NMSIS-DSP  Version 1.2.0
NMSIS DSP Software Library
Here is a list of all modules:
[detail level 123]
 Basic Math Functions
 Bayesian estimatorsImplement the naive gaussian Bayes estimator. The training must be done from scikit-learn
 Complex Math FunctionsThis 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 functionsDistance functions for use with clustering algorithms. There are distance functions for float vectors and boolean vectors
 Fast Math FunctionsThis 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 FunctionsThese 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 FunctionsThis 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 FunctionsFunctions to operates on quaternions and convert between a rotation and quaternion representation
 Statistics Functions
 Support Functions
 SVM FunctionsThis 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