| 1 | flag_ts |
| 1 | flag_cross |
| 0 | flag_prepro |
| 1 | flag_end_____0_none_____1_all_outputs_____2_condpro |
Row 1
Flag variable toggling between interpolation (0)
and time series prediction (>1).
In the latter case, the positive integer number indicates
how many time steps ahead a prediction is to be made.
Row 2
Flag variable deciding whether to do
the training with (1) or without (0) cross-validation.
If set to 1, the data for cross-validation need to be
available in file
cross.in.
Row 3
Flag variable for preprocessing of the data.
If set to 1, the data are normalised.
Row 4
Flag variable for defining the output after
training is completed.
0: No output.
1: For every data point, the input to each
of the nodes in the kernel layer (that is,
the kernel centres) is calculated.
The results are written out to file
predict.out.
From this you can obtain a
state-space plot
for the kernel centres.
2: Predicting the
conditional probability density
P(y|x)
for a
given input vector x.