(data_instances, cols_dict, abnormal_list, error)
return summary_dict
Statics sums, sum_square, max and min value through one traversal

        Parameters
        ----------
        data_instances : Table
            The input data

        cols_dict : dict
            Specify which column(s) need to apply statistic.

        abnormal_list: list
            Specify which values are not permitted.

        Returns
        -------
        Dict of SummaryStatistics object
Statics sums, sum_square, max and min value through one traversal
Statics sums sum_square max and min value through one traversal
def static_summaries_in_partition(data_instances, cols_dict, abnormal_list, error):
        """
        Statics sums, sum_square, max and min value through one traversal

        Parameters
        ----------
        data_instances : Table
            The input data

        cols_dict : dict
            Specify which column(s) need to apply statistic.

        abnormal_list: list
            Specify which values are not permitted.

        Returns
        -------
        Dict of SummaryStatistics object

        """
        summary_dict = {}
        for col_name in cols_dict:
            summary_dict[col_name] = QuantileSummaries(abnormal_list=abnormal_list, error=error)

        for k, instances in data_instances:
            if isinstance(instances, Instance):
                features = instances.features
            else:
                features = instances

            for col_name, col_index in cols_dict.items():
                value = features[col_index]
                summary_obj = summary_dict[col_name]
                summary_obj.insert(value)

        return summary_dict
def static_summaries_in_partition ( data_instances , cols_dict , abnormal_list , error ) : summary_dict = { } for col_name in cols_dict : summary_dict [ col_name ] = QuantileSummaries ( abnormal_list = abnormal_list , error = error ) for k , instances in data_instances : if isinstance ( instances , Instance ) : features = instances . features else : features = instances for col_name , col_index in cols_dict . items ( ) : value = features [ col_index ] summary_obj = summary_dict [ col_name ] summary_obj . insert ( value ) return summary_dict