end_index=(i+1)*len(x_sorted)//n x_segment=x_sorted[start_index:end_index]y_segment=model.predict(x_segment.reshape(-1,1))plt.plot(x_segment,y_segment,color='red',label=f'segment{i+1}')# 设置图例和标题plt.legend()plt.title('Piecewise Linear Regression')# 显示图形plt.show() 1...
分段回归(Piecewise Regression)是一种用于估计具有不同斜率的线性关系的统计方法。它特别适用于变量在特定范围内具有不同的行为或趋势。本文将引导你实现分段回归的步骤,并提供相关的代码实例及注释。 流程概览 我们可以将实现分段回归的步骤总结为以下几个阶段: 2023-10-012023-10-022023-10-022023-10-032023-10-032...
通过以上步骤,你可以使用Python进行分段线性拟合,并得到可视化的拟合结果。如果你需要更高级的功能,如自动选择分段点或更复杂的模型验证,可以考虑使用专门的库,如pwlf(piecewise linear fit)。
(bin_mapping) pred2 = fit3.predict(X_valid_2) # 可视化 fig, (ax1) = plt.subplots(1,1, figsize=(12,5)) fig.suptitle('Piecewise Constant', fontsize=14) # 多项式回归线散点图 ax1.scatter(train_x, train_y, facecolor='None', edgecolor='k', alpha=0.3) ax1.plot(xp, pred2, c=...
from sklearn.linear_model import LinearRegression #拟合线性回归模型 x = train_x.reshape(-1,1) model = LinearRegression() model.fit(x,train_y) print(model.coef_) print(model.intercept_) ->array([0.72190831]) -> 80.65287740759283 #在验证集上进行预测 ...
(bin_mapping) pred2 = fit3.predict(X_valid_2) #进行可视化 fig, (ax1) = plt.subplots(1,1, figsize=(12,5)) fig.suptitle('Piecewise Constant', fontsize=14) #画出样条回归的散点图 ax1.scatter(train_x, train_y, facecolor='None', edgecolor='k', alpha=0.3) ax1.plot(xp, pred2,...
import numpy as np from sklearn.linear_model import LinearRegression # 定义分段函数 def piecewise_function(x): if x < 0: return 0 elif x < 5: return x else: return 2 * x - 5 # 生成数据 x = np.linspace(-5, 10, num=1000) y = np.array([piecewise_function(xi) for xi in x...
model = LinearRegression() model.fit(x,train_y) print(model.coef_) print(model.intercept_) -> array([0.72190831]) -> 80.65287740759283 #在验证集上进行预测 valid_x = valid_x.reshape(-1,1) pred = model.predict(valid_x) #可视化
The features created with this basis expansion can be used to fit a piecewise cubic function under the constraint that the fitted curve is linear *outside* the range of the knots.. The fitted curve is continuously differentiable to the second order at all of the knots. This transformer can ...
xp = np.linspace(valid_x.min(),valid_x.max()-1,70)bin_mapping = np.digitize(xp, bins)X_valid_2 = pd.get_dummies(bin_mapping)pred2 = fit3.predict(X_valid_2)#进行可视化 fig, (ax1) = plt.subplots(1,1, figsize=(12,5))fig.suptitle('Piecewise Constant', fontsize=14)#画出样条...