fixunknowns

If you have input data with unknown values, you can represent them with NaN values. For example, here are five 2-element vectors with unknown values in the first element of two of the vectors:

p1 = [1 NaN 3 2 NaN; 3 1 -1 2 4];

The network will not be able to process the NaN values properly. Use the function fixunknowns to transform each row with NaN values (in this case only the first row) into two rows that encode that same information numerically.

[p2,ps] = fixunknowns(p1);

Here is how the first row of values was recoded as two rows.

p2 =
   1  2  3  2  2
   1  0  1  1  0
   3  1 -1  2  4

The first new row is the original first row, but with the mean value for that row (in this case 2) replacing all NaN values. The elements of the second new row are now either 1, indicating the original element was a known value, or 0 indicating that it was unknown. The original second row is now the new third row. In this way both known and unknown values are encoded numerically in a way that lets the network be trained and simulated.

Whenever supplying new data to the network, you should transform the inputs in the same way, using the settings ps returned by fixunknowns when it was used to transform the training input data.

p2new = fixunknowns('apply',p1new,ps);

The function fixunkowns is only recommended for input processing. Unknown targets represented by NaN values can be handled directly by the toolbox learning algorithms. For instance, performance functions used by backpropagation algorithms recognize NaN values as unknown or unimportant values.