K Means

# importing required tools

import numpy as np

from matplotlib import pyplot as plt

Z = pd.read_csv("/home/umesh/Desktop/icfoss/data_kmeans.csv")

import numpy as np

import cv2

from matplotlib import pyplot as plt

# convert to np.float32

Z = np.float32(Z)

# define criteria and apply kmeans()

criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

ret,label,center = cv2.kmeans(Z,2,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)

# Now separate the data

A = Z[label.ravel()==0]

B = Z[label.ravel()==1]

# Plot the data

plt.scatter(A[:,0],A[:,1])

plt.scatter(B[:,0],B[:,1],c = 'r')

plt.scatter(center[:,0],center[:,1],s = 80,c = 'y', marker = 's')

plt.xlabel('Test Data'),plt.ylabel('Z samples')

plt.show()