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Discussion of article "Third Generation Neural Networks: Deep Networks" - page 5

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Vladimir Perervenko
3473
Vladimir Perervenko  
jake89:

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 .

newX <- predict(spSign, X)
pr.sae <- nn.predict(SAE, newXX)
# Calculate parameters preprocessing
spSign <- preProcess(x[t$tr, ], method = "spatialSign")
# Using these parameters (spSign) carry out the actual preprocessing 
x.tr<-predict(spSign, x[t$tr, ])
# Using these parameters (spSign) carry out the actual preprocessing  
x.ts<-predict(spSign, x[t$ts, ]

Description of the function preProcess(), refer to the package "caret".

Best regards


jake89
7
jake89  
Vladimir Perervenko:

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 ?  

Vladimir Perervenko
3473
Vladimir Perervenko  
jake89:

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

jake89
7
jake89  
Vladimir Perervenko:

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 . 

Vladimir Perervenko
3473
Vladimir Perervenko  
jake89:

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

Seng Wee Ngui
276
Seng Wee Ngui  
Hello have you found a way to fix the problem with server socket?
Huy Dao The
15
Huy Dao The  
Hi I see you used 11 oscilator indicator for input, I have some indicators in Mt4 and they are not oscilator, How can i add or replace these indicator likes in your article 

Stacked RBM (DN_SRBM) https://www.mql5.com/en/articles/1628

Deep neural network with Stacked RBM. Self-training, self-control
Deep neural network with Stacked RBM. Self-training, self-control
  • 2016.04.26
  • Vladimir Perervenko
  • www.mql5.com
This article is a continuation of previous articles on deep neural network and predictor selection. Here we will cover features of a neural network initiated by Stacked RBM, and its implementation in the "darch" package.
Lorentzos Roussos
13491
Lorentzos Roussos  
Fascinating.

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
Carl Schreiber
7353
Carl Schreiber  
Vladimir Perervenko:


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).

  1. Is it correct that you should firstly optimize the indicators to be in putted into the NN?
  2. Then you optimize the parameters of the NN?
  3. Or do you optimize the parameters of the NN and the indicators at the same time?
  4. Isn't it true that the more variables you have to optimize the greater is the danger of over adapting?
  5. If the data-sets for 1. and 2. are the same wouldn't that lead me to a kind of over adapting to the data set?
  6. Isn't exactly that indicated by "Again we have a profitable phase of about 5 weeks until the model deteriorates."
  7. a) Lets assume we have a bunch of indicators all together optimized by the tester and now
    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?
  8. Do you know an estimation about how big the data set has to be for a NN due to the number of inputs, layers and perceptrons?


(*) 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.


Lorentzos Roussos
13491
Lorentzos Roussos  
Carl Schreiber:

Hi,

please help me to clarify some my negative prejudgements about neural networks (NN).

  1. Is it correct that you should firstly optimize the indicators to be in putted into the NN?
  2. Then you optimize the parameters of the NN?
  3. Or do you optimize the parameters of the NN and the indicators at the same time?
  4. Isn't it true that the more variables you have to optimize the greater is the danger of over adapting?
  5. If the data-sets for 1. and 2. are the same wouldn't that lead me to a kind of over adapting to the data set?
  6. Isn't exactly that indicated by "Again we have a profitable phase of about 5 weeks until the model deteriorates."
  7. a) Lets assume we have a bunch of indicators all together optimized by the tester and now
    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?
  8. Do you know an estimation about how big the data set has to be for a NN due to the number of inputs, layers and perceptrons?


(*) 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.


1,2,3 and 4 , i believe whatever indicators and settings are passed in , inherently adjust to the underlying asset.

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).
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