--- a
+++ b/A_Simple_Example.R
@@ -0,0 +1,81 @@
+library(pROC)
+library(MASS)
+library(glmnet)
+# ------------------------------------------
+# Please first set the path
+# working_dir = "C:/Users/dell/Desktop/NSLR"
+# setwd(working_dir)
+
+source('Fun_Auxiliary.R')
+source('Fun_NSLR.R')
+
+# ------------------------------------------
+# Generate a simple simulation data
+snrlam0 = 3
+f.num0  = 100
+sim.data = GET.SIM.DATA2(smaple.num = 700, feature.num=f.num0, random.seed = 10, snrlam=snrlam0)
+adj = get_sim_prior_Net(f.num0, 40, 0.3,0.05)
+
+Train.id = 1:300
+Valid.id = 301:500
+w.true = sim.data$w
+
+# Training data
+X1 = sim.data$X[Train.id,]; y1 =sim.data$y[Train.id]; 
+# Testing data
+X2 = sim.data$X[Valid.id,]; y2 =sim.data$y[Valid.id]; 
+
+# Non Normalized Laplacian Matrix"
+PM = get.penalityMatrix(adj,X1, y1)
+
+# ----------------------------------------
+# ----------------------------------------
+# Two regularization parameters
+lambda = 0.2 
+alpha  = 0.3
+
+# ----------------------------------------
+# A typical logistic regression model and four regularized logistic regession models
+# ----------------------------------------
+
+# Typical Logistic Regression Model
+out1 =    SGNLR(X1, y1, PM$M.c,        lambda=0, alpha=0, niter=20)
+
+# L1 (Lasso) Regularized Logistic Regression Model
+out2 =    SGNLR(X1, y1, PM$M.lasso,    lambda,   alpha,   niter=20)
+
+# Elastic Net Regularized Logistic Regression Model
+out3 =    SGNLR(X1, y1, PM$M.elastic,  lambda,   alpha,   niter=20)
+
+# Classical Network-regularized Logistic Regression Model
+out4 =    SGNLR(X1, y1, PM$M.network,  lambda,   alpha,   niter=20)
+
+# Adaptive Network-regularized Logistic Regression Model
+out5 =    SGNLR(X1, y1, PM$M.AdaptNet, lambda,   alpha,   niter=20)
+
+# Absolute Network-regularized Logistic Regression Model
+out6 = abs.SNLR(X1, y1, PM$M.network,  lambda,   alpha,   niter=20)
+
+# Testing
+res1 = predict.SGNLR(X2,y2,out1$w)
+res2 = predict.SGNLR(X2,y2,out2$w)
+res3 = predict.SGNLR(X2,y2,out3$w)
+res4 = predict.SGNLR(X2,y2,out4$w)
+res5 = predict.SGNLR(X2,y2,out5$w)
+res6 = predict.SGNLR(X2,y2,out6$w)
+
+multi.method.AUC = c(res1$AUC,res2$AUC,res3$AUC,res4$AUC,res5$AUC,res6$AUC)
+names(multi.method.AUC) = c("LR","Lasso.LR","Elastic.LR","Network.LR","AdaNet.LR","AbsNet.LR")
+
+# Result in term of AUC
+print(multi.method.AUC)
+
+
+
+
+
+
+
+
+
+