afdee1c/symbolic-regression
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Silviu Marian Udrescu 2020-06-26 08:54:38 -04:00 • committed by GitHub
parent 2dd591d346
commit 5e059634a6
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1 changed files with 2 additions and 7 deletions
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9
Code/S_final_gd.py
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@ -30,7 +30,7 @@ def final_gd(data, math_expr, lr = 1e-2, N_epochs = 5000):
"""Recursively transform each numerical value into a learnable parameter."""
import sympy
from sympy import Symbol
if isinstance(expr, sympy.numbers.Float):
if isinstance(expr, sympy.numbers.Float) or isinstance(expr, sympy.numbers.Integer) or isinstance(expr, sympy.numbers.Rational) or isinstance(expr, sympy.numbers.Pi):
used_param_names = list(param_dict.keys()) + list(unsnapped_param_dict)
unsnapped_param_name = get_next_available_key(used_param_names, "p", is_underscore=False)
unsnapped_param_dict[unsnapped_param_name] = float(expr)
@ -45,7 +45,6 @@ def final_gd(data, math_expr, lr = 1e-2, N_epochs = 5000):
unsnapped_sub_expr_list.append(unsnapped_sub_expr)
return expr.func(*unsnapped_sub_expr_list)
def get_next_available_key(iterable, key, midfix="", suffix="", is_underscore=True):
"""Get the next available key that does not collide with the keys in the dictionary."""
if key + suffix not in iterable:
@ -61,10 +60,9 @@ def final_gd(data, math_expr, lr = 1e-2, N_epochs = 5000):
# Turn BF expression to pytorch expression
eq = parse_expr(math_expr)
eq = unsnap_recur(eq,param_dict,unsnapped_param_dict)
N_vars = len(data[0])-1
N_params = len(unsnapped_param_dict)
possible_vars = ["x%s" %i for i in np.arange(0,30,1)]
variables = []
params = []
@ -76,7 +74,6 @@ def final_gd(data, math_expr, lr = 1e-2, N_epochs = 5000):
symbols = params + variables
f = lambdify(symbols, N(eq), torch)
# Set the trainable parameters in the expression
trainable_parameters = []
@ -143,5 +140,3 @@ def final_gd(data, math_expr, lr = 1e-2, N_epochs = 5000):
error = get_symbolic_expr_error(data,str(eq))
return error, complexity, eq