用模型前缀注释拟合峰

import numpy as np
from sklearn.linear_model import LinearRegression

# 模拟数据
X = np.random.rand(100, 1)
y = 3 * X + 2 + np.random.randn(100, 1) * 0.5  # 添加一些噪声

# 假设有两个峰，分别为peak_1和peak_2
peaks = {'peak_1': (0, 0.5), 'peak_2': (0.5, 1)}  # 峰的位置和范围

# 为每个峰训练一个线性回归模型，并添加前缀
models = {}
for peak_name, (start, end) in peaks.items():
    mask = (X >= start) & (X <= end)
    X_peak = X[mask]
    y_peak = y[mask]
    
    model = LinearRegression()
    model.fit(X_peak, y_peak)
    
    # 添加前缀
    model.coef_ = {'{}_coef'.format(peak_name): model.coef_[0][0]}
    model.intercept_ = {'{}_intercept'.format(peak_name): model.intercept_[0]}
    
    models[peak_name] = model

# 输出结果
for peak_name, model in models.items():
    print(f"{peak_name}的斜率：{model.coef_['{}_coef'.format(peak_name)]}")
    print(f"{peak_name}的截距：{model.intercept_['{}_intercept'.format(peak_name)]}")