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
342 lines
9.8 KiB
C
342 lines
9.8 KiB
C
/******************************************************************************
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* @file distance_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 _DISTANCE_FUNCTIONS_H_
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#define _DISTANCE_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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#include "dsp/basic_math_functions.h"
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#include "dsp/fast_math_functions.h"
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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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* @defgroup groupDistance Distance functions
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*
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* Distance functions for use with clustering algorithms.
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* There are distance functions for float vectors and boolean vectors.
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*
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*/
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/* 6.14 bug */
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#if defined (__ARMCC_VERSION) && (__ARMCC_VERSION >= 6100100) && (__ARMCC_VERSION < 6150001)
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__attribute__((weak)) float __powisf2(float a, int b);
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#endif
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/**
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* @brief Euclidean distance between two vectors
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float32_t arm_euclidean_distance_f32(const float32_t *pA,const float32_t *pB, uint32_t blockSize);
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/**
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* @brief Euclidean distance between two vectors
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float64_t arm_euclidean_distance_f64(const float64_t *pA,const float64_t *pB, uint32_t blockSize);
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/**
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* @brief Bray-Curtis distance between two vectors
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float32_t arm_braycurtis_distance_f32(const float32_t *pA,const float32_t *pB, uint32_t blockSize);
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/**
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* @brief Canberra distance between two vectors
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*
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* This function may divide by zero when samples pA[i] and pB[i] are both zero.
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* The result of the computation will be correct. So the division per zero may be
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* ignored.
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*
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float32_t arm_canberra_distance_f32(const float32_t *pA,const float32_t *pB, uint32_t blockSize);
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/**
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* @brief Chebyshev distance between two vectors
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float32_t arm_chebyshev_distance_f32(const float32_t *pA,const float32_t *pB, uint32_t blockSize);
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/**
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* @brief Chebyshev distance between two vectors
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float64_t arm_chebyshev_distance_f64(const float64_t *pA,const float64_t *pB, uint32_t blockSize);
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/**
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* @brief Cityblock (Manhattan) distance between two vectors
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float32_t arm_cityblock_distance_f32(const float32_t *pA,const float32_t *pB, uint32_t blockSize);
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/**
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* @brief Cityblock (Manhattan) distance between two vectors
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float64_t arm_cityblock_distance_f64(const float64_t *pA,const float64_t *pB, uint32_t blockSize);
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/**
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* @brief Correlation distance between two vectors
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*
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* The input vectors are modified in place !
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*
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float32_t arm_correlation_distance_f32(float32_t *pA,float32_t *pB, uint32_t blockSize);
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/**
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* @brief Cosine distance between two vectors
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*
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float32_t arm_cosine_distance_f32(const float32_t *pA,const float32_t *pB, uint32_t blockSize);
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/**
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* @brief Cosine distance between two vectors
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*
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float64_t arm_cosine_distance_f64(const float64_t *pA,const float64_t *pB, uint32_t blockSize);
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/**
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* @brief Jensen-Shannon distance between two vectors
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*
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* This function is assuming that elements of second vector are > 0
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* and 0 only when the corresponding element of first vector is 0.
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* Otherwise the result of the computation does not make sense
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* and for speed reasons, the cases returning NaN or Infinity are not
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* managed.
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*
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* When the function is computing x log (x / y) with x 0 and y 0,
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* it will compute the right value (0) but a division per zero will occur
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* and shoudl be ignored in client code.
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*
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float32_t arm_jensenshannon_distance_f32(const float32_t *pA,const float32_t *pB,uint32_t blockSize);
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/**
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* @brief Minkowski distance between two vectors
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*
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* @param[in] pA First vector
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* @param[in] pB Second vector
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* @param[in] n Norm order (>= 2)
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* @param[in] blockSize vector length
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* @return distance
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*
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*/
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float32_t arm_minkowski_distance_f32(const float32_t *pA,const float32_t *pB, int32_t order, uint32_t blockSize);
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/**
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* @brief Dice distance between two vectors
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*
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* @param[in] pA First vector of packed booleans
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* @param[in] pB Second vector of packed booleans
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* @param[in] order Distance order
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* @param[in] blockSize Number of samples
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* @return distance
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*
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*/
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float32_t arm_dice_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools);
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/**
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* @brief Hamming distance between two vectors
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*
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* @param[in] pA First vector of packed booleans
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* @param[in] pB Second vector of packed booleans
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* @param[in] numberOfBools Number of booleans
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* @return distance
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*
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*/
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float32_t arm_hamming_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools);
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/**
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* @brief Jaccard distance between two vectors
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*
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* @param[in] pA First vector of packed booleans
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* @param[in] pB Second vector of packed booleans
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* @param[in] numberOfBools Number of booleans
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* @return distance
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*
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*/
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float32_t arm_jaccard_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools);
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/**
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* @brief Kulsinski distance between two vectors
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*
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* @param[in] pA First vector of packed booleans
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* @param[in] pB Second vector of packed booleans
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* @param[in] numberOfBools Number of booleans
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* @return distance
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*
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*/
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float32_t arm_kulsinski_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools);
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/**
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* @brief Roger Stanimoto distance between two vectors
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*
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* @param[in] pA First vector of packed booleans
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* @param[in] pB Second vector of packed booleans
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* @param[in] numberOfBools Number of booleans
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* @return distance
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*
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*/
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float32_t arm_rogerstanimoto_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools);
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/**
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* @brief Russell-Rao distance between two vectors
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*
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* @param[in] pA First vector of packed booleans
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* @param[in] pB Second vector of packed booleans
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* @param[in] numberOfBools Number of booleans
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* @return distance
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*
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*/
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float32_t arm_russellrao_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools);
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/**
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* @brief Sokal-Michener distance between two vectors
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*
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* @param[in] pA First vector of packed booleans
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* @param[in] pB Second vector of packed booleans
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* @param[in] numberOfBools Number of booleans
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* @return distance
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*
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*/
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float32_t arm_sokalmichener_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools);
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/**
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* @brief Sokal-Sneath distance between two vectors
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*
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* @param[in] pA First vector of packed booleans
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* @param[in] pB Second vector of packed booleans
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* @param[in] numberOfBools Number of booleans
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* @return distance
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*
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*/
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float32_t arm_sokalsneath_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools);
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/**
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* @brief Yule distance between two vectors
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*
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* @param[in] pA First vector of packed booleans
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* @param[in] pB Second vector of packed booleans
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* @param[in] numberOfBools Number of booleans
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* @return distance
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*
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*/
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float32_t arm_yule_distance(const uint32_t *pA, const uint32_t *pB, uint32_t numberOfBools);
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#ifdef __cplusplus
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}
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#endif
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#endif /* ifndef _DISTANCE_FUNCTIONS_H_ */
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