[机器学习]机器学习笔记整理13-线性回归简单实现

概念请参考:[机器学习]机器学习笔记整理12-线性回归概念理解

1. 简单线性回归模型举例：

汽车卖家做电视广告数量与卖出的汽车数量：

1.1 如何练处适合简单线性回归模型的最佳回归线？

1.2 计算

预测：

假设有一周广告数量为6，预测的汽车销售量是多少？

代码实现

x_given = 6Y_hat = 5*6 + 10 = 401.3 Python实现：import numpy as npdef fitSLR(x, y):    n = len(x)    dinominator = 0    numerator = 0    for i in range(0, n):        numerator += (x[i] - np.mean(x))*(y[i] - np.mean(y))        dinominator += (x[i] - np.mean(x))**2    b1 = numerator/float(dinominator)    b0 = np.mean(y)/float(np.mean(x))    return b0, b1def predict(x, b0, b1):    return b0 + x*b1x = [1, 3, 2, 1, 3]y = [14, 24, 18, 17, 27]    b0, b1 = fitSLR(x, y)print "intercept:", b0, " slope:", b1x_test = 6y_test = predict(6, b0, b1)print "y_test:", y_test