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/* ----------------------------------------------------------------------
* Project: CMSIS DSP Library
* Title: arm_naive_gaussian_bayes_predict_f16
* 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_f16.h"
#if defined(ARM_FLOAT16_SUPPORTED)
#include <limits.h>
#include <math.h>
/**
* @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_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE)
#include "arm_helium_utils.h"
#include "arm_vec_math_f16.h"
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;
const float16_t *pTheta = S->theta;
const float16_t *pSigma = S->sigma;
float16_t *buffer = pOutputProbabilities;
const float16_t *pIn = in;
float16_t result;
f16x8_t vsigma;
_Float16 tmp;
f16x8_t vacc1, vacc2;
uint32_t index;
float16_t *logclassPriors=pBufferB;
float16_t *pLogPrior = logclassPriors;
arm_vlog_f16((float16_t *) S->classPriors, logclassPriors, S->numberOfClasses);
pTheta = S->theta;
pSigma = S->sigma;
for (nbClass = 0; nbClass < S->numberOfClasses; nbClass++) {
pIn = in;
vacc1 = vdupq_n_f16(0.0f16);
vacc2 = vdupq_n_f16(0.0f16);
uint32_t blkCnt =S->vectorDimension >> 3;
while (blkCnt > 0U) {
f16x8_t vinvSigma, vtmp;
vsigma = vaddq_n_f16(vld1q(pSigma), S->epsilon);
vacc1 = vaddq(vacc1, vlogq_f16(vmulq_n_f16(vsigma, 2.0f16 * (_Float16)PI)));
vinvSigma = vrecip_medprec_f16(vsigma);
vtmp = vsubq(vld1q(pIn), vld1q(pTheta));
/* squaring */
vtmp = vmulq(vtmp, vtmp);
vacc2 = vfmaq(vacc2, vtmp, vinvSigma);
pIn += 8;
pTheta += 8;
pSigma += 8;
blkCnt--;
}
blkCnt = S->vectorDimension & 7;
if (blkCnt > 0U) {
mve_pred16_t p0 = vctp16q(blkCnt);
f16x8_t vinvSigma, vtmp;
vsigma = vaddq_n_f16(vld1q(pSigma), S->epsilon);
vacc1 =
vaddq_m_f16(vacc1, vacc1, vlogq_f16(vmulq_n_f16(vsigma, 2.0f16 * (_Float16)PI)), p0);
vinvSigma = vrecip_medprec_f16(vsigma);
vtmp = vsubq(vld1q(pIn), vld1q(pTheta));
/* squaring */
vtmp = vmulq(vtmp, vtmp);
vacc2 = vfmaq_m_f16(vacc2, vtmp, vinvSigma, p0);
pTheta += blkCnt;
pSigma += blkCnt;
}
tmp = -0.5f16 * (_Float16)vecAddAcrossF16Mve(vacc1);
tmp -= 0.5f16 * (_Float16)vecAddAcrossF16Mve(vacc2);
*buffer = (_Float16)tmp + (_Float16)*pLogPrior++;
buffer++;
}
arm_max_f16(pOutputProbabilities, S->numberOfClasses, &result, &index);
return (index);
}
#else
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;
float16_t *buffer = pOutputProbabilities;
const float16_t *pIn=in;
float16_t result;
_Float16 sigma;
_Float16 tmp;
_Float16 acc1,acc2;
uint32_t index;
(void)pBufferB;
pTheta=S->theta;
pSigma=S->sigma;
for(nbClass = 0; nbClass < S->numberOfClasses; nbClass++)
{
pIn = in;
tmp = 0.0f16;
acc1 = 0.0f16;
acc2 = 0.0f16;
for(nbDim = 0; nbDim < S->vectorDimension; nbDim++)
{
sigma = (_Float16)*pSigma + (_Float16)S->epsilon;
acc1 += (_Float16)logf(2.0f * PI * (float32_t)sigma);
acc2 += ((_Float16)*pIn - (_Float16)*pTheta) * ((_Float16)*pIn - (_Float16)*pTheta) / (_Float16)sigma;
pIn++;
pTheta++;
pSigma++;
}
tmp = -0.5f16 * (_Float16)acc1;
tmp -= 0.5f16 * (_Float16)acc2;
*buffer = (_Float16)tmp + (_Float16)logf((float32_t)*pPrior++);
buffer++;
}
arm_max_f16(pOutputProbabilities,S->numberOfClasses,&result,&index);
return(index);
}
#endif /* defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) */
/**
* @} end of groupBayes group
*/
#endif /* #if defined(ARM_FLOAT16_SUPPORTED) */
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