def _compute_baseline_hazard(self):
        # http://courses.nus.edu.sg/course/stacar/internet/st3242/handouts/notes3.pdf
        ind_hazards = self.predict_partial_hazard(self.data).values

        event_table = survival_table_from_events(self.durations.values,
                                                 self.event_observed.values)

        baseline_hazard_ = pd.DataFrame(np.zeros((event_table.shape[0], 1)),
                                        index=event_table.index,
                                        columns=['baseline hazard'])

        for t, s in event_table.iterrows():
            less = np.array(t >= self.durations)
            if ind_hazards[less].sum() == 0:
                v = 0
            else:
                v = (s['observed'] / ind_hazards[less].sum())
            baseline_hazard_.ix[t] = v

        return baseline_hazard_
dataset/ETHPy150Open CamDavidsonPilon/lifelines/lifelines/fitters/coxph_fitter.py/CoxPHFitter._compute_baseline_hazard/swapped operands of `>=`