Python实现3种回归模型（LinearRegression，Lasso，Ridge）的示例_ElasticNetCV - CSDN文库

130 浏览量更新于2023-03-03 评论 1 收藏 179KB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

资源详情

资源评论

资源推荐

Python 实现实现3种回归模型（种回归模型（Linear

Regression，，Lasso，，Ridge）的示例）的示例

公共的抽象基类公共的抽象基类

import numpy as np

from abc import ABCMeta, abstractmethod

class LinearModel(metaclass=ABCMeta):

"""

Abstract base class of Linear Model.

"""

def __init__(self):

# Before fit or predict, please transform samples' mean to 0, var to 1.

self.scaler = StandardScaler()

@abstractmethod

def fit(self, X, y):

"""fit func"""

def predict(self, X):

# before predict, you must run fit func.

if not hasattr(self, 'coef_'):

raise Exception('Please run `fit` before predict')

X = self.scaler.transform(X)

X = np.c_[np.ones(X.shape[0]), X]

# `x @ y` == `np.dot(x, y)`

return X @ self.coef_

Linear Regression

class LinearRegression(LinearModel):

"""

Linear Regression.

"""

def __init__(self):

super().__init__()

def fit(self, X, y):

"""

:param X_: shape = (n_samples + 1, n_features)

:param y: shape = (n_samples])

:return: self

"""

self.scaler.fit(X)

X = self.scaler.transform(X)

X = np.c_[np.ones(X.shape[0]), X] self.coef_ = np.linalg.inv(X.T @ X) @ X.T @ y

return self

Lasso

class Lasso(LinearModel):

"""

Lasso Regression, training by Coordinate Descent.

cost = ||X @ coef_||^2 + alpha * ||coef_||_1

"""

def __init__(self, alpha=1.0, n_iter=1000, e=0.1):

self.alpha = alpha

self.n_iter = n_iter

self.e = e

super().__init__()

def fit(self, X, y):

self.scaler.fit(X)

X = self.scaler.transform(X)

本内容试读结束，登录后可阅读更多

下载后可阅读完整内容，剩余3页未读，立即下载

评论0

weixin_38697808

粉丝: 5
资源: 899

会员权益专享

图片转文字

全年可省5，000元立即开通

最新资源

资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！点击此处反馈