import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cluster import KMeans
import sklearn.metrics as sm
import pandas as pd
import numpy as np
import matplotlib
l1 = [0,1,2]

def rename(s):
    l2 = []
    for i in s:
        if i not in l2:
            l2.append(i)

    for i in range(len(s)):
        pos = l2.index(s[i])
        s[i] = l1[pos]

    return s

iris = datasets.load_iris()

X = pd.DataFrame(iris.data)
print(X)
X.columns = ['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width']

y = pd.DataFrame(iris.target)
y.columns = ['Targets']

print("Actual Target is:\n", iris.target)


model = KMeans(n_clusters=3)
model.fit(X)

plt.figure(figsize=(14,7))
colormap = np.array(['red', 'lime', 'black'])
plt.subplot(1, 2, 1)
plt.scatter(X.Petal_Length, X.Petal_Width, c=colormap[y.Targets], s=40)
plt.title('Real Classification')

plt.subplot(1, 2, 2)
plt.scatter(X.Petal_Length, X.Petal_Width, c=colormap[model.labels_], s=40)
plt.title('K Mean Classification')
plt.show()

km = rename(model.labels_)
print("\nWhat KMeans thought: \n", km)
print("Accuracy of KMeans is ",sm.accuracy_score(y, km))
print("Confusion Matrix for KMeans is \n",sm.confusion_matrix(y, km))

from sklearn import preprocessing
scaler = preprocessing.StandardScaler()
scaler.fit(X)
xsa = scaler.transform(X)
xs = pd.DataFrame(xsa, columns = X.columns)
print("\n",xs.sample(5))

from sklearn.mixture import GaussianMixture
gmm = GaussianMixture(n_components=3)
gmm.fit(xs)

y_cluster_gmm = gmm.predict(xs)

plt.subplot(1, 2, 1)
plt.scatter(X.Petal_Length, X.Petal_Width, c=colormap[y_cluster_gmm], s=40)
plt.title('GMM Classification')
plt.show()

em = rename(y_cluster_gmm)
print("\nWhat EM thought: \n", em)
print("Accuracy of EM is ",sm.accuracy_score(y, em))
print("Confusion Matrix for EM is \n", sm.confusion_matrix(y, em))
