![]() ![]() Most of these are also developed as open source projects and they all can be downloaded from the internet. Whether you need these non-commercial external toolboxes depends on the data formats that you will use and whether you want to use their specific functionality. Using these external toolboxes allows us to focus on specifically improving FieldTrip and to join forces with other open source software projects. The following functions depend on the MathWorks Statistics and Machine Learning Toolbox (stats)īesides the non-free MathWorks toolboxes that are used by some functions, FieldTrip also makes use of other free toolboxes for certain functionality, such as access to specific file formats. The following functions depend on the MathWorks Signal Processing Toolbox (signal) ![]() The following functions depend on the MathWorks Optimization Toolbox (optim) The following functions depend on the MathWorks Image Processing Toolbox (images) We try to avoid using these additional MathWorks toolboxes to the extent that we will look for alternatives (e.g., from GNU Octave) or use drop-in replacement functions for certain functions, as long as the time required to implement these alternatives is not too large. Whether you need to buy these toolboxes depends on whether you want to use specific functionality in FieldTrip. MATLAB includes a large number of functions in standard toolboxes that come with every installation, but certain functions are included in additional (commercial) toolboxes from MathWorks, such as the Signal Processing or the Statistics toolbox. In general we attempt to support MATLAB versions up to 5 years old.Īn online poll in April 2011 showed that a large proportion (>98%) of our users access the FieldTrip toolbox with a MATLAB version younger than 2006 (<5 years). Consequently, sometimes we will use MATLAB code that is only supported from a certain version upwards. However, the MATLAB syntax and the availability of functions in the standard MathWorks toolboxes changes over time. We try to develop FieldTrip in such a way that it works with the latest MATLAB release on the most popular operating system platforms, but at the same time we try to have it work with as many older MATLAB versions as possible. Many toolbox algorithms can be used on data sets that are too big to be stored in memory.Faq matlab toolbox What are the MATLAB and external requirements? MATLAB version Native Simulink blocks let you use predictive models with simulations and Model-Based design. You can apply interpretability techniques such as partial dependence plots, Shapley values and LIME, and automatically generate C/C++ code for embedded deployment. The toolbox provides supervised, semi-supervised, and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted decision trees, shallow neural nets, k-means, and other clustering methods. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.įor multidimensional data analysis and feature extraction, the toolbox provides principal component analysis (PCA), regularization, dimensionality reduction, and feature selection methods that let you identify variables with the best predictive power. ![]() ![]() You can use descriptive statistics, visualizations, and clustering for exploratory data analysis fit probability distributions to data generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Statistics and Machine Learning Toolbox provides functions and apps to describe, analyze, and model data. ![]()
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