KNN算法3-归一化

1.数据归一化处理

仅仅适合数据有明显边界的数据

import numpy as np
import matplotlib.pyplot as plt
x = np.random.randint(0, 100, 100)
(x - np.min(x)) / (np.max(x) - np.min(x))

矩阵

X = np.random.randint(0, 100, (50, 2))
X[:,0] = (X[:,0] - np.min(X[:,0])) / (np.max(X[:,0]) - np.min(X[:,0]))
X[:,1] = (X[:,1] - np.min(X[:,1])) / (np.max(X[:,1]) - np.min(X[:,1]))
plt.scatter(X[:,0], X[:,1])
plt.show()

np.mean(X[:,0])

np.std(X[:,0])

2.均值方差归一化 Standardization

X2 = np.random.randint(0, 100, (50, 2))
X2 = np.array(X2, dtype=float)
X2[:10,:]

X2[:,0] = (X2[:,0] - np.mean(X2[:,0])) / np.std(X2[:,0])
X2[:,1] = (X2[:,1] - np.mean(X2[:,1])) / np.std(X2[:,1])