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framework
Machine learning, computer vision, statistics and general scientific computing for .NET
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Accord.NET Framework 3.4.0
Build 3.4.0.5853, released on 14.01.2017
Accord.NET Framework 3.4.0 release notes
14.01.2017.
Version updates and fixes:
- GH-19: Implement Grubbs' test;
- GH-129: Possible error in Special.BSpline function;
- GH-153: Visual Studio 2015;
- GH-172: Add Random Forest Implementation;
- GH-177: AugmentedLagrangian with NonlinearConstraints - Gradient NullReferenceException issue;
- GH-183: Severity Check in NumberOfVertices Set Property on DiscreteCurveEvolution Class;
- GH-229: Can't build cloned repository;
- GH-250: Prediction interval - Accord.Statistics.Models.Regression.LogisticRegression;
- GH-264: Integer division instead of double in GetSpectralResolution;
- GH-264: Incorrect use of loop variables in sample converter;
- GH-264: Checking same arguments multiple times in blob counter;
- GH-264: Checking length of same vector in a loop;
- GH-264: Integer division instead of double in Math.Tools;
- GH-264: Dependency classes of Denavit Harternberg IK solver should be marked as Serializable;
GH-264: Error when checking whether component mixtures implement IFormattable;GH-264: Multivariate Empirical Distribution outdated/unecessary argument checks;GH-264: Correcting the support for weighted samples in Inverse Gaussian Distribution;GH-275: Examples for the GoldfarbIdnani solver are not up to date and do not compile;GH-291: Accord.Imaging nuget dependencies;GH-295: Accord.Video.FFMPEG.VideoFileWriter ignores bitrate;GH-296: Update documentation for hidden Markov models;GH-299: Update to .NET 4.6 and VS2015;GH-302: Regression (SVMs) : NullReferenceException on clicking 'Create Machine';GH-309: Compile error with release 3.2.2;GH-310: Examples for L1-regularized (Logistic) regression;GH-313: Inaccuracy in Accord.Math Pseudoinverse;GH-314: V3.3.0 Cannot set input and output names in LogisticRegressionAnalysis;GH-320: Shared Covariance Matrix for Gaussian Mixture Models;GH-325: ClusterCollection doesn't implement IEnumerable properly (runtime error);GH-327: NegativeBinomialDistribution Cum Dist func not returning expected value;GH-301: Bug in Accord.Statistics.Analysis.DistributionAnalysisGH-304: Bug in GammaDistribution.ProbabilityDensityFunctionGH-330: Liblinear (Linear SVMs) does not train, exits with "index out of range";GH-331: RandomForest is not serializable;GH-332: Partial Least Squares issue with NIPALS method and the new API;GH-337: ExpectationMaximization max Iterations can't be changed;GH-340: PoissonDistribution InverseDistributionFunction not returning expected value;GH-365: Can HOG to work with BoW'2 with SVM or OSVM.General
- Fixing make install on Linux/Mono.
Imaging
- Updating BagOfVisualWords to implement the updated IBagOfWords interface;
- Adding methods to facilitate the creation of BoVW with arbitrary extractors;
- Adding examples in the documentation on how to learn SVMs on the extracted Bo(V)Ws;
- Updating IFeatureDetector interfaces to use covariance and contravariance to avoid element-by-element type conversions.
Math
- Adding support for computing the full QR decomposition (besides only the economy one);
- Adding methods to compute the null-space of a given matrix.
MachineLearning
- Updating the IBagOfWords interface and implementing classes to implement the IUnsupervisedLearning and ITransform interfaces;
- Updating ZeroOneLoss to handle class labels in the -1/+1 format;
- Updating the kernel cache to pre-compute the entire kernel matrix by default.
Statistics
- Adding random generators for the von-Mises Fisher distribution;
- Updating documentation examples for Hidden Markov Models, Hidden Markov Classifiers and their respective algorithms;
- Adding a new GammaOptions class to pass fitting options to Gamma distributions;
- Updating DistributionAnalysis to use the new machine learning interfaces/API;
- Updating code and documentation for Dynamic Time warping kernel;
- Updating Gamma distribution so probabilities are computed in the log-domain by default;
- Marking Moving and Running statistics as ISerializable;
- Adding methods to compute the marginals in multivariate discrete distributions;
- Adding RunningRangeStatistics and MovingRangeStatistics.