Neural network bet selectio on even chances.

[mr.ore]

So I finally found time to create quite naive implemntation of counter propagation neural network. It is the one which is more simple to understand and implement than back propagation, but it's nature prefers more generalization, so that severeral slightly different inputs (for example index of tracked events like red=0, black=1 in last x spins like RBRRB = 0 1 0 0 1) are mapped to same output (for example bet on 0 = red). The aim is to reduce variance on even chances. And  here are first results - could it be a path to that mythical "consistent winnig bet"

[superman]

Keep going mr.ore, watching with interest as always, don't go getting drundk again lol

[mr.ore]

Important part of the method is that it contains "vector quantization" method in self organizing map (link:://en.wikipedia.org/wiki/Self-organizing_map).

Quoting wiki:

The algorithm can be iteratively updated with 'live' data, rather than by picking random points from a data set, but this will introduce some bias if the data is temporally correlated over many samples.

link:://en.wikipedia.org/wiki/Vector_quantization

I have the simple version now, so improving it might be of utmost importance.

link:://en.wikipedia.org/wiki/Counterpropagation_network

[Bayes]

The aim is to reduce variance on even chances.

The way I'm attempting to do that is by using diversification which is mathematically proven to reduce variance (the proof is in the article). Instead of "assets" I'm "investing" in different EC patterns. Ridiculous? maybe so; it's stretching the definition to call a pattern an asset, but combined with regression to the mean it's working pretty well. 

I suppose the analogous technique in horseracing is "dutching". Spread your stake over 2 or more likely winners so that the return is the same whichever horse wins. It's really just common sense to not put all your eggs in one basket.

[mr.ore]

I have saved initial state of rng, so that I can test the strategy over same spins. Now I returned back to programing after some break and a nap during to various circumstances and runned it for 30k spins. Wow, it seems that banrkoll balance trend follow a nice predictable line, like if a variance was really lovered, it is pretty "consistent". Maybe I am up to something  I will try to fine tune it to these spins without looking on more, so that I can have that "up to something" feeling at least this evening and enjoy myself  .

[birdhands]

Please excuse the stupid question, but this is a European wheel, right?  That's why there is a steady loss?  I understand that the variance is what's being tested, I just want to be sure.


Mr. Ore, I believe that you and Bayes are probably our best hope; if there's a solution, you will find it.

[mr.ore]

Yes that's Europen wheel.

[mr.ore]

Mr. Ore, I believe that you and Bayes are probably our best hope; if there's a solution, you will find it.

Mathematically there is no solution, so finding anything that works is like prooving that intelligence is something really special. There is more in it than just beating the game, which should be impossible . But the more I watch results over thousands of spins the more I thing, there MUST be something. Intelligence MUST outperform random, because I see again and again the similar patterns even in such a large set of spins. While there MUST exist a sequence that beats anything, how much have been done to make almost infallible system? It does not matter that somewhere in an infinity it will lose. Utility is what matters.  Expected value = over inifinite number of spins, our results would be -2.7%, but what about FINITE number of spins? Let's fight random again, it's saturday evening, oh yeah  !

[mr.ore]

Curve fitting excersise - first 1000 spins were used to train neural network so that it learns "reading random", other spins are those in which it was not trained. This time no-zero roulette to see clearly the results. The main problem is that "trained" network can "broke" and my implementation is not stable - it does not converge to stable state where it does not learn because it already know how to win over trained spins. While I am still quite far from ability to automatically generate a roulette system that wins over a set of spins flat betting, first steps have been done.

When/if I master it over even chances, I will try it to create some wheel based method (so that it can learn dealer's signatures or any other forms of bias - to get Advantage-play system ). Well created AI should be able found even unexpected biases, if any exist. Random can't produce bias forever, but maybe human hand or physical wheel could do that. Wiesbaden spins also contain information about dealer change - what about trying to categorize unknown dealers? If they have signature, it should be possible for NN to found it. But there is no information about who was the dealer, that's not good.

What I would like someone to send me is data in this form:

dealer 0
13
5
7
0
13
21
14
13
22
0
dealer 1
14
19
26
17
0
8
dealer 0
9
19
18
17
22

and so on

I need a set of spins where a dealer is identified.
They can have a name of course, but I will transform them to numbers anyway

Could someone provide me with that? Next time you go to casino, track dealers names too and connect them to their spins, so that we can study human nature instead that of random.

[mr.ore]

And image of curve fitting excersise, of course Remember - this time it is without zero, but it should be obvious.

[mr.ore]

First 1000+ spins to see what my new "baby" can do. It has to mature though

[mr.ore]

Anyone remember this w3c guy who proposed the idea of consistent winning bet and that it should be tested over 30 sessions 100 spins long flat betting? What would happened if you actually found a bet that wins over 3000 (=30 sessions X 100 spins) spins flat betting? It would look like those two images in attachement, first on no-zero, second one single zero roulette. Neural network found such a bet and mastered it.

[mr.ore]

And what would happened if you took it into a casino  ...

[Dutchy]

To Bayes and Mr.Ore,guys thanks for all your efforts,really amazing ideas!Math on a scale most of us will never know!Retracement always interests me but many times I have found it does not rebound enough.Tried comparing singles with 2's and up should be 50/50 right, with moderate success.Keep up the great work.Mr.Ore it is saturday night,"go get em"

[mr.ore]

Well, those 10k spins were beated, but I had to manually change parameters of network and run it 100 times over spin data to learn it. I will have to read another chapter in a textbook to learn improved versions of neural networks, maybe it would help to create a good method. In the end I want the network just to avoid long stretches without upwards trends so that my positive progression works. I have quiet a good positive up as you go progression which works wonders, but a good hit ratio is really needed

In any way - this curve fitting is just to test the networks efficiency and not a real attempt to create a system. The system is going to have a progression, it is A MUST for a game with negative expectation.

As usual - one image is without zero, the other is with zero kicking in.