Hyperreal

Latest version: v0.5.0

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0.5.0

Further refinements and simplifications of the clustering process - it is now possible to control the grouping size in the hierarchical clustering improvement.

This release also migrates the packaging infrastructure to fully use pyproject.toml.

0.4.2.dev2

0.4.2.dev1

0.4.1

- Bugfix for an edge case in cluster refinement. 64
- Very rough initial implementation to allow searching for a single value in a single field - this currently only supports exact matches and has no error handling.

0.4.0

Lots of new features in this release:

- support for network export of cluster-cluster cooccurrence via networkx (see the new `export` subcommand on the CLI, or the `Index.create_cluster_cooccurrence_graph` method.
- support for structured sampling using the model as a guide for sampling documents
- interface for corpus implementations to support exporting tabular renderings of documents
- refactoring of the clustering method so more of the pieces can be used on their own - this will be used for further functionality in the future
- reworking of the web UI render pathway, so the top of the page is calculated first and streamed as soon as it's ready - this enables better on interactivity on very large datasets
- more sophisticated tokenisation of stackexchange data: codeblocks are now pulled out and indexed separately.
- initial examples for two large scale document collections: stackoverflow and Australian Federal Hansard

Experimental initial features:

- initial experimental indexing of word positions to enable phrase and proximity queries - this is likely to change a lot in future releases
- initial support for rendering of concordances (keywords in context)
- initial support for intersecting a set of queries with a given field - this will be used in the future to enable first class support for temporal queries

0.3.1

This is a small utility release:

- make available a bugfix that prevents zombie processes from automatically created process pools
- factors out some functionality from the feature clustering algorithm into their own methods to allow more flexibility in future.

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