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/******************************************************************************
* @file bayes_functions.h
* @brief Public header file for CMSIS DSP Library
* @version V1.10.0
* @date 08 July 2021
* Target Processor: Cortex-M and Cortex-A cores
******************************************************************************/
/*
* Copyright (c) 2010-2020 Arm Limited or its affiliates. All rights reserved.
*
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the License); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef _BAYES_FUNCTIONS_H_
#define _BAYES_FUNCTIONS_H_
#include "arm_math_types.h"
#include "arm_math_memory.h"
#include "dsp/none.h"
#include "dsp/utils.h"
#include "dsp/statistics_functions.h"
/**
* @defgroup groupBayes Bayesian estimators
*
* 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
*/
#ifdef __cplusplus
extern "C"
{
#endif
/**
* @brief Instance structure for Naive Gaussian Bayesian estimator.
*/
typedef struct
{
uint32_t vectorDimension; /**< Dimension of vector space */
uint32_t numberOfClasses; /**< Number of different classes */
const float32_t *theta; /**< Mean values for the Gaussians */
const float32_t *sigma; /**< Variances for the Gaussians */
const float32_t *classPriors; /**< Class prior probabilities */
float32_t epsilon; /**< Additive value to variances */
} arm_gaussian_naive_bayes_instance_f32;
/**
* @brief Naive Gaussian Bayesian Estimator
*
* @param[in] S points to a naive bayes instance structure
* @param[in] in points to the elements of the input vector.
* @param[out] *pOutputProbabilities points to a buffer of length numberOfClasses containing estimated probabilities
* @param[out] *pBufferB points to a temporary buffer of length numberOfClasses
* @return 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);
#ifdef __cplusplus
}
#endif
#endif /* ifndef _BAYES_FUNCTIONS_H_ */
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