ACCORD.NET MACHINE LEARNING FRAMEWORKABOUTEXAMPLESPUBLICATIONSONLINE DOCSLICENSEACCORD.NEURO NAMESPACE Machine learning made in a minute. The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use.
INTRODUCTION
ACCORD.MATH NAMESPACE Gets the default matrix representation, where each row is separated by a new line, and columns are separated by spaces. SAMPLE GALLERY
Download the application; Browse the source code; The Wave Recorder sample application demonstrates how to use the IAudioOutput and IAudioSource interfaces to capture and output sound. This is just a sample application, however. The intent of the framework is not to allow building of audio players, but to support the use of audio signals in machine learning and statistics experiments. PUBLICATIONS
MATRIX CLASS
Static class Matrix. Defines a set of extension methods that operates mainly on multidimensional arrays and vectors. BINARYSPLIT CLASS
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. SUPPORTVECTORMACHINE CLASS Linear Support Vector Machine (SVM). // As an example, we will try to learn a linear machine that can // replicate the "exclusive-or" logical function. However, since we // will be using a linear SVM, we will not be able to solve this // problem perfectly as the XOR is a non-linear classification problem: double inputs = { new double { 0, 0}, // the XOR function takes two booleans new CSVWRITER CLASS
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. MATRIX.MESHGRID(T) METHOD Generates a 2-D mesh grid from two vectors a and b, generating two matrices len(a) x len(b) with all all possible combinations of values between the two vectors. This method is analogous to MATLAB/Octave's meshgrid function.
ACCORD.NET MACHINE LEARNING FRAMEWORKABOUTEXAMPLESPUBLICATIONSONLINE DOCSLICENSEACCORD.NEURO NAMESPACE Machine learning made in a minute. The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. INTRODUCTION
ACCORD.MATH NAMESPACE Gets the default matrix representation, where each row is separated by a new line, and columns are separated by spaces. SAMPLE GALLERY
Download the application; Browse the source code; The Wave Recorder sample application demonstrates how to use the IAudioOutput and IAudioSource interfaces to capture and output sound. This is just a sample application, however. The intent of the framework is not to allow building of audio players, but to support the use of audio signals in machine learning and statistics experiments. PUBLICATIONS
MATRIX CLASS
Static class Matrix. Defines a set of extension methods that operates mainly on multidimensional arrays and vectors. BINARYSPLIT CLASS
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. SUPPORTVECTORMACHINE CLASS Linear Support Vector Machine (SVM). // As an example, we will try to learn a linear machine that can // replicate the "exclusive-or" logical function. However, since we // will be using a linear SVM, we will not be able to solve this // problem perfectly as the XOR is a non-linear classification problem: double inputs = { new double { 0, 0}, // the XOR function takes two booleans new CSVWRITER CLASS
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. BINARYSPLIT CLASS
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. SUPPORTVECTORMACHINE CLASS Linear Support Vector Machine (SVM). // As an example, we will try to learn a linear machine that can // replicate the "exclusive-or" logical function. However, since we // will be using a linear SVM, we will not be able to solve this // problem perfectly as the XOR is a non-linear classification problem: double inputs = { new double { 0, 0}, // the XOR function takes two booleans new EIGENVALUEDECOMPOSITION CLASS In the mathematical discipline of linear algebra, eigendecomposition or sometimes spectral decomposition is the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors. ORDINARYLEASTSQUARES CLASS Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. CSVWRITER CLASS
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. PRINCIPALCOMPONENTANALYSIS CLASS A question often asked by users is "why my matrices have inverted signs" or "why my results differ from ". In short, despite any differences, the results are most likely correct (unless you firmly believe you have found a bug; in this case, please fill in a bug report).
NONLINEARREGRESSION CLASS Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. HISTOGRAM CLASS
In a more general mathematical sense, a histogram is a mapping Mi that counts the number of observations that fall into various disjoint categories (known as bins). NONNEGATIVELEASTSQUARES CLASS Non-negative Least Squares for optimization. The following example shows how to fit a multiple linear regression model with the additional constraint that none of its coefficients should be negative.
AUDIOOUTPUTDEVICE CLASS // To create an audio output device, DirectSound requires a handle to // the parent form of the application (or other application handle). In // Windows.Forms, this could be achieved by providing the Handle property // of the currently displayed form. int sampleRate = 22000; // 22kHz int channels = 2; // stereo // Create the audio output device with the desired values AudioOutputDevice output Toggle navigation Accord.NET Framework * About
* Examples
* Publications
* Support
* Sample apps
* Online docs
* GitHub Wiki
* License
*
* Contribute
* Report an issue
* Donate (Paypal)
* How to
* Get Started
*
* Mathematics
* Classification
* Regression
* Clustering
* Distributions
* Hypothesis Tests
* Kernel Methods
* Image
* Audio
* Vision
* Books
* F# for Machine Learning Essentials * Mastering .NET Machine Learning * Machine Learning Projects for .NET Developers * GitHub
v3.8
* Download INSTALLER * Download ARCHIVE
* Read the MANUAL
* Consult the .NET API * Fork On GITHUB
MACHINE LEARNING MADE IN A MINUTE The ACCORD.NET FRAMEWORK is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even FOR COMMERCIAL USE. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive documentation and wiki helps fill in the
details.
CLASSIFICATION
.
Support Vector Machines ,
Logistic Regression
,
Decision Trees
,
Neural Networks
, Deep
Learning (Deep Neural Networks) ,
Levenberg-Marquardt with Bayesian Regularization ,
Restricted Boltzmann Machines ,
Sequence classification ,
Hidden Markov Classifiers and Hidden Conditional Random Fields .
REGRESSION .
Multiple linear regression ,
Multivariate linear regression ,
polynomial regression ,
logarithmic regression. Logistic regression ,
multinomial logistic regression (softmax) and generalized linear models .
L2-regularized L2-loss logistic regression ,
L2-regularized logistic regression ,
L1-regularized logistic regression ,
L2-regularized logistic regression in the dual form and regression support vector machines .
CLUSTERING .
K-Means
,
K-Modes
,
Mean-Shift
,
Gaussian Mixture Models ,
Binary Split
,
Deep Belief Networks ,
Restricted Boltzmann Machines .
Clustering algorithms can be applied in arbitrary data, including images
,
data tables, videos and audio .
DISTRIBUTIONS
.
Parametric and non-parametric estimation of more than 40 distributions. Univariate distributions such as Normal ,
Cauchy
,
Hypergeometric
,
Poisson
,
Bernoulli
,
and specialized distributions such as the Kolmogorov-Smirnov ,
Nakagami
,
Weibull
,
and Von-Mises
distributions. Multivariate distributions such as the multivariate Normal ,
Multinomial
,
Independent
,
Joint
and Mixture distributions .
HYPOTHESIS TESTS
.
More than 35 statistical hypothesis tests ,
including one way
and two-way ANOVA tests ,
non-parametric tests such as the Kolmogorov-Smirnov test and the Sign Test for the Median ,
contingency table
tests such as the Kappa test ,
with variations for multiple tables ,
as well as the Bhapkar and Bowker
tests; and the more traditional Chi-Square ,
Z
,
F
,
T
and Wald tests
.
KERNEL METHODS
.
Kernel Support Vector Machines ,
Multi-class
and Multi-label machines ,
Sequential Minimal Optimization ,
Least-Squares Learning ,
probabilistic learning ,
including special methods for linear machines such as LIBLINEAR's methods for Linear Coordinate Descent ,
Linear Newton Method ,
Probabilistic Coordinate Descent ,
Probabilistic Coordinate Descent in the Dual ,
Probabilistic Newton Method for L1 and L2 machines in both the dual and primal formulations .
IMAGING .
Interest and feature point detectors such as Harris ,
FREAK
,
SURF
,
and FAST
.
Grey-level Co-occurrence matrices ,
Border following
,
Bag-of-Visual-Words (BoW) ,
RANSAC-based homography estimation ,
integral images
,
haralick textural feature extraction ,
and dense descriptors such as histogram of oriented gradients (HOG) and Local Binary Pattern (LBP) .
Several image filters for image processing applications such as difference of Gaussians ,
Gabor
,
Niblack
and Sauvola thresholding .
AUDIO AND SIGNAL
.
Load, parse, save, filter and transform audio signals ,
such as applying audio processing filters in
both space and frequency domain .
WAV files
,
audio capture
,
time-domain filters such as envelope ,
high-pass
,
low-pass
,
wave rectification
filters. Frequency-domain operators such as differential rectification filter
and comb filter with Dirac's delta functions .
Signal generators for Cosine ,
Impulse
,
Square
signals.
VISION .
Real-time face detection and tracking
,
as well as general methods for detecting ,
tracking
and transforming objects in image streams . Contains
cascade definitions
,
Camshift
and Dynamic Template Matching trackers .
Includes pre-created classifiers for human faces and some facial features such as noses .
GET STARTED NOW!
SAMPLE APPLICATIONS . Sample applications help you start writing your applications quickly. Just get one of the sample applications that is closest to your goal ,
and start from there. CREATE AND TEST NEW LEARNING ALGORITHMS WITH EASE .
Strategy and template method patterns help you swap learning algorithms quickly. Create, build and compare different approaches without delving too deep in code. Check a list of works that have been made possible with the framework . ASK YOUR QUESTIONS
.
Stackoverflow is continuously monitored for new questions containing the "Accord.NET" tag . Ask your new
question and mark it with this tag, and it will be answered in a minute by the framework authors and the user community. NOTE: If you would like to ask questions concerning the framework itself, such as questions on _"why"_ something was done in a particular way, suggestions and general discussions, DO NOT USE STACKOVERFLOW - please refer to the project's issue tracker and mark your issue with the "question" tag instead. For bugs and feature requests, please also use issue tracker . If the project has been useful for you, please consider helping us improve the wiki . Thanks!
* .NET FRAMEWORK
* Windows Phone
* Android
* iOS
* Java
*
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This software is completely free and will ALWAYS STAY FREE. Enjoy! Have you found this software useful? Consider donating just US$10 so it can get even better! This software is completely free and will ALWAYS STAY FREE . Enjoy! 
