Metadata-Version: 2.4
Name: scalableDistances
Version: 0.1.0
Summary: My Python package related to scaling distances
Author-email: Prakhar Gandhi <gprakhar0@gmail.com>
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: numpy==1.26.4
Requires-Dist: mpire
Requires-Dist: pandas

# scalableDistances


Python package related to scaling distances

```python
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
    
```

