MiniBatchKMeans
MiniBatchKMeans# class sklearn.cluster.MiniBatchKMeans(n_clusters=8, *, init='k-means++', max_iter=100, batch_size=1024, verbose=0, compute_labels=True, random_state=None, tol=0.0, max_no_improvement=10, init_size=None, n_init='auto', reassignment_ratio=0.01)[source]# Mini-Batch K-Means clustering. Read more in the User Guide. Parameters: n_clustersint, default=8The number of clusters to form as w
API Reference — scikit-learn 0.19.2 documentation
API Reference# This is the class and function reference of scikit-learn. Please refer to the full user guide for further details, as the raw specifications of classes and functions may not be enough to give full guidelines on their uses. For reference on concepts repeated across the API, see Glossary of Common Terms and API Elements.
Compressive sensing: tomography reconstruction with L1 prior (Lasso)
Compressive sensing: tomography reconstruction with L1 prior (Lasso)# This example shows the reconstruction of an image from a set of parallel projections, acquired along different angles. Such a dataset is acquired in computed tomography (CT). Without any prior information on the sample, the number of projections required to reconstruct the image is of the order of the linear size l of the image
7. Dataset loading utilities — scikit-learn 0.23.2 documentation
7. Dataset loading utilities¶ The sklearn.datasets package embeds some small toy datasets as introduced in the Getting Started section. This package also features helpers to fetch larger datasets commonly used by the machine learning community to benchmark algorithms on data that comes from the ‘real world’. To evaluate the impact of the scale of the dataset (n_samples and n_features) while contro
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Developer’s Guide
