API Documentation¶
This is the API documentation for scikit-hubness
.
Analysis: skhubness.analysis
¶
The skhubness.analysis
package provides methods for measuring hubness.
Examine hubness characteristics of data. |
|
Built-in mutable sequence. |
Neighbors: skhubness.neighbors
¶
The skhubness.neighbors
package is a drop-in replacement for sklearn.neighbors
,
providing all of its features, while adding transparent support for hubness reduction
and approximate nearest neighbor search.
BallTree for fast generalized N-point problems |
|
DistanceMetric class |
|
KDTree for fast generalized N-point problems |
|
Wrapper for using nmslib |
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Classifier implementing the k-nearest neighbors vote. |
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Regression based on k-nearest neighbors. |
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Wrapper for using falconn LSH |
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Nearest centroid classifier. |
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Unsupervised learner for implementing neighbor searches. |
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Wrapper for ngtpy and NNG variants. |
|
Wrap Puffinn LSH for scikit-learn compatibility. |
|
Classifier implementing a vote among neighbors within a given radius |
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Regression based on neighbors within a fixed radius. |
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Wrapper for using annoy.AnnoyIndex |
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Computes the (weighted) graph of k-Neighbors for points in X |
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Computes the (weighted) graph of Neighbors for points in X |
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Kernel Density Estimation. |
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Unsupervised Outlier Detection using Local Outlier Factor (LOF) |
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Neighborhood Components Analysis |
Reduction: skhubness.reduction
¶
The skhubness.reduction
package provides methods for hubness reduction.
Hubness reduction with Mutual Proximity [R5a390b9a9956-1]. |
|
Hubness reduction with Local Scaling [Rf1f7cb70176a-1]. |
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Hubness reduction with DisSimLocal [R3ede0f1b99b2-1]. |
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Supported hubness reduction algorithms |