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Closed 10 years ago.
How can I tell whether an algorithm is stable or not?..
Also, how does this algorithm Bucketsort compare to Mergesort, Quicksort, Bubblesort, and Insertionsort
?
At first glance it would seem that if your queues are FIFO then it is stable. However I think there some context from class or other homework that would help you make a more solid determination.
From wikipedia:
Stability
Stable sorting algorithms maintain the relative order of records with equal keys. If all keys are different then this distinction is not necessary. But if there are equal keys, then a sorting algorithm is stable if whenever there are two records (let's say R and S) with the same key, and R appears before S in the original list, then R will always appear before S in the sorted list. When equal elements are indistinguishable, such as with integers, or more generally, any data where the entire element is the key, stability is not an issue. However, assume that the following pairs of numbers are to be sorted by their first component:
http://en.wikipedia.org/wiki/Sorting_algorithm#Stability
As far as comparing to other algorithms. Wikipedia has a concise entry on it:
http://en.wikipedia.org/wiki/Bucket_sort#Comparison_with_other_sorting_algorithms
Also: https://stackoverflow.com/a/7341355/1416221
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Closed 10 years ago.
I have an array of floats Float_t xbins[41] that defines 40 bins i.e. ranges of floats.
E.g. y is in bin 7 if y > xbins[7] && !(y > xbins[8]).
How do I determine what bin a given float should belong to without having 40 if statements?
Please answer in C++ as I don't speak other languages.
If the array is sorted, then do a binary search to locate the correct bin. You'll need a combination of std::sort (if not sorted), then something like std::lower_bound, to locate. You'll need to ensure that operator< is implemented correctly for Float_t.
As it turned out that the bins are not uniformly spaced but have integer bounds, the probably fastest method is to have a (inverse) look up table that apparently has about 100 entries. One needs to make basically two comparisons for the lower & higher bounds.
If the array bounds are derived with a formula, it could be possible to write an inverse formula that outperforms the LUT method.
For a generic case binary search is the way -- and even that can be improved a bit by doing linear interpolation instead of exactly subdividing the range to half. The speed (if the data is not pathological) would be O(loglogn) compared to O(logn) for binary search.
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Closed 10 years ago.
I am working on a project in school and I need to create a 160 bit value. I haven't programmed in a while so I can't figure out how I would implement this. Any help would be appreciated.
You need a library for big integers (assuming you can't just take a ready-to-use cryptographic library).
First you create a random 160-bit value, not necessarily prime. Depending on the platform, you may use /dev/random, CryptGenRandom, or some other enthropy source(s), (possible several ones, combined).
Then you increment the value in a loop, applying e.g. Miller-Rabin (pseudo-)primality test to each candidate, until you find a prime number.
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Closed 10 years ago.
To use vector efficiently we need to reserve the memory before setting the elements. But for map and set which are not contiguous containers how we can make them fast and efficient?
I have a vector/set/map of size 10s Millions of doubles and want to add non-repeated elements. I want to make it as fast as possible.
Q1) all STL containers are already as efficient as they can be. It's up to you the programmer to choose what data structures suits the given requirement. You need to understand the pros and cons of each data structures.
Q2) Map[key] = value calls operator[] which can also be used to access elements, not just inserting, whereas insert() function is only specific to inserting. insert() has few other overloading feature not available on operator[], check http://www.cplusplus.com/reference/map/map/insert/
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Closed 12 years ago.
I have ordederd to make a function, which takes a list ex [3,4,6,1,29] and returns a list of tuples [(3,4),(4,6),(6,1),(1,29)]
This is a very easy question, it's really hard to help without defeating the purpose...
If you are allowed to use predifined functions, there is already one which can do almost all work for you (if you don't know which one, try finding it with http://www.haskell.org/hoogle/ ). Take a step back and think about the easier question how to produce a list [(3,3),(4,4),(6,6),(1,1),(29,29)].
If you can't use predefined functions, then recursion is your friend: What do you need to do for an empty list? What for a list with one element? With two elements?
Without any own effort I can't give more hints. If you're stuck, extend your question and show what you already got, and we'll try to help.
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Closed 12 years ago.
I'm trying to pick out my next hackery project. It'll likely be one of the following:
A sparse radix trie Implementation with extremely fast set operations
A really good soft heap implementation
A bloomier filter implementation
A collection of small financial algorithms, such as deriving total returns given a set of dividends and minimal information about them.
But I can't choose. So I thought I'd put my fate in the hands of my peers. Which of those four would you find most useful? Most interesting to work on? Which do you think is the most needed?
I didn't know what a bloomier (maybe Bloom?) filter is until reading your question. Sounds cool and useful.