GOOD.utils.fast_pytorch_kmeans.kmeans
Classes
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Kmeans clustering algorithm implemented with PyTorch |
- class GOOD.utils.fast_pytorch_kmeans.kmeans.KMeans(n_clusters, max_iter=300, tol=0.0001, verbose=0, mode='euclidean', init_method='kmeans++', minibatch=None, n_init=None, algorithm=None, device=None)[source]
Bases:
objectKmeans clustering algorithm implemented with PyTorch
- Parameters
n_clusters – int, Number of clusters
max_iter – int, default: 100 Maximum number of iterations
tol – float, default: 0.0001 Tolerance
verbose – int, default: 0 Verbosity
mode – {‘euclidean’, ‘cosine’}, default: ‘euclidean’ Type of distance measure
init_method – {‘random’, ‘point’, ‘++’} Type of initialization
minibatch – {None, int}, default: None Batch size of MinibatchKmeans algorithm if None perform full KMeans algorithm
- centroids
torch.Tensor, shape: [n_clusters, n_features] cluster centroids
- static cos_sim(a, b)[source]
Compute cosine similarity of 2 sets of vectors
Parameters: a: torch.Tensor, shape: [m, n_features]
b: torch.Tensor, shape: [n, n_features]
- static euc_sim(a, b)[source]
Compute euclidean similarity of 2 sets of vectors
Parameters: a: torch.Tensor, shape: [m, n_features]
b: torch.Tensor, shape: [n, n_features]
- fit(X, sample_weight=None, centroids=None)[source]
Perform kmeans clustering
Parameters: X: torch.Tensor, shape: [n_samples, n_features]
- fit_predict(X, sample_weight=None, centroids=None)[source]
Combination of fit() and predict() methods. This is faster than calling fit() and predict() seperately.
Parameters: X: torch.Tensor, shape: [n_samples, n_features]
- centroids: {torch.Tensor, None}, default: None
if given, centroids will be initialized with given tensor if None, centroids will be randomly chosen from X
Return: labels: torch.Tensor, shape: [n_samples]
- max_sim(a, b)[source]
Compute maximum similarity (or minimum distance) of each vector in a with all of the vectors in b
Parameters: a: torch.Tensor, shape: [m, n_features]
b: torch.Tensor, shape: [n, n_features]