135 lines
3.7 KiB
C++
135 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 algorythm 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 10KB 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 <math.h>
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#include "ubl.h"
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#include "Marlin.h"
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double linear_fit_average(double *, int);
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double linear_fit_average_squared(double *, int);
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double linear_fit_average_mixed_terms(double *, double *, int );
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double linear_fit_average_product(double *matrix1, double *matrix2, int n);
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void linear_fit_subtract_mean(double *matrix, double bar, int n);
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double linear_fit_max_abs(double *, int);
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struct linear_fit linear_fit_results;
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struct linear_fit *lsf_linear_fit(double *x, double *y, double *z, int n) {
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double xbar, ybar, zbar;
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double x2bar, y2bar;
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double xybar, xzbar, yzbar;
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double D;
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int i;
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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(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, int n)
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{
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int i;
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double sum=0.0;
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for (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, int n) {
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int i;
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double sum = 0.0;
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for (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, int n) {
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int i;
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for (i = 0; i < n; i++) {
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matrix[i] -= bar;
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}
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return;
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}
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double linear_fit_max_abs(double *matrix, int n) {
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int i;
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double max_abs = 0.0;
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for(i=0; i<n; i++)
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if ( max_abs < fabs(matrix[i]))
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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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