# -*- 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 matclustering.core import TrajectoryClustering
[docs]
class SSOCoClus(TrajectoryClustering):
# UNDER DEV.
def __init__(self,
# Params here
random_state=1, # Not used, only for compatibility
n_jobs=1,
verbose=False):
super().__init__('SSOCoClus', random_state=random_state, n_jobs=n_jobs, verbose=verbose)
#self.add_config(k=k)
#self.grid_search(k)
[docs]
def if_config(self, config=None):
if config == None:
config = [
]
return config
[docs]
def create(self, config=None):
pass
[docs]
def fit(self, X, config=None):
pass