# 导入所需的库
import cv2
import numpy as np
import matplotlib.pyplot as plt

# 包含25个已知/训练数据的特征集（x，y）值
trainData = np.random.randint(0,100,(25,2)).astype(np.float32)

# 表示每个人都是红色或蓝色，用0和1表示标签
responses = np.random.randint(0,2,(25,1)).astype(np.float32)

# 取出红色家庭并绘制它们
red = trainData[responses.ravel()==0]
plt.scatter(red[:,0],red[:,1],80,'r','^')

# 取出蓝色家庭并绘制它们
blue = trainData[responses.ravel()==1]
plt.scatter(blue[:,0],blue[:,1],80,'b','s')
plt.show()

# 导入必要的库
import cv2
import numpy as np
import matplotlib.pyplot aplt

# 特征集，包含25个已知/训练数据的（x,y）值
trainData = np.random.randint(0,100,(25,2)).astype(np.float32)

# 将每个数据标记为红色或蓝色，使用0或1进行编码
responses = np.random.randint(0,2,(25,1)).astype(np.float32)

# 将标记为红色的数据进行绘图
red = trainData[responses.ravel()==0]
plt.scatter(red[:,0],red[:,1],80,'r','^')

# 将标记为蓝色的数据进行绘图
blue = trainData[responses.ravel()==1]
plt.scatter(blue[:,0],blue[:,1],80,'b','s')

# 构建一个新的数据点
newcomer = np.random.randint(0,100,(1,2)).astype(np.float32)
plt.scatter(newcomer[:,0],newcomer[:,1],80,'g','o')

# 构建k-NN分类器模型并进行训练
knn = cv2.ml.KNearest_create()
knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)

# 对新的数据点进行分类预测，并返回距离最近的三个邻居的标签、对应的数据和距离
ret, results, neighbors ,dist = knn.findNearest(newcomer, 3)

# 输出预测结果以及邻居的标签、对应的数据和距离
print("Label of New Member: {}\n".format(results) )
print("Nearest Neighbors: {}\n".format(neighbors) )
print("Distance of Each Neighbor: {}\n".format(dist) )

# 显示绘制的图形
plt.show()

Label of New Member: [[1.]]
Nearest Neighbors: [[0. 1. 1.]]
Distance of Each Neighbor: [[ 85. 85. 405.]]