!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
! Keywords for the target properties
!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
ptype=1              ! property type 1: continuous for regression,2:categorical for classification
ntask=1              ! number of tasks (properties or maps) 1: single-task learning, >1: multi-task learning
nsample=5            ! number of samples. If ntask>1, input a number for each task seperated by comma
task_weighting=1     ! 1: no weighting (tasks treated equally) 2: weighted by #sample_task_i/total_sample.a
desc_dim=3           ! dimension of the descriptor
restart=.false.      ! set .true. to continue an unfinished job

!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
! Keywords for feature construction (FC) and sure independence screening (SIS)
! FC recursively do H*Phi->Phi, where H: operators set, Phi: feature space. Number of repeat: rung of the Phi.
! Implemented operators:(+)(-)(*)(/)(exp)(exp-)(^-1)(^2)(^3)(sqrt)(cbrt)(log)(|-|)(scd)(^6)(sin)(cos)
! scd: standard Cauchy distribution
!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
nsf=3                ! number of input scalar features in the train.dat file
rung=2               ! rung of the feature space to be constructed
opset='(+)(-)(*)(/)(exp)(exp-)(^-1)(^2)(^3)(sqrt)(log)(|-|)(^6)(sin)(cos)'    ! SAME one set of operators for recursive FC. Otherwise, input a set for each rung seperated by comma
maxcomplexity=10     ! max feature complexity (number of operators in a feature)
dimclass=(1:2)(3:3)  ! group features according to their dimension/unit; those not in any () are dimensionless
maxfval_lb=1e-3      ! features having the max. abs. data value <maxfval_lb will not be selected
maxfval_ub=1e5       ! features having the max. abs. data value >maxfval_ub will not be selected
subs_sis=7           ! SAME one size for every SIS-selected subspace. Otherwise, input a size for each dimension seperated by comma

!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
! keywords for descriptor identification via a sparsifying operator
!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
method='L0'          ! sparsification operator: 'L1L0' or 'L0'; L0 is recommended!
L1L0_size4L0= 1      ! If method='L1L0', specify the number of features to be screened by L1 for L0
fit_intercept=.true. ! fit to a nonzero intercept (.true.) or force the intercept to zero (.false.)
metric='RMSE'        ! for regression only, the metric for model selection: RMSE,MaxAE
nm_output=100        ! number of the best models to output
