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metrics-sampler-extension-jmx from group com.github.dimovelev (version 0.9.1)
Group: com.github.dimovelev Artifact: metrics-sampler-extension-jmx
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Artifact metrics-sampler-extension-jmx
Group com.github.dimovelev
Version 0.9.1
Last update 31. December 2019
Organization not specified
URL Not specified
License not specified
Dependencies amount 2
Dependencies metrics-sampler-core, slf4j-api,
There are maybe transitive dependencies!
Group com.github.dimovelev
Version 0.9.1
Last update 31. December 2019
Organization not specified
URL Not specified
License not specified
Dependencies amount 2
Dependencies metrics-sampler-core, slf4j-api,
There are maybe transitive dependencies!
metrics-sampler-extension-graphite from group com.github.dimovelev (version 0.9.1)
Group: com.github.dimovelev Artifact: metrics-sampler-extension-graphite
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Artifact metrics-sampler-extension-graphite
Group com.github.dimovelev
Version 0.9.1
Last update 31. December 2019
Organization not specified
URL Not specified
License not specified
Dependencies amount 2
Dependencies metrics-sampler-core, slf4j-api,
There are maybe transitive dependencies!
Group com.github.dimovelev
Version 0.9.1
Last update 31. December 2019
Organization not specified
URL Not specified
License not specified
Dependencies amount 2
Dependencies metrics-sampler-core, slf4j-api,
There are maybe transitive dependencies!
metrics-sampler-extension-base from group com.github.dimovelev (version 0.9.1)
Group: com.github.dimovelev Artifact: metrics-sampler-extension-base
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Artifact metrics-sampler-extension-base
Group com.github.dimovelev
Version 0.9.1
Last update 31. December 2019
Organization not specified
URL Not specified
License not specified
Dependencies amount 3
Dependencies metrics-sampler-core, slf4j-api, juel,
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Group com.github.dimovelev
Version 0.9.1
Last update 31. December 2019
Organization not specified
URL Not specified
License not specified
Dependencies amount 3
Dependencies metrics-sampler-core, slf4j-api, juel,
There are maybe transitive dependencies!
metrics-sampler-core from group com.github.dimovelev (version 0.9.1)
Group: com.github.dimovelev Artifact: metrics-sampler-core
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Artifact metrics-sampler-core
Group com.github.dimovelev
Version 0.9.1
Last update 31. December 2019
Organization not specified
URL Not specified
License not specified
Dependencies amount 8
Dependencies slf4j-api, xstream, commons-beanutils, commons-io, logback-classic, logback-core, httpclient, jcommander,
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Group com.github.dimovelev
Version 0.9.1
Last update 31. December 2019
Organization not specified
URL Not specified
License not specified
Dependencies amount 8
Dependencies slf4j-api, xstream, commons-beanutils, commons-io, logback-classic, logback-core, httpclient, jcommander,
There are maybe transitive dependencies!
metrics-sampler from group com.github.dimovelev (version 0.9.1)
metrics-sampler is a java program which regularly queries metrics from a configured set of inputs,
selects and renames them using regular expressions and sends them to a configured set of outputs. It supports
JMX and JDBC as inputs and Graphite as output out of the box.
Group: com.github.dimovelev Artifact: metrics-sampler
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Artifact metrics-sampler
Group com.github.dimovelev
Version 0.9.1
Last update 31. December 2019
Organization not specified
URL https://github.com/dimovelev/metrics-sampler
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
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Group com.github.dimovelev
Version 0.9.1
Last update 31. December 2019
Organization not specified
URL https://github.com/dimovelev/metrics-sampler
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
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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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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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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,
There are maybe transitive dependencies!
linearForwardSelection from group nz.ac.waikato.cms.weka (version 1.0.2)
Extension of BestFirst. Takes a restricted number of k attributes into account. Fixed-set selects a fixed number k of attributes, whereas k is increased in each step when fixed-width is selected. The search uses either the initial ordering to select the top k attributes, or performs a ranking (with the same evalutator the search uses later on). The search direction can be forward, or floating forward selection (with opitional backward search steps). For more information see: Martin Guetlein (2006). Large Scale Attribute Selection Using Wrappers. Freiburg, Germany.
Group: nz.ac.waikato.cms.weka Artifact: linearForwardSelection
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Artifact linearForwardSelection
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/linearForwardSelection
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, classifierBasedAttributeSelection,
There are maybe transitive dependencies!
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/linearForwardSelection
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, classifierBasedAttributeSelection,
There are maybe transitive dependencies!
raceSearch from group nz.ac.waikato.cms.weka (version 1.0.2)
Races the cross validation error of competing attribute subsets. Use in conjuction with a ClassifierSubsetEval. RaceSearch has four modes:
forward selection races all single attribute additions to a base set (initially no attributes), selects the winner to become the new base set and then iterates until there is no improvement over the base set.
Backward elimination is similar but the initial base set has all attributes included and races all single attribute deletions.
Schemata search is a bit different. Each iteration a series of races are run in parallel. Each race in a set determines whether a particular attribute should be included or not---ie the race is between the attribute being "in" or "out". The other attributes for this race are included or excluded randomly at each point in the evaluation. As soon as one race has a clear winner (ie it has been decided whether a particular attribute should be inor not) then the next set of races begins, using the result of the winning race from the previous iteration as new base set.
Rank race first ranks the attributes using an attribute evaluator and then races the ranking. The race includes no attributes, the top ranked attribute, the top two attributes, the top three attributes, etc.
It is also possible to generate a raked list of attributes through the forward racing process. If generateRanking is set to true then a complete forward race will be run---that is, racing continues until all attributes have been selected. The order that they are added in determines a complete ranking of all the attributes.
Racing uses paired and unpaired t-tests on cross-validation errors of competing subsets. When there is a significant difference between the means of the errors of two competing subsets then the poorer of the two can be eliminated from the race. Similarly, if there is no significant difference between the mean errors of two competing subsets and they are within some threshold of each other, then one can be eliminated from the race.
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Artifact raceSearch
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/raceSearch
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, classifierBasedAttributeSelection,
There are maybe transitive dependencies!
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/raceSearch
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, classifierBasedAttributeSelection,
There are maybe transitive dependencies!
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