import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.metrics import mean_squared_error
import time
# 开始时间
start_time = time.time()
# 加载数据
data = np.loadtxt('E:\\工作\\*\\*AB数据监测预测\\data.csv', delimiter=',',encoding="utf-8-sig") #改成自己的数据地址
X = data[:, :-1]
y = data[:, -1]
# 数据划分与标准化
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# 训练模型
model = MLPRegressor(hidden_layer_sizes=(10,), activation='relu', max_iter=2000)
model.fit(X_train, y_train)
# 预测与评估
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"MSE: {mse:.2f}, R² Score: {model.score(X_test, y_test):.2f}")
'''
#输入数据
input_array = np.array([5])
scaled_input = scaler.transform(input_array)
scaled_output = y_pred(scaled_input)
print(scaled_output)
'''
# 结束时间
end_time = time.time()
# 计算并打印执行时间
execution_time = end_time - start_time
print(f"Execution time: {execution_time} seconds")
评价结果：
