Search Results

Search found 1098 results on 44 pages for 'continuous'.

Page 17/44 | < Previous Page | 13 14 15 16 17 18 19 20 21 22 23 24  | Next Page >

  • Agile Testing Days 2012 – Day 2 – Learn through disagreement

    - by Chris George
    I think I was in the right place! During Day 1 I kept on reading tweets about Lean Coffee that has happened earlier that morning. It intrigued me and I figured in for a penny in for a pound, and set my alarm for 6:45am. Following the award night the night before, it was _really_ hard getting up when it went off, but I did and after a very early breakfast, set off for the 10 min walk to the Dorint. With Lean Coffee due to start at 07:30, I arrived at the hotel and made my way to one of the hotel bars. I soon realised I was in the right place as although the bar was empty, there was a table with post-it’s and pens! This MUST be the place! The premise of Lean Coffee is to have several small timeboxed discussions. Everyone writes down what they would like to discuss on post-its that are then briefly explained and submitted to the pile. Once everyone is done, the group dot-votes on the topics. The topics are then sorted by the dot vote counts and the discussions begin. Each discussion had 8 mins to start with, which meant it prevented the discussions getting off topic too much. After the time elapsed, the group had a vote whether to extend the discussion by a further 4 mins or move on. Several discussion were had around training, soft skills etc. The conversations were really interesting and there were quite a few good ideas. Overall it was a very enjoyable experience, certainly worth the early start! Make Melly Happy Following Lean Coffee was real coffee, and much needed that was! The first keynote of the day was “Let’s help Melly (Changing Work into Life)”by Jurgen Appelo. Draw lines to track happiness This was a very interesting presentation, and set the day nicely. The theme to the keynote was projects are about the people, more-so than the actual tasks. So he started by showing a photo of an employee ‘Melly’ who looked happy enough. He then stated that she looked happy but actually hated her job. In fact 50% of Americans hate their jobs. He went on to say that the world over 50% of people hate Americans their jobs. Jurgen talked about many ways to reduce the feedback cycle, not only of the project, but of the people management. Ideas such as Happiness doors, happiness tracking (drawing lines on a wall indicating your happiness for that day), kudo boxes (to compliment a colleague for good work). All of these (and more) ideas stimulate conversation amongst the team, lead to early detection of issues and investigation of solutions. I’ve massively simplified Jurgen’s keynote and have certainly not done it justice, so I will post a link to the video once it’s available. Following more coffee, the next talk was “How releasing faster changes testing” by Alexander Schwartz. This is a topic very close to our hearts at the moment, so I was eager to find out any juicy morsels that could help us achieve more frequent releases, and Alex did not disappoint. He started off by confirming something that I have been a firm believer in for a number of years now; adding more people can do more harm than good when trying to release. This is for a number of reasons, but just adding new people to a team at such a critical time can be more of a drain on resources than they add. The alternative is to have the whole team have shared responsibility for faster delivery. So the whole team is responsible for quality and testing. Obviously you will have the test engineers on the project who have the specialist skills, but there is no reason that the entire team cannot do exploratory testing on the product. This links nicely with the Developer Exploratory testing presented by Sigge on Day 1, and certainly something that my team are really striving towards. Focus on cycle time, so what can be done to reduce the time between dev cycles, release cycles. What’s stops a release, what delays a release? all good solid questions that can be answered. Alex suggested that perhaps the product doesn’t need to be fully tested. Doing less testing will reduce the cycle time therefore get the release out faster. He suggested a risk-based approach to planning what testing needs to happen. Reducing testing could have an impact on revenue if it causes harm to customers, so test the ‘right stuff’! Determine a set of tests that are ‘face saving’ or ‘smoke’ tests. These tests cover the core functionality of the product and aim to prevent major embarrassment if these areas were to fail! Amongst many other very good points, Alex suggested that a good approach would be to release after every new feature is added. So do a bit of work -> release, do some more work -> release. By releasing small increments of work, the impact on the customer of bugs being introduced is reduced. Red Pill, Blue Pill The second keynote of the day was “Adaptation and improvisation – but your weakness is not your technique” by Markus Gartner and proved to be another very good presentation. It started off quoting lines from the Matrix which relate to adapting, improvising, realisation and mastery. It has alot of nerds in the room smiling! Markus went on to explain how through deliberate practice ( and a lot of it!) you can achieve mastery, but then you never stop learning. Through methods such as code retreats, testing dojos, workshops you can continually improve and learn. The code retreat idea was one that interested me. It involved pairing to write an automated test for, say, 45 mins, they deleting all the code, finding a different partner and writing the same test again! This is another keynote where the video will speak louder than anything I can write here! Markus did elaborate on something that Lisa and Janet had touched on yesterday whilst busting the myth that “Testers Must Code”. Whilst it is true that to be a tester, you don’t need to code, it is becoming more common that there is this crossover happening where more testers are coding and more programmers are testing. Markus made a special distinction between programmers and developers as testers develop tests code so this helped to make that clear. “Extending Continuous Integration and TDD with Continuous Testing” by Jason Ayers was my next talk after lunch. We already do CI and a bit of TDD on my project team so I was interested to see what this continuous testing thing was all about and whether it would actually work for us. At the start of the presentation I was of the opinion that it just would not work for us because our tests are too slow, and that would be the case for many people. Jason started off by setting the scene and saying that those doing TDD spend between 10-15% of their time waiting for tests to run. This can be reduced by testing less often, reducing the test time but this then increases the risk of introduced bugs not being spotted quickly. Therefore, in comes Continuous Testing (CT). CT systems run your unit tests whenever you save some code and runs them in the background so you can continue working. This is a really nice idea, but to do this, your tests must be fast, independent and reliable. The latter two should be the case anyway, and the first is ideal, but hard! Jason makes several suggestions to make tests fast. Firstly keep the scope of the test small, secondly spin off any expensive tests into a suite which is run, perhaps, overnight or outside of the CT system at any rate. So this started to change my mind, perhaps we could re-engineer our tests, and continuously run the quick ones to give an element of coverage. This talk was very interesting and I’ve already tried a couple of the tools mentioned on our product (Mighty Moose and NCrunch). Sadly due to the way our solution is built, it currently doesn’t work, but we will look at whether we can make this work because this has the potential to be a mini-game-changer for us. Using the wrong data Gojko’s Hierarchy of Quality The final keynote of the day was “Reinventing software quality” by Gojko Adzic. He opened the talk with the statement “We’ve got quality wrong because we are using the wrong data”! Gojko then went on to explain that we should judge a bug by whether the customer cares about it, not by whether we think it’s important. Why spend time fixing issues that the customer just wouldn’t care about and releasing months later because of this? Surely it’s better to release now and get customer feedback? This was another reference to the idea of how it’s better to build the right thing wrong than the wrong thing right. Get feedback early to make sure you’re making the right thing. Gojko then showed something which was very analogous to Maslow’s heirachy of needs. Successful – does it contribute to the business? Useful – does it do what the user wants Usable – does it do what it’s supposed to without breaking Performant/Secure – is it secure/is the performance acceptable Deployable Functionally ok – can it be deployed without breaking? He then explained that User Stories should focus on change. In other words they should focus on the users needs, not the users process. Describe what the change will be, how that change will happen then measure it! Networking and Beer Following the day’s closing keynote, there were drinks and nibble for the ‘Networking’ evening. This was a great opportunity to talk to people. I find approaching strangers very uncomfortable but once again, when in Rome! Pete Walen and I had a long conversation about only fixing issues that the customer cares about versus fixing issues that make you proud of your software! Without saying much, and asking the right questions, Pete made me re-evaluate my thoughts on the matter. Clever, very clever!  Oh and he ‘bought’ me a beer! My Takeaway Triple from Day 2: release small and release often to minimize issues creeping in and get faster feedback from ‘the real world’ Focus on issues that the customers care about, not what we think is important It’s okay to disagree with someone, even if they are well respected agile testing gurus, that’s how discussion and learning happens!  

    Read the article

  • In search of database delivery practitioners and enthusiasts

    - by Claire Brooking
    We know from speaking with many of you at tradeshows and user groups that database delivery is not a factory production line. During planning, evaluation, quality control, and disaster mitigation, the people having their say at each step means that successful database deployment is a carefully managed course of action. With so many factors involved at every stage, we would love to find a way for our software to help out, by simplifying processes, speeding them up or joining together the people and the steps that make it all happen. We’re hoping our new research group for database delivery (SQL Server and Oracle) will help us understand the views and experiences of those of you out there in the trenches managing database changes. As part of our new group, we’ll be running a variety of research sessions, including surveys and phone interviews, over coming months. If you have opinions to share on Continuous Integration or Continuous Delivery for databases, we’d love to hear from you. Your feedback really will count as the product teams at Red Gate build plans. For some of our more in-depth sessions, we’ll also be offering participants an Amazon voucher as a thank-you for your time. If you’re not yet practising automated database deployment processes, but are contemplating or planning it, please do consider joining our research group too. If you’d like to sign up to the group and find out more, please fill in a quick form online, and we’ll be in touch to let you know about new research opportunities you might be interested in. We look forward to hearing your stories!

    Read the article

  • ArchBeat Link-o-Rama for October 18, 2013

    - by OTN ArchBeat
    Enriching XMLType data using relational data – XQuery and fn:collection in action | Lucas Jellema Another detailed technical post from the always prolific Lucas Jellema. Evil Behind ChangeEventPolicy PPR in CRUD ADF 12c and WebLogic Stuck Threads | Andrejus Baranovskis The latest post from Oracle ACE Director Andrejus Baranovskis is a bit of a preview of his presentation at the upcoming UKOUG 2013 event. Podcast: Interview with authors of "Hudson Continuous Integration in Practice" For your listening pleasure... Here's an Oracle Author Podcast Interview with "Hudson Continuous Integration in Practice" authors Ed Burns and Winston Prakash. Manual Recovery Mechanisms in SOA Suite and AIA | Shreenidhi Raghuram Solution architect Shreenidhi Raghuram's post combines information from several sources to provide "a quick reference for Manual Recovery of Faults within the SOA and AIA contexts." Event: Harnessing Oracle Weblogic and Oracle Coherence This OTN Virtual Developer Day event features eight sessions in two tracks, with presentations and hands-on labs for developers and architects delivered by experts in Weblogic, Coherence, and ADF. Registration is free. November 5th, 2013. 9am-1pm PT / 12pm-4pm ET / 1pm-5pm BRT Podcast: IoT Challenges and Opportunities - Part 2 Part 2 of the OTN ArchBeat Internet of Things podcast features a roundtable discussion of IoT challenges: massive data streams, security and privacy issues, evolving standards and protocols. Listen! Video: Design - ADF Architectural Patterns - Two for One Deal | Chris Muir Chris Muir explores the reuse of BTF workspaces across multiple applications and the advantages and disadvantages of reuse at the application level. Thought for the Day "Can't nothing make your life work if you ain't the architect." — Terry McMillan, American author (Born October 18, 1951) Source: brainyquote.com

    Read the article

  • LOD in modern games

    - by Firas Assaad
    I'm currently working on my master's thesis about LOD and mesh simplification, and I've been reading many academic papers and articles about the subject. However, I can't find enough information about how LOD is being used in modern games. I know many games use some sort of dynamic LOD for terrain, but what about elsewhere? Level of Detail for 3D Graphics for example points out that discrete LOD (where artists prepare several models in advance) is widely used because of the performance overhead of continuous LOD. That book was published in 2002 however, and I'm wondering if things are different now. There has been some research in performing dynamic LOD using the geometry shader (this paper for example, with its implementation in ShaderX6), would that be used in a modern game? To summarize, my question is about the state of LOD in modern video games, what algorithms are used and why? In particular, is view dependent continuous simplification used or does the runtime overhead make using discrete models with proper blending and impostors a more attractive solution? If discrete models are used, is an algorithm used (e.g. vertex clustering) to generate them offline, do artists manually create the models, or perhaps a combination of both methods is used?

    Read the article

  • I'm a SubVersion geek, why I should consider or not consider Mercurial or Git or any other DRCS?

    - by Pierre 303
    I tried to understand the benefits of DRCS. I must recognize I still doesn't get it. Here are my current beliefs. I'm ready to destroy them thanks to your expertise. I know I'm probably resisting to change. I just want to evaluate how much that change will cost me. Merging hell can be solved by just applying good practices such as continuous integration. There is no such good practice than having a private branch for a few days when you are in a self managing team with real collaboration. I use branching for that for very rare cases, and I keep a branch for every major version, in which I fix bugs merged from the trunk. I see the value of committing offline then pushing online. But continuous integration can help on this too. I work on very large projects, and I never noticed SubVersion to be slow even when the server is 5000km away on the internet and my small connection (less than 1024D/128U). Harddisk space is cheap, so having a copy of source code locally doesn't look like a problem to me. I already have a full copy of the last version on my disk. I don't understand the distributed thing there (maybe THIS IS the key to my understanding?) I not new in the industry, and judging by my difficulty to understand, I don't think DRCS are easier to understand than SubVersion like. If fact, I don't understand... Doctor, give me your diagnostic.

    Read the article

  • A better alternative to incompatible implementations for the same interface?

    - by glenatron
    I am working on a piece of code which performs a set task in several parallel environments where the behaviour of the different components in the task are similar but quite different. This means that my implementations are quite different but they are all based on the relationships between the same interfaces, something like this: IDataReader -> ContinuousDataReader -> ChunkedDataReader IDataProcessor -> ContinuousDataProcessor -> ChunkedDataProcessor IDataWriter -> ContinuousDataWriter -> ChunkedDataWriter So that in either environment we have an IDataReader, IDataProcessor and IDataWriter and then we can use Dependency Injection to ensure that we have the correct one of each for the current environment, so if we are working with data in chunks we use the ChunkedDataReader, ChunkedDataProcessor and ChunkedDataWriter and if we have continuous data we have the continuous versions. However the behaviour of these classes is quite different internally and one could certainly not go from a ContinuousDataReader to the ChunkedDataReader even though they are both IDataProcessors. This feels to me as though it is incorrect ( possibly an LSP violation? ) and certainly not a theoretically correct way of working. It is almost as though the "real" interface here is the combination of all three classes. Unfortunately in the project I am working on with the deadlines we are working to, we're pretty much stuck with this design, but if we had a little more elbow room, what would be a better design approach in this kind of scenario?

    Read the article

  • Toolset agnostic build server and Silverlight projects

    - by Marko Apfel
    Problem Normally I try to have my continuous integration as most a possible toolset free to ensure that no local stuff could have an impact to my build. My Silverlight app references a special compile target in a folder outside my developer tree: <Import Project="$(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v10.0\WebApplications\Microsoft.WebApplication.targets" /> So I copied the stuff from this folder to a local one and changed the call to this target in my csproj: <Import Project="..\..\..\tools\WebApplications\Microsoft.WebApplication.targets" /> And now Visual Studio Conversion Wizard welcomes my with this: Solution Regardless of which line I write – this conversion comes back again and again, if the line has another form than <Import Project="$(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v10.0\WebApplications\Microsoft.WebApplication.targets" /> So it seems that there is no simple way to change this behaviour. Workaraound I must accept, that this line must be in the csproj and to run the build the toolset must be copied to the build server at the correct location. So go to your development machine where Visual Studio is installed and copy the folder “C:\Program Files (x86)\MSBuild\Microsoft\VisualStudio\v10.0\WebApplications” to your build server at the equivalent location.   Xmas wishes to Microsoft: Please provide technologies to let us developers bundle all needed stuff for a project in one developer tree. It should be possible that one checkout starts us up! No additional installations regardless whether it is a developing machine or dedicated build or continuous integration server. Silverlight is only one example, code analysis configurations could also be terrible and much more …

    Read the article

  • Is there a (family of) monotonically non-decreasing noise function(s)?

    - by Joe Wreschnig
    I'd like a function to animate an object moving from point A to point B over time, such that it reaches B at some fixed time, but its position at any time is randomly perturbed in a continuous fashion, but never goes backwards. The objects move along straight lines, so I only need one dimension. Mathematically, that means I'm looking for some continuous f(x), x ? [0,1], such that: f(0) = 0 f(1) = 1 x < y ? f(x) = f(y) At "most" points f(x + d) - f(x) bears no obvious relation to d. (The function is not uniformly increasing or otherwise predictable; I think that's also equivalent to saying no degree of derivative is a constant.) Ideally, I would actually like some way to have a family of these functions, providing some seed state. I'd need at least 4 bits of seed (16 possible functions), for my current use, but since that's not much feel free to provide even more. To avoid various issues with accumulation errors, I'd prefer the function not require any kind of internal state. That is, I want it to be a real function, not a programming "function".

    Read the article

  • Space Invaders-type game: Keeping the enemies aligned with each other as they turn around?

    - by CorundumGames
    OK, so here's the lowdown of the problem I'm trying to solve. I'm developing a game in PyGame that's a cross between Space Invaders and Columns. I'm trying to make the motion of the enemies similar to that of the aliens in Space Invaders; that is, they're all clustered in a grid, and if even one hits the side of the screen, the entire formation moves down and turns around. However, the motion of these aliens is continuous (as continuous as a monitor can be, anyway), not on a discrete grid like in the original. The enemies are instances of an Enemy class, and in turn they're held by a 2D array in a enemysquadron module (which, if you don't use Python, is in this case essentially a singleton due to the way Python modules work). Inside the Enemy class I have a class-scope velocity vector that is reversed every time an Enemy object touches the edge of the screen. This won't do, though, because as time goes on the enemies just become disorganized and jumbled (i.e. not in a grid as planned). I haven't implemented the Enemies going downward yet, so let's not worry about that right now. Any tips?

    Read the article

  • At the Java DEMOgrounds - ZeroTurnaround and its LiveRebel 2.5

    - by Janice J. Heiss
    At the ZeroTurnaround demo, I spoke with Krishnan Badrinarayanan, their Product Marketing Manager. ZeroTurnaround, the creator of JRebel and LiveRebel, describes itself on their site as a company “dedicated to changing the way the world develops, tests and runs Java applications."“We just launched LiveRebel 2.5 today,” stated Badrinarayanan, “which enables companies to embrace the concept and practice of continuous delivery, which means having a pipeline that takes products right from the developers to an end-user, faster, more frequently -- all the while ensuring that it’s a quality product that does not break in production. So customers don’t feel the discontinuity that something has changed under them and that they can’t deal with the change. And all this happens while there is zero down time.”He pointed out that Salesforce.com is not useable from 3 a.m. to 5 a.m. on Saturday because they are engaged in maintenance. “With LiveRebel 2.5, you can unify the whole delivery chain without having any downtime at all,” he said. “There are many products that tell customers to take their tools and change how they work as an organization so that you they have to conform to the way the tool prescribes them to work as an application team. We take a more pragmatic approach. A lot of companies might use Jenkins or Bamboo to do continuous integration. We extend that. We say, take our product, take LiveRebel okay, and integrate it with Jenkins – you can do that quickly, so that, in half a day, you will be up and running. And let LiveRebel automate your deployment processes and all the automated tasks that go with it. Right from tests to the staging environment to production -- all with zero downtime and with no impact on users currently using the system.” “So if you were to make the update right now and you had 100 users on your system, they would not even know this was happening. It would maintain their sessions and transfer them over to the new version, all in the background.”

    Read the article

  • How to restore your production database without needing additional storage

    - by David Atkinson
    Production databases can get very large. This in itself is to be expected, but when a copy of the database is needed the database must be restored, requiring additional and costly storage.  For example, if you want to give each developer a full copy of your production server, you'll need n times the storage cost for your n-developer team. The same is true for any test databases that are created during the course of your project lifecycle. If you've read my previous blog posts, you'll be aware that I've been focusing on the database continuous integration theme. In my CI setup I create a "production"-equivalent database directly from its source control representation, and use this to test my upgrade scripts. Despite this being a perfectly valid and practical thing to do as part of a CI setup, it's not the exact equivalent to running the upgrade script on a copy of the actual production database. So why shouldn't I instead simply restore the most recent production backup as part of my CI process? There are two reasons why this would be impractical. 1. My CI environment isn't an exact copy of my production environment. Indeed, this would be the case in a perfect world, and it is strongly recommended as a good practice if you follow Jez Humble and David Farley's "Continuous Delivery" teachings, but in practical terms this might not always be possible, especially where storage is concerned. It may just not be possible to restore a huge production database on the environment you've been allotted. 2. It's not just about the storage requirements, it's also the time it takes to do the restore. The whole point of continuous integration is that you are alerted as early as possible whether the build (yes, the database upgrade script counts!) is broken. If I have to run an hour-long restore each time I commit a change to source control I'm just not going to get the feedback quickly enough to react. So what's the solution? Red Gate has a technology, SQL Virtual Restore, that is able to restore a database without using up additional storage. Although this sounds too good to be true, the explanation is quite simple (although I'm sure the technical implementation details under the hood are quite complex!) Instead of restoring the backup in the conventional sense, SQL Virtual Restore will effectively mount the backup using its HyperBac technology. It creates a data and log file, .vmdf, and .vldf, that becomes the delta between the .bak file and the virtual database. This means that both read and write operations are permitted on a virtual database as from SQL Server's point of view it is no different from a conventional database. Instead of doubling the storage requirements upon a restore, there is no 'duplicate' storage requirements, other than the trivially small virtual log and data files (see illustration below). The benefit is magnified the more databases you mount to the same backup file. This technique could be used to provide a large development team a full development instance of a large production database. It is also incredibly easy to set up. Once SQL Virtual Restore is installed, you simply run a conventional RESTORE command to create the virtual database. This is what I have running as part of a nightly "release test" process triggered by my CI tool. RESTORE DATABASE WidgetProduction_virtual FROM DISK=N'C:\WidgetWF\ProdBackup\WidgetProduction.bak' WITH MOVE N'WidgetProduction' TO N'C:\WidgetWF\ProdBackup\WidgetProduction_WidgetProduction_Virtual.vmdf', MOVE N'WidgetProduction_log' TO N'C:\WidgetWF\ProdBackup\WidgetProduction_log_WidgetProduction_Virtual.vldf', NORECOVERY, STATS=1, REPLACE GO RESTORE DATABASE mydatabase WITH RECOVERY   Note the only change from what you would do normally is the naming of the .vmdf and .vldf files. SQL Virtual Restore intercepts this by monitoring the extension and applies its magic, ensuring the 'virtual' restore happens rather than the conventional storage-heavy restore. My automated release test then applies the upgrade scripts to the virtual production database and runs some validation tests, giving me confidence that were I to run this on production for real, all would go smoothly. For illustration, here is my 8Gb production database: And its corresponding backup file: Here are the .vldf and .vmdf files, which represent the only additional used storage for the new database following the virtual restore.   The beauty of this product is its simplicity. Once it is installed, the interaction with the backup and virtual database is exactly the same as before, as the clever stuff is being done at a lower level. SQL Virtual Restore can be downloaded as a fully functional 14-day trial. Technorati Tags: SQL Server

    Read the article

  • How to restore your production database without needing additional storage

    - by David Atkinson
    Production databases can get very large. This in itself is to be expected, but when a copy of the database is needed the database must be restored, requiring additional and costly storage.  For example, if you want to give each developer a full copy of your production server, you’ll need n times the storage cost for your n-developer team. The same is true for any test databases that are created during the course of your project lifecycle. If you’ve read my previous blog posts, you’ll be aware that I’ve been focusing on the database continuous integration theme. In my CI setup I create a “production”-equivalent database directly from its source control representation, and use this to test my upgrade scripts. Despite this being a perfectly valid and practical thing to do as part of a CI setup, it’s not the exact equivalent to running the upgrade script on a copy of the actual production database. So why shouldn’t I instead simply restore the most recent production backup as part of my CI process? There are two reasons why this would be impractical. 1. My CI environment isn’t an exact copy of my production environment. Indeed, this would be the case in a perfect world, and it is strongly recommended as a good practice if you follow Jez Humble and David Farley’s “Continuous Delivery” teachings, but in practical terms this might not always be possible, especially where storage is concerned. It may just not be possible to restore a huge production database on the environment you’ve been allotted. 2. It’s not just about the storage requirements, it’s also the time it takes to do the restore. The whole point of continuous integration is that you are alerted as early as possible whether the build (yes, the database upgrade script counts!) is broken. If I have to run an hour-long restore each time I commit a change to source control I’m just not going to get the feedback quickly enough to react. So what’s the solution? Red Gate has a technology, SQL Virtual Restore, that is able to restore a database without using up additional storage. Although this sounds too good to be true, the explanation is quite simple (although I’m sure the technical implementation details under the hood are quite complex!) Instead of restoring the backup in the conventional sense, SQL Virtual Restore will effectively mount the backup using its HyperBac technology. It creates a data and log file, .vmdf, and .vldf, that becomes the delta between the .bak file and the virtual database. This means that both read and write operations are permitted on a virtual database as from SQL Server’s point of view it is no different from a conventional database. Instead of doubling the storage requirements upon a restore, there is no ‘duplicate’ storage requirements, other than the trivially small virtual log and data files (see illustration below). The benefit is magnified the more databases you mount to the same backup file. This technique could be used to provide a large development team a full development instance of a large production database. It is also incredibly easy to set up. Once SQL Virtual Restore is installed, you simply run a conventional RESTORE command to create the virtual database. This is what I have running as part of a nightly “release test” process triggered by my CI tool. RESTORE DATABASE WidgetProduction_Virtual FROM DISK=N'D:\VirtualDatabase\WidgetProduction.bak' WITH MOVE N'WidgetProduction' TO N'C:\WidgetWF\ProdBackup\WidgetProduction_WidgetProduction_Virtual.vmdf', MOVE N'WidgetProduction_log' TO N'C:\WidgetWF\ProdBackup\WidgetProduction_log_WidgetProduction_Virtual.vldf', NORECOVERY, STATS=1, REPLACE GO RESTORE DATABASE WidgetProduction_Virtual WITH RECOVERY   Note the only change from what you would do normally is the naming of the .vmdf and .vldf files. SQL Virtual Restore intercepts this by monitoring the extension and applies its magic, ensuring the ‘virtual’ restore happens rather than the conventional storage-heavy restore. My automated release test then applies the upgrade scripts to the virtual production database and runs some validation tests, giving me confidence that were I to run this on production for real, all would go smoothly. For illustration, here is my 8Gb production database: And its corresponding backup file: Here are the .vldf and .vmdf files, which represent the only additional used storage for the new database following the virtual restore.   The beauty of this product is its simplicity. Once it is installed, the interaction with the backup and virtual database is exactly the same as before, as the clever stuff is being done at a lower level. SQL Virtual Restore can be downloaded as a fully functional 14-day trial. Technorati Tags: SQL Server

    Read the article

  • How to restore your production database without needing additional storage

    - by David Atkinson
    Production databases can get very large. This in itself is to be expected, but when a copy of the database is needed the database must be restored, requiring additional and costly storage.  For example, if you want to give each developer a full copy of your production server, you'll need n times the storage cost for your n-developer team. The same is true for any test databases that are created during the course of your project lifecycle. If you've read my previous blog posts, you'll be aware that I've been focusing on the database continuous integration theme. In my CI setup I create a "production"-equivalent database directly from its source control representation, and use this to test my upgrade scripts. Despite this being a perfectly valid and practical thing to do as part of a CI setup, it's not the exact equivalent to running the upgrade script on a copy of the actual production database. So why shouldn't I instead simply restore the most recent production backup as part of my CI process? There are two reasons why this would be impractical. 1. My CI environment isn't an exact copy of my production environment. Indeed, this would be the case in a perfect world, and it is strongly recommended as a good practice if you follow Jez Humble and David Farley's "Continuous Delivery" teachings, but in practical terms this might not always be possible, especially where storage is concerned. It may just not be possible to restore a huge production database on the environment you've been allotted. 2. It's not just about the storage requirements, it's also the time it takes to do the restore. The whole point of continuous integration is that you are alerted as early as possible whether the build (yes, the database upgrade script counts!) is broken. If I have to run an hour-long restore each time I commit a change to source control I'm just not going to get the feedback quickly enough to react. So what's the solution? Red Gate has a technology, SQL Virtual Restore, that is able to restore a database without using up additional storage. Although this sounds too good to be true, the explanation is quite simple (although I'm sure the technical implementation details under the hood are quite complex!) Instead of restoring the backup in the conventional sense, SQL Virtual Restore will effectively mount the backup using its HyperBac technology. It creates a data and log file, .vmdf, and .vldf, that becomes the delta between the .bak file and the virtual database. This means that both read and write operations are permitted on a virtual database as from SQL Server's point of view it is no different from a conventional database. Instead of doubling the storage requirements upon a restore, there is no 'duplicate' storage requirements, other than the trivially small virtual log and data files (see illustration below). The benefit is magnified the more databases you mount to the same backup file. This technique could be used to provide a large development team a full development instance of a large production database. It is also incredibly easy to set up. Once SQL Virtual Restore is installed, you simply run a conventional RESTORE command to create the virtual database. This is what I have running as part of a nightly "release test" process triggered by my CI tool. RESTORE DATABASE WidgetProduction_virtual FROM DISK=N'C:\WidgetWF\ProdBackup\WidgetProduction.bak' WITH MOVE N'WidgetProduction' TO N'C:\WidgetWF\ProdBackup\WidgetProduction_WidgetProduction_Virtual.vmdf', MOVE N'WidgetProduction_log' TO N'C:\WidgetWF\ProdBackup\WidgetProduction_log_WidgetProduction_Virtual.vldf', NORECOVERY, STATS=1, REPLACE GO RESTORE DATABASE mydatabase WITH RECOVERY   Note the only change from what you would do normally is the naming of the .vmdf and .vldf files. SQL Virtual Restore intercepts this by monitoring the extension and applies its magic, ensuring the 'virtual' restore happens rather than the conventional storage-heavy restore. My automated release test then applies the upgrade scripts to the virtual production database and runs some validation tests, giving me confidence that were I to run this on production for real, all would go smoothly. For illustration, here is my 8Gb production database: And its corresponding backup file: Here are the .vldf and .vmdf files, which represent the only additional used storage for the new database following the virtual restore.   The beauty of this product is its simplicity. Once it is installed, the interaction with the backup and virtual database is exactly the same as before, as the clever stuff is being done at a lower level. SQL Virtual Restore can be downloaded as a fully functional 14-day trial. Technorati Tags: SQL Server

    Read the article

  • LIBGDX "parsing error emitter" with 2 or more emitters [on hold]

    - by flow969
    I have a problem with the use of particle effect of LIBGDX with 2 or more emitters. After using ParticleEditor to create my .p file, I use it in my code BUT...when I use only 1 emitter it's fine but with more than 1, not fine ! :( Here is my error code in java console : Exception in thread "LWJGL Application" java.lang.RuntimeException: Error parsing emitter: - Delay - at com.badlogic.gdx.graphics.g2d.ParticleEmitter.load(ParticleEmitter.java:910) at com.badlogic.gdx.graphics.g2d.ParticleEmitter.<init>(ParticleEmitter.java:95) at com.badlogic.gdx.graphics.g2d.ParticleEffect.loadEmitters(ParticleEffect.java:154) at com.badlogic.gdx.graphics.g2d.ParticleEffect.load(ParticleEffect.java:138) at com.fasgame.fishtrip.android.screens.GameScreen.show(GameScreen.java:313) at com.badlogic.gdx.Game.setScreen(Game.java:61) at com.fasgame.fishtrip.android.screens.MainMenuScreen.render(MainMenuScreen.java:71) at com.badlogic.gdx.Game.render(Game.java:46) at com.badlogic.gdx.backends.lwjgl.LwjglApplication.mainLoop(LwjglApplication.java:206) at com.badlogic.gdx.backends.lwjgl.LwjglApplication$1.run(LwjglApplication.java:114) Caused by: java.lang.NumberFormatException: For input string: "- Count -" at sun.misc.FloatingDecimal.readJavaFormatString(Unknown Source) at sun.misc.FloatingDecimal.parseFloat(Unknown Source) at java.lang.Float.parseFloat(Unknown Source) at com.badlogic.gdx.graphics.g2d.ParticleEmitter.readFloat(ParticleEmitter.java:929) at com.badlogic.gdx.graphics.g2d.ParticleEmitter$RangedNumericValue.load(ParticleEmitter.java:1062) at com.badlogic.gdx.graphics.g2d.ParticleEmitter.load(ParticleEmitter.java:866) ... 9 more And here is my particle effect .p file : Blanc - Delay - active: false - Duration - lowMin: 3000.0 lowMax: 3000.0 - Count - min: 0 max: 200 - Emission - lowMin: 0.0 lowMax: 0.0 highMin: 250.0 highMax: 250.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Life - lowMin: 500.0 lowMax: 500.0 highMin: 500.0 highMax: 500.0 relative: false scalingCount: 3 scaling0: 1.0 scaling1: 0.47058824 scaling2: 0.0 timelineCount: 3 timeline0: 0.0 timeline1: 0.51369864 timeline2: 1.0 - Life Offset - active: false - X Offset - active: false - Y Offset - active: false - Spawn Shape - shape: point - Spawn Width - lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Spawn Height - lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Scale - lowMin: 0.0 lowMax: 0.0 highMin: 70.0 highMax: 70.0 relative: true scalingCount: 2 scaling0: 1.0 scaling1: 0.0 timelineCount: 2 timeline0: 0.0 timeline1: 1.0 - Velocity - active: true lowMin: 0.0 lowMax: 0.0 highMin: 30.0 highMax: 300.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Angle - active: true lowMin: 220.0 lowMax: 320.0 highMin: 220.0 highMax: 320.0 relative: false scalingCount: 2 scaling0: 0.0 scaling1: 0.98039216 timelineCount: 2 timeline0: 0.0 timeline1: 1.0 - Rotation - active: false - Wind - active: false - Gravity - active: true lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Tint - colorsCount: 3 colors0: 0.50980395 colors1: 0.7647059 colors2: 0.7921569 timelineCount: 1 timeline0: 0.0 - Transparency - lowMin: 0.0 lowMax: 0.0 highMin: 1.0 highMax: 1.0 relative: false scalingCount: 4 scaling0: 1.0 scaling1: 1.0 scaling2: 1.0 scaling3: 1.0 timelineCount: 4 timeline0: 0.0 timeline1: 0.36301368 timeline2: 0.6164383 timeline3: 1.0 - Options - attached: false continuous: true aligned: false additive: true behind: false premultipliedAlpha: false pre_particle.png Bleu - Delay - active: false - Duration - lowMin: 3000.0 lowMax: 3000.0 - Count - min: 0 max: 200 - Emission - lowMin: 0.0 lowMax: 0.0 highMin: 250.0 highMax: 250.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Life - lowMin: 500.0 lowMax: 500.0 highMin: 500.0 highMax: 500.0 relative: false scalingCount: 3 scaling0: 1.0 scaling1: 0.47058824 scaling2: 0.0 timelineCount: 3 timeline0: 0.0 timeline1: 0.51369864 timeline2: 1.0 - Life Offset - active: false - X Offset - active: false - Y Offset - active: false - Spawn Shape - shape: point - Spawn Width - lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Spawn Height - lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Scale - lowMin: 0.0 lowMax: 0.0 highMin: 70.0 highMax: 70.0 relative: true scalingCount: 2 scaling0: 1.0 scaling1: 0.0 timelineCount: 2 timeline0: 0.0 timeline1: 1.0 - Velocity - active: true lowMin: 0.0 lowMax: 0.0 highMin: 30.0 highMax: 300.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Angle - active: true lowMin: 220.0 lowMax: 320.0 highMin: 220.0 highMax: 320.0 relative: false scalingCount: 2 scaling0: 0.0 scaling1: 0.98039216 timelineCount: 2 timeline0: 0.0 timeline1: 1.0 - Rotation - active: false - Wind - active: false - Gravity - active: true lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Tint - colorsCount: 3 colors0: 0.0 colors1: 0.7254902 colors2: 0.7921569 timelineCount: 1 timeline0: 0.0 - Transparency - lowMin: 0.0 lowMax: 0.0 highMin: 1.0 highMax: 1.0 relative: false scalingCount: 6 scaling0: 0.0 scaling1: 1.0 scaling2: 1.0 scaling3: 1.0 scaling4: 1.0 scaling5: 0.0 timelineCount: 6 timeline0: 0.0 timeline1: 0.047945205 timeline2: 0.34246576 timeline3: 0.6712329 timeline4: 0.94520545 timeline5: 1.0 - Options - attached: false continuous: true aligned: false additive: true behind: false premultipliedAlpha: false pre_particle.png BleuFonce - Delay - active: false - Duration - lowMin: 3000.0 lowMax: 3000.0 - Count - min: 0 max: 200 - Emission - lowMin: 0.0 lowMax: 0.0 highMin: 250.0 highMax: 250.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Life - lowMin: 500.0 lowMax: 500.0 highMin: 500.0 highMax: 500.0 relative: false scalingCount: 3 scaling0: 1.0 scaling1: 0.47058824 scaling2: 0.0 timelineCount: 3 timeline0: 0.0 timeline1: 0.51369864 timeline2: 1.0 - Life Offset - active: false - X Offset - active: false - Y Offset - active: false - Spawn Shape - shape: point - Spawn Width - lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Spawn Height - lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Scale - lowMin: 0.0 lowMax: 0.0 highMin: 70.0 highMax: 70.0 relative: true scalingCount: 2 scaling0: 1.0 scaling1: 0.0 timelineCount: 2 timeline0: 0.0 timeline1: 1.0 - Velocity - active: true lowMin: 0.0 lowMax: 0.0 highMin: 30.0 highMax: 300.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Angle - active: true lowMin: 220.0 lowMax: 320.0 highMin: 220.0 highMax: 320.0 relative: false scalingCount: 2 scaling0: 0.0 scaling1: 0.98039216 timelineCount: 2 timeline0: 0.0 timeline1: 1.0 - Rotation - active: false - Wind - active: false - Gravity - active: true lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Tint - colorsCount: 3 colors0: 0.0 colors1: 0.7294118 colors2: 1.0 timelineCount: 1 timeline0: 0.0 - Transparency - lowMin: 0.0 lowMax: 0.0 highMin: 1.0 highMax: 1.0 relative: false scalingCount: 4 scaling0: 1.0 scaling1: 0.0 scaling2: 0.0 scaling3: 1.0 timelineCount: 4 timeline0: 0.0 timeline1: 0.001 timeline2: 0.5753425 timeline3: 0.79452056 - Options - attached: false continuous: true aligned: false additive: true behind: false premultipliedAlpha: false pre_particle.png For the "- Image Path -" missing it's normal if I let them in it doesn't work even with only 1 emitter PS : I've already updated my lib to the last release

    Read the article

  • Screen capture during testing

    - by Edwward
    This is an application for reviewing performance tests. Simple in concept, tricky to describe. Picture: 1) Recording interactions with a WPF program so the inputs can be played back. 2) Playing the inputs back while doing a continuous screen capture. 3) Capturing wall time as well as continuous CPU percentages during playback. 4) Repeating steps (2) and (3) lots of times. 5) Writing the relevant stuff out to files/db. 6) Reading it and putting it all in a fancy UI for easy review/analysis. The killer for me is (2). I could use some guidance on a good, possibly commercial, screen capture SDK. I would also welcome the news that my whole problem already has a solution. And of course any thoughts on the overall idea would also be great. Thanks. Ed

    Read the article

  • API For Flex Apps To Interact

    - by dimo414
    I have a large flex application (the app) running on one server, and many small flex applications (widgets) running on another server, which are to be included in the app so that visually the user see's one continuous application. Due to proprietary third party software, this structure cannot be changed. I am looking for some way to allow the app and the widgets to communicate, allowing the app to make changes to the widgets and the the widgets to notify the app when events are triggered, so that user interaction is fluid and continuous. There are a few related questions which indicate it's possible to do this by setting up event triggers and listeners. I am wondering if there is any standardized way to do this (the answers aren't very clear) or if anyone has developed a library or API to make this easier.

    Read the article

  • Efficiency: what block size of kernel-mode memory allocations?

    - by Robert
    I need a big, driver-internal memory buffer with several tens of megabytes (non-paged, since accessed at dispatcher level). Since I think that allocating chunks of non-continuous memory will more likely succeed than allocating one single continuous memory block (especially when memory becomes fragmented) I want to implement that memory buffer as a linked list of memory blocks. What size should the blocks have to efficiently load the memory pages? (read: not to waste any page space) A multiple of 4096? (equally to the page size of the OS) A multiple of 4000? (not to waste another page for OS-internal memory allocation information) Another size? Target platform is Windows NT = 5.1 (XP and above) Target architectures are x86 and amd64 (not Itanium)

    Read the article

  • 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.

    Read the article

  • JD Edwards Delivers Once Again with Significant Announcements

    Listen to Lenley Hensarling, JD Edwards Group Vice President,talk about the significant JD Edwards announcements made during Oracle OpenWorld 2008.Lenley will highlight how JD Edwards’ customers can benefit from the latest product releases from EnterpriseOne and World,discuss the wave of companies who are upgrading to the most recent JD Edwards releases to take advantage of an array of industry specific enhancements,and elaborate on JD Edwards’ strategy about integrating to other Oracle solutions,bringing continuous value to customers.

    Read the article

  • Search Engine Ranking Competition

    Search engine ranking competition just got tougher. With individuals and businesses pooling a team of SEO experts to update their website, SEO software, working on intensive keyword research, as well as tapping into social media marketing, continuous marketing is necessary to improve and maintain search engine ranking competitiveness.

    Read the article

  • java development of products and automation development

    - by momo
    I'm a java developer working on j2ee development, on real products (not inhouse tools). I found another job to work on development of test automation frameworks / continuous integration. is development of test automation frameworks will affect my skill set ?is it considered to be less reputed and less needed? (the reason im confused is that the new role salary is higher).. do you think I should give up this offer and continue seeking a development role within the domain technolgies (java / j2ee) ?

    Read the article

< Previous Page | 13 14 15 16 17 18 19 20 21 22 23 24  | Next Page >