Machine learning in trading: theory, models, practice and algo-trading - page 1865
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ok.
And this is Maxim's code,
https://www.mql5.com/ru/forum/86386/page1862#comment_17290073
personally reminds me of a digital filter
As far as I remember, in a digital filter, the constant values are static.
That is, from these constantvalues, a certain model isimmediately assembled.
With Max, it's a logical branching of If, so it's a tree.
By the way, Max.
Have you thought about replacing the tree of Ifs with a tree of ternary operators z = (x > y) ? x : y;
Because ternary branching is faster than If
. It's perfect for big trees.
As far as I remember, in the digital filter the constant values are static.
That is, from these constantvalues, a certain model isimmediately assembled.
In Max, this is a logical branching of If, so it is a tree.
By the way, Max.
Have you thought about replacing the tree of Ifs with a tree of ternary operators z = (x > y) ? x : y;
Because ternary branching works faster than If
- just right for big trees.
No, why
there are knobs for adjusting output AFR, use them to do it
It's just that the algorithm is basically the same.
And here, does it change the processing algorithm or the best way is chosen by trying and substituting constants into one and the same algorithm?
Well, why not?
there are knobs for adjusting the output frequency response, and they change it
It's just that the algorithm is basically the same.
And here the processing algorithm is changed or the best way is chosen by constant search?
Yes, in digital filters, you can change the constants themselves, but they are set by you, forming a kind of model at once.
The tree structure searches for these values for you according to the activation function.
In general, everywhere you look, there is a structure of the tree :)
MLM-men, structure of the tree.
Power, tree structure.
Subordination in employment, tree structure.
Kinship heritage, tree structure.
etc.
:)
Just look at the tree outside at your leisure, and mentally reason from the base of the tree.
Just fantasize. Decompose that tree into different kinds of subspecies, etc. who runs on them, what color, etc. )))
I've fantasized about ants, caterpillars, butterflies running around on their branches :))
Fascinating training to understand.
It's easier for you to google, activation functions in neural network.
Everywhere you look there is a tree structure :)
MLM people, tree structure.
Power, tree structure.
Employment, structure of the tree.
Just look at the tree outside at your leisure, and mentally reason from the base of the tree.
Just fantasize. Decompose that tree into different kinds of subspecies, etc. who runs on them, what color, etc. )))
I've fantasized about ants, caterpillars, butterflies running around on their branches :))
Fascinating training to understand.
It's easier for you to google, activation functions in a neural network.
read
I get it, I get how this thing works.
so we decided to bang the composter to save our skulls.
hmm, interesting ;)
Yes, you can change the constants themselves in digital filters, but they are set rigidly by you, forming some model at once.
The tree structure looks for these values for you, according to the activation function.
In general, everywhere you look, there is a structure of the tree :)
MLM-men, structure of the tree.
Power, tree structure.
Subordination in employment, tree structure.
Kinship heritage, tree structure.
etc.
:)
Just look at the tree outside at your leisure, and mentally reason from the base of the tree.
Just fantasize. Decompose that tree into different kinds of subspecies, etc. who runs on them, what color, etc. )))
I've fantasized about ants, caterpillars, butterflies running around on their branches :))
Fascinating training to understand.
It's easier for you to google, activation functions in a neural network.
That's exactly what's in a neural network. The tree doesn't have an activation function. You just have a comparison.
That's exactly what's in the neural network. The tree has no activation function. There is just a comparison.
Hirase how you got here. That's right, and I understand that branching of the tree itself is good to have its own level of generalization. That is, this node is responsible for a certain aspect of the model between the input vector and the target vector. And if it were possible to decompose the problem from the general to the particular independently, then there would be no need to do training, but here, when this structure is large and not known, we have to resort to training. Isn't that right? Gentlemen of the HOGORADIANS :-)))))) I almost burst my belly :-)
not known yet, because it's written with a lot of grammatical errors and:
input/output is a normal filter
and the output is the AFC.
Obtaining the desired AFC at the output is notoriously called "learning" here
and one of the tree experts now is crying hard for his big pennies in one of his own threads on the forum
That's why I started this conversation about yellow leaves...That's exactly what's in the neural network. The tree has no activation function. There is simply a comparison.
Well, the meshes themselves can also be represented as a tree.
It turns out that a neural network tree has an activation function ))
So a tree is a digital filter after all?
The tree is just a branching structure.
How you apply this structure is a matter of fantasy.)