mirror of
https://gitee.com/Lyon1998/pikapython.git
synced 2025-01-22 17:12:55 +08:00
90 lines
2.8 KiB
C
90 lines
2.8 KiB
C
/******************************************************************************
|
|
* @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_ */
|