Python执行遗传编程gplearn库使用实例探究

pip install gplearn

import numpy as np
from gplearn.genetic import SymbolicRegressor
import matplotlib.pyplot as plt

# Generating synthetic data
np.random.seed(0)
x = np.linspace(-10, 10, 100)
y = x + np.log2(x**2) + 3*np.sin(x) + np.random.normal(0, 0.1, 100)

# Creating Symbolic Regressor
function_set = [
    'add', 'sub', 'mul', 'div', 'sqrt', 'log', 'abs', 'sin', 'cos', 'tan']
symbolic_regressor = SymbolicRegressor(population_size=5000,
                                       generations=20,
                                       function_set=function_set,
                                       stopping_criteria=0.01,
                                       p_crossover=0.6, p_subtree_mutation=0.2,
                                       p_hoist_mutation=0.05, p_point_mutation=0.1,
                                       max_samples=0.9, verbose=1,
                                       parsimony_coefficient=0.01, random_state=0)

# Fitting the model
symbolic_regressor.fit(x.reshape(-1, 1), y)

# Print the best program
print(symbolic_regressor._program)

# Predictions
y_pred = symbolic_regressor.predict(x.reshape(-1, 1))

# Plotting
plt.figure(figsize=(10, 6))
plt.scatter(x, y, label="Actual Data")
plt.plot(x, y_pred, color='red', label="Symbolic Regression Model")
plt.legend()
plt.xlabel("X")
plt.ylabel("Y")
plt.title("Symbolic Regression Example")
plt.show()