scalableDistances 0.1.0

My Python package related to scaling distances

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