fish_length = [25.4, 26.3, 26.5, 29.0, 29.0, 29.7, 29.7, 30.0, 30.0, 30.7, 31.0, 31.0,
31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0,
35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0, 9.8,
10.5, 10.6, 11.0, 11.2, 11.3, 11.8, 11.8, 12.0, 12.2, 12.4, 13.0, 14.3, 15.0]
fish_weight = [242.0, 290.0, 340.0, 363.0, 430.0, 450.0, 500.0, 390.0, 450.0, 500.0, 475.0, 500.0,
500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0,
700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0, 6.7,
7.5, 7.0, 9.7, 9.8, 8.7, 10.0, 9.9, 9.8, 12.2, 13.4, 12.2, 19.7, 19.9]
fish_data = [[l, w] for l, w in zip(fish_length, fish_weight)]
fish_target = [1]*35 + [0]*14
from sklearn.neighbors import KNeighborsClassifier
kn = KNeighborsClassifier()
print(fish_data[4])
[29.0, 430.0]
print(fish_data[0:5])
[[25.4, 242.0], [26.3, 290.0], [26.5, 340.0], [29.0, 363.0], [29.0, 430.0]]
print(fish_data[:5])
[[25.4, 242.0], [26.3, 290.0], [26.5, 340.0], [29.0, 363.0], [29.0, 430.0]]
print(fish_data[44:])
[[12.2, 12.2], [12.4, 13.4], [13.0, 12.2], [14.3, 19.7], [15.0, 19.9]]
train_input = fish_data[:35]
train_target = fish_target[:35]
test_input = fish_data[35:]
test_target = fish_target[35:]
kn.fit(train_input, train_target)
kn.score(test_input, test_target)
0.0
import numpy as np
input_arr = np.array(fish_data)
target_arr = np.array(fish_target)
print(input_arr)
[[ 25.4 242. ] [ 26.3 290. ] [ 26.5 340. ] [ 29. 363. ] [ 29. 430. ] [ 29.7 450. ] [ 29.7 500. ] [ 30. 390. ] [ 30. 450. ] [ 30.7 500. ] [ 31. 475. ] [ 31. 500. ] [ 31.5 500. ] [ 32. 340. ] [ 32. 600. ] [ 32. 600. ] [ 33. 700. ] [ 33. 700. ] [ 33.5 610. ] [ 33.5 650. ] [ 34. 575. ] [ 34. 685. ] [ 34.5 620. ] [ 35. 680. ] [ 35. 700. ] [ 35. 725. ] [ 35. 720. ] [ 36. 714. ] [ 36. 850. ] [ 37. 1000. ] [ 38.5 920. ] [ 38.5 955. ] [ 39.5 925. ] [ 41. 975. ] [ 41. 950. ] [ 9.8 6.7] [ 10.5 7.5] [ 10.6 7. ] [ 11. 9.7] [ 11.2 9.8] [ 11.3 8.7] [ 11.8 10. ] [ 11.8 9.9] [ 12. 9.8] [ 12.2 12.2] [ 12.4 13.4] [ 13. 12.2] [ 14.3 19.7] [ 15. 19.9]]
print(input_arr.shape)
(49, 2)
np.random.seed(42)
index = np.arange(49)
np.random.shuffle(index)
print(index)
[13 45 47 44 17 27 26 25 31 19 12 4 34 8 3 6 40 41 46 15 9 16 24 33 30 0 43 32 5 29 11 36 1 21 2 37 35 23 39 10 22 18 48 20 7 42 14 28 38]
print(input_arr[[1,3]])
[[ 26.3 290. ] [ 29. 363. ]]
train_input = input_arr[index[:35]]
train_target = target_arr[index[:35]]
print(input_arr[13], train_input[0])
[ 32. 340.] [ 32. 340.]
test_input = input_arr[index[35:]]
test_target = target_arr[index[35:]]
import matplotlib.pyplot as plt
plt.scatter(train_input[:, 0], train_input[:, 1])
plt.scatter(test_input[:, 0], test_input[:, 1])
plt.xlabel('length')
plt.ylabel('weight')
plt.show()
kn.fit(train_input, train_target)
KNeighborsClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
KNeighborsClassifier()
kn.score(test_input, test_target)
1.0
kn.predict(test_input)
array([0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0])
test_target
array([0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0])