msmbuilder.cluster.KCenters

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

[1]Gonzalez, Teofilo F. “Clustering to minimize the maximum intercluster distance.” Theor. Comput. Sci. 38 (1985): 293-306.
[2]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() Return some diagnostic summary statistics about this Markov model
transform(sequences) Alias for predict
__init__(n_clusters=8, metric='euclidean', random_state=None)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([n_clusters, metric, random_state]) Initialize self.
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() Return some diagnostic summary statistics about this Markov model
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
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

fit_transform(sequences, y=None)

Alias for fit_predict

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.

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

partial_transform(X)

Alias for partial_predict

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.

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 latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:
self
summarize()

Return some diagnostic summary statistics about this Markov model

transform(sequences)

Alias for predict