# Config
    seed = 2021 # 2021 Train, 2022 Val, 2023 Test, you have to change the generateData.py seed as well
    #from GenerateData import seed
    import random
    random.seed(seed)
    np.random.seed(seed=seed) # fix the seed for reproducibility

    #NOTE: For linux you can only use unique numVars, in Windows, it is possible to use [1,2,3,4] * 10!
    numVars = [1] #list(range(31)) #[1,2,3,4,5]
    decimals = 8
    numberofPoints = [30,31] # only usable if support points has not been provided
    numSamples = 10000 # number of generated samples
    folder = './Dataset'
    dataPath = folder +'/{}_{}_{}.json'

    testPoints = False
    trainRange = [-3.0,3.0] 
    testRange = [[-5.0, 3.0],[-3.0, 5.0]] # this means Union((-5,-1),(1,5))

    supportPoints = None
    #supportPoints = np.linspace(xRange[0],xRange[1],numberofPoints[1])
    #supportPoints = [[np.round(p,decimals)] for p in supportPoints]
    #supportPoints = [[np.round(p,decimals), np.round(p,decimals)] for p in supportPoints]
    #supportPoints = [[np.round(p,decimals) for i in range(numVars[0])] for p in supportPoints]

    supportPointsTest = None
    #supportPoints = None # uncomment this line if you don't want to use support points
    #supportPointsTest = np.linspace(xRange[0],xRange[1],numberofPoints[1])
    #supportPointsTest = [[np.round(p,decimals) for i in range(numVars[0])] for p in supportPointsTest]
    
    n_levels = 4
    allow_constants = True
    const_range = [-2.1, 2.1]
    const_ratio = 0.5
    op_list=[
                "id", "add", "mul",
                "sin", "pow", "cos", 
                "exp", "div", "sub", "log"
            ]
    exponents=[3, 4, 5, 6]

    sortY = False # if the data is sorted based on y
    numSamplesEachEq = 50
    threshold = 5000
    templatesEQs = None