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wire-schema-metadata from group com.squareup.wire (version 4.0.0-alpha.7)

Multiplatform proto parsing and pruning library

Group: com.squareup.wire Artifact: wire-schema-metadata
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Download wire-schema-metadata.jar (4.0.0-alpha.7)
 

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Artifact wire-schema-metadata
Group com.squareup.wire
Version 4.0.0-alpha.7
Last update 09. August 2021
Organization not specified
URL https://github.com/square/wire
License The Apache Software License, Version 2.0
Dependencies amount 2
Dependencies wire-runtime-metadata, kotlin-stdlib-common,
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wire-schema-multiplatform from group com.squareup.wire (version 4.0.0-alpha.7)

Group: com.squareup.wire Artifact: wire-schema-multiplatform
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Artifact wire-schema-multiplatform
Group com.squareup.wire
Version 4.0.0-alpha.7


lucene-pruning from group org.apache.lucene (version 3.6.2)

Pruning Lucene indexes by various criteria.

Group: org.apache.lucene Artifact: lucene-pruning
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Download lucene-pruning.jar (3.6.2)
 

1 downloads
Artifact lucene-pruning
Group org.apache.lucene
Version 3.6.2
Last update 24. December 2012
Organization not specified
URL Not specified
License not specified
Dependencies amount 1
Dependencies lucene-core,
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entity-pruner from group net.saliman (version 3.1.0)

A Utility for making an ORM managed object graph safe for sending to a client by pruning out database proxies circular references and other problematic items

Group: net.saliman Artifact: entity-pruner
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Artifact entity-pruner
Group net.saliman
Version 3.1.0
Last update 24. July 2012
Organization not specified
URL https://github.com/stevesaliman/entity-pruner
License GNU Lesser General Public License Version 3.0
Dependencies amount 4
Dependencies slf4j-log4j12, log4j, slf4j-api, jcl-over-slf4j,
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minimax4j from group fr.avianey (version 1.0.0)

Minimax4j is a pure java implementation of the minimax algorithm. It provides implementation of standard minimax but also provides alpha-beta pruning, negamax or negascout. This project can be use to implement two-person zero-sum games of perfect information such as chess, reversi, connect four, ...

Group: fr.avianey Artifact: minimax4j
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Download minimax4j.jar (1.0.0)
 

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Artifact minimax4j
Group fr.avianey
Version 1.0.0
Last update 29. December 2013
Organization not specified
URL Not specified
License GNU Lesser General Public License version 3
Dependencies amount 0
Dependencies No dependencies
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simpleCART from group nz.ac.waikato.cms.weka (version 1.0.2)

Class implementing minimal cost-complexity pruning. Note when dealing with missing values, use "fractional instances" method instead of surrogate split method. For more information, see: Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, California.

Group: nz.ac.waikato.cms.weka Artifact: simpleCART
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Download simpleCART.jar (1.0.2)
 

9 downloads
Artifact simpleCART
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/simpleCART
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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conjunctiveRule from group nz.ac.waikato.cms.weka (version 1.0.4)

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents. For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule. For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule. In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.

Group: nz.ac.waikato.cms.weka Artifact: conjunctiveRule
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Download conjunctiveRule.jar (1.0.4)
 

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Artifact conjunctiveRule
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 29. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/conjunctiveRule
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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