mirror of
https://github.com/MarlinFirmware/Marlin.git
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119 lines
3.7 KiB
C++
119 lines
3.7 KiB
C++
/**
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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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* 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.
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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 "ubl.h"
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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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double linear_fit_average(double m[], const int);
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//double linear_fit_average_squared(double m[], const int);
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//double linear_fit_average_mixed_terms(double m1[], double m2[], const int);
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double linear_fit_average_product(double matrix1[], double matrix2[], const int n);
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void linear_fit_subtract_mean(double matrix[], double bar, const int n);
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double linear_fit_max_abs(double m[], const int);
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linear_fit linear_fit_results;
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linear_fit* lsf_linear_fit(double x[], double y[], double z[], const int n) {
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double xbar, ybar, zbar,
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x2bar, y2bar,
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xybar, xzbar, yzbar,
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D;
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linear_fit_results.A = 0.0;
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linear_fit_results.B = 0.0;
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linear_fit_results.D = 0.0;
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xbar = linear_fit_average(x, n);
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ybar = linear_fit_average(y, n);
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zbar = linear_fit_average(z, n);
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linear_fit_subtract_mean(x, xbar, n);
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linear_fit_subtract_mean(y, ybar, n);
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linear_fit_subtract_mean(z, zbar, n);
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x2bar = linear_fit_average_product(x, x, n);
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y2bar = linear_fit_average_product(y, y, n);
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xybar = linear_fit_average_product(x, y, n);
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xzbar = linear_fit_average_product(x, z, n);
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yzbar = linear_fit_average_product(y, z, n);
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D = x2bar * y2bar - xybar * xybar;
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for (int i = 0; i < n; i++) {
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if (fabs(D) <= 1e-15 * (linear_fit_max_abs(x, n) + linear_fit_max_abs(y, n))) {
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printf("error: x,y points are collinear at index:%d\n", i);
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return NULL;
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}
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}
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linear_fit_results.A = -(xzbar * y2bar - yzbar * xybar) / D;
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linear_fit_results.B = -(yzbar * x2bar - xzbar * xybar) / D;
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// linear_fit_results.D = -(zbar - linear_fit_results->A * xbar - linear_fit_results->B * ybar);
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linear_fit_results.D = -(zbar + linear_fit_results.A * xbar + linear_fit_results.B * ybar);
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return &linear_fit_results;
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}
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double linear_fit_average(double *matrix, const int n) {
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double sum = 0.0;
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for (int i = 0; i < n; i++)
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sum += matrix[i];
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return sum / (double)n;
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}
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double linear_fit_average_product(double *matrix1, double *matrix2, const int n) {
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double sum = 0.0;
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for (int i = 0; i < n; i++)
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sum += matrix1[i] * matrix2[i];
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return sum / (double)n;
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}
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void linear_fit_subtract_mean(double *matrix, double bar, const int n) {
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for (int i = 0; i < n; i++)
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matrix[i] -= bar;
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}
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double linear_fit_max_abs(double *matrix, const int n) {
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double max_abs = 0.0;
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for (int i = 0; i < n; i++)
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NOLESS(max_abs, fabs(matrix[i]));
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return max_abs;
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}
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#endif
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