MilesCranmer commited on
Commit
e8f42f8
1 Parent(s): eefdfef

Add fast_cycle option back in

Browse files
Files changed (2) hide show
  1. benchmarks/benchmark.sh +3 -1
  2. setup.py +1 -1
benchmarks/benchmark.sh CHANGED
@@ -6,7 +6,9 @@ import numpy as np
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  from pysr import pysr
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  X=np.random.randn(100, 2)*5
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  y=2*np.sin((X[:, 0]+X[:, 1]))*np.exp(X[:, 1]/3)
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- if version[1] >= 3 and version[2] >= 16:
 
 
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  eq = pysr(X, y, binary_operators=["plus", "mult", "div", "pow"], unary_operators=["sin"], niterations=20, procs=4, parsimony=1e-10, npop=1000, ncyclesperiteration=1000, maxdepth=6)
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  elif version[1] >= 3 and version[2] >= 2:
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  eq = pysr(X, y, binary_operators=["plus", "mult", "div", "pow"], unary_operators=["sin"], niterations=20, procs=4, parsimony=1e-10, npop=1000, ncyclesperiteration=1000)
 
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  from pysr import pysr
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  X=np.random.randn(100, 2)*5
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  y=2*np.sin((X[:, 0]+X[:, 1]))*np.exp(X[:, 1]/3)
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+ if version[1] >= 3 and version[2] >= 17:
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+ eq = pysr(X, y, binary_operators=["plus", "mult", "div", "pow"], unary_operators=["sin"], niterations=20, procs=4, parsimony=1e-10, npop=1000, ncyclesperiteration=1000, maxdepth=6, fast_cycle=True)
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+ elif version[1] >= 3 and version[2] >= 16:
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  eq = pysr(X, y, binary_operators=["plus", "mult", "div", "pow"], unary_operators=["sin"], niterations=20, procs=4, parsimony=1e-10, npop=1000, ncyclesperiteration=1000, maxdepth=6)
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  elif version[1] >= 3 and version[2] >= 2:
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  eq = pysr(X, y, binary_operators=["plus", "mult", "div", "pow"], unary_operators=["sin"], niterations=20, procs=4, parsimony=1e-10, npop=1000, ncyclesperiteration=1000)
setup.py CHANGED
@@ -5,7 +5,7 @@ with open("README.md", "r") as fh:
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  setuptools.setup(
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  name="pysr", # Replace with your own username
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- version="0.3.16",
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  author="Miles Cranmer",
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  author_email="[email protected]",
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  description="Simple and efficient symbolic regression",
 
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  setuptools.setup(
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  name="pysr", # Replace with your own username
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+ version="0.3.17",
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  author="Miles Cranmer",
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  author_email="[email protected]",
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  description="Simple and efficient symbolic regression",