机器学习(五) 线性回归法 （上）

一、简单额线性回归

解决回归问题

思想简单、实现容易

许多强大的非线性模型的基础

结果具有很好的可解释性

蕴含机器学习中的很多重要思想

 

 

二、最小二乘法

 

 

 

 

三、简单线性回归的实现

 SimpleLinearRegression.py

import numpy as np


class SimpleLinearRegression1:

    def __init__(self):
        """初始化Simple Linear Regression 模型"""
        self.a_ = None
        self.b_ = None

    def fit(self, x_train, y_train):
        """根据训练数据集x_train,y_train训练Simple Linear Regression模型"""
        assert x_train.ndim == 1, 
            "Simple Linear Regressor can only solve single feature training data."
        assert len(x_train) == len(y_train), 
            "the size of x_train must be equal to the size of y_train"

        x_mean = np.mean(x_train)
        y_mean = np.mean(y_train)

        num = 0.0
        d = 0.0
        for x, y in zip(x_train, y_train):
            num += (x - x_mean) * (y - y_mean)
            d += (x - x_mean) ** 2

        self.a_ = num / d
        self.b_ = y_mean - self.a_ * x_mean

        return self

    def predict(self, x_predict):
        """给定待预测数据集x_predict，返回表示x_predict的结果向量"""
        assert x_predict.ndim == 1, 
            "Simple Linear Regressor can only solve single feature training data."
        assert self.a_ is not None and self.b_ is not None, 
            "must fit before predict!"

        return np.array([self._predict(x) for x in x_predict])

    def _predict(self, x_single):
        """给定单个待预测数据x，返回x的预测结果值"""
        return self.a_ * x_single + self.b_

    def __repr__(self):
        return "SimpleLinearRegression1()"

 

 

四、向量化

 

SimpleLinearRegression.py

class SimpleLinearRegression2:

    def __init__(self):
        """初始化Simple Linear Regression模型"""
        self.a_ = None
        self.b_ = None

    def fit(self, x_train, y_train):
        """根据训练数据集x_train,y_train训练Simple Linear Regression模型"""
        assert x_train.ndim == 1, 
            "Simple Linear Regressor can only solve single feature training data."
        assert len(x_train) == len(y_train), 
            "the size of x_train must be equal to the size of y_train"

        x_mean = np.mean(x_train)
        y_mean = np.mean(y_train)

        self.a_ = (x_train - x_mean).dot(y_train - y_mean) / (x_train - x_mean).dot(x_train - x_mean)
        self.b_ = y_mean - self.a_ * x_mean

        return self

    def predict(self, x_predict):
        """给定待预测数据集x_predict，返回表示x_predict的结果向量"""
        assert x_predict.ndim == 1, 
            "Simple Linear Regressor can only solve single feature training data."
        assert self.a_ is not None and self.b_ is not None, 
            "must fit before predict!"

        return np.array([self._predict(x) for x in x_predict])

    def _predict(self, x_single):
        """给定单个待预测数据x_single，返回x_single的预测结果值"""
        return self.a_ * x_single + self.b_

    def __repr__(self):
        return "SimpleLinearRegression2()"

五、衡量线性回归法的指标：MSE、RMS 和 MAE

 

metrics.py

import numpy as np
from math import sqrt


def accuracy_score(y_true, y_predict):
    """计算y_true和y_predict之间的准确率"""
    assert len(y_true) == len(y_predict), 
        "the size of y_true must be equal to the size of y_predict"

    return np.sum(y_true == y_predict) / len(y_true)


def mean_squared_error(y_true, y_predict):
    """计算y_true和y_predict之间的MSE"""
    assert len(y_true) == len(y_predict), 
        "the size of y_true must be equal to the size of y_predict"

    return np.sum((y_true - y_predict)**2) / len(y_true)


def root_mean_squared_error(y_true, y_predict):
    """计算y_true和y_predict之间的RMSE"""

    return sqrt(mean_squared_error(y_true, y_predict))


def mean_absolute_error(y_true, y_predict):
    """计算y_true和y_predict之间的MAE"""

    return np.sum(np.absolute(y_true - y_predict)) / len(y_true)

 

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