def get_atan(pathdir,
pathdir_write_to,
filename,
BF_try_time,
BF_ops_file_type,
PA,
polyfit_deg=4):
try:
os.mkdir(pathdir_write_to)
except:
pass
try:
n_variables = np.loadtxt(pathdir + "%s" % filename,
dtype='str').shape[1] - 1
variables = np.loadtxt(pathdir + "%s" % filename, usecols=(0, ))
for j in range(1, n_variables):
v = np.loadtxt(pathdir + "%s" % filename, usecols=(j, ))
variables = np.column_stack((variables, v))
f_dependent = np.loadtxt(pathdir + "%s" % filename,
usecols=(n_variables, ))
dt = np.column_stack((variables, np.arctan(f_dependent)))
np.savetxt(pathdir_write_to + filename, dt)
PA = run_bf_polyfit(pathdir, pathdir_write_to, filename, BF_try_time,
BF_ops_file_type, PA, polyfit_deg, "atan")
except:
return PA
return PA
def get_log(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3):
try:
os.mkdir(pathdir_write_to)
except:
pass
data = np.loadtxt(pathdir+filename)
try:
data[:,-1] = np.log(data[:,-1])
np.savetxt(pathdir_write_to+filename,data)
PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "log")
except:
return PA
return PA
def run_AI_all(pathdir,filename,BF_try_time=60,BF_ops_file_type="14ops", polyfit_deg=4, NN_epochs=4000, PA=PA):
try:
os.mkdir("results/")
except:
pass
# load the data for different checks
data = np.loadtxt(pathdir+filename)
# Run bf and polyfit
PA = run_bf_polyfit(pathdir,pathdir,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
# 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_asin(pathdir,"results/mystery_world_asin/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
PA = get_atan(pathdir,"results/mystery_world_atan/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
PA = get_cos(pathdir,"results/mystery_world_cos/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
PA = get_exp(pathdir,"results/mystery_world_exp/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
PA = get_inverse(pathdir,"results/mystery_world_inverse/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
PA = get_log(pathdir,"results/mystery_world_log/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
PA = get_sin(pathdir,"results/mystery_world_sin/",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_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.
print("Checking for symmetry \n", filename)
if len(data[0])<3:
print("Just one variable!")
pass
elif path.exists("results/NN_trained_models/models/" + filename + ".h5"):# or len(data[0])<3:
print("NN already trained \n")
print("NN loss: ", NN_eval(pathdir,filename), "\n")
elif path.exists("results/NN_trained_models/models/" + filename + "_pretrained.h5"):
print("Found pretrained NN \n")
NN_train(pathdir,filename,NN_epochs/2,lrs=1e-3,N_red_lr=3,pretrained_path="results/NN_trained_models/models/" + filename + "_pretrained.h5")
print("NN loss after training: ", NN_eval(pathdir,filename), "\n")
else:
print("Training a NN on the data... \n")
NN_train(pathdir,filename,NN_epochs)
print("NN loss: ", NN_eval(pathdir,filename), "\n")
# Check which symmetry/separability is the best
# Symmetries
symmetry_minus_result = check_translational_symmetry_minus(pathdir,filename)
symmetry_divide_result = check_translational_symmetry_divide(pathdir,filename)
symmetry_multiply_result = check_translational_symmetry_multiply(pathdir,filename)
symmetry_plus_result = check_translational_symmetry_plus(pathdir,filename)
# Separabilities
separability_plus_result = check_separability_plus(pathdir,filename)
separability_multiply_result = check_separability_multiply(pathdir,filename)
if symmetry_plus_result[0]==-1:
idx_min = -1
else:
idx_min = np.argmin(np.array([symmetry_plus_result[0], symmetry_minus_result[0], symmetry_multiply_result[0], symmetry_divide_result[0], separability_plus_result[0], separability_multiply_result[0]]))
# Apply the best symmetry/separability and rerun the main function on this new file
if idx_min == 0:
new_pathdir, new_filename = do_translational_symmetry_plus(pathdir,filename,symmetry_plus_result[1],symmetry_plus_result[2])
PA1_ = ParetoSet()
PA1 = run_AI_all(new_pathdir,new_filename,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA1_)
PA = add_sym_on_pareto(pathdir,filename,PA1,symmetry_plus_result[1],symmetry_plus_result[2],PA,"+")
return PA
elif idx_min == 1:
new_pathdir, new_filename = do_translational_symmetry_minus(pathdir,filename,symmetry_minus_result[1],symmetry_minus_result[2])
PA1_ = ParetoSet()
PA1 = run_AI_all(new_pathdir,new_filename,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA1_)
PA = add_sym_on_pareto(pathdir,filename,PA1,symmetry_minus_result[1],symmetry_minus_result[2],PA,"-")
return PA
elif idx_min == 2:
new_pathdir, new_filename = do_translational_symmetry_multiply(pathdir,filename,symmetry_multiply_result[1],symmetry_multiply_result[2])
PA1_ = ParetoSet()
PA1 = run_AI_all(new_pathdir,new_filename,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA1_)
PA = add_sym_on_pareto(pathdir,filename,PA1,symmetry_multiply_result[1],symmetry_multiply_result[2],PA,"*")
return PA
elif idx_min == 3:
new_pathdir, new_filename = do_translational_symmetry_divide(pathdir,filename,symmetry_divide_result[1],symmetry_divide_result[2])
PA1_ = ParetoSet()
PA1 = run_AI_all(new_pathdir,new_filename,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA1_)
PA = add_sym_on_pareto(pathdir,filename,PA1,symmetry_divide_result[1],symmetry_divide_result[2],PA,"/")
return PA
elif idx_min == 4:
new_pathdir1, new_filename1, new_pathdir2, new_filename2, = do_separability_plus(pathdir,filename,separability_plus_result[1],separability_plus_result[2])
PA1_ = ParetoSet()
PA1 = run_AI_all(new_pathdir1,new_filename1,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA1_)
PA2_ = ParetoSet()
PA2 = run_AI_all(new_pathdir2,new_filename2,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA2_)
PA = combine_pareto(pathdir,filename,PA1,PA2,separability_plus_result[1],separability_plus_result[2],PA,"+")
return PA
elif idx_min == 5:
new_pathdir1, new_filename1, new_pathdir2, new_filename2, = do_separability_multiply(pathdir,filename,separability_multiply_result[1],separability_multiply_result[2])
PA1_ = ParetoSet()
PA1 = run_AI_all(new_pathdir1,new_filename1,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA1_)
PA2_ = ParetoSet()
PA2 = run_AI_all(new_pathdir2,new_filename2,BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA2_)
PA = combine_pareto(pathdir,filename,PA1,PA2,separability_multiply_result[1],separability_multiply_result[2],PA,"*")
return PA
else:
return PA
