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# Config |
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seed = 2021 # 2021 Train, 2022 Val, 2023 Test, you have to change the generateData.py seed as well |
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#from GenerateData import seed |
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import random |
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random.seed(seed) |
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np.random.seed(seed=seed) # fix the seed for reproducibility |
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#NOTE: For linux you can only use unique numVars, in Windows, it is possible to use [1,2,3,4] * 10! |
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numVars = [1] #list(range(31)) #[1,2,3,4,5] |
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decimals = 8 |
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numberofPoints = [30,31] # only usable if support points has not been provided |
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numSamples = 10000 # number of generated samples |
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folder = './Dataset' |
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dataPath = folder +'/{}_{}_{}.json' |
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testPoints = False |
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trainRange = [-3.0,3.0] |
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testRange = [[-5.0, 3.0],[-3.0, 5.0]] # this means Union((-5,-1),(1,5)) |
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supportPoints = None |
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#supportPoints = np.linspace(xRange[0],xRange[1],numberofPoints[1]) |
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#supportPoints = [[np.round(p,decimals)] for p in supportPoints] |
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#supportPoints = [[np.round(p,decimals), np.round(p,decimals)] for p in supportPoints] |
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#supportPoints = [[np.round(p,decimals) for i in range(numVars[0])] for p in supportPoints] |
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supportPointsTest = None |
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#supportPoints = None # uncomment this line if you don't want to use support points |
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#supportPointsTest = np.linspace(xRange[0],xRange[1],numberofPoints[1]) |
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#supportPointsTest = [[np.round(p,decimals) for i in range(numVars[0])] for p in supportPointsTest] |
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n_levels = 4 |
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allow_constants = True |
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const_range = [-2.1, 2.1] |
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const_ratio = 0.5 |
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op_list=[ |
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"id", "add", "mul", |
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"sin", "pow", "cos", |
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"exp", "div", "sub", "log" |
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] |
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exponents=[3, 4, 5, 6] |
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sortY = False # if the data is sorted based on y |
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numSamplesEachEq = 50 |
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threshold = 5000 |
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templatesEQs = None |