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

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]	*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

Returns

The predicted class

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]	*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

Returns

The predicted class