电子商务之价格优化算法：非线性回归：动态定价策略与算法.docx

import numpy as np

import matplotlib.pyplot as plt

from sklearn.preprocessing import PolynomialFeatures

X_poly = poly_features.fit_transform(X)

#

poly_model = LinearRegression()

poly_model.fit(X_poly, y)

#

X_test = np.linspace(10, 50, 100).reshape(-1, 1)

X_test_poly = poly_features.transform(X_test)

y_pred = poly_model.predict(X_test_poly)

#

import numpy as np

from scipy.optimize import curve_fit

#

params, _ = curve_fit(exp_func, X, y)

#

X_test = np.linspace(10, 50, 100)

y_pred = exp_func(X_test, *params)

#

plt.legend()

plt.show()

from sklearn.model_selection import train_test_split

from sklearn.metrics import mean_squared_error, r2_score

#

X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

#

degrees = [1, 2, 3]

poly_features = PolynomialFeatures(degree=degree, include_bias=False)

X_train_poly = poly_features.fit_transform(X_train)

X_val_poly = poly_features.transform(X_val)

r2 = r2_score(y_val, y_val_pred)

print(f"Degree {degree}: MSE={mse:.2f}, R^2={r2:.2f}")