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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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.""" """Recursively transform each numerical value into a learnable parameter."""
import sympy import sympy
from sympy import Symbol 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) 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_name = get_next_available_key(used_param_names, "p", is_underscore=False)
unsnapped_param_dict[unsnapped_param_name] = float(expr) 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) unsnapped_sub_expr_list.append(unsnapped_sub_expr)
return expr.func(*unsnapped_sub_expr_list) return expr.func(*unsnapped_sub_expr_list)
def get_next_available_key(iterable, key, midfix="", suffix="", is_underscore=True): 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.""" """Get the next available key that does not collide with the keys in the dictionary."""
if key + suffix not in iterable: 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 # Turn BF expression to pytorch expression
eq = parse_expr(math_expr) eq = parse_expr(math_expr)
eq = unsnap_recur(eq,param_dict,unsnapped_param_dict) eq = unsnap_recur(eq,param_dict,unsnapped_param_dict)
N_vars = len(data[0])-1 N_vars = len(data[0])-1
N_params = len(unsnapped_param_dict) N_params = len(unsnapped_param_dict)
possible_vars = ["x%s" %i for i in np.arange(0,30,1)] possible_vars = ["x%s" %i for i in np.arange(0,30,1)]
variables = [] variables = []
params = [] params = []
@ -76,7 +74,6 @@ def final_gd(data, math_expr, lr = 1e-2, N_epochs = 5000):
symbols = params + variables symbols = params + variables
f = lambdify(symbols, N(eq), torch) f = lambdify(symbols, N(eq), torch)
# Set the trainable parameters in the expression # Set the trainable parameters in the expression
trainable_parameters = [] 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)) error = get_symbolic_expr_error(data,str(eq))
return error, complexity, eq return error, complexity, eq