Overview What is a Support Vector Machine? Train an SVM classifier with scikit-learn Implement your SVM with CMSIS-DSP What is a Bayesian estimator? Train your Bayesian estimator with scikit-learn Implement your Bayesian estimator with CMSIS-DSP What is clustering? Use CMSIS-DSP distance functions Miscellaneous new CMSIS-DSP functions Related information Next steps
Use CMSIS-DSP distance functions
CMSIS-DSP provides most of the distance functions which are generally used In clustering algorithms. It includes distance functions for float but also for booleans.
All distance functions in the CMSIS-DSP have a similar API. Let's use the Manhattan distance, also known as the city block distance, as an example.
The API of the city block distance is described in the following code:
float32_t arm_cityblock_distance_f32(const float32_t *pA, const float32_t *pB, uint32_t blockSize);
This function computes the distance between two vectors, pA and pB, of the dimension blockSize.
The folder CMSIS/DSP/DistanceFunctions also contains functions which are not really distances from a mathematical point of view. For instance, the cosine distance is a measure of similarity.