RSNNS v0.4-12
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Neural Networks using the Stuttgart Neural Network Simulator (SNNS)
The Stuttgart Neural Network Simulator (SNNS) is a library
containing many standard implementations of neural networks. This
package wraps the SNNS functionality to make it available from
within R. Using the 'RSNNS' low-level interface, all of the
algorithmic functionality and flexibility of SNNS can be accessed.
Furthermore, the package contains a convenient high-level
interface, so that the most common neural network topologies and
learning algorithms integrate seamlessly into R.
Functions in RSNNS
| Name | Description | |
| SnnsRObject$createPatSet | Create a pattern set | |
| SnnsRObjectMethodCaller | Method caller for SnnsR objects | |
| SnnsRObject$getCompleteWeightMatrix | Get the complete weight matrix. | |
| elman | Create and train an Elman network | |
| SnnsRObject$getAllUnitsTType | Get all units in the net of a certain ttype. | |
| SnnsRObjectFactory | SnnsR object factory | |
| dlvq | Create and train a dlvq network | |
| encodeClassLabels | Encode a matrix of (decoded) class labels | |
| exportToSnnsNetFile | Export the net to a file in the original SNNS file format | |
| SnnsRObject$resetRSNNS | Reset the SnnsR object. | |
| SnnsRObject$setTTypeUnitsActFunc | Set the activation function for all units of a certain ttype. | |
| decodeClassLabels | Decode class labels to a binary matrix | |
| readResFile | Rudimentary parser for res files. | |
| train | Internal generic train function for rsnns objects | |
| print.rsnns | Generic print function for rsnns objects | |
| getSnnsRDefine | Get a define of the SNNS kernel | |
| vectorToActMap | Convert a vector to an activation map | |
| resolveSnnsRDefine | Resolve a define of the SNNS kernel | |
| denormalizeData | Revert data normalization | |
| getSnnsRFunctionTable | Get SnnsR function table | |
| rbf | Create and train a radial basis function (RBF) network | |
| plotRegressionError | Plot a regression error plot | |
| artmap | Create and train an artmap network | |
| SnnsRObject$whereAreResults | Get a list of output units of a net | |
| mlp | Create and train a multi-layer perceptron (MLP) | |
| SnnsRObject$predictCurrPatSet | Predict values with a trained net | |
| art2 | Create and train an art2 network | |
| SnnsRObject$initializeNet | Initialize the network | |
| SnnsRObject$train | Train a network and test it in every training iteration | |
| normTrainingAndTestSet | Function to normalize training and test set | |
| matrixToActMapList | Convert matrix of activations to activation map list | |
| normalizeData | Data normalization | |
| SnnsRObject$getTypeDefinitions | Get the FType definitions of the network. | |
| SnnsRObject$somPredictCurrPatSetWinners | Get most of the relevant results from a som | |
| SnnsRObject$somPredictCurrPatSetWinnersSpanTree | Get the spanning tree of the SOM | |
| SnnsRObject$getUnitDefinitions | Get the unit definitions of the network. | |
| confusionMatrix | Computes a confusion matrix | |
| SnnsRObject$getInfoHeader | Get an info header of the network. | |
| assoz | Create and train an (auto-)associative memory | |
| som | Create and train a self-organizing map (SOM) | |
| SnnsRObject$getSiteDefinitions | Get the sites definitions of the network. | |
| predict.rsnns | Generic predict function for rsnns object | |
| SnnsRObject$getUnitsByName | Find all units whose name begins with a given prefix. | |
| SnnsRObject$getWeightMatrix | Get the weight matrix between two sets of units | |
| setSnnsRSeedValue | DEPRECATED, Set the SnnsR seed value | |
| SnnsRObject$setUnitDefaults | Set the unit defaults | |
| SnnsRObject$somPredictComponentMaps | Calculate the som component maps | |
| snnsData | Example data of the package | |
| extractNetInfo | Extract information from a network | |
| art1 | Create and train an art1 network | |
| analyzeClassification | Converts continuous outputs to class labels | |
| getNormParameters | Get normalization parameters of the input data | |
| splitForTrainingAndTest | Function to split data into training and test set | |
| inputColumns | Get the columns that are inputs | |
| summary.rsnns | Generic summary function for rsnns objects | |
| savePatFile | Save data to a pat file | |
| rsnnsObjectFactory | Object factory for generating rsnns objects | |
| weightMatrix | Function to extract the weight matrix of an rsnns object | |
| jordan | Create and train a Jordan network | |
| plotActMap | Plot activation map | |
| plotIterativeError | Plot iterative errors of an rsnns object | |
| outputColumns | Get the columns that are targets | |
| plotROC | Plot a ROC curve | |
| rbfDDA | Create and train an RBF network with the DDA algorithm | |
| readPatFile | Load data from a pat file | |
| toNumericClassLabels | Convert a vector (of class labels) to a numeric vector | |
| SnnsRObject$getAllOutputUnits | Get all output units of the net. | |
| SnnsRObject$getAllUnits | Get all units present in the net. | |
| RSNNS-package | Getting started with the RSNNS package | |
| SnnsRObject$getAllInputUnits | Get all input units of the net | |
| SnnsRObject$createNet | Create a layered network | |
| SnnsRObject$getAllHiddenUnits | Get all hidden units of the net | |
| SnnsR-class | The main class of the package | |
| SnnsRObject$extractNetInfo | Get characteristics of the network. | |
| SnnsRObject$extractPatterns | Extract the current pattern set to a matrix | |
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Details
| License | LGPL (>= 2) | file LICENSE |
| LinkingTo | Rcpp |
| Type | Package |
| LazyLoad | yes |
| Copyright | Original SNNS software Copyright (C) 1990-1995 SNNS Group, IPVR, Univ. Stuttgart, FRG; 1996-1998 SNNS Group, WSI, Univ. Tuebingen, FRG. R interface Copyright (C) DiCITS Lab, Sci2s group, DECSAI, University of Granada. |
| URL | https://github.com/cbergmeir/RSNNS |
| BugReports | https://github.com/cbergmeir/RSNNS/issues |
| MailingList | rsnns@googlegroups.com |
| Date | 2019-09-16 |
| Encoding | UTF-8 |
| RoxygenNote | 6.1.0 |
| NeedsCompilation | yes |
| Packaged | 2019-09-16 23:27:46 UTC; bergmeir |
| Repository | CRAN |
| Date/Publication | 2019-09-17 04:40:13 UTC |
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