Andrea Gavana wrote: >>> 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. >> Ah, magnifico! Thank you Robert and Friedrich, it seems to be working >> now...
One other thought -- core to much engineering is dimensional analysis -- you know how we like those non-dimensional number! I think this situation is less critical, as you are interpolating, not optimizing or something, but many interpolation methods are built on the idea of some data points being closer than others to your point of interest. Who is to say if a point that is 2 hours away is closer or father than one 2 meters away? This is essentially what you are doing. Scaling everything to the same range is a start, but then you've still given them an implicit weighting. An alternative to to figure out a way to non-dimensionalize your parameters -- that *may* give you a more physically based scaling. And you might invent the "Gavana Number" in the process ;-) -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
