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  • How to Use RDA to Generate WLS Thread Dumps At Specified Intervals?

    - by Daniel Mortimer
    Introduction There are many ways to generate a thread dump of a WebLogic Managed Server. For example, take a look at: Taking Thread Dumps - [an excellent blog post on the Middleware Magic site]or  Different ways to take thread dumps in WebLogic Server (Document 1098691.1) There is another method - use Remote Diagnostic Agent! The solution described below is not documented, but it is relatively straightforward to execute. One advantage of using RDA to collect the thread dumps is RDA will also collect configuration, log files, network, system, performance information at the same time. Instructions 1. Not familiar with Remote Diagnostic Agent? Take a look at my previous blog "Resolve SRs Faster Using RDA - Find the Right Profile" 2. Choose a profile, which includes the WebLogic Server data collection modules (for example the profile "WebLogicServer"). At RDA setup time you should see the prompt below: ------------------------------------------------------------------------------- S301WLS: Collects Oracle WebLogic Server Information ------------------------------------------------------------------------------- Enter the location of the directory where the domains to analyze are located (For example in UNIX, <BEA Home>/user_projects/domains or <Middleware Home>/user_projects/domains) Hit 'Return' to accept the default (/oracle/11AS/Middleware/user_projects/domains) > For a successful WLS connection, ensure that the domain Admin Server is up and running. Data Collection Type:   1  Collect for a single server (offline mode)   2  Collect for a single server (using WLS connection)   3  Collect for multiple servers (using WLS connection) Enter the item number Hit 'Return' to accept the default (1) > 2 Choose option 2 or 3. Note: Collect for a single server or multiple servers using WLS connection means that RDA will attempt to connect to execute online WLST commands against the targeted server(s). The thread dumps are collected using the WLST function - "threadDumps()". If WLST cannot connect to the managed server, RDA will proceed to collect other data and ignore the request to collect thread dumps. If in the final output you see no Thread Dump menu item, then it's likely that the managed server is in a state which prevents new connections to it. If faced with this scenario, you would have to employ alternative methods for collecting thread dumps. 3. The RDA setup will create a setup.cfg file in the RDA_HOME directory. Open this file in an editor. You will find the following parameters which govern the number of thread dumps and thread dump interval. #N.Number of thread dumps to capture WREQ_THREAD_DUMP=10 #N.Thread dump interval WREQ_THREAD_DUMP_INTERVAL=5000 The example lines above show the default settings. In other words, RDA will collect 10 thread dumps at 5000 millisecond (5 second) intervals. You may want to change this to something like: #N.Number of thread dumps to capture WREQ_THREAD_DUMP=10 #N.Thread dump interval WREQ_THREAD_DUMP_INTERVAL=30000 However, bear in mind, that such change will increase the total amount of time it takes for RDA to complete its run. 4. Once you are happy with the setup.cfg, run RDA. RDA will collect, render, generate and package all files in the output directory. 5. For ease of viewing, open up the RDA Start html file - "xxxx__start.htm". The thread dumps can be found under the WLST Collections for the target managed server(s). See screenshots belowScreenshot 1:RDA Start Page - Main Index Screenshot 2: Managed Server Sub Index Screenshot 3: WLST Collections Screenshot 4: Thread Dump Page - List of dump file links Screenshot 5: Thread Dump Dat File Link Additional Comment: A) You can view the thread dump files within the RDA Start Page framework, but most likely you will want to download the dat files for in-depth analysis via thread dump analysis tools such as: Thread Dump Analyzer -  Samurai - a GUI based tail , thread dump analysis tool If you are new to thread dump analysis - take a look at this recorded Support Advisor Webcast  Oracle WebLogic Server: Diagnosing Performance Issues through Java Thread Dumps[Slidedeck from webcast in PDF format]B) I have logged a couple of enhancement requests for the RDA Development Team to consider: Add timestamp to dump file links, dat filename and at the top of the body of the dat file Package the individual thread dumps in a zip so all dump files can be conveniently downloaded in one go.

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  • Haskell newbie on types

    - by garulfo
    I'm completely new to Haskell (and more generally to functional programming), so forgive me if this is really basic stuff. To get more than a taste, I try to implement in Haskell some algorithmic stuff I'm working on. I have a simple module Interval that implements intervals on the line. It contains the type data Interval t = Interval t t the helper function makeInterval :: (Ord t) => t -> t -> Interval t makeInterval l r | l <= r = Interval l r | otherwise = error "bad interval" and some utility functions about intervals. Here, my interest lies in multidimensional intervals (d-intervals), those objects that are composed of d intervals. I want to separately consider d-intervals that are the union of d disjoint intervals on the line (multiple interval) from those that are the union of d interval on d separate lines (track interval). With distinct algorithmic treatments in mind, I think it would be nice to have two distinct types (even if both are lists of intervals here) such as import qualified Interval as I -- Multilple interval newtype MInterval t = MInterval [I.Interval t] -- Track interval newtype TInterval t = TInterval [I.Interval t] to allow for distinct sanity checks, e.g. makeMInterval :: (Ord t) => [I.Interval t] -> MInterval t makeMInterval is = if foldr (&&) True [I.precedes i i' | (i, i') <- zip is (tail is)] then (MInterval is) else error "bad multiple interval" makeTInterval :: (Ord t) => [I.Interval t] -> TInterval t makeTInterval = TInterval I now get to the point, at last! But some functions are naturally concerned with both multiple intervals and track intervals. For example, a function order would return the number of intervals in a multiple interval or a track interval. What can I do? Adding -- Dimensional interval data DInterval t = MIntervalStuff (MInterval t) | TIntervalStuff (TInterval t) does not help much, since, if I understand well (correct me if I'm wrong), I would have to write order :: DInterval t -> Int order (MIntervalStuff (MInterval is)) = length is order (TIntervalStuff (TInterval is)) = length is and call order as order (MIntervalStuff is) or order (TIntervalStuff is) when is is a MInterval or a TInterval. Not that great, it looks odd. Neither I want to duplicate the function (I have many functions that are concerned with both multiple and track intevals, and some other d-interval definitions such as equal length multiple and track intervals). I'm left with the feeling that I'm completely wrong and have missed some important point about types in Haskell (and/or can't forget enough here about OO programming). So, quite a newbie question, what would be the best way in Haskell to deal with such a situation? Do I have to forget about introducing MInterval and TInterval and go with one type only? Thanks a lot for your help, Garulfo

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  • How do I get confidence intervals without inverting a singular Hessian matrix?

    - by AmalieNot
    Hello. I recently posted this to reddit and it was suggested I come here, so here I am. I'm a student working on an epidemiology model in R, using maximum likelihood methods. I created my negative log likelihood function. It's sort of gross looking, but here it is: NLLdiff = function(v1, CV1, v2, CV2, st1 = (czI01 - czV01), st2 = (czI02 - czV02), st01 = czI01, st02 = czI02, tt1 = czT01, tt2 = czT02) { prob1 = (1 + v1 * CV1 * tt1)^(-1/CV1) prob2 = ( 1 + v2 * CV2 * tt2)^(-1/CV2) -(sum(dbinom(st1, st01, prob1, log = T)) + sum(dbinom(st2, st02, prob2, log = T))) } The reason the first line looks so awful is because most of the data it takes is inputted there. czI01, for example, is already declared. I did this simply so that my later calls to the function don't all have to have awful vectors in them. I then optimized for CV1, CV2, v1 and v2 using mle2 (library bbmle). That's also a bit gross looking, and looks like: ml.cz.diff = mle2 (NLLdiff, start=list(v1 = vguess, CV1 = cguess, v2 = vguess, CV2 = cguess), method="L-BFGS-B", lower = 0.0001) Now, everything works fine up until here. ml.cz.diff gives me values that I can turn into a plot that reasonably fits my data. I also have several different models, and can get AICc values to compare them. However, when I try to get confidence intervals around v1, CV1, v2 and CV2 I have problems. Basically, I get a negative bound on CV1, which is impossible as it actually represents a square number in the biological model as well as some warnings. The warnings are this: http://i.imgur.com/B3H2l.png . Is there a better way to get confidence intervals? Or, really, a way to get confidence intervals that make sense here? What I see happening is that, by coincidence, my hessian matrix is singular for some values in the optimization space. But, since I'm optimizing over 4 variables and don't have overly extensive programming knowledge, I can't come up with a good method of optimization that doesn't rely on the hessian. I have googled the problem - it suggested that my model's bad, but I'm reconstructing some work done before which suggests that my model's really not awful (the plots I make using the ml.cz.diff look like the plots of the original work). I have also read the relevant parts of the manual as well as Bolker's book Ecological Models in R. I have also tried different optimization methods, which resulted in a longer run time but the same errors. The "SANN" method didn't finish running within an hour, so I didn't wait around to see the result. tl;dr : my confidence intervals are bad, is there a relatively straightforward way to fix them in R. My vectors are: czT01 = c(5, 5, 5, 5, 5, 5, 5, 25, 25, 25, 25, 25, 25, 25, 50, 50, 50, 50, 50, 50, 50) czT02 = c(5, 5, 5, 5, 5, 10, 10, 10, 10, 10, 25, 25, 25, 25, 25, 50, 50, 50, 50, 50, 75, 75, 75, 75, 75) czI01 = c(25, 24, 22, 22, 26, 23, 25, 25, 25, 23, 25, 18, 21, 24, 22, 23, 25, 23, 25, 25, 25) czI02 = c(13, 16, 5, 18, 16, 13, 17, 22, 13, 15, 15, 22, 12, 12, 13, 13, 11, 19, 21, 13, 21, 18, 16, 15, 11) czV01 = c(1, 4, 5, 5, 2, 3, 4, 11, 8, 1, 11, 12, 10, 16, 5, 15, 18, 12, 23, 13, 22) czV02 = c(0, 3, 1, 5, 1, 6, 3, 4, 7, 12, 2, 8, 8, 5, 3, 6, 4, 6, 11, 5, 11, 1, 13, 9, 7) and I get my guesses by: v = -log((c(czI01, czI02) - c(czV01, czV02))/c(czI01, czI02))/c(czT01, czT02) vguess = mean(v) cguess = var(v)/vguess^2 It's also possible that I'm doing something else completely wrong, but my results seem reasonable so I haven't caught it.

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  • How Do I Schedule Cron to Run at Specific intervals?

    - by Russ
    I have 6 scripts that each take about 20 minutes to run, I want to schedule cron to run the first 3 at 00, 20, and 40 on the odd hours and the second 3 at the same intervals on the even hours. How can I tell cron to do this? is it something like this: 0 2,4,6,8,10,12,14,16,18,20,22,24 * * * root Script1 20 2,4,6,8,10,12,14,16,18,20,22,24 * * * root Script2 40 2,4,6,8,10,12,14,16,18,20,22,24 * * * root Script3 0 1,3,5,7,9,11,13,17,19,21,23 * * * root Script4 20 1,3,5,7,9,11,13,17,19,21,23 * * * root Script5 40 1,3,5,7,9,11,13,17,19,21,23 * * * root Script6

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  • Apache crashing at random intervals. Can not find a reason in log files

    - by Nick Downton
    We are having an issue with a VPS running plesk 9.5 on ubuntu 8.04 At seemingly random intervals Apache will disappear and needs to be started manually. I have checked the apache error log, /var/log/messages, individual virtual host apache error files and cannot find anything that coincides with the time of the failure. dmesg is empty which is a bit odd. We have also had the psa service go down for no apparent reason but apache stay up. I'm at a loss to diagnose this really because all the log files I can find do not point to any issues. Are there any others I can look at? Memory usage sits at about 55% (out of 400mb) and it isn't a particularly high trafficed server. Any pointers as to where else I can find out what is going on would be very much appreciated. Nick

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  • Changing time intervals for vSphere performance monitoring, and is there a better way?

    - by user991710
    I have a set of experiments running on a cluster node which is running ESXi 5.1, and I want to monitor the resource consumption on the node itself. Specifically, I am currently running experiments on a subset of the VMs on the ESXi host and wish to monitor resource consumption on those specific VMs. Right now, since I'm using only a single ESXi host, I am using vSphere to access it and the performance reports. Ideally, I would like to get these reports for different time intervals. I can already get the charts for a time interval of 1h, but these are rather long-running experiments and something like 2h, 3h,... would be preferable. However, I cannot seem to change the time interval. Here is an example of what my Customize Performance Chart dialog shows: I am also running on a trial key at the moment. How can I change this interval? Do I need a standard license, or do I just need to turn off the VM (unlikely, but I haven't attempted it yet as these are long-running experiments)? Any help (or pointers to documentation which deals with the above -- I've already looked but did not find much) would be greatly appreciated.

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  • How to write text(using CGContextShowTextAtPoint) dynamically near graph x and y-axis intervals?

    - by Rajendra Bhole
    I developed graph using NSObject class and using CGContext method. The following code displaying dynamically in X and Y-axis intervals, CGContextSetRGBStrokeColor(ctx, 2.0, 2.0, 2.0, 1.0); CGContextSetLineWidth(ctx, 2.0); CGContextMoveToPoint(ctx, 30.0, 200.0); CGContextAddLineToPoint(ctx, 30.0, 440.0); for(float y = 400.0; y >= 200.0; y-=30) { CGContextSetRGBStrokeColor(ctx, 2.0, 2.0, 2.0, 1.0); CGContextMoveToPoint(ctx, 28, y); CGContextAddLineToPoint(ctx, 32, y); CGContextStrokePath(ctx); //CGContextClosePath(ctx); } CGContextMoveToPoint(ctx, 10, 420.0); CGContextAddLineToPoint(ctx, 320, 420.0); //CGContextAddLineToPoint(ctx, 320.0, 420.0); //CGContextStrokePath(ctx); for(float x = 60.0; x <= 260.0; x+=30) { CGContextSetRGBStrokeColor(ctx, 2.0, 2.0, 2.0, 1.0); CGContextMoveToPoint(ctx, x, 418.0); CGContextAddLineToPoint(ctx, x, 422.0); CGContextStrokePath(ctx); CGContextClosePath(ctx); } I want to write the dynamic text on the X and Y-axis lines near the intervals (like X-axis is denoting number of days per week and Y-axis denoting something per someting)? Thanks.

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  • How to write text(using CGContextShowTextAtPoint) on graph x and y-axis intervals points?

    - by Rajendra Bhole
    I developed graph using NSObject class and using CGContext method. The following code displaying dynamically in X and Y-axis intervals, CGContextSetRGBStrokeColor(ctx, 2.0, 2.0, 2.0, 1.0); CGContextSetLineWidth(ctx, 2.0); CGContextMoveToPoint(ctx, 30.0, 200.0); CGContextAddLineToPoint(ctx, 30.0, 440.0); for(float y = 400.0; y >= 200.0; y-=30) { CGContextSetRGBStrokeColor(ctx, 2.0, 2.0, 2.0, 1.0); CGContextMoveToPoint(ctx, 28, y); CGContextAddLineToPoint(ctx, 32, y); CGContextStrokePath(ctx); //CGContextClosePath(ctx); } CGContextMoveToPoint(ctx, 10, 420.0); CGContextAddLineToPoint(ctx, 320, 420.0); //CGContextAddLineToPoint(ctx, 320.0, 420.0); //CGContextStrokePath(ctx); for(float x = 60.0; x <= 260.0; x+=30) { CGContextSetRGBStrokeColor(ctx, 2.0, 2.0, 2.0, 1.0); CGContextMoveToPoint(ctx, x, 418.0); CGContextAddLineToPoint(ctx, x, 422.0); CGContextStrokePath(ctx); CGContextClosePath(ctx); } I want to write the dynamic text on the X and Y-axis lines near the intervals (like X-axis is denoting number of days per week and Y-axis denoting something per someting)? Thanks.

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  • [R] how do I quickly group the time column in a dataframe into intervals?

    - by Derek
    Hi, Assuming in R, I have a data.frame with the first column representing the time (as POSIXct). The rest of the columns (e.g., columns 2) are numeric data. I would like to group time into 3-minute intervals. Each interval will the the average of values that falls into that particular interval. Right now, I have a for-loop that iterates through the time column and generate the interval on the fly. I am wondering if there's a more elegant way to accomplish the same thing? Thanks in advance. Derek

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  • Why does my DNS change (and break) at regular intervals?

    - by Peter Kelly
    I have a laptop running Windows 7. Up until recently, everything was fine. We have multiple devices in my house connecting to the one access point with no problems. No issues with ISP. Now my Windows 7 PC DNS settings change every minute or so. Before the problem occurs if I do an ipconfig /all I have two DNS settings (primary/secondary) and everything is fine. After a short period of time this change to a sole DNS, 10.0.0.1. Webpages no longer resolve. If I do an ipconfig /renew, this fixes the problem. I have tried uninstalling various programs I thought might be related but the problem persists. Any ideas of potential causes?

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  • Capturing time intervals when somebody was online? How would you impement this feature?

    - by Kirzilla
    Hello, Our aim is to build timelines saying about periods of time when user was online. (It really doesn't matter what user we are talking about and where he was online) To get information about onliners we can call API method, someservice.com/api/?call=whoIsOnline whoIsOnline method will give us a list of users currently online. But there is no API method to get information about who IS NOT online. So, we should build our timelines using information we got from whoIsOnline. Of course there will be a measurement error (we can't track information in realtime). Let's suppose that we will call whoIsOnline method every 2 minutes (yes, we will run our script by cron every 2 minutes). For example, calling whoIsOnline at 08:00 will return Peter_id Michal_id Andy_id calling whoIsOnline at 08:02 will return Michael_id Andy_id George_id As you can see, Peter has gone offline, but we have new onliner - George. Available instruments are Db(MySQL) / text files / key-value storage (Redis/memcache); feel free to choose any of them (or even all of them). So, we have to get information like this George_id was online... 12 May: 08:02-08:30, 12:40-12:46, 20:14-22:36 11 May: 09:10-12:30, 21:45-23:00 10 May: was not online And now question... How would you store information to implement such timelines? How would you query/calculate information about periods of time when user was online? Additional information.. You cannot update information about offline users, only users who are "currently" online. Solution should be flexible: timeline information could be represented relating to any timezone. We should keep information only for last 7 days. Every user seen online is automatically getting his own identifier in our database. Uff.. it was really hard for me to write it because my English is pretty bad, but I hope my question will be clear for you. Thank you.

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  • How to discard time intervals with Time Series / XYPlots using JFreeChart?

    - by Alex Arnon
    Hi All, I am building a set of chart displays, one of which is for a month display of daily trading - that is, one point of data per day (closing). Since there is no trade during weekends and holidays, I need to discard these data points. Not only that, but data points should still appear adjacent to each other, regardless of any gaps in time. This can be seen in any such chart e.g. in the 3 month graph for Nasdaq on Yahoo Finance - see how weekends are skipped. My question is: how should one correctly implement this in JFreeChart? Thanks in advance!

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  • (iphone) can i give different intervals between images when animating?

    - by Eugene
    Hi, I'm animating several image as follows. UIImageView* animationView = [[UIImageView alloc] initWithFrame: self.animationViewContainer.bounds]; animationView.animationImages = animationArray; animationView.animationDuration = 0.5; animationView.animationRepeatCount = 5; [animationView startAnimating]; What I'd like to do is, controlling duration between animationImages. For instance, show image1 for 0.3 sec image2 for 0.5 sec.. There must be some way to do this, but hard to find an answer. I've asked the same question here before, but wording of the question wasn't so clear. Thank you

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  • iPhone app getting XML then refreshing it at intervals...

    - by user157733
    I have an app which gets some data from the web via an XML document. I have this working fine and have followed apples SeismicXML example (uses NSURLRequest etc). I am very new to this so I have to admit that I do not totally understand all the code that gets the XML - but it is working. My problem is that my app may be running for some time so I want to be able to refresh the XML every now and again and check to see if it is different. If it is different I need to update my contents. Basically this means my questions are.... Is there a standard way of doing this? I was thinking of creating a timer to call the function which parses the XML but I can't figure out which function to call. If anyone can give me any pointers or even better examples of this it would be fab. Thanks

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  • xbox thumbstick used to rotate sprite, basic formula makes it "stick" or feel "sticky" at 90 degree intervals! how do get smooth rotation?

    - by Hugh
    Context: C#, XNA game I am using a very basic formula to calculate what angle my sprite (spaceship for example) should be facing based on the xbox controller thumbstick ie. you use the thumbstick to rotate the ship! in my main update method: shuttleAngle = (float) Math.Atan2(newGamePadState.ThumbSticks.Right.X, newGamePadState.ThumbSticks.Right.Y); in my main draw method: spriteBatch.Draw(shuttle, shuttleCoords, sourceRectangle, Color.White, shuttleAngle, origin, 1.0f, SpriteEffects.None, 1); as you can see its quite simple, i take the current radians from the thumbstick and store it in a float "shuttleAngle" and then use this as the rotation angle (in radians) arguement for drawing the shuttle. For some reason when i rotate the sprint it feels sticky at 0, 90, 180 and 270 degrees angles, it wants to settle at those angles. its not giving me a smooth and natural rotation like i would feel in a game that uses a similar mechanic. PS: my xbox controller is fine!

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  • Violating 1st normal form, is it okay for my purpose?

    - by Nick
    So I'm making a running log, and I have the workouts stored as entries in a table. For each workout, the user can add intervals (which consist of a time and a distance), so I have an array like this: [workout] => [description] => [comments] => ... [intervals] => [0] => [distance] => 200m [time] => 32 [1] => [distance] => 400m [time] => 65 ... I'm really tempted to throw the "intervals" array into serialize() or json_encode() and put it in an "intervals" field in my table, however this violates the principles of good database design (which, incidentally, I know hardly anything about). Is there any disadvantage to doing this? I never plan on querying my table based on the contents of "intervals". Creating a separate table just for intervals seems like a lot of unnecessary complexity, so if anyone with more experience has had a situation like this, what route did you take and how did it work out?

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  • Recursive SQL giving ORA-01790

    - by PenFold
    Using Oracle 11g release 2, the following query gives an ORA-01790: expression must have same datatype as corresponding expression: with intervals(time_interval) AS (select trunc(systimestamp) from dual union all select (time_interval + numtodsinterval(10, 'Minute')) from intervals where time_interval < systimestamp) select time_interval from intervals; The error suggests that the datatype of both subqueries of the UNION ALL are returning different datatypes. Even if I cast to TIMESTAMP in each of the subqueries, then I get the same error. What am I missing?

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  • On ESXi, guest machines hang for significant intervals compared to real machines. How can I fix this?

    - by Tarbox
    This is ESXi version 5.0.0. We plan on upgrading to 5.5 eventually. I have four code profiles, two taken on a real, unvirtualized machine, two taken on a virtual machine. Ordering the list of subroutines by time spent in each one, the two real profiles are practically identical. The two virtual profiles are different from each other and from the real profiles: a subset of subroutines are taking a lot more time on the virtual machines, and the subset is different for each run. The two virtual profiles take a similar amount of time, which is 3 times the amount of time the real profiles take. This gross "how long does it take?" result is consistent after hundreds of tests across three different virtual machines on two different host machines -- the virtual machine is just slower. I've only the code profiling on the four, however. Here's the most guilty set of lines: This is the real machine: 8µs $text = '' unless defined $text; 1.48ms foreach ( split( "\n", $text ) ) { This is the first run on the virtual machine: 20.1ms $text = '' unless defined $text; 1.49ms foreach ( split( "\n", $text ) ) { This is the second run on the virtual machine: 6µs $text = '' unless defined $text; 21.9ms foreach ( split( "\n", $text ) ) { My WAG is that the VM is swapping out the thread and then swapping it back in, destroying some level of cache in the process, but these code profiles were taken when the vm in question was the only active vm on the host, so... what? What does that mean? The guest itself is under light load, this is a latency problem for my users rather than throughput. The host is also under a light load, if I knew what resources to assign where, I could do it without worrying about the cost. I've attempted to lock memory, reserve cpu, assign a restrictive affinity, and disable hyperthread sharing. They don't help, it still takes the VM 2-4x the amount of time to do the same thing as the real machine. The host the tests were run on is 6x2.50GHz, Intel Xeon E5-26400 w/ 16gigs of ram. The guest exhibits the same performance under a wide combination of settings. The real machine is 4x2.13GHz, Xeon E5506 w/ 2 gigs of ram. Thank you for all advice.

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  • interval overlapping in tsql

    - by Nico
    hi folks, i need to get splited intervals and the number of overlapping intervals, eg basedata: interval A: startTime 08:00, endTime 12:00 interval B: startTime 09:00, endTime 12:00 interval C: startTime 12:00, endTime 16:00 interval D: startTime 13:00, endTime 14:00 now i have a separate interval from 10:00 to 15:00 and have to determine what intervals are intersected at first. result should be something like: 1: 10:00 - 12:00 ( intersecting with interval A ) 2: 10:00 - 12:00 ( intersecting with interval B ) 3: 12:00 - 15:00 ( intersecting with interval C ) 4: 13:00 - 14:00 ( intersecting with interval D ) this part works fine, the following causes the trouble: i need some kind of weighting for parallel intervals. this also means, that it can occur that an interval-intersection must be splitted n times, if it's ( partly ) intersected by another one. in the upper example the expecting result would be: 1: 10:00 - 12:00 -> weightage: 50% 2: 10:00 - 12:00 -> weightage: 50% 3.1: 12:00 - 13:00 -> weightage: 1oo% 3.2: 13:00 - 14:00 -> weightage: 50% 3.3: 14:00 - 15:00 -> weightage: 50% 4: 13:00 - 14:00 -< weightage: 100% the splitting of interval 3 is caused by the intersecting with interval 4 between 13:00 and 14:00. sql-server is ms-sql 2008. thanks for help in advance!

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  • MSSQL DATEDIFF accuracy

    - by jomi
    Hello, I have to store some intervals in mssql db. I'm aware that the datetime's accuracy is approx. 3.3ms (can only end 0, 3 and 7). But when I calculate intervals between datetimes I see that the result can only end with 0, 3 and 6. So the more intervals I sum up the more precision I loose. Is it possible to get an accurate DATEDIFF in milliseconds ? declare @StartDate datetime declare @EndDate datetime set @StartDate='2010-04-01 12:00:00.000' set @EndDate='2010-04-01 12:00:00.007' SELECT DATEDIFF(millisecond, @StartDate, @EndDate),@EndDate-@StartDate, @StartDate, @EndDate I would like to see 7 ad not 6. (And it should be as fast as possible) Thanks,

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  • In C# is there a function that correlates sequential values on a IEnumerable

    - by Mike Q
    Hi all, I have a IEnumerable. I have a custom Interval class which just has two DateTimes inside it. I want to convert the IEnumerable to IEnumerable where n DateTimes would enumerate to n-1 Intervals. So if I had 1st Jan, 1st Feb and 1st Mar as the DateTime then I want two intervals out, 1st Jan/1st Feb and 1st Feb/1st March. Is there an existing C# Linq function that does this. Something like the below Correlate... IEnumerable<Interval> intervals = dttms.Correlate<DateTime, Interval>((dttm1, dttm2) => new Interval(dttm1, dttm2)); If not I'll just roll my own.

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  • A Taxonomy of Numerical Methods v1

    - by JoshReuben
    Numerical Analysis – When, What, (but not how) Once you understand the Math & know C++, Numerical Methods are basically blocks of iterative & conditional math code. I found the real trick was seeing the forest for the trees – knowing which method to use for which situation. Its pretty easy to get lost in the details – so I’ve tried to organize these methods in a way that I can quickly look this up. I’ve included links to detailed explanations and to C++ code examples. I’ve tried to classify Numerical methods in the following broad categories: Solving Systems of Linear Equations Solving Non-Linear Equations Iteratively Interpolation Curve Fitting Optimization Numerical Differentiation & Integration Solving ODEs Boundary Problems Solving EigenValue problems Enjoy – I did ! Solving Systems of Linear Equations Overview Solve sets of algebraic equations with x unknowns The set is commonly in matrix form Gauss-Jordan Elimination http://en.wikipedia.org/wiki/Gauss%E2%80%93Jordan_elimination C++: http://www.codekeep.net/snippets/623f1923-e03c-4636-8c92-c9dc7aa0d3c0.aspx Produces solution of the equations & the coefficient matrix Efficient, stable 2 steps: · Forward Elimination – matrix decomposition: reduce set to triangular form (0s below the diagonal) or row echelon form. If degenerate, then there is no solution · Backward Elimination –write the original matrix as the product of ints inverse matrix & its reduced row-echelon matrix à reduce set to row canonical form & use back-substitution to find the solution to the set Elementary ops for matrix decomposition: · Row multiplication · Row switching · Add multiples of rows to other rows Use pivoting to ensure rows are ordered for achieving triangular form LU Decomposition http://en.wikipedia.org/wiki/LU_decomposition C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-lu-decomposition-for-solving.html Represent the matrix as a product of lower & upper triangular matrices A modified version of GJ Elimination Advantage – can easily apply forward & backward elimination to solve triangular matrices Techniques: · Doolittle Method – sets the L matrix diagonal to unity · Crout Method - sets the U matrix diagonal to unity Note: both the L & U matrices share the same unity diagonal & can be stored compactly in the same matrix Gauss-Seidel Iteration http://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method C++: http://www.nr.com/forum/showthread.php?t=722 Transform the linear set of equations into a single equation & then use numerical integration (as integration formulas have Sums, it is implemented iteratively). an optimization of Gauss-Jacobi: 1.5 times faster, requires 0.25 iterations to achieve the same tolerance Solving Non-Linear Equations Iteratively find roots of polynomials – there may be 0, 1 or n solutions for an n order polynomial use iterative techniques Iterative methods · used when there are no known analytical techniques · Requires set functions to be continuous & differentiable · Requires an initial seed value – choice is critical to convergence à conduct multiple runs with different starting points & then select best result · Systematic - iterate until diminishing returns, tolerance or max iteration conditions are met · bracketing techniques will always yield convergent solutions, non-bracketing methods may fail to converge Incremental method if a nonlinear function has opposite signs at 2 ends of a small interval x1 & x2, then there is likely to be a solution in their interval – solutions are detected by evaluating a function over interval steps, for a change in sign, adjusting the step size dynamically. Limitations – can miss closely spaced solutions in large intervals, cannot detect degenerate (coinciding) solutions, limited to functions that cross the x-axis, gives false positives for singularities Fixed point method http://en.wikipedia.org/wiki/Fixed-point_iteration C++: http://books.google.co.il/books?id=weYj75E_t6MC&pg=PA79&lpg=PA79&dq=fixed+point+method++c%2B%2B&source=bl&ots=LQ-5P_taoC&sig=lENUUIYBK53tZtTwNfHLy5PEWDk&hl=en&sa=X&ei=wezDUPW1J5DptQaMsIHQCw&redir_esc=y#v=onepage&q=fixed%20point%20method%20%20c%2B%2B&f=false Algebraically rearrange a solution to isolate a variable then apply incremental method Bisection method http://en.wikipedia.org/wiki/Bisection_method C++: http://numericalcomputing.wordpress.com/category/algorithms/ Bracketed - Select an initial interval, keep bisecting it ad midpoint into sub-intervals and then apply incremental method on smaller & smaller intervals – zoom in Adv: unaffected by function gradient à reliable Disadv: slow convergence False Position Method http://en.wikipedia.org/wiki/False_position_method C++: http://www.dreamincode.net/forums/topic/126100-bisection-and-false-position-methods/ Bracketed - Select an initial interval , & use the relative value of function at interval end points to select next sub-intervals (estimate how far between the end points the solution might be & subdivide based on this) Newton-Raphson method http://en.wikipedia.org/wiki/Newton's_method C++: http://www-users.cselabs.umn.edu/classes/Summer-2012/csci1113/index.php?page=./newt3 Also known as Newton's method Convenient, efficient Not bracketed – only a single initial guess is required to start iteration – requires an analytical expression for the first derivative of the function as input. Evaluates the function & its derivative at each step. Can be extended to the Newton MutiRoot method for solving multiple roots Can be easily applied to an of n-coupled set of non-linear equations – conduct a Taylor Series expansion of a function, dropping terms of order n, rewrite as a Jacobian matrix of PDs & convert to simultaneous linear equations !!! Secant Method http://en.wikipedia.org/wiki/Secant_method C++: http://forum.vcoderz.com/showthread.php?p=205230 Unlike N-R, can estimate first derivative from an initial interval (does not require root to be bracketed) instead of inputting it Since derivative is approximated, may converge slower. Is fast in practice as it does not have to evaluate the derivative at each step. Similar implementation to False Positive method Birge-Vieta Method http://mat.iitm.ac.in/home/sryedida/public_html/caimna/transcendental/polynomial%20methods/bv%20method.html C++: http://books.google.co.il/books?id=cL1boM2uyQwC&pg=SA3-PA51&lpg=SA3-PA51&dq=Birge-Vieta+Method+c%2B%2B&source=bl&ots=QZmnDTK3rC&sig=BPNcHHbpR_DKVoZXrLi4nVXD-gg&hl=en&sa=X&ei=R-_DUK2iNIjzsgbE5ID4Dg&redir_esc=y#v=onepage&q=Birge-Vieta%20Method%20c%2B%2B&f=false combines Horner's method of polynomial evaluation (transforming into lesser degree polynomials that are more computationally efficient to process) with Newton-Raphson to provide a computational speed-up Interpolation Overview Construct new data points for as close as possible fit within range of a discrete set of known points (that were obtained via sampling, experimentation) Use Taylor Series Expansion of a function f(x) around a specific value for x Linear Interpolation http://en.wikipedia.org/wiki/Linear_interpolation C++: http://www.hamaluik.com/?p=289 Straight line between 2 points à concatenate interpolants between each pair of data points Bilinear Interpolation http://en.wikipedia.org/wiki/Bilinear_interpolation C++: http://supercomputingblog.com/graphics/coding-bilinear-interpolation/2/ Extension of the linear function for interpolating functions of 2 variables – perform linear interpolation first in 1 direction, then in another. Used in image processing – e.g. texture mapping filter. Uses 4 vertices to interpolate a value within a unit cell. Lagrange Interpolation http://en.wikipedia.org/wiki/Lagrange_polynomial C++: http://www.codecogs.com/code/maths/approximation/interpolation/lagrange.php For polynomials Requires recomputation for all terms for each distinct x value – can only be applied for small number of nodes Numerically unstable Barycentric Interpolation http://epubs.siam.org/doi/pdf/10.1137/S0036144502417715 C++: http://www.gamedev.net/topic/621445-barycentric-coordinates-c-code-check/ Rearrange the terms in the equation of the Legrange interpolation by defining weight functions that are independent of the interpolated value of x Newton Divided Difference Interpolation http://en.wikipedia.org/wiki/Newton_polynomial C++: http://jee-appy.blogspot.co.il/2011/12/newton-divided-difference-interpolation.html Hermite Divided Differences: Interpolation polynomial approximation for a given set of data points in the NR form - divided differences are used to approximately calculate the various differences. For a given set of 3 data points , fit a quadratic interpolant through the data Bracketed functions allow Newton divided differences to be calculated recursively Difference table Cubic Spline Interpolation http://en.wikipedia.org/wiki/Spline_interpolation C++: https://www.marcusbannerman.co.uk/index.php/home/latestarticles/42-articles/96-cubic-spline-class.html Spline is a piecewise polynomial Provides smoothness – for interpolations with significantly varying data Use weighted coefficients to bend the function to be smooth & its 1st & 2nd derivatives are continuous through the edge points in the interval Curve Fitting A generalization of interpolating whereby given data points may contain noise à the curve does not necessarily pass through all the points Least Squares Fit http://en.wikipedia.org/wiki/Least_squares C++: http://www.ccas.ru/mmes/educat/lab04k/02/least-squares.c Residual – difference between observed value & expected value Model function is often chosen as a linear combination of the specified functions Determines: A) The model instance in which the sum of squared residuals has the least value B) param values for which model best fits data Straight Line Fit Linear correlation between independent variable and dependent variable Linear Regression http://en.wikipedia.org/wiki/Linear_regression C++: http://www.oocities.org/david_swaim/cpp/linregc.htm Special case of statistically exact extrapolation Leverage least squares Given a basis function, the sum of the residuals is determined and the corresponding gradient equation is expressed as a set of normal linear equations in matrix form that can be solved (e.g. using LU Decomposition) Can be weighted - Drop the assumption that all errors have the same significance –-> confidence of accuracy is different for each data point. Fit the function closer to points with higher weights Polynomial Fit - use a polynomial basis function Moving Average http://en.wikipedia.org/wiki/Moving_average C++: http://www.codeproject.com/Articles/17860/A-Simple-Moving-Average-Algorithm Used for smoothing (cancel fluctuations to highlight longer-term trends & cycles), time series data analysis, signal processing filters Replace each data point with average of neighbors. Can be simple (SMA), weighted (WMA), exponential (EMA). Lags behind latest data points – extra weight can be given to more recent data points. Weights can decrease arithmetically or exponentially according to distance from point. Parameters: smoothing factor, period, weight basis Optimization Overview Given function with multiple variables, find Min (or max by minimizing –f(x)) Iterative approach Efficient, but not necessarily reliable Conditions: noisy data, constraints, non-linear models Detection via sign of first derivative - Derivative of saddle points will be 0 Local minima Bisection method Similar method for finding a root for a non-linear equation Start with an interval that contains a minimum Golden Search method http://en.wikipedia.org/wiki/Golden_section_search C++: http://www.codecogs.com/code/maths/optimization/golden.php Bisect intervals according to golden ratio 0.618.. Achieves reduction by evaluating a single function instead of 2 Newton-Raphson Method Brent method http://en.wikipedia.org/wiki/Brent's_method C++: http://people.sc.fsu.edu/~jburkardt/cpp_src/brent/brent.cpp Based on quadratic or parabolic interpolation – if the function is smooth & parabolic near to the minimum, then a parabola fitted through any 3 points should approximate the minima – fails when the 3 points are collinear , in which case the denominator is 0 Simplex Method http://en.wikipedia.org/wiki/Simplex_algorithm C++: http://www.codeguru.com/cpp/article.php/c17505/Simplex-Optimization-Algorithm-and-Implemetation-in-C-Programming.htm Find the global minima of any multi-variable function Direct search – no derivatives required At each step it maintains a non-degenerative simplex – a convex hull of n+1 vertices. Obtains the minimum for a function with n variables by evaluating the function at n-1 points, iteratively replacing the point of worst result with the point of best result, shrinking the multidimensional simplex around the best point. Point replacement involves expanding & contracting the simplex near the worst value point to determine a better replacement point Oscillation can be avoided by choosing the 2nd worst result Restart if it gets stuck Parameters: contraction & expansion factors Simulated Annealing http://en.wikipedia.org/wiki/Simulated_annealing C++: http://code.google.com/p/cppsimulatedannealing/ Analogy to heating & cooling metal to strengthen its structure Stochastic method – apply random permutation search for global minima - Avoid entrapment in local minima via hill climbing Heating schedule - Annealing schedule params: temperature, iterations at each temp, temperature delta Cooling schedule – can be linear, step-wise or exponential Differential Evolution http://en.wikipedia.org/wiki/Differential_evolution C++: http://www.amichel.com/de/doc/html/ More advanced stochastic methods analogous to biological processes: Genetic algorithms, evolution strategies Parallel direct search method against multiple discrete or continuous variables Initial population of variable vectors chosen randomly – if weighted difference vector of 2 vectors yields a lower objective function value then it replaces the comparison vector Many params: #parents, #variables, step size, crossover constant etc Convergence is slow – many more function evaluations than simulated annealing Numerical Differentiation Overview 2 approaches to finite difference methods: · A) approximate function via polynomial interpolation then differentiate · B) Taylor series approximation – additionally provides error estimate Finite Difference methods http://en.wikipedia.org/wiki/Finite_difference_method C++: http://www.wpi.edu/Pubs/ETD/Available/etd-051807-164436/unrestricted/EAMPADU.pdf Find differences between high order derivative values - Approximate differential equations by finite differences at evenly spaced data points Based on forward & backward Taylor series expansion of f(x) about x plus or minus multiples of delta h. Forward / backward difference - the sums of the series contains even derivatives and the difference of the series contains odd derivatives – coupled equations that can be solved. Provide an approximation of the derivative within a O(h^2) accuracy There is also central difference & extended central difference which has a O(h^4) accuracy Richardson Extrapolation http://en.wikipedia.org/wiki/Richardson_extrapolation C++: http://mathscoding.blogspot.co.il/2012/02/introduction-richardson-extrapolation.html A sequence acceleration method applied to finite differences Fast convergence, high accuracy O(h^4) Derivatives via Interpolation Cannot apply Finite Difference method to discrete data points at uneven intervals – so need to approximate the derivative of f(x) using the derivative of the interpolant via 3 point Lagrange Interpolation Note: the higher the order of the derivative, the lower the approximation precision Numerical Integration Estimate finite & infinite integrals of functions More accurate procedure than numerical differentiation Use when it is not possible to obtain an integral of a function analytically or when the function is not given, only the data points are Newton Cotes Methods http://en.wikipedia.org/wiki/Newton%E2%80%93Cotes_formulas C++: http://www.siafoo.net/snippet/324 For equally spaced data points Computationally easy – based on local interpolation of n rectangular strip areas that is piecewise fitted to a polynomial to get the sum total area Evaluate the integrand at n+1 evenly spaced points – approximate definite integral by Sum Weights are derived from Lagrange Basis polynomials Leverage Trapezoidal Rule for default 2nd formulas, Simpson 1/3 Rule for substituting 3 point formulas, Simpson 3/8 Rule for 4 point formulas. For 4 point formulas use Bodes Rule. Higher orders obtain more accurate results Trapezoidal Rule uses simple area, Simpsons Rule replaces the integrand f(x) with a quadratic polynomial p(x) that uses the same values as f(x) for its end points, but adds a midpoint Romberg Integration http://en.wikipedia.org/wiki/Romberg's_method C++: http://code.google.com/p/romberg-integration/downloads/detail?name=romberg.cpp&can=2&q= Combines trapezoidal rule with Richardson Extrapolation Evaluates the integrand at equally spaced points The integrand must have continuous derivatives Each R(n,m) extrapolation uses a higher order integrand polynomial replacement rule (zeroth starts with trapezoidal) à a lower triangular matrix set of equation coefficients where the bottom right term has the most accurate approximation. The process continues until the difference between 2 successive diagonal terms becomes sufficiently small. Gaussian Quadrature http://en.wikipedia.org/wiki/Gaussian_quadrature C++: http://www.alglib.net/integration/gaussianquadratures.php Data points are chosen to yield best possible accuracy – requires fewer evaluations Ability to handle singularities, functions that are difficult to evaluate The integrand can include a weighting function determined by a set of orthogonal polynomials. Points & weights are selected so that the integrand yields the exact integral if f(x) is a polynomial of degree <= 2n+1 Techniques (basically different weighting functions): · Gauss-Legendre Integration w(x)=1 · Gauss-Laguerre Integration w(x)=e^-x · Gauss-Hermite Integration w(x)=e^-x^2 · Gauss-Chebyshev Integration w(x)= 1 / Sqrt(1-x^2) Solving ODEs Use when high order differential equations cannot be solved analytically Evaluated under boundary conditions RK for systems – a high order differential equation can always be transformed into a coupled first order system of equations Euler method http://en.wikipedia.org/wiki/Euler_method C++: http://rosettacode.org/wiki/Euler_method First order Runge–Kutta method. Simple recursive method – given an initial value, calculate derivative deltas. Unstable & not very accurate (O(h) error) – not used in practice A first-order method - the local error (truncation error per step) is proportional to the square of the step size, and the global error (error at a given time) is proportional to the step size In evolving solution between data points xn & xn+1, only evaluates derivatives at beginning of interval xn à asymmetric at boundaries Higher order Runge Kutta http://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods C++: http://www.dreamincode.net/code/snippet1441.htm 2nd & 4th order RK - Introduces parameterized midpoints for more symmetric solutions à accuracy at higher computational cost Adaptive RK – RK-Fehlberg – estimate the truncation at each integration step & automatically adjust the step size to keep error within prescribed limits. At each step 2 approximations are compared – if in disagreement to a specific accuracy, the step size is reduced Boundary Value Problems Where solution of differential equations are located at 2 different values of the independent variable x à more difficult, because cannot just start at point of initial value – there may not be enough starting conditions available at the end points to produce a unique solution An n-order equation will require n boundary conditions – need to determine the missing n-1 conditions which cause the given conditions at the other boundary to be satisfied Shooting Method http://en.wikipedia.org/wiki/Shooting_method C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-shooting-method-for-solving.html Iteratively guess the missing values for one end & integrate, then inspect the discrepancy with the boundary values of the other end to adjust the estimate Given the starting boundary values u1 & u2 which contain the root u, solve u given the false position method (solving the differential equation as an initial value problem via 4th order RK), then use u to solve the differential equations. Finite Difference Method For linear & non-linear systems Higher order derivatives require more computational steps – some combinations for boundary conditions may not work though Improve the accuracy by increasing the number of mesh points Solving EigenValue Problems An eigenvalue can substitute a matrix when doing matrix multiplication à convert matrix multiplication into a polynomial EigenValue For a given set of equations in matrix form, determine what are the solution eigenvalue & eigenvectors Similar Matrices - have same eigenvalues. Use orthogonal similarity transforms to reduce a matrix to diagonal form from which eigenvalue(s) & eigenvectors can be computed iteratively Jacobi method http://en.wikipedia.org/wiki/Jacobi_method C++: http://people.sc.fsu.edu/~jburkardt/classes/acs2_2008/openmp/jacobi/jacobi.html Robust but Computationally intense – use for small matrices < 10x10 Power Iteration http://en.wikipedia.org/wiki/Power_iteration For any given real symmetric matrix, generate the largest single eigenvalue & its eigenvectors Simplest method – does not compute matrix decomposition à suitable for large, sparse matrices Inverse Iteration Variation of power iteration method – generates the smallest eigenvalue from the inverse matrix Rayleigh Method http://en.wikipedia.org/wiki/Rayleigh's_method_of_dimensional_analysis Variation of power iteration method Rayleigh Quotient Method Variation of inverse iteration method Matrix Tri-diagonalization Method Use householder algorithm to reduce an NxN symmetric matrix to a tridiagonal real symmetric matrix vua N-2 orthogonal transforms     Whats Next Outside of Numerical Methods there are lots of different types of algorithms that I’ve learned over the decades: Data Mining – (I covered this briefly in a previous post: http://geekswithblogs.net/JoshReuben/archive/2007/12/31/ssas-dm-algorithms.aspx ) Search & Sort Routing Problem Solving Logical Theorem Proving Planning Probabilistic Reasoning Machine Learning Solvers (eg MIP) Bioinformatics (Sequence Alignment, Protein Folding) Quant Finance (I read Wilmott’s books – interesting) Sooner or later, I’ll cover the above topics as well.

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  • Arithmetic Coding Questions

    - by Xophmeister
    I have been reading up on arithmetic coding and, while I understand how it works, all the guides and instructions I've read start with something like: Set up your intervals based upon the frequency of symbols in your data; i.e., more likely symbols get proportionally larger intervals. My main query is, once I have encoded my data, presumably I also need to include this statistical model with the encoding, otherwise the compressed data can't be decoded. Is that correct? I don't see this mentioned anywhere -- the most I've seen is that you need to include the number of iterations (i.e., encoded symbols) -- but unless I'm missing something, this also seems necessary to me. If this is true, that will obviously add an overhead to the final output. At what point does this outweigh the benefits of compression (e.g., say if I'm trying to compress just a few thousand bits)? Will the choice of symbol size also make a significant difference (e.g., if I'm looking at 2-bit words, rather than full octets/whatever)?

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