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| <title>lmfit: a self-contained C library for Levenberg-Marquardt least-squares minimization and curve fitting</title> |
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| <h1 id="NAME">NAME</h1> |
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| <p>lmcurve - Levenberg-Marquardt least-squares fit of a curve (t,y)</p> |
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| <h1 id="SYNOPSIS">SYNOPSIS</h1> |
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| <p><b>#include <lmcurve.h</b>></p> |
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| <p><b>void lmcurve( const int</b> <i>n_par</i><b>, double *</b><i>par</i><b>, const int</b> <i>m_dat</i><b>, const<span style="white-space: nowrap;"> </span>double *</b><i>t</i><b>, const<span style="white-space: nowrap;"> </span>double *</b><i>y</i><b>, double (*</b><i>f</i><b>)( const double </b><i>ti</i><b>, const double *</b><i>par</i><b> ), const<span style="white-space: nowrap;"> </span>lm_control_struct *</b><i>control</i><b>, lm_status_struct *</b><i>status</i><b>);</b></p> |
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| <p><b>void lmcurve_tyd( const int</b> <i>n_par</i><b>, double *</b><i>par</i><b>, const int</b> <i>m_dat</i><b>, const<span style="white-space: nowrap;"> </span>double *</b><i>t</i><b>, const<span style="white-space: nowrap;"> </span>double *</b><i>y</i><b>, const<span style="white-space: nowrap;"> </span>double *</b><i>dy</i><b>, double (*</b><i>f</i><b>)( const double </b><i>ti</i><b>, const double *</b><i>par</i><b> ), const<span style="white-space: nowrap;"> </span>lm_control_struct *</b><i>control</i><b>, lm_status_struct *</b><i>status</i><b>);</b></p> |
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| <p><b>extern const lm_control_struct lm_control_double;</b></p> |
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| <p><b>extern const lm_control_struct lm_control_float;</b></p> |
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| <p><b>extern const char *lm_infmsg[];</b></p> |
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| <p><b>extern const char *lm_shortmsg[];</b></p> |
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| <h1 id="DESCRIPTION">DESCRIPTION</h1> |
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| <p><b>lmcurve()</b> and <b>lmcurve_tyd()</b> wrap the more generic minimization function <b>lmmin()</b>, for use in curve fitting.</p> |
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| <p><b>lmcurve()</b> determines a vector <i>par</i> that minimizes the sum of squared elements of a residue vector <i>r</i>[i] := <i>y</i>[i] - <i>f</i>(<i>t</i>[i];<i>par</i>). Typically, <b>lmcurve()</b> is used to approximate a data set <i>t</i>,<i>y</i> by a parametric function <i>f</i>(<i>ti</i>;<i>par</i>). On success, <i>par</i> represents a local minimum, not necessarily a global one; it may depend on its starting value.</p> |
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| <p><b>lmcurve_tyd()</b> does the same for a data set <i>t</i>,<i>y</i>,<i>dy</i>, where <i>dy</i> represents the standard deviation of empirical data <i>y</i>. Residues are computed as <i>r</i>[i] := (<i>y</i>[i] - <i>f</i>(<i>t</i>[i];<i>par</i>))/<i>dy</i>[i]. Users must ensure that all <i>dy</i>[i] are positive.</p> |
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| <p>Function arguments:</p> |
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| <dl> |
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| <dt id="n_par"><i>n_par</i></dt> |
| <dd> |
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| <p>Number of free variables. Length of parameter vector <i>par</i>.</p> |
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| </dd> |
| <dt id="par"><i>par</i></dt> |
| <dd> |
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| <p>Parameter vector. On input, it must contain a reasonable guess. On output, it contains the solution found to minimize ||<i>r</i>||.</p> |
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| </dd> |
| <dt id="m_dat"><i>m_dat</i></dt> |
| <dd> |
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| <p>Number of data points. Length of vectors <i>t</i> and <i>y</i>. Must statisfy <i>n_par</i> <= <i>m_dat</i>.</p> |
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| </dd> |
| <dt id="t"><i>t</i></dt> |
| <dd> |
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| <p>Array of length <i>m_dat</i>. Contains the abcissae (time, or "x") for which function <i>f</i> will be evaluated.</p> |
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| </dd> |
| <dt id="y"><i>y</i></dt> |
| <dd> |
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| <p>Array of length <i>m_dat</i>. Contains the ordinate values that shall be fitted.</p> |
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| </dd> |
| <dt id="dy"><i>dy</i></dt> |
| <dd> |
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| <p>Only in <b>lmcurve_tyd()</b>. Array of length <i>m_dat</i>. Contains the standard deviations of the values <i>y</i>.</p> |
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| </dd> |
| <dt id="f"><i>f</i></dt> |
| <dd> |
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| <p>A user-supplied parametric function <i>f</i>(ti;<i>par</i>).</p> |
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| </dd> |
| <dt id="control"><i>control</i></dt> |
| <dd> |
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| <p>Parameter collection for tuning the fit procedure. In most cases, the default &<i>lm_control_double</i> is adequate. If <i>f</i> is only computed with single-precision accuracy, <i>&lm_control_float</i> should be used. Parameters are explained in <b>lmmin(3)</b>.</p> |
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| </dd> |
| <dt id="status"><i>status</i></dt> |
| <dd> |
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| <p>A record used to return information about the minimization process: For details, see <b>lmmin(3)</b>.</p> |
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| </dd> |
| </dl> |
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| <h1 id="EXAMPLE">EXAMPLE</h1> |
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| <p>Fit a data set y(x) by a curve f(x;p):</p> |
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| <pre><code> #include "lmcurve.h" |
| #include <stdio.h> |
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| /* model function: a parabola */ |
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| double f( double t, const double *p ) |
| { |
| return p[0] + p[1]*t + p[2]*t*t; |
| } |
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| int main() |
| { |
| int n = 3; /* number of parameters in model function f */ |
| double par[3] = { 100, 0, -10 }; /* really bad starting value */ |
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| /* data points: a slightly distorted standard parabola */ |
| int m = 9; |
| int i; |
| double t[9] = { -4., -3., -2., -1., 0., 1., 2., 3., 4. }; |
| double y[9] = { 16.6, 9.9, 4.4, 1.1, 0., 1.1, 4.2, 9.3, 16.4 }; |
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| lm_control_struct control = lm_control_double; |
| lm_status_struct status; |
| control.verbosity = 7; |
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| printf( "Fitting ...\n" ); |
| lmcurve( n, par, m, t, y, f, &control, &status ); |
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| printf( "Results:\n" ); |
| printf( "status after %d function evaluations:\n %s\n", |
| status.nfev, lm_infmsg[status.outcome] ); |
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| printf("obtained parameters:\n"); |
| for ( i = 0; i < n; ++i) |
| printf(" par[%i] = %12g\n", i, par[i]); |
| printf("obtained norm:\n %12g\n", status.fnorm ); |
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| printf("fitting data as follows:\n"); |
| for ( i = 0; i < m; ++i) |
| printf( " t[%2d]=%4g y=%6g fit=%10g residue=%12g\n", |
| i, t[i], y[i], f(t[i],par), y[i] - f(t[i],par) ); |
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| return 0; |
| }</code></pre> |
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| <h1 id="COPYING">COPYING</h1> |
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| <p>Copyright (C) 2009-2015 Joachim Wuttke, Forschungszentrum Juelich GmbH</p> |
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| <p>Software: FreeBSD License</p> |
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| <p>Documentation: Creative Commons Attribution Share Alike</p> |
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| <h1 id="SEE-ALSO">SEE ALSO</h1> |
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| <a href="http://apps.jcns.fz-juelich.de/man/lmmin.html"><b>lmmin</b>(3)</a> |
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| <p>Homepage: http://apps.jcns.fz-juelich.de/lmfit</p> |
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| <h1 id="BUGS">BUGS</h1> |
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| <p>Please send bug reports and suggestions to the author <[email protected]>.</p> |
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