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  • Whats the difference between Paxos and W+R>=N in Cassandra?

    - by user1128016
    Dynamo-like databases (e.g. Cassandra) provide ability to enforce consistency by means of quorum, i.e. a number of synchronously written replicas (W) and a number of replicas to read (R) should be chosen in such a way that W+RN where N is a replication factor. On the other hand, PAXOS-based systems like Zookeeper are also used as a consistent fault-tolerant storage. What is the difference between these two approaches? Does PAXOS provide guarantees that are not provided by W+RN schema?

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  • Naive Bayesian classification (spam filtering) - Doubt in one calculation? Which one is right? Plz c

    - by Microkernel
    Hi guys, I am implementing Naive Bayesian classifier for spam filtering. I have doubt on some calculation. Please clarify me what to do. Here is my question. In this method, you have to calculate P(S|W) - Probability that Message is spam given word W occurs in it. P(W|S) - Probability that word W occurs in a spam message. P(W|H) - Probability that word W occurs in a Ham message. So to calculate P(W|S), should I do (1) (Number of times W occuring in spam)/(total number of times W occurs in all the messages) OR (2) (Number of times word W occurs in Spam)/(Total number of words in the spam message) So, to calculate P(W|S), should I do (1) or (2)? (I thought it to be (2), but I am not sure, so plz clarify me) I am refering http://en.wikipedia.org/wiki/Bayesian_spam_filtering for the info by the way. I got to complete the implementation by this weekend :( Thanks and regards, MicroKernel :) @sth: Hmm... Shouldn't repeated occurrence of word 'W' increase a message's spam score? In the your approach it wouldn't, right?. Lets take a scenario and discuss... Lets say, we have 100 training messages, out of which 50 are spam and 50 are Ham. and say word_count of each message = 100. And lets say, in spam messages word W occurs 5 times in each message and word W occurs 1 time in Ham message. So total number of times W occuring in all the spam message = 5*50 = 250 times. And total number of times W occuring in all Ham messages = 1*50 = 50 times. Total occurance of W in all of the training messages = (250+50) = 300 times. So, in this scenario, how do u calculate P(W|S) and P(W|H) ? Naturally we should expect, P(W|S) P(W|H)??? right. Please share your thought...

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  • Compact data structure for storing a large set of integral values

    - by Odrade
    I'm working on an application that needs to pass around large sets of Int32 values. The sets are expected to contain ~1,000,000-50,000,000 items, where each item is a database key in the range 0-50,000,000. I expect distribution of ids in any given set to be effectively random over this range. The operations I need on the set are dirt simple: Add a new value Iterate over all of the values. There is a serious concern about the memory usage of these sets, so I'm looking for a data structure that can store the ids more efficiently than a simple List<int>or HashSet<int>. I've looked at BitArray, but that can be wasteful depending on how sparse the ids are. I've also considered a bitwise trie, but I'm unsure how to calculate the space efficiency of that solution for the expected data. A Bloom Filter would be great, if only I could tolerate the false negatives. I would appreciate any suggestions of data structures suitable for this purpose. I'm interested in both out-of-the-box and custom solutions. EDIT: To answer your questions: No, the items don't need to be sorted By "pass around" I mean both pass between methods and serialize and send over the wire. I clearly should have mentioned this. There could be a decent number of these sets in memory at once (~100).

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  • Efficiently solving sparse matrices

    - by anon
    For solving spare matrices, in general, how big does the matrix have to be (as a rule of thumb) for methods like congraduate descent to be faster than brute force solvers (that do not take advantage o sparsity)?

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  • Programming Contest Question: Counting Polyominos

    - by Martijn Courteaux
    Hi, An example question for a programming contest was to write a program that finds out how much polyominos are possible with a given number of stones. So for two stones (n = 2) there is only one polyominos: XX You might think this is a second solution: X X But it isn't. The polyominos are not unique if you can rotate them. So, for 4 stones (n = 4), there are 7 solutions: X X XX X X X X X X XX X XX XX XX X X X XX X X XX The application has to be able to find the solution for 1 <= n <=10 PS: Using the list of polyominos on Wikipedia isn't allowed ;) EDIT: Of course the question is: How to do this in Java, C/C++, C#

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  • BFS algorithm problem

    - by Gorkamorka
    The problem is as follows: A wanderer begins on the grid coordinates (x,y) and wants to reach the coordinates (0,0). From every gridpoint, the wanderer can go 8 steps north OR 3 steps south OR 5 steps east OR 6 steps west (8N/3S/5E/6W). How can I find the shortest route from (X,Y) to (0,0) using breadth-first search? Clarifications: Unlimited grid Negative coordinates are allowed A queue (linked list or array) must be used No obstacles present

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  • Why does Java's hashCode() in String use 31 as a multiplier?

    - by jacobko
    In Java, the hash code for a String object is computed as s[0]*31^(n-1) + s[1]*31^(n-2) + ... + s[n-1] using int arithmetic, where s[i] is the ith character of the string, n is the length of the string, and ^ indicates exponentiation. Why is 31 used as a multiplier? I understand that the multiplier should be a relatively large prime number. So why not 29, or 37, or even 97?

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  • Travelling Salesman Problem Constraint Representation

    - by alex25
    Hey! I read a couple of articles and sample code about how to solve TSP with Genetic Algorithms and Ant Colony Optimization etc. But everything I found didn't include time (window) constraints, eg. "I have to be at customer x before 12am)" and assumed symmetry. Can somebody point me into the direction of some sample code or articles that explain how I can add constraints to TSP and how I can represent those in code. Thanks!

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  • incremental way of counting quantiles for large set of data

    - by Gacek
    I need to count the quantiles for a large set of data. Let's assume we can get the data only through some portions (i.e. one row of a large matrix). To count the Q3 quantile one need to get all the portions of the data and store it somewhere, then sort it and count the quantile: List<double> allData = new List<double>(); foreach(var row in matrix) // this is only example. In fact the portions of data are not rows of some matrix { allData.AddRange(row); } allData.Sort(); double p = 0.75*allData.Count; int idQ3 = (int)Math.Ceiling(p) - 1; double Q3 = allData[idQ3]; Now, I would like to find a way of counting this without storing the data in some separate variable. The best solution would be to count some parameters od mid-results for first row and then adjust it step by step for next rows. Note: These datasets are really big (ca 5000 elements in each row) The Q3 can be estimated, it doesn't have to be an exact value. I call the portions of data "rows", but they can have different leghts! Usually it varies not so much (+/- few hundred samples) but it varies! This question is similar to this one: http://stackoverflow.com/questions/1058813/on-line-iterator-algorithms-for-estimating-statistical-median-mode-skewness But I need to count quantiles. ALso there are few articles in this topic, i.e.: http://web.cs.wpi.edu/~hofri/medsel.pdf http://portal.acm.org/citation.cfm?id=347195&dl But before I would try to implement these, I wanted to ask you if there are maybe any other, qucker ways of counting the 0.25/0.75 quantiles?

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  • How to sort a list so that managers are always ahead of their subordinates (How do I do a topologica

    - by James Black
    I am working on a project using Groovy, and I would like to take an array of employees, so that no manager follows their subordinates in the array. The reason being that I need to add people to a database and I would prefer not to do it in two passes. So, I basically have: <employees> <employee> <employeeid>12</employeeid> <manager>3</manager> </employee> <employee> <employeeid>1</employeeid> <manager></manager> </employee> <employee> <employeeid>3</employeeid> <manager>1</manager> </employee> </employees> So, it should be sorted as such: employeeid = 1 employeeid = 3 employeeid = 12 The first person should have a null for managers. I am thinking about a binary tree representation, but I expect it will be very unbalanced, and I am not certain the best way to do this using Groovy properly. Is there a way to do this that isn't going to involve using nested loops?

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  • List of Big-O for PHP functions?

    - by Kendall Hopkins
    After using PHP for a while now, I've noticed that not all PHP built in functions as fast as expected. Consider the below two possible implementations of a function that finds if a number is prime using a cached array of primes. //very slow for large $prime_array $prime_array = array( 2, 3, 5, 7, 11, 13, .... 104729, ... ); $result_array = array(); foreach( $array_of_number => $number ) { $result_array[$number] = in_array( $number, $large_prime_array ); } //still decent performance for large $prime_array $prime_array => array( 2 => NULL, 3 => NULL, 5 => NULL, 7 => NULL, 11 => NULL, 13 => NULL, .... 104729 => NULL, ... ); foreach( $array_of_number => $number ) { $result_array[$number] = array_key_exists( $number, $large_prime_array ); } This is because in_array is implemented with a linear search O(n) which will linearly slow down as $prime_array grows. Where the array_key_exists function is implemented with a hash lookup O(1) which will not slow down unless the hash table gets extremely populated (in which case it's only O(logn)). So far I've had to discover the big-O's via trial and error, and occasionally looking at the source code. Now for the question... I was wondering if there was a list of the theoretical (or practical) big O times for all* the PHP built in functions. *or at least the interesting ones For example find it very hard to predict what the big O of functions listed because the possible implementation depends on unknown core data structures of PHP: array_merge, array_merge_recursive, array_reverse, array_intersect, array_combine, str_replace (with array inputs), etc.

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  • How can I test if a point lies within a 3d shape with its surface defined by a point cloud?

    - by Ben
    Hi I have a collection of points which describe the surface of a shape that should be roughly spherical, and I need a method with which to determine if any other given point lies within this shape. I've previously been approximating the shape as an exact sphere, but this has proven too inaccurate and I need a more accurate method. Simplicity and speed is favourable over complete accuracy, a good approximation will suffice. I've come across techniques for converting a point cloud to a 3d mesh, but most things I have found have been very complicated, and I am looking for something as simple as possible. Any ideas? Many thanks, Ben.

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  • Converting to a column oriented array in Java

    - by halfwarp
    Although I have Java in the title, this could be for any OO language. I'd like to know a few new ideas to improve the performance of something I'm trying to do. I have a method that is constantly receiving an Object[] array. I need to split the Objects in this array through multiple arrays (List or something), so that I have an independent list for each column of all arrays the method receives. Example: List<List<Object>> column-oriented = new ArrayList<ArrayList<Object>>(); public void newObject(Object[] obj) { for(int i = 0; i < obj.length; i++) { column-oriented.get(i).add(obj[i]); } } Note: For simplicity I've omitted the initialization of objects and stuff. The code I've shown above is slow of course. I've already tried a few other things, but would like to hear some new ideas. How would you do this knowing it's very performance sensitive?

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  • How can I group an array of rectangles into "Islands" of connected regions?

    - by Eric
    The problem I have an array of java.awt.Rectangles. For those who are not familiar with this class, the important piece of information is that they provide an .intersects(Rectangle b) function. I would like to write a function that takes this array of Rectangles, and breaks it up into groups of connected rectangles. Lets say for example, that these are my rectangles (constructor takes the arguments x, y, width,height): Rectangle[] rects = new Rectangle[] { new Rectangle(0, 0, 4, 2), //A new Rectangle(1, 1, 2, 4), //B new Rectangle(0, 4, 8, 2), //C new Rectangle(6, 0, 2, 2) //D } A quick drawing shows that A intersects B and B intersects C. D intersects nothing. A tediously drawn piece of ascii art does the job too: +-------+ +---+ ¦A+---+ ¦ ¦ D ¦ +-+---+-+ +---+ ¦ B ¦ +-+---+---------+ ¦ +---+ C ¦ +---------------+ Therefore, the output of my function should be: new Rectangle[][]{ new Rectangle[] {A,B,C}, new Rectangle[] {D} } The failed code This was my attempt at solving the problem: public List<Rectangle> getIntersections(ArrayList<Rectangle> list, Rectangle r) { List<Rectangle> intersections = new ArrayList<Rectangle>(); for(Rectangle rect : list) { if(r.intersects(rect)) { list.remove(rect); intersections.add(rect); intersections.addAll(getIntersections(list, rect)); } } return intersections; } public List<List<Rectangle>> mergeIntersectingRects(Rectangle... rectArray) { List<Rectangle> allRects = new ArrayList<Rectangle>(rectArray); List<List<Rectangle>> groups = new ArrayList<ArrayList<Rectangle>>(); for(Rectangle rect : allRects) { allRects.remove(rect); ArrayList<Rectangle> group = getIntersections(allRects, rect); group.add(rect); groups.add(group); } return groups; } Unfortunately, there seems to be an infinite recursion loop going on here. My uneducated guess would be that java does not like me doing this: for(Rectangle rect : allRects) { allRects.remove(rect); //... } Can anyone shed some light on the issue?

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  • What is it about Fibonacci numbers?

    - by Ian Bishop
    Fibonacci numbers have become a popular introduction to recursion for Computer Science students and there's a strong argument that they persist within nature. For these reasons, many of us are familiar with them. They also exist within Computer Science elsewhere too; in surprisingly efficient data structures and algorithms based upon the sequence. There are two main examples that come to mind: Fibonacci heaps which have better amortized running time than binomial heaps. Fibonacci search which shares O(log N) running time with binary search on an ordered array. Is there some special property of these numbers that gives them an advantage over other numerical sequences? Is it a density quality? What other possible applications could they have? It seems strange to me as there are many natural number sequences that occur in other recursive problems, but I've never seen a Catalan heap.

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  • Clamping a vector to a minimum and maximum?

    - by user146780
    I came accross this: t = Clamp(t/d, 0, 1) but I'm not sure how to perform this operation on a vector. What are the steps to clamp a vector if one was writing their own vector implementation? Thanks clamp clamping a vector to a minimum and a maximum ex: pc = # the point you are coloring now p0 = # start point p1 = # end point v = p1 - p0 d = Length(v) v = Normalize(v) # or Scale(v, 1/d) v0 = pc - p0 t = Dot(v0, v) t = Clamp(t/d, 0, 1) color = (start_color * t) + (end_color * (1 - t))

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  • question about api functions

    - by davit-datuashvili
    i have question we have API functions in java can user create it's own function and add to his java IDE? for example i am using netbeans can i create my own function add to netbean IDE?let say create binary function or something else thanks

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  • Data Structure for a particular problem??

    - by AGeek
    Hi, Which data structure can perform insertion, deletion and searching operation in O(1) time in the worst case. We may assume the set of elements are integers drawn from a finite set 1,2,...,n, and initialization can take O(n) time. I can only think of implementing a hash table. Implementing it with Trees will not give O(1) time complexity for any of the operation. Or is it possible?? Kindly share your views on this, or any other data structure apart from these.. Thanks..

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  • Given an even number of vertices, how to find an optimum set of pairs based on proximity?

    - by Alex Z
    The problem: We have a set of n vertices in 3D euclidean space, and there is an even number of these vertices. We want to pair them up based on their proximity. In other words, we'd like to be able to find a set of vertex pairs, where the vertices in each pair are as close as possible together. We want to minimise sacrificing the proximity between the vertices of any other pairs as much as possible in doing this. I am not looking for the most optimal solution (if it even strictly exists/can be done), just a reasonable one that can be computed relatively quickly. A relatively awful brute force approach involves choosing a vertex and looping through the rest to find its nearest neighbor and then repeating until there are none left. Of course as we near the end of the list the closest vertex could be very far away, but it is the only choice, therefore this can fail badly on the third point above.

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  • algorithm q: Fuzzy matching of structured data

    - by user86432
    I have a fairly small corpus of structured records sitting in a database. Given a tiny fraction of the information contained in a single record, submitted via a web form (so structured in the same way as the table schema), (let us call it the test record) I need to quickly draw up a list of the records that are the most likely matches for the test record, as well as provide a confidence estimate of how closely the search terms match a record. The primary purpose of this search is to discover whether someone is attempting to input a record that is duplicate to one in the corpus. There is a reasonable chance that the test record will be a dupe, and a reasonable chance the test record will not be a dupe. The records are about 12000 bytes wide and the total count of records is about 150,000. There are 110 columns in the table schema and 95% of searches will be on the top 5% most commonly searched columns. The data is stuff like names, addresses, telephone numbers, and other industry specific numbers. In both the corpus and the test record it is entered by hand and is semistructured within an individual field. You might at first blush say "weight the columns by hand and match word tokens within them", but it's not so easy. I thought so too: if I get a telephone number I thought that would indicate a perfect match. The problem is that there isn't a single field in the form whose token frequency does not vary by orders of magnitude. A telephone number might appear 100 times in the corpus or 1 time in the corpus. The same goes for any other field. This makes weighting at the field level impractical. I need a more fine-grained approach to get decent matching. My initial plan was to create a hash of hashes, top level being the fieldname. Then I would select all of the information from the corpus for a given field, attempt to clean up the data contained in it, and tokenize the sanitized data, hashing the tokens at the second level, with the tokens as keys and frequency as value. I would use the frequency count as a weight: the higher the frequency of a token in the reference corpus, the less weight I attach to that token if it is found in the test record. My first question is for the statisticians in the room: how would I use the frequency as a weight? Is there a precise mathematical relationship between n, the number of records, f(t), the frequency with which a token t appeared in the corpus, the probability o that a record is an original and not a duplicate, and the probability p that the test record is really a record x given the test and x contain the same t in the same field? How about the relationship for multiple token matches across multiple fields? Since I sincerely doubt that there is, is there anything that gets me close but is better than a completely arbitrary hack full of magic factors? Barring that, has anyone got a way to do this? I'm especially keen on other suggestions that do not involve maintaining another table in the database, such as a token frequency lookup table :). This is my first post on StackOverflow, thanks in advance for any replies you may see fit to give.

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