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3520135210 of 37014 results
72088.
The following main features are supported:
- View revision logs, commit messages, diffstats, diffs, archive trees and
file contents.
- Visualize revision graphs.
- Stage / unstage changes and add untracked files.
- Merge files.
- Cherry-pick commits.
Description
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Located in Package: tig
72108.
TileCache is an implementation of a WMS-C compliant server made available by MetaCarta. TileCache provides a Python-based WMS/TMS server, with pluggable caching mechanisms and rendering backends. In the simplest use case, TileCache requires only write access to a disk, the ability to run Python CGI scripts, and a WMS you want to be cached. With these resources, you can create your own local disk-based cache of any WMS server, and use the result in any WMS-C supporting client, like OpenLayers, or any TMS supporting client, like OpenLayers and worldKit.
Description
(no translation yet)
Located in Package: tilecache
72111.
lightweight Mapnik tile-server
Summary
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Located in Package: tilelite
72112.
TileLite is a lightweight Mapnik tile-server written as a WSGI application, designed to serve tiles in the OSM (OpenStreetMap)-scheme or the Google-scheme.
Description
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Located in Package: tilelite
72113.
map tiles caching system
Summary
(no translation yet)
Located in Package: tilestache
72114.
TileStache is a Python-based server application that can serve up map tiles based on rendered geographic data.
Description
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Located in Package: tilestache
72118.
Tilburg Memory Based Learner
Summary
(no translation yet)
Located in Package: timbl
72119.
Memory-Based Learning (MBL) is a machine-learning method applicable to a wide range of tasks in Natural Language Processing (NLP).
Description
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Located in Package: timbl
72120.
The Tilburg Memory Based Learner, TiMBL, is a tool for NLP research, and for many other domains where classification tasks are learned from examples. It is an efficient implementation of k-nearest neighbor classifier.
Description
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Located in Package: timbl
72121.
TiMBL's features are:
* Fast, decision-tree-based implementation of k-nearest neighbor
classification;
* Implementations of IB1 and IB2, IGTree, TRIBL, and TRIBL2 algorithms;
* Similarity metrics: Overlap, MVDM, Jeffrey Divergence, Dot product, Cosine;
* Feature weighting metrics: information gain, gain ratio, chi squared,
shared variance;
* Distance weighting metrics: inverse, inverse linear, exponential decay;
* Extensive verbosity options to inspect nearest neighbor sets;
* Server functionality and extensive API;
* Fast leave-one-out testing and internal cross-validation;
* and Handles user-defined example weighting.
Description
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Located in Package: timbl
3520135210 of 37014 results

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