[SciPy-User] ODR fitting several equations to the same parameters
ms
devicerandom@gmail....
Fri Nov 13 10:30:07 CST 2009
josef.pktd@gmail.com ha scritto:
> On Fri, Nov 13, 2009 at 10:28 AM, ms <devicerandom@gmail.com> wrote:
>> josef.pktd@gmail.com ha scritto:
>>> On Thu, Nov 12, 2009 at 10:04 AM, ms <devicerandom@gmail.com> wrote:
>>>> josef.pktd@gmail.com ha scritto:
>>> an example
>>> (quickly written and not optimized, there are parts I don't remember
>>> about curve_fit, fixed parameters could be better handled by a class)
>> Hmm, it seems I don't have curve_fit -I am constrained to use
>> scipy-0.6.0 and there's no chance to change that (it's an external server).
>
> You can just copy the function (plus 2 helper functions) from the
> current trunk. You would need to add the imports. Alternatively you
> can just use optimize.leastsq directly, using curve_fit as a recipe.
Thanks, but I've seen that with a bit of tweaking it works good with ODR
too. Thanks a lot, really nice trick! A polished version of it should go
in the cookbook in my opinion.
thanks!
m.
>
> Josef
>
> http://projects.scipy.org/scipy/browser/trunk/scipy/optimize/minpack.py#L338
>
> 338 def _general_function(params, xdata, ydata, function):
> 339 return function(xdata, *params) - ydata
> 340
> 341 def _weighted_general_function(params, xdata, ydata, function, weights):
> 342 return weights * (function(xdata, *params) - ydata)
> 343
> 344 def curve_fit(f, xdata, ydata, p0=None, sigma=None, **kw):
>
>
>
>> I am going to have a good look at what's doable with your approach
>> anyway, but I am happy if someone gives me old-school alternatives :)
>>
>> cheers,
>> m.
>>
>>> ####################
>>> """stack equations with different known parameters
>>>
>>> I didn't get curve_fit to work with only 1 parameter to estimate
>>>
>>> Created on Thu Nov 12 11:17:21 2009
>>> Author: josef-pktd
>>> """
>>> import numpy as np
>>> from scipy import optimize
>>>
>>>
>>> def fsingle(a,c,b,x):
>>> return b*x**a + c
>>>
>>> atrue = 1.
>>> ctrue = 10.
>>> b = np.array([[1.]*10, [2.]*10, [3.]*10])
>>> b = np.array([1.,2.,3.])
>>> x = np.random.uniform(size=(3,10))
>>> y = np.hstack([fsingle(atrue, ctrue, b[i], x[i]) for i in range(x.shape[0])])
>>> y += 0.1*np.random.normal(size=y.shape)
>>>
>>> def fun(x,a,c):
>>> #b is taken from enclosing scope
>>> #print x.shape
>>> xx=x.reshape((3,10))
>>> return np.hstack([fsingle(a, c, b[i], xx[i]) for i in range(xx.shape[0])])
>>>
>>> res = optimize.curve_fit(fun,x.ravel(),y, p0=np.array([2.,1.]))
>>>
>>> print 'true parameters ', atrue, ctrue
>>> print 'parameter estimate', res[0]
>>> print 'standard deviation', np.sqrt(np.diag(res[1]))
>>> ####################
>>>
>>>
>>>
>>>
>>>> sorry,
>>>> m.
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