No BSD License
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assocNeighbours(node, connect...
ASSOCNEIGHBOURS Set the array "input" in the NODE structure-array according to the
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assocRules(node,rulesMatrix)
ASSOCRULES Set the array "rule" in the NODE structure-array according to the network's
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averageConnectivity(node)
AVERAGECONNECTIVITY Calculate average network connectivity.
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bnKav(n, kavdesired)
BNKAV Generate network with N nodes and at average KAV incoming connections per node.
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bsn(n, k, optionString)
BSN Build and show network.
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connectivityDistribution(node...
CONNECTIVITYDISTRIBUTION Calculate and display the network's connectivity distribution.
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countTransitionsPerNode(tsm)
COUNTTRANSITIONSPERNODE Count number of changes of a node through evolution.
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displayEvolution(node, k, mod...
DISPLAYEVOLUTION Calculate and visualize evolution of NODE over K discrete time steps according to MODE update scheme
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displayNodeStats(node,tsm)
DISPLAYNODESTATS Visualize node statistics (number of updates / nb of state-transitions).
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displayTimeStateMatrix(tsm,va...
DISPLAYTIMESTATEMATRIX Visualize Time-State-Matrix.
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displayTopology(node, connect...
DISPLAYTOPOLOGY Visualize network topology, set xy-components and "ouput" field in node structure-array.
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entropy(rulesMatrix)
ENTROPY Calculate the entropy of the system. Indicator for the diversity of the rules in the network.
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evolveARBN(node, varargin)
EVOLVEARBN Develop network gradually K discrete time-steps according to ARBN (Asynchronous
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evolveCRBN(node, varargin)
EVOLVECRBN Develop network gradually K discrete time-steps according to CRBN (Classical
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evolveDARBN(node, varargin)
EVOLVEDARBN Develop network gradually K discrete time-steps according to DARBN (Deterministic
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evolveDGARBN(node, varargin)
EVOLVEDGARBN Develop network gradually K discrete time steps according to DGARBN (Deterministic
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evolveGARBN(node, varargin)
EVOLVEGARBN Develop network gradually K discrete time steps according to GARBN (Generalized
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evolveTopology(node, mode, tS...
EVOLVETOPOLOGY Evolve and display topology according to algorithm described by Christof Teuscher and Eduardo Sanchez
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findAttractor(varargin)
FINDATTRACTOR Return attractor length and states in attractor.
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frozenComp(kMax, n, r, step ,...
FROZENCOMP Visualize frozen components graph.
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getStateVector(node)
GETSTATEVECTOR Return the states of the nodes in NODE as row vector.
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initConnections(varargin)
INITCONNECTIONS Generate N x N adjacent matrix with K incoming connections per node.
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initNodes(n, varargin)
INITNODES Generate structure-array containing node-state information.
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initRules(varargin)
INITRULES Generate a 2^k x n or 2^kMax x n matrix containing logic transition
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resetNodeStats(node)
RESETNODESTATS Reset all variables of the node structure which are involved in statistical evaluation to zero.
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saveFigure(figure, varargin)
SAVEFIGURE Save a figure to the current directory in two formats (*.fig and *.eps).
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saveMatrix(matrix, varargin)
SAVEMATRIX Save a matrix to the current directory.
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scalingLaw(n, mode, tSteps, r...
SCALINGLAW Visualize Scaling Law.
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setLUTLines(node, varargin)
SETLUTLINES Updates the "lineNumber" field of the NODE structure-array.
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setNodeNextState(node)
SETNODENEXTSTATE Look up next state for each node and update "nextState" field of the NODE
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x(~out);
end;
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View all files
from
Random Boolean Network Toolbox
by Christian Schwarzer
Simulation und visualization of Random Boolean Networks.
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| bsn(n, k, optionString)
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function [node, conn, rules] = bsn(n, k, optionString)
% BSN Build and show network.
%
% BSN(N, K, OPTIONSTRING) builds network with N nodes and K connections per node
% and displays the topology using OPTIONSTRING-LineStyle.
%
% This is just a script that calls some standard initialisation functions with
% common parameters.
% To exercice more control over the parameters of the network, make sure to
% call all initializing functions with specific parameters individually.
%
% Input:
% n - Number of nodes in network
% k - Incoming connections per node
% optionString - LineStyle ('line' or 'arrow')
%
% Output:
% node - Structure-array containing node information
% conn - n x n adjacent matrix with at average k incoming
% connections per node
% rules - 2^k x n (2^kMax x n) matrix containing transition logic rules for each node
%
% Author: Christian Schwarzer - SSC EPFL
% CreationDate: 8.11.2002 LastModified: 20.01.2003
node = initNodes(n);
conn= initConnections(n, k);
rules = initRules(n, k);
node = assocRules(node, rules);
node = assocNeighbours(node, conn);
node = displayTopology(node, conn, optionString);
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