Hey all,
I am trying to use the PDL::Fit::LM module to fit a 2D circle to some data.
The equation of a 2D circle is y=(r^2-(x-x0)^2)^0.5+y0. I am running into
problems. I get my guess parameters back as the optimal parameters. Is there
anything that jumps out at you as wrong? I have appended the code below.
Thanks
Ganesh.
use PDL;
use PDL::Fit::LM;
my $xdata=pdl [3,0,5];
my $ydata=pdl [4,5,0];
my $initp=pdl [1,1,1];
my $wt=1;
my ($yf,$pf,$cf,$if) = lmfit $xdata, $ydata, $wt, \&circlefit, $initp,
{Maxiter => 300, Eps => 1e-3};
print "Parameters are ",$pf,"\n";
sub circlefit {
# leave this line as is
my ($x,$par,$ym,$dyda) = @_;
# $m and $b are fit parameters, internal to this function
# call them whatever make sense to you, but replace (0..1)
# with (0..x) where x is equal to your number of fit parameters
# minus 1
my ($x0, $y0, $r) = map { $par->slice("($_)") } (0..2);
# Write function with dependent variable $ym,
# independent variable $x, and fit parameters as specified above.
# Use the .= (dot equals) assignment operator to express the
equality
# (not just a plain equals)
$ym .= ($r**2 - ($x - $x0)**2)**0.5 + $y0;
# Edit only the (0..1) part to (0..x) as above
my (@dy) = map {$dyda -> slice(",($_)") } (0..2);
# Partial derivative of the function with respect to first
# fit parameter ($m in this case). Again, note .= assignment
# operator (not just "equals")
$dy[0] .= ($r**2 - ($x - $x0)**2)**(-0.5)*($x - $x0);
# Partial derivative of the function with respect to next
# fit parameter ($b in this case)
$dy[1] .= 1;
$dy[2] .= $r*($r**2 - ($x - $x0)**2)**(-0.5);
# Add $dy[ ] .= () lines as necessary to supply
# partial derivatives for all floating paramters.
}
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