Dbscan algorithm in python

db_scan.py

import string
import numpy as np


UNCLASSIFIED = 0
OUTLIER = -1


class DBSCAN(object):
    def __init__(self, epsilon=1.0, min_samples=10, dist=np.linalg.norm):
        self.epsilon = epsilon
        self.min_samples = min_samples
        self.dist = dist

    def fit_predict(self, X):
        # we instantiate all the elements as unclassified
        if len(X) == 0:
            raise Exception("X is empty")

        labels = len(X) * [UNCLASSIFIED]

        cluster = 0
        for index in range(0, len(X)):
            point = X[index]
            if not labels[index] == UNCLASSIFIED:
                continue

            neighbours = self._region_query(point, X)
            if len(neighbours) < self.min_samples:
                labels[index] = OUTLIER
            else:
                cluster += 1
                self._expand_cluster(index, cluster, X, neighbours, labels)

        return labels

    def _region_query(self, point, X):
        result = []
        for other in range(0, len(X)):
            if self.dist(point - X[other]) < self.epsilon:
                result.append(other)
        return result

    def _expand_cluster(self, index, cluster, dataset, neighbours, labels):
        labels[index] = cluster
        i = 0
        while i < len(neighbours):
            current = neighbours[i]
            if labels[current] == OUTLIER:
                labels[current] = cluster
            elif labels[current] == UNCLASSIFIED:
                labels[current] = cluster
                neighbour_of_neighbours = self._region_query(current, dataset)
                if len(neighbour_of_neighbours) >= self.min_samples:
                    neighbours = neighbours + neighbour_of_neighbours
            i += 1


print(DBSCAN(epsilon=4.5, min_samples=1).fit_predict(
    np.matrix('1 2;3 4;519 20; 47 50; 11 3;4 6;7 8;10.0 11;1.4 5.0')))