Gaussianmixture

class geoanalytics.clustering.Gaussianmixture.Gaussianmixture(dataframe)

Bases: object

About this algorithm

Description:

Gaussian Mixture Model (GMM) is a probabilistic clustering algorithm that clusters feature-rich data by modeling it as a mixture of Gaussians, with runtime and memory tracking and exportable label results.

Parameters:

• Dataset (pandas DataFrame) must be provided during object initialization.

• No other parameters are required during instantiation.

Attributes:

• df (pd.DataFrame) – The input data with ‘x’, ‘y’ coordinates and features.

• labelsDF (pd.DataFrame) – DataFrame containing ‘x’, ‘y’, and assigned cluster labels.

• model (GaussianMixture) – The trained scikit-learn GaussianMixture model instance for reuse or further analysis.

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.clustering import Gaussianmixture

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

gm = Gaussianmixture(df)

output = gm.clustering(n_components=3)

labels_df = output[0]

weights = output[1]

centers = output[2]

gm.save('GaussianMixtureLabels.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(n_components=4, max_iters=100, covariance_type='full', init_params='kmeans', random_state=0)

Performs Gaussian Mixture Model clustering on the feature columns.

Parameters:

• n_components – Number of Gaussian components (clusters) to use.

• max_iters – Maximum number of iterations allowed during EM algorithm.

• covariance_type – Type of covariance parameters (‘full’, ‘tied’, ‘diag’, ‘spherical’).

• init_params – Initialization method (‘kmeans’ or ‘random’).

• random_state – Random seed to ensure reproducibility.

Returns:

Tuple of (DataFrame with labels, array of component weights, array of component means).

save(outputFileLabels='GaussianMixtureLabels.csv', outputFileWeights='GaussianMixtureWeights.csv', outputFileMeans='GaussianMixtureMeans.csv')

Saves labels, weights, and means to separate CSV files.