msmbuilder.cluster.KMedoids¶
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class
msmbuilder.cluster.KMedoids(n_clusters=8, n_passes=1, metric='euclidean', random_state=None)¶ K-Medoids clustering
This method finds a set of cluster centers that are themselves data points, attempting to minimize the mean-squared distance from the datapoints to their assigned cluster centers.
This algorithm requires computing the full distance matrix between all pairs of data points, requiring O(N^2) memory. The implementation of this method is based on the C clustering library [1].
Parameters: n_clusters : int, optional, default: 8
The number of clusters to be found.
n_passes : int, default=1
The number of times clustering is performed. Clustering is performed n_passes times, each time starting from a different (random) initial assignment.
metric : {“euclidean”, “sqeuclidean”, “cityblock”, “chebyshev”, “canberra”,
“braycurtis”, “hamming”, “jaccard”, “cityblock”, “rmsd”}
The distance metric to use. metric = “rmsd” requires that sequences passed to
fit()be`md.Trajectory`; other distance metrics require ``np.ndarray``s.random_state : integer or numpy.RandomState, optional
The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
See also
MiniBatchKMedoids- Alternative online implementation that does incremental updates of the cluster centers using mini-batches, for more memory efficiency.
References
[R3] de Hoon, Michiel JL, et al. “Open source clustering software.” Bioinformatics 20.9 (2004): 1453-1454. Attributes
cluster_centers_ (array, [n_clusters, n_features]) Coordinates of cluster centers labels_ (list of arrays, each of shape [sequence_length, ]) labels_[i] is an array of the labels of each point in sequence i. The label of each point is an integer in [0, n_clusters). Methods
fit(sequences[, y])Fit the kcenters clustering on the data fit_predict(sequences[, y])Performs clustering on X and returns cluster labels. fit_transform(sequences[, y])Alias for fit_predict get_params([deep])Get parameters for this estimator. partial_predict(X[, y])Predict the closest cluster each sample in X belongs to. partial_transform(X)Alias for partial_predict predict(sequences[, y])Predict the closest cluster each sample in each sequence in sequences belongs to. set_params(**params)Set the parameters of this estimator. summarize()transform(sequences)Alias for predict -
__init__(n_clusters=8, n_passes=1, metric='euclidean', random_state=None)¶
Methods
__init__([n_clusters, n_passes, metric, ...])fit(sequences[, y])Fit the kcenters clustering on the data fit_predict(sequences[, y])Performs clustering on X and returns cluster labels. fit_transform(sequences[, y])Alias for fit_predict get_params([deep])Get parameters for this estimator. partial_predict(X[, y])Predict the closest cluster each sample in X belongs to. partial_transform(X)Alias for partial_predict predict(sequences[, y])Predict the closest cluster each sample in each sequence in sequences belongs to. set_params(**params)Set the parameters of this estimator. summarize()transform(sequences)Alias for predict -
fit(sequences, y=None)¶ Fit the kcenters clustering on the data
Parameters: sequences : list of array-like, each of shape [sequence_length, n_features]
A list of multivariate timeseries, or
md.Trajectory. Each sequence may have a different length, but they all must have the same number of features, or the same number of atoms if they are ``md.Trajectory``s.Returns: self
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fit_predict(sequences, y=None)¶ Performs clustering on X and returns cluster labels.
Parameters: sequences : list of array-like, each of shape [sequence_length, n_features]
A list of multivariate timeseries. Each sequence may have a different length, but they all must have the same number of features.
Returns: Y : list of ndarray, each of shape [sequence_length, ]
Cluster labels
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fit_transform(sequences, y=None)¶ Alias for fit_predict
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get_params(deep=True)¶ Get parameters for this estimator.
Parameters: deep: boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
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partial_predict(X, y=None)¶ Predict the closest cluster each sample in X belongs to.
In the vector quantization literature, cluster_centers_ is called the code book and each value returned by predict is the index of the closest code in the code book.
Parameters: X : array-like shape=(n_samples, n_features)
A single timeseries.
Returns: Y : array, shape=(n_samples,)
Index of the cluster that each sample belongs to
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partial_transform(X)¶ Alias for partial_predict
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predict(sequences, y=None)¶ Predict the closest cluster each sample in each sequence in sequences belongs to.
In the vector quantization literature, cluster_centers_ is called the code book and each value returned by predict is the index of the closest code in the code book.
Parameters: sequences : list of array-like, each of shape [sequence_length, n_features]
A list of multivariate timeseries. Each sequence may have a different length, but they all must have the same number of features.
Returns: Y : list of arrays, each of shape [sequence_length,]
Index of the closest center each sample belongs to.
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set_params(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form
<component>__<parameter>so that it’s possible to update each component of a nested object.Returns: self
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transform(sequences)¶ Alias for predict