# This program computes the function f -- the similarity of one data item
# with a set of others. The data items defined here are example 2D vectors. 
# That is, each 'cell' is a 2-item tuple or list: cell = [_, _].
# See lab slides for an illustration.

import numpy as np

# Compute the similarity of 'center_cell' with 'neighbors'
center_cell = (4,1)
neighbors = [(2,1), (3,6), (5,4)]

alpha = 4  #Scale of dissimilarity or discrimination factor
neighborhood_size = 3

xi1 = center_cell[0]
xi2 = center_cell[1]

total_similarity = 0

print('Distance values ---')

# Compute similarity of [xi1, xi2] cell with neighboring [xj1, xj2] cells.
for cell in neighbors:
    xj1 = cell[0]
    xj2 = cell[1]
    distance =  np.sqrt(pow(xi1-xj1,2) + pow(xi2-xj2,2))
    print('Distance between', center_cell, 'and', cell, ' = ', distance)
    current_similarity = 1 - (distance / alpha)
    total_similarity += current_similarity

total_similarity = total_similarity / neighborhood_size ** 2

print('Similarity between', center_cell, 'and its neighbors', neighbors, ' = ', total_similarity)

# NOTE that for the same set of data, changing 'alpha' will yield different similarity values