#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 15 15:19:07 2019

@author: pippo
"""

#Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Importing the DataSet
dataset = pd.read_csv('Mall_Customers.csv')
X = dataset.iloc[:, [3, 4]].values

# Using the Dedograms to find the optimal number of clusters:
import scipy.cluster.hierarchy as sch
dedograms = sch.dendrogram(sch.linkage(X, method = 'ward'))
plt.title('Dedograms')
plt.xlabel('Customers')
plt.ylabel('Euclidean distances')

#Fitting HC to the mall dataset
from sklearn.cluster import AgglomerativeClustering
hc = AgglomerativeClustering(n_clusters=5,affinity='euclidean',linkage='ward')
y_hc = hc.fit_predict(X)

#Visualising the clusters
plt.scatter(X[y_hc == 0, 0], X[y_hc == 0,1], s=100, c='red',     label = 'Careful' )
plt.scatter(X[y_hc == 1, 0], X[y_hc == 1,1], s=100, c='blue',    label = 'Standard' )
plt.scatter(X[y_hc == 2, 0], X[y_hc == 2,1], s=100, c='green',   label = 'Target' )
plt.scatter(X[y_hc == 3, 0], X[y_hc == 3,1], s=100, c='cyan',    label = 'Careless' )
plt.scatter(X[y_hc == 4, 0], X[y_hc == 4,1], s=100, c='magenta', label = 'Sensible' )
plt.title('Cluster of clients')
plt.xlabel('Annual Income(K$)')
plt.ylabel('Spending Score (1-100)')
plt.legend()
plt.show()