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Anyone can link good article about ELO Rating system algorithm? How it works and why there is a constant? Also what are score and expected Score?
Wikipedia page is a good starting point.
In Elo formula you have two constants:
denominator (400),
K-factor (usually 10 to 40).
The base formula is simple:
Updated Elo points are previous points corrected by the performance, where S_A is the score (three values: 1 for win, 0.5 for draw, 0 for loss) and E_A is the expected score (that is "his probability of winning plus half his probability of drawing"),
Here's a comparison between Elo and some post-Elo rating systems.
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For example I have 4 numbers: 10, 3, 2, 1
Then the question is "How to make 4 numbers above to 24 ?"
The answer is (10-2) * (3 * 1) = (8 * 3) = 24
What is the name of the algorithm in the programming language? to predict the operator (-, +, /, *) in the case above
These fit within the broad class of constraint satisfaction problems - essentially you can easily test a solution, but you need to search to find one. There are several algorithms for dealing with various formulations of constraint satisfaction problems.
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I am looking for a book that contains descriptions of the topics: Shor's algorithm, McEliece cryptosystem, Lattice-based cryptography, Discrete logarithm; and possibly others.
Does anyone know a single book or a few that would contain there topics?
There is "Post-Quantum Cryptography" published by Daniel J. Bernstein. The book is more of a general overview and doesn't go that far into details.
It contains sections about lattice based, hash based and code based cryptography.
Shor's algorithm as well as discrete logarithm aren't handled in depth, but there is a general overview.
I think the best way to get into the details is by studying the respective scientific papers.
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I am trying to train tenserflow U-net, for multi-class segmentation of heart. I have 3 labels and the prediction has 3 probability maps (one probability map for every label). I trained with momentum optimizer which is also the default optimizer of the network. In the very beginning iterations, the probability mapping of label 1 and label 2 are different but after some iterations (or epochs) the probability map of label 1 and label 2 become exactly like each other and technically I have a binary label segmentation. I have seen other networks that have similar architecture like U-net and they have trained on the multi-class dataset. I want to find some multiclass segmentation examples with U-net but all examples are binary.
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I need to calculate my shipping based on dimensions. Some of my items are less than 2 pounds but are larger in size.
I googled and found a $240 extension for this but it's too expensive.
Is there any alternative where I can send dimensions to UPS, USPS and Fedex and get the shipping rates from them in my Magento store?
Shipping based on dimensions just ends up being reduced back to weight. Which is why it is called dimensional weight.
http://www.fedex.com/gb/tools/dimweight.html
Basically just enter the dimensional weight or the actual weight of the product when creating your products and shipping will be accurate.
Technically you could program this problem away, but it would be hard to justify the effort.
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What would be the best approach to create the AI for a Real Time Strategy game? any books or tutorials that you like?
There is a good book by Peter Norvig on artificial intelligence: http://norvig.com/paip.html
And you can even take part in a free course by this author at Stanford on artificial intelligence in the next few months:
http://www.ai-class.com/
This might teach you the best approach to create a real AI system. However, maybe you want something simpler for a game.
A good place to start, especially since you mention Solarmax, would be the Google/University of Waterloo AI challenge from a while back called Planet Wars.
And most useful of all, you can find the analysis of the game from the guy who won the challenge here.
And another one here.