class ClusterModel¶

class deeptime.clustering.ClusterModel(cluster_centers: ndarray, metric: str = 'euclidean', converged: bool = False)¶

A generic clustering model. Stores the number of cluster centers, the position of cluster centers, the metric that was used to compute the cluster centers and whether the estimation converged. It can be used to transform data by assigning each frame to its closest cluster center.

Parameters:

cluster_centers ((k, d) ndarray) – The cluster centers, length of the array should match n_clusters.
metric (str, default='euclidean') – The metric that was used for estimation, defaults to Euclidean metric.
converged (bool, optional, default=False) – Whether the estimation converged.

Examples

Let us create an artificial cluster model with three cluster centers in a three-dimensional space. The cluster centers are just the canonical basis vectors \(c_1 = (1,0,0)^\top\), \(c_2 = (0,1,0)^\top\), and \(c_3 = (0,0,1)^\top\).

We can transform data with the model. The data are five frames sampled around the third cluster center.

>>> model = ClusterModel(cluster_centers=np.eye(3))
>>> data = np.random.normal(loc=[0, 0, 1], scale=0.01, size=(5, 3))
>>> assignments = model.transform(data)
>>> print(assignments)
[2 2 2 2 2]

Attributes

`cluster_centers`	Gets the cluster centers that were estimated for this model.
`converged`	Whether the estimation process converged.
`dim`
`metric`	The metric that was used.
`n_clusters`	The number of cluster centers.

Methods

`copy`()	Makes a deep copy of this model.
`get_params`([deep])	Get the parameters.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(data[, n_jobs])	For each frame in data, yields the index of the closest point in `cluster_centers`.

__call__(*args, **kwargs)¶: Call self as a function.

copy() → Model¶

Makes a deep copy of this model.

Returns:: A new copy of this model.
Return type:: copy

get_params(deep=False)¶

Get the parameters.

Returns:: params – Parameter names mapped to their values.
Return type:: mapping of string to any

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.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: object

transform(data, n_jobs=None) → ndarray¶

For each frame in data, yields the index of the closest point in cluster_centers.

Parameters:

data ((T, d) ndarray) – frames
n_jobs (int, optional, default=None) – number of jobs to use for assignment

Returns:

discrete_trajectory – A discrete trajectory where each frame denotes the closest cluster center.

Return type:

(T, 1) ndarray

property cluster_centers: ndarray¶

Gets the cluster centers that were estimated for this model.

Returns:: Array containing estimated cluster centers.
Return type:: np.ndarray

property converged: bool¶

Whether the estimation process converged. Per default this is set to False, which can also indicate that the model was created manually and does not stem from an Estimator directly.

Returns:: converged – Whether the clustering converged
Return type:: bool

property metric: str¶

The metric that was used.

Returns:: metric – Name of the metric that was used. The name is related to the implementation via the metric registry.
Return type:: str

property n_clusters: int¶

The number of cluster centers.

Returns:: The number of cluster centers.
Return type:: int