afdee1c/symbolic-regression
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Silviu Marian Udrescu 2020-04-16 00:00:35 -04:00 • committed by GitHub
parent ae3d4e9a8d
commit 23184662e3
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2 changed files with 19 additions and 12 deletions
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7
Code/S_get_symbolic_expr_error.py
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@ -26,8 +26,15 @@ def get_symbolic_expr_error(pathdir,filename,expr):
if check_var in np.array(variables).astype('str'): if check_var in np.array(variables).astype('str'):
real_variables = real_variables + [data[:,i]] real_variables = real_variables + [data[:,i]]
# Remove accidental nan's
good_idx = np.where(np.isnan(f(*real_variables))==False)
# use this to get rid of cases where the loss gets complex because of transformations of the output variable # use this to get rid of cases where the loss gets complex because of transformations of the output variable
if isinstance(np.mean((f(*real_variables)-data[:,-1])**2), complex): if isinstance(np.mean((f(*real_variables)-data[:,-1])**2), complex):
return 1000000 return 1000000
else: else:
try:
return np.sqrt(np.mean((f(*real_variables)[good_idx]-data[good_idx][:,-1])**2))/np.sqrt(np.mean(data[good_idx][:,-1]**2))
except:
# use this for the case in which the expression is just one number (i.e. not array)
return np.sqrt(np.mean((f(*real_variables)-data[:,-1])**2))/np.sqrt(np.mean(data[:,-1]**2)) return np.sqrt(np.mean((f(*real_variables)-data[:,-1])**2))/np.sqrt(np.mean(data[:,-1]**2))

18
Code/S_run_aifeynman.py
View file

@ -174,28 +174,28 @@ def run_aifeynman(pathdir,filename,BF_try_time,BF_ops_file_type, polyfit_deg=4,
np.savetxt("results/solution_before_snap_%s.txt" %filename,PA_list,fmt="%s") np.savetxt("results/solution_before_snap_%s.txt" %filename,PA_list,fmt="%s")
# Run zero, integer and rational snap on the resulted equations # Run zero, integer and rational snap on the resulted equations
PA_snapped_1 = ParetoSet()
for j in range(len(PA_list)): for j in range(len(PA_list)):
PA_snapped_1 = add_snap_expr_on_pareto(pathdir,filename,PA_list[j][-1],PA_snapped_1, "") PA = add_snap_expr_on_pareto(pathdir,filename,PA_list[j][-1],PA, "")
PA_list = PA_snapped_1.get_pareto_points() PA_list = PA.get_pareto_points()
np.savetxt("results/solution_first_snap_%s.txt" %filename,PA_list,fmt="%s") np.savetxt("results/solution_first_snap_%s.txt" %filename,PA_list,fmt="%s")
# Run gradient descent on the data one more time # Run gradient descent on the data one more time
for i in range(len(PA_list)): for i in range(len(PA_list)):
try: try:
gd_update = final_gd(pathdir+filename,PA_list[i][-1]) gd_update = final_gd(pathdir+filename,PA_list[i][-1])
PA_snapped_1.add(Point(x=gd_update[1],y=gd_update[0],data=gd_update[2])) PA.add(Point(x=gd_update[1],y=gd_update[0],data=gd_update[2]))
except: except:
continue continue
PA_list = PA_snapped_1.get_pareto_points() PA_list = PA.get_pareto_points()
print("test_1 ", PA_list)
PA_snapped = ParetoSet()
for j in range(len(PA_list)): for j in range(len(PA_list)):
PA_snapped = add_snap_expr_on_pareto(pathdir,filename,PA_list[j][-1],PA_snapped, DR_file) print(PA_list[j][-1])
PA = add_snap_expr_on_pareto(pathdir,filename,PA_list[j][-1],PA, DR_file)
list_dt = np.array(PA_snapped.get_pareto_points()) print("test_2 ", PA.get_pareto_points())
list_dt = np.array(PA.get_pareto_points())
data_file_len = len(np.loadtxt(pathdir+filename)) data_file_len = len(np.loadtxt(pathdir+filename))
log_err = [] log_err = []
log_err_all = [] log_err_all = []