alg.py 文件源码

def cluster_dbscan(self, image_cols):
        print 'DBSCAN'
        # TODO handle outliers/noise
        # Look at different metrics?

        db = DBSCAN(eps=self.params.epsilon, min_samples=10, metric='euclidean')
        db.fit(image_cols)

        # from IPython import embed; embed(); import ipdb; ipdb.set_trace()
        self.number_of_clusters = np.max(db.labels_) + 1
        # Ignore -1 cluster, it's noise

        print 'number of clusters', self.number_of_clusters

        # Clusters
        centers = np.zeros((self.number_of_clusters, 3))
        for i in range(0, self.number_of_clusters):
            cluster_points = image_cols[db.labels_ == i]
            cluster_mean = np.mean(cluster_points, axis=0)
            centers[i, :] = cluster_mean

        return centers