afdee1c/symbolic-regression
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Silviu Marian Udrescu 2020-05-15 04:26:45 -04:00 • committed by GitHub
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Code/S_run_aifeynman.py
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@ -37,7 +37,6 @@ def run_AI_all(pathdir,filename,BF_try_time=60,BF_ops_file_type="14ops", polyfit
# Run bf and polyfit # Run bf and polyfit
PA = run_bf_polyfit(pathdir,pathdir,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = run_bf_polyfit(pathdir,pathdir,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
'''
# Run bf and polyfit on modified output # Run bf and polyfit on modified output
PA = get_acos(pathdir,"results/mystery_world_acos/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_acos(pathdir,"results/mystery_world_acos/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
PA = get_asin(pathdir,"results/mystery_world_asin/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_asin(pathdir,"results/mystery_world_asin/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
@ -50,7 +49,7 @@ def run_AI_all(pathdir,filename,BF_try_time=60,BF_ops_file_type="14ops", polyfit
PA = get_sqrt(pathdir,"results/mystery_world_sqrt/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_sqrt(pathdir,"results/mystery_world_sqrt/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
PA = get_squared(pathdir,"results/mystery_world_squared/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_squared(pathdir,"results/mystery_world_squared/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
PA = get_tan(pathdir,"results/mystery_world_tan/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg) PA = get_tan(pathdir,"results/mystery_world_tan/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
'''
############################################################################################################################# #############################################################################################################################
# check if the NN is trained. If it is not, train it on the data. # check if the NN is trained. If it is not, train it on the data.
print("Checking for symmetry \n", filename) print("Checking for symmetry \n", filename)