NMSIS-DSP  Version 1.2.0 NMSIS DSP Software Library
Modules
Here is a list of all modules:
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 ►Basic Math Functions Bayesian estimators Implement the naive gaussian Bayes estimator. The training must be done from scikit-learn ►Complex Math Functions This set of functions operates on complex data vectors. The data in the complex arrays is stored in an interleaved fashion (real, imag, real, imag, ...). In the API functions, the number of samples in a complex array refers to the number of complex values; the array contains twice this number of real values ►Controller Functions ►Distance functions Distance functions for use with clustering algorithms. There are distance functions for float vectors and boolean vectors ►Fast Math Functions This set of functions provides a fast approximation to sine, cosine, and square root. As compared to most of the other functions in the NMSIS math library, the fast math functions operate on individual values and not arrays. There are separate functions for Q15, Q31, and floating-point data ►Filtering Functions ►Interpolation Functions These functions perform 1- and 2-dimensional interpolation of data. Linear interpolation is used for 1-dimensional data and bilinear interpolation is used for 2-dimensional data ►Matrix Functions This set of functions provides basic matrix math operations. The functions operate on matrix data structures. For example, the type definition for the floating-point matrix structure is shown below: ►Quaternion Math Functions Functions to operates on quaternions and convert between a rotation and quaternion representation ►Statistics Functions ►Support Functions ►SVM Functions This set of functions is implementing SVM classification on 2 classes. The training must be done from scikit-learn. The parameters can be easily generated from the scikit-learn object. Some examples are given in DSP/Testing/PatternGeneration/SVM.py ►Transform Functions ►Window Functions ►Examples