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

Matthieu Rigal rigal@rapideye...
Thu Jun 10 09:02:23 CDT 2010


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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