rt-ai-8

'''
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
sns.set()

𝑋 = 𝑛𝑝.𝑎𝑟𝑟𝑎𝑦([[5,3],[10,15],[15,12],[24,10],[30,45],[85,70],[71,80],[60,78],[55,52],[80,91]])
print(X.shape)
kmeans = KMeans(n_clusters=6, random_state=4)
kmeans.fit(X)
y_kmeans = kmeans.predict(X)
print(y_kmeans)

centers = kmeans.cluster_centers_
print(len(centers))

X,y_true = make_blobs(n_samples=6, centers=6, cluster_std=0.4, random_state=4)
plt.scatter(X[:,0], X[:, 1], c=np.unique(y_kmeans), s=50, cmap="summer")
plt.scatter(centers[:,0], centers[:,1], c="blue", s=100, alpha=0.9)
plt.show()




import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

from sklearn.datasets import load_iris
from sklearn.cluster import KMeans
sns.set()

iris = load_iris()
x = iris.data
print(x.shape)
kmeans = KMeans(n_clusters=10, random_state=4)
y_kmeans = kmeans.fit_predict(x)
print(kmeans.cluster_centers_.shape)

#Visualising the clusters
plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], s = 100, c = 'purple', label = 'setosa')
plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], s = 100, c = 'orange', label = 'versicolour')
plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], s = 100, c = 'green', label = 'virginica')

#Plotting the centroids of the clusters
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], s = 100, c = 'red', label = 'centroids')

plt.legend()
plt.show()




import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.cluster.hierarchy as shc

from sklearn.datasets import load_iris
from sklearn.cluster import AgglomerativeClustering
sns.set()

iris = load_iris()
data = iris.data[:, 0:2]
plt.figure(figsize=(10,7),dpi=180)
plt.title("IRIS DENDROGRAM")
dend = shc.dendrogram(shc.linkage(data,method='ward'))

cluster = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage="ward")
cluster.fit_predict(data)
plt.figure(figsize=(10,7))
plt.scatter(data[:,0],data[:,1],c=cluster.labels_,cmap='rainbow')
plt.show()

'''