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  • Laplacian of Gaussian

    - by Don
    I am having trouble implementing a LoG kernel. I am trying to implement 9x9 kernal with theta = 1.4 as shown in this link http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm. However, I am having difficulty with the formula itself.For whatever values I input into the formula, I don't get any of the values in a 9x9 LoG kernel with theta = 1. 4. If someone can provide an example of how they got one of the big values ie -40 or -23, or the code to implement it, It'd be greatly appreciated. Thank you

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  • probability and relative frequency

    - by Alexandru
    If I use relative frequency to estimate the probability of an event, how good is my estimate based on the number of experiments? Is standard deviation a good measure? A paper/link/online book would be perfect. http://en.wikipedia.org/wiki/Frequentist

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  • How to find a binary logarithm very fast? (O(1) at best)

    - by psihodelia
    Is there any very fast method to find a binary logarithm of an integer number? For example, given a number x=52656145834278593348959013841835216159447547700274555627155488768 such algorithm must find y=log(x,2) which is 215. x is always a power of 2. The problem seems to be really simple. All what is required is to find the position of the most significant 1 bit. There is a well-known method FloorLog, but it is not very fast especially for the very long multi-words integers. What is the fastest method?

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  • Pool Billiard AI

    - by Sebi
    Im implementing a pool billiard game in Java and it all works fine. It is a multiplayer game, but nevertheless, it should also be possible to play it alone. For this purpose I'm trying to implement a simple KI. At the moment, the KI choose just randomly a direction and a random intensity of the impulse (don't know the correct english word for that). Of course this AI is very poor and unlikely to ever challenge a player. So i thought about improving the KI, but there are several hard to solve problems. First I thought of just choosing the nearest ball and to try to put it directly into the nearest hole. This isn't that bad, but if there other balls in the line between, it isn't really working anymore. Additionally this dosn't solve te problem of calculating the intensity of the impulse. So are there any general advice? Or any ideas? Best practices?

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  • Algorithm for dividing very large numbers

    - by pocoa
    I need to write an algorithm(I cannot use any 3rd party library, because this is an assignment) to divide(integer division, floating parts are not important) very large numbers like 100 - 1000 digits. I found http://en.wikipedia.org/wiki/Fourier_division algorithm but I don't know if it's the right way to go. Do you have any suggestions?

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  • An implementation of Sharir's or Aurenhammer's deterministic algorithm for calculating the intersect

    - by RGrey
    The problem of finding the intersection/union of 'N' discs/circles on a flat plane was first proposed by M. I. Shamos in his 1978 thesis: Shamos, M. I. “Computational Geometry” Ph.D. thesis, Yale Univ., New Haven, CT 1978. Since then, in 1985, Micha Sharir presented an O(n log2n) time and O(n) space deterministic algorithm for the disc intersection/union problem (based on modified Voronoi diagrams): Sharir, M. Intersection and closest-pair problems for a set of planar discs. SIAM .J Comput. 14 (1985), pp. 448-468. In 1988, Franz Aurenhammer presented a more efficient O(n log n) time and O(n) space algorithm for circle intersection/union using power diagrams (generalizations of Voronoi diagrams): Aurenhammer, F. Improved algorithms for discs and balls using power diagrams. Journal of Algorithms 9 (1985), pp. 151-161. Earlier in 1983, Paul G. Spirakis also presented an O(n^2) time deterministic algorithm, and an O(n) probabilistic algorithm: Spirakis, P.G. Very Fast Algorithms for the Area of the Union of Many Circles. Rep. 98, Dept. Comput. Sci., Courant Institute, New York University, 1983. I've been searching for any implementations of the algorithms above, focusing on computational geometry packages, and I haven't found anything yet. As neither appear trivial to put into practice, it would be really neat if someone could point me in the right direction!

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  • Sparse constrained linear least-squares solver

    - by Jacob
    This great SO answer points to a good sparse solver, but I've got constraints on x (for Ax = b) such that each element in x is >=0 an <=N. The first thing which comes to mind is an QP solver for large sparse matrices. Also, A is huge (around 2e6x2e6) but very sparse with <=4 elements per row. Any ideas/recommendations? I'm looking for something like MATLAB's lsqlin but with huge sparse matrices.

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  • Curve fitting: Find the smoothest function that satisfies a list of constraints.

    - by dreeves
    Consider the set of non-decreasing surjective (onto) functions from (-inf,inf) to [0,1]. (Typical CDFs satisfy this property.) In other words, for any real number x, 0 <= f(x) <= 1. The logistic function is perhaps the most well-known example. We are now given some constraints in the form of a list of x-values and for each x-value, a pair of y-values that the function must lie between. We can represent that as a list of {x,ymin,ymax} triples such as constraints = {{0, 0, 0}, {1, 0.00311936, 0.00416369}, {2, 0.0847077, 0.109064}, {3, 0.272142, 0.354692}, {4, 0.53198, 0.646113}, {5, 0.623413, 0.743102}, {6, 0.744714, 0.905966}} Graphically that looks like this: We now seek a curve that respects those constraints. For example: Let's first try a simple interpolation through the midpoints of the constraints: mids = ({#1, Mean[{#2,#3}]}&) @@@ constraints f = Interpolation[mids, InterpolationOrder->0] Plotted, f looks like this: That function is not surjective. Also, we'd like it to be smoother. We can increase the interpolation order but now it violates the constraint that its range is [0,1]: The goal, then, is to find the smoothest function that satisfies the constraints: Non-decreasing. Tends to 0 as x approaches negative infinity and tends to 1 as x approaches infinity. Passes through a given list of y-error-bars. The first example I plotted above seems to be a good candidate but I did that with Mathematica's FindFit function assuming a lognormal CDF. That works well in this specific example but in general there need not be a lognormal CDF that satisfies the constraints.

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  • formula for best approximation for center of 2D rotation with small angles

    - by RocketSurgeon
    This is not a homework. I am asking to see if problem is classical (trivial) or non-trivial. It looks simple on a surface, and I hope it is truly a simple problem. Have N points (N = 2) with coordinates Xn, Yn on a surface of 2D solid body. Solid body has some small rotation (below Pi/180) combined with small shifts (below 1% of distance between any 2 points of N). Possibly some small deformation too (<<0.001%) Same N points have new coordinates named XXn, YYn Calculate with best approximation the location of center of rotation as point C with coordinates XXX, YYY. Thank you

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  • Automatic camera calibration

    - by srand
    From Wikipedia, camera resectioning is the process of finding the true parameters of the camera that produced a given photograph or video. Camera resectioning is also known as geometric camera calibration. Currently I am using Camera Calibration Toolbox for Matlab for my camera calibration. The toolbox returns calibration parameters such as focal length, principle point, skew, and distortion. However, the issue with this method is that it requires an extra step in calibrating the camera by using a special calibration object like a checkerboard. Additionally, it only works for one focus of the camera. How can I get the calibration parameters without manually calibrating? For example, how does Microsoft's Photosynth perform camera calibration on its images?

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  • Finding subsets that can be completed to tuples without duplicates

    - by Jules
    We have a collection of sets A_1,..,A_n. The goal is to find new sets for each of the old sets. newA_i = {a_i in A_i such that there exist (a_1,..,a_n) in (A1,..,An) with no a_k = a_j for all k and j} So in words this says that we remove all the elements from A_i that can't be used to form a tuple (a_1,..a_n) from the sets (A_1,..,A_n) such that the tuple doesn't contain duplicates. My question is how to compute these new sets quickly. If you just implement this definition by generating all possible v's this will take exponential time. Do you know a better algorithm? Edit: here's an example. Take A_1 = {1,2,3,4} A_2 = {2}. Now the new sets look like this: newA_1 = {1,3,4} newA_2 = {2} The 2 has been removed from A_1 because if you choose it the tuple will always be (2,2) which is invalid because it contains duplicates. On the other hand 1,3,4 are valid because (1,2), (3,2) and (4,2) are valid tuples. Another example: A_1 = {1,2,3} A_2 = {1,4,5} A_3 = {2,4,5} A_4 = {1,2,3} A_5 = {1,2,3} Now the new sets are: newA_1 = {1,2,3} newA_2 = {4,5} newA_3 = {4,5} newA_4 = {1,2,3} newA_5 = {1,2,3} The 1 and 2 are removed from sets 2 and 3 because if you choose the 1 or 2 from these sets you'll only have 2 values left for sets 1, 4 and 5, so you will always have duplicates in tuples that look like (_,1,_,_,_) or like (_,_,2,_,_).

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  • Algorithm to fill slots

    - by Peter Lang
    I am searching for an algorithm to fill several slots, which are already filled to some level. The current levels and the available quantity to fill are known Resulting levels should be as equal as possible, but existing level cannot be reduced Slots are filled from left to right, so left slots get higher level if equal level is impossible       The image above shows six examples, each column represents a slot. The grey area is already filled, the blue are is the expected position of the new elements. I could iterate through my slots and increase the quantity on the lowest slot by 1 until the available quantity is consumed, but I wonder about how to actually calculate the new filling levels. I am going to implement this with SQL/PL/SQL, other code is just as welcome though :)

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  • Why is Java not telling me when I can't use Integer?

    - by Sebi
    For a small project (Problem 10 Project Euler) i tried to sum up all prime numbers below 2 millions. So I used a brute force method and iterated from 0 to 2'000'000 and checked if the number is a prime. If it is I added it to the sum: private int sum = 0; private void calculate() { for (int i = 0; i < 2000000; i++) { if (i.isPrime()) { sum = sum + i; } } sysout(sum) } The result of this calculation is 1179908154, but this is incorrect. So i changed int to BigInteger and now i get the correct sum 142913828922. Obviously the range of int was overflowed. But why can't Java tell my that? (e.g. by an exception)

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  • Finding the string length of a integer in .NET

    - by James Newton-King
    In .NET what is the best way to find the length of an integer in characters if it was represented as a string? e.g. 1 = 1 character 10 = 2 characters 99 = 2 characters 100 = 3 characters 1000 = 4 characters The obvious answer is to convert the int to a string and get its length but I want the best performance possible without the overhead of creating a new string.

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  • Best way to do powerOf(int x, int n)?

    - by Mike
    So given x, and power, n, solve for X^n. There's the easy way that's O(n)... I can get it down to O(n/2), by doing numSquares = n/2; numOnes = n%2; return (numSquares * x * x + numOnes * x); Now there's a log(n) solution, does anyone know how to do it? It can be done recursively.

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  • Clustering [assessment] algorithm with distance matrix as an input

    - by Max
    Can anyone suggest some clustering algorithm which can work with distance matrix as an input? Or the algorithm which can assess the "goodness" of the clustering also based on the distance matrix? At this moment I'm using a modification of Kruskal's algorithm (http://en.wikipedia.org/wiki/Kruskal%27s_algorithm) to split data into two clusters. It has a problem though. When the data has no distinct clusters the algorithm will still create two clusters with one cluster containing one element and the other containing all the rest. In this case I would rather have one cluster containing all the elements and another one which is empty. Are there any algorithms which are capable of doing this type of clustering? Are there any algorithms which can estimate how well the clustering was done or even better how many clusters are there in the data? The algorithms should work only with distance(similarity) matrices as an input.

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  • how to calculate a bill from several tables on mysql?

    - by Audel
    I'm using mysql to create an hotel booking system, but i am struggling a little bit to calculate the final bill. I need a SELECT command to get data from several tables and make some calculations. Basically I just need to get the 'night cost' from a table called 'room_types'. Then, use DATEDIFF function to get the difference of days between the 'checkin' and 'checkout' columns in the table 'room_booking' and multiply the difference with the night cost and display the total. These are the tables I would be using: are room_booking, room_types, booking, and room. One booking may have several room bookings, so Im looking for a table that looks something like this: +------------+------------+---------------+------------------+ | bookingid | Room price | nights stayed | total room price | +------------+------------+---------------+------------------+ | B001 | 30.00 | 4 | 120.00 | +------------+------------+---------------+------------------+ | B001 | 40.00 | 3 | 120.00 | +------------+------------+---------------+------------------+ booking id comes from table 'booking' room price from 'room_types', 'nights stayed' is calculated from the table room_booking, using the datediff command between checkin and checkout . I hope i was clear

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  • Fastest way to list all primes below N in python

    - by jbochi
    This is the best algorithm I could come up with after struggling with a couple of Project Euler's questions. def get_primes(n): numbers = set(range(n, 1, -1)) primes = [] while numbers: p = numbers.pop() primes.append(p) numbers.difference_update(set(range(p*2, n+1, p))) return primes >>> timeit.Timer(stmt='get_primes.get_primes(1000000)', setup='import get_primes').timeit(1) 1.1499958793645562 Can it be made even faster? EDIT: This code has a flaw: Since numbers is an unordered set, there is no guarantee that numbers.pop() will remove the lowest number from the set. Nevertheless, it works (at least for me) for some input numbers: >>> sum(get_primes(2000000)) 142913828922L #That's the correct sum of all numbers below 2 million >>> 529 in get_primes(1000) False >>> 529 in get_primes(530) True EDIT: The rank so far (pure python, no external sources, all primes below 1 million): Sundaram's Sieve implementation by myself: 327ms Daniel's Sieve: 435ms Alex's recipe from Cookbok: 710ms EDIT: ~unutbu is leading the race.

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  • Java: minimum number of operations for conjunctive inequalities?

    - by HH
    I try to simplify conditionals in for (int t=0,size=fo.getPrintViewsPerFile().size();t<size&&t<countPerFile;t++){, more precisely: t<s&&t<c You need to compare two times, then calc the boolean value from them. Is there any simpler way to do it? If no, how can you prove it? I can simplify it to some extent, proof tree.

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  • JavaScript Image zoom with CSS3 Transforms, How to calculate Origin? (with example)

    - by Sunday Ironfoot
    I'm trying to implement an image zoom effect, a bit like how the zoom works with Google Maps, but with a grid of fix position images. I've uploaded an example of what I have so far here: http://www.dominicpettifer.co.uk/Files/MosaicZoom.html (uses CSS3 transforms so only works with Firefox, Opera, Chrome or Safari) Use your mouse wheel to zoom in/out. The HTML source is basically an outer div with an inner-div, and that inner-div contains 16 images arranged using absolute position. It's going to be a Photo Mosaic basically. I've got the zoom bit working using CSS3 transforms: $(this).find('div').css('-moz-transform', 'scale(' + scale + ')'); ...however, I'm relying on the mouse X/Y position on the outer div to zoom in on where the mouse cursor is, similar to how Google Maps functions. The problem is that if you zoom right in on an image, move the cursor to the bottom/left corner and zoom again, instead of zooming to the bottom/left corner of the image, it zooms to the bottom/left of the entire mosaic. This has the effect of appearing to jump about the mosaic as you zoom in closer while moving the mouse around, even slightly. That's basically the problem, I want the zoom to work exactly like Google Maps where it zooms exactly to where your mouse cursor position is, but I can't get my head around the Maths to calculate the transform-origin: X/Y values correctly. Please help, been stuck on this for 3 days now. Here is the full code listing for the mouse wheel event: var scale = 1; $("#mosaicContainer").mousewheel(function(e, delta) { if (delta > 0) { scale += 1; } else { scale -= 1; } scale = scale < 1 ? 1 : (scale > 40 ? 40 : scale); var x = e.pageX - $(this).offset().left; var y = e.pageY - $(this).offset().top; $(this).find('div').css('-moz-transform', 'scale(' + scale + ')') .css('-moz-transform-origin', x + 'px ' + y + 'px'); return false; });

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