Warning 461 ‘OxyPlot.Axes.LinearAxis.LinearAxis(OxyPlot.Axes.AxisPosition, double, double, string)’ is obsolete
If you try to use OxyPlot.Axes.LinearAxis‘ constructor with parameters, the compiler will complain telling you the method has been deprecated and shouldn’t be used. I couldn’t find on the web which was the alternative solution to resolve this issue, but then it occurred to me that, what really is being deprecated, is the passage of arguments through the constructor’s parameters.
As such, the solution is simply to rewrite your code and call the axis’ default constructor instead, using C# object initialization syntax to configure your object:
Hope it can be of help if you were getting those warnings like me.
The Accord.NET Framework is not only an image processing and computer vision framework, but also a machine learning framework for .NET. One of its features is to encompass the exact same algorithms that can be found in other libraries, such as LIBLINEAR, but offer them in .NET withing a common interface ready to be incorporated in your application.
Principal Component Analysis (PCA) is a technique for exploratory data analysis with many success applications in several research fields. It is often used in image processing, data analysis, data pre-processing, visualization and is often used as one of the most basic building steps in many complex algorithms.
One of the most popular resources for learning about PCA is the excellent tutorial due to Lindsay I Smith. On her tutorial, Lindsay gives an example application for PCA, presenting and discussing the steps involved in the analysis.
This said, the above technical report aims to show, discuss and otherwise present the reader to the Principal Component Analysis while also reproducing all Lindsay’s example calculations using the Accord.NET Framework. The report comes with complete source C# code listings and also has a companion Visual Studio solution file containing all sample source codes ready to be tinkered inside a debug application.
While the text leaves out the more detailed discussions about the exposed concepts to Lindsay and does not addresses them in full details, it presents some practical examples to reproduce most of the calculations given by Lindsay on her tutorial using solely Accord.NET.
If you like a practical example on how to perform matrix operations in C#, this tutorial may help getting you started.
After a preliminary article on hidden Markov models, some months ago I had finally posted the article on Hidden Conditional Random Fields (HCRF) on CodeProject. The HCRF is a discriminative model, forming the generative-discriminative pair with the hidden Markov model classifers.
This CodeProject article is a second on a series of articles about sequence classification, the first being about Hidden Markov Models. I’ve used this opportunity to write a little about generative versus discriminative models, and also provide a brief discussion on how Vapnik’s ideas apply to these learning paradigms.
All the code available on those articles are also available within the Accord.NET Framework. Those articles provide good examples on how to use the framework and can be regarded as a practical implementation on how to use those models with the framework.
So a few days ago I bought a Windows Phone device. So far, I am impressed with the Windows Phone ecosystem. The nicest thing is that I was finally able to test an app I’ve been eager to try for months: Point and Call.
The app author, Antti Savolainen, was kind enough to share some details about his app. It uses part of the SVMs framelet from Accord.NET to do the digit recognition, mostly based on one of the earlier CodeProject articles I’ve posted in the past. Needless to say, Antti did an awesome job, as the SVM part was surely just a tiny fraction of all the work in preprocessing, adjusting, locating, and doing the right things at the right times that I would never be able to figure out alone. Surely, he and his company, Sadiga, deserves all the credits for this neat app!
If you would like find more interesting uses of the Accord.NET Framework, don’t forget to check the framework’s publication page for details!
In the meantime, this article is also serving as a hook to a future article, an article about Hidden Conditional Random Fields (HCRFs). The HCRF models can serve the same purpose as the HMMs but can be generalized to arbitrary graph structures and be trained discriminatively, which could be an advantage on classification tasks.
As always, I hope readers can find it a good read 🙂
Message=External component has thrown an exception.
when trying to create a SlimDX.Direct3D9.Direct3D object, most likely you will be able to solve this by placing D3DX9_43.dll into your output folder. Please note that the proper solution would be to install the full DirectX 9 runtime into your system, but it may be too much for some quick testing.
A similar error may occur when using SharpDX, but in SharpDX’s case, the error message will be much more helpful and actually tell you what is missing.
I’ve recently started to record videos to demonstrate some capabilities of the Accord.NET Framework. Surprisingly, there were only a few, free, opensource applications to achieve this goal – and none of them had all the features I needed.
It is, until I decided to roll my own.
Screencast Capture Lite is a tool for recording the desktop screen and saving it to a video file, preserving quality as much as possible. However, this does not mean it produces gigantic files which take a long time to be uploaded to the web. The application encodes everything using solely H624 in an almost lossless setting.
As a demonstration, please take a look on the Youtube video sample shown below. However, note that Youtube actually reduced the quality of the video, even if you watch it in HD. The local copy produced by Screencast Capture has an even higher quality than what is being shown, while the generated video file occupied less than 2 megabytes on disk.
And by the way what would be a better approach to demonstrate the capabilities of the Accord.NET frameworks other than writing this application using them?
Well, actually this application has been created specifically for two things:
With the beginning of this year, I would like to share a video I wish I had found earlier. It is about the recent breakthrough given by Deep Neural Networks in the field of speech recognition – which, despite I had known was a breakthrough, I didn’t know it was already leading to such surprising great results.
Deep neural networks are also available in the Accord.NET Framework. However, they’ve been a very recent addition – if you find any issues, bugs, or just wish to collaborate on development, please let me know!