apop_missing_data.c File Reference

Defines

#define Switch_back

Functions

apop_data * apop_data_listwise_delete (apop_data *d, char inplace)
apop_model * apop_ml_imputation (apop_data *d, apop_model *mvn)

Detailed Description

Some missing data handlers.

Define Documentation

#define Switch_back

Value:

apop_data *real_data = ml_model->parameters;   \
    apop_model *actual_base = ml_model->more; \
    actual_base->parameters = d;

Function Documentation

apop_data* apop_data_listwise_delete	(	apop_data *	d,
		char	inplace
	)

If there is an NaN anywhere in the row of data (including the matrix, the vector, and the weights) then delete the row from the data set.

The function returns a new data set with the NaNs removed, so the original data set is left unmolested. You may want to apop_data_free the original immediately after this function.

If every row has an NaN, then this returns NULL.
If there is text, it gets pruned as well.
If inplace = 'y', then I'll free each element of the input data set and refill it with the pruned elements. Again, I'll take up (up to) twice the size of the data set in memory during the function. If every row has an NaN, then your apop_data set will have a lot of NULL elements.
This function uses the Designated initializers syntax for inputs.

Parameters:

	d	The data, with NaNs
	inplace	If `'y'`, clear out the pointer-to-apop_data that you sent in and refill with the pruned data. If `'n'`, leave the set alone and return a new data set.

Returns:: A (potentially shorter) copy of the data set, without NaNs. If inplace=='y', redundant with the input.

apop_model* apop_ml_imputation	(	apop_data *	d,
		apop_model *	mvn
	)

Impute the most likely data points to replace NaNs in the data, and insert them into the given data. That is, the data set is modified in place.