Machine learning in trading: theory, models, practice and algo-trading - page 256
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Each array[][] is a set of single-quality information, that is, for each input, a separate array[][]. I want to feed a lot of arrays, I'm ready for now 4, I'll create more in the plans, each array describes the state of the price, it turns out from different angles, something like that.
Each array contains 1000 rows and 1000 columns, well, in general, I have a three-dimensional, it turns out that the K-dimension is the new two-dimensionalUsually networks work with two-dimensional matrices. You could combine all the matrices into one broad matrix with 2000 (3000 or 4000) columns and 1000 rows, and then train the neural network as usual. You can train neuronics in R with rattle, it's a special program with a visual interface for data analysis, where you can use buttons and menus to process data and teach the model, and then in the "log" tab look at the generated R script for all these operations, and then you can just change and run the resulting code in the R console itself. That is you do all operations with your mouse and then you can watch the generated R script which does the same thing, it's convenient to study the possibilities of the language.
https://www.mql5.com/ru/articles/1165 - there is a lot and good about rattle.
There's also the mxnet package for R, it's a deep learning neuron, and trains on 4-dimensional arrays, even more measurements than you need
There are installation instructions (Installlation) and some examples in vignettes and demo folders -https://github.com/dmlc/mxnet/tree/master/R-package
I do not understand what you want to do, so I will not say anything about it, but I say something else judging by the number of inputs, you can start to study methods of compressing information such as "PCA" or others, you will need them
The logic of your question is probably deeper than my explanation.
I want to feed a couple of two-dimensional arrays into the network.
The arrays are the same along the axes, i.e. the columns are graded by period and the rows by bar.
Usually networks work with two-dimensional matrices. You could combine all the matrices into one broad matrix with 2000 (3000 or 4000) columns and 1000 rows, and then train the neural network as usual. You can train neuronics in R with rattle, it's a special program with a visual interface for data analysis, where you can use buttons and menus to process data and teach the model, and then in the "log" tab you can see the generated R script for all these operations, and then you can just change and run the resulting code in the R console itself. That is you do all operations with your mouse and then you can watch the generated R script which does the same thing, it's convenient to study the possibilities of the language.
https://www.mql5.com/ru/articles/1165 - there is a lot and good about rattle.
There's also the mxnet package for R, it's a deep learning neuron, and trains on 4-dimensional arrays, even more measurements than you need
There's an installation guide (Installlation) and some examples in the vignettes and demo folders -https://github.com/dmlc/mxnet/tree/master/R-package
The logic of your question is probably deeper than my explanation.
I want to feed a couple of two-dimensional arrays into the network.
The arrays on the axes are the same, that is, the columns are graded by period, and the rows by bar.
I don't have any deep logic)))
You just have a lot of inputs into the network, it will take a long time to learn, you will come to the point where you have to reduce the number of inputs, that's why I advised you to study compression methods
Thanks!
Usually networks work with two-dimensional matrices. You could combine all the matrices into one broad matrix with 2000 (3000 or 4000) columns and 1000 rows, and then train the neural network as usual. You can train neuronics in R with rattle, it's a special program with a visual interface for data analysis, where you can use buttons and menus to process data and teach the model, and then in the "log" tab look at the generated R script for all these operations, and then you can just change and run the resulting code in the R console itself. That is you do all operations with your mouse and then you can watch the generated R script which does the same thing, it's convenient to study the possibilities of the language.
https://www.mql5.com/ru/articles/1165 - there is a lot and good about rattle.
There's also the mxnet package for R, it's a deep learning neuron, and trains on 4-dimensional arrays, even more measurements than you need
There's an installation guide (Installlation) and some examples in the vignettes and demo folders -https://github.com/dmlc/mxnet/tree/master/R-package
Would you happen to tell me if I did everything according to the instructionshttps://www.mql5.com/ru/articles/1165
And it's silent.
Would you happen to tell me if I did everything according to the instructionshttps://www.mql5.com/ru/articles/1165
It doesn't say anything.
Try just typing
Try just typing
Man, I don't want to bother you with this nonsense, but if you know, maybe something standard is missing.
*** added
All simply entered rattle()
Man, I don't want to bother you with this nonsense, but if you know, maybe something standard is missing
*** added
It's easy to enter rattle()
You can't see rattle() on the picture.
After entering this command, the rattle window itself should appear, as described in the article
TC.RData (as I understand from the article) must be loaded into rattle().