Add files via upload
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Silviu Marian Udrescu
GitHub
parent
be88c45a8a
commit
f1f13db1b9
2 changed files with 266 additions and 355 deletions
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@ -2,212 +2,141 @@ import numpy as np
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import os
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from S_run_bf_polyfit import run_bf_polyfit
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def get_acos(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=4):
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def get_acos(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3):
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try:
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os.mkdir(pathdir_write_to)
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except:
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pass
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data = np.loadtxt(pathdir+filename)
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try:
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n_variables = np.loadtxt(pathdir+"%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"%s" %filename, usecols=(0,))
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"%s" %filename, usecols=(n_variables,))
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dt = np.column_stack((variables,np.arccos(f_dependent)))
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np.savetxt(pathdir_write_to+filename,dt)
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data[:,-1] = np.arccos(data[:,-1])
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np.savetxt(pathdir_write_to+filename,data)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "acos")
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except:
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return PA
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return PA
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def get_asin(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=4):
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def get_asin(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3):
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try:
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os.mkdir(pathdir_write_to)
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except:
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pass
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data = np.loadtxt(pathdir+filename)
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try:
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n_variables = np.loadtxt(pathdir+"%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"%s" %filename, usecols=(0,))
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"%s" %filename, usecols=(n_variables,))
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dt = np.column_stack((variables,np.arcsin(f_dependent)))
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np.savetxt(pathdir_write_to+filename,dt)
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data[:,-1] = np.arcsin(data[:,-1])
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np.savetxt(pathdir_write_to+filename,data)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "asin")
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except:
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return PA
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return PA
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def get_atan(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=4):
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def get_atan(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3):
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try:
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os.mkdir(pathdir_write_to)
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except:
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pass
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data = np.loadtxt(pathdir+filename)
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try:
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n_variables = np.loadtxt(pathdir+"%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"%s" %filename, usecols=(0,))
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"%s" %filename, usecols=(n_variables,))
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dt = np.column_stack((variables,np.arctan(f_dependent)))
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np.savetxt(pathdir_write_to+filename,dt)
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data[:,-1] = np.arctan(data[:,-1])
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np.savetxt(pathdir_write_to+filename,data)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "atan")
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except:
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return PA
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return PA
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def get_cos(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=4):
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def get_cos(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3):
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try:
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os.mkdir(pathdir_write_to)
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except:
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pass
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data = np.loadtxt(pathdir+filename)
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try:
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n_variables = np.loadtxt(pathdir+"%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"%s" %filename, usecols=(0,))
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"%s" %filename, usecols=(n_variables,))
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dt = np.column_stack((variables,np.cos(f_dependent)))
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np.savetxt(pathdir_write_to+filename,dt)
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data[:,-1] = np.cos(data[:,-1])
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np.savetxt(pathdir_write_to+filename,data)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "cos")
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except:
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return PA
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return PA
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def get_exp(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=4):
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def get_exp(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3):
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try:
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os.mkdir(pathdir_write_to)
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except:
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pass
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data = np.loadtxt(pathdir+filename)
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try:
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n_variables = np.loadtxt(pathdir+"%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"%s" %filename, usecols=(0,))
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"%s" %filename, usecols=(n_variables,))
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dt = np.column_stack((variables,np.exp(f_dependent)))
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np.savetxt(pathdir_write_to+filename,dt)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "exp")
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data[:,-1] = np.exp(data[:,-1])
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np.savetxt(pathdir_write_to+filename,data)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "exp")
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except:
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return PA
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return PA
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def get_inverse(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=4):
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def get_inverse(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3):
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try:
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os.mkdir(pathdir_write_to)
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except:
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pass
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data = np.loadtxt(pathdir+filename)
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try:
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n_variables = np.loadtxt(pathdir+"%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"%s" %filename, usecols=(0,))
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"%s" %filename, usecols=(n_variables,))
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dt = np.column_stack((variables,1/f_dependent))
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np.savetxt(pathdir_write_to+filename,dt)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "inverse")
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data[:,-1] = 1/data[:,-1]
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np.savetxt(pathdir_write_to+filename,data)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "inverse")
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except:
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return PA
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return PA
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def get_log(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=4):
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def get_log(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3):
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try:
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os.mkdir(pathdir_write_to)
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except:
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pass
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data = np.loadtxt(pathdir+filename)
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try:
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n_variables = np.loadtxt(pathdir+"%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"%s" %filename, usecols=(0,))
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"%s" %filename, usecols=(n_variables,))
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dt = np.column_stack((variables,np.log(f_dependent)))
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np.savetxt(pathdir_write_to+filename,dt)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "log")
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data[:,-1] = np.log(data[:,-1])
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np.savetxt(pathdir_write_to+filename,data)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "log")
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except:
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return PA
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return PA
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def get_sin(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=4):
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def get_sin(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3):
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try:
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os.mkdir(pathdir_write_to)
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except:
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pass
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data = np.loadtxt(pathdir+filename)
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try:
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n_variables = np.loadtxt(pathdir+"%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"%s" %filename, usecols=(0,))
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"%s" %filename, usecols=(n_variables,))
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dt = np.column_stack((variables,np.sin(f_dependent)))
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np.savetxt(pathdir_write_to+filename,dt)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "sin")
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data[:,-1] = np.sin(data[:,-1])
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np.savetxt(pathdir_write_to+filename,data)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "sin")
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except:
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return PA
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return PA
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def get_sqrt(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=4):
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def get_sqrt(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3):
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try:
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os.mkdir(pathdir_write_to)
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except:
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pass
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data = np.loadtxt(pathdir+filename)
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try:
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n_variables = np.loadtxt(pathdir+"%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"%s" %filename, usecols=(0,))
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"%s" %filename, usecols=(n_variables,))
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dt = np.column_stack((variables,np.sqrt(f_dependent)))
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np.savetxt(pathdir_write_to+filename,dt)
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data[:,-1] = np.sqrt(data[:,-1])
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np.savetxt(pathdir_write_to+filename,data)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "sqrt")
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except:
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@ -216,22 +145,15 @@ def get_sqrt(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA,
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return PA
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def get_squared(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=4):
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def get_squared(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3):
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try:
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os.mkdir(pathdir_write_to)
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except:
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pass
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data = np.loadtxt(pathdir+filename)
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try:
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n_variables = np.loadtxt(pathdir+"%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"%s" %filename, usecols=(0,))
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"%s" %filename, usecols=(n_variables,))
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dt = np.column_stack((variables,f_dependent**2))
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np.savetxt(pathdir_write_to+filename,dt)
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data[:,-1] = data[:,-1]**2
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np.savetxt(pathdir_write_to+filename,data)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "squared")
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except:
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@ -240,22 +162,15 @@ def get_squared(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type,
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return PA
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def get_tan(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=4):
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def get_tan(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3):
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try:
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os.mkdir(pathdir_write_to)
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except:
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pass
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data = np.loadtxt(pathdir+filename)
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try:
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n_variables = np.loadtxt(pathdir+"%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"%s" %filename, usecols=(0,))
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+"%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"%s" %filename, usecols=(n_variables,))
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dt = np.column_stack((variables,np.tan(f_dependent)))
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np.savetxt(pathdir_write_to+filename,dt)
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data[:,-1] = np.tan(data[:,-1])
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np.savetxt(pathdir_write_to+filename,data)
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PA = run_bf_polyfit(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg, "tan")
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except:
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@ -265,9 +180,3 @@ def get_tan(pathdir,pathdir_write_to,filename,BF_try_time,BF_ops_file_type, PA,
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@ -20,225 +20,227 @@ from os import path
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def run_bf_polyfit(pathdir,pathdir_transformed,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg=3, output_type=""):
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input_data = np.loadtxt(pathdir_transformed+filename)
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#############################################################################################################################
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# run BF on the data (+)
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print("Checking for brute force + \n")
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brute_force(pathdir_transformed,filename,BF_try_time,BF_ops_file_type,"+")
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try:
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# load the BF output data
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bf_all_output = np.loadtxt("results.dat", dtype="str")
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express = bf_all_output[:,2]
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prefactors = bf_all_output[:,1]
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prefactors = [str(i) for i in prefactors]
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if np.isnan(input_data).any()==False:
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# run BF on the data (+)
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print("Checking for brute force + \n")
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brute_force(pathdir_transformed,filename,BF_try_time,BF_ops_file_type,"+")
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# Calculate the complexity of the bf expression the same way as for gradient descent case
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complexity = []
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errors = []
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eqns = []
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for i in range(len(prefactors)):
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try:
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if output_type=="":
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eqn = prefactors[i] + "+" + RPN_to_eq(express[i])
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elif output_type=="acos":
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eqn = "cos(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
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elif output_type=="asin":
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eqn = "sin(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
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elif output_type=="atan":
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eqn = "tan(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
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elif output_type=="cos":
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eqn = "acos(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
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elif output_type=="exp":
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eqn = "log(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
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elif output_type=="inverse":
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eqn = "1/(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
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elif output_type=="log":
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eqn = "exp(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
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elif output_type=="sin":
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eqn = "asin(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
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elif output_type=="sqrt":
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eqn = "(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")**2"
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elif output_type=="squared":
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eqn = "sqrt(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
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elif output_type=="tan":
|
||||
eqn = "atan(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
|
||||
|
||||
eqns = eqns + [eqn]
|
||||
errors = errors + [get_symbolic_expr_error(input_data,eqn)]
|
||||
expr = parse_expr(eqn)
|
||||
is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
|
||||
numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
|
||||
compl = 0
|
||||
for j in numbers_expr:
|
||||
try:
|
||||
compl = compl + get_number_DL_snapped(float(j))
|
||||
except:
|
||||
compl = compl + 1000000
|
||||
|
||||
# Add the complexity due to symbols
|
||||
n_variables = len(expr.free_symbols)
|
||||
n_operations = len(count_ops(expr,visual=True).free_symbols)
|
||||
if n_operations!=0 or n_variables!=0:
|
||||
compl = compl + (n_variables+n_operations)*np.log2((n_variables+n_operations))
|
||||
|
||||
complexity = complexity + [compl]
|
||||
except:
|
||||
continue
|
||||
|
||||
for i in range(len(complexity)):
|
||||
PA.add(Point(x=complexity[i], y=errors[i], data=eqns[i]))
|
||||
|
||||
# run gradient descent of BF output parameters and add the results to the Pareto plot
|
||||
for i in range(len(express)):
|
||||
try:
|
||||
bf_gd_update = RPN_to_pytorch(input_data,eqns[i])
|
||||
PA.add(Point(x=bf_gd_update[1],y=bf_gd_update[0],data=bf_gd_update[2]))
|
||||
except:
|
||||
continue
|
||||
except:
|
||||
pass
|
||||
|
||||
#############################################################################################################################
|
||||
# run BF on the data (*)
|
||||
print("Checking for brute force * \n")
|
||||
brute_force(pathdir_transformed,filename,BF_try_time,BF_ops_file_type,"*")
|
||||
|
||||
try:
|
||||
# load the BF output data
|
||||
bf_all_output = np.loadtxt("results.dat", dtype="str")
|
||||
express = bf_all_output[:,2]
|
||||
prefactors = bf_all_output[:,1]
|
||||
prefactors = [str(i) for i in prefactors]
|
||||
|
||||
# Calculate the complexity of the bf expression the same way as for gradient descent case
|
||||
complexity = []
|
||||
errors = []
|
||||
eqns = []
|
||||
for i in range(len(prefactors)):
|
||||
try:
|
||||
if output_type=="":
|
||||
eqn = prefactors[i] + "*" + RPN_to_eq(express[i])
|
||||
elif output_type=="acos":
|
||||
eqn = "cos(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="asin":
|
||||
eqn = "sin(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="atan":
|
||||
eqn = "tan(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="cos":
|
||||
eqn = "acos(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="exp":
|
||||
eqn = "log(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="inverse":
|
||||
eqn = "1/(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="log":
|
||||
eqn = "exp(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="sin":
|
||||
eqn = "asin(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="sqrt":
|
||||
eqn = "(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")**2"
|
||||
elif output_type=="squared":
|
||||
eqn = "sqrt(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="tan":
|
||||
eqn = "atan(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
|
||||
eqns = eqns + [eqn]
|
||||
errors = errors + [get_symbolic_expr_error(input_data,eqn)]
|
||||
expr = parse_expr(eqn)
|
||||
is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
|
||||
numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
|
||||
compl = 0
|
||||
for j in numbers_expr:
|
||||
try:
|
||||
compl = compl + get_number_DL_snapped(float(j))
|
||||
except:
|
||||
compl = compl + 1000000
|
||||
|
||||
# Add the complexity due to symbols
|
||||
n_variables = len(expr.free_symbols)
|
||||
n_operations = len(count_ops(expr,visual=True).free_symbols)
|
||||
if n_operations!=0 or n_variables!=0:
|
||||
compl = compl + (n_variables+n_operations)*np.log2((n_variables+n_operations))
|
||||
|
||||
complexity = complexity + [compl]
|
||||
except:
|
||||
continue
|
||||
|
||||
# add the BF output to the Pareto plot
|
||||
for i in range(len(complexity)):
|
||||
PA.add(Point(x=complexity[i], y=errors[i], data=eqns[i]))
|
||||
|
||||
# run gradient descent of BF output parameters and add the results to the Pareto plot
|
||||
for i in range(len(express)):
|
||||
try:
|
||||
bf_gd_update = RPN_to_pytorch(input_data,eqns[i])
|
||||
PA.add(Point(x=bf_gd_update[1],y=bf_gd_update[0],data=bf_gd_update[2]))
|
||||
except:
|
||||
continue
|
||||
except:
|
||||
pass
|
||||
|
||||
#############################################################################################################################
|
||||
# run polyfit on the data
|
||||
print("Checking polyfit \n")
|
||||
try:
|
||||
polyfit_result = polyfit(polyfit_deg, pathdir_transformed+filename)
|
||||
eqn = str(polyfit_result[0])
|
||||
|
||||
# Calculate the complexity of the polyfit expression the same way as for gradient descent case
|
||||
if output_type=="":
|
||||
eqn = eqn
|
||||
elif output_type=="acos":
|
||||
eqn = "cos(" + eqn + ")"
|
||||
elif output_type=="asin":
|
||||
eqn = "sin(" + eqn + ")"
|
||||
elif output_type=="atan":
|
||||
eqn = "tan(" + eqn + ")"
|
||||
elif output_type=="cos":
|
||||
eqn = "acos(" + eqn + ")"
|
||||
elif output_type=="exp":
|
||||
eqn = "log(" + eqn + ")"
|
||||
elif output_type=="inverse":
|
||||
eqn = "1/(" + eqn + ")"
|
||||
elif output_type=="log":
|
||||
eqn = "exp(" + eqn + ")"
|
||||
elif output_type=="sin":
|
||||
eqn = "asin(" + eqn + ")"
|
||||
elif output_type=="sqrt":
|
||||
eqn = "(" + eqn + ")**2"
|
||||
elif output_type=="squared":
|
||||
eqn = "sqrt(" + eqn + ")"
|
||||
elif output_type=="tan":
|
||||
eqn = "atan(" + eqn + ")"
|
||||
|
||||
polyfit_err = get_symbolic_expr_error(input_data,eqn)
|
||||
expr = parse_expr(eqn)
|
||||
is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
|
||||
numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
|
||||
complexity = 0
|
||||
for j in numbers_expr:
|
||||
complexity = complexity + get_number_DL_snapped(float(j))
|
||||
try:
|
||||
# Add the complexity due to symbols
|
||||
n_variables = len(polyfit_result[0].free_symbols)
|
||||
n_operations = len(count_ops(polyfit_result[0],visual=True).free_symbols)
|
||||
if n_operations!=0 or n_variables!=0:
|
||||
complexity = complexity + (n_variables+n_operations)*np.log2((n_variables+n_operations))
|
||||
# load the BF output data
|
||||
bf_all_output = np.loadtxt("results.dat", dtype="str")
|
||||
express = bf_all_output[:,2]
|
||||
prefactors = bf_all_output[:,1]
|
||||
prefactors = [str(i) for i in prefactors]
|
||||
|
||||
# Calculate the complexity of the bf expression the same way as for gradient descent case
|
||||
complexity = []
|
||||
errors = []
|
||||
eqns = []
|
||||
for i in range(len(prefactors)):
|
||||
try:
|
||||
if output_type=="":
|
||||
eqn = prefactors[i] + "+" + RPN_to_eq(express[i])
|
||||
elif output_type=="acos":
|
||||
eqn = "cos(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="asin":
|
||||
eqn = "sin(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="atan":
|
||||
eqn = "tan(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="cos":
|
||||
eqn = "acos(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="exp":
|
||||
eqn = "log(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="inverse":
|
||||
eqn = "1/(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="log":
|
||||
eqn = "exp(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="sin":
|
||||
eqn = "asin(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="sqrt":
|
||||
eqn = "(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")**2"
|
||||
elif output_type=="squared":
|
||||
eqn = "sqrt(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="tan":
|
||||
eqn = "atan(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
|
||||
|
||||
eqns = eqns + [eqn]
|
||||
errors = errors + [get_symbolic_expr_error(input_data,eqn)]
|
||||
expr = parse_expr(eqn)
|
||||
is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
|
||||
numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
|
||||
compl = 0
|
||||
for j in numbers_expr:
|
||||
try:
|
||||
compl = compl + get_number_DL_snapped(float(j))
|
||||
except:
|
||||
compl = compl + 1000000
|
||||
|
||||
# Add the complexity due to symbols
|
||||
n_variables = len(expr.free_symbols)
|
||||
n_operations = len(count_ops(expr,visual=True).free_symbols)
|
||||
if n_operations!=0 or n_variables!=0:
|
||||
compl = compl + (n_variables+n_operations)*np.log2((n_variables+n_operations))
|
||||
|
||||
complexity = complexity + [compl]
|
||||
except:
|
||||
continue
|
||||
|
||||
for i in range(len(complexity)):
|
||||
PA.add(Point(x=complexity[i], y=errors[i], data=eqns[i]))
|
||||
|
||||
# run gradient descent of BF output parameters and add the results to the Pareto plot
|
||||
for i in range(len(express)):
|
||||
try:
|
||||
bf_gd_update = RPN_to_pytorch(input_data,eqns[i])
|
||||
PA.add(Point(x=bf_gd_update[1],y=bf_gd_update[0],data=bf_gd_update[2]))
|
||||
except:
|
||||
continue
|
||||
except:
|
||||
pass
|
||||
|
||||
#run zero snap on polyfit output
|
||||
PA_poly = ParetoSet()
|
||||
PA_poly.add(Point(x=complexity, y=polyfit_err, data=str(eqn)))
|
||||
PA_poly = add_snap_expr_on_pareto_polyfit(pathdir, filename, str(eqn), PA_poly)
|
||||
#############################################################################################################################
|
||||
# run BF on the data (*)
|
||||
print("Checking for brute force * \n")
|
||||
brute_force(pathdir_transformed,filename,BF_try_time,BF_ops_file_type,"*")
|
||||
|
||||
for l in range(len(PA_poly.get_pareto_points())):
|
||||
PA.add(Point(PA_poly.get_pareto_points()[l][0],PA_poly.get_pareto_points()[l][1],PA_poly.get_pareto_points()[l][2]))
|
||||
|
||||
except:
|
||||
pass
|
||||
|
||||
print("Complexity RMSE Expression")
|
||||
for pareto_i in range(len(PA.get_pareto_points())):
|
||||
print(PA.get_pareto_points()[pareto_i])
|
||||
|
||||
return PA
|
||||
try:
|
||||
# load the BF output data
|
||||
bf_all_output = np.loadtxt("results.dat", dtype="str")
|
||||
express = bf_all_output[:,2]
|
||||
prefactors = bf_all_output[:,1]
|
||||
prefactors = [str(i) for i in prefactors]
|
||||
|
||||
# Calculate the complexity of the bf expression the same way as for gradient descent case
|
||||
complexity = []
|
||||
errors = []
|
||||
eqns = []
|
||||
for i in range(len(prefactors)):
|
||||
try:
|
||||
if output_type=="":
|
||||
eqn = prefactors[i] + "*" + RPN_to_eq(express[i])
|
||||
elif output_type=="acos":
|
||||
eqn = "cos(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="asin":
|
||||
eqn = "sin(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="atan":
|
||||
eqn = "tan(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="cos":
|
||||
eqn = "acos(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="exp":
|
||||
eqn = "log(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="inverse":
|
||||
eqn = "1/(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="log":
|
||||
eqn = "exp(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="sin":
|
||||
eqn = "asin(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="sqrt":
|
||||
eqn = "(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")**2"
|
||||
elif output_type=="squared":
|
||||
eqn = "sqrt(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
elif output_type=="tan":
|
||||
eqn = "atan(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
|
||||
|
||||
eqns = eqns + [eqn]
|
||||
errors = errors + [get_symbolic_expr_error(input_data,eqn)]
|
||||
expr = parse_expr(eqn)
|
||||
is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
|
||||
numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
|
||||
compl = 0
|
||||
for j in numbers_expr:
|
||||
try:
|
||||
compl = compl + get_number_DL_snapped(float(j))
|
||||
except:
|
||||
compl = compl + 1000000
|
||||
|
||||
# Add the complexity due to symbols
|
||||
n_variables = len(expr.free_symbols)
|
||||
n_operations = len(count_ops(expr,visual=True).free_symbols)
|
||||
if n_operations!=0 or n_variables!=0:
|
||||
compl = compl + (n_variables+n_operations)*np.log2((n_variables+n_operations))
|
||||
|
||||
complexity = complexity + [compl]
|
||||
except:
|
||||
continue
|
||||
|
||||
# add the BF output to the Pareto plot
|
||||
for i in range(len(complexity)):
|
||||
PA.add(Point(x=complexity[i], y=errors[i], data=eqns[i]))
|
||||
|
||||
# run gradient descent of BF output parameters and add the results to the Pareto plot
|
||||
for i in range(len(express)):
|
||||
try:
|
||||
bf_gd_update = RPN_to_pytorch(input_data,eqns[i])
|
||||
PA.add(Point(x=bf_gd_update[1],y=bf_gd_update[0],data=bf_gd_update[2]))
|
||||
except:
|
||||
continue
|
||||
except:
|
||||
pass
|
||||
|
||||
#############################################################################################################################
|
||||
# run polyfit on the data
|
||||
print("Checking polyfit \n")
|
||||
try:
|
||||
polyfit_result = polyfit(polyfit_deg, pathdir_transformed+filename)
|
||||
eqn = str(polyfit_result[0])
|
||||
|
||||
# Calculate the complexity of the polyfit expression the same way as for gradient descent case
|
||||
if output_type=="":
|
||||
eqn = eqn
|
||||
elif output_type=="acos":
|
||||
eqn = "cos(" + eqn + ")"
|
||||
elif output_type=="asin":
|
||||
eqn = "sin(" + eqn + ")"
|
||||
elif output_type=="atan":
|
||||
eqn = "tan(" + eqn + ")"
|
||||
elif output_type=="cos":
|
||||
eqn = "acos(" + eqn + ")"
|
||||
elif output_type=="exp":
|
||||
eqn = "log(" + eqn + ")"
|
||||
elif output_type=="inverse":
|
||||
eqn = "1/(" + eqn + ")"
|
||||
elif output_type=="log":
|
||||
eqn = "exp(" + eqn + ")"
|
||||
elif output_type=="sin":
|
||||
eqn = "asin(" + eqn + ")"
|
||||
elif output_type=="sqrt":
|
||||
eqn = "(" + eqn + ")**2"
|
||||
elif output_type=="squared":
|
||||
eqn = "sqrt(" + eqn + ")"
|
||||
elif output_type=="tan":
|
||||
eqn = "atan(" + eqn + ")"
|
||||
|
||||
polyfit_err = get_symbolic_expr_error(input_data,eqn)
|
||||
expr = parse_expr(eqn)
|
||||
is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
|
||||
numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
|
||||
complexity = 0
|
||||
for j in numbers_expr:
|
||||
complexity = complexity + get_number_DL_snapped(float(j))
|
||||
try:
|
||||
# Add the complexity due to symbols
|
||||
n_variables = len(polyfit_result[0].free_symbols)
|
||||
n_operations = len(count_ops(polyfit_result[0],visual=True).free_symbols)
|
||||
if n_operations!=0 or n_variables!=0:
|
||||
complexity = complexity + (n_variables+n_operations)*np.log2((n_variables+n_operations))
|
||||
except:
|
||||
pass
|
||||
|
||||
#run zero snap on polyfit output
|
||||
PA_poly = ParetoSet()
|
||||
PA_poly.add(Point(x=complexity, y=polyfit_err, data=str(eqn)))
|
||||
PA_poly = add_snap_expr_on_pareto_polyfit(pathdir, filename, str(eqn), PA_poly)
|
||||
|
||||
for l in range(len(PA_poly.get_pareto_points())):
|
||||
PA.add(Point(PA_poly.get_pareto_points()[l][0],PA_poly.get_pareto_points()[l][1],PA_poly.get_pareto_points()[l][2]))
|
||||
|
||||
except:
|
||||
pass
|
||||
|
||||
print("Complexity RMSE Expression")
|
||||
for pareto_i in range(len(PA.get_pareto_points())):
|
||||
print(PA.get_pareto_points()[pareto_i])
|
||||
|
||||
return PA
|
||||
else:
|
||||
return PA
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue