本文實例講述了Python實現的線性回歸算法。分享給大家供大家參考，具體如下：

用python實現線性回歸

Using Python to Implement Line Regression Algorithm

小菜鳥記錄學習過程

代碼：

#encoding:utf-8"""  Author:   njulpy  Version:   1.0  Data:   2018/04/09  Project： Using Python to Implement LineRegression Algorithm"""import numpy as npimport pandas as pdfrom numpy.linalg import invfrom numpy import dotfrom sklearn.model_selection import train_test_splitimport matplotlib.pyplot as pltfrom sklearn import linear_model# 最小二乘法def lms(x_train,y_train,x_test):  theta_n = dot(dot(inv(dot(x_train.T, x_train)), x_train.T), y_train) # theta = (X'X)^(-1)X'Y  #print(theta_n)  y_pre = dot(x_test,theta_n)  mse = np.average((y_test-y_pre)**2)  #print(len(y_pre))  #print(mse)  return theta_n,y_pre,mse#梯度下降算法def train(x_train, y_train, num, alpha,m, n):  beta = np.ones(n)  for i in range(num):    h = np.dot(x_train, beta)       # 計算預測值    error = h - y_train.T         # 計算預測值與訓練集的差值    delt = 2*alpha * np.dot(error, x_train)/m # 計算參數的梯度變化值    beta = beta - delt    #print('error', error)  return betaif __name__ == "__main__":  iris = pd.read_csv('iris.csv')  iris['Bias'] = float(1)  x = iris[['Sepal.Width', 'Petal.Length', 'Petal.Width', 'Bias']]  y = iris['Sepal.Length']  x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=5)  t = np.arange(len(x_test))  m, n = np.shape(x_train)  # Leastsquare  theta_n, y_pre, mse = lms(x_train, y_train, x_test)  # plt.plot(t, y_test, label='Test')  # plt.plot(t, y_pre, label='Predict')  # plt.show()  # GradientDescent  beta = train(x_train, y_train, 1000, 0.001, m, n)  y_predict = np.dot(x_test, beta.T)  # plt.plot(t, y_predict)  # plt.plot(t, y_test)  # plt.show()  # sklearn  regr = linear_model.LinearRegression()  regr.fit(x_train, y_train)  y_p = regr.predict(x_test)  print(regr.coef_,theta_n,beta)  l1,=plt.plot(t, y_predict)  l2,=plt.plot(t, y_p)  l3,=plt.plot(t, y_pre)  l4,=plt.plot(t, y_test)  plt.legend(handles=[l1, l2,l3,l4 ], labels=['GradientDescent', 'sklearn','Leastsquare','True'], loc='best')  plt.show()

輸出結果

sklearn: [ 0.65368836  0.70955523 -0.54193454  0.        ]
 LeastSquare: [ 0.65368836  0.70955523 -0.54193454  1.84603897]
 GradientDescent: [ 0.98359285  0.29325906  0.60084232  1.006859  ]

附：上述示例中的iris.csv文件點擊此處本站下載。

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