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# Class: LevenbergMarquardt Source Location: /PHPExcel/Shared/JAMA/examples/LevenbergMarquardt.php
### Class Overview
| ### Methods chiSquared solve
### Class Details [line 7] [ Top ] ### Class Methods ### method chiSquared [line 21]
`void chiSquared( double[][] $x, double[] $a, $y, $s, object $f, double[] $y,, double[] $s,)`
Calculate the current sum-squared-error Chi-squared is the distribution of squared Gaussian errors, thus the name. Calculate the current sum-squared-error Chi-squared is the distribution of squared Gaussian errors, thus the name. #### Parameters:
double[][] | $x |
double[] | $a |
double[] | $y, |
double[] | $s, |
object | $f |
| $y |
| $s |
[ Top ] ### method solve [line 57]
`the solve( x $x, y $a, a $y, vary $s, s2 $vary, lambda $f, termepsilon $lambda, maxiter $termepsilon, verbose $maxiter, $verbose)`
Minimize E = sum {(y[k] - f(x[k],a)) / s[k]}^2 The individual errors are optionally scaled by s[k]. Minimize E = sum {(y[k] - f(x[k],a)) / s[k]}^2 The individual errors are optionally scaled by s[k]. Note that LMfunc implements the value and gradient of f(x,a), NOT the value and gradient of E with respect to a! #### Tags:
return: | new lambda for future iterations. Can use this and maxiter to interleave the LM descent with some other task, setting maxiter to something small.
#### Parameters:
x | $x | array of domain points, each may be multidimensional
y | $a | corresponding array of values
a | $y | the parameters/state of the model
vary | $s | false to indicate the corresponding a[k] is to be held fixed
s2 | $vary | sigma^2 for point i
lambda | $f | blend between steepest descent (lambda high) and jump to bottom of quadratic (lambda zero). Start with 0.001.
termepsilon | $lambda | termination accuracy (0.01)
maxiter | $termepsilon | stop and return after this many iterations if not done
verbose | $maxiter | set to zero (no prints), 1, 2
| $verbose |
[ Top ] Documentation generated on Mon, 11 Jan 2010 08:11:51 +0100 by phpDocumentor 1.4.1