-neighborhood graph clustering using python, Libraries networkx and scilearn

Part1: implement epsilon_graph

Write a function that takes as an input a collection of points and a positive real number 0 and returns the -neighborhood graph. This can be done in number of ways(choose either 1 or 2 you do not have to do both only one):

1-Use the definition of the -neighborhood graph: you create a graph with nodes at the points of X and edges between x and y whenever . You will also need to insert the weight on the edges being the distance between the points.

2-Use the adjacency matrix of the radius graph available at sklearn. If you want to choose this option make sure to choose mode='distance'so that the output adjacency matrix gives back the correct weight between the edges.

Part2 implement epsilon_graph_clusters Write a function that takes as an input a collection of points and a positive real number 0and returns the clusters induced by the connected components of the -neighborhood graph you computed in part

This can be done in the following two steps :

1-Use the function you implemented in part 1 to compute the neighborhood graph G.

2-Use the function you implemented in problem 1 to compute the connected components of G