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1470 changed files with 1054449 additions and 0 deletions
29
Drivers/CMSIS/DSP/Source/BayesFunctions/BayesFunctions.c
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29
Drivers/CMSIS/DSP/Source/BayesFunctions/BayesFunctions.c
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/* ----------------------------------------------------------------------
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* Project: CMSIS DSP Library
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||||
* Title: BayesFunctions.c
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* Description: Combination of all bayes function source files.
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*
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||||
* $Date: 16. March 2020
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||||
* $Revision: V1.0.0
|
||||
*
|
||||
* Target Processor: Cortex-M cores
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||||
* -------------------------------------------------------------------- */
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||||
/*
|
||||
* Copyright (C) 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.
|
||||
*/
|
||||
|
||||
#include "arm_gaussian_naive_bayes_predict_f32.c"
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27
Drivers/CMSIS/DSP/Source/BayesFunctions/BayesFunctionsF16.c
Normal file
27
Drivers/CMSIS/DSP/Source/BayesFunctions/BayesFunctionsF16.c
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|||
/* ----------------------------------------------------------------------
|
||||
* Project: CMSIS DSP Library
|
||||
* Title: BayesFunctions.c
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||||
* Description: Combination of all bayes function f16 source files.
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||||
*
|
||||
*
|
||||
* Target Processor: Cortex-M cores
|
||||
* -------------------------------------------------------------------- */
|
||||
/*
|
||||
* Copyright (C) 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.
|
||||
*/
|
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|
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#include "arm_gaussian_naive_bayes_predict_f16.c"
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23
Drivers/CMSIS/DSP/Source/BayesFunctions/CMakeLists.txt
Normal file
23
Drivers/CMSIS/DSP/Source/BayesFunctions/CMakeLists.txt
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|
@ -0,0 +1,23 @@
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|||
cmake_minimum_required (VERSION 3.14)
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|
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project(CMSISDSPBayes)
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include(configLib)
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include(configDsp)
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file(GLOB SRC "./*_*.c")
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add_library(CMSISDSPBayes STATIC)
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target_sources(CMSISDSPBayes PRIVATE arm_gaussian_naive_bayes_predict_f32.c)
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|
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configLib(CMSISDSPBayes ${ROOT})
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configDsp(CMSISDSPBayes ${ROOT})
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|
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### Includes
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target_include_directories(CMSISDSPBayes PUBLIC "${DSP}/Include")
|
||||
|
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if ((NOT ARMAC5) AND (NOT DISABLEFLOAT16))
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target_sources(CMSISDSPBayes PRIVATE arm_gaussian_naive_bayes_predict_f16.c)
|
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endif()
|
||||
|
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|
|
@ -0,0 +1,207 @@
|
|||
/* ----------------------------------------------------------------------
|
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* Project: CMSIS DSP Library
|
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* Title: arm_naive_gaussian_bayes_predict_f16
|
||||
* Description: Naive Gaussian Bayesian Estimator
|
||||
*
|
||||
* $Date: 23 April 2021
|
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* $Revision: V1.9.0
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||||
*
|
||||
* Target Processor: Cortex-M and Cortex-A cores
|
||||
* -------------------------------------------------------------------- */
|
||||
/*
|
||||
* Copyright (C) 2010-2021 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.
|
||||
*/
|
||||
|
||||
#include "dsp/bayes_functions_f16.h"
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|
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#if defined(ARM_FLOAT16_SUPPORTED)
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#include <limits.h>
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#include <math.h>
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||||
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||||
|
||||
/**
|
||||
* @addtogroup groupBayes
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* @{
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*/
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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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*/
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|
||||
#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE)
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#include "arm_helium_utils.h"
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#include "arm_vec_math_f16.h"
|
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|
||||
uint32_t arm_gaussian_naive_bayes_predict_f16(const arm_gaussian_naive_bayes_instance_f16 *S,
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const float16_t * in,
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float16_t *pOutputProbabilities,
|
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float16_t *pBufferB
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||||
)
|
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{
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uint32_t nbClass;
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const float16_t *pTheta = S->theta;
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const float16_t *pSigma = S->sigma;
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float16_t *buffer = pOutputProbabilities;
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const float16_t *pIn = in;
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float16_t result;
|
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f16x8_t vsigma;
|
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_Float16 tmp;
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f16x8_t vacc1, vacc2;
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uint32_t index;
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float16_t *logclassPriors=pBufferB;
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||||
float16_t *pLogPrior = logclassPriors;
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|
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arm_vlog_f16((float16_t *) S->classPriors, logclassPriors, S->numberOfClasses);
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|
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pTheta = S->theta;
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pSigma = S->sigma;
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|
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for (nbClass = 0; nbClass < S->numberOfClasses; nbClass++) {
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pIn = in;
|
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|
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vacc1 = vdupq_n_f16(0.0f16);
|
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vacc2 = vdupq_n_f16(0.0f16);
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|
||||
uint32_t blkCnt =S->vectorDimension >> 3;
|
||||
while (blkCnt > 0U) {
|
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f16x8_t vinvSigma, vtmp;
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|
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vsigma = vaddq_n_f16(vld1q(pSigma), S->epsilon);
|
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vacc1 = vaddq(vacc1, vlogq_f16(vmulq_n_f16(vsigma, 2.0f16 * (_Float16)PI)));
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|
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vinvSigma = vrecip_medprec_f16(vsigma);
|
||||
|
||||
vtmp = vsubq(vld1q(pIn), vld1q(pTheta));
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/* squaring */
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||||
vtmp = vmulq(vtmp, vtmp);
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vacc2 = vfmaq(vacc2, vtmp, vinvSigma);
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||||
|
||||
pIn += 8;
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pTheta += 8;
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pSigma += 8;
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blkCnt--;
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||||
}
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||||
|
||||
blkCnt = S->vectorDimension & 7;
|
||||
if (blkCnt > 0U) {
|
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mve_pred16_t p0 = vctp16q(blkCnt);
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f16x8_t vinvSigma, vtmp;
|
||||
|
||||
vsigma = vaddq_n_f16(vld1q(pSigma), S->epsilon);
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vacc1 =
|
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vaddq_m_f16(vacc1, vacc1, vlogq_f16(vmulq_n_f16(vsigma, 2.0f16 * (_Float16)PI)), p0);
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|
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vinvSigma = vrecip_medprec_f16(vsigma);
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vtmp = vsubq(vld1q(pIn), vld1q(pTheta));
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/* squaring */
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vtmp = vmulq(vtmp, vtmp);
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vacc2 = vfmaq_m_f16(vacc2, vtmp, vinvSigma, p0);
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||||
pTheta += blkCnt;
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pSigma += blkCnt;
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}
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tmp = -0.5f16 * (_Float16)vecAddAcrossF16Mve(vacc1);
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tmp -= 0.5f16 * (_Float16)vecAddAcrossF16Mve(vacc2);
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*buffer = (_Float16)tmp + (_Float16)*pLogPrior++;
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buffer++;
|
||||
}
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arm_max_f16(pOutputProbabilities, S->numberOfClasses, &result, &index);
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return (index);
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}
|
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#else
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|
||||
uint32_t arm_gaussian_naive_bayes_predict_f16(const arm_gaussian_naive_bayes_instance_f16 *S,
|
||||
const float16_t * in,
|
||||
float16_t *pOutputProbabilities,
|
||||
float16_t *pBufferB)
|
||||
{
|
||||
uint32_t nbClass;
|
||||
uint32_t nbDim;
|
||||
const float16_t *pPrior = S->classPriors;
|
||||
const float16_t *pTheta = S->theta;
|
||||
const float16_t *pSigma = S->sigma;
|
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float16_t *buffer = pOutputProbabilities;
|
||||
const float16_t *pIn=in;
|
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float16_t result;
|
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_Float16 sigma;
|
||||
_Float16 tmp;
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_Float16 acc1,acc2;
|
||||
uint32_t index;
|
||||
(void)pBufferB;
|
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|
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pTheta=S->theta;
|
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pSigma=S->sigma;
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|
||||
for(nbClass = 0; nbClass < S->numberOfClasses; nbClass++)
|
||||
{
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||||
|
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pIn = in;
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|
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tmp = 0.0f16;
|
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acc1 = 0.0f16;
|
||||
acc2 = 0.0f16;
|
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for(nbDim = 0; nbDim < S->vectorDimension; nbDim++)
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{
|
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sigma = (_Float16)*pSigma + (_Float16)S->epsilon;
|
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acc1 += (_Float16)logf(2.0f * PI * (float32_t)sigma);
|
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acc2 += ((_Float16)*pIn - (_Float16)*pTheta) * ((_Float16)*pIn - (_Float16)*pTheta) / (_Float16)sigma;
|
||||
|
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pIn++;
|
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pTheta++;
|
||||
pSigma++;
|
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}
|
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|
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tmp = -0.5f16 * (_Float16)acc1;
|
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tmp -= 0.5f16 * (_Float16)acc2;
|
||||
|
||||
|
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*buffer = (_Float16)tmp + (_Float16)logf((float32_t)*pPrior++);
|
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buffer++;
|
||||
}
|
||||
|
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arm_max_f16(pOutputProbabilities,S->numberOfClasses,&result,&index);
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return(index);
|
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}
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|
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#endif /* defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) */
|
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|
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/**
|
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* @} end of groupBayes group
|
||||
*/
|
||||
|
||||
#endif /* #if defined(ARM_FLOAT16_SUPPORTED) */
|
||||
|
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|
|
@ -0,0 +1,396 @@
|
|||
/* ----------------------------------------------------------------------
|
||||
* Project: CMSIS DSP Library
|
||||
* Title: arm_naive_gaussian_bayes_predict_f32
|
||||
* Description: Naive Gaussian Bayesian Estimator
|
||||
*
|
||||
* $Date: 23 April 2021
|
||||
* $Revision: V1.9.0
|
||||
*
|
||||
* Target Processor: Cortex-M and Cortex-A cores
|
||||
* -------------------------------------------------------------------- */
|
||||
/*
|
||||
* Copyright (C) 2010-2021 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.
|
||||
*/
|
||||
|
||||
#include "dsp/bayes_functions.h"
|
||||
#include <limits.h>
|
||||
#include <math.h>
|
||||
|
||||
#define PI_F 3.1415926535897932384626433832795f
|
||||
#define DPI_F (2.0f*3.1415926535897932384626433832795f)
|
||||
|
||||
/**
|
||||
* @addtogroup groupBayes
|
||||
* @{
|
||||
*/
|
||||
|
||||
/**
|
||||
* @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
|
||||
*
|
||||
*
|
||||
*/
|
||||
|
||||
#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE)
|
||||
|
||||
#include "arm_helium_utils.h"
|
||||
#include "arm_vec_math.h"
|
||||
|
||||
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
|
||||
)
|
||||
{
|
||||
uint32_t nbClass;
|
||||
const float32_t *pTheta = S->theta;
|
||||
const float32_t *pSigma = S->sigma;
|
||||
float32_t *buffer = pOutputProbabilities;
|
||||
const float32_t *pIn = in;
|
||||
float32_t result;
|
||||
f32x4_t vsigma;
|
||||
float32_t tmp;
|
||||
f32x4_t vacc1, vacc2;
|
||||
uint32_t index;
|
||||
float32_t *logclassPriors=pBufferB;
|
||||
float32_t *pLogPrior = logclassPriors;
|
||||
|
||||
arm_vlog_f32((float32_t *) S->classPriors, logclassPriors, S->numberOfClasses);
|
||||
|
||||
pTheta = S->theta;
|
||||
pSigma = S->sigma;
|
||||
|
||||
for (nbClass = 0; nbClass < S->numberOfClasses; nbClass++) {
|
||||
pIn = in;
|
||||
|
||||
vacc1 = vdupq_n_f32(0);
|
||||
vacc2 = vdupq_n_f32(0);
|
||||
|
||||
uint32_t blkCnt =S->vectorDimension >> 2;
|
||||
while (blkCnt > 0U) {
|
||||
f32x4_t vinvSigma, vtmp;
|
||||
|
||||
vsigma = vaddq_n_f32(vld1q(pSigma), S->epsilon);
|
||||
vacc1 = vaddq(vacc1, vlogq_f32(vmulq_n_f32(vsigma, 2.0f * PI)));
|
||||
|
||||
vinvSigma = vrecip_medprec_f32(vsigma);
|
||||
|
||||
vtmp = vsubq(vld1q(pIn), vld1q(pTheta));
|
||||
/* squaring */
|
||||
vtmp = vmulq(vtmp, vtmp);
|
||||
|
||||
vacc2 = vfmaq(vacc2, vtmp, vinvSigma);
|
||||
|
||||
pIn += 4;
|
||||
pTheta += 4;
|
||||
pSigma += 4;
|
||||
blkCnt--;
|
||||
}
|
||||
|
||||
blkCnt = S->vectorDimension & 3;
|
||||
if (blkCnt > 0U) {
|
||||
mve_pred16_t p0 = vctp32q(blkCnt);
|
||||
f32x4_t vinvSigma, vtmp;
|
||||
|
||||
vsigma = vaddq_n_f32(vld1q(pSigma), S->epsilon);
|
||||
vacc1 =
|
||||
vaddq_m_f32(vacc1, vacc1, vlogq_f32(vmulq_n_f32(vsigma, 2.0f * PI)), p0);
|
||||
|
||||
vinvSigma = vrecip_medprec_f32(vsigma);
|
||||
|
||||
vtmp = vsubq(vld1q(pIn), vld1q(pTheta));
|
||||
/* squaring */
|
||||
vtmp = vmulq(vtmp, vtmp);
|
||||
|
||||
vacc2 = vfmaq_m_f32(vacc2, vtmp, vinvSigma, p0);
|
||||
|
||||
pTheta += blkCnt;
|
||||
pSigma += blkCnt;
|
||||
}
|
||||
|
||||
tmp = -0.5f * vecAddAcrossF32Mve(vacc1);
|
||||
tmp -= 0.5f * vecAddAcrossF32Mve(vacc2);
|
||||
|
||||
*buffer = tmp + *pLogPrior++;
|
||||
buffer++;
|
||||
}
|
||||
|
||||
arm_max_f32(pOutputProbabilities, S->numberOfClasses, &result, &index);
|
||||
|
||||
return (index);
|
||||
}
|
||||
|
||||
#else
|
||||
|
||||
#if defined(ARM_MATH_NEON)
|
||||
|
||||
#include "NEMath.h"
|
||||
|
||||
|
||||
|
||||
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)
|
||||
{
|
||||
|
||||
const float32_t *pPrior = S->classPriors;
|
||||
|
||||
const float32_t *pTheta = S->theta;
|
||||
const float32_t *pSigma = S->sigma;
|
||||
|
||||
const float32_t *pTheta1 = S->theta + S->vectorDimension;
|
||||
const float32_t *pSigma1 = S->sigma + S->vectorDimension;
|
||||
|
||||
float32_t *buffer = pOutputProbabilities;
|
||||
const float32_t *pIn=in;
|
||||
|
||||
float32_t result;
|
||||
float32_t sigma,sigma1;
|
||||
float32_t tmp,tmp1;
|
||||
uint32_t index;
|
||||
uint32_t vecBlkCnt;
|
||||
uint32_t classBlkCnt;
|
||||
float32x4_t epsilonV;
|
||||
float32x4_t sigmaV,sigmaV1;
|
||||
float32x4_t tmpV,tmpVb,tmpV1;
|
||||
float32x2_t tmpV2;
|
||||
float32x4_t thetaV,thetaV1;
|
||||
float32x4_t inV;
|
||||
(void)pBufferB;
|
||||
|
||||
epsilonV = vdupq_n_f32(S->epsilon);
|
||||
|
||||
classBlkCnt = S->numberOfClasses >> 1;
|
||||
while(classBlkCnt > 0)
|
||||
{
|
||||
|
||||
|
||||
pIn = in;
|
||||
|
||||
tmp = logf(*pPrior++);
|
||||
tmp1 = logf(*pPrior++);
|
||||
tmpV = vdupq_n_f32(0.0f);
|
||||
tmpV1 = vdupq_n_f32(0.0f);
|
||||
|
||||
vecBlkCnt = S->vectorDimension >> 2;
|
||||
while(vecBlkCnt > 0)
|
||||
{
|
||||
sigmaV = vld1q_f32(pSigma);
|
||||
thetaV = vld1q_f32(pTheta);
|
||||
|
||||
sigmaV1 = vld1q_f32(pSigma1);
|
||||
thetaV1 = vld1q_f32(pTheta1);
|
||||
|
||||
inV = vld1q_f32(pIn);
|
||||
|
||||
sigmaV = vaddq_f32(sigmaV, epsilonV);
|
||||
sigmaV1 = vaddq_f32(sigmaV1, epsilonV);
|
||||
|
||||
tmpVb = vmulq_n_f32(sigmaV,DPI_F);
|
||||
tmpVb = vlogq_f32(tmpVb);
|
||||
tmpV = vmlsq_n_f32(tmpV,tmpVb,0.5f);
|
||||
|
||||
tmpVb = vmulq_n_f32(sigmaV1,DPI_F);
|
||||
tmpVb = vlogq_f32(tmpVb);
|
||||
tmpV1 = vmlsq_n_f32(tmpV1,tmpVb,0.5f);
|
||||
|
||||
tmpVb = vsubq_f32(inV,thetaV);
|
||||
tmpVb = vmulq_f32(tmpVb,tmpVb);
|
||||
tmpVb = vmulq_f32(tmpVb, vinvq_f32(sigmaV));
|
||||
tmpV = vmlsq_n_f32(tmpV,tmpVb,0.5f);
|
||||
|
||||
tmpVb = vsubq_f32(inV,thetaV1);
|
||||
tmpVb = vmulq_f32(tmpVb,tmpVb);
|
||||
tmpVb = vmulq_f32(tmpVb, vinvq_f32(sigmaV1));
|
||||
tmpV1 = vmlsq_n_f32(tmpV1,tmpVb,0.5f);
|
||||
|
||||
pIn += 4;
|
||||
pTheta += 4;
|
||||
pSigma += 4;
|
||||
pTheta1 += 4;
|
||||
pSigma1 += 4;
|
||||
|
||||
vecBlkCnt--;
|
||||
}
|
||||
tmpV2 = vpadd_f32(vget_low_f32(tmpV),vget_high_f32(tmpV));
|
||||
tmp += vget_lane_f32(tmpV2, 0) + vget_lane_f32(tmpV2, 1);
|
||||
|
||||
tmpV2 = vpadd_f32(vget_low_f32(tmpV1),vget_high_f32(tmpV1));
|
||||
tmp1 += vget_lane_f32(tmpV2, 0) + vget_lane_f32(tmpV2, 1);
|
||||
|
||||
vecBlkCnt = S->vectorDimension & 3;
|
||||
while(vecBlkCnt > 0)
|
||||
{
|
||||
sigma = *pSigma + S->epsilon;
|
||||
sigma1 = *pSigma1 + S->epsilon;
|
||||
|
||||
tmp -= 0.5f*logf(2.0f * PI_F * sigma);
|
||||
tmp -= 0.5f*(*pIn - *pTheta) * (*pIn - *pTheta) / sigma;
|
||||
|
||||
tmp1 -= 0.5f*logf(2.0f * PI_F * sigma1);
|
||||
tmp1 -= 0.5f*(*pIn - *pTheta1) * (*pIn - *pTheta1) / sigma1;
|
||||
|
||||
pIn++;
|
||||
pTheta++;
|
||||
pSigma++;
|
||||
pTheta1++;
|
||||
pSigma1++;
|
||||
vecBlkCnt--;
|
||||
}
|
||||
|
||||
*buffer++ = tmp;
|
||||
*buffer++ = tmp1;
|
||||
|
||||
pSigma += S->vectorDimension;
|
||||
pTheta += S->vectorDimension;
|
||||
pSigma1 += S->vectorDimension;
|
||||
pTheta1 += S->vectorDimension;
|
||||
|
||||
classBlkCnt--;
|
||||
}
|
||||
|
||||
classBlkCnt = S->numberOfClasses & 1;
|
||||
|
||||
while(classBlkCnt > 0)
|
||||
{
|
||||
|
||||
|
||||
pIn = in;
|
||||
|
||||
tmp = logf(*pPrior++);
|
||||
tmpV = vdupq_n_f32(0.0f);
|
||||
|
||||
vecBlkCnt = S->vectorDimension >> 2;
|
||||
while(vecBlkCnt > 0)
|
||||
{
|
||||
sigmaV = vld1q_f32(pSigma);
|
||||
thetaV = vld1q_f32(pTheta);
|
||||
inV = vld1q_f32(pIn);
|
||||
|
||||
sigmaV = vaddq_f32(sigmaV, epsilonV);
|
||||
|
||||
tmpVb = vmulq_n_f32(sigmaV,DPI_F);
|
||||
tmpVb = vlogq_f32(tmpVb);
|
||||
tmpV = vmlsq_n_f32(tmpV,tmpVb,0.5f);
|
||||
|
||||
tmpVb = vsubq_f32(inV,thetaV);
|
||||
tmpVb = vmulq_f32(tmpVb,tmpVb);
|
||||
tmpVb = vmulq_f32(tmpVb, vinvq_f32(sigmaV));
|
||||
tmpV = vmlsq_n_f32(tmpV,tmpVb,0.5f);
|
||||
|
||||
pIn += 4;
|
||||
pTheta += 4;
|
||||
pSigma += 4;
|
||||
|
||||
vecBlkCnt--;
|
||||
}
|
||||
tmpV2 = vpadd_f32(vget_low_f32(tmpV),vget_high_f32(tmpV));
|
||||
tmp += vget_lane_f32(tmpV2, 0) + vget_lane_f32(tmpV2, 1);
|
||||
|
||||
vecBlkCnt = S->vectorDimension & 3;
|
||||
while(vecBlkCnt > 0)
|
||||
{
|
||||
sigma = *pSigma + S->epsilon;
|
||||
tmp -= 0.5f*logf(2.0f * PI_F * sigma);
|
||||
tmp -= 0.5f*(*pIn - *pTheta) * (*pIn - *pTheta) / sigma;
|
||||
|
||||
pIn++;
|
||||
pTheta++;
|
||||
pSigma++;
|
||||
vecBlkCnt--;
|
||||
}
|
||||
|
||||
*buffer++ = tmp;
|
||||
|
||||
classBlkCnt--;
|
||||
}
|
||||
|
||||
arm_max_f32(pOutputProbabilities,S->numberOfClasses,&result,&index);
|
||||
|
||||
return(index);
|
||||
}
|
||||
|
||||
#else
|
||||
|
||||
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)
|
||||
{
|
||||
uint32_t nbClass;
|
||||
uint32_t nbDim;
|
||||
const float32_t *pPrior = S->classPriors;
|
||||
const float32_t *pTheta = S->theta;
|
||||
const float32_t *pSigma = S->sigma;
|
||||
float32_t *buffer = pOutputProbabilities;
|
||||
const float32_t *pIn=in;
|
||||
float32_t result;
|
||||
float32_t sigma;
|
||||
float32_t tmp;
|
||||
float32_t acc1,acc2;
|
||||
uint32_t index;
|
||||
|
||||
(void)pBufferB;
|
||||
|
||||
pTheta=S->theta;
|
||||
pSigma=S->sigma;
|
||||
|
||||
for(nbClass = 0; nbClass < S->numberOfClasses; nbClass++)
|
||||
{
|
||||
|
||||
|
||||
pIn = in;
|
||||
|
||||
tmp = 0.0;
|
||||
acc1 = 0.0f;
|
||||
acc2 = 0.0f;
|
||||
for(nbDim = 0; nbDim < S->vectorDimension; nbDim++)
|
||||
{
|
||||
sigma = *pSigma + S->epsilon;
|
||||
acc1 += logf(2.0f * PI_F * sigma);
|
||||
acc2 += (*pIn - *pTheta) * (*pIn - *pTheta) / sigma;
|
||||
|
||||
pIn++;
|
||||
pTheta++;
|
||||
pSigma++;
|
||||
}
|
||||
|
||||
tmp = -0.5f * acc1;
|
||||
tmp -= 0.5f * acc2;
|
||||
|
||||
|
||||
*buffer = tmp + logf(*pPrior++);
|
||||
buffer++;
|
||||
}
|
||||
|
||||
arm_max_f32(pOutputProbabilities,S->numberOfClasses,&result,&index);
|
||||
|
||||
return(index);
|
||||
}
|
||||
|
||||
#endif
|
||||
#endif /* defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) */
|
||||
|
||||
/**
|
||||
* @} end of groupBayes group
|
||||
*/
|
||||
Loading…
Add table
Add a link
Reference in a new issue