bootstrap_NN.R

In chunchentu/smogllim:

library(R.matlab)

cvID = 1
test_num = 5

data_path = "sim_data"
target_size = 30
full_data_path = file.path(data_path, sprintf('size%d.mat', target_size))
temp = readMat(full_data_path)

t1 = temp$subcluster1.t
t2 = temp$subcluster2.t
y1 = temp$subcluster1.y
y2 = temp$subcluster2.y

cluster_N = dim(t1)[2]

set.seed(cvID)
rand_index = sample(1:cluster_N)
test_index = sort(rand_index[1:test_num])
train_index = setdiff(1:cluster_N, test_index)

train_t = cbind(t1[,train_index], t2[,train_index])
train_y = cbind(y1[,train_index], y2[,train_index])

test_t = cbind(t1[,test_index], t2[,test_index])
test_y = cbind(y1[,test_index], y2[,test_index])


cstr = list(Sigma="*")
K = 2
Lw = 9
Lt = dim(train_t)[1]
minSize = 5
M = 2
dropTh = 0.5
# fit gllim model
set.seed(cvID)
in_r = array(0, c(cluster_N*2, K))
in_r[1:cluster_N, 1] = 1
in_r[(cluster_N+1):(cluster_N*2), 2] = 1
in_r = list(R=in_r)

gllim_result = gllim(train_t, train_y, K, in_r=in_r, Lw=Lw, cstr=cstr)
gllim_train_pred = gllim_inverse_map(train_y, gllim_result)
gllim_train_pred_t = gllim_train_pred$x_exp[1:Lt, , drop=FALSE]
gllim_train_diff = train_t - gllim_train_pred_t
gllim_train_mse = mean(apply(gllim_train_diff^2, 2, sum))

gllim_test_pred = gllim_inverse_map(test_y, gllim_result)
gllim_test_pred_t = gllim_test_pred$x_exp[1:Lt, , drop=FALSE]
gllim_test_diff = test_t - gllim_test_pred_t
gllim_test_mse = mean(apply(gllim_test_diff^2, 2, sum))
cat(sprintf("GLLiM: Train MSE: %.4g, test MSE: %.4g\n",
                                            gllim_train_mse, gllim_test_mse))

smogllim_result = smogllim(train_t, train_y, K, M, Lw=Lw, cstr=cstr,
                                                    minSize=minSize, dropTh=dropTh)

smogllim_train_pred = smogllim_inverse_map(train_y, smogllim_result$theta)
smogllim_train_pred_t = smogllim_train_pred$x_exp[1:Lt, , drop=FALSE]
smogllim_train_diff = train_t - smogllim_train_pred_t
smogllim_train_mse = mean(apply(smogllim_train_diff^2, 2, sum))


smogllim_test_pred = smogllim_inverse_map(test_y, smogllim_result$theta)
smogllim_test_pred_t = smogllim_test_pred$x_exp[1:Lt, , drop=FALSE]
smogllim_test_diff = test_t - smogllim_test_pred_t
smogllim_test_mse = mean(apply(smogllim_test_diff^2, 2, sum))


cat(sprintf("SMoGLLiM Train MSE: %.4f, test MSE: %.4f\n",
                                        smogllim_train_mse, smogllim_test_mse))

save(list = ls(all.names = TRUE), file = save_name, envir =
  environment())