242 lines
11 KiB
Python
242 lines
11 KiB
Python
# add a function to compte complexity
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from get_pareto import Point, ParetoSet
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from RPN_to_pytorch import RPN_to_pytorch
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from RPN_to_eq import RPN_to_eq
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import numpy as np
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import matplotlib.pyplot as plt
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from S_brute_force import brute_force
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from S_combine_pareto import combine_pareto
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from S_get_number_DL_snapped import get_number_DL_snapped
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from sympy.parsing.sympy_parser import parse_expr
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from sympy import preorder_traversal, count_ops
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from S_polyfit import polyfit
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from S_get_symbolic_expr_error import get_symbolic_expr_error
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from S_add_sym_on_pareto import add_sym_on_pareto
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from S_add_snap_expr_on_pareto_polyfit import add_snap_expr_on_pareto_polyfit
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import os
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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=4, output_type=""):
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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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# 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":
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eqn = "atan(" + prefactors[i] + "+" + RPN_to_eq(express[i]) + ")"
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eqns = eqns + [eqn]
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errors = errors + [get_symbolic_expr_error(pathdir,filename,eqn)]
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expr = parse_expr(eqn)
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is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
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numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
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compl = 0
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for j in numbers_expr:
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try:
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compl = compl + get_number_DL_snapped(float(j))
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except:
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compl = compl + 1000000
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# Add the complexity due to symbols
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n_variables = len(expr.free_symbols)
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n_operations = len(count_ops(expr,visual=True).free_symbols)
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if n_operations!=0 or n_variables!=0:
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compl = compl + (n_variables+n_operations)*np.log2((n_variables+n_operations))
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complexity = complexity + [compl]
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except:
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continue
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for i in range(len(complexity)):
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PA.add(Point(x=complexity[i], y=errors[i], data=eqns[i]))
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# run gradient descent of BF output parameters and add the results to the Pareto plot
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for i in range(len(express)):
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try:
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bf_gd_update = RPN_to_pytorch(pathdir,filename,eqns[i])
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PA.add(Point(x=bf_gd_update[1],y=bf_gd_update[0],data=bf_gd_update[2]))
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except:
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continue
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except:
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pass
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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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# 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":
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eqn = "atan(" + prefactors[i] + "*" + RPN_to_eq(express[i]) + ")"
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eqns = eqns + [eqn]
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errors = errors + [get_symbolic_expr_error(pathdir,filename,eqn)]
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expr = parse_expr(eqn)
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is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
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numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
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compl = 0
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for j in numbers_expr:
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try:
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compl = compl + get_number_DL_snapped(float(j))
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except:
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compl = compl + 1000000
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# Add the complexity due to symbols
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n_variables = len(expr.free_symbols)
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n_operations = len(count_ops(expr,visual=True).free_symbols)
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if n_operations!=0 or n_variables!=0:
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compl = compl + (n_variables+n_operations)*np.log2((n_variables+n_operations))
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complexity = complexity + [compl]
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except:
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continue
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# add the BF output to the Pareto plot
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for i in range(len(complexity)):
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PA.add(Point(x=complexity[i], y=errors[i], data=eqns[i]))
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# run gradient descent of BF output parameters and add the results to the Pareto plot
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for i in range(len(express)):
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try:
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bf_gd_update = RPN_to_pytorch(pathdir,filename,eqns[i])
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PA.add(Point(x=bf_gd_update[1],y=bf_gd_update[0],data=bf_gd_update[2]))
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except:
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continue
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except:
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pass
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#############################################################################################################################
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# run polyfit on the data
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print("Checking polyfit \n")
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polyfit_result = polyfit(polyfit_deg, pathdir_transformed+filename)
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eqn = str(polyfit_result[0])
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# Calculate the complexity of the polyfit expression the same way as for gradient descent case
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if output_type=="":
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eqn = eqn
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elif output_type=="acos":
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eqn = "cos(" + eqn + ")"
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elif output_type=="asin":
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eqn = "sin(" + eqn + ")"
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elif output_type=="atan":
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eqn = "tan(" + eqn + ")"
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elif output_type=="cos":
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eqn = "acos(" + eqn + ")"
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elif output_type=="exp":
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eqn = "log(" + eqn + ")"
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elif output_type=="inverse":
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eqn = "1/(" + eqn + ")"
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elif output_type=="log":
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eqn = "exp(" + eqn + ")"
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elif output_type=="sin":
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eqn = "asin(" + eqn + ")"
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elif output_type=="sqrt":
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eqn = "(" + eqn + ")**2"
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elif output_type=="squared":
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eqn = "sqrt(" + eqn + ")"
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elif output_type=="tan":
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eqn = "atan(" + eqn + ")"
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polyfit_err = get_symbolic_expr_error(pathdir,filename,eqn)
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expr = parse_expr(eqn)
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is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
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numbers_expr = [subexpression for subexpression in preorder_traversal(expr) if is_atomic_number(subexpression)]
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complexity = 0
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for j in numbers_expr:
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complexity = complexity + get_number_DL_snapped(float(j))
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try:
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# Add the complexity due to symbols
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n_variables = len(polyfit_result[0].free_symbols)
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n_operations = len(count_ops(polyfit_result[0],visual=True).free_symbols)
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if n_operations!=0 or n_variables!=0:
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complexity = complexity + (n_variables+n_operations)*np.log2((n_variables+n_operations))
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except:
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pass
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#run zero snap on polyfit output
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PA_poly = ParetoSet()
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PA_poly.add(Point(x=complexity, y=polyfit_err, data=str(eqn)))
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PA_poly = add_snap_expr_on_pareto_polyfit(pathdir, filename, str(eqn), PA_poly)
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for l in range(len(PA_poly.get_pareto_points())):
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PA.add(Point(PA_poly.get_pareto_points()[l][0],PA_poly.get_pareto_points()[l][1],PA_poly.get_pareto_points()[l][2]))
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print("Complexity RMSE Expression")
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for pareto_i in range(len(PA.get_pareto_points())):
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print(PA.get_pareto_points()[pareto_i])
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return PA
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