Training of the classificator with one data set.
learn_sampset2 trains the current classificator with data for the key SampKey (see read_sampset). The training sequence is terminated at least after NSamples examples. If NSamples is bigger than the number of examples in SampKey, then a cyclic start at the beginning occurs. If the error underpasses the value StopError, then the training sequence is terminated in advance. StopError is calculated with N / ErrorN. Whereby N significates the number of examples which were classified wrong during the last ErrorN training examples. Typically ErrorN is the number of examples in SampKey and NSamples is a multiple of it. If you want a data set with 100 examples to run 5 times at most and if you want it to terminate with an error lower than 5, then the corresponding values are NSamples = 500, ErrorN = 100 and StopError = 0.05. A protocol of the training activity is going to be written in file Outfile.
SampKey (input_control) |
integer -> integer |
Number of the data set to train. |
Outfile (input_control) |
filename.named -> string |
Name of the protocol file. | |
Default value: 'training_prot' |
NSamples (input_control) |
integer -> integer |
Number of arrays of attributes to learn. | |
Default value: 500 |
StopError (input_control) |
real -> real |
Classification error for termination. | |
Default value: 0.05 |
ErrorN (input_control) |
integer -> integer |
Error during the assignment. | |
Default value: 100 |
learn_sampset2 returns TRUE. An exception handling is raised, if there is no key SampKey or if there are problems while opening the file.
test_sampset2, enquire_classif2, write_classif2, close_classif2, free_sampset
test_sampset2, enquire_classif2, learn_classif2, read_sampset