wangfq
95808f9f25
- 用 DLD154V4B vd1_task/per_channel 替换 vds_task 复杂算法
- 移除 FUNCTION_B/二次判断/快速变化/多重确认等增强特性
- 保留平坦性离开算法 (CN200910309382)，每通道独立状态
- 灵敏度表改为 DLD154V4B 4级: {216,108,36,10} / {108,72,18,9}
- 清理废弃类型: FltHistoryManager, Loop_ACS_Info, StageRangeConfig 等
- 首次添加 vd960DBN 完整源码
90 lines
2.8 KiB
C
90 lines
2.8 KiB
C
/******************************************************************************
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* @file bayes_functions.h
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* @brief Public header file for CMSIS DSP Library
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* @version V1.10.0
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* @date 08 July 2021
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* Target Processor: Cortex-M and Cortex-A cores
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******************************************************************************/
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/*
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* Copyright (c) 2010-2020 Arm Limited or its affiliates. All rights reserved.
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*
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the License); you may
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* not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an AS IS BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef _BAYES_FUNCTIONS_H_
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#define _BAYES_FUNCTIONS_H_
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#include "arm_math_types.h"
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#include "arm_math_memory.h"
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#include "dsp/none.h"
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#include "dsp/utils.h"
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#include "dsp/statistics_functions.h"
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/**
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* @defgroup groupBayes Bayesian estimators
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*
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* Implement the naive gaussian Bayes estimator.
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* The training must be done from scikit-learn.
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*
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* The parameters can be easily
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* generated from the scikit-learn object. Some examples are given in
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* DSP/Testing/PatternGeneration/Bayes.py
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*/
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#ifdef __cplusplus
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extern "C"
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{
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#endif
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/**
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* @brief Instance structure for Naive Gaussian Bayesian estimator.
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*/
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typedef struct
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{
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uint32_t vectorDimension; /**< Dimension of vector space */
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uint32_t numberOfClasses; /**< Number of different classes */
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const float32_t *theta; /**< Mean values for the Gaussians */
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const float32_t *sigma; /**< Variances for the Gaussians */
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const float32_t *classPriors; /**< Class prior probabilities */
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float32_t epsilon; /**< Additive value to variances */
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} arm_gaussian_naive_bayes_instance_f32;
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/**
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* @brief Naive Gaussian Bayesian Estimator
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*
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* @param[in] S points to a naive bayes instance structure
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* @param[in] in points to the elements of the input vector.
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* @param[out] *pOutputProbabilities points to a buffer of length numberOfClasses containing estimated probabilities
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* @param[out] *pBufferB points to a temporary buffer of length numberOfClasses
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* @return The predicted class
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*
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*/
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uint32_t arm_gaussian_naive_bayes_predict_f32(const arm_gaussian_naive_bayes_instance_f32 *S,
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const float32_t * in,
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float32_t *pOutputProbabilities,
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float32_t *pBufferB);
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#ifdef __cplusplus
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}
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#endif
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#endif /* ifndef _BAYES_FUNCTIONS_H_ */
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