I need to iterate all different "Connect-Four" games possible.
The grid has 42 cells, and there are 21 red and 21 yellow pieces.
Every game generated must use every pieces, and all pieces of the same color are indistinguishable (e.g if you swap two reds in a solution, it doesn't count as another solution)
From that I can draw the conclusion that there's
I'm thinking about generating binary strings containing 21 0 and 21 1 but beside generating every 42-char long binary string and testing them one by one, I don't have any idea how to do that. That would be 42! (1.4050061e+51) string to test, so that's not an option.
How would you go about generating all these possible games ?
It seems that you do not care that some of these games will have ended early. To simply generate all of the possible combinations, you should think of the board as a matrix, where a 1 represents a black, and a 0 represents a red.
Now if we vectorize the matrix of values for a full board, then we will get something like
[0,1,1,0,...]
where the exact order depends on the permutation. Now since we have 21 of each color, that means that you are essentially asking for all of the possible permutations of the vector
[ones(1,21),zeros(1,21)]
(in Matlab and Python notation). In Matlab, you would then generate the list of all permutations by using the function
perms([ones(1,21),zeros(1,21)])
I am not sure what you want here because obviously it is not feasible to enumerate all of these in practice. If you are just interested in how to do it, I would suggest that you look in the Matlab implementation. It looks like 10 lines of pretty simple code.
Related
I am looking for any direction on how to implement the process below, you should not need to understand much at all about poker.
Below is a grid of possible two-card combinations.
Pocket pairs in blue, suited cards in yellow and off-suited in red.
Essentially there is a slider under the matrix which selects a percentage of possible combinations of two cards which a player could be dealt. However, you can see that it moves in a sort of linear fashion, towards the "better" cards.
These selections are also able to be parsed from strings e.g AA-88,AKo-AJo,KQo,AKs-AJs,KQs,QJs,JTs is 8.6% of the matrix.
I've looked around but cannot find questions about the specific selection process. I am not looking for "how to create this grid" or , more like how would I go about the selection process based on the sliding percentage. I am primarily a JavaScript developer but snippets in any language are appreciated, if applicable.
My initial assumptions are that there is some sort of weighting involved i.e. (favoured towards pairs over suited and suited over non-suited) or could it just be predetermined and I'm overthinking this?
In my opinion there should be something along the lines of "grouping(s)" AND "a subsequent weighting" process. It should also be customisable for the user to provide an optimal experience (imo).
For example, if you look at the below:
https://en.wikipedia.org/wiki/Texas_hold_%27em_starting_hands#Sklansky_hand_groups
These are/were standard hand rankings created back in the 1970s/1980s however since then, hand selection has become much more complicated. These kind of groupings have changed a lot in 30 years so poker players will want a custom user experience here.
But lets take a basic preflop scenario.
Combinations:- pairs = 6, suited = 4, nonsuited = 12
1 (AA:6, KK:6, QQ:6, JJ:6, AKs:4) = 28combos
2 (AQs:4, TT:6, AK:16, AJs:4, KQs:4, 99:6) = 40
3 (ATs:4, AQ:16, KJs:4, 88:6, KTs:4, QJs:4) = 38
....
9 (87s:4, QT:12, Q8s:4, 44:6, A9:16, J8s:4, 76s:4, JT:16) = 66
Say for instance we only reraise the top 28/1326 of combinations (in theory there should be some deduction here but for simplicity let's ignore that). We are only 3betting or reraising a very very obvious and small percentage of hands, our holdings are obvious at around 2-4% of total hands. So a player may want to disguise their reraise or 3bet range with say 50% of the weakest hands from group 9. As a basic example.
Different decision trees and game theory can be used with "range building" so a simple ordered list may not be suitable for what you're trying to achieve. depends on your programs purpose.
That said, if you just looking to build an ordered list then you could just take X% of hands that players open with, say average is 27% and run a hand equity calculator simulation tweaking the below GitHub to get different hand rankings. https://github.com/andrewprock/pokerstove
Theres also some lists here at the bottom this page.
http://www.propokertools.com/help/simulator_docs
Be lucky!
I am in the making of a simple Sudoku for my exams at school. I have decided to have only one sudoku. This ones numbers are then shuffled around to make it look like a new one every time. The problem here is that I need to handle 81 integers. Some of them have to be visible, and some not. I can not myself see an easy way to handle these ints with ease, except with arrays, but that didn't go very well.
If you have any suggestions let me know :)
int[][]
Make it a 9x9 array like the visual sudoku.
Any non-visible number can be negated e.g. -5 instead of 5.
To validate the grid as having a solution check the Math.abs(value) (or whatever the absolute function is in your language of choice). Iterate from 1 to 9 in each 'square' and then for each row and column.
This will only let you know that you have a starting arrangement in which you can fill in numbers in a valid way it won't tell you that you can use logically to find that answer exclusively (e.g. an empty grid is valid but has thousands of solutions).
I am trying to generate a Settlers of Catan game board and am stuck trying to create an efficient implementation of hex numbers.
The goal is to randomly generate a set of numbers from 2-12 (with only one instance of 2 and 12, and two instances of all numbers in between), ensuring that the values 6 and 8 they are not hexagonally (?) adjacent to one another. 6 & 8 are special because they are the numbers you are most likely to roll so the game does not want these next to one another as players get disproportionately higher resources of that kind. A 7 means you have to discard resources.
The expected result: http://imgur.com/Ng7Siy8
Right now I have a working brute force implementation that is very slow and I am hoping to optimize it, but I am not sure how. The implementation is in VBA, which has constrained the data structures I can use.
In pseudo code I am doing something like this:
For Each of the 19 hexes
Loop Until we have a valid number
Generate a random number between 1 and 12
Check
Have we already placed too many of that number?
Is the number equal to 6 or 8?
Is the number being placed on a hex next to another hex with 6 or 8 placed on it?
If valid
Place
If invalid
Regenerate random number
It's very manual and subject to the random generator function, which means it can be anywhere from being really short to being really really long (compounded over 19 hexes).
Note: How my numbers are being placed seems important. I start at the outside of the gameboard (see here http://imgur.com/Ng7Siy8) on the gray hex with number 6, and then move counter clockwise around the board inward. This means that my next hex is 2 light green, 4 light orange...continuing around to 9 dark green and then coming inwards to 4 light orange.
This pattern limits the number of comparisons I need to make.
There are several optimizations you can do - first of all you know exactly how many numbers are present prom each tile - you have 2,3,3,4,4,5,5,6,6,8,8,9,9,10,10,11,11,12. So start off with this set of numbers - you will eliminate the check if the number has been generated too many times. now you can do a random shuffle of this set of numbers and check if it is "valid". This will still result in too many negative checks I think but it should perform better than your current approach.
Place the 8 first, calculate which of the remaining tiles you'd be happy to place the 6 on (i.e. non-adjacent), then choose on at random for the 6. Then place the rest.
I've seen some questions on deciding the best OCR result given output from different engines, and the answer is typically "choose the best engine".
I want, however, to capture several frames of text images, with possible temporary occlusions or temporary failures.
I'm using tesseract-ocr with python-tesseract.
Considering the OCR outputs of the last N frames, I want to decide what is the best result (line by line, for simplicity).
For example, for N=3, we could use a median filtering:
ABXD
XBCX
AXCD
When there are 2 out of 3 equal characters, the majority will win, so the result would be ABCD.
However, that's not so easy with different string sizes. If I expect a given size M (if scanning a price table, the rows are typically XX.XX), I can always penalize on strings bigger than M.
If we were talking numbers, a median filtering would work quite well (simple background subtraction in computer vision), or some least mean squares adaptive filtering.
There's also the problem of similar characters: l and 1 can be very similar, depending on the font.
I was also thinking of using string distances between each string. For example, choose the string with the smallest sum of distances with the others.
Has anyone addressed this kind of problem before? Is there any known algorithm for this kind of problem that I should know?
This problem is called multiple sequence alignment and you can read about it here
Here is the facts first.
In the game of bridge there are 4
players named North, South, East and
West.
All 52 cards are dealt with 13 cards
to each player.
There is a Honour counting systems.
Ace=4 points, King=3 points, Queen=2
points and Jack=1 point.
I'm creating a "Card dealer" with constraints where for example you might say that the hand dealt to north has to have exactly 5 spades and between 13 to 16 Honour counting points, the rest of the hands are random.
How do I accomplish this without affecting the "randomness" in the best way and also having effective code?
I'm coding in C# and .Net but some idea in Pseudo code would be nice!
Since somebody already mentioned my Deal 3.1, I'd like to point out some of the optimizations I made in that code.
First of all, to get the most flexibly constraints, I wanted to add a complete programming language to my dealer, so you could generate whole libraries of constraints with different types of evaluators and rules. I used Tcl for that language, because I was already learning it for work, and, in 1994 when Deal 0.0 was released, Tcl was the easiest language to embed inside a C application.
Second, I needed the constraint language to run fairly fast. The constraints are running deep inside the loop. Quite a lot of code in my dealer is little optimizations with lookup tables and the like.
One of the most surprising and simple optimizations was to not deal cards to a seat until a constraint is checked on that seat. For example, if you want north to match constraint A and south to match constraint B, and your constraint code is:
match constraint A to north
match constraint B to south
Then only when you get to the first line do you fill out the north hand. If it fails, you reject the complete deal. If it passes, next fill out the south hand and check its constraint. If it fails, throw out the entire deal. Otherwise, finish the deal and accept it.
I found this optimization when doing some profiling and noticing that most of the time was spent in the random number generator.
There is one fancy optimization, which can work in some instances, call "smart stacking."
deal::input smartstack south balanced hcp 20 21
This generates a "factory" for the south hand which takes some time to build but which can then very quickly fill out the one hand to match this criteria. Smart stacking can only be applied to one hand per deal at a time, because of conditional probability problems. [*]
Smart stacking takes a "shape class" - in this case, "balanced," a "holding evaluator", in this case, "hcp", and a range of values for the holding evaluator. A "holding evaluator" is any evaluator which is applied to each suit and then totaled, so hcp, controls, losers, and hcp_plus_shape, etc. are all holding evalators.
For smartstacking to be effective, the holding evaluator needs to take a fairly limited set of values. How does smart stacking work? That might be a bit more than I have time to post here, but it's basically a huge set of tables.
One last comment: If you really only want this program for bidding practice, and not for simulations, a lot of these optimizations are probably unnecessary. That's because the very nature of practicing makes it unworthy of the time to practice bids that are extremely rare. So if you have a condition which only comes up once in a billion deals, you really might not want to worry about it. :)
[Edit: Add smart stacking details.]
Okay, there are exactly 8192=2^13 possible holdings in a suit. Group them by length and honor count:
Holdings(length,points) = { set of holdings with this length and honor count }
So
Holdings(3,7) = {AK2, AK3,...,AKT,AQJ}
and let
h(length,points) = |Holdings(length,points)|
Now list all shapes that match your shape condition (spades=5):
5-8-0-0
5-7-1-0
5-7-0-1
...
5-0-0-8
Note that the collection of all possible hand shapes has size 560, so this list is not huge.
For each shape, list the ways you can get the total honor points you are looking for by listing the honor points per suit. For example,
Shape Points per suit
5-4-4-0 10-3-0-0
5-4-4-0 10-2-1-0
5-4-4-0 10-1-2-0
5-4-4-0 10-0-3-0
5-4-4-0 9-4-0-0
...
Using our sets Holdings(length,points), we can compute the number of ways to get each of these rows.
For example, for the row 5-4-4-0 10-3-0-0, you'd have:
h(5,10)*h(4,3)*h(4,0)*h(0,0)
So, pick one of these rows at random, with relative probability based on the count, and then, for each suit, choose a holding at random from the correct Holdings() set.
Obviously, the wider the range of hand shapes and points, the more rows you will need to pre-compute. A little more code, you can still do this with some cards pre-determined - if you know where the ace of spades or west's whole hand or whatever.
[*] In theory, you can solve these conditional probability issues for smart stacking with multiple hands, but the solution to the problem would make it effective only for extremely rare types of deals. That's because the number of rows in the factory's table is roughly the product of the number of rows for stacking one hand times the number of rows for stacking the other hand. Also, the h() table has to be keyed on the number of ways of dividing the n cards amongst hand 1, hand 2, and other hands, which changes the number of values from roughly 2^13 to 3^13 possible values, which is about two orders of magnitude bigger.
Since the numbers are quite small here, you could just take the heuristic approach: Randomly deal your cards, evaluate the constraints and just deal again if they are not met.
Depending on how fast your computer is, it might be enough to do this:
Repeat:
do a random deal
Until the board meets all the constraints
As with all performance questions, the thing to do is try it and see!
edit I tried it and saw:
done 1000000 hands in 12914 ms, 4424 ok
This is without giving any thought to optimisation - and it produces 342 hands per second meeting your criteria of "North has 5 spades and 13-16 honour points". I don't know the details of your application but it seems to me that this might be enough.
I would go for this flow, which I think does not affect the randomness (other than by pruning solutions that do not meet constraints):
List in your program all possible combinations of "valued" cards whose total Honour points count is between 13 and 16. Then pick randomly one of these combinations, removing the cards from a fresh deck.
Count how many spades you already have among the valued cards, and pick randomly among the remaining spades of the deck until you meet the count.
Now pick from the deck as much non-spades, non-valued cards as you need to complete the hand.
Finally pick the other hands among the remaining cards.
You can write a program that generates the combinations of my first point, or simply hardcode them while accounting for color symmetries to reduce the number of lines of code :)
Since you want to practise bidding, I guess you will likely be having various forms of constraints (and not just 1S opening, as I guess for this current problem) coming up in the future. Trying to come up with the optimal hand generation tailored to the constraints could be a huge time sink and not really worth the effort.
I would suggest you use rejection sampling: Generate a random deal (without any constraints) and test if it satisfies your constraints.
In order to make this feasible, I suggest you concentrate on making the random deal generation (without any constraints) as fast as you can.
To do this, map each hand to a 12byte integer (the total number of bridge hands fits in 12 bytes). Generating a random 12 byte integer can be done in just 3, 4 byte random number calls, of course since the number of hands is not exactly fitting in 12 bytes, you might have a bit of processing to do here, but I expect it won't be too much.
Richard Pavlicek has an excellent page (with algorithms) to map a deal to a number and back.
See here: http://www.rpbridge.net/7z68.htm
I would also suggest you look at the existing bridge hand dealing software (like Deal 3.1, which is freely available) too. Deal 3.1 also supports doing double dummy analysis. Perhaps you could make it work for you without having to roll one of your own.
Hope that helps.