# -*- coding: utf-8 -*-
"""
MAT-Tools: Python Framework for Multiple Aspect Trajectory Data Mining
The present application offers a tool, to support the user in the clustering of multiple aspect trajectory data.It integrates into a unique framework for multiple aspects trajectories and in general for multidimensional sequence data mining methods.
Copyright (C) 2022, MIT license (this portion of code is subject to licensing from source project distribution)
Created on Apr, 2024
Copyright (C) 2024, License GPL Version 3 or superior (see LICENSE file)
Authors:
- Tarlis Portela
- Yuri Santos
"""
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from matclustering.core import SimilarityClustering
[docs]
class TSKMeans(SimilarityClustering): # Trajectory KMeans
"""
Trajectory K-Means Clustering for Trajectory Data using similarity matrix.
The `TSKMeans` class implements the KMeans clustering algorithm, specifically
designed for clustering trajectory data. This implementation allows
for dynamic configuration of the number of clusters (k) and supports grid
search for hyperparameter tuning.
Parameters
----------
k : int or list of int, optional
The number of clusters to form. Can also be a list for grid search.
Default is 5.
random_state : int, optional
Seed for random number generation, ensuring reproducibility.
Default is 1.
n_jobs : int, optional
The number of jobs to run in parallel for both `fit` and `predict`.
Default is 1.
verbose : bool, optional
If True, enables verbose output during processing. Default is False.
Methods
-------
create(config=None):
Initializes and returns a KMeans model with the specified parameters.
"""
def __init__(self,
k=5,
random_state=1,
n_jobs=1,
verbose=False):
super().__init__('TSKMeans', random_state=random_state, n_jobs=n_jobs, verbose=verbose)
self.add_config(k=k)
if isinstance(k, list):
self.grid_search(k) # list of k values transform in a 2D configs
else:
self.grid = [[k]] # just one config
[docs]
def if_config(self, config=None):
if config == None:
config = [
self.config['k']
]
return config
[docs]
def create(self, config=None):
k, = self.if_config(config)
return KMeans(n_clusters = k, random_state=self.config['random_state'])