DecimalScaling

class geoanalytics.normalization.DecimalScaling.DecimalScaling(dataframe)

Bases: object

About this algorithm

Description:

DecimalScaling is a normalization technique that transforms features by dividing them by powers of 10 based on the maximum absolute value. This class normalizes all feature columns (excluding coordinates) to scale them within a decimal range, while also tracking memory usage and execution time.

Parameters:

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

Attributes:

• df (pd.DataFrame) – Original DataFrame with renamed columns.

• normalizedDF (pd.DataFrame) – DataFrame after normalization.

• startTime, endTime (float) – Timestamps for runtime tracking.

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

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.normalization import DecimalScaling

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

scaler = DecimalScaling(df)

normalized_df = scaler.run()

scaler.getRuntime()

scaler.getMemoryUSS()

scaler.getMemoryRSS()

scaler.save("DecimalScaling.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()

Applies Decimal Scaling normalization to the input features.

Returns:

Normalized DataFrame with ‘x’, ‘y’, and scaled features.

Return type:

pd.DataFrame

save(outputFile='DecimalScaling.csv')

Saves the Normalized DataFrame to a CSV file.