Implement the naive gaussian Bayes estimator. The training must be done from scikit-learn. More...

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. More...

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. More...

Detailed 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

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

[in]	*S	points to a naive bayes instance structure
[in]	*in	points to the elements of the input vector.
[out]	*pOutputProbabilities	points to a buffer of length numberOfClasses containing estimated probabilities
[out]	*pBufferB	points to a temporary buffer of length numberOfClasses

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

[in]	*S	points to a naive bayes instance structure
[in]	*in	points to the elements of the input vector.
[out]	*pOutputProbabilities	points to a buffer of length numberOfClasses containing estimated probabilities
[out]	*pBufferB	points to a temporary buffer of length numberOfClasses