[SciPy-User] Value that compare two
Tue Apr 2 11:20:34 CDT 2013
On Tue, Apr 2, 2013 at 10:15 AM, Florian Lindner <email@example.com> wrote:
> this is not exactly a scipy question... but I want to implement it with scipy.
> I have two datasets of shape: (n, 2), each row consists of a coordinate and a
> pressure value from experiments or simulations.
> I want to compare these two sets and get some kind of integral distance value.
> delta = abs(data2 - data1)
> delta[:,0] = data1[:,0] # I don't want to delta the coordinates
> sum_delta = np.trapz(delta[:,1], x = delta[:,0])
> This works fine, but I also want to have a normalized delta value (aka
> Before I try to invent another wheel which at the end will look rather
> Is there some best practice way to compute such a value?
> If one could also give a quotable source of the algorithm it would be even
> more perfect!
I'm more familiar with quadratic than absolute error for comparing
functions or distributions in applications
ISE integratead squared error
MISE mean integrated squared error
MIAE mean integrated absolute error
these are common measures for non-parametric estimation.
google search for mean integrated absolute error gave me the above link.
There are various other functional distance measures, where I also
know mainly the goodness-of-fit measures for probability
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