mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-12-27 09:59:10 +00:00
63 lines
1.4 KiB
Python
63 lines
1.4 KiB
Python
# Titan Robotics Team 2022: Clustering submodule
|
|
# Written by Arthur Lu
|
|
# Notes:
|
|
# this should be imported as a python module using 'from tra_analysis import Clustering'
|
|
# setup:
|
|
|
|
__version__ = "2.0.2"
|
|
|
|
# changelog should be viewed using print(analysis.__changelog__)
|
|
__changelog__ = """changelog:
|
|
2.0.2:
|
|
- generalized optional args to **kwargs
|
|
2.0.1:
|
|
- added normalization preprocessing to clustering, expects instance of sklearn.preprocessing.Normalizer()
|
|
2.0.0:
|
|
- added dbscan clustering algo
|
|
- added spectral clustering algo
|
|
1.0.0:
|
|
- created this submodule
|
|
- copied kmeans clustering from Analysis
|
|
"""
|
|
|
|
__author__ = (
|
|
"Arthur Lu <learthurgo@gmail.com>",
|
|
)
|
|
|
|
__all__ = [
|
|
"kmeans",
|
|
"dbscan",
|
|
"spectral",
|
|
]
|
|
|
|
import sklearn
|
|
|
|
def kmeans(data, normalizer = None, **kwargs):
|
|
|
|
if normalizer != None:
|
|
data = normalizer.transform(data)
|
|
|
|
kernel = sklearn.cluster.KMeans(**kwargs)
|
|
kernel.fit(data)
|
|
predictions = kernel.predict(data)
|
|
centers = kernel.cluster_centers_
|
|
|
|
return centers, predictions
|
|
|
|
def dbscan(data, normalizer=None, **kwargs):
|
|
|
|
if normalizer != None:
|
|
data = normalizer.transform(data)
|
|
|
|
model = sklearn.cluster.DBSCAN(**kwargs).fit(data)
|
|
|
|
return model.labels_
|
|
|
|
def spectral(data, normalizer=None, **kwargs):
|
|
|
|
if normalizer != None:
|
|
data = normalizer.transform(data)
|
|
|
|
model = sklearn.cluster.SpectralClustering(**kwargs).fit(data)
|
|
|
|
return model.labels_ |