Inspired by this post & TF comment I tried symbollic regression [0] Basically it uses genetic algorithm to find a formula that matches known input and output ve

Inspired by this post & TF comment I tried symbollic regression [0] Basically it uses genetic algorithm to find a formula that matches known input and output vectors with minimal loss I tried to force it to use pi constant but was unable I don't have much expreience with this library but I'm sure with more tweaks you'll get the right result from pysr import PySRRegressor def f(n): if n % 15 == 0: return 3 elif n%5 == 0: return 2 elif n%3 == 0: return 1 return 0 n = 500 X = np.array(range(1,n)).reshape(-1,1) Y = np.array([f(n) for n in range(1,n)]).reshape(-1,1) model = PySRRegressor( maxsize=25, niterations=200, # < Increase me for better results binary_operators=["+", "*"], unary_operators=["cos", "sin", "exp"], elementwise_loss="loss(prediction, target) = (prediction - target)^2", ) model.fit(X,Y) Result I got is this: ((cos((x0 + x0) * 1.0471969) * 0.66784626) + ((cos(sin(x0 * 0.628323) * -4.0887628) + 0.06374673) * 1.1508249)) + 1.1086457 with compleixty 22 loss: 0.000015800686 The first term is close to 2/3 * cos(2pi*n/3) which is featured in the actual formula in the article. the constant doesn't compare to 11/15 though [0] https://github.com/MilesCranmer/PySR https://github.com/MilesCranmer/PySR