[SciPy-User] leastsq returns bizarre, not fitted, output for float values

[Matthieu Rigal]

OK, I've found the bug...

Somehow the leastsq function is not working if both data sets are float 32 
type.
By just adding following line the problem is solved :
aX = numpy.asarray(aX, dtype=numpy.float64)

Is it a known bug ? Should I add it to the bug tracker ?

Best regards,
Matthieu

On Thursday 10 June 2010 14:57:10 Matthieu Rigal wrote:
> Hi folks,
>
> Thanks for your help last time, even if I had not reply to my second
> message...
>
> I am using leastsq for several things, but it is returning strange
> values for one of the case I'm using it for. I simplified it to the
> following code I'll paste below.
> The effect is that it is fitting nothing, just giving back the
> parameters given for initialization. Thus some fitting is possible as
> you will see in the plotted graph.
> I'm using SciPy 0.7.
>
> It might be a bug, a misusage from my side... or some data type
> incompatibility I was not able to find on the net or in the source...
>
> As you will see, if you transform the x-data to a numpy.int array (by
> uncommenting a line below), the fitting is working... is it to be
> expected ? It should then be somewhere in the doc, isn't it ?
>
>
> import numpy
> from scipy.optimize import leastsq
> import matplotlib.pyplot as plt
>
> def LinearFit(p, y, x):
>     a, b = p
>     return y - (a*x +b)
>
> aX = numpy.asarray([ 22.08742332,  23.43987274,  21.59165192, 
> 24.80192566, 26.11182976,  29.18944931,  27.89473152,  30.00043106,
>         36.24227142,  30.45967293,  30.04778099,  28.11702538,
>         29.31716728,  27.89473152,  20.59804916,  34.19070053,
>         48.33156204,  50.82163239,  45.22343063,  42.80136108,
>         30.71160889,  29.31716728,  25.14836884,  23.50605965,
>         26.89011765,  40.35306168,  55.074543  ,  58.57307816,
>         60.77198792,  56.14603043,  39.29994583,  38.14756012,
>         35.76476288,  27.31066895,  23.45325851,  30.46047974,
>         37.53346634,  41.04254532,  54.47524643,  61.14104462,
>         61.03421402,  56.14603043,  44.67305756,  35.13313675],
> dtype=numpy.float32)
>
> aY = numpy.asarray([ 25.45091248,  25.50468063,  27.15722656, 
> 25.10549927, 28.44662094,  30.3882637 ,  31.90523148,  34.12581253,
>         36.62049484,  33.90032196,  34.04083252,  29.66094398,
>         30.68564224,  29.31051826,  25.17509079,  37.28609848,
>         42.86494827,  48.25041199,  46.88908005,  34.44023132,
>         31.26217461,  31.8005867 ,  28.34657669,  26.77126312,
>         31.06710815,  41.03251266,  49.48557281,  52.79579926,
>         50.865448  ,  48.03937531,  39.30026245,  38.50889969,
>         37.07154083,  31.61130905,  27.42698288,  30.84166718,
>         30.84166718,  40.47367096,  50.37258148,  53.13900757,
>         53.75816727,  52.74428177,  43.87319183,  33.70808029],
> dtype=numpy.float32)
>
> #aX = numpy.asarray(numpy.rint(aX), dtype=numpy.int)
>
> p0 = [1.0] + [aY.min()-aX.min()]
> aParams, err, i, j, k = leastsq(LinearFit, p0,  args=(aY, aX),
> maxfev=10000,full_output=True)
> aY0 = aParams[0] * aX + aParams[1]
> print err, i, j, k
> print aParams
>
> plt.plot(aX, aY, '+', aX, aY0, '+')
> plt.legend(['input','model'])
> plt.show()
>
>
> Thanks in advance for the help,
> Best Regards,
> M



-- 
Matthieu Rigal
Product Development

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