# initializes centers using kmeans++ method
    def _init_plus(self):
        first = self.data[np.random.choice(self.data.shape[0], 1)]
        clusters = [ first ]

        # for each point, find distance squared from the cluster, then normalize into weights
        weights = np.apply_along_axis(lambda row: np.linalg.norm(row - first)**2 , 1, self.data)
        weights /= sum(weights)

        while len(clusters) < self.n_clusters:
            new_cluster = self.data[np.random.choice(self.data.shape[0], 1, p = weights)]
            clusters.append(new_cluster)
            #update weights with distances -- looking for distance to closest cluster, so just take min
            for (i, row) in enumerate(self.data):
                old_dist = weights[i]
                new_dist = np.linalg.norm(row - new_cluster)**2
                weights[i] = min(old_dist, new_dist)
            weights /= sum(weights)

        return clusters