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Patches to UBL

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Scott Lahteine
2017-04-08 03:16:13 -05:00
committed by Roxy-3D
parent 14cf527bb8
commit 15edb41cee
3 changed files with 178 additions and 204 deletions
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148
Marlin/least_squares_fit.cpp
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@ -23,111 +23,95 @@
/**
* Least Squares Best Fit By Roxy and Ed Williams
*
* This algorythm 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 10KB of program memory.
* 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.
*
*/
#include "MarlinConfig.h"
#if ENABLED(AUTO_BED_LEVELING_UBL) // Currently only used by UBL, but is applicable to Grid Based (Linear) Bed Leveling
#include <math.h>
#include "ubl.h"
#include "Marlin.h"
#if ENABLED(AUTO_BED_LEVELING_UBL) // Currently only used by UBL, but is applicable to Grid Based (Linear) Bed Leveling
double linear_fit_average(double *, int);
double linear_fit_average_squared(double *, int);
double linear_fit_average_mixed_terms(double *, double *, int );
double linear_fit_average_product(double *matrix1, double *matrix2, int n);
void linear_fit_subtract_mean(double *matrix, double bar, int n);
double linear_fit_max_abs(double *, int);
#include "ubl.h"
#include "Marlin.h"
#include "macros.h"
#include <math.h>
struct linear_fit linear_fit_results;
double linear_fit_average(double m[], const int);
//double linear_fit_average_squared(double m[], const int);
//double linear_fit_average_mixed_terms(double m1[], double m2[], const int);
double linear_fit_average_product(double matrix1[], double matrix2[], const int n);
void linear_fit_subtract_mean(double matrix[], double bar, const int n);
double linear_fit_max_abs(double m[], const int);
struct linear_fit *lsf_linear_fit(double *x, double *y, double *z, int n) {
double xbar, ybar, zbar;
double x2bar, y2bar;
double xybar, xzbar, yzbar;
double D;
int i;
linear_fit linear_fit_results;
linear_fit_results.A = 0.0;
linear_fit_results.B = 0.0;
linear_fit_results.D = 0.0;
linear_fit* lsf_linear_fit(double x[], double y[], double z[], const int n) {
double xbar, ybar, zbar,
x2bar, y2bar,
xybar, xzbar, yzbar,
D;
xbar = linear_fit_average(x, n);
ybar = linear_fit_average(y, n);
zbar = linear_fit_average(z, n);
linear_fit_results.A = 0.0;
linear_fit_results.B = 0.0;
linear_fit_results.D = 0.0;
linear_fit_subtract_mean( x, xbar, n);
linear_fit_subtract_mean( y, ybar, n);
linear_fit_subtract_mean( z, zbar, n);
xbar = linear_fit_average(x, n);
ybar = linear_fit_average(y, n);
zbar = linear_fit_average(z, n);
x2bar = linear_fit_average_product( x, x, n);
y2bar = linear_fit_average_product( y, y, n);
xybar = linear_fit_average_product( x, y, n);
xzbar = linear_fit_average_product( x, z, n);
yzbar = linear_fit_average_product( y, z, n);
linear_fit_subtract_mean(x, xbar, n);
linear_fit_subtract_mean(y, ybar, n);
linear_fit_subtract_mean(z, zbar, n);
D = x2bar*y2bar - xybar*xybar;
for(i=0; i<n; i++) {
if (fabs(D) <= 1e-15*( linear_fit_max_abs(x, n) + linear_fit_max_abs(y, n))) {
printf( "error: x,y points are collinear at index:%d \n", i );
return NULL;
}
}
x2bar = linear_fit_average_product(x, x, n);
y2bar = linear_fit_average_product(y, y, n);
xybar = linear_fit_average_product(x, y, n);
xzbar = linear_fit_average_product(x, z, n);
yzbar = linear_fit_average_product(y, z, n);
linear_fit_results.A = -(xzbar*y2bar - yzbar*xybar) / D;
linear_fit_results.B = -(yzbar*x2bar - xzbar*xybar) / D;
// linear_fit_results.D = -(zbar - linear_fit_results->A*xbar - linear_fit_results->B*ybar);
linear_fit_results.D = -(zbar + linear_fit_results.A*xbar + linear_fit_results.B*ybar);
D = x2bar * y2bar - xybar * xybar;
for (int i = 0; i < n; i++) {
if (fabs(D) <= 1e-15 * (linear_fit_max_abs(x, n) + linear_fit_max_abs(y, n))) {
printf("error: x,y points are collinear at index:%d\n", i);
return NULL;
}
}
return &linear_fit_results;
linear_fit_results.A = -(xzbar * y2bar - yzbar * xybar) / D;
linear_fit_results.B = -(yzbar * x2bar - xzbar * xybar) / D;
// linear_fit_results.D = -(zbar - linear_fit_results->A * xbar - linear_fit_results->B * ybar);
linear_fit_results.D = -(zbar + linear_fit_results.A * xbar + linear_fit_results.B * ybar);
return &linear_fit_results;
}
double linear_fit_average(double *matrix, int n)
{
int i;
double sum=0.0;
for (i = 0; i < n; i++)
sum += matrix[i];
return sum / (double) n;
double linear_fit_average(double *matrix, const int n) {
double sum = 0.0;
for (int i = 0; i < n; i++)
sum += matrix[i];
return sum / (double)n;
}
double linear_fit_average_product(double *matrix1, double *matrix2, int n) {
int i;
double sum = 0.0;
for (i = 0; i < n; i++)
sum += matrix1[i] * matrix2[i];
return sum / (double) n;
double linear_fit_average_product(double *matrix1, double *matrix2, const int n) {
double sum = 0.0;
for (int i = 0; i < n; i++)
sum += matrix1[i] * matrix2[i];
return sum / (double)n;
}
void linear_fit_subtract_mean(double *matrix, double bar, int n) {
int i;
for (i = 0; i < n; i++) {
matrix[i] -= bar;
}
return;
void linear_fit_subtract_mean(double *matrix, double bar, const int n) {
for (int i = 0; i < n; i++)
matrix[i] -= bar;
}
double linear_fit_max_abs(double *matrix, int n) {
int i;
double max_abs = 0.0;
for(i=0; i<n; i++)
if ( max_abs < fabs(matrix[i]))
max_abs = fabs(matrix[i]);
return max_abs;
double linear_fit_max_abs(double *matrix, const int n) {
double max_abs = 0.0;
for (int i = 0; i < n; i++)
NOLESS(max_abs, fabs(matrix[i]));
return max_abs;
}
#endif