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structural-type-jackson from group codes.rafael.structuraltype (version 1.5)

Group: codes.rafael.structuraltype Artifact: structural-type-jackson
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Artifact structural-type-jackson
Group codes.rafael.structuraltype
Version 1.5
Last update 14. April 2023
Organization not specified
URL Not specified
License not specified
Dependencies amount 2
Dependencies structural-type-api, jackson-databind,
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structural-type-maven-plugin from group codes.rafael.structuraltype (version 1.5)

Group: codes.rafael.structuraltype Artifact: structural-type-maven-plugin
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Artifact structural-type-maven-plugin
Group codes.rafael.structuraltype
Version 1.5
Last update 14. April 2023
Organization not specified
URL Not specified
License not specified
Dependencies amount 4
Dependencies structural-type-generator, classgraph, maven-plugin-api, maven-core,
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structural-type-generator from group codes.rafael.structuraltype (version 1.5)

Group: codes.rafael.structuraltype Artifact: structural-type-generator
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Artifact structural-type-generator
Group codes.rafael.structuraltype
Version 1.5
Last update 14. April 2023
Organization not specified
URL Not specified
License not specified
Dependencies amount 2
Dependencies structural-type-api, javapoet,
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structural-type-api from group codes.rafael.structuraltype (version 1.5)

Group: codes.rafael.structuraltype Artifact: structural-type-api
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Artifact structural-type-api
Group codes.rafael.structuraltype
Version 1.5
Last update 14. April 2023
Organization not specified
URL Not specified
License not specified
Dependencies amount 0
Dependencies No dependencies
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structural-type from group codes.rafael.structuraltype (version 1.5)

This project aims to create a structural representation of similar nominal Java classes. A structural type is represented by an interface that contains property accessors for all properties that are defined by those classes. For each class, a projection class is generated that implements that interface for delegation to the nominal classes for which the structure was created. Additionally, a template implementation is created that implements the structural interface as a Java bean.

Group: codes.rafael.structuraltype Artifact: structural-type
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Artifact structural-type
Group codes.rafael.structuraltype
Version 1.5
Last update 14. April 2023
Organization not specified
URL https://github.com/raphw/skatteprosessen-structural-type
License Apache License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
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winnow from group nz.ac.waikato.cms.weka (version 1.0.2)

Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318; N. Littlestone (1989). Mistake bounds and logarithmic linear-threshold learning algorithms. University of California, Santa Cruz. Does classification for problems with nominal attributes (which it converts into binary attributes)

Group: nz.ac.waikato.cms.weka Artifact: winnow
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1 downloads
Artifact winnow
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/winnow
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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hotSpot from group nz.ac.waikato.cms.weka (version 1.0.14)

HotSpot learns a set of rules (displayed in a tree-like structure) that maximize/minimize a target variable/value of interest. With a nominal target, one might want to look for segments of the data where there is a high probability of a minority value occuring (given the constraint of a minimum support). For a numeric target, one might be interested in finding segments where this is higher on average than in the whole data set. For example, in a health insurance scenario, find which health insurance groups are at the highest risk (have the highest claim ratio), or, which groups have the highest average insurance payout.

Group: nz.ac.waikato.cms.weka Artifact: hotSpot
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447 downloads
Artifact hotSpot
Group nz.ac.waikato.cms.weka
Version 1.0.14
Last update 09. August 2021
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/hotSpot
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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RBFNetwork from group nz.ac.waikato.cms.weka (version 1.0.8)

RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many parameters, and to use normalized basis functions instead of unnormalized ones.

Group: nz.ac.waikato.cms.weka Artifact: RBFNetwork
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11 downloads
Artifact RBFNetwork
Group nz.ac.waikato.cms.weka
Version 1.0.8
Last update 16. January 2015
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/RBFNetwork
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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denormalize from group nz.ac.waikato.cms.weka (version 1.0.3)

An instance filter that collapses instances with a common grouping ID value into a single instance. Useful for converting transactional data into a format that Weka's association rule learners can handle. IMPORTANT: assumes that the incoming batch of instances has been sorted on the grouping attribute. The values of nominal attributes are converted to indicator attributes. These can be either binary (with f and t values) or unary with missing values used to indicate absence. The later is Weka's old market basket format, which is useful for Apriori. Numeric attributes can be aggregated within groups by computing the average, sum, minimum or maximum.

Group: nz.ac.waikato.cms.weka Artifact: denormalize
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Artifact denormalize
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 29. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/denormalize
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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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,
There are maybe transitive dependencies!



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