CMSIS-DSP  
CMSIS DSP Software Library
Bayesian estimators

Functions

uint32_t arm_gaussian_naive_bayes_predict_f16 (const arm_gaussian_naive_bayes_instance_f16 *S, const float16_t *in, float16_t *pOutputProbabilities, float16_t *pBufferB)
 Naive Gaussian Bayesian Estimator. More...
 
uint32_t arm_gaussian_naive_bayes_predict_f32 (const arm_gaussian_naive_bayes_instance_f32 *S, const float32_t *in, float32_t *pOutputProbabilities, float32_t *pBufferB)
 Naive Gaussian Bayesian Estimator. More...
 

Description

Implement the naive gaussian Bayes estimator. 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/Bayes.py

Function Documentation

◆ arm_gaussian_naive_bayes_predict_f16()

uint32_t arm_gaussian_naive_bayes_predict_f16 ( const arm_gaussian_naive_bayes_instance_f16 S,
const float16_t *  in,
float16_t *  pOutputProbabilities,
float16_t *  pBufferB 
)
Parameters
[in]*Spoints to a naive bayes instance structure
[in]*inpoints to the elements of the input vector.
[out]*pOutputProbabilitiespoints to a buffer of length numberOfClasses containing estimated probabilities
[out]*pBufferBpoints to a temporary buffer of length numberOfClasses
Returns
The predicted class

◆ arm_gaussian_naive_bayes_predict_f32()

uint32_t arm_gaussian_naive_bayes_predict_f32 ( const arm_gaussian_naive_bayes_instance_f32 S,
const float32_t in,
float32_t pOutputProbabilities,
float32_t pBufferB 
)
Parameters
[in]*Spoints to a naive bayes instance structure
[in]*inpoints to the elements of the input vector.
[out]*pOutputProbabilitiespoints to a buffer of length numberOfClasses containing estimated probabilities
[out]*pBufferBpoints to a temporary buffer of length numberOfClasses
Returns
The predicted class