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Silviu Marian Udrescu 2020-06-27 19:10:29 -04:00 • committed by GitHub
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1 changed files with 29 additions and 27 deletions
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Code/S_get_symbolic_expr_error.py
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@ -9,32 +9,34 @@ from os import path
from sympy import Symbol, lambdify, N
def get_symbolic_expr_error(data,expr):
N_vars = len(data[0])-1
possible_vars = ["x%s" %i for i in np.arange(0,30,1)]
variables = []
for i in range(N_vars):
variables = variables + [possible_vars[i]]
eq = parse_expr(expr)
f = lambdify(variables, N(eq))
real_variables = []
try:
N_vars = len(data[0])-1
possible_vars = ["x%s" %i for i in np.arange(0,30,1)]
variables = []
for i in range(N_vars):
variables = variables + [possible_vars[i]]
eq = parse_expr(expr)
f = lambdify(variables, N(eq))
real_variables = []
for i in range(len(data[0])-1):
check_var = "x"+str(i)
if check_var in np.array(variables).astype('str'):
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
if isinstance(np.mean((f(*real_variables)-data[:,-1])**2), complex):
for i in range(len(data[0])-1):
check_var = "x"+str(i)
if check_var in np.array(variables).astype('str'):
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
if isinstance(np.mean((f(*real_variables)-data[:,-1])**2), complex):
return 1000000
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))
return np.mean(np.log2(1+abs(f(*real_variables)[good_idx]-data[good_idx][:,-1])*2**30))
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.mean(np.log2(1+abs(f(*real_variables)-data[:,-1])*2**30))
except:
return 1000000
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))
return np.mean(np.log2(1+abs(f(*real_variables)[good_idx]-data[good_idx][:,-1])*2**30))
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.mean(np.log2(1+abs(f(*real_variables)-data[:,-1])*2**30))