CrossDistance

class geoanalytics.scoreCalculator.CrossDistance.CrossDistance(TopkDF, TrainDF, startBandTopkDF=2, startBandTrainDF=2)[source]

Bases: object

About this algorithm

Description:

CrossDistance computes the average pairwise distances between two datasets using a specified distance metric. This is useful for evaluating the similarity between two sets of feature vectors, such as in retrieval, matching, or clustering validation.

Parameters:

  • TopkDF (pd.DataFrame): First dataset (e.g., top-k retrieved samples).

  • TrainDF (pd.DataFrame): Second dataset (e.g., original training samples).

  • startBandTopkDF (int): Column index from which to start using features in TopkDF (default: 2).

  • startBandTrainDF (int): Column index from which to start using features in TrainDF (default: 2).

Attributes:

  • TopkDF (np.ndarray) – Sliced feature matrix from TopkDF.

  • TrainDF (np.ndarray) – Sliced feature matrix from TrainDF.

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.scoreCalculator import CrossDistance

topk_df = pd.read_csv("topk.csv")

train_df = pd.read_csv("train.csv")

cd = CrossDistance(topk_df, train_df, startBandTopkDF=2, startBandTrainDF=2)

mean_distance = cd.run(metric='euclidean')

Credits

This implementation was created by Raashika and revised by M. Charan Teja under the guidance of Professor Rage Uday Kiran.

run(metric='euclidean')[source]

Computes and returns the mean pairwise distance between the rows of two datasets.

Parameters:

metric (str) – Distance metric to use (default: ‘euclidean’).

Returns:

Mean of all computed distances.

Return type:

float