(self)
Statics sum, sum_square, max_value, min_value,
        so that variance is available.
Statics sum, sum_square, max_value, min_value,
        so that variance is available.
Statics sum sum_square max_value min_value so that variance is available .
def _static_sums(self):
        """
        Statics sum, sum_square, max_value, min_value,
        so that variance is available.
        """
        is_sparse = data_overview.is_sparse_data(self.data_instances)
        partition_cal = functools.partial(self.static_in_partition,
                                          cols_index=self.cols_index,
                                          summary_statistics=copy.deepcopy(self.summary_statistics),
                                          is_sparse=is_sparse)
        self.summary_statistics = self.data_instances.applyPartitions(partition_cal). \
            reduce(lambda x, y: self.copy_merge(x, y))
        # self.summary_statistics = summary_statistic_dict.reduce(self.aggregate_statics)
        self.finish_fit_statics = True
def _static_sums ( self ) : is_sparse = data_overview . is_sparse_data ( self . data_instances ) partition_cal = functools . partial ( self . static_in_partition , cols_index = self . cols_index , summary_statistics = copy . deepcopy ( self . summary_statistics ) , is_sparse = is_sparse ) self . summary_statistics = self . data_instances . applyPartitions ( partition_cal ) . reduce ( lambda x , y : self . copy_merge ( x , y ) ) # self.summary_statistics = summary_statistic_dict.reduce(self.aggregate_statics) self . finish_fit_statics = True