PyTcgpr/
    __init__.py
    TCGPR.py
    data/
        OutliersIdentification.py
        DatasetPartition.py
    feature/
        FeaturesSelection.py

from PyTcgpr import TCGPR
dataSet = "data.csv"
initial_set_cap = 3
sampling_cap =2
up_search = 500
CV = 'LOOCV'
Task = 'Partition'
TCGPR.fit(
    filePath = dataSet, initial_set_cap = initial_set_cap,Task=Task, sampling_cap = sampling_cap,
    up_search = up_search, CV=CV
        )
# note: default setting of Mission = 'DATA', No need to declare

from PyTcgpr import TCGPR
dataSet = "data.csv"
sampling_cap =2
up_search = 500
Task = 'Identification'
CV = 'LOOCV'
TCGPR.fit(
    filePath = dataSet, Task = Task, sampling_cap = sampling_cap,
    up_search = up_search,CV=CV
        )
# note: default setting of Mission = 'DATA', No need to declare; initial_set_cap is masked 

from PyTcgpr import TCGPR
dataSet = "data.csv"
sampling_cap =2
Mission = 'FEATURE'
up_search = 500
CV = 'LOOCV'
TCGPR.fit(
    filePath = dataSet, Mission = 'FEATURE', initial_set_cap = initial_set_cap, sampling_cap = sampling_cap,
    up_search = up_search,CV=CV
        )
# note: for feature selection, Mission should be declared as Mission = 'FEATURE' ! 

    :param Mission : str, the mission of TCGPR, 
        default Mission = 'DATA' for data screening. 
        Mission = 'FEATURE' for feature selection.

    :param filePath: the input dataset in csv format

    :param initial_set_cap: 
    for Mission = 'DATA':
        if Task = 'Partition':
        initial_set_cap : the capacity of the initial dataset
        int, default = 3, recommend = 3-10
        or a list : 
        i.e.,  
        [3,4,8], means the 4-th, 5-th, 9-th datum will be collected as the initial dataset
        elif Task = 'Identification':
        param initial_set_cap is masked 
    for Mission = 'FEATURE':
        initial_set_cap : the capacity of the initial featureset
        int, default = 1, recommend = 1-5
        or a list : i.e.,  
        [3,4,8], means the 4-th, 5-th, 9-th feature will be selected as the initial characterize

    :param sampling_cap: 
    for Mission = 'DATA':
        int, the number of data added to the updating dataset at each iteration, default = 1, recommend = 1-5
    for Mission = 'FEATURE':
        int, the number of features added to the updating feature set at each iteration, default = 1, recommend = 1-3

    :param measure :Correlation criteria, default 'Pearson' means R values are used
        or measure = 'Determination' means R^2 values are used

    :param ratio: 
    for Mission = 'DATA':
        if Task = 'Partition':
        tolerance, lower boundary of R is (1-ratio)Rmax, default = 0.2, recommend = 0~0.3
        elif Task = 'Identification':
        tolerance, lower boundary of R is (1+ratio)R[last], default = 0.0, recommend = -0.01~0.01
    for Mission = 'FEATURE':
        tolerance, lower boundary of R is (1+ratio)R[last], default = -0.01, recommend = -0.01~0.01

    :param target:
    used in feature selection when Mission = 'FEATURE'
        int, default 1, the number of target in regression mission
        target = 1 for single_task regression and =k for k_task regression (Multiobjective regression)
    otherwise : param target is masked 
    
    :param weight
    a weight imposed on R value in calculating GGMF, default = .2 , recommend =  .1-1
    i.e.,
        weight * (1-R) +  mean / std (mean, std is the mean and standard deviation of length scales) 

    :param up_search: 
    for Mission = 'DATA':
        up boundary of candidates for brute force search, default = 5e2 , recommend =  2e2-2e3
    for Mission = 'FEATURE':
        up boundary of candidates for brute force search, default = 10 , recommend = 10-20

    :param exploit_coef: constrains to the magnitude of variance in Cal_EI function, default = 2, recommend = 2

    :param exploit_model: boolean, default, False
        exploit_model == True, the searching direction will be R only! GGMF will not be used!

    :param CV: cross validation, default = 10
        e.g. (int) CV = 5,10,... or str CV = 'LOOCV' for leave one out cross validation