import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures from sklearn.externals import joblib X_train = [[5],[6], [8], [10], [14], [18], [20], [20.1]] y_train = [[5],[7], [9], [13], [17.5], [18], [20], [25]] X_test = [[6], [8], [11], [16]] y_test = [[8], [12], [15], [18]] regressor = LinearRegression() regressor.fit(X_train, y_train) xx = np.linspace(0, 26, 100) #根据线性预测分析0-26的Y值 yy = regressor.predict(xx.reshape(xx.shape[0], 1)) #绘画X_Y关系直线 plt.plot(xx, yy) plt.title('Pizza price regressed on diameter') plt.xlabel('Diameter in inches') plt.ylabel('Price in dollars') plt.axis([0, 25, 0, 25]) plt.grid(True) plt.scatter(X_train, y_train) #持久化保存模型 joblib.dump(value=regressor,filename="regressorModel20191023.gz",compress=True) print("model has saved!") #加载先前保存的模型 model=joblib.load(filename="regressorModel20191023.gz") print("model has loaded!") print(type(model)) #导入模型后再次预测分析0-26的Y值 yy1= model.predict(xx.reshape(xx.shape[0], 1)) #绘画X_Y关系直线 plt.plot(xx, yy1) plt.show()