y[50:100] = 1
y[100:150] = 2
#
# standardize data
data = cl.my_math.standardize(data)
#apply one algorithm at a time
####################################################################
print('PAM')
start = time.time()
[u, centroids, ite, dist_tmp] = cl.pam(data, 3, replicates=10)
print('Time elapsed: ', time.time()-start)
print('Accuracy: ', cl.my_math.compare_categorical_vectors(u, y)[0])
####################################################################
print('Build PAM')
start = time.time()
[u, medoids, ite, dist_tmp] = cl.build_pam(data, 3)
print('Time elapsed: ', time.time()-start)
print('Accuracy: ', cl.my_math.compare_categorical_vectors(u, y)[0])
####################################################################
print('Minkowski Weighted PAM')
start = time.time()
[u, medoids, weights, ite, dist_tmp] = cl.mwpam(data, 3, 1.1, False, 10)
print('Time elapsed: ', time.time()-start)
print('Accuracy: ', cl.my_math.compare_categorical_vectors(u, y)[0])
####################################################################
print('Minkowski Weighted PAM (Initialized with Minkowski Build)')
start = time.time()
[u, medoids, weights, ite, dist_tmp] = cl.mwpam(data, 3, 1.1)
print('Time elapsed: ', time.time()-start)
print('Accuracy: ', cl.my_math.compare_categorical_vectors(u, y)[0])
####################################################################
