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From "Gilles (JIRA)" <j...@apache.org>
Subject [jira] Commented: (MATH-519) GaussianFitter Unexpectedly Throws NotStrictlyPositiveException
Date Mon, 21 Feb 2011 12:19:38 GMT

    [ https://issues.apache.org/jira/browse/MATH-519?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12997370#comment-12997370
] 

Gilles commented on MATH-519:
-----------------------------

{quote}
I'm assuming that the value that results from passing in a negative sigma is closer to the
optimal than POSITIVE infinity or NaN [...]
{quote}

POSITIVE_INFINITY or NaN are the returned values of the objective function and its gradient,
specially chosen because they are quite likely to be different from the actual values of the
function and its gradient; it's not "sigma" that is assumed to infinity or NaN.

If you think is that we can accept a negative sigma as the result of the fitting, I don't
agree. In the case of the Gaussian, it's by "chance" that a semantically invalid parameter
(negative sigma) would still be usable (as it is being squared before use).
In most case you cannot expect such a forgiving situation. For example, if you want to fit
"a" in the following function:
{noformat}
  log(a * x)
{noformat}
no invalid values for "a" are usable.
The "Gaussian" class should not be unsafe (no validation of sigma) just because of its particular
use here.
[Moreover the workaround is useful in showing users how to setup a fitting of a function that
can raise an exception.]

{quote}
[...] and that this will result in faster convergence.
{quote}

Did you try?


> GaussianFitter Unexpectedly Throws NotStrictlyPositiveException
> ---------------------------------------------------------------
>
>                 Key: MATH-519
>                 URL: https://issues.apache.org/jira/browse/MATH-519
>             Project: Commons Math
>          Issue Type: Bug
>    Affects Versions: 3.0
>            Reporter: Ole Ersoy
>             Fix For: 3.0
>
>         Attachments: GaussianFitter.java, GaussianFitter2Test.java
>
>   Original Estimate: 4h
>  Remaining Estimate: 4h
>
> Running the following:
>     	double[] observations = 
>     	{ 
>     			1.1143831578403364E-29, 
>     			 4.95281403484594E-28, 
>     			 1.1171347211930288E-26, 
>     			 1.7044813962636277E-25, 
>     			 1.9784716574832164E-24, 
>     			 1.8630236407866774E-23, 
>     			 1.4820532905097742E-22, 
>     			 1.0241963854632831E-21, 
>     			 6.275077366673128E-21, 
>     			 3.461808994532493E-20, 
>     			 1.7407124684715706E-19, 
>     			 8.056687953553974E-19, 
>     			 3.460193945992071E-18, 
>     			 1.3883326374011525E-17, 
>     			 5.233894983671116E-17, 
>     			 1.8630791465263745E-16, 
>     			 6.288759227922111E-16, 
>     			 2.0204433920597856E-15, 
>     			 6.198768938576155E-15, 
>     			 1.821419346860626E-14, 
>     			 5.139176445538471E-14, 
>     			 1.3956427429045787E-13, 
>     			 3.655705706448139E-13, 
>     			 9.253753324779779E-13, 
>     			 2.267636001476696E-12, 
>     			 5.3880460095836855E-12, 
>     			 1.2431632654852931E-11 
>     	};
>   
>     	GaussianFitter g = 
>     		new GaussianFitter(new LevenbergMarquardtOptimizer());
>     	
>     	for (int index = 0; index < 27; index++)
>     	{
>     		g.addObservedPoint(index, observations[index]);
>     	}
>        	g.fit();
> Results in:
> org.apache.commons.math.exception.NotStrictlyPositiveException: -1.277 is smaller than,
or equal to, the minimum (0)
> 	at org.apache.commons.math.analysis.function.Gaussian$Parametric.validateParameters(Gaussian.java:184)
> 	at org.apache.commons.math.analysis.function.Gaussian$Parametric.value(Gaussian.java:129)
> I'm guessing the initial guess for sigma is off.  

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