modifiedkNN.py

import cv2
import numpy as np
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()

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

# 10 new comers
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,responses)
knn.train(trainData,cv2.ml.ROW_SAMPLE,responses)
ret, results,neighbours,dist = knn.findNearest(newcomers, 3)
# The results also will contain 10 labels.


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

print "result: ", results,"\n"
print "neighbours: ", neighbours,"\n"
print "distance: ", dist

plt.show()