How to use the kmodes.kmodes.KModes function in kmodes

#!/usr/bin/env python

import numpy as np
from kmodes.kmodes import KModes

# reproduce results on small soybean data set
x = np.genfromtxt('soybean.csv', dtype=int, delimiter=',')[:, :-1]
y = np.genfromtxt('soybean.csv', dtype=str, delimiter=',', usecols=(35, ))

kmodes_huang = KModes(n_clusters=4, init='Huang', verbose=1)
kmodes_huang.fit(x)

# Print cluster centroids of the trained model.
print('k-modes (Huang) centroids:')
print(kmodes_huang.cluster_centroids_)
# Print training statistics
print('Final training cost: {}'.format(kmodes_huang.cost_))
print('Training iterations: {}'.format(kmodes_huang.n_iter_))

kmodes_cao = KModes(n_clusters=4, init='Cao', verbose=1)
kmodes_cao.fit(x)

# Print cluster centroids of the trained model.
print('k-modes (Cao) centroids:')
print(kmodes_cao.cluster_centroids_)
# Print training statistics

def _kmodes(k, n_init, n_jobs, seed):
    KModes(n_clusters=k, init='Huang', n_init=n_init, n_jobs=n_jobs,
           random_state=seed) \
        .fit(data[:N_kmodes, :])

# reproduce results on small soybean data set
x = np.genfromtxt('soybean.csv', dtype=int, delimiter=',')[:, :-1]
y = np.genfromtxt('soybean.csv', dtype=str, delimiter=',', usecols=(35, ))

kmodes_huang = KModes(n_clusters=4, init='Huang', verbose=1)
kmodes_huang.fit(x)

# Print cluster centroids of the trained model.
print('k-modes (Huang) centroids:')
print(kmodes_huang.cluster_centroids_)
# Print training statistics
print('Final training cost: {}'.format(kmodes_huang.cost_))
print('Training iterations: {}'.format(kmodes_huang.n_iter_))

kmodes_cao = KModes(n_clusters=4, init='Cao', verbose=1)
kmodes_cao.fit(x)

# Print cluster centroids of the trained model.
print('k-modes (Cao) centroids:')
print(kmodes_cao.cluster_centroids_)
# Print training statistics
print('Final training cost: {}'.format(kmodes_cao.cost_))
print('Training iterations: {}'.format(kmodes_cao.n_iter_))

print('Results tables:')
for result in (kmodes_huang, kmodes_cao):
    classtable = np.zeros((4, 4), dtype=int)
    for ii, _ in enumerate(y):
        classtable[int(y[ii][-1]) - 1, result.labels_[ii]] += 1

    print("\n")

def cao():
    KModes(
        n_clusters=K,
        init='Cao',
        verbose=2
    ).fit_predict(data)

def huang():
    KModes(
        n_clusters=K,
        init='Huang',
        n_init=1,
        verbose=2
    ).fit_predict(data)

def huang_ng_dissim():
    KModes(
        n_clusters=K,
        init='Huang',
        cat_dissim=ng_dissim,
        n_init=1,
        verbose=2
    ).fit_predict(data)