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  • Webcrawler, feedback?

    - by Jan Kuboschek
    Hey folks, every once in a while I have the need to automate data collection tasks from websites. Sometimes I need a bunch of URLs from a directory, sometimes I need an XML sitemap (yes, I know there is lots of software for that and online services). Anyways, as follow up to my previous question I've written a little webcrawler that can visit websites. Basic crawler class to easily and quickly interact with one website. Override "doAction(String URL, String content)" to process the content further (e.g. store it, parse it). Concept allows for multi-threading of crawlers. All class instances share processed and queued lists of links. Instead of keeping track of processed links and queued links within the object, a JDBC connection could be established to store links in a database. Currently limited to one website at a time, however, could be expanded upon by adding an externalLinks stack and adding to it as appropriate. JCrawler is intended to be used to quickly generate XML sitemaps or parse websites for your desired information. It's lightweight. Is this a good/decent way to write the crawler, provided the limitations above? http://pastebin.com/VtgC4qVE - Main.java http://pastebin.com/gF4sLHEW - JCrawler.java http://pastebin.com/VJ1grArt - HTMLUtils.java Thanks for your feedback in advance! :)

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  • PostgreSQL - fetch the row which has the Max value for a column

    - by Joshua Berry
    I'm dealing with a Postgres table (called "lives") that contains records with columns for time_stamp, usr_id, transaction_id, and lives_remaining. I need a query that will give me the most recent lives_remaining total for each usr_id There are multiple users (distinct usr_id's) time_stamp is not a unique identifier: sometimes user events (one by row in the table) will occur with the same time_stamp. trans_id is unique only for very small time ranges: over time it repeats remaining_lives (for a given user) can both increase and decrease over time example: time_stamp|lives_remaining|usr_id|trans_id ----------------------------------------- 07:00 | 1 | 1 | 1 09:00 | 4 | 2 | 2 10:00 | 2 | 3 | 3 10:00 | 1 | 2 | 4 11:00 | 4 | 1 | 5 11:00 | 3 | 1 | 6 13:00 | 3 | 3 | 1 As I will need to access other columns of the row with the latest data for each given usr_id, I need a query that gives a result like this: time_stamp|lives_remaining|usr_id|trans_id ----------------------------------------- 11:00 | 3 | 1 | 6 10:00 | 1 | 2 | 4 13:00 | 3 | 3 | 1 As mentioned, each usr_id can gain or lose lives, and sometimes these timestamped events occur so close together that they have the same timestamp! Therefore this query won't work: SELECT b.time_stamp,b.lives_remaining,b.usr_id,b.trans_id FROM (SELECT usr_id, max(time_stamp) AS max_timestamp FROM lives GROUP BY usr_id ORDER BY usr_id) a JOIN lives b ON a.max_timestamp = b.time_stamp Instead, I need to use both time_stamp (first) and trans_id (second) to identify the correct row. I also then need to pass that information from the subquery to the main query that will provide the data for the other columns of the appropriate rows. This is the hacked up query that I've gotten to work: SELECT b.time_stamp,b.lives_remaining,b.usr_id,b.trans_id FROM (SELECT usr_id, max(time_stamp || '*' || trans_id) AS max_timestamp_transid FROM lives GROUP BY usr_id ORDER BY usr_id) a JOIN lives b ON a.max_timestamp_transid = b.time_stamp || '*' || b.trans_id ORDER BY b.usr_id Okay, so this works, but I don't like it. It requires a query within a query, a self join, and it seems to me that it could be much simpler by grabbing the row that MAX found to have the largest timestamp and trans_id. The table "lives" has tens of millions of rows to parse, so I'd like this query to be as fast and efficient as possible. I'm new to RDBM and Postgres in particular, so I know that I need to make effective use of the proper indexes. I'm a bit lost on how to optimize. I found a similar discussion here. Can I perform some type of Postgres equivalent to an Oracle analytic function? Any advice on accessing related column information used by an aggregate function (like MAX), creating indexes, and creating better queries would be much appreciated! P.S. You can use the following to create my example case: create TABLE lives (time_stamp timestamp, lives_remaining integer, usr_id integer, trans_id integer); insert into lives values ('2000-01-01 07:00', 1, 1, 1); insert into lives values ('2000-01-01 09:00', 4, 2, 2); insert into lives values ('2000-01-01 10:00', 2, 3, 3); insert into lives values ('2000-01-01 10:00', 1, 2, 4); insert into lives values ('2000-01-01 11:00', 4, 1, 5); insert into lives values ('2000-01-01 11:00', 3, 1, 6); insert into lives values ('2000-01-01 13:00', 3, 3, 1);

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  • efficacy of register allocation algorithms!

    - by aksci
    i'm trying to do a research/project on register allocation using graph coloring where i am to test the efficiency of different optimizing register allocation algorithms in different scenarios. how do i start? what are the prerequisites and the grounds with which i can test them. what all algos can i use? thank you!

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  • One letter game problem?

    - by Alex K
    Recently at a job interview I was given the following problem: Write a script capable of running on the command line as python It should take in two words on the command line (or optionally if you'd prefer it can query the user to supply the two words via the console). Given those two words: a. Ensure they are of equal length b. Ensure they are both words present in the dictionary of valid words in the English language that you downloaded. If so compute whether you can reach the second word from the first by a series of steps as follows a. You can change one letter at a time b. Each time you change a letter the resulting word must also exist in the dictionary c. You cannot add or remove letters If the two words are reachable, the script should print out the path which leads as a single, shortest path from one word to the other. You can /usr/share/dict/words for your dictionary of words. My solution consisted of using breadth first search to find a shortest path between two words. But apparently that wasn't good enough to get the job :( Would you guys know what I could have done wrong? Thank you so much. import collections import functools import re def time_func(func): import time def wrapper(*args, **kwargs): start = time.time() res = func(*args, **kwargs) timed = time.time() - start setattr(wrapper, 'time_taken', timed) return res functools.update_wrapper(wrapper, func) return wrapper class OneLetterGame: def __init__(self, dict_path): self.dict_path = dict_path self.words = set() def run(self, start_word, end_word): '''Runs the one letter game with the given start and end words. ''' assert len(start_word) == len(end_word), \ 'Start word and end word must of the same length.' self.read_dict(len(start_word)) path = self.shortest_path(start_word, end_word) if not path: print 'There is no path between %s and %s (took %.2f sec.)' % ( start_word, end_word, find_shortest_path.time_taken) else: print 'The shortest path (found in %.2f sec.) is:\n=> %s' % ( self.shortest_path.time_taken, ' -- '.join(path)) def _bfs(self, start): '''Implementation of breadth first search as a generator. The portion of the graph to explore is given on demand using get_neighboors. Care was taken so that a vertex / node is explored only once. ''' queue = collections.deque([(None, start)]) inqueue = set([start]) while queue: parent, node = queue.popleft() yield parent, node new = set(self.get_neighbours(node)) - inqueue inqueue = inqueue | new queue.extend([(node, child) for child in new]) @time_func def shortest_path(self, start, end): '''Returns the shortest path from start to end using bfs. ''' assert start in self.words, 'Start word not in dictionnary.' assert end in self.words, 'End word not in dictionnary.' paths = {None: []} for parent, child in self._bfs(start): paths[child] = paths[parent] + [child] if child == end: return paths[child] return None def get_neighbours(self, word): '''Gets every word one letter away from the a given word. We do not keep these words in memory because bfs accesses a given vertex only once. ''' neighbours = [] p_word = ['^' + word[0:i] + '\w' + word[i+1:] + '$' for i, w in enumerate(word)] p_word = '|'.join(p_word) for w in self.words: if w != word and re.match(p_word, w, re.I|re.U): neighbours += [w] return neighbours def read_dict(self, size): '''Loads every word of a specific size from the dictionnary into memory. ''' for l in open(self.dict_path): l = l.decode('latin-1').strip().lower() if len(l) == size: self.words.add(l) if __name__ == '__main__': import sys if len(sys.argv) not in [3, 4]: print 'Usage: python one_letter_game.py start_word end_word' else: g = OneLetterGame(dict_path = '/usr/share/dict/words') try: g.run(*sys.argv[1:]) except AssertionError, e: print e

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  • Try to fill the GAE datastore but the code consumes to much cpu time. How to optimize this?

    - by Neverland
    I try to get the list of images in Amazon EC2 inside the Google datastore. I want to realize this with a cron job inside the GAE. class AmazonEC2uswest(db.Model): ami = db.StringProperty(required=True) mani = db.StringProperty() typ = db.StringProperty() arch = db.StringProperty() state = db.StringProperty() owner = db.StringProperty() class CronAMIsAmazonUS_WEST(webapp.RequestHandler): def get(self): aws_access_key_id_admin = "<secret>" aws_secret_access_key_admin = "<secret>" conn_us_west = boto.ec2.connect_to_region('us-west-1', aws_access_key_id=aws_access_key_id_admin, aws_secret_access_key=aws_secret_access_key_admin, is_secure = False) liste_images_us_west = conn_us_west.get_all_images() laenge_liste_images_us_west = len(liste_images_us_west) for i in range(laenge_liste_images_us_west): datastore_uswest_AMIs = AmazonEC2uswest(ami=liste_images_us_west[i].id, mani=str(liste_images_us_west[i].location), typ=liste_images_us_west[i].type, arch=liste_images_us_west[i].architecture, state=liste_images_us_west[i].state, owner=liste_images_us_west[i].ownerId) datastore_uswest_AMIs.put() The problem: Getting the list with get_all_images() lasts only a few seconds. But writing the data to the Google datastore needs way too much CPU time. My IBM T42p (P4M with 2GHz) needs for that piece of code approx. 1 Minute! Is it possible to optimize my code in a way that it needs fewer CPU time?

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  • Float compile-time calculation not happening?

    - by Klaim
    A little test program: #include <iostream> const float TEST_FLOAT = 1/60; const float TEST_A = 1; const float TEST_B = 60; const float TEST_C = TEST_A / TEST_B; int main() { std::cout << TEST_FLOAT << std::endl; std::cout << TEST_C << std::endl; std::cin.ignore(); return 0; } Result : 0 0.0166667 Tested on Visual Studio 2008 & 2010. I worked on other compilers that, if I remember well, made the first result like the second result. Now my memory could be wrong, but shouldn't TEST_FLOAT have the same value than TEST_C? If not, why? Is TEST_C value resolved at compile time or at runtime? I always assumed the former but now that I see those results I have some doubts...

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  • Very slow Eclipse 4.2, how to make it more responsive?

    - by Laurent
    I'm using Eclipse PDT on a rather large PHP project and the IDE is almost unusable. It takes nearly 30 seconds to open a file, and other actions, like selecting a folder in the file explorer, editing some text, etc. are equally slow. I followed various instructions to speed it up but nothing seems to work. This is my current eclipse.ini file. Any idea how I can improve it? -startup plugins/org.eclipse.equinox.launcher_1.3.0.v20120522-1813.jar --launcher.library plugins/org.eclipse.equinox.launcher.win32.win32.x86_1.1.200.v20120522-1813 -showsplash org.eclipse.platform --launcher.XXMaxPermSize 256m --launcher.defaultAction openFile -vmargs -server -Dosgi.requiredJavaVersion=1.7 -Xmn128m -Xms1024m -Xmx1024m -Xss2m -XX:PermSize=128m -XX:MaxPermSize=128m -XX:+UseParallelGC System: Eclipse 4.2.0, Windows 7, 4 GB RAM

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  • How Do You Profile & Optimize CUDA Kernels?

    - by John Dibling
    I am somewhat familiar with the CUDA visual profiler and the occupancy spreadsheet, although I am probably not leveraging them as well as I could. Profiling & optimizing CUDA code is not like profiling & optimizing code that runs on a CPU. So I am hoping to learn from your experiences about how to get the most out of my code. There was a post recently looking for the fastest possible code to identify self numbers, and I provided a CUDA implementation. I'm not satisfied that this code is as fast as it can be, but I'm at a loss as to figure out both what the right questions are and what tool I can get the answers from. How do you identify ways to make your CUDA kernels perform faster?

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  • The best way to implement drawing features like Keynote

    - by Shamseddine
    Hi all, I'm trying to make a little iPad tool's for drawing simple geometrical objects (rect, rounded rect, ellipse, star, ...). My goal is to make something very close to Keynote (drawing feature), i.e. let the user add a rect (for instance), resizing it and moving it. I want too the user can select many objects and move them together. I've thought about at least 3 differents ways to do that : Extends UIView for each object type, a class for Rect, another for Ellipse, ... With custom drawing method. Then add this view as subview of the global view. Extends CALayer for each object type, a class for Rect, another for Ellipse, ... With custom drawing method. Then add this layer as sublayer of the global view layer's. Extends NSObject for each object type, a class for Rect, another for Ellipse, ... With just a drawing method which will get as argument a CGContext and a Rect and draw directly the form in it. Those methods will be called by the drawing method of the global view. I'm aware that the two first ways come with functions to detect touch on each object, to add easily shadows,... but I'm afraid that they are a little too heavy ? That's why I thought about the last way, which it seems to be straight forward. Which way will be the more efficient ??? Or maybe I didn't thought another way ? Any help will be appreciated ;-) Thanks.

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  • Can anyone recommend a decent tool for optimizing images other than Photoshop

    - by toomanyairmiles
    Can anyone recommend a decent tool for optimising images other than adobe photoshop, the gimp etc? I'm looking to optimise images for the web preferably online and free. Basically I have a client who can't install additional software on their work PC but needs to optimise photographs and other images for their website and is presently uploading 1 or 2 Mb files. On a personal level I'm interested to see what other people are using...

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  • Random Complete System Unresponsiveness Running Mathematical Functions

    - by Computer Guru
    I have a program that loads a file (anywhere from 10MB to 5GB) a chunk at a time (ReadFile), and for each chunk performs a set of mathematical operations (basically calculates the hash). After calculating the hash, it stores info about the chunk in an STL map (basically <chunkID, hash>) and then writes the chunk itself to another file (WriteFile). That's all it does. This program will cause certain PCs to choke and die. The mouse begins to stutter, the task manager takes 2 min to show, ctrl+alt+del is unresponsive, running programs are slow.... the works. I've done literally everything I can think of to optimize the program, and have triple-checked all objects. What I've done: Tried different (less intensive) hashing algorithms. Switched all allocations to nedmalloc instead of the default new operator Switched from stl::map to unordered_set, found the performance to still be abysmal, so I switched again to Google's dense_hash_map. Converted all objects to store pointers to objects instead of the objects themselves. Caching all Read and Write operations. Instead of reading a 16k chunk of the file and performing the math on it, I read 4MB into a buffer and read 16k chunks from there instead. Same for all write operations - they are coalesced into 4MB blocks before being written to disk. Run extensive profiling with Visual Studio 2010, AMD Code Analyst, and perfmon. Set the thread priority to THREAD_MODE_BACKGROUND_BEGIN Set the thread priority to THREAD_PRIORITY_IDLE Added a Sleep(100) call after every loop. Even after all this, the application still results in a system-wide hang on certain machines under certain circumstances. Perfmon and Process Explorer show minimal CPU usage (with the sleep), no constant reads/writes from disk, few hard pagefaults (and only ~30k pagefaults in the lifetime of the application on a 5GB input file), little virtual memory (never more than 150MB), no leaked handles, no memory leaks. The machines I've tested it on run Windows XP - Windows 7, x86 and x64 versions included. None have less than 2GB RAM, though the problem is always exacerbated under lower memory conditions. I'm at a loss as to what to do next. I don't know what's causing it - I'm torn between CPU or Memory as the culprit. CPU because without the sleep and under different thread priorities the system performances changes noticeably. Memory because there's a huge difference in how often the issue occurs when using unordered_set vs Google's dense_hash_map. What's really weird? Obviously, the NT kernel design is supposed to prevent this sort of behavior from ever occurring (a user-mode application driving the system to this sort of extreme poor performance!?)..... but when I compile the code and run it on OS X or Linux (it's fairly standard C++ throughout) it performs excellently even on poor machines with little RAM and weaker CPUs. What am I supposed to do next? How do I know what the hell it is that Windows is doing behind the scenes that's killing system performance, when all the indicators are that the application itself isn't doing anything extreme? Any advice would be most welcome.

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  • Why better isolation level means better performance in SQL Server

    - by Oleg Zhylin
    When measuring performance on my query I came up with a dependency between isolation level and elapsed time that was surprising to me READUNCOMMITTED - 409024 READCOMMITTED - 368021 REPEATABLEREAD - 358019 SERIALIZABLE - 348019 Left column is table hint, and the right column is elapsed time in microseconds (sys.dm_exec_query_stats.total_elapsed_time). Why better isolation level gives better performance? This is a development machine and no concurrency whatsoever happens. I would expect READUNCOMMITTED to be the fasted due to less locking overhead. Update: I did measure this with DBCC DROPCLEANBUFFERS DBCC FREEPROCCACHE issued and Profiler confirms there're no cache hits happening. Update2: The query in question is an OLAP one and we need to run it as fast as possible. Closing the production server from outside world to get the computation done is not out of question if this gives performance benefits.

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  • How does loop address alignment affect the speed on Intel x86_64?

    - by Alexander Gololobov
    I'm seeing 15% performance degradation of the same C++ code compiled to exactly same machine instructions but located on differently aligned addresses. When my tiny main loop starts at 0x415220 it's faster then when it is at 0x415250. I'm running this on Intel Core2 Duo. I use gcc 4.4.5 on x86_64 Ubuntu. Can anybody explain the cause of slowdown and how I can force gcc to optimally align the loop? Here is the disassembly for both cases with profiler annotation: 415220 576 12.56% |XXXXXXXXXXXXXX 48 c1 eb 08 shr $0x8,%rbx 415224 110 2.40% |XX 0f b6 c3 movzbl %bl,%eax 415227 0.00% | 41 0f b6 04 00 movzbl (%r8,%rax,1),%eax 41522c 40 0.87% | 48 8b 04 c1 mov (%rcx,%rax,8),%rax 415230 806 17.58% |XXXXXXXXXXXXXXXXXXX 4c 63 f8 movslq %eax,%r15 415233 186 4.06% |XXXX 48 c1 e8 20 shr $0x20,%rax 415237 102 2.22% |XX 4c 01 f9 add %r15,%rcx 41523a 414 9.03% |XXXXXXXXXX a8 0f test $0xf,%al 41523c 680 14.83% |XXXXXXXXXXXXXXXX 74 45 je 415283 ::Run(char const*, char const*)+0x4b3 41523e 0.00% | 41 89 c7 mov %eax,%r15d 415241 0.00% | 41 83 e7 01 and $0x1,%r15d 415245 0.00% | 41 83 ff 01 cmp $0x1,%r15d 415249 0.00% | 41 89 c7 mov %eax,%r15d 415250 679 13.05% |XXXXXXXXXXXXXXXX 48 c1 eb 08 shr $0x8,%rbx 415254 124 2.38% |XX 0f b6 c3 movzbl %bl,%eax 415257 0.00% | 41 0f b6 04 00 movzbl (%r8,%rax,1),%eax 41525c 43 0.83% |X 48 8b 04 c1 mov (%rcx,%rax,8),%rax 415260 828 15.91% |XXXXXXXXXXXXXXXXXXX 4c 63 f8 movslq %eax,%r15 415263 388 7.46% |XXXXXXXXX 48 c1 e8 20 shr $0x20,%rax 415267 141 2.71% |XXX 4c 01 f9 add %r15,%rcx 41526a 634 12.18% |XXXXXXXXXXXXXXX a8 0f test $0xf,%al 41526c 749 14.39% |XXXXXXXXXXXXXXXXXX 74 45 je 4152b3 ::Run(char const*, char const*)+0x4c3 41526e 0.00% | 41 89 c7 mov %eax,%r15d 415271 0.00% | 41 83 e7 01 and $0x1,%r15d 415275 0.00% | 41 83 ff 01 cmp $0x1,%r15d 415279 0.00% | 41 89 c7 mov %eax,%r15d

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  • Benefits of 'Optimize code' option in Visual Studio build

    - by gt
    Much of our C# release code is built with the 'Optimize code' option turned off. I believe this is to allow code built in Release mode to be debugged more easily. Given that we are creating fairly simple desktop software which connects to backend Web Services, (ie. not a particularly processor-intensive application) then what if any sort of performance hit might be expected? And is any particular platform likely to be worse affected? Eg. multi-processor / 64 bit.

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  • SEO for Ultraseek 5.7

    - by Adam N
    We've got Ultraseek 5.7 indexing the content on our corporate intranet site, and we'd like to make sure our web pages are being optimized for it. Which SEO techniques are useful for Ultraseek, and where can I find documentation about these features? Features I've considered implementing: Make the title and first H1 contain the most valuable information about the page Implement a sitemap.xml file Ping the Ultraseek xpa interface when new content is added Use "SEO-Friendly" URL strings Add Meta keywords to the HTML pages.

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  • PHP Increasing write to page speed.

    - by Frederico
    I'm currently writing out xml and have done the following: header ("content-type: text/xml"); header ("content-length: ".strlen($xml)); $xml being the xml to be written out. I'm near about 1.8 megs of text (which I found via firebug), it seems as the writing is taking more time than the script to run.. is there a way to increase this write speed? Thank you in advance.

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  • speed up wamp server + drupal on windows vista

    - by Andrew Welch
    Hi, My localhost performance with drupal six is pretty slow. I found a solution to add a # before the :: localhost line of the system32/etc/hosts file but this was something I had already done and didn't help much. does anyone know of any other optimisations that might work? tHanks Andy

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  • Grand Central Strategy for Opening Multiple Files

    - by user276632
    I have a working implementation using Grand Central dispatch queues that (1) opens a file and computes an OpenSSL DSA hash on "queue1", (2) writing out the hash to a new "side car" file for later verification on "queue2". I would like to open multiple files at the same time, but based on some logic that doesn't "choke" the OS by having 100s of files open and exceeding the hard drive's sustainable output. Photo browsing applications such as iPhoto or Aperture seem to open multiple files and display them, so I'm assuming this can be done. I'm assuming the biggest limitation will be disk I/O, as the application can (in theory) read and write multiple files simultaneously. Any suggestions? TIA

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  • help optimize sql query

    - by msony
    I have tracking table tbl_track with id, session_id, created_date fields I need count unique session_id for one day here what i got: select count(0) from ( select distinct session_id from tbl_track where created_date between getdate()-1 and getdate() group by session_id )tbl im feeling that it could be better solution for it

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  • Optimizing processing and management of large Java data arrays

    - by mikera
    I'm writing some pretty CPU-intensive, concurrent numerical code that will process large amounts of data stored in Java arrays (e.g. lots of double[100000]s). Some of the algorithms might run millions of times over several days so getting maximum steady-state performance is a high priority. In essence, each algorithm is a Java object that has an method API something like: public double[] runMyAlgorithm(double[] inputData); or alternatively a reference could be passed to the array to store the output data: public runMyAlgorithm(double[] inputData, double[] outputData); Given this requirement, I'm trying to determine the optimal strategy for allocating / managing array space. Frequently the algorithms will need large amounts of temporary storage space. They will also take large arrays as input and create large arrays as output. Among the options I am considering are: Always allocate new arrays as local variables whenever they are needed (e.g. new double[100000]). Probably the simplest approach, but will produce a lot of garbage. Pre-allocate temporary arrays and store them as final fields in the algorithm object - big downside would be that this would mean that only one thread could run the algorithm at any one time. Keep pre-allocated temporary arrays in ThreadLocal storage, so that a thread can use a fixed amount of temporary array space whenever it needs it. ThreadLocal would be required since multiple threads will be running the same algorithm simultaneously. Pass around lots of arrays as parameters (including the temporary arrays for the algorithm to use). Not good since it will make the algorithm API extremely ugly if the caller has to be responsible for providing temporary array space.... Allocate extremely large arrays (e.g. double[10000000]) but also provide the algorithm with offsets into the array so that different threads will use a different area of the array independently. Will obviously require some code to manage the offsets and allocation of the array ranges. Any thoughts on which approach would be best (and why)?

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  • Is it possible to do A/B testing by page rather than by individual?

    - by mojones
    Lets say I have a simple ecommerce site that sells 100 different t-shirt designs. I want to do some a/b testing to optimise my sales. Let's say I want to test two different "buy" buttons. Normally, I would use AB testing to randomly assign each visitor to see button A or button B (and try to ensure that that the user experience is consistent by storing that assignment in session, cookies etc). Would it be possible to take a different approach and instead, randomly assign each of my 100 designs to use button A or B, and measure the conversion rate as (number of sales of design n) / (pageviews of design n) This approach would seem to have some advantages; I would not have to worry about keeping the user experience consistent - a given page (e.g. www.example.com/viewdesign?id=6) would always return the same html. If I were to test different prices, it would be far less distressing to the user to see different prices for different designs than different prices for the same design on different computers. I also wonder whether it might be better for SEO - my suspicion is that Google would "prefer" that it always sees the same html when crawling a page. Obviously this approach would only be suitable for a limited number of sites; I was just wondering if anyone has tried it?

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  • What is the best algorithm for this array-comparison problem?

    - by mark
    What is the most efficient for speed algorithm to solve the following problem? Given 6 arrays, D1,D2,D3,D4,D5 and D6 each containing 6 numbers like: D1[0] = number D2[0] = number ...... D6[0] = number D1[1] = another number D2[1] = another number .... ..... .... ...... .... D1[5] = yet another number .... ...... .... Given a second array ST1, containing 1 number: ST1[0] = 6 Given a third array ans, containing 6 numbers: ans[0] = 3, ans[1] = 4, ans[2] = 5, ......ans[5] = 8 Using as index for the arrays D1,D2,D3,D4,D5 and D6, the number that goes from 0, to the number stored in ST1[0] minus one, in this example 6, so from 0 to 6-1, compare each res array against each D array My algorithm so far is: I tried to keep everything unlooped as much as possible. EML := ST1[0] //number contained in ST1[0] EML1 := 0 //start index for the arrays D While EML1 < EML if D1[ELM1] = ans[0] goto two if D2[ELM1] = ans[0] goto two if D3[ELM1] = ans[0] goto two if D4[ELM1] = ans[0] goto two if D5[ELM1] = ans[0] goto two if D6[ELM1] = ans[0] goto two ELM1 = ELM1 + 1 return 0 //If the ans[0] number is not found in either D1[0-6], D2[0-6].... D6[0-6] return 0 which will then exclude ans[0-6] numbers two: EML1 := 0 start index for arrays Ds While EML1 < EML if D1[ELM1] = ans[1] goto three if D2[ELM1] = ans[1] goto three if D3[ELM1] = ans[1] goto three if D4[ELM1] = ans[1] goto three if D5[ELM1] = ans[1] goto three if D6[ELM1] = ans[1] goto three ELM1 = ELM1 + 1 return 0 //If the ans[1] number is not found in either D1[0-6], D2[0-6].... D6[0-6] return 0 which will then exclude ans[0-6] numbers three: EML1 := 0 start index for arrays Ds While EML1 < EML if D1[ELM1] = ans[2] goto four if D2[ELM1] = ans[2] goto four if D3[ELM1] = ans[2] goto four if D4[ELM1] = ans[2] goto four if D5[ELM1] = ans[2] goto four if D6[ELM1] = ans[2] goto four ELM1 = ELM1 + 1 return 0 //If the ans[2] number is not found in either D1[0-6], D2[0-6].... D6[0-6] return 0 which will then exclude ans[0-6] numbers four: EML1 := 0 start index for arrays Ds While EML1 < EML if D1[ELM1] = ans[3] goto five if D2[ELM1] = ans[3] goto five if D3[ELM1] = ans[3] goto five if D4[ELM1] = ans[3] goto five if D5[ELM1] = ans[3] goto five if D6[ELM1] = ans[3] goto five ELM1 = ELM1 + 1 return 0 //If the ans[3] number is not found in either D1[0-6], D2[0-6].... D6[0-6] return 0 which will then exclude ans[0-6] numbers five: EML1 := 0 start index for arrays Ds While EML1 < EML if D1[ELM1] = ans[4] goto six if D2[ELM1] = ans[4] goto six if D3[ELM1] = ans[4] goto six if D4[ELM1] = ans[4] goto six if D5[ELM1] = ans[4] goto six if D6[ELM1] = ans[4] goto six ELM1 = ELM1 + 1 return 0 //If the ans[4] number is not found in either D1[0-6], D2[0-6].... D6[0-6] return 0 which will then exclude ans[0-6] numbers six: EML1 := 0 start index for arrays Ds While EML1 < EML if D1[ELM1] = ans[5] return 1 ////If the ans[1] number is not found in either D1[0-6]..... if D2[ELM1] = ans[5] return 1 which will then include ans[0-6] numbers return 1 if D3[ELM1] = ans[5] return 1 if D4[ELM1] = ans[5] return 1 if D5[ELM1] = ans[5] return 1 if D6[ELM1] = ans[5] return 1 ELM1 = ELM1 + 1 return 0 As language of choice, it would be pure c

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  • Whats faster in Javascript a bunch of small setInterval loops, or one big one?

    - by RobertWHurst
    Just wondering if its worth it to make a monolithic loop function or just add loops were they're needed. The big loop option would just be a loop of callbacks that are added dynamically with an add function. adding a function would look like this setLoop(function(){ alert('hahaha! I\'m a really annoying loop that bugs you every tenth of a second'); }); setLoop would add the function to the monolithic loop. so is the is worth anything in performance or should I just stick to lots of little loops using setInterval?

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  • Find the closest vector

    - by Alexey Lebedev
    Hello! Recently I wrote the algorithm to quantize an RGB image. Every pixel is represented by an (R,G,B) vector, and quantization codebook is a couple of 3-dimensional vectors. Every pixel of the image needs to be mapped to (say, "replaced by") the codebook pixel closest in terms of euclidean distance (more exactly, squared euclidean). I did it as follows: class EuclideanMetric(DistanceMetric): def __call__(self, x, y): d = x - y return sqrt(sum(d * d, -1)) class Quantizer(object): def __init__(self, codebook, distanceMetric = EuclideanMetric()): self._codebook = codebook self._distMetric = distanceMetric def quantize(self, imageArray): quantizedRaster = zeros(imageArray.shape) X = quantizedRaster.shape[0] Y = quantizedRaster.shape[1] for i in xrange(0, X): print i for j in xrange(0, Y): dist = self._distMetric(imageArray[i,j], self._codebook) code = argmin(dist) quantizedRaster[i,j] = self._codebook[code] return quantizedRaster ...and it works awfully, almost 800 seconds on my Pentium Core Duo 2.2 GHz, 4 Gigs of memory and an image of 2600*2700 pixels:( Is there a way to somewhat optimize this? Maybe the other algorithm or some Python-specific optimizations.

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