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('Desktop/LABEM.csv')
print("INPUT DATA AND SHAPE")
print(data.shape)
print(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=7)
plt.show()
kmeans = KMeans(20, random_state=0)
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).fir(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()