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https://github.com/IcedRooibos/py32f0-template.git
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248 lines
5.9 KiB
C
248 lines
5.9 KiB
C
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/* ----------------------------------------------------------------------
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* Project: CMSIS DSP Library
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* Title: arm_jensenshannon_distance_f32.c
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* Description: Jensen-Shannon distance between two vectors
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*
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* $Date: 23 April 2021
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* $Revision: V1.9.0
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*
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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) 2010-2021 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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#include "dsp/distance_functions.h"
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#include <limits.h>
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#include <math.h>
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/**
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@addtogroup JensenShannon
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@{
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*/
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#if !defined(ARM_MATH_MVEF) || defined(ARM_MATH_AUTOVECTORIZE)
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/// @private
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__STATIC_INLINE float32_t rel_entr(float32_t x, float32_t y)
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{
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return (x * logf(x / y));
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}
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#endif
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#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE)
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#include "arm_helium_utils.h"
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#include "arm_vec_math.h"
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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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uint32_t blkCnt;
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float32_t tmp;
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f32x4_t a, b, t, tmpV, accumV;
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accumV = vdupq_n_f32(0.0f);
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blkCnt = blockSize >> 2;
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while (blkCnt > 0U) {
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a = vld1q(pA);
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b = vld1q(pB);
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t = vaddq(a, b);
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t = vmulq(t, 0.5f);
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tmpV = vmulq(a, vrecip_medprec_f32(t));
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tmpV = vlogq_f32(tmpV);
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accumV = vfmaq(accumV, a, tmpV);
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tmpV = vmulq_f32(b, vrecip_medprec_f32(t));
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tmpV = vlogq_f32(tmpV);
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accumV = vfmaq(accumV, b, tmpV);
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pA += 4;
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pB += 4;
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blkCnt--;
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}
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/*
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* tail
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* (will be merged thru tail predication)
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*/
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blkCnt = blockSize & 3;
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if (blkCnt > 0U) {
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mve_pred16_t p0 = vctp32q(blkCnt);
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a = vldrwq_z_f32(pA, p0);
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b = vldrwq_z_f32(pB, p0);
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t = vaddq(a, b);
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t = vmulq(t, 0.5f);
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tmpV = vmulq_f32(a, vrecip_medprec_f32(t));
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tmpV = vlogq_f32(tmpV);
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accumV = vfmaq_m_f32(accumV, a, tmpV, p0);
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tmpV = vmulq_f32(b, vrecip_medprec_f32(t));
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tmpV = vlogq_f32(tmpV);
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accumV = vfmaq_m_f32(accumV, b, tmpV, p0);
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}
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arm_sqrt_f32(vecAddAcrossF32Mve(accumV) / 2.0f, &tmp);
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return (tmp);
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}
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#else
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#if defined(ARM_MATH_NEON)
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#include "NEMath.h"
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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 result (0) but a division by zero will occur
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* and should 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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float32_t accum, result, tmp,a,b;
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uint32_t blkCnt;
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float32x4_t aV,bV,t, tmpV, accumV;
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float32x2_t accumV2;
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accum = 0.0f;
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accumV = vdupq_n_f32(0.0f);
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blkCnt = blockSize >> 2;
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while(blkCnt > 0)
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{
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aV = vld1q_f32(pA);
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bV = vld1q_f32(pB);
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t = vaddq_f32(aV,bV);
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t = vmulq_n_f32(t, 0.5f);
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tmpV = vmulq_f32(aV, vinvq_f32(t));
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tmpV = vlogq_f32(tmpV);
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accumV = vmlaq_f32(accumV, aV, tmpV);
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tmpV = vmulq_f32(bV, vinvq_f32(t));
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tmpV = vlogq_f32(tmpV);
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accumV = vmlaq_f32(accumV, bV, tmpV);
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pA += 4;
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pB += 4;
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blkCnt --;
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}
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accumV2 = vpadd_f32(vget_low_f32(accumV),vget_high_f32(accumV));
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accum = vget_lane_f32(accumV2, 0) + vget_lane_f32(accumV2, 1);
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blkCnt = blockSize & 3;
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while(blkCnt > 0)
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{
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a = *pA;
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b = *pB;
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tmp = (a + b) / 2.0f;
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accum += rel_entr(a, tmp);
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accum += rel_entr(b, tmp);
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pA++;
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pB++;
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blkCnt --;
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}
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arm_sqrt_f32(accum/2.0f, &result);
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return(result);
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}
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#else
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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 result (0) but a division by zero will occur
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* and should 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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float32_t left, right,sum, result, tmp;
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uint32_t i;
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left = 0.0f;
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right = 0.0f;
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for(i=0; i < blockSize; i++)
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{
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tmp = (pA[i] + pB[i]) / 2.0f;
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left += rel_entr(pA[i], tmp);
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right += rel_entr(pB[i], tmp);
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}
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sum = left + right;
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arm_sqrt_f32(sum/2.0f, &result);
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return(result);
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}
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#endif
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#endif /* defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) */
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/**
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* @} end of JensenShannon group
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*/
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