[SciPy-User] scipy.interpolate.rbf sensitive to input noise ?
Fri Feb 19 10:36:44 CST 2010
On Fri, Feb 19, 2010 at 10:26, denis <email@example.com> wrote:
> On Feb 18, 4:48 pm, Robert Kern <robert.k...@gmail.com> wrote:
>> On Thu, Feb 18, 2010 at 09:19, denis <denis-bz...@t-online.de> wrote:
>> > Running rbf on 100 1d random.uniform input points
>> > (after changing the linalg.solve in rbf.py to lstsq)
>> Why would you do this? This does not magically turn Rbf interpolation
>> into a smoothing approximation. Use the "smooth" keyword to specify a
>> smoothing parameter.
> i'm interpolating y = np.sin(x) + np.random.normal( 0, .1 ), not
> random noise.
Okay. You said otherwise. It would help if you simply attached the
code that you are using.
> The idea was to look at gaussian vs thin-plate;
> true, that 1d snippet doesn't say much,
> but my 2d plots were so noisy that I went down to 1d.
> Use "smooth" ? rbf.py just does
> self.A = self._function(r) - eye(self.N)*self.smooth
> and you don't know A .
I have no idea what you mean by the last part of your sentence. Did
you actually try using the smooth keyword? What were your results?
> Bytheway (googling), http://www.farfieldtechnology.com/products/toolbox
> have an O(N lnN) FastRBF TM in matlab, $
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
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