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							91 lines
						
					
					
						
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							91 lines
						
					
					
						
							3.0 KiB
						
					
					
				/**
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 * Marlin 3D Printer Firmware
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 * Copyright (C) 2016 MarlinFirmware [https://github.com/MarlinFirmware/Marlin]
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 *
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 * Based on Sprinter and grbl.
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 * Copyright (C) 2011 Camiel Gubbels / Erik van der Zalm
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 *
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 * This program is free software: you can redistribute it and/or modify
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 * it under the terms of the GNU General Public License as published by
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 * the Free Software Foundation, either version 3 of the License, or
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 * (at your option) any later version.
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 *
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 * This program is distributed in the hope that it will be useful,
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 * but WITHOUT ANY WARRANTY; without even the implied warranty of
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 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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 * GNU General Public License for more details.
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 *
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 * You should have received a copy of the GNU General Public License
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 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
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 *
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 */
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/**
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 * Incremental Least Squares Best Fit  By Roxy and Ed Williams
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 *
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 * This algorithm is high speed and has a very small code footprint.
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 * Its results are identical to both the Iterative Least-Squares published
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 * earlier by Roxy and the QR_SOLVE solution. If used in place of QR_SOLVE
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 * it saves roughly 10K of program memory.   And even better...  the data
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 * fed into the algorithm does not need to all be present at the same time.
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 * A point can be probed and its values fed into the algorithm and then discarded.
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 *
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 */
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#include "MarlinConfig.h"
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#if ENABLED(AUTO_BED_LEVELING_UBL)  // Currently only used by UBL, but is applicable to Grid Based (Linear) Bed Leveling
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#include "Marlin.h"
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#include "macros.h"
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#include <math.h>
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struct linear_fit_data {
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  float xbar, ybar, zbar,
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        x2bar, y2bar, z2bar,
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        xybar, xzbar, yzbar,
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        max_absx, max_absy,
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        A, B, D, N;
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};
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void inline incremental_LSF_reset(struct linear_fit_data *lsf) {
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  memset(lsf, 0, sizeof(linear_fit_data));
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}
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void inline incremental_WLSF(struct linear_fit_data *lsf, const float &x, const float &y, const float &z, const float &w) {
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  // weight each accumulator by factor w, including the "number" of samples
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  // (analagous to calling inc_LSF twice with same values to weight it by 2X)
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  lsf->xbar  += w * x;
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  lsf->ybar  += w * y;
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  lsf->zbar  += w * z;
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  lsf->x2bar += w * x * x;  // don't use sq(x) -- let compiler re-use w*x four times
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  lsf->y2bar += w * y * y;
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  lsf->z2bar += w * z * z;
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  lsf->xybar += w * x * y;
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  lsf->xzbar += w * x * z;
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  lsf->yzbar += w * y * z;
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  lsf->N     += w;
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  lsf->max_absx = max(fabs(w * x), lsf->max_absx);
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  lsf->max_absy = max(fabs(w * y), lsf->max_absy);
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}
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void inline incremental_LSF(struct linear_fit_data *lsf, const float &x, const float &y, const float &z) {
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  lsf->xbar += x;
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  lsf->ybar += y;
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  lsf->zbar += z;
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  lsf->x2bar += sq(x);
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  lsf->y2bar += sq(y);
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  lsf->z2bar += sq(z);
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  lsf->xybar += x * y;
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  lsf->xzbar += x * z;
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  lsf->yzbar += y * z;
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  lsf->max_absx = max(fabs(x), lsf->max_absx);
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  lsf->max_absy = max(fabs(y), lsf->max_absy);
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  lsf->N += 1.0;
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}
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int finish_incremental_LSF(struct linear_fit_data *);
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#endif
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