MinMax

class geoanalytics.normalization.MinMax.MinMax(dataframe)

Bases: object

About this algorithm

Description:

Min-Max normalization scales each feature to a fixed range—typically [0, 1]—by subtracting the minimum and dividing by the range (max - min). It is useful when features are on different scales and bounded values are required for machine learning models. This class includes memory and runtime tracking.

Parameters:

• dataframe (pd.DataFrame): Input DataFrame with ‘x’, ‘y’ and multiple feature columns.

Attributes:

• df (pd.DataFrame) – Input DataFrame with renamed columns ‘x’, ‘y’, and features.

• normalizedDF (pd.DataFrame) – Output DataFrame after min-max normalization.

• startTime, endTime (float) – Runtime measurement markers.

• memoryUSS, memoryRSS (float) – Memory usage in kilobytes.

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.normalization import MinMax

df = pd.read_csv("input.csv")

scaler = MinMax(df)

normalized_df = scaler.run()

scaler.getRuntime()

scaler.getMemoryUSS()

scaler.getMemoryRSS()

scaler.save("MinMax.csv")

Credits

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

getMemoryRSS()

Prints the memory usage (RSS) of the process in kilobytes.

getMemoryUSS()

Prints the memory usage (USS) of the process in kilobytes.

getRuntime()

Prints the total runtime of the clustering algorithm.

run()

Executes min-max normalization on the dataset using sklearn’s MinMaxScaler.

Returns:

DataFrame with ‘x’, ‘y’, and normalized feature columns.

Return type:

pd.DataFrame

save(outputFile='MinMax.csv')

Saves the Normalized DataFrame to a CSV file.