import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.mixture import GaussianMixture
from sklearn.cluster import KMeans
data = pd.read_csv('knnvsempgm2.csv')
print("Input Data and shape")
print(data.shape)
data.head()

f1=data['V1'].values
f2=data['V2'].values
X=np.array(list(zip(f1,f2)))

print("X",X)
print('Graph for whole dataset')
plt.scatter(f1,f2,c='black',s=15)
plt.show()

Kmeans=KMeans(10,random_state=42)
labels=Kmeans.fit(X).predict(X)
print("labels ",labels)
centroids=Kmeans.cluster_centers_
print("centroids ",centroids)
plt.scatter(X[:,0],X[:,1],c=labels,s=40,cmap='viridis');
print('Graph using Kmeans Algorithm')
plt.scatter(centroids[:,0],centroids[:,1],marker='*',s=200,c='#050505')
plt.show()

gmm= GaussianMixture(n_components=3).fit(X)
labels=gmm.predict(X)

probs=gmm.predict_proba(X)
size=10*probs.max(1)**3
print('Graph using EM Algorithm')

plt.scatter(X[:,0],X[:,1],c=labels,s=size,cmap='viridis');
plt.show()