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CMSIS-DSP/Source/MatrixFunctions/arm_householder_f64.c

118 lines
2.5 KiB
C

/* ----------------------------------------------------------------------
* Project: CMSIS DSP Library
* Title: arm_householder_f64.c
* Description: Double floating-point Householder transform
*
* $Date: 15 June 2022
* $Revision: V1.11.0
*
* Target Processor: Cortex-M and Cortex-A cores
* -------------------------------------------------------------------- */
/*
* Copyright (C) 2010-2022 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"
#include "dsp/basic_math_functions.h"
#include "dsp/fast_math_functions.h"
#include "dsp/matrix_utils.h"
#include <math.h>
/**
@ingroup groupMatrix
*/
/**
@addtogroup MatrixHouseholder
@{
*/
/**
@brief Householder transform of a double floating point vector.
@param[in] pSrc points to the input vector.
@param[in] threshold norm2 threshold.
@param[in] blockSize dimension of the vector space.
@param[out] pOut points to the output vector.
@return beta return the scaling factor beta
*/
float64_t arm_householder_f64(
const float64_t * pSrc,
const float64_t threshold,
uint32_t blockSize,
float64_t * pOut
)
{
uint32_t i;
float64_t epsilon;
float64_t x1norm2,alpha;
float64_t beta,tau,r;
epsilon = threshold;
alpha = pSrc[0];
for(i=1; i < blockSize; i++)
{
pOut[i] = pSrc[i];
}
pOut[0] = 1.0;
arm_dot_prod_f64(pSrc+1,pSrc+1,blockSize-1,&x1norm2);
if (x1norm2<=epsilon)
{
tau = 0.0;
memset(pOut,0,blockSize * sizeof(float64_t));
}
else
{
beta = alpha * alpha + x1norm2;
beta=sqrt(beta);
if (alpha > 0.0)
{
beta = -beta;
}
r = 1.0 / (alpha -beta);
arm_scale_f64(pOut,r,pOut,blockSize);
pOut[0] = 1.0;
tau = (beta - alpha) / beta;
}
return(tau);
}
/**
@} end of MatrixHouseholder group
*/