def get_idxs_to_balance_class_count(self, class_counts):
        """
        Determine indices with with which we can sample from the dataset
        and get a balanced inter-class distribution
        """
        mx = np.max(class_counts)
        num_examples, num_classes = self.l.shape
        idxs = np.arange(num_examples).reshape(-1, 1)
        for i, c in enumerate(class_counts):
            delta_ = mx - c
            if delta_ > 0:
                if num_classes < i:
                    rows = np.where(self.l[:, i]==1)[0]
                else: # cannot index other class
                    rows = np.where(np.sum(self.l, axis=-1) <= 0)[0]
                rows_to_sample = rows[np.random.randint(0, high=rows.size,
                                                        size=(delta_, 1))
                                      ]
                idxs = np.vstack((idxs, rows_to_sample))
        return idxs.ravel()
dataset/ETHPy150Open kashefy/nideep/nideep/datasets/balance.py/Balancer.get_idxs_to_balance_class_count/swapped operands of `<`