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