File size: 1,801 Bytes
f492854 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
# 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 |