class KMeansModel¶

class deeptime.clustering.KMeansModel(cluster_centers, metric: str, tolerance: Optional[float] = None, inertias: Optional[ndarray] = None, converged: bool = False)¶

The K-means clustering model. Stores all important information which are result of the estimation procedure. It can also be used to transform data by assigning each frame to its closest cluster center. For an example please see the documentation of the superclass ClusterModel.

Parameters:

cluster_centers ((k, d) ndarray) – The d-dimensional cluster centers, length of the array should coincide with n_clusters.
metric (str) – The metric that was used
tolerance (float, optional, default=None) – Tolerance which was used as convergence criterium. Defaults to None so that clustering models can be constructed purely from cluster centers and metric.
inertias ((t,) ndarray or None, optional, default=None) – Value of the cost function over t iterations. Defaults to None so that clustering models can be constructed purely from cluster centers and metric.
converged (bool, optional, default=False) – Whether the convergence criterium was met.

Attributes

`cluster_centers`	Gets the cluster centers that were estimated for this model.
`converged`	Whether the estimation process converged.
`dim`
`inertia`	Sum of squared distances to assigned centers of training data
`inertias`	Series of inertias over the the iterations of k-means.
`metric`	The metric that was used.
`n_clusters`	The number of cluster centers.
`tolerance`	The tolerance used as stopping criterion in the kmeans clustering loop.

Methods

`copy`()	Makes a deep copy of this model.
`get_params`([deep])	Get the parameters.
`score`(data[, n_jobs])	Computes how well the model fits to given data by computing the `inertia`.
`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

score(data: ndarray, n_jobs: Optional[int] = None) → float¶

Computes how well the model fits to given data by computing the inertia.

Parameters:

data ((T, d) ndarray, dtype=float or double) – dataset with T entries and d dimensions
n_jobs (int, optional, default=None) – number of jobs to use

Returns:

score – the inertia

Return type:

float

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 inertia: Optional[int]¶

Sum of squared distances to assigned centers of training data

\[\sum_{i=1}^k \sum_{x\in S_i} d(x, \mu_i)^2, \]

where \(x\) are the frames assigned to their respective cluster center \(S_i\).

Type:: float or None

property inertias: Optional[ndarray]¶

Series of inertias over the the iterations of k-means.

Type:: (t, dtype=float) ndarray or None

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

property tolerance¶

The tolerance used as stopping criterion in the kmeans clustering loop. In particular, when the relative change in the inertia is smaller than the given tolerance value.

Returns:: tolerance – the tolerance
Return type:: float