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- /* fit/linear.c
- *
- * Copyright (C) 2000, 2007 Brian Gough
- *
- * 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
- * along with this program; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
- */
- #include "gsl__config.h"
- #include "gsl_errno.h"
- #include "gsl_fit.h"
- /* Fit the data (x_i, y_i) to the linear relationship
- Y = c0 + c1 x
- returning,
- c0, c1 -- coefficients
- cov00, cov01, cov11 -- variance-covariance matrix of c0 and c1,
- sumsq -- sum of squares of residuals
- This fit can be used in the case where the errors for the data are
- uknown, but assumed equal for all points. The resulting
- variance-covariance matrix estimates the error in the coefficients
- from the observed variance of the points around the best fit line.
- */
- int
- gsl_fit_linear (const double *x, const size_t xstride,
- const double *y, const size_t ystride,
- const size_t n,
- double *c0, double *c1,
- double *cov_00, double *cov_01, double *cov_11, double *sumsq)
- {
- double m_x = 0, m_y = 0, m_dx2 = 0, m_dxdy = 0;
- size_t i;
- for (i = 0; i < n; i++)
- {
- m_x += (x[i * xstride] - m_x) / (i + 1.0);
- m_y += (y[i * ystride] - m_y) / (i + 1.0);
- }
- for (i = 0; i < n; i++)
- {
- const double dx = x[i * xstride] - m_x;
- const double dy = y[i * ystride] - m_y;
- m_dx2 += (dx * dx - m_dx2) / (i + 1.0);
- m_dxdy += (dx * dy - m_dxdy) / (i + 1.0);
- }
- /* In terms of y = a + b x */
- {
- double s2 = 0, d2 = 0;
- double b = m_dxdy / m_dx2;
- double a = m_y - m_x * b;
- *c0 = a;
- *c1 = b;
- /* Compute chi^2 = \sum (y_i - (a + b * x_i))^2 */
- for (i = 0; i < n; i++)
- {
- const double dx = x[i * xstride] - m_x;
- const double dy = y[i * ystride] - m_y;
- const double d = dy - b * dx;
- d2 += d * d;
- }
- s2 = d2 / (n - 2.0); /* chisq per degree of freedom */
- *cov_00 = s2 * (1.0 / n) * (1 + m_x * m_x / m_dx2);
- *cov_11 = s2 * 1.0 / (n * m_dx2);
- *cov_01 = s2 * (-m_x) / (n * m_dx2);
- *sumsq = d2;
- }
- return GSL_SUCCESS;
- }
- /* Fit the weighted data (x_i, w_i, y_i) to the linear relationship
- Y = c0 + c1 x
- returning,
- c0, c1 -- coefficients
- s0, s1 -- the standard deviations of c0 and c1,
- r -- the correlation coefficient between c0 and c1,
- chisq -- weighted sum of squares of residuals */
- int
- gsl_fit_wlinear (const double *x, const size_t xstride,
- const double *w, const size_t wstride,
- const double *y, const size_t ystride,
- const size_t n,
- double *c0, double *c1,
- double *cov_00, double *cov_01, double *cov_11,
- double *chisq)
- {
- /* compute the weighted means and weighted deviations from the means */
- /* wm denotes a "weighted mean", wm(f) = (sum_i w_i f_i) / (sum_i w_i) */
- double W = 0, wm_x = 0, wm_y = 0, wm_dx2 = 0, wm_dxdy = 0;
- size_t i;
- for (i = 0; i < n; i++)
- {
- const double wi = w[i * wstride];
- if (wi > 0)
- {
- W += wi;
- wm_x += (x[i * xstride] - wm_x) * (wi / W);
- wm_y += (y[i * ystride] - wm_y) * (wi / W);
- }
- }
- W = 0; /* reset the total weight */
- for (i = 0; i < n; i++)
- {
- const double wi = w[i * wstride];
- if (wi > 0)
- {
- const double dx = x[i * xstride] - wm_x;
- const double dy = y[i * ystride] - wm_y;
- W += wi;
- wm_dx2 += (dx * dx - wm_dx2) * (wi / W);
- wm_dxdy += (dx * dy - wm_dxdy) * (wi / W);
- }
- }
- /* In terms of y = a + b x */
- {
- double d2 = 0;
- double b = wm_dxdy / wm_dx2;
- double a = wm_y - wm_x * b;
- *c0 = a;
- *c1 = b;
- *cov_00 = (1 / W) * (1 + wm_x * wm_x / wm_dx2);
- *cov_11 = 1 / (W * wm_dx2);
- *cov_01 = -wm_x / (W * wm_dx2);
- /* Compute chi^2 = \sum w_i (y_i - (a + b * x_i))^2 */
- for (i = 0; i < n; i++)
- {
- const double wi = w[i * wstride];
- if (wi > 0)
- {
- const double dx = x[i * xstride] - wm_x;
- const double dy = y[i * ystride] - wm_y;
- const double d = dy - b * dx;
- d2 += wi * d * d;
- }
- }
- *chisq = d2;
- }
- return GSL_SUCCESS;
- }
- int
- gsl_fit_linear_est (const double x,
- const double c0, const double c1,
- const double cov00, const double cov01, const double cov11,
- double *y, double *y_err)
- {
- *y = c0 + c1 * x;
- *y_err = sqrt (cov00 + x * (2 * cov01 + cov11 * x));
- return GSL_SUCCESS;
- }
- int
- gsl_fit_mul (const double *x, const size_t xstride,
- const double *y, const size_t ystride,
- const size_t n,
- double *c1, double *cov_11, double *sumsq)
- {
- double m_x = 0, m_y = 0, m_dx2 = 0, m_dxdy = 0;
- size_t i;
- for (i = 0; i < n; i++)
- {
- m_x += (x[i * xstride] - m_x) / (i + 1.0);
- m_y += (y[i * ystride] - m_y) / (i + 1.0);
- }
- for (i = 0; i < n; i++)
- {
- const double dx = x[i * xstride] - m_x;
- const double dy = y[i * ystride] - m_y;
- m_dx2 += (dx * dx - m_dx2) / (i + 1.0);
- m_dxdy += (dx * dy - m_dxdy) / (i + 1.0);
- }
- /* In terms of y = b x */
- {
- double s2 = 0, d2 = 0;
- double b = (m_x * m_y + m_dxdy) / (m_x * m_x + m_dx2);
- *c1 = b;
- /* Compute chi^2 = \sum (y_i - b * x_i)^2 */
- for (i = 0; i < n; i++)
- {
- const double dx = x[i * xstride] - m_x;
- const double dy = y[i * ystride] - m_y;
- const double d = (m_y - b * m_x) + dy - b * dx;
- d2 += d * d;
- }
- s2 = d2 / (n - 1.0); /* chisq per degree of freedom */
- *cov_11 = s2 * 1.0 / (n * (m_x * m_x + m_dx2));
- *sumsq = d2;
- }
- return GSL_SUCCESS;
- }
- int
- gsl_fit_wmul (const double *x, const size_t xstride,
- const double *w, const size_t wstride,
- const double *y, const size_t ystride,
- const size_t n,
- double *c1, double *cov_11, double *chisq)
- {
- /* compute the weighted means and weighted deviations from the means */
- /* wm denotes a "weighted mean", wm(f) = (sum_i w_i f_i) / (sum_i w_i) */
- double W = 0, wm_x = 0, wm_y = 0, wm_dx2 = 0, wm_dxdy = 0;
- size_t i;
- for (i = 0; i < n; i++)
- {
- const double wi = w[i * wstride];
- if (wi > 0)
- {
- W += wi;
- wm_x += (x[i * xstride] - wm_x) * (wi / W);
- wm_y += (y[i * ystride] - wm_y) * (wi / W);
- }
- }
- W = 0; /* reset the total weight */
- for (i = 0; i < n; i++)
- {
- const double wi = w[i * wstride];
- if (wi > 0)
- {
- const double dx = x[i * xstride] - wm_x;
- const double dy = y[i * ystride] - wm_y;
- W += wi;
- wm_dx2 += (dx * dx - wm_dx2) * (wi / W);
- wm_dxdy += (dx * dy - wm_dxdy) * (wi / W);
- }
- }
- /* In terms of y = b x */
- {
- double d2 = 0;
- double b = (wm_x * wm_y + wm_dxdy) / (wm_x * wm_x + wm_dx2);
- *c1 = b;
- *cov_11 = 1 / (W * (wm_x * wm_x + wm_dx2));
- /* Compute chi^2 = \sum w_i (y_i - b * x_i)^2 */
- for (i = 0; i < n; i++)
- {
- const double wi = w[i * wstride];
- if (wi > 0)
- {
- const double dx = x[i * xstride] - wm_x;
- const double dy = y[i * ystride] - wm_y;
- const double d = (wm_y - b * wm_x) + (dy - b * dx);
- d2 += wi * d * d;
- }
- }
- *chisq = d2;
- }
- return GSL_SUCCESS;
- }
- int
- gsl_fit_mul_est (const double x,
- const double c1, const double cov11,
- double *y, double *y_err)
- {
- *y = c1 * x;
- *y_err = sqrt (cov11) * fabs (x);
- return GSL_SUCCESS;
- }
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