On Sun, Mar 28, 2010 at 18:30, Andrea Gavana <andrea.gav...@gmail.com> wrote: > Hi Friedrich & All, > > On 28 March 2010 23:51, Friedrich Romstedt wrote: >> 2010/3/28 Andrea Gavana <andrea.gav...@gmail.com>: >>> Example 1 >>> >>> # o2 and o3 are the number of production wells, split into 2 >>> # different categories >>> # inj is the number of injection wells >>> # fomts is the final oil recovery >>> >>> rbf = Rbf(oilPlateau, gasPlateau, gasInjPlateau, o2, o3, inj, fomts) >>> >>> op = [50380] >>> gp = [103014000] >>> gi = [53151000] >>> o2w = [45] >>> o3w = [20] >>> inw = [15] >>> >>> fi = rbf(op, gp, gi, o2w, o3w, inw) >>> >>> # I => KNOW <= the answer to be close to +3.5e8 >>> >>> print fi >>> >>> [ -1.00663296e+08] >>> >>> (yeah right...) >>> >>> >>> Example 2 >>> >>> Changing o2w from 45 to 25 (again, the answer should be close to 3e8, >>> less wells => less production) >>> >>> fi = rbf(op, gp, gi, o2w, o3w, inw) >>> >>> print fi >>> >>> [ 1.30023424e+08] >>> >>> And keep in mind, that nowhere I have such low values of oil recovery >>> in my data... the lowest one are close to 2.8e8... >> >> I want to put my2 cents in, fwiw ... >> >> What I see from >> http://docs.scipy.org/doc/scipy-0.7.x/reference/generated/scipy.interpolate.Rbf.html#scipy.interpolate.Rbf >> are three things: >> >> 1. Rbf uses some weighting based on the radial functions. >> 2. Rbf results go through the nodal points without *smooth* set to >> some value != 0 >> 3. Rbf is isotropic >> >> (3.) is most important. I see from your e-mail that the values you >> pass in to Rbf are of very different order of magnitude. But the >> default norm used in Rbf is for sure isotropic, i.e., it will result >> in strange and useless "mean distances" in R^N where there are N >> parameters. You have to either pass in a *norm* which weights the >> coords according to their extent, or to scale the data such that the >> aspect ratios of the hypecube's edges are sensible.
> I believe I need a technical dictionary to properly understand all > that... :-D . Sorry, I am no expert at all, really, just an amateur > with some imagination, but your suggestion about the different > magnitude of the matrix is a very interesting one. Although I have > absolutely no idea on how to re-scale them properly to avoid RBFs > going crazy. Scaling each axis by its standard deviation is a typical first start. Shifting and scaling the values such that they each go from 0 to 1 is another useful thing to try. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
