[SciPy-User] Sigmoid Curve Fitting

[Chris Spencer]

Right, I noticed that negating k should theoretically have the same
effect. However, when I reversed your sample data (i.e. ydata =
ydata[::-1]), I was surprised to find that curve_fit gives me
f(x)=0.92 instead of a proper sigmoid curve. Adding my parameter seems
to work around this problem.

Regards,
Chris

On Tue, Sep 21, 2010 at 2:21 PM, Warren Weckesser
<warren.weckesser at enthought.com> wrote:
>
> On 9/21/10 1:04 PM, Chris Spencer wrote:
>> I found this modification allows for the inversion of the estimated
>> sigmoid curve:
>>
>> def sigmoid(x, x0, k, a, c, d):
>>      y = 1 / (1 + np.exp(-k*(x-x0)))
>>      y = (1 - y)*(1 - d) + y*d
>>      y = a * y + c
>>      return y
>>
>
>
> A negative value of k "reverses" the sigmoid shape, so you shouldn't
> have to define a new function.  If you prefer to have k be positive, you
> could use
>
> def sigmoid(x, x0, k, a, c):
>     y = a / (1 + np.exp(k*(x-x0))) + c
>     return y
>
> (I changed "-k" to "k".)
>
>
> Warren
>
>> Regards,
>> Chris
>>
>> On Tue, Sep 21, 2010 at 1:02 PM, Chris Spencer<chrisspen at gmail.com>  wrote:
>>> On Tue, Sep 21, 2010 at 12:16 PM, Warren Weckesser
>>> <warren.weckesser at enthought.com>  wrote:
>>>> The following is a variation that includes more parameters in the family
>>>> of sigmoid functions.  But bear in mind, I chose this family of
>>>> functions just as a demonstration of curve_fit.  I don't know if it
>>>> makes sense to use this family for your data.  The appropriate family to
>>>> use depends on the nature of the data.
>>> I see what you mean. That modification only fits a low-to-high
>>> sigmoid, but that's close enough for me to adapt by reversing my data
>>> set. Thank you for the excellent example.
>>>
>>> Regards,
>>> Chris
>>>
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