How to use the kmodes.kprototypes.KPrototypes function in kmodes

def _kprototypes(k, n_init, n_jobs, seed):
    KPrototypes(n_clusters=k, init='Huang', n_init=n_init, n_jobs=n_jobs,
                random_state=seed) \
        .fit(data[:N_kproto, :], categorical=list(range(M - MN, M)))

def kproto(self):  # TODO- solve clustering issue with PCA + K-means
        cluster_data = self.data
        opt_k = self.silouhette_analysis(cluster_data, prototype=True)

        kp = KPrototypes(n_clusters=opt_k)
        kp.fit(cluster_data, categorical=self.categorical_features)
        labels = kp.predict(
            cluster_data, categorical=self.categorical_features)

        cluster_data['labels'] = labels
        self.data_clustered = cluster_data

        return cluster_data

def huang():
    KPrototypes(n_clusters=K, init='Huang', n_init=1, verbose=2)\
        .fit_predict(data, categorical=list(range(M - MN, M)))

#!/usr/bin/env python

import numpy as np
from kmodes.kprototypes import KPrototypes

# stocks with their market caps, sectors and countries
syms = np.genfromtxt('stocks.csv', dtype=str, delimiter=',')[:, 0]
X = np.genfromtxt('stocks.csv', dtype=object, delimiter=',')[:, 1:]
X[:, 0] = X[:, 0].astype(float)

kproto = KPrototypes(n_clusters=4, init='Cao', verbose=2)
clusters = kproto.fit_predict(X, categorical=[1, 2])

# Print cluster centroids of the trained model.
print(kproto.cluster_centroids_)
# Print training statistics
print(kproto.cost_)
print(kproto.n_iter_)

for s, c in zip(syms, clusters):
    print("Symbol: {}, cluster:{}".format(s, c))

def __init__(self, n_clusters=8, max_iter=100, num_dissim=euclidean_dissim,
                 cat_dissim=matching_dissim, init='Huang', n_init=10, gamma=None,
                 verbose=0, random_state=None, n_jobs=1):

        super(KPrototypes, self).__init__(n_clusters, max_iter, cat_dissim, init,
                                          verbose=verbose, random_state=random_state,
                                          n_jobs=n_jobs)
        self.num_dissim = num_dissim
        self.gamma = gamma
        self.n_init = n_init
        if isinstance(self.init, list) and self.n_init > 1:
            if self.verbose:
                print("Initialization method is deterministic. "
                      "Setting n_init to 1.")
            self.n_init = 1

def cao():
    KPrototypes(n_clusters=K, init='Cao', verbose=2)\
        .fit_predict(data, categorical=list(range(M - MN, M)))