SunRed/Marlin-Artillery-M600
Archived
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity

Save 7714 bytes of program memory when doing AUTO_BED_LEVELING_LINEAR (#7276)

Browse source 
We can save more and a pile of RAM by eleminating the eqnBVector and
EqnAMatrix arrays next.
This commit is contained in:
Roxy-3D 2017-07-10 19:05:11 -05:00 committed by GitHub
parent e5904c4df8
commit 9af67e2446
5 changed files with 28 additions and 1653 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

36
Marlin/Marlin_main.cpp
View file

@ -261,7 +261,7 @@
#if HAS_ABL #if HAS_ABL
#include "vector_3.h" #include "vector_3.h"
#if ENABLED(AUTO_BED_LEVELING_LINEAR) #if ENABLED(AUTO_BED_LEVELING_LINEAR)
#include "qr_solve.h" #include "least_squares_fit.h"
#endif #endif
#elif ENABLED(MESH_BED_LEVELING) #elif ENABLED(MESH_BED_LEVELING)
#include "mesh_bed_leveling.h" #include "mesh_bed_leveling.h"
@ -4353,6 +4353,11 @@ void home_all_axes() { gcode_G28(true); }
#endif // AUTO_BED_LEVELING_3POINT #endif // AUTO_BED_LEVELING_3POINT
#if ENABLED(AUTO_BED_LEVELING_LINEAR)
struct linear_fit_data lsf_results;
incremental_LSF_reset(&lsf_results);
#endif
/** /**
* On the initial G29 fetch command parameters. * On the initial G29 fetch command parameters.
*/ */
@ -4549,11 +4554,7 @@ void home_all_axes() { gcode_G28(true); }
abl_should_enable = false; abl_should_enable = false;
} }
#elif ENABLED(AUTO_BED_LEVELING_LINEAR) #endif // AUTO_BED_LEVELING_BILINEAR
mean = 0.0;
#endif // AUTO_BED_LEVELING_LINEAR
#if ENABLED(AUTO_BED_LEVELING_3POINT) #if ENABLED(AUTO_BED_LEVELING_3POINT)
@ -4616,11 +4617,11 @@ void home_all_axes() { gcode_G28(true); }
#if ENABLED(AUTO_BED_LEVELING_LINEAR) #if ENABLED(AUTO_BED_LEVELING_LINEAR)
mean += measured_z; // mean += measured_z; // I believe this is unused code?
eqnBVector[abl_probe_index] = measured_z; // eqnBVector[abl_probe_index] = measured_z; // I believe this is unused code?
eqnAMatrix[abl_probe_index + 0 * abl2] = xProbe; // eqnAMatrix[abl_probe_index + 0 * abl2] = xProbe; // I believe this is unused code?
eqnAMatrix[abl_probe_index + 1 * abl2] = yProbe; // eqnAMatrix[abl_probe_index + 1 * abl2] = yProbe; // I believe this is unused code?
eqnAMatrix[abl_probe_index + 2 * abl2] = 1; // eqnAMatrix[abl_probe_index + 2 * abl2] = 1; // I believe this is unused code?
#elif ENABLED(AUTO_BED_LEVELING_BILINEAR) #elif ENABLED(AUTO_BED_LEVELING_BILINEAR)
@ -4794,6 +4795,11 @@ void home_all_axes() { gcode_G28(true); }
eqnAMatrix[abl_probe_index + 1 * abl2] = yProbe; eqnAMatrix[abl_probe_index + 1 * abl2] = yProbe;
eqnAMatrix[abl_probe_index + 2 * abl2] = 1; eqnAMatrix[abl_probe_index + 2 * abl2] = 1;
incremental_LSF(&lsf_results, xProbe, yProbe, measured_z);
#if ENABLED(AUTO_BED_LEVELING_LINEAR)
indexIntoAB[xCount][yCount] = abl_probe_index;
#endif
#elif ENABLED(AUTO_BED_LEVELING_BILINEAR) #elif ENABLED(AUTO_BED_LEVELING_BILINEAR)
z_values[xCount][yCount] = measured_z + zoffset; z_values[xCount][yCount] = measured_z + zoffset;
@ -4894,7 +4900,11 @@ void home_all_axes() { gcode_G28(true); }
* so Vx = -a Vy = -b Vz = 1 (we want the vector facing towards positive Z * so Vx = -a Vy = -b Vz = 1 (we want the vector facing towards positive Z
*/ */
float plane_equation_coefficients[3]; float plane_equation_coefficients[3];
qr_solve(plane_equation_coefficients, abl2, 3, eqnAMatrix, eqnBVector);
finish_incremental_LSF(&lsf_results);
plane_equation_coefficients[0] = -lsf_results.A; // We should be able to eliminate the '-' on these three lines and down below
plane_equation_coefficients[1] = -lsf_results.B; // but that is not yet tested.
plane_equation_coefficients[2] = -lsf_results.D;
mean /= abl2; mean /= abl2;
@ -4916,7 +4926,7 @@ void home_all_axes() { gcode_G28(true); }
// Create the matrix but don't correct the position yet // Create the matrix but don't correct the position yet
if (!dryrun) { if (!dryrun) {
planner.bed_level_matrix = matrix_3x3::create_look_at( planner.bed_level_matrix = matrix_3x3::create_look_at(
vector_3(-plane_equation_coefficients[0], -plane_equation_coefficients[1], 1) vector_3(-plane_equation_coefficients[0], -plane_equation_coefficients[1], 1) // We can eleminate the '-' here and up above
); );
} }

4
Marlin/least_squares_fit.cpp
View file

@ -34,7 +34,7 @@
#include "MarlinConfig.h" #include "MarlinConfig.h"
#if ENABLED(AUTO_BED_LEVELING_UBL) // Currently only used by UBL, but is applicable to Grid Based (Linear) Bed Leveling #if ENABLED(AUTO_BED_LEVELING_UBL) || ENABLED(AUTO_BED_LEVELING_LINEAR)
#include "macros.h" #include "macros.h"
#include <math.h> #include <math.h>
@ -68,4 +68,4 @@ int finish_incremental_LSF(struct linear_fit_data *lsf) {
return 0; return 0;
} }
#endif // AUTO_BED_LEVELING_UBL #endif // AUTO_BED_LEVELING_UBL || ENABLED(AUTO_BED_LEVELING_LINEAR)

2
Marlin/least_squares_fit.h
View file

@ -34,7 +34,7 @@
#include "MarlinConfig.h" #include "MarlinConfig.h"
#if ENABLED(AUTO_BED_LEVELING_UBL) // Currently only used by UBL, but is applicable to Grid Based (Linear) Bed Leveling #if ENABLED(AUTO_BED_LEVELING_UBL) || ENABLED(AUTO_BED_LEVELING_LINEAR)
#include "Marlin.h" #include "Marlin.h"
#include "macros.h" #include "macros.h"

1591
Marlin/qr_solve.cpp
View file

@ -1,1591 +0,0 @@
/**
* Marlin 3D Printer Firmware
* Copyright (C) 2016 MarlinFirmware [https://github.com/MarlinFirmware/Marlin]
*
* Based on Sprinter and grbl.
* Copyright (C) 2011 Camiel Gubbels / Erik van der Zalm
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*
*/
#include "qr_solve.h"
#if ENABLED(AUTO_BED_LEVELING_LINEAR)
#include <stdlib.h>
#include <math.h>
//# include "r8lib.h"
int i4_min(int i1, int i2)
/******************************************************************************/
/**
Purpose:
I4_MIN returns the smaller of two I4's.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
29 August 2006
Author:
John Burkardt
Parameters:
Input, int I1, I2, two integers to be compared.
Output, int I4_MIN, the smaller of I1 and I2.
*/
{
return (i1 < i2) ? i1 : i2;
}
float r8_epsilon(void)
/******************************************************************************/
/**
Purpose:
R8_EPSILON returns the R8 round off unit.
Discussion:
R8_EPSILON is a number R which is a power of 2 with the property that,
to the precision of the computer's arithmetic,
1 < 1 + R
but
1 = ( 1 + R / 2 )
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
01 September 2012
Author:
John Burkardt
Parameters:
Output, float R8_EPSILON, the R8 round-off unit.
*/
{
const float value = 2.220446049250313E-016;
return value;
}
float r8_max(float x, float y)
/******************************************************************************/
/**
Purpose:
R8_MAX returns the maximum of two R8's.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
07 May 2006
Author:
John Burkardt
Parameters:
Input, float X, Y, the quantities to compare.
Output, float R8_MAX, the maximum of X and Y.
*/
{
return (y < x) ? x : y;
}
float r8_abs(float x)
/******************************************************************************/
/**
Purpose:
R8_ABS returns the absolute value of an R8.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
07 May 2006
Author:
John Burkardt
Parameters:
Input, float X, the quantity whose absolute value is desired.
Output, float R8_ABS, the absolute value of X.
*/
{
return (x < 0.0) ? -x : x;
}
float r8_sign(float x)
/******************************************************************************/
/**
Purpose:
R8_SIGN returns the sign of an R8.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
08 May 2006
Author:
John Burkardt
Parameters:
Input, float X, the number whose sign is desired.
Output, float R8_SIGN, the sign of X.
*/
{
return (x < 0.0) ? -1.0 : 1.0;
}
float r8mat_amax(int m, int n, float a[])
/******************************************************************************/
/**
Purpose:
R8MAT_AMAX returns the maximum absolute value entry of an R8MAT.
Discussion:
An R8MAT is a doubly dimensioned array of R8 values, stored as a vector
in column-major order.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
07 September 2012
Author:
John Burkardt
Parameters:
Input, int M, the number of rows in A.
Input, int N, the number of columns in A.
Input, float A[M*N], the M by N matrix.
Output, float R8MAT_AMAX, the maximum absolute value entry of A.
*/
{
float value = r8_abs(a[0 + 0 * m]);
for (int j = 0; j < n; j++) {
for (int i = 0; i < m; i++) {
NOLESS(value, r8_abs(a[i + j * m]));
}
}
return value;
}
void r8mat_copy(float a2[], int m, int n, float a1[])
/******************************************************************************/
/**
Purpose:
R8MAT_COPY_NEW copies one R8MAT to a "new" R8MAT.
Discussion:
An R8MAT is a doubly dimensioned array of R8 values, stored as a vector
in column-major order.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
26 July 2008
Author:
John Burkardt
Parameters:
Input, int M, N, the number of rows and columns.
Input, float A1[M*N], the matrix to be copied.
Output, float R8MAT_COPY_NEW[M*N], the copy of A1.
*/
{
for (int j = 0; j < n; j++) {
for (int i = 0; i < m; i++)
a2[i + j * m] = a1[i + j * m];
}
}
/******************************************************************************/
void daxpy(int n, float da, float dx[], int incx, float dy[], int incy)
/******************************************************************************/
/**
Purpose:
DAXPY computes constant times a vector plus a vector.
Discussion:
This routine uses unrolled loops for increments equal to one.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
30 March 2007
Author:
C version by John Burkardt
Reference:
Jack Dongarra, Cleve Moler, Jim Bunch, Pete Stewart,
LINPACK User's Guide,
SIAM, 1979.
Charles Lawson, Richard Hanson, David Kincaid, Fred Krogh,
Basic Linear Algebra Subprograms for Fortran Usage,
Algorithm 539,
ACM Transactions on Mathematical Software,
Volume 5, Number 3, September 1979, pages 308-323.
Parameters:
Input, int N, the number of elements in DX and DY.
Input, float DA, the multiplier of DX.
Input, float DX[*], the first vector.
Input, int INCX, the increment between successive entries of DX.
Input/output, float DY[*], the second vector.
On output, DY[*] has been replaced by DY[*] + DA * DX[*].
Input, int INCY, the increment between successive entries of DY.
*/
{
if (n <= 0 || da == 0.0) return;
int i, ix, iy, m;
/**
Code for unequal increments or equal increments
not equal to 1.
*/
if (incx != 1 || incy != 1) {
if (0 <= incx)
ix = 0;
else
ix = (- n + 1) * incx;
if (0 <= incy)
iy = 0;
else
iy = (- n + 1) * incy;
for (i = 0; i < n; i++) {
dy[iy] = dy[iy] + da * dx[ix];
ix = ix + incx;
iy = iy + incy;
}
}
/**
Code for both increments equal to 1.
*/
else {
m = n % 4;
for (i = 0; i < m; i++)
dy[i] = dy[i] + da * dx[i];
for (i = m; i < n; i = i + 4) {
dy[i ] = dy[i ] + da * dx[i ];
dy[i + 1] = dy[i + 1] + da * dx[i + 1];
dy[i + 2] = dy[i + 2] + da * dx[i + 2];
dy[i + 3] = dy[i + 3] + da * dx[i + 3];
}
}
}
/******************************************************************************/
float ddot(int n, float dx[], int incx, float dy[], int incy)
/******************************************************************************/
/**
Purpose:
DDOT forms the dot product of two vectors.
Discussion:
This routine uses unrolled loops for increments equal to one.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
30 March 2007
Author:
C version by John Burkardt
Reference:
Jack Dongarra, Cleve Moler, Jim Bunch, Pete Stewart,
LINPACK User's Guide,
SIAM, 1979.
Charles Lawson, Richard Hanson, David Kincaid, Fred Krogh,
Basic Linear Algebra Subprograms for Fortran Usage,
Algorithm 539,
ACM Transactions on Mathematical Software,
Volume 5, Number 3, September 1979, pages 308-323.
Parameters:
Input, int N, the number of entries in the vectors.
Input, float DX[*], the first vector.
Input, int INCX, the increment between successive entries in DX.
Input, float DY[*], the second vector.
Input, int INCY, the increment between successive entries in DY.
Output, float DDOT, the sum of the product of the corresponding
entries of DX and DY.
*/
{
if (n <= 0) return 0.0;
int i, m;
float dtemp = 0.0;
/**
Code for unequal increments or equal increments
not equal to 1.
*/
if (incx != 1 || incy != 1) {
int ix = (incx >= 0) ? 0 : (-n + 1) * incx,
iy = (incy >= 0) ? 0 : (-n + 1) * incy;
for (i = 0; i < n; i++) {
dtemp += dx[ix] * dy[iy];
ix = ix + incx;
iy = iy + incy;
}
}
/**
Code for both increments equal to 1.
*/
else {
m = n % 5;
for (i = 0; i < m; i++)
dtemp += dx[i] * dy[i];
for (i = m; i < n; i = i + 5) {
dtemp += dx[i] * dy[i]
+ dx[i + 1] * dy[i + 1]
+ dx[i + 2] * dy[i + 2]
+ dx[i + 3] * dy[i + 3]
+ dx[i + 4] * dy[i + 4];
}
}
return dtemp;
}
/******************************************************************************/
float dnrm2(int n, float x[], int incx)
/******************************************************************************/
/**
Purpose:
DNRM2 returns the euclidean norm of a vector.
Discussion:
DNRM2 ( X ) = sqrt ( X' * X )
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
30 March 2007
Author:
C version by John Burkardt
Reference:
Jack Dongarra, Cleve Moler, Jim Bunch, Pete Stewart,
LINPACK User's Guide,
SIAM, 1979.
Charles Lawson, Richard Hanson, David Kincaid, Fred Krogh,
Basic Linear Algebra Subprograms for Fortran Usage,
Algorithm 539,
ACM Transactions on Mathematical Software,
Volume 5, Number 3, September 1979, pages 308-323.
Parameters:
Input, int N, the number of entries in the vector.
Input, float X[*], the vector whose norm is to be computed.
Input, int INCX, the increment between successive entries of X.
Output, float DNRM2, the Euclidean norm of X.
*/
{
float norm;
if (n < 1 || incx < 1)
norm = 0.0;
else if (n == 1)
norm = r8_abs(x[0]);
else {
float scale = 0.0, ssq = 1.0;
int ix = 0;
for (int i = 0; i < n; i++) {
if (x[ix] != 0.0) {
float absxi = r8_abs(x[ix]);
if (scale < absxi) {
ssq = 1.0 + ssq * (scale / absxi) * (scale / absxi);
scale = absxi;
}
else
ssq = ssq + (absxi / scale) * (absxi / scale);
}
ix += incx;
}
norm = scale * SQRT(ssq);
}
return norm;
}
/******************************************************************************/
void dqrank(float a[], int lda, int m, int n, float tol, int* kr,
int jpvt[], float qraux[])
/******************************************************************************/
/**
Purpose:
DQRANK computes the QR factorization of a rectangular matrix.
Discussion:
This routine is used in conjunction with DQRLSS to solve
overdetermined, underdetermined and singular linear systems
in a least squares sense.
DQRANK uses the LINPACK subroutine DQRDC to compute the QR
factorization, with column pivoting, of an M by N matrix A.
The numerical rank is determined using the tolerance TOL.
Note that on output, ABS ( A(1,1) ) / ABS ( A(KR,KR) ) is an estimate
of the condition number of the matrix of independent columns,
and of R. This estimate will be <= 1/TOL.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
21 April 2012
Author:
C version by John Burkardt.
Reference:
Jack Dongarra, Cleve Moler, Jim Bunch, Pete Stewart,
LINPACK User's Guide,
SIAM, 1979,
ISBN13: 978-0-898711-72-1,
LC: QA214.L56.
Parameters:
Input/output, float A[LDA*N]. On input, the matrix whose
decomposition is to be computed. On output, the information from DQRDC.
The triangular matrix R of the QR factorization is contained in the
upper triangle and information needed to recover the orthogonal
matrix Q is stored below the diagonal in A and in the vector QRAUX.
Input, int LDA, the leading dimension of A, which must
be at least M.
Input, int M, the number of rows of A.
Input, int N, the number of columns of A.
Input, float TOL, a relative tolerance used to determine the
numerical rank. The problem should be scaled so that all the elements
of A have roughly the same absolute accuracy, EPS. Then a reasonable
value for TOL is roughly EPS divided by the magnitude of the largest
element.
Output, int *KR, the numerical rank.
Output, int JPVT[N], the pivot information from DQRDC.
Columns JPVT(1), ..., JPVT(KR) of the original matrix are linearly
independent to within the tolerance TOL and the remaining columns
are linearly dependent.
Output, float QRAUX[N], will contain extra information defining
the QR factorization.
*/
{
float work[n];
for (int i = 0; i < n; i++)
jpvt[i] = 0;
int job = 1;
dqrdc(a, lda, m, n, qraux, jpvt, work, job);
*kr = 0;
int k = i4_min(m, n);
for (int j = 0; j < k; j++) {
if (r8_abs(a[j + j * lda]) <= tol * r8_abs(a[0 + 0 * lda]))
return;
*kr = j + 1;
}
}
/******************************************************************************/
void dqrdc(float a[], int lda, int n, int p, float qraux[], int jpvt[],
float work[], int job)
/******************************************************************************/
/**
Purpose:
DQRDC computes the QR factorization of a real rectangular matrix.
Discussion:
DQRDC uses Householder transformations.
Column pivoting based on the 2-norms of the reduced columns may be
performed at the user's option.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
07 June 2005
Author:
C version by John Burkardt.
Reference:
Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
LINPACK User's Guide,
SIAM, (Society for Industrial and Applied Mathematics),
3600 University City Science Center,
Philadelphia, PA, 19104-2688.
ISBN 0-89871-172-X
Parameters:
Input/output, float A(LDA,P). On input, the N by P matrix
whose decomposition is to be computed. On output, A contains in
its upper triangle the upper triangular matrix R of the QR
factorization. Below its diagonal A contains information from
which the orthogonal part of the decomposition can be recovered.
Note that if pivoting has been requested, the decomposition is not that
of the original matrix A but that of A with its columns permuted
as described by JPVT.
Input, int LDA, the leading dimension of the array A. LDA must
be at least N.
Input, int N, the number of rows of the matrix A.
Input, int P, the number of columns of the matrix A.
Output, float QRAUX[P], contains further information required
to recover the orthogonal part of the decomposition.
Input/output, integer JPVT[P]. On input, JPVT contains integers that
control the selection of the pivot columns. The K-th column A(*,K) of A
is placed in one of three classes according to the value of JPVT(K).
> 0, then A(K) is an initial column.
= 0, then A(K) is a free column.
< 0, then A(K) is a final column.
Before the decomposition is computed, initial columns are moved to
the beginning of the array A and final columns to the end. Both
initial and final columns are frozen in place during the computation
and only free columns are moved. At the K-th stage of the
reduction, if A(*,K) is occupied by a free column it is interchanged
with the free column of largest reduced norm. JPVT is not referenced
if JOB == 0. On output, JPVT(K) contains the index of the column of the
original matrix that has been interchanged into the K-th column, if
pivoting was requested.
Workspace, float WORK[P]. WORK is not referenced if JOB == 0.
Input, int JOB, initiates column pivoting.
0, no pivoting is done.
nonzero, pivoting is done.
*/
{
int jp;
int j;
int lup;
int maxj;
float maxnrm, nrmxl, t, tt;
int pl = 1, pu = 0;
/**
If pivoting is requested, rearrange the columns.
*/
if (job != 0) {
for (j = 1; j <= p; j++) {
int swapj = (0 < jpvt[j - 1]);
jpvt[j - 1] = (jpvt[j - 1] < 0) ? -j : j;
if (swapj) {
if (j != pl)
dswap(n, a + 0 + (pl - 1)*lda, 1, a + 0 + (j - 1), 1);
jpvt[j - 1] = jpvt[pl - 1];
jpvt[pl - 1] = j;
pl++;
}
}
pu = p;
for (j = p; 1 <= j; j--) {
if (jpvt[j - 1] < 0) {
jpvt[j - 1] = -jpvt[j - 1];
if (j != pu) {
dswap(n, a + 0 + (pu - 1)*lda, 1, a + 0 + (j - 1)*lda, 1);
jp = jpvt[pu - 1];
jpvt[pu - 1] = jpvt[j - 1];
jpvt[j - 1] = jp;
}
pu = pu - 1;
}
}
}
/**
Compute the norms of the free columns.
*/
for (j = pl; j <= pu; j++)
qraux[j - 1] = dnrm2(n, a + 0 + (j - 1) * lda, 1);
for (j = pl; j <= pu; j++)
work[j - 1] = qraux[j - 1];
/**
Perform the Householder reduction of A.
*/
lup = i4_min(n, p);
for (int l = 1; l <= lup; l++) {
/**
Bring the column of largest norm into the pivot position.
*/
if (pl <= l && l < pu) {
maxnrm = 0.0;
maxj = l;
for (j = l; j <= pu; j++) {
if (maxnrm < qraux[j - 1]) {
maxnrm = qraux[j - 1];
maxj = j;
}
}
if (maxj != l) {
dswap(n, a + 0 + (l - 1)*lda, 1, a + 0 + (maxj - 1)*lda, 1);
qraux[maxj - 1] = qraux[l - 1];
work[maxj - 1] = work[l - 1];
jp = jpvt[maxj - 1];
jpvt[maxj - 1] = jpvt[l - 1];
jpvt[l - 1] = jp;
}
}
/**
Compute the Householder transformation for column L.
*/
qraux[l - 1] = 0.0;
if (l != n) {
nrmxl = dnrm2(n - l + 1, a + l - 1 + (l - 1) * lda, 1);
if (nrmxl != 0.0) {
if (a[l - 1 + (l - 1)*lda] != 0.0)
nrmxl = nrmxl * r8_sign(a[l - 1 + (l - 1) * lda]);
dscal(n - l + 1, 1.0 / nrmxl, a + l - 1 + (l - 1)*lda, 1);
a[l - 1 + (l - 1)*lda] = 1.0 + a[l - 1 + (l - 1) * lda];
/**
Apply the transformation to the remaining columns, updating the norms.
*/
for (j = l + 1; j <= p; j++) {
t = -ddot(n - l + 1, a + l - 1 + (l - 1) * lda, 1, a + l - 1 + (j - 1) * lda, 1)
/ a[l - 1 + (l - 1) * lda];
daxpy(n - l + 1, t, a + l - 1 + (l - 1)*lda, 1, a + l - 1 + (j - 1)*lda, 1);
if (pl <= j && j <= pu) {
if (qraux[j - 1] != 0.0) {
tt = 1.0 - POW(r8_abs(a[l - 1 + (j - 1) * lda]) / qraux[j - 1], 2);
tt = r8_max(tt, 0.0);
t = tt;
tt = 1.0 + 0.05 * tt * POW(qraux[j - 1] / work[j - 1], 2);
if (tt != 1.0)
qraux[j - 1] = qraux[j - 1] * SQRT(t);
else {
qraux[j - 1] = dnrm2(n - l, a + l + (j - 1) * lda, 1);
work[j - 1] = qraux[j - 1];
}
}
}
}
/**
Save the transformation.
*/
qraux[l - 1] = a[l - 1 + (l - 1) * lda];
a[l - 1 + (l - 1)*lda] = -nrmxl;
}
}
}
}
/******************************************************************************/
int dqrls(float a[], int lda, int m, int n, float tol, int* kr, float b[],
float x[], float rsd[], int jpvt[], float qraux[], int itask)
/******************************************************************************/
/**
Purpose:
DQRLS factors and solves a linear system in the least squares sense.
Discussion:
The linear system may be overdetermined, underdetermined or singular.
The solution is obtained using a QR factorization of the
coefficient matrix.
DQRLS can be efficiently used to solve several least squares
problems with the same matrix A. The first system is solved
with ITASK = 1. The subsequent systems are solved with
ITASK = 2, to avoid the recomputation of the matrix factors.
The parameters KR, JPVT, and QRAUX must not be modified
between calls to DQRLS.
DQRLS is used to solve in a least squares sense
overdetermined, underdetermined and singular linear systems.
The system is A*X approximates B where A is M by N.
B is a given M-vector, and X is the N-vector to be computed.
A solution X is found which minimimzes the sum of squares (2-norm)
of the residual, A*X - B.
The numerical rank of A is determined using the tolerance TOL.
DQRLS uses the LINPACK subroutine DQRDC to compute the QR
factorization, with column pivoting, of an M by N matrix A.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
10 September 2012
Author:
C version by John Burkardt.
Reference:
David Kahaner, Cleve Moler, Steven Nash,
Numerical Methods and Software,
Prentice Hall, 1989,
ISBN: 0-13-627258-4,
LC: TA345.K34.
Parameters:
Input/output, float A[LDA*N], an M by N matrix.
On input, the matrix whose decomposition is to be computed.
In a least squares data fitting problem, A(I,J) is the
value of the J-th basis (model) function at the I-th data point.
On output, A contains the output from DQRDC. The triangular matrix R
of the QR factorization is contained in the upper triangle and
information needed to recover the orthogonal matrix Q is stored
below the diagonal in A and in the vector QRAUX.
Input, int LDA, the leading dimension of A.
Input, int M, the number of rows of A.
Input, int N, the number of columns of A.
Input, float TOL, a relative tolerance used to determine the
numerical rank. The problem should be scaled so that all the elements
of A have roughly the same absolute accuracy EPS. Then a reasonable
value for TOL is roughly EPS divided by the magnitude of the largest
element.
Output, int *KR, the numerical rank.
Input, float B[M], the right hand side of the linear system.
Output, float X[N], a least squares solution to the linear
system.
Output, float RSD[M], the residual, B - A*X. RSD may
overwrite B.
Workspace, int JPVT[N], required if ITASK = 1.
Columns JPVT(1), ..., JPVT(KR) of the original matrix are linearly
independent to within the tolerance TOL and the remaining columns
are linearly dependent. ABS ( A(1,1) ) / ABS ( A(KR,KR) ) is an estimate
of the condition number of the matrix of independent columns,
and of R. This estimate will be <= 1/TOL.
Workspace, float QRAUX[N], required if ITASK = 1.
Input, int ITASK.
1, DQRLS factors the matrix A and solves the least squares problem.
2, DQRLS assumes that the matrix A was factored with an earlier
call to DQRLS, and only solves the least squares problem.
Output, int DQRLS, error code.
0: no error
-1: LDA < M (fatal error)
-2: N < 1 (fatal error)
-3: ITASK < 1 (fatal error)
*/
{
int ind;
if (lda < m) {
/*fprintf ( stderr, "\n" );
fprintf ( stderr, "DQRLS - Fatal error!\n" );
fprintf ( stderr, " LDA < M.\n" );*/
ind = -1;
return ind;
}
if (n <= 0) {
/*fprintf ( stderr, "\n" );
fprintf ( stderr, "DQRLS - Fatal error!\n" );
fprintf ( stderr, " N <= 0.\n" );*/
ind = -2;
return ind;
}
if (itask < 1) {
/*fprintf ( stderr, "\n" );
fprintf ( stderr, "DQRLS - Fatal error!\n" );
fprintf ( stderr, " ITASK < 1.\n" );*/
ind = -3;
return ind;
}
ind = 0;
/**
Factor the matrix.
*/
if (itask == 1)
dqrank(a, lda, m, n, tol, kr, jpvt, qraux);
/**
Solve the least-squares problem.
*/
dqrlss(a, lda, m, n, *kr, b, x, rsd, jpvt, qraux);
return ind;
}
/******************************************************************************/
void dqrlss(float a[], int lda, int m, int n, int kr, float b[], float x[],
float rsd[], int jpvt[], float qraux[])
/******************************************************************************/
/**
Purpose:
DQRLSS solves a linear system in a least squares sense.
Discussion:
DQRLSS must be preceded by a call to DQRANK.
The system is to be solved is
A * X = B
where
A is an M by N matrix with rank KR, as determined by DQRANK,
B is a given M-vector,
X is the N-vector to be computed.
A solution X, with at most KR nonzero components, is found which
minimizes the 2-norm of the residual (A*X-B).
Once the matrix A has been formed, DQRANK should be
called once to decompose it. Then, for each right hand
side B, DQRLSS should be called once to obtain the
solution and residual.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
10 September 2012
Author:
C version by John Burkardt
Parameters:
Input, float A[LDA*N], the QR factorization information
from DQRANK. The triangular matrix R of the QR factorization is
contained in the upper triangle and information needed to recover
the orthogonal matrix Q is stored below the diagonal in A and in
the vector QRAUX.
Input, int LDA, the leading dimension of A, which must
be at least M.
Input, int M, the number of rows of A.
Input, int N, the number of columns of A.
Input, int KR, the rank of the matrix, as estimated by DQRANK.
Input, float B[M], the right hand side of the linear system.
Output, float X[N], a least squares solution to the
linear system.
Output, float RSD[M], the residual, B - A*X. RSD may
overwrite B.
Input, int JPVT[N], the pivot information from DQRANK.
Columns JPVT[0], ..., JPVT[KR-1] of the original matrix are linearly
independent to within the tolerance TOL and the remaining columns
are linearly dependent.
Input, float QRAUX[N], auxiliary information from DQRANK
defining the QR factorization.
*/
{
int i;
int info;
int j;
int job;
int k;
float t;
if (kr != 0) {
job = 110;
info = dqrsl(a, lda, m, kr, qraux, b, rsd, rsd, x, rsd, rsd, job); UNUSED(info);
}
for (i = 0; i < n; i++)
jpvt[i] = - jpvt[i];
for (i = kr; i < n; i++)
x[i] = 0.0;
for (j = 1; j <= n; j++) {
if (jpvt[j - 1] <= 0) {
k = - jpvt[j - 1];
jpvt[j - 1] = k;
while (k != j) {
t = x[j - 1];
x[j - 1] = x[k - 1];
x[k - 1] = t;
jpvt[k - 1] = -jpvt[k - 1];
k = jpvt[k - 1];
}
}
}
}
/******************************************************************************/
int dqrsl(float a[], int lda, int n, int k, float qraux[], float y[],
float qy[], float qty[], float b[], float rsd[], float ab[], int job)
/******************************************************************************/
/**
Purpose:
DQRSL computes transformations, projections, and least squares solutions.
Discussion:
DQRSL requires the output of DQRDC.
For K <= min(N,P), let AK be the matrix
AK = ( A(JPVT[0]), A(JPVT(2)), ..., A(JPVT(K)) )
formed from columns JPVT[0], ..., JPVT(K) of the original
N by P matrix A that was input to DQRDC. If no pivoting was
done, AK consists of the first K columns of A in their
original order. DQRDC produces a factored orthogonal matrix Q
and an upper triangular matrix R such that
AK = Q * (R)
(0)
This information is contained in coded form in the arrays
A and QRAUX.
The parameters QY, QTY, B, RSD, and AB are not referenced
if their computation is not requested and in this case
can be replaced by dummy variables in the calling program.
To save storage, the user may in some cases use the same
array for different parameters in the calling sequence. A
frequently occurring example is when one wishes to compute
any of B, RSD, or AB and does not need Y or QTY. In this
case one may identify Y, QTY, and one of B, RSD, or AB, while
providing separate arrays for anything else that is to be
computed.
Thus the calling sequence
dqrsl ( a, lda, n, k, qraux, y, dum, y, b, y, dum, 110, info )
will result in the computation of B and RSD, with RSD
overwriting Y. More generally, each item in the following
list contains groups of permissible identifications for
a single calling sequence.
1. (Y,QTY,B) (RSD) (AB) (QY)
2. (Y,QTY,RSD) (B) (AB) (QY)
3. (Y,QTY,AB) (B) (RSD) (QY)
4. (Y,QY) (QTY,B) (RSD) (AB)
5. (Y,QY) (QTY,RSD) (B) (AB)
6. (Y,QY) (QTY,AB) (B) (RSD)
In any group the value returned in the array allocated to
the group corresponds to the last member of the group.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
07 June 2005
Author:
C version by John Burkardt.
Reference:
Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
LINPACK User's Guide,
SIAM, (Society for Industrial and Applied Mathematics),
3600 University City Science Center,
Philadelphia, PA, 19104-2688.
ISBN 0-89871-172-X
Parameters:
Input, float A[LDA*P], contains the output of DQRDC.
Input, int LDA, the leading dimension of the array A.
Input, int N, the number of rows of the matrix AK. It must
have the same value as N in DQRDC.
Input, int K, the number of columns of the matrix AK. K
must not be greater than min(N,P), where P is the same as in the
calling sequence to DQRDC.
Input, float QRAUX[P], the auxiliary output from DQRDC.
Input, float Y[N], a vector to be manipulated by DQRSL.
Output, float QY[N], contains Q * Y, if requested.
Output, float QTY[N], contains Q' * Y, if requested.
Output, float B[K], the solution of the least squares problem
minimize norm2 ( Y - AK * B),
if its computation has been requested. Note that if pivoting was
requested in DQRDC, the J-th component of B will be associated with
column JPVT(J) of the original matrix A that was input into DQRDC.
Output, float RSD[N], the least squares residual Y - AK * B,
if its computation has been requested. RSD is also the orthogonal
projection of Y onto the orthogonal complement of the column space
of AK.
Output, float AB[N], the least squares approximation Ak * B,
if its computation has been requested. AB is also the orthogonal
projection of Y onto the column space of A.
Input, integer JOB, specifies what is to be computed. JOB has
the decimal expansion ABCDE, with the following meaning:
if A != 0, compute QY.
if B != 0, compute QTY.
if C != 0, compute QTY and B.
if D != 0, compute QTY and RSD.
if E != 0, compute QTY and AB.
Note that a request to compute B, RSD, or AB automatically triggers
the computation of QTY, for which an array must be provided in the
calling sequence.
Output, int DQRSL, is zero unless the computation of B has
been requested and R is exactly singular. In this case, INFO is the
index of the first zero diagonal element of R, and B is left unaltered.
*/
{
int cab;
int cb;
int cqty;
int cqy;
int cr;
int i;
int info;
int j;
int jj;
int ju;
float t;
float temp;
/**
Set INFO flag.
*/
info = 0;
/**
Determine what is to be computed.
*/
cqy = ( job / 10000 != 0);
cqty = ((job % 10000) != 0);
cb = ((job % 1000) / 100 != 0);
cr = ((job % 100) / 10 != 0);
cab = ((job % 10) != 0);
ju = i4_min(k, n - 1);
/**
Special action when N = 1.
*/
if (ju == 0) {
if (cqy)
qy[0] = y[0];
if (cqty)
qty[0] = y[0];
if (cab)
ab[0] = y[0];
if (cb) {
if (a[0 + 0 * lda] == 0.0)
info = 1;
else
b[0] = y[0] / a[0 + 0 * lda];
}
if (cr)
rsd[0] = 0.0;
return info;
}
/**
Set up to compute QY or QTY.
*/
if (cqy) {
for (i = 1; i <= n; i++)
qy[i - 1] = y[i - 1];
}
if (cqty) {
for (i = 1; i <= n; i++)
qty[i - 1] = y[i - 1];
}
/**
Compute QY.
*/
if (cqy) {
for (jj = 1; jj <= ju; jj++) {
j = ju - jj + 1;
if (qraux[j - 1] != 0.0) {
temp = a[j - 1 + (j - 1) * lda];
a[j - 1 + (j - 1)*lda] = qraux[j - 1];
t = -ddot(n - j + 1, a + j - 1 + (j - 1) * lda, 1, qy + j - 1, 1) / a[j - 1 + (j - 1) * lda];
daxpy(n - j + 1, t, a + j - 1 + (j - 1)*lda, 1, qy + j - 1, 1);
a[j - 1 + (j - 1)*lda] = temp;
}
}
}
/**
Compute Q'*Y.
*/
if (cqty) {
for (j = 1; j <= ju; j++) {
if (qraux[j - 1] != 0.0) {
temp = a[j - 1 + (j - 1) * lda];
a[j - 1 + (j - 1)*lda] = qraux[j - 1];
t = -ddot(n - j + 1, a + j - 1 + (j - 1) * lda, 1, qty + j - 1, 1) / a[j - 1 + (j - 1) * lda];
daxpy(n - j + 1, t, a + j - 1 + (j - 1)*lda, 1, qty + j - 1, 1);
a[j - 1 + (j - 1)*lda] = temp;
}
}
}
/**
Set up to compute B, RSD, or AB.
*/
if (cb) {
for (i = 1; i <= k; i++)
b[i - 1] = qty[i - 1];
}
if (cab) {
for (i = 1; i <= k; i++)
ab[i - 1] = qty[i - 1];
}
if (cr && k < n) {
for (i = k + 1; i <= n; i++)
rsd[i - 1] = qty[i - 1];
}
if (cab && k + 1 <= n) {
for (i = k + 1; i <= n; i++)
ab[i - 1] = 0.0;
}
if (cr) {
for (i = 1; i <= k; i++)
rsd[i - 1] = 0.0;
}
/**
Compute B.
*/
if (cb) {
for (jj = 1; jj <= k; jj++) {
j = k - jj + 1;
if (a[j - 1 + (j - 1)*lda] == 0.0) {
info = j;
break;
}
b[j - 1] = b[j - 1] / a[j - 1 + (j - 1) * lda];
if (j != 1) {
t = -b[j - 1];
daxpy(j - 1, t, a + 0 + (j - 1)*lda, 1, b, 1);
}
}
}
/**
Compute RSD or AB as required.
*/
if (cr || cab) {
for (jj = 1; jj <= ju; jj++) {
j = ju - jj + 1;
if (qraux[j - 1] != 0.0) {
temp = a[j - 1 + (j - 1) * lda];
a[j - 1 + (j - 1)*lda] = qraux[j - 1];
if (cr) {
t = -ddot(n - j + 1, a + j - 1 + (j - 1) * lda, 1, rsd + j - 1, 1)
/ a[j - 1 + (j - 1) * lda];
daxpy(n - j + 1, t, a + j - 1 + (j - 1)*lda, 1, rsd + j - 1, 1);
}
if (cab) {
t = -ddot(n - j + 1, a + j - 1 + (j - 1) * lda, 1, ab + j - 1, 1)
/ a[j - 1 + (j - 1) * lda];
daxpy(n - j + 1, t, a + j - 1 + (j - 1)*lda, 1, ab + j - 1, 1);
}
a[j - 1 + (j - 1)*lda] = temp;
}
}
}
return info;
}
/******************************************************************************/
/******************************************************************************/
void dscal(int n, float sa, float x[], int incx)
/******************************************************************************/
/**
Purpose:
DSCAL scales a vector by a constant.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
30 March 2007
Author:
C version by John Burkardt
Reference:
Jack Dongarra, Cleve Moler, Jim Bunch, Pete Stewart,
LINPACK User's Guide,
SIAM, 1979.
Charles Lawson, Richard Hanson, David Kincaid, Fred Krogh,
Basic Linear Algebra Subprograms for Fortran Usage,
Algorithm 539,
ACM Transactions on Mathematical Software,
Volume 5, Number 3, September 1979, pages 308-323.
Parameters:
Input, int N, the number of entries in the vector.
Input, float SA, the multiplier.
Input/output, float X[*], the vector to be scaled.
Input, int INCX, the increment between successive entries of X.
*/
{
int i;
int ix;
int m;
if (n <= 0) return;
if (incx == 1) {
m = n % 5;
for (i = 0; i < m; i++)
x[i] = sa * x[i];
for (i = m; i < n; i = i + 5) {
x[i] = sa * x[i];
x[i + 1] = sa * x[i + 1];
x[i + 2] = sa * x[i + 2];
x[i + 3] = sa * x[i + 3];
x[i + 4] = sa * x[i + 4];
}
}
else {
if (0 <= incx)
ix = 0;
else
ix = (- n + 1) * incx;
for (i = 0; i < n; i++) {
x[ix] = sa * x[ix];
ix = ix + incx;
}
}
}
/******************************************************************************/
void dswap(int n, float x[], int incx, float y[], int incy)
/******************************************************************************/
/**
Purpose:
DSWAP interchanges two vectors.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
30 March 2007
Author:
C version by John Burkardt
Reference:
Jack Dongarra, Cleve Moler, Jim Bunch, Pete Stewart,
LINPACK User's Guide,
SIAM, 1979.
Charles Lawson, Richard Hanson, David Kincaid, Fred Krogh,
Basic Linear Algebra Subprograms for Fortran Usage,
Algorithm 539,
ACM Transactions on Mathematical Software,
Volume 5, Number 3, September 1979, pages 308-323.
Parameters:
Input, int N, the number of entries in the vectors.
Input/output, float X[*], one of the vectors to swap.
Input, int INCX, the increment between successive entries of X.
Input/output, float Y[*], one of the vectors to swap.
Input, int INCY, the increment between successive elements of Y.
*/
{
if (n <= 0) return;
int i, ix, iy, m;
float temp;
if (incx == 1 && incy == 1) {
m = n % 3;
for (i = 0; i < m; i++) {
temp = x[i];
x[i] = y[i];
y[i] = temp;
}
for (i = m; i < n; i = i + 3) {
temp = x[i];
x[i] = y[i];
y[i] = temp;
temp = x[i + 1];
x[i + 1] = y[i + 1];
y[i + 1] = temp;
temp = x[i + 2];
x[i + 2] = y[i + 2];
y[i + 2] = temp;
}
}
else {
ix = (incx >= 0) ? 0 : (-n + 1) * incx;
iy = (incy >= 0) ? 0 : (-n + 1) * incy;
for (i = 0; i < n; i++) {
temp = x[ix];
x[ix] = y[iy];
y[iy] = temp;
ix = ix + incx;
iy = iy + incy;
}
}
}
/******************************************************************************/
/******************************************************************************/
void qr_solve(float x[], int m, int n, float a[], float b[])
/******************************************************************************/
/**
Purpose:
QR_SOLVE solves a linear system in the least squares sense.
Discussion:
If the matrix A has full column rank, then the solution X should be the
unique vector that minimizes the Euclidean norm of the residual.
If the matrix A does not have full column rank, then the solution is
not unique; the vector X will minimize the residual norm, but so will
various other vectors.
Licensing:
This code is distributed under the GNU LGPL license.
Modified:
11 September 2012
Author:
John Burkardt
Reference:
David Kahaner, Cleve Moler, Steven Nash,
Numerical Methods and Software,
Prentice Hall, 1989,
ISBN: 0-13-627258-4,
LC: TA345.K34.
Parameters:
Input, int M, the number of rows of A.
Input, int N, the number of columns of A.
Input, float A[M*N], the matrix.
Input, float B[M], the right hand side.
Output, float QR_SOLVE[N], the least squares solution.
*/
{
float a_qr[n * m], qraux[n], r[m], tol;
int ind, itask, jpvt[n], kr, lda;
r8mat_copy(a_qr, m, n, a);
lda = m;
tol = r8_epsilon() / r8mat_amax(m, n, a_qr);
itask = 1;
ind = dqrls(a_qr, lda, m, n, tol, &kr, b, x, r, jpvt, qraux, itask); UNUSED(ind);
}
/******************************************************************************/
#endif

44
Marlin/qr_solve.h
View file

@ -1,44 +0,0 @@
/**
* Marlin 3D Printer Firmware
* Copyright (C) 2016 MarlinFirmware [https://github.com/MarlinFirmware/Marlin]
*
* Based on Sprinter and grbl.
* Copyright (C) 2011 Camiel Gubbels / Erik van der Zalm
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*
*/
#include "MarlinConfig.h"
#if ENABLED(AUTO_BED_LEVELING_LINEAR)
void daxpy(int n, float da, float dx[], int incx, float dy[], int incy);
float ddot(int n, float dx[], int incx, float dy[], int incy);
float dnrm2(int n, float x[], int incx);
void dqrank(float a[], int lda, int m, int n, float tol, int* kr,
int jpvt[], float qraux[]);
void dqrdc(float a[], int lda, int n, int p, float qraux[], int jpvt[],
float work[], int job);
int dqrls(float a[], int lda, int m, int n, float tol, int* kr, float b[],
float x[], float rsd[], int jpvt[], float qraux[], int itask);
void dqrlss(float a[], int lda, int m, int n, int kr, float b[], float x[],
float rsd[], int jpvt[], float qraux[]);
int dqrsl(float a[], int lda, int n, int k, float qraux[], float y[],
float qy[], float qty[], float b[], float rsd[], float ab[], int job);
void dscal(int n, float sa, float x[], int incx);
void dswap(int n, float x[], int incx, float y[], int incy);
void qr_solve(float x[], int m, int n, float a[], float b[]);
#endif