线性回归算法-4.多元线性回归算法

野性酷女 2023-08-17 16:44 141阅读 0赞

# 多元线性回归算法 #

![1465169-20190714195903677-324118699.png][]

\\(x^\{(i)\}\\)由一个特征变为多个特征，此时拟合函数不是简单的\\(y^\{(i)\} = ax^\{(i)\}+b\\), 而是：  
**\\\[\\hat y^\{(i)\} = \\theta \_0x^\{(i)\}\_0 + \\theta \_1x^\{(i)\}\_1+ \\theta \_2x^\{(i)\}\_2+...+\\theta \_\{n\}x^\{(i)\}\_n，x^\{(i)\}\_0\\equiv 1 \\\]**  
*注：上标i为第i个样本，下标1-n为样本i的多个特征值*

由上式可得：  
\\\[\\theta = （\\theta\_0,\\theta\_1,\\theta\_2,\\theta \_0,...,\\theta \_n）^T\\\]  
\\\[X^\{(i)\} = (X^\{(i)\}\_0,X^\{(i)\}\_1,X^\{(i)\}\_2,...,X^\{(i)\}\_n)\\\]  
\\\[\\hat y^\{(i)\} = X^\{(i)\}\\cdot \\theta\\\]  
把上式推广到所有样本中：  
\\\[ X\_b = \\begin\{bmatrix\} 1 & x^\{(1)\}\_1 & x^\{(1)\}\_2 & ... & x^\{(1)\}\_n\\\\ 1 & x^\{(2)\}\_1 & x^\{(2)\}\_2 & ... & x^\{(2)\}\_n\\\\ ... & & & & ...\\\\ 1 & x^\{(m)\}\_1 & x^\{(m)\}\_2 & ... & x^\{(m)\}\_n \\end\{bmatrix\}. \\theta = \\begin\{bmatrix\} \\theta\_0\\\\ \\theta\_1\\\\ \\theta\_2\\\\ ...\\\\ \\theta\_n\\\\ \\end\{bmatrix\}\\\]  
\\\[ \\hat y = X\_b\\cdot \\theta\\\]

![1465169-20190714201323376-1674921949.png][]

得到\\(\\theta\\)的表达式（此推导过程较为复杂,感兴趣的话在网上找）：  
\\\[\\theta = (X^\{T\}\_bX\_b)^\{-1\}X^\{T\}\_by\\\]  
上式为多元线性回归的正规方程解（Normal Equation）  
问题：时间复杂度较高，\\(O(n^3)\\)

## 多元线性回归的实现 ##

封装LineRegression算法库

import numpy
    from .metrics import r2_score
    
    class LineRegression(object):
        """docstring fos LineRegression"""
        def __init__(self):
            self.coef_ = None
            self.interception_ = None
            self._theta = None
    
        def fit_normal(self,X_train,y_train):
    
            assert X_train.shape[0] == y_train.shape[0],\
                "size of x_train must be equal to the size of y_train"
    
            X_b = numpy.hstack([numpy.ones((len(X_train),1)),X_train])
            self._theta = numpy.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y_train)
    
            self.coef_ = self._theta[1:]    #系数
            self.interception_ = self._theta[0] #截距
            return self
    
        def predict(self,X_predict):
    
            assert self.coef_ is not None and self.interception_ is not None,\
                "must be fit before predict"
            assert X_predict.shape[1] == len(self.coef_),\
                "the feature number of X_predict must be equal X_train"
    
            X_b = numpy.hstack([numpy.ones((len(X_predict),1)),X_predict])
            y_predict = X_b.dot(self._theta)
            return y_predict
    
        def score(self,x_test,y_test):
            y_predict = self.predict(x_test)
            return r2_score(y_test,y_predict)
    
    
        def __repr__(self):
            return "LineRegression"

算法调用

import numpy
    import matplotlib.pyplot as plt
    from sklearn import datasets
    
    # 加载波士顿房产数据集
    boston = datasets.load_boston()
    X = boston.data
    y = boston.target
    
    X = X[y<50]
    y = y[y<50]

![1465169-20190714200015784-402222874.png][]

from mylib.model_selection import train_test_split
    from mylib.LineRegression import LineRegression
    
    X_train,X_test,y_train,y_test = train_test_split(X,y,seed=666)
    
    reg = LineRegression()
    reg.fit_normal(X_train,y_train)
    # reg.predict(X_test)
    reg.score(X_test,y_test)

![1465169-20190714200027857-1963534936.png][]

## scikit-learn 中的LinearRegression ##

from sklearn.linear_model import LinearRegression
    line_reg = LinearRegression()
    line_reg.fit(X,y)

转载于:https://www.cnblogs.com/shuai-long/p/11185430.html

[1465169-20190714195903677-324118699.png]: /images/20230809/2a7aecf059884a55ba6be8c487318d51.png
[1465169-20190714201323376-1674921949.png]: /images/20230809/4384f9bb360a426684070612b30889ec.png
[1465169-20190714200015784-402222874.png]: /images/20230809/0361fd408ba44b4d92bc84fc2a16860b.png
[1465169-20190714200027857-1963534936.png]: /images/20230809/38b229200790475ba3c726ad04b765b2.png