luv090909/py32f0-template
1
0
Fork 0
mirror of https://github.com/IcedRooibos/py32f0-template.git synced 2025-10-31 01:42:04 -07:00
Code Issues Packages Projects Releases Wiki Activity
py32f0-template/Libraries/CMSIS/DSP/Source/MatrixFunctions/arm_mat_cholesky_f32.c
IOsetting e19b6df4e3 fix: incorrect file attributes
2023-01-19 22:45:57 +08:00

439 lines
11 KiB
C
Raw Blame History

/* ----------------------------------------------------------------------
* Project: CMSIS DSP Library
* Title: arm_mat_cholesky_f32.c
* Description: Floating-point Cholesky decomposition
*
* $Date: 05 October 2021
* $Revision: V1.9.1
*
* 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/matrix_functions.h"
/**
@ingroup groupMatrix
*/
/**
@defgroup MatrixChol Cholesky and LDLT decompositions
Computes the Cholesky or LDL^t decomposition of a matrix.
If the input matrix does not have a decomposition, then the
algorithm terminates and returns error status ARM_MATH_DECOMPOSITION_FAILURE.
*/
/**
@addtogroup MatrixChol
@{
*/
/**
* @brief Floating-point Cholesky decomposition of positive-definite matrix.
* @param[in] pSrc points to the instance of the input floating-point matrix structure.
* @param[out] pDst points to the instance of the output floating-point matrix structure.
* @return The function returns ARM_MATH_SIZE_MISMATCH, if the dimensions do not match.
* @return execution status
- \ref ARM_MATH_SUCCESS : Operation successful
- \ref ARM_MATH_SIZE_MISMATCH : Matrix size check failed
- \ref ARM_MATH_DECOMPOSITION_FAILURE : Input matrix cannot be decomposed
* @par
* If the matrix is ill conditioned or only semi-definite, then it is better using the LDL^t decomposition.
* The decomposition of A is returning a lower triangular matrix U such that A = U U^t
*/
#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE)
#include "arm_helium_utils.h"
arm_status arm_mat_cholesky_f32(
const arm_matrix_instance_f32 * pSrc,
arm_matrix_instance_f32 * pDst)
{
arm_status status; /* status of matrix inverse */
#ifdef ARM_MATH_MATRIX_CHECK
/* Check for matrix mismatch condition */
if ((pSrc->numRows != pSrc->numCols) ||
(pDst->numRows != pDst->numCols) ||
(pSrc->numRows != pDst->numRows) )
{
/* Set status as ARM_MATH_SIZE_MISMATCH */
status = ARM_MATH_SIZE_MISMATCH;
}
else
#endif /* #ifdef ARM_MATH_MATRIX_CHECK */
{
int i,j,k;
int n = pSrc->numRows;
float32_t invSqrtVj;
float32_t *pA,*pG;
int kCnt;
mve_pred16_t p0;
f32x4_t acc, acc0, acc1, acc2, acc3;
f32x4_t vecGi;
f32x4_t vecGj,vecGj0,vecGj1,vecGj2,vecGj3;
pA = pSrc->pData;
pG = pDst->pData;
for(i=0 ;i < n ; i++)
{
for(j=i ; j+3 < n ; j+=4)
{
pG[(j + 0) * n + i] = pA[(j + 0) * n + i];
pG[(j + 1) * n + i] = pA[(j + 1) * n + i];
pG[(j + 2) * n + i] = pA[(j + 2) * n + i];
pG[(j + 3) * n + i] = pA[(j + 3) * n + i];
kCnt = i;
acc0 = vdupq_n_f32(0.0f);
acc1 = vdupq_n_f32(0.0f);
acc2 = vdupq_n_f32(0.0f);
acc3 = vdupq_n_f32(0.0f);
for(k=0; k < i ; k+=4)
{
p0 = vctp32q(kCnt);
vecGi=vldrwq_z_f32(&pG[i * n + k],p0);
vecGj0=vldrwq_z_f32(&pG[(j + 0) * n + k],p0);
vecGj1=vldrwq_z_f32(&pG[(j + 1) * n + k],p0);
vecGj2=vldrwq_z_f32(&pG[(j + 2) * n + k],p0);
vecGj3=vldrwq_z_f32(&pG[(j + 3) * n + k],p0);
acc0 = vfmaq_m(acc0, vecGi, vecGj0, p0);
acc1 = vfmaq_m(acc1, vecGi, vecGj1, p0);
acc2 = vfmaq_m(acc2, vecGi, vecGj2, p0);
acc3 = vfmaq_m(acc3, vecGi, vecGj3, p0);
kCnt -= 4;
}
pG[(j + 0) * n + i] -= vecAddAcrossF32Mve(acc0);
pG[(j + 1) * n + i] -= vecAddAcrossF32Mve(acc1);
pG[(j + 2) * n + i] -= vecAddAcrossF32Mve(acc2);
pG[(j + 3) * n + i] -= vecAddAcrossF32Mve(acc3);
}
for(; j < n ; j++)
{
pG[j * n + i] = pA[j * n + i];
kCnt = i;
acc = vdupq_n_f32(0.0f);
for(k=0; k < i ; k+=4)
{
p0 = vctp32q(kCnt);
vecGi=vldrwq_z_f32(&pG[i * n + k],p0);
vecGj=vldrwq_z_f32(&pG[j * n + k],p0);
acc = vfmaq_m(acc, vecGi, vecGj,p0);
kCnt -= 4;
}
pG[j * n + i] -= vecAddAcrossF32Mve(acc);
}
if (pG[i * n + i] <= 0.0f)
{
return(ARM_MATH_DECOMPOSITION_FAILURE);
}
invSqrtVj = 1.0f/sqrtf(pG[i * n + i]);
for(j=i; j < n ; j++)
{
pG[j * n + i] = pG[j * n + i] * invSqrtVj ;
}
}
status = ARM_MATH_SUCCESS;
}
/* Return to application */
return (status);
}
#else
#if defined(ARM_MATH_NEON) && !defined(ARM_MATH_AUTOVECTORIZE)
arm_status arm_mat_cholesky_f32(
const arm_matrix_instance_f32 * pSrc,
arm_matrix_instance_f32 * pDst)
{
arm_status status; /* status of matrix inverse */
#ifdef ARM_MATH_MATRIX_CHECK
/* Check for matrix mismatch condition */
if ((pSrc->numRows != pSrc->numCols) ||
(pDst->numRows != pDst->numCols) ||
(pSrc->numRows != pDst->numRows) )
{
/* Set status as ARM_MATH_SIZE_MISMATCH */
status = ARM_MATH_SIZE_MISMATCH;
}
else
#endif /* #ifdef ARM_MATH_MATRIX_CHECK */
{
int i,j,k;
int n = pSrc->numRows;
float32_t invSqrtVj;
float32_t *pA,*pG;
int kCnt;
f32x4_t acc, acc0, acc1, acc2, acc3;
f32x4_t vecGi;
f32x4_t vecGj,vecGj0,vecGj1,vecGj2,vecGj3;
#if !defined(__aarch64__)
f32x2_t tmp = vdup_n_f32(0);
#endif
float32_t sum=0.0f;
float32_t sum0=0.0f,sum1=0.0f,sum2=0.0f,sum3=0.0f;
pA = pSrc->pData;
pG = pDst->pData;
for(i=0 ;i < n ; i++)
{
for(j=i ; j+3 < n ; j+=4)
{
pG[(j + 0) * n + i] = pA[(j + 0) * n + i];
pG[(j + 1) * n + i] = pA[(j + 1) * n + i];
pG[(j + 2) * n + i] = pA[(j + 2) * n + i];
pG[(j + 3) * n + i] = pA[(j + 3) * n + i];
acc0 = vdupq_n_f32(0.0f);
acc1 = vdupq_n_f32(0.0f);
acc2 = vdupq_n_f32(0.0f);
acc3 = vdupq_n_f32(0.0f);
kCnt = i >> 2;
k=0;
while(kCnt > 0)
{
vecGi=vld1q_f32(&pG[i * n + k]);
vecGj0=vld1q_f32(&pG[(j + 0) * n + k]);
vecGj1=vld1q_f32(&pG[(j + 1) * n + k]);
vecGj2=vld1q_f32(&pG[(j + 2) * n + k]);
vecGj3=vld1q_f32(&pG[(j + 3) * n + k]);
acc0 = vfmaq_f32(acc0, vecGi, vecGj0);
acc1 = vfmaq_f32(acc1, vecGi, vecGj1);
acc2 = vfmaq_f32(acc2, vecGi, vecGj2);
acc3 = vfmaq_f32(acc3, vecGi, vecGj3);
kCnt--;
k+=4;
}
#if defined(__aarch64__)
sum0 = vpadds_f32(vpadd_f32(vget_low_f32(acc0), vget_high_f32(acc0)));
sum1 = vpadds_f32(vpadd_f32(vget_low_f32(acc1), vget_high_f32(acc1)));
sum2 = vpadds_f32(vpadd_f32(vget_low_f32(acc2), vget_high_f32(acc2)));
sum3 = vpadds_f32(vpadd_f32(vget_low_f32(acc3), vget_high_f32(acc3)));
#else
tmp = vpadd_f32(vget_low_f32(acc0), vget_high_f32(acc0));
sum0 = vget_lane_f32(tmp, 0) + vget_lane_f32(tmp, 1);
tmp = vpadd_f32(vget_low_f32(acc1), vget_high_f32(acc1));
sum1 = vget_lane_f32(tmp, 0) + vget_lane_f32(tmp, 1);
tmp = vpadd_f32(vget_low_f32(acc2), vget_high_f32(acc2));
sum2 = vget_lane_f32(tmp, 0) + vget_lane_f32(tmp, 1);
tmp = vpadd_f32(vget_low_f32(acc3), vget_high_f32(acc3));
sum3 = vget_lane_f32(tmp, 0) + vget_lane_f32(tmp, 1);
#endif
kCnt = i & 3;
while(kCnt > 0)
{
sum0 = sum0 + pG[i * n + k] * pG[(j + 0) * n + k];
sum1 = sum1 + pG[i * n + k] * pG[(j + 1) * n + k];
sum2 = sum2 + pG[i * n + k] * pG[(j + 2) * n + k];
sum3 = sum3 + pG[i * n + k] * pG[(j + 3) * n + k];
kCnt--;
k++;
}
pG[(j + 0) * n + i] -= sum0;
pG[(j + 1) * n + i] -= sum1;
pG[(j + 2) * n + i] -= sum2;
pG[(j + 3) * n + i] -= sum3;
}
for(; j < n ; j++)
{
pG[j * n + i] = pA[j * n + i];
acc = vdupq_n_f32(0.0f);
kCnt = i >> 2;
k=0;
while(kCnt > 0)
{
vecGi=vld1q_f32(&pG[i * n + k]);
vecGj=vld1q_f32(&pG[j * n + k]);
acc = vfmaq_f32(acc, vecGi, vecGj);
kCnt--;
k+=4;
}
#if defined(__aarch64__)
sum = vpadds_f32(vpadd_f32(vget_low_f32(acc), vget_high_f32(acc)));
#else
tmp = vpadd_f32(vget_low_f32(acc), vget_high_f32(acc));
sum = vget_lane_f32(tmp, 0) + vget_lane_f32(tmp, 1);
#endif
kCnt = i & 3;
while(kCnt > 0)
{
sum = sum + pG[i * n + k] * pG[(j + 0) * n + k];
kCnt--;
k++;
}
pG[j * n + i] -= sum;
}
if (pG[i * n + i] <= 0.0f)
{
return(ARM_MATH_DECOMPOSITION_FAILURE);
}
invSqrtVj = 1.0f/sqrtf(pG[i * n + i]);
for(j=i; j < n ; j++)
{
pG[j * n + i] = pG[j * n + i] * invSqrtVj ;
}
}
status = ARM_MATH_SUCCESS;
}
/* Return to application */
return (status);
}
#else
arm_status arm_mat_cholesky_f32(
const arm_matrix_instance_f32 * pSrc,
arm_matrix_instance_f32 * pDst)
{
arm_status status; /* status of matrix inverse */
#ifdef ARM_MATH_MATRIX_CHECK
/* Check for matrix mismatch condition */
if ((pSrc->numRows != pSrc->numCols) ||
(pDst->numRows != pDst->numCols) ||
(pSrc->numRows != pDst->numRows) )
{
/* Set status as ARM_MATH_SIZE_MISMATCH */
status = ARM_MATH_SIZE_MISMATCH;
}
else
#endif /* #ifdef ARM_MATH_MATRIX_CHECK */
{
int i,j,k;
int n = pSrc->numRows;
float32_t invSqrtVj;
float32_t *pA,*pG;
pA = pSrc->pData;
pG = pDst->pData;
for(i=0 ; i < n ; i++)
{
for(j=i ; j < n ; j++)
{
pG[j * n + i] = pA[j * n + i];
for(k=0; k < i ; k++)
{
pG[j * n + i] = pG[j * n + i] - pG[i * n + k] * pG[j * n + k];
}
}
if (pG[i * n + i] <= 0.0f)
{
return(ARM_MATH_DECOMPOSITION_FAILURE);
}
invSqrtVj = 1.0f/sqrtf(pG[i * n + i]);
for(j=i ; j < n ; j++)
{
pG[j * n + i] = pG[j * n + i] * invSqrtVj ;
}
}
status = ARM_MATH_SUCCESS;
}
/* Return to application */
return (status);
}
#endif /* #if defined(ARM_MATH_NEON) */
#endif /* defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) */
/**
@} end of MatrixChol group
*/
Reference in New Issue View Git Blame Copy Permalink