Regular spatial clustering.
This method finds a set of cluster centers that are themselves data points, chosen to be approximately equally separated in conformation space with respect to the distance metric used. In pseudocode, the algorithm, from Senne et al. [1], is:
Initialize a list of cluster centers containing only the first data point in the data set
- Iterating over all conformations in the input dataset (in order),
- If the data point is farther than d_min from all existing cluster center, add it to the list of cluster centers
| Parameters: | d_min : float
metric : {“euclidean”, “sqeuclidean”, “cityblock”, “chebyshev”, “canberra”,
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References
| [R11] | Senne, Martin, et al. J. Chem Theory Comput. 8.7 (2012): 2223-2238 |
Attributes
| cluster_centers_ | (array, [n_clusters, n_features]) Coordinates of cluster centers |
| n_clusters_ | (int) The number of clusters located. |
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 |
Fit the kcenters clustering on the data
| Parameters: | sequences : list of array-like, each of shape [sequence_length, n_features]
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| Returns: | self |
Performs clustering on X and returns cluster labels.
| Parameters: | sequences : list of array-like, each of shape [sequence_length, n_features]
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| Returns: | Y : list of ndarray, each of shape [sequence_length, ]
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Alias for fit_predict
Get parameters for this estimator.
| Parameters: | deep: boolean, optional
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| Returns: | params : mapping of string to any
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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)
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| Returns: | Y : array, shape=(n_samples,)
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Alias for partial_predict
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]
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| Returns: | Y : list of arrays, each of shape [sequence_length,]
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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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Alias for predict