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