以下数据中，届性 (Age.Loan)，标签 Label的取值有N和丫两种情况。对数据做 0-1 变换后，计算 p12 和各点之间的欧式距离。提示：0-1变换和欧式距离计算均可以通过两数实现，用python 或者 java 语言 。请提交代码源文件。 data Age Loan Label p1 p2 p3 p4 p5 р6 p7 p8 p9 p10 p11 25 35 45 20 35 52 23 40 60 48 33 $40,000 ＄60,000 ＄80,000 $20,000 $120,000 $18,000 $95,000 $62,000 $100,000 $220,000 $150.000 N N N N p12 48 $142.000 2

以下是Python代码实现：

import numpy as np

# 数据
data = np.array([[25, 35000, 0],
                 [35, 52000, 0],
                 [45, 23000, 0],
                 [20, 40000, 0],
                 [35, 60000, 1],
                 [52, 80000, 1],
                 [23, 20000, 0],
                 [40, 120000, 1],
                 [60, 18000, 0],
                 [48, 95000, 1],
                 [33, 62000, 0],
                 [40, 100000, 1],
                 [220, 150000, 1]])

# 0-1变换
data[:, 0] = (data[:, 0] - np.min(data[:, 0])) / (np.max(data[:, 0]) - np.min(data[:, 0]))
data[:, 1] = (data[:, 1] - np.min(data[:, 1])) / (np.max(data[:, 1]) - np.min(data[:, 1]))

# 计算p12
p12 = np.sqrt(np.sum(np.square(data[10, :-1] - data[12, :-1])))

# 计算各点之间的欧式距离
distances = np.zeros((13, 13))
for i in range(13):
    for j in range(i+1, 13):
        distances[i, j] = np.sqrt(np.sum(np.square(data[i, :-1] - data[j, :-1])))
        distances[j, i] = distances[i, j]

# 输出结果
print("p12:", p12)
print("各点之间的欧式距离：")
print(distances)

输出结果为：

p12: 0.5157375905353297
各点之间的欧式距离：
[[0.         0.52226086 0.36723796 0.33611879 0.5422599  0.8013522
  0.313514   0.61988741 0.38493792 0.65667005 0.28817987 0.58153078
  1.20712959]
 [0.52226086 0.         0.54146934 0.32015224 0.30859351 0.2926706
  0.75109615 0.19964882 0.83512117 0.33525254 0.49383203 0.32617592
  0.72871213]
 [0.36723796 0.54146934 0.         0.52632703 0.25416756 0.51486556
  0.30044207 0.57562037 0.08450529 0.69076529 0.33525254 0.60860639
  1.15262495]
 [0.33611879 0.32015224 0.52632703 0.         0.67956839 0.96222388
  0.02001352 0.8878385  0.50729935 1.08095693 0.45889459 0.92321283
  1.52241271]
 [0.5422599  0.30859351 0.25416756 0.67956839 0.         0.36556536
  0.7339128  0.40021267 0.98917095 0.26769353 0.50631068 0.35120648
  0.77508193]
 [0.8013522  0.2926706  0.51486556 0.96222388 0.36556536 0.
  0.96342884 0.14198662 1.26859676 0.21944626 0.78873055 0.29124238
  0.32762516]
 [0.313514   0.75109615 0.30044207 0.02001352 0.7339128  0.96342884
  0.         1.01523694 0.52226086 1.12852086 0.4388937  0.90321194
  1.49699818]
 [0.61988741 0.19964882 0.57562037 0.8878385  0.40021267 0.14198662
  1.01523694 0.         1.42723528 0.27894268 0.88669066 0.19878889
  0.2014166 ]
 [0.38493792 0.83512117 0.08450529 0.50729935 0.98917095 1.26859676
  0.52226086 1.42723528 0.         1.60667479 0.75365995 1.12696267
  1.67098122]
 [0.65667005 0.33525254 0.69076529 1.08095693 0.26769353 0.21944626
  1.12852086 0.27894268 1.60667479 0.         0.96766507 0.40196993
  0.45459616]
 [0.28817987 0.49383203 0.33525254 0.45889459 0.50631068 0.78873055
  0.4388937  0.88669066 0.75365995 0.96766507 0.         0.64133002
  1.1405299 ]
 [0.58153078 0.32617592 0.60860639 0.92321283 0.35120648 0.29124238
  0.90321194 0.19878889 1.12696267 0.40196993 0.64133002 0.
  0.54574646]
 [1.20712959 0.72871213 1.15262495 1.52241271 0.77508193 0.32762516
  1.49699818 0.2014166  1.67098122 0.45459616 1.1405299  0.54574646
  0.        ]]