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")
print(sl.expr)
And return the results:
>>>62.33 - 2.156*x10
- Note
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: