Bayesian estimators

RISCV_DSP_ATTRIBUTE 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)
RISCV_DSP_ATTRIBUTE 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

RISCV_DSP_ATTRIBUTE 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

RISCV_DSP_ATTRIBUTE 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