Bayesian estimators
-
uint32_t riscv_gaussian_naive_bayes_predict_f16(const riscv_gaussian_naive_bayes_instance_f16 *S, const float16_t *in, float16_t *pOutputProbabilities, float16_t *pBufferB)
-
uint32_t riscv_gaussian_naive_bayes_predict_f32(const riscv_gaussian_naive_bayes_instance_f32 *S, const float32_t *in, float32_t *pOutputProbabilities, float32_t *pBufferB)
- group groupBayes
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
Functions
-
uint32_t riscv_gaussian_naive_bayes_predict_f16(const riscv_gaussian_naive_bayes_instance_f16 *S, const float16_t *in, float16_t *pOutputProbabilities, float16_t *pBufferB)
Naive Gaussian Bayesian Estimator.
- Parameters
*S – [in] points to a naive bayes instance structure
*in – [in] points to the elements of the input vector.
*pOutputProbabilities – [out] points to a buffer of length numberOfClasses containing estimated probabilities
*pBufferB – [out] points to a temporary buffer of length numberOfClasses
- Returns
The predicted class
-
uint32_t riscv_gaussian_naive_bayes_predict_f32(const riscv_gaussian_naive_bayes_instance_f32 *S, const float32_t *in, float32_t *pOutputProbabilities, float32_t *pBufferB)
Naive Gaussian Bayesian Estimator.
- Parameters
*S – [in] points to a naive bayes instance structure
*in – [in] points to the elements of the input vector.
*pOutputProbabilities – [out] points to a buffer of length numberOfClasses containing estimated probabilities
*pBufferB – [out] points to a temporary buffer of length numberOfClasses
- Returns
The predicted class
-
uint32_t riscv_gaussian_naive_bayes_predict_f16(const riscv_gaussian_naive_bayes_instance_f16 *S, const float16_t *in, float16_t *pOutputProbabilities, float16_t *pBufferB)