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Marlin-Artillery-M600/Marlin/least_squares_fit.h
oldmcg 48f7652143 UBL G29 -P3.1 smart fill (#6823)
* UBL G29 -P3.1 mesh fill with distance-weighted least squares fit.

* Back to original -O0 on G29 for now.
2017-05-22 12:33:50 -05:00

90 lines
2.9 KiB
C

/**
* 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/>.
*
*/
/**
* Incremental Least Squares Best Fit By Roxy and Ed Williams
*
* This algorithm is high speed and has a very small code footprint.
* Its results are identical to both the Iterative Least-Squares published
* earlier by Roxy and the QR_SOLVE solution. If used in place of QR_SOLVE
* it saves roughly 10K of program memory. And even better... the data
* fed into the algorithm does not need to all be present at the same time.
* A point can be probed and its values fed into the algorithm and then discarded.
*
*/
#include "MarlinConfig.h"
#if ENABLED(AUTO_BED_LEVELING_UBL) // Currently only used by UBL, but is applicable to Grid Based (Linear) Bed Leveling
#include "Marlin.h"
#include "macros.h"
#include <math.h>
struct linear_fit_data {
float xbar, ybar, zbar,
x2bar, y2bar, z2bar,
xybar, xzbar, yzbar,
max_absx, max_absy,
A, B, D, N;
};
void inline incremental_LSF_reset(struct linear_fit_data *lsf) {
memset(lsf, 0, sizeof(linear_fit_data));
}
void inline incremental_WLSF(struct linear_fit_data *lsf, float x, float y, float z, float w) {
// weight each accumulator by factor w, including the "number" of samples
// (analagous to calling inc_LSF twice with same values to weight it by 2X)
lsf->xbar += w * x;
lsf->ybar += w * y;
lsf->zbar += w * z;
lsf->x2bar += w * x * x; // don't use sq(x) -- let compiler re-use w*x four times
lsf->y2bar += w * y * y;
lsf->z2bar += w * z * z;
lsf->xybar += w * x * y;
lsf->xzbar += w * x * z;
lsf->yzbar += w * y * z;
lsf->N += w;
lsf->max_absx = max(fabs( w * x ), lsf->max_absx);
lsf->max_absy = max(fabs( w * y ), lsf->max_absy);
}
void inline incremental_LSF(struct linear_fit_data *lsf, float x, float y, float z) {
lsf->xbar += x;
lsf->ybar += y;
lsf->zbar += z;
lsf->x2bar += sq(x);
lsf->y2bar += sq(y);
lsf->z2bar += sq(z);
lsf->xybar += x * y;
lsf->xzbar += x * z;
lsf->yzbar += y * z;
lsf->max_absx = max(fabs(x), lsf->max_absx);
lsf->max_absy = max(fabs(y), lsf->max_absy);
lsf->N += 1.0;
}
int finish_incremental_LSF(struct linear_fit_data *);
#endif