Quick start

The symbol are conformed with the ‘’sklearn-style’’ type, which can be easily to modeling with fit, predict, score.

if __name__ == "__main__":
    # data
    from sklearn.datasets import load_boston
    from bgp.skflow import SymbolLearning

    data = load_boston()
    x = data["data"]
    y = data["target"]
    c = [6, 3, 4]

    # start->
    sl = SymbolLearning(loop="MultiMutateLoop", pop=500, gen=2, random_state=1)
    sl.fit(x, y, c=c)
    score = sl.score(x, y, "r2")

And return the results:

>>>62.33 - 2.156*x10

When the result of one problem is not stable, the final expression is changed with random_state (random seed). The random seeds between window and linux are different.

More Examples: