Firmware2/Marlin/src/libs/least_squares_fit.h

88 lines
3.0 KiB
C
Raw Normal View History

/**
* Marlin 3D Printer Firmware
2020-02-03 15:00:57 +01:00
* Copyright (c) 2020 MarlinFirmware [https://github.com/MarlinFirmware/Marlin]
*
* Based on Sprinter and grbl.
2019-06-28 06:57:50 +02:00
* 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
2020-07-23 05:20:14 +02:00
* along with this program. If not, see <https://www.gnu.org/licenses/>.
*
*/
#pragma once
/**
* 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
2017-04-29 00:33:28 +02:00
* 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.
*/
2017-09-06 13:28:32 +02:00
#include "../inc/MarlinConfig.h"
#include <math.h>
struct linear_fit_data {
2017-04-29 00:33:28 +02:00
float xbar, ybar, zbar,
x2bar, y2bar,
2017-04-29 00:33:28 +02:00
xybar, xzbar, yzbar,
max_absx, max_absy,
A, B, D, N;
};
inline void incremental_LSF_reset(struct linear_fit_data *lsf) {
memset(lsf, 0, sizeof(linear_fit_data));
}
inline void incremental_WLSF(struct linear_fit_data *lsf, const_float_t x, const_float_t y, const_float_t z, const_float_t w) {
// weight each accumulator by factor w, including the "number" of samples
2017-09-06 13:28:32 +02:00
// (analogous to calling inc_LSF twice with same values to weight it by 2X)
const float wx = w * x, wy = w * y, wz = w * z;
lsf->xbar += wx;
lsf->ybar += wy;
lsf->zbar += wz;
lsf->x2bar += wx * x;
lsf->y2bar += wy * y;
lsf->xybar += wx * y;
lsf->xzbar += wx * z;
lsf->yzbar += wy * z;
lsf->N += w;
lsf->max_absx = _MAX(ABS(wx), lsf->max_absx);
lsf->max_absy = _MAX(ABS(wy), lsf->max_absy);
}
inline void incremental_WLSF(struct linear_fit_data *lsf, const xy_pos_t &pos, const_float_t z, const_float_t w) {
2019-09-29 11:25:39 +02:00
incremental_WLSF(lsf, pos.x, pos.y, z, w);
}
inline void incremental_LSF(struct linear_fit_data *lsf, const_float_t x, const_float_t y, const_float_t z) {
lsf->xbar += x;
lsf->ybar += y;
lsf->zbar += z;
lsf->x2bar += sq(x);
lsf->y2bar += sq(y);
lsf->xybar += x * y;
lsf->xzbar += x * z;
lsf->yzbar += y * z;
lsf->max_absx = _MAX(ABS(x), lsf->max_absx);
lsf->max_absy = _MAX(ABS(y), lsf->max_absy);
lsf->N += 1.0;
}
inline void incremental_LSF(struct linear_fit_data *lsf, const xy_pos_t &pos, const_float_t z) {
2019-09-29 11:25:39 +02:00
incremental_LSF(lsf, pos.x, pos.y, z);
}
int finish_incremental_LSF(struct linear_fit_data *);