We are trying to calculate probabilities(and odds) for "bet on poker" game on which we are working now.
To calculate probabilities and odds for each hand we used https://github.com/cookpete/poker-odds library.
Now, having probabilities of "Royal Flush,Straight Flush, Four of a kind, Full house, Flush, Straight, Three of kind, Two pairs, One pair, High card" for each hand we are trying to calculate the same probabilities for entire table (for example we need probability that the winning combination of the table will be Royal flush) in this image we have probabilities of each hand but not for entire table
We have modified https://github.com/cookpete/poker-odds lib (which we used to calculate probabilities for each hands), especially calculate.js file and added 10 variables for each combination(var flush = 0, straight = 0 etc...), and after each iteration depending on winning combination I increment corresponding variable, at the end I have for example from 1000 iterations flush won 500 times , straight won 300 times and lets say pair won 200 times, after this we assume that the probability of flush 50%, pair 20% and the straight is 30%.
I know this is not very accurate and not very professional approach, but it seems working until we can find better way :)
I have a following problem Im trying to solve.
I have hundreds of particles with their corresponding chemical composition (elements with their weight percentages).
As an example, here are some made-up simplified particles:
Particle 1 - S (32%), K (25%), C (43%)
Particle 2 - S (33%), K (12%), C (15%), O (40%)
Particle 3 - Ti (18%), S (72%)
Particle 4 - Ti (10%), S (79%), K (12%)
In reality there are hundreds of them, some of them quite different to one another, some of them quite similar. As you can see, some particles do not have certain elements (i.e. they could be used as 0%).
What I would try to achieve is perform a cluster analysis, that would group the particles into groups with similar particles and give me some averages in terms of that cluster element composition.
I was looking at how cluster analysis works, but usually it only uses 2 parameters, whereas I have many elements for each particle and I want to take into account more than just one element for each particle while clustering it. I am not so much interested in the exact match in terms of all the elements contained. In other words, if for example some 2 particles were quite similar except that one contained one extra element in a very small quantity, that would be ok too. Very low percentages are sometimes caused by background noise when measuring it.
Once I know which strategy to use I would ideally use R to do it. But giving me just a hint as to how to go about it, or a link, would be enough.
Question :
You are a teacher at the brand new Little Coders kindergarten. You have N kids in your class, and each one has a different student ID number from 1 through N. Every kid in your class has a single best friend forever (BFF), and you know who that BFF is for each kid. BFFs are not necessarily reciprocal -- that is, B being A's BFF does not imply that A is B's BFF.
Your lesson plan for tomorrow includes an activity in which the participants must sit in a circle. You want to make the activity as successful as possible by building the largest possible circle of kids such that each kid in the circle is sitting directly next to their BFF, either to the left or to the right. Any kids not in the circle will watch the activity without participating.
What is the greatest number of kids that can be in the circle?
Input
The first line of the input gives the number of test cases, T. T test cases follow. Each test case consists of two lines. The first line of a test case contains a single integer N, the total number of kids in the class. The second line of a test case contains N integers F1, F2, ..., FN, where Fi is the student ID number of the BFF of the kid with student ID i.
Output
For each test case, output one line containing "Case #x: y", where x is the test case number (starting from 1) and y is the maximum number of kids in the group that can be arranged in a circle such that each kid in the circle is sitting next to his or her BFF.
My problem : There is the contest analysis on the code jam site, but I don't understand it. Where is the optimization happening? If someone can explain this problem and its solution in a detailed manner, it will be very helpful.
Edit : I am not adding any pseudo-code, because I want to better my understanding of the problem, and it's not a coding issue.
I am making a roguelike where the setting is open world on a procedurally generated planet. I want the distribution of each biome to be organic. There are 5 different biomes. Is there a way to organically distribute them without a huge complicated algorithm? I want the amount of space each biome takes up to be nearly equal.
I have worked with cellular automata before when I was making the terrain generators for each biome. There were 2 different states for each tile there. Is there an efficient way to do 5?
I'm using python 2.5, although specific code isn't necessary. Programming theory on it is fine.
If the question is too open ended, are there any resources out there that I could look at for this kind of problem?
You can define a cellular automaton on any cell state space. Just formulate the cell update function as F:Q^n->Q where Q is your state space (here Q={0,1,2,3,4,5}) and n is the size of your neighborhood.
As a start, just write F as a majority rule, that is, 0 being the neutral state, F(c) should return the value in 1-5 with the highest count in the neighborhood, and 0 if none is present. In case of equality, you may pick one of the max at random.
As an initial state, start with a configuration with 5 relatively equidistant cells with the states 1-5 (you may build them deterministically through a fixed position that can be shifted/mirrored, or generate these points randomly).
When all cells have a value different than 0, you have your map.
Feel free to improve on the update function, for example by applying the rule with a given probability.
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I have a large sales database of a 'home and construction' retail.
And I need to know who are the electricians, plumbers, painters, etc. in the store.
My first approach was to select the articles related to a specialty (wires [article] is related to an electrician [specialty], for example) And then, based on customer sales, know who the customers are.
But this is a lot of work.
My second approach is to make a cluster segmentation first, and then discover which cluster belong to a specialty. (this is a lot better because I would be able to discover new segments)
But, how can I do that? What type of clustering should I occupy? Kmeans, fuzzy? What variables should I take to that model? Should I use PCA to know how many cluster to search?
The header of my data (simplified):
customer_id | transaction_id | transaction_date | item_article_id | item_group_id | item_category_id | item_qty | sales_amt
Any help would be appreciated
(sorry my english)
You want to identify classes of customers based on what they buy (I presume this is for marketing reasons). This calls for a clustering approach. I will talk you through the entire setup.
The clustering space
Let us first consider what exactly you are clustering: either orders or customers. In either case, the way you characterize the items and the distances between them is the same. I will discuss the basic case for orders first, and then explain the considerations that apply to clustering by customers instead.
For your purpose, an order is characterized by what articles were purchased, and possibly also how many of them. In terms of a space, this means that you have a dimension for each type of article (item_article_id), for example the "wire" dimension. If all you care about is whether an article is bought or not, each item has a coordinate of either 0 or 1 in each dimension. If some order includes wire but not pipe, then it has a value of 1 on the "wire" dimension and 0 on the "pipe" dimension.
However, there is something to say for caring about the quantities. Perhaps plumbers buy lots of glue while electricians buy only small amounts. In that case, you can set the coordinate in each dimension to the quantity of the corresponding article (presumably item_qty). So suppose you have three articles, wire, pipe and glue, then an order described by the vector (2, 3, 0) includes 2 wire, 3 pipe and 0 glue, while an order described by the vector (0, 1, 4) includes 0 wire, 1 pipe and 4 glue.
If there is a large spread in the quantities for a given article, i.e. if some orders include order of magnitude more of some article than other orders, then it may be helpful to work with a log scale. Suppose you have these four orders:
2 wire, 2 pipe, 1 glue
3 wire, 2 pipe, 0 glue
0 wire, 100 pipe, 1 glue
0 wire, 300 pipe, 3 glue
The former two orders look like they may belong to electricians while the latter two look like they belong to plumbers. However, if you work with a linear scale, order 3 will turn out to be closer to orders 1 and 2 than to order 4. We fix that by using a log scale for the vectors that encode these orders (I use the base 10 logarithm here, but it does not matter which base you take because they differ only by a constant factor):
(0.30, 0.30, 0)
(0.48, 0.30, -2)
(-2, 2, 0)
(-2, 2.48, 0.48)
Now order 3 is closest to order 4, as we would expect. Note that I have used -2 as a special value to indicate the absence of an article, because the logarithm of 0 is not defined (log(x) tends to negative infinity as x tends to 0). -2 means that we pretend that the order included 1/100th of the article; you could make the special value more or less extreme, depending on how much weight you want to give to the fact that an article was not included.
The input to your clustering algorithm (regardless of which algorithm you take, see below) will be a position matrix with one row for each item (order or customer), one column for each dimension (article), and either the presence (0/1), amount, or logarithm of the amount in each cell, depending on which you choose based on the discussion above. If you cluster by customers, you can simply sum the amounts from all orders that belong to that customer before you calculate what goes into each cell of your position matrix (if you use the log scale, sum the amounts before taking the logarithm).
Clustering by orders rather than by customers gives you more detail, but also more noise. Customers may be consistent within an order but not between them; perhaps a customer sometimes behaves like a plumber and sometimes like an electrician. This is a pattern that you will only find if you cluster by orders. You will then find how often each customer belongs to each cluster; perhaps 70% of somebody's orders belong to the electrician type and 30% belong to the plumber type. On the other hand, a plumber may only buy pipe in one order and then only buy glue in the next order. Only if you cluster by customers and sum the amounts of their orders, you get a balanced view of what each customer needs on average.
From here on I will refer to your position matrix by the name my.matrix.
The clustering algorithm
If you want to be able to discover new customer types, you probably want to let the data speak for themselves as much as possible. A good old fashioned
hierarchical clustering with complete linkage (CLINK) may be an appropriate choice in this case. In R, you simply do hclust(dist(my.matrix)) (this will use the Euclidean distance measure, which is probably good enough in your case). It will join closely neighbouring items or clusters together until all items are categorized in a hierarchical tree. You can treat any branch of the tree as a cluster, observe typical article amounts for that branch and decide whether that branch represents a customer segment by itself, should be split in sub-branches, or joined with a sibling branch instead. The advantage is that you find the "full story" of which items and clusters of items are most similar to each other and how much. The disadvantage is that the outcome of the algorithm does not tell you where to draw the borders between your customer segments; you can cut up the clustering tree in many ways, so it's up to your interpretation how you want to identify your customer types.
On the other hand, if you are comfortable fixing the number of clusters (k) beforehand, k-means is a very robust way to get just any segmentation of your customers in k distinct types. In R, you would do kmeans(my.matrix, k). For marketing purposes, it may be sufficient to have (say) 5 different profiles of customers that you make custom advertisement for, rather than treating all customers the same. With k-means you don't explore all of the diversity that is present in your data, but you might not need to do so anyway.
If you don't want to fix the number of clusters beforehand, but you also don't want to manually decide where to draw the borders between the segments afterwards, there is a third possibility. You start with the k-means algorithm, where you let it generate an amount of cluster centers that is much larger than the number of clusters that you hope to end up with (for example, if you hope to end up with somewhere about 10 clusters, let the k-means algorithm look for 200 clusters). Then, use the mean shift algorithm to further cluster the resulting centers. You will end up with a smaller number of compact clusters. The approach is explained in more detail by James Li over here. You can use the mean shift algorithm in R with the ms function from the LPCM package, see this documentation.
About using PCA
PCA will not tell you how many clusters you need. PCA answers a different question: which variables seem to represent a common underlying (hidden) factor. In a sense, it is a way to cluster variables, i.e. properties of entities, not to cluster the entities themselves. The number of principal components (common underlying factors) is not indicative of the number of clusters needed. PCA can still be interesting if you want to learn something about the predictive value of each article about a customer's interests.
Sources
Michael J. Crawley, 2005. Statistics. An Introduction using R.
Gerry P. Quinn and Michael J. Keough, 2002. Experimental Design and Data Analysis for Biologists.
Wikipedia: hierarchical clustering, k-means, mean shift, PCA