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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 time-steps. 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 time-steps, 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 time-steps. 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()

 

 
 
 
      Christian Schwarzer - EPFL