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Sparse_logistic_regression/LogisticRegressionFramework/Simulations/Miscellaneous/RSCHMM_Simulate_Subject_Balanced.m
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RSCHMM_Simulate_Subject_Balanced.m
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function [R_TC] = RSCHMM_Simulate_Subject_Balanced(Alpha_baseline,...
Alpha_active,Beta_baseline,Beta_active,Gamma_baseline,Gamma_active,...
Noise,S,T)
% Number of regions at hand
R = size(Gamma_baseline,1);
% 1. For networks, generation of the time courses
% Will contain the data
N_TC = nan(N,T);
% First time point: random selection between active and baseline
N_TC(:,1) = randi([0,1],N,1);
for t = 2:T
% Current state of activity of the other networks
h = N_TC(:,t-1);
for n = 1:N
% Only the N-1 other networks
Modulators = Beta(:,n);
Modulators(n) = [];
h_modulators = h;
h_modulators(n) = [];
% Defines the probability to go up from 0
P_up(n) = Alpha_A(n);
% Defines the probability to go down from 1
P_down(n) = Alpha_B(n);
% P_up modulated by the other networks
for n2 = 1:N-1
P_up(n) = P_up(n) + h_modulators(n2)*Modulators(n2)*DeltaP;
end
for n2 = 1:N-1
P_down(n) = P_down(n) - h_modulators(n2)*Modulators(n2)*DeltaP;
end
end
P_up(P_up > 1) = 1;
P_up(P_up < 0) = 0;
P_down(P_down > 1) = 1;
P_down(P_down < 0) = 0;
N_TC(:,t) = RSCHMM_PickState(P_up,P_down,h);
end
% 2. Upscaling to the regional level
R_TC = zeros(R,T);
for r = 1:R
tmp = Network_assignment(r,:);
tmp(isnan(tmp)) = [];
for i = 1:length(tmp)
R_TC(r,:) = R_TC(r,:) + N_TC(tmp(i),:);
end
end
R_TC(R_TC > 1) = 1;
Indices_null = isnan(Network_assignment(:,1)) & isnan(Network_assignment(:,2));
Null_TC = R_TC(Indices_null,:);
Null_TC(:,1) = randi([0,1],sum(Indices_null),1);
for t = 2:T
Null_TC(:,t) = RSCHMM_PickState(Alpha_null_A,Alpha_null_B,Null_TC(:,t-1));
end
R_TC(Indices_null,:) = Null_TC;
R_TC = R_TC + Noise*randn(size(R_TC));
end
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