[Preferences]
alpha=0
bounds[0]=[-10000000000, 10000000000]
bounds[1]=[-10000000000, 10000000000]
bounds[2]=[0, 10000000000]
bounds[3]=[0, 200]
bounds[4]=[0.01, 10]
bounds_init[0]=[-0.717039, 1.282961]
bounds_init[1]=[0.09494000000000002, 2.0949400000000002]
bounds_init[2]=[1.29765, 3.29765]
bounds_init[3]=[0.7026699999999999, 2.70267]
bounds_init[4]=[0.01, 1.725037]
converted_file=
cv=
dx=1e-05
figsize[0]=8
figsize[1]=6
file1=
file2=
file3=
file4=
file5=
fitfile_template={filebody}_parameters.csv
fmax_plot=10
fmax_record=2000
fmin_max_mlr=60
fontsize=16
ga_nmaxiter=3
graphupdateinterval=10
help=0
hidden_layer_sizes=3,3,3
initialize_method=simplex
input_template=
interval=50
ix_plot=
ix_sort=
kvariance=6
l1_ratio=0.5
legend_fontsize=12
max_iter_mlp=1000
max_num_probes=1
mutation_rate=0.01
n_restarts_optimizer=50
ncal_error=11
nmax_surrogate_trial=5
nmaxdata=20
nparents=20
nsample=100
num_rand_basis=400
plot_interval=5
print_interval=5
run=1
score_mode=EI
seed=
surrogate_method=cg
surrogate_model=gpr
surrogate_scores=max
test_data_ratio=0.2
trainfile=fit_db.xlsx
use_cv=0
use_tkplt=0
x_scale=
xlabel=0
ylabel=1

[Files]
datafile=mu.xlsx
infile=peaks.xlsx
outfile=input-repeat.xlsx
historyfile=D:\git\tkProg\tkprog_COE\optimize\optimize_peakfit\test\peaks-history.xlsx
fitfile=mu-fit.xlsx

[Data]
sample=
Tmin=0
Tmax=1000
Nmin=0
Nmax=1e+23

[Flags]
fhistory=1
ffitfiles=0

[Condition]
method=nelder-mead
jac=3-points
nmaxiter=1000
nmaxcall=10000
tol=1e-05
xatol=0.0001
y_scale=

[Scan]
target_var=0
x0=
x1=
nx=5