initial commit

5 files changed, 682 insertions, 0 deletions

diff --git a/Drivers/CMSIS/DSP/Source/BayesFunctions/BayesFunctions.c b/Drivers/CMSIS/DSP/Source/BayesFunctions/BayesFunctions.c
new file mode 100644
index 0000000..4a553dc
--- /dev/null
+++ b/Drivers/CMSIS/DSP/Source/BayesFunctions/BayesFunctions.c
@@ -0,0 +1,29 @@
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS DSP Library
+ * Title:        BayesFunctions.c
+ * Description:  Combination of all bayes function source files.
+ *
+ * $Date:        16. March 2020
+ * $Revision:    V1.0.0
+ *
+ * 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.
+ */
+
+#include "arm_gaussian_naive_bayes_predict_f32.c"
diff --git a/Drivers/CMSIS/DSP/Source/BayesFunctions/BayesFunctionsF16.c b/Drivers/CMSIS/DSP/Source/BayesFunctions/BayesFunctionsF16.c
new file mode 100644
index 0000000..57ced8c
--- /dev/null
+++ b/Drivers/CMSIS/DSP/Source/BayesFunctions/BayesFunctionsF16.c
@@ -0,0 +1,27 @@
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS DSP Library
+ * Title:        BayesFunctions.c
+ * Description:  Combination of all bayes function f16 source files.
+ *
+ *
+ * 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.
+ */
+
+#include "arm_gaussian_naive_bayes_predict_f16.c"
diff --git a/Drivers/CMSIS/DSP/Source/BayesFunctions/CMakeLists.txt b/Drivers/CMSIS/DSP/Source/BayesFunctions/CMakeLists.txt
new file mode 100644
index 0000000..72a202d
--- /dev/null
+++ b/Drivers/CMSIS/DSP/Source/BayesFunctions/CMakeLists.txt
@@ -0,0 +1,23 @@
+cmake_minimum_required (VERSION 3.14)
+
+project(CMSISDSPBayes)
+
+include(configLib)
+include(configDsp)
+
+file(GLOB SRC "./*_*.c")
+
+add_library(CMSISDSPBayes STATIC)
+
+target_sources(CMSISDSPBayes PRIVATE arm_gaussian_naive_bayes_predict_f32.c)
+
+configLib(CMSISDSPBayes ${ROOT})
+configDsp(CMSISDSPBayes ${ROOT})
+
+### Includes
+target_include_directories(CMSISDSPBayes PUBLIC "${DSP}/Include")
+
+if ((NOT ARMAC5) AND (NOT DISABLEFLOAT16))
+target_sources(CMSISDSPBayes PRIVATE arm_gaussian_naive_bayes_predict_f16.c)
+endif()
+
diff --git a/Drivers/CMSIS/DSP/Source/BayesFunctions/arm_gaussian_naive_bayes_predict_f16.c b/Drivers/CMSIS/DSP/Source/BayesFunctions/arm_gaussian_naive_bayes_predict_f16.c
new file mode 100644
index 0000000..e3b2ef6
--- /dev/null
+++ b/Drivers/CMSIS/DSP/Source/BayesFunctions/arm_gaussian_naive_bayes_predict_f16.c
@@ -0,0 +1,207 @@
+/* ----------------------------------------------------------------------
+ * 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) */ 
+
diff --git a/Drivers/CMSIS/DSP/Source/BayesFunctions/arm_gaussian_naive_bayes_predict_f32.c b/Drivers/CMSIS/DSP/Source/BayesFunctions/arm_gaussian_naive_bayes_predict_f32.c
new file mode 100644
index 0000000..56331ff
--- /dev/null
+++ b/Drivers/CMSIS/DSP/Source/BayesFunctions/arm_gaussian_naive_bayes_predict_f32.c
@@ -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
+ */