Understanding k-Nearest Neighbour

我们将Red系列标记为Class-0（由0表示），将Blue 系列标记为Class-1（由1表示）。 我们创建了25个系列或25个训练数据，并将它们标记为0级或1级.在Matplotlib的帮助下绘制它,红色系列显示为红色三角形，蓝色系列显示为蓝色方块.

import numpy as np
import cv2
import matplotlib.pyplot as plt

# Feature set containing (x,y) values of 25 known/training data
trainData = np.random.randint(0,100,(25,2)).astype(np.float32)

# Labels each one either Red or Blue with numbers 0 and 1
responses = np.random.randint(0,2,(25,1)).astype(np.float32)

# Take Red families and plot them
red = trainData[responses.ravel()==0]
plt.scatter(red[:,0],red[:,1],80,'r','^')

# Take Blue families and plot them
blue = trainData[responses.ravel()==1]
plt.scatter(blue[:,0],blue[:,1],80,'b','s')

plt.show()

接下来初始化kNN算法并传递trainData和响应以训练kNN（它构造搜索树）.然后我们将对一个new-comer，并在OpenCV的kNN帮助下将它归类为一个系列.KNN之前，我们需要了解一下我们的测试数据（new-comer),数据应该是一个浮点数组，其大小为numberoftestdata×numberoffeatures.然后找到new-comer的最近的邻居并分类.

newcomer = np.random.randint(0,100,(1,2)).astype(np.float32)
plt.scatter(newcomer[:,0],newcomer[:,1],80,'g','o')

knn = cv2.ml.KNearest_create()
knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)
ret, results, neighbours ,dist = knn.findNearest(newcomer, 3)

print( "result:  {}\n".format(results) )
print( "neighbours:  {}\n".format(neighbours) )
print( "distance:  {}\n".format(dist) )

plt.show()

输出：

result:  [[1.]]

neighbours:  [[1. 1. 0.]]

distance:  [[ 29. 149. 160.]]

上面返回的是：

1. newcomer的标签，如果最近邻算法，k=1
2. k-Nearest Neighbors的标签
3. 从newcomer到每个最近邻居的相应距离

如果newcomer有大量数据，则可以将其作为数组传递,相应的结果也作为矩阵获得.

newcomers = np.random.randint(0,100,(10,2)).astype(np.float32)


plt.scatter(newcomers[:,0],newcomers[:,1],80,'g','o')

knn = cv2.ml.KNearest_create()
knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)
ret, results, neighbours ,dist = knn.findNearest(newcomers, 3)

print( "result:  {}\n".format(results) )
print( "neighbours:  {}\n".format(neighbours) )
print( "distance:  {}\n".format(dist) )

plt.show()

输出：

result:  [[1.]
 [0.]
 [1.]
 [0.]
 [0.]
 [0.]
 [0.]
 [0.]
 [0.]
 [0.]]

neighbours:  [[0. 1. 1.]
 [0. 0. 0.]
 [1. 1. 1.]
 [0. 1. 0.]
 [1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 0.]
 [0. 1. 0.]
 [0. 0. 0.]
 [0. 0. 1.]]

distance:  [[ 229.  392.  397.]
 [   4.   10.  233.]
 [  73.  146.  185.]
 [ 130.  145. 1681.]
 [  61.  100.  125.]
 [   8.   29.  169.]
 [  41.   41.  306.]
 [  85.  505.  733.]
 [ 242.  244.  409.]
 [  61.  260.  493.]]