import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from scipy.cluster.hierarchy import linkage
from scipy.cluster.hierarchy import dendrogram
import matplotlib.pyplot as plt

########################################################################

# load data
cars = pd.read_csv('auto-mpg.data.txt',header=None, sep='\s+')

# keep a sample of 50 cars
cars = cars.sample(50, random_state=0)

# extract labels
labels = cars.iloc[:,8].values

# extract power and weight as data matrix X
X = cars.iloc[:, [3,4]].values

# normalize data
min_max_scaler = MinMaxScaler()
min_max_scaler.fit(X) # determine min and max
X_normalized = min_max_scaler.transform(X)

# perform hierarchical agglomerative clustering using complete linkage
clusters = linkage(X_normalized, method='complete', metric='euclidean')

# plot dendrogram
dendrogram = dendrogram(clusters, labels=labels)
plt.tight_layout()
plt.ylabel('Euclidean distance')
plt.show()