Kernel K-means 実装 in Python

環境: Mac, Jupyter-Notebook, Python2.7
Kernel K-meansを実装したんですが、うまく動作しません。コードにエラーは出ません。どこかの計算が間違っているんだと思いますが、どこかわからないので誰か助けてください。

Algorithmについてはこちらがわかりやすいかと思います
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とりあえずデータを貼ります、下に実際のコードを書きました。

    from sklearn.metrics.pairwise import pairwise_kernels

    class Kernel_Kmeans(object):

        def __init__(self, n_clusters=3, max_iter=50, kernel="linear",
                    gamma=None, degree=3, coef0=1, kernel_params=None):
            self.n_clusters = n_clusters
            self.max_iter = max_iter
            self.kernel = kernel
            self.gamma = gamma
            self.degree = degree
            self.coef0 = coef0
            self.kernel_params = kernel_params

        def kernel_matrix(self, X, Y = None):
            if callable(self.kernel):
                prams = self.kernel_params or {}
            else:
                params = {"gamma": self.gamma,
                          "degree": self.degree,
                          "coef0": self.coef0}

            return pairwise_kernels(X, Y, metric = self.kernel,
                                    filter_params = True, **params)

        def initialize_cluster(self, X):
            self.labels = np.random.choice(xrange(self.n_clusters), X.shape[0])
            #self.cluster_size = np.bincount(self.labels)


        def fit(self, X):
            n_samples = X.shape[0]
            self.initialize_cluster(X)

            for ite in xrange(self.max_iter):
                self.cluster_size = np.bincount(self.labels)
                for i in xrange(n_samples):
                    data = X[self.labels == 0]
                    xm = np.sum(self.kernel_matrix(data, Y = X[i].reshape(1, X.shape[1])))/self.cluster_size[0]
                    mm = np.sum(self.kernel_matrix(data))/(self.cluster_size[0]**2)
                    min_dist = mm - 2*xm 
                    for j in xrange(self.n_clusters

):
                    if j == (self.n_clusters-1):
                        continue
                    tmp_data = X[self.labels == j+1]
                    tmp_xm = np.sum(self.kernel_matrix(data, Y = X[i].reshape(1, X.shape[1])))/self.cluster_size[j+1]
                    tmp_mm = np.sum(self.kernel_matrix(data))/(self.cluster_size[j+1]**2)
                    tmp_dist = tmp_mm - 2*tmp_xm 
                    if tmp_dist < min_dist:
                        min_dist = tmp_dist
                        self.labels[i] = j