msmbuilder.cluster.KCenters¶
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class
msmbuilder.cluster.KCenters(n_clusters=8, metric='euclidean', random_state=None)¶ K-Centers clustering
Cluster a vector or Trajectory dataset using a simple heuristic to minimize the maximum distance from any data point to its assigned cluster center.
The runtime of this algorithm is O(kN), where k is the number of clusters and N is the size of the dataset, making it one of the least expensive clustering algorithms available.
Parameters: n_clusters : int, optional, default: 8
The number of clusters to form as well as the number of centroids to generate.
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.
References
[R1] Gonzalez, Teofilo F. “Clustering to minimize the maximum intercluster distance.” Theor. Comput. Sci. 38 (1985): 293-306. [R2] Beauchamp, Kyle A., et al. “MSMBuilder2: modeling conformational dynamics on the picosecond to millisecond scale.” J. Chem. Theory. Comput. 7.10 (2011): 3412-3419. 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). distances_ (list of arrays, each of shape [sequence_length, ]) distances_[i] is an array of the labels of each point in sequence i. Distance from each sample to the cluster center it is assigned to. 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, metric='euclidean', random_state=None)¶
Methods
__init__([n_clusters, metric, random_state])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
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