Iterate version

README.md

@@ -305,6 +305,7 @@ pd.DataFrame, Results dataframe, giving complexity, MSE, and equations
 - [x] Try @spawn over each sub-population. Do random sort, compute mutation for each, then replace 10% oldest.
 - [x] Control max depth, rather than max number of nodes?
 - [x] Allow user to pass names for variables - use these when printing
+- [x] Check for domain errors in an equation quickly before actually running the entire array over it. (We do this now recursively - every single equation is checked for nans/infs when being computed.)
 - [ ] Sort these todo lists by priority
 
 ## Feature ideas

benchmarks/benchmark.sh

@@ -6,7 +6,9 @@ import numpy as np
 from pysr import pysr
 X=np.random.randn(100, 2)*5
 y=2*np.sin((X[:, 0]+X[:, 1]))*np.exp(X[:, 1]/3)
-if version[1] >= 3 and version[2] >= 17:
+if version[1] >= 3 and version[2] >= 20:
+    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, batching=True, batchSize=50)
+elif version[1] >= 3 and version[2] >= 17:
     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)
 elif version[1] >= 3 and version[2] >= 16:
     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)

setup.py

@@ -5,7 +5,7 @@ with open("README.md", "r") as fh:
 
 setuptools.setup(
     name="pysr", # Replace with your own username
-    version="0.3.19",
+    version="0.3.20",
     author="Miles Cranmer",
     author_email="[email protected]",
     description="Simple and efficient symbolic regression",