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smp.smooth [2010/10/27 18:32]
jochen
smp.smooth [2010/10/28 02:09] (current)
jochen updated the algorithm description to reflect an update in code
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 In other words, the central kernel weight remains with the old value and the remainder to 1 is split across the next neighbors according to the kernel weight given by their distance to the vertex. This approach takes both the neighborhood and the distance of vertices into account. In other words, the central kernel weight remains with the old value and the remainder to 1 is split across the next neighbors according to the kernel weight given by their distance to the vertex. This approach takes both the neighborhood and the distance of vertices into account.
  
-After the smoothing is performed, the mean of the map is adjusted to reflect the original mean value (to counter rounding errors if a high number of iterations was used), **if and only if** the standard deviation is not smaller than 0.1 times the mean (for most skewed maps this is the case).+After the smoothing is performed, the mean of the map is adjusted to reflect the original mean value (to counter rounding errors if a high number of iterations was used), **if and only if** the standard deviation is not smaller than the sum (for most skewed maps this is the case).
smp.smooth.txt ยท Last modified: 2010/10/28 02:09 by jochen