線性最小二乘法——python

算法編寫

class LinearRegression:
    '''
        alpha：擬合參數
        fit  ：訓練函數，得到擬合參數
        predict：預測並輸出結果
    '''
    def __init__(self):
        pass
        
    def fit(self,x,y):
        x = np.insert(x,0,1,axis=1)
        xt = np.linalg.inv(x.T.dot(x))
        self.alpha=xt.dot(x.T).dot(y)
        self.alpha=self.alpha.reshape(-1,1)

    def predict(self,x):
        try:
            x=np.insert(x,0,1,axis=1)
            return np.sum(self.alpha.T*x,axis=1)
        except:
            x=np.insert(x,0,1)
            return np.sum(self.alpha.T*x)

實例演示

from sklearn import datasets
import numpy as np

data = datasets.load_diabetes()
x = data['data']
y = data['target']
line = LinearRegression()
line.fit(x,y)

line.predict(np.random.randn(10))