[SciPy-User] leastsq not converging to optimal solution, fmin won't accept parameters

[Joses Ho]

Hi,

I am trying to fit to my qPCR data an exponential function where each successive y-value depends on the previous v-value in the following fashion:
y[n]  =  y[n-1] + k ln (1 + (y[n-1]/k) + b
where y[n] = current y-value being evaluated
and y[n-1] = previous y-value

I therefore have three values to fit - y0 (starting value of y), k, and b.

I define a residuals function where I compute 20 values of y, given an initial estimate of y0, k and b, and then subtract these from my 20 measured values of y.

I have tried to use scipy.optimize.leastsq on the above residuals function, but it doesn't seem to be converging on the correct values of y0,k and b.
I have tried scipy.optimize.fmin on the same function, but I get the following error:
...fsim[0] = func(x0)
...ValueError: setting an array element with a sequence.
which seems to me that it refuses to accept the x0 array I give to it, consisting of [y0,k,b].

Basically, I am trying to implement the recent method of Boggy and Woolf (PLoS 2010) [doi:10.1371/ journal.pone.0012355] in Python.
I am using SciPy 0.8.0b1 and NumPy 1.4.0 on Python 2.6.5 of the Enthought Python Distribution 6.2-2.

Any help here would be very much appreciated!

Thanks
Joses

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