.. _aggregation_model-library:

Aggregation model library
=========================

An aggregation model takes one attribute as its input parameter. The parameter is an instance of the **AggregationArg** class (see simo.simulation.model.aggregationarg).

The object instance has the following attributes:

- *operands* -- the number of operands for the model
 
- *target_variable* -- index of the target variable in the data matrix

- *remove_targets* -- indices of the objects for which the aggregation model is not evaluated

- *use_nan_funcs* -- boolean indicating NaN values in the data

- *values* -- operand values

- *weights* -- weight attribute values

- *target_index* -- indices of the object for which the model is evaluated

- *oper_target_index* -- target index tables for operand(s)

- *oper_data_level* -- operand data level indice, int

- *success* -- boolean for successfull model evaluation

- *errors* -- list of errors encountered during model evaluation

- *deterministic* -- boolean that can be used to control whether random values are used in aggregation models or not (among others, random number generation is treated as an aggregation model)

- *results* -- a numpy vector for storing aggregation results, NOTE: aggregation model implementation is responsible for making sure that this actually is a numpy array

Below is an example of a very simple **substract** aggregation model::

    def substract(arg):
        """Substract two operands
        """
        if arg.operands != 2:
            arg.success = False
            arg.errors.append("Two operands required in 'substract'")
        else:
            arg.results = arg.values[0] - arg.values[1]