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61 lines
2.5 KiB
Python
61 lines
2.5 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__ = "2.0.1"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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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, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):
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if normalizer != None:
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data = normalizer.transform(data)
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kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm)
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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, eps=0.5, min_samples=5, metric='euclidean', metric_params=None, algorithm='auto', leaf_size=30, p=None, n_jobs=None):
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if normalizer != None:
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data = normalizer.transform(data)
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model = sklearn.cluster.DBSCAN(eps = eps, min_samples = min_samples, metric = metric, metric_params = metric_params, algorithm = algorithm, leaf_size = leaf_size, p = p, n_jobs = n_jobs).fit(data)
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return model.labels_
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def spectral(data, normalizer=None, n_clusters=8, eigen_solver=None, n_components=None, random_state=None, n_init=10, gamma=1.0, affinity='rbf', n_neighbors=10, eigen_tol=0.0, assign_labels='kmeans', degree=3, coef0=1, kernel_params=None, n_jobs=None, verbose=False):
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if normalizer != None:
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data = normalizer.transform(data)
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model = sklearn.cluster.SpectralClustering(n_clusters = n_clusters, eigen_solver = eigen_solver, n_components = n_components, random_state = random_state, n_init = n_init, gamma = gamma, affinity = affinity, n_neighbors = n_neighbors, eigen_tol = eigen_tol, assign_labels = assign_labels, degree = degree, coef0 = coef0, kernel_params = kernel_params, n_jobs = n_jobs).fit(data)
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return model.labels_ |