scalingLaw()
Visualize Scaling Law ([SP1]).
Syntax
[x, y] = scalingLaw(n, mode, tSteps, r)
[x, y] = scalingLaw(n, mode, tSteps, r, runLength, threshold)
Description
scalingLaw(n, mode, tSteps,
r)
visualizes scaling law for networks with n(.) nodes and mode
update scheme over tSteps timesteps. The averages are formed over
r networks at each n(.). Paramters of algorithm: runLength is set to
n (number of nodes), threshold to 0 and tMax to infinity.
scalingLaw(n, mode, tSteps,
r, runLength, threshold) visualizes scaling law for networks with
n nodes and mode update scheme over tSteps timesteps, using
runLength, threshold and tMax as parameters for the algorithm. The averages are formed over
r networks at each n(.).
Input:
n  Array containing different values for N
(number of nodes)
mode  String defining update scheme. Currently supported modes are:CRBN, ARBN, DARBN, GARBN, DGARBN
tSteps  Number of time steps to run (Parameter T)
r  Number of networks to evaluate to form average
runLength  (Optional) Array containing lengths of 'activity measuring' (Parameter L)
threshold  (Optional) Array containing activity thresholds
Output:
y_max  Kev(N)  2 (Max)
y_min  Kev(N)  2 (Min)
y_av  Kev(N)  2 (Average)
x  n
Example
The following command plots the
scaling Law for networks with N=[3,4,5,6,7,8,9,10,15]. Each
average is built over 10 networks running over 50 timesteps.
RunLength and threshold are set to 50 and 0 respectively
>> scalingLaw([3,4,5,6,7,8,9,10,15],'CRBN',50,10,50*ones(1,9),zeros(1,9))
See also
initNodes(),
initConnections(), initRules(),
assocNeighbours(), assocRules(),
evolveTopology(), findAttractor(),
countTransitionsPerNode()
