Jupyter Notebook
k近邻算法
import numpy as np
from matplotlib import pyplot as plt
import math
X
X_train = np.array([[1,1.2],[3,3.3],[5,2.2],[7,5.7],[9,8.4]])
X
X_train
array([[1. , 1.2],
[3. , 3.3],
[5. , 2.2],
[7. , 5.7],
[9. , 8.4]])
Y
Y_train = np.array([0,0,0,1,1])
Y
Y_train
array([0, 0, 0, 1, 1])
plt.scatter(x_train[y_train == 0,0],x_train[y_train == 0,1],color=“r”,label=“normal”)
plt.scatter(x_train[y_train == 1,0],x_train[y_train == 1,1],color=“g”,label=“bad”)
plt.title(“search”)
Text(0.5, 1.0, ‘search’)
#x是我们要测试的值在下图用蓝色表示
x = np.array([6,3.7])
b
plt.scatter(x_train[y_train == 0,0],x_train[y_train == 0,1],color=“r”,label=“normal”)
plt.scatter(x_train[y_train == 1,0],x_train[y_train == 1,1],color=“g”,label=“bad”)
plt.scatter(x[0],x[1],color=“b”)
plt.title(“search”)
Text(0.5, 1.0, ‘search’)
KNN，求x与其他点的距离，并保存进distant中去
distant = []
for x_train in X_train:
d = math.sqrt(np.sum((x - x_train)**2))
distant.append(d)
distant
[5.5901699437494745,
3.026549190084311,
1.8027756377319946,
2.23606797749979,
5.57584074378026]
对所获取到的距离进行下标排序
np.argsort(distant)
np.argsort(distant)
array([2, 3, 1, 4, 0], dtype=int64)
nearst = np.argsort(distant)
获取到排序号的点坐标，进行相应Y_train中坐标的值，才是我们想要的
k = 3
k = 3
k =3 说明与三个数进行比较，看离谁近，则进行投票
topk_y = [Y_train[i] for i in nearst[:k]]
topk_y
[0, 1, 0]
此时我们得出了0，1，0说明 0胜出，预测到的结果应该是0
from collections import Counter
Counter(topk_y)
Counter({0: 2, 1: 1})
计数完进行投票
votes = Counter(topk_y)
votes.most_common(1)
[(0, 2)]
votes.most_common(1)[0][0]
votes.most_common(1)[0][0]
0
把值存在predict中来
predict =
predict = votes.most_common(1)[0][0]
predict
0
至此，简单的K临近算法已经差不多了呀