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Sparse_logistic_regression/LogisticRegressionFramework/Framework/RSCHMM_Solve_Equation_Optimized_Deluxe_SingleVersion.m
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RSCHMM_Solve_Equation_Optimized_Deluxe_SingleVersion.m
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%% Runs an iterative process to solve the K-dim logistic regression equation
%% coefficient by coefficient
% Stops and returns the matrix when there is no more change in the
% coefficients that are being estimated
function [alpha0,beta0,gamma0,alpha1,beta1,gamma1] = RSCHMM_Solve_Equation_Optimized_Deluxe_SingleVersion(W,Z,...
X_causal,X_coact,alpha0,beta0,gamma0,alpha1,beta1,gamma1,lambda,rho,epsilon,EN,n_iter)
R = length(beta0)+1;
% This will determine whether we keep running or not
Delta = realmax;
iter = 1;
% We will perform the shrinkage steps for as long as needed until we
% converge
while Delta > epsilon %&& iter < (n_iter+1)
order = randperm(2*R-1);
j = order(1);
if j == 1
[alpha0,delta0] = RSCHMM_Shrinkage_Estimate_Optimized_Deluxe(W(:,1),Z(:,1),X_causal(:,1),X_coact(:,1),...
alpha0,beta0,gamma0,'Alpha',j,lambda,rho,EN);
[alpha1,delta1] = RSCHMM_Shrinkage_Estimate_Optimized_Deluxe(W(:,2),Z(:,2),X_causal(:,2),X_coact(:,2),...
alpha1,beta1,gamma1,'Alpha',j,lambda,rho,EN);
elseif j <= R
[beta0(j-1),delta0] = RSCHMM_Shrinkage_Estimate_Optimized_Deluxe(W(:,1),Z(:,1),X_causal(:,1),X_coact(:,1),...
alpha0,beta0,gamma0,'Beta',j-1,lambda,rho,EN);
[beta1(j-1),delta1] = RSCHMM_Shrinkage_Estimate_Optimized_Deluxe(W(:,2),Z(:,2),X_causal(:,2),X_coact(:,2),...
alpha1,beta1,gamma1,'Beta',j-1,lambda,rho,EN);
else
[gamma0(j-1-(R-1)),delta0] = RSCHMM_Shrinkage_Estimate_Optimized_Deluxe(W(:,1),Z(:,1),X_causal(:,1),X_coact(:,1),...
alpha0,beta0,gamma0,'Gamma',j-1-(R-1),lambda,rho,EN);
[gamma1(j-1-(R-1)),delta1] = RSCHMM_Shrinkage_Estimate_Optimized_Deluxe(W(:,2),Z(:,2),X_causal(:,2),X_coact(:,2),...
alpha1,beta1,gamma1,'Gamma',j-1-(R-1),lambda,rho,EN);
end
Delta = sqrt(delta0^2 + delta1^2);
Delta
iter = iter + 1;
end
end
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