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Hi
Also , following code :
I am not sure I understand it . What happens if I have a new X vector and I want to preprocess it and run pr.sae<-nn.predict(SAE, X);
How do I do it ? Thank you .
Description of the function preProcess(), refer to the package "caret".
Best regards
Description of the function preProcess(), refer to the package "caret".
Best regards
I decided to just use your code ... But I am stuck on the "No calculation results! Symbol" error .
I see in the code , a server with port is referenced . What server is this referring to ?
I decided to just use your code ... But I am stuck on the "No calculation results! Symbol" error .
I see in the code , a server with port is referenced . What server is this referring to ?
Hi,
What do you run that carried out?
I can not read minds at a distance.
Please describe your problem more detail.
Best regards
Vlad
Hi,
What do you run that carried out?
I can not read minds at a distance.
Please describe your problem more detail.
Best regards
Vlad
ok sorry . I will see what else I can figure out . I get the "No calculation results! Symbol" and I install the indicator and still get the error .
I made some changes but markets is closed right now . Will let you know next week .
ok sorry . I will see what else I can figure out . I get the "No calculation results! Symbol" and I install the indicator and still get the error .
I made some changes but markets is closed right now . Will let you know next week .
Hi,
The problem appeared after the release of a new version of the svSocket () package.
I have not found the cause of the data block between the client and the server.
I rewrote the expert, and attached it to a new article which is to be released a few days ago (today at checkout).
Best regards
Vladimir
Stacked RBM (DN_SRBM) https://www.mql5.com/en/articles/1628
Its interesting to note , if a human is immersed in a task the human will improve
while if a machine does the same it may stick on a local optimum.
Maybe the algorithmic immersion could evolve from a "Study" paradigm to an "Execute" paradigm.
Great Article.Props
Again we have a profitable phase of about 5 weeks until the model deteriorates.
This is normal. The model can and should be periodically re-learn.
I believe the splitting into test and training data is unnecessary: we can use all data for training.
Can. It is important to remember a few important points:1. training and test sets should not be crossed.
2. The training set should be mixed
3. If the ratio of classes of balance - to make the adjustment.
I am glad that there were colleagues using R.
Best Regards
Vladimir
Hi,
please help me to clarify some my negative prejudgements about neural networks (NN).
b) we run a second optimization by the tester only to check which of the optimized indicators we need(*)
c) so that we have a smaller bunch of our optimized indicators
d) for what do I need the NN?
(*) Unfortunately if you run mt4' optimizer in genetic mode and you want to try to bypass certain parameter sets (e.g. don't test if "indicator-A" is 'on') by returning from OnInit() with "INIT_PARAMETERS_INCORRECT" the genetic algorithm still counts this as a valid pass and that reduces the number of actually executed passes before this algorithm stops due to the number passes which is one of termination criteria.
Hi,
please help me to clarify some my negative prejudgements about neural networks (NN).
b) we run a second optimization by the tester only to check which of the optimized indicators we need(*)
c) so that we have a smaller bunch of our optimized indicators
d) for what do I need the NN?
(*) Unfortunately if you run mt4' optimizer in genetic mode and you want to try to bypass certain parameter sets (e.g. don't test if "indicator-A" is 'on') by returning from OnInit() with "INIT_PARAMETERS_INCORRECT" the genetic algorithm still counts this as a valid pass and that reduces the number of actually executed passes before this algorithm stops due to the number passes which is one of termination criteria.
For example , lets say we create a simple optimization using the RSI and ZigZag Highs , ZigZag Lows .
We produce an average oversold on highs by summing the RSI value at the ZigZag Highs , and an average overbought
on lows by summing the RSI value at the ZigZag Lows . Our averages will essentially be the adjustment of RSI regardless
of settings to that asset .
The question is not if indicators should be optimized in my humble opinion ,but whether or not the indicator is utilizable
fundamentally.
In the above example you can grasp my point by viewing the Averages for a RSI(3) versus an RSI(16) .
The RSI(3) will constantly trigger our optimized levels versus the RSI(16).