scalableDistances

Python package related to scaling distances

import scalableDistances 
from scalableDistances import distance
import pandas as pd
import random
from pprint import pprint
N = 2
main_arr2 = []
arr2 = [random.randint(1, 100) for _ in range(N)]

    
for i in range(N):
    main_arr2.append(arr2)


print(f"main_arr2 = {main_arr2}")
progress_bar = True  # Set to True to enable progress bar
print("Starting the parallel processing...")
for distance_metric in [
        "euclidean", "squared_euclidean", "manhattan", "chebyshev",
        "minkowski", "l0", "canberra", "bray_curtis", 
        "hamming", "hamming_count",
        "jaccard", 
        "standardized_euclidean",
        "huber",
        "maximum_relative_difference"
]:
    print(f"Calculating distances using {distance_metric} metric...")
    results = distance.get_distance_metrics(main_arr2, distance_metric, progress_bar)
    print(f"Results for {distance_metric}:")
    pprint(results[:5])  # Print the first 5 results for verification