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finimej,
That's good you discovered the bug, but it's strange the EA gives 100% winners during the run within teaching period. It means it classifies trades correctly. Not trying to criticize you or anything, just a note on the side. I'm still interested in the fixed version though, please keep posting.
Quote:
Originally Posted by finimej
I've double posted the code, can not edit my own code any more?
Orest above,
The EA trainer does not use the pnnclassificator at all. It backlooked and set the classficiation by the code from trainer itself. The pnn_system is using the classificator.
It's hard to debug somebodyelse elegent code, where it mixed global variable and local variable, and lots arrays.
I think better to write our own code, then I would like to have two files
pnn_buy_trained.dat
pnn_sell_trained.dat
then have two class array, one for buy and one for sell, to get the data to/from files. This would be easier to write the code and debug and also faster. just get no energy right now, somebody else take over the debug comez pnnclassification? The logic is written in the lecture notes pattern2.pdf and pattern3.pdf.
Canon-ball problem using a Multi Layer Perceptron in MT4
with 4 input neuron and 6 hidden neuron:
#define IN 4
#define IN1 5 //IN+1
#define OUT 2
#define N_HU 6
#define N_HU1 7 //N_HU + 1
Canon-ball problem using a Multi Layer Perceptron in MT4
with 6 input neuron and 9 hidden neuron:
#define IN 6
#define IN1 7 //IN+1
#define OUT 2
#define N_HU 9
#define N_HU1 10 //N_HU + 1
Canon-ball problem using a Multi Layer Perceptron in MT4
with 8 input neuron and 12 hidden neuron:
#define IN 8
#define IN1 9 //IN+1
#define OUT 2
#define N_HU 12
Canon-ball problem using a Multi Layer Perceptron in MT4
with 18 input neuron and 27 hidden neuron:
#define IN 18 //2*9
#define IN1 19 //IN+1
#define OUT 2
#define N_HU 27 //3*9
#define N_HU1 28 //N_HU + 1
Linear Mode
