《python与机器学习实战》笔记(一)

机器学习追求的是合理的假设空间的选取和模型的泛化能力。

人生苦短，我用python。

单纯的lambda表达式

f = lambda x:pow(x,2)

f(2)

如上两行代码，定义一个lambda表达式f,输入参数为x，返回为x的平方

机器学习的过程：

1. 获取与处理数据

2. 选择与处理数据

3. 评估与可视化结果

   import numpy as np
   import matplotlib.pyplot as plt
   
   
   # Read dataset
   x, y = [], []
   for sample in open("../_Data/prices.txt", "r"):
       xx, yy = sample.split(",")
       x.append(float(xx))
       y.append(float(yy))
   x, y = np.array(x), np.array(y)
   # Perform normalization
   x = (x - x.mean()) / x.std()
   # Scatter dataset
   plt.figure()
   plt.scatter(x, y, c="g", s=20)
   plt.show()
   
   x0 = np.linspace(-2, 4, 100)
   
   
   # Get regression model under LSE criterion with degree 'deg'
   def get_model(deg):
       return lambda input_x=x0: np.polyval(np.polyfit(x, y, deg), input_x)
   
   
   # Get the cost of regression model above under given x, y
   def get_cost(deg, input_x, input_y):
       return 0.5 * ((get_model(deg)(input_x) - input_y) ** 2).sum()
   
   # Set degrees
   test_set = (1, 4, 10)
   for d in test_set:
       print(get_cost(d, x, y))
   
   # Visualize results
   plt.scatter(x, y, c="g", s=20)
   for d in test_set:
       plt.plot(x0, get_model(d)(), label="degree = {}".format(d))
   plt.xlim(-2, 4)
   plt.ylim(1e5, 8e5)
   plt.legend()
   plt.show()
   

   
   

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