]
+                    param = -1 * cim[current_values.at[self._vnames[i]]][current_values.at[self._vnames[i]]]
 
                     time[i] = t + random.exponential(scale = param)
         
@@ -57,8 +57,13 @@ class TrajectoryGenerator(object):
             if t >= t_end:
                 return sigma
             else:
+                cim_row = np.array(cim[current_values.at[self._vnames[next]]])
+                cim_row[current_values.at[self._vnames[next]]] = 0
+                cim_row /= sum(cim_row)
+                rand_mult = np.random.multinomial(1, cim_row, size=1)
+
                 new_row = pd.DataFrame(sigma[-1:].values, columns = sigma.columns)
-                new_row.loc[0].at[self._vnames[next]] = 1 - new_row.loc[0].at[self._vnames[next]]
+                new_row.loc[0].at[self._vnames[next]] = np.where(rand_mult[0] == 1)[0][0]
                 new_row.loc[0].at["Time"] = round(t, 4)
                 sigma = sigma.append(new_row, ignore_index = True)