Package: ANN2 2.3.4
ANN2: Artificial Neural Networks for Anomaly Detection
Training of neural networks for classification and regression tasks using mini-batch gradient descent. Special features include a function for training autoencoders, which can be used to detect anomalies, and some related plotting functions. Multiple activation functions are supported, including tanh, relu, step and ramp. For the use of the step and ramp activation functions in detecting anomalies using autoencoders, see Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore, several loss functions are supported, including robust ones such as Huber and pseudo-Huber loss, as well as L1 and L2 regularization. The possible options for optimization algorithms are RMSprop, Adam and SGD with momentum. The package contains a vectorized C++ implementation that facilitates fast training through mini-batch learning.
Authors:
ANN2_2.3.4.tar.gz
ANN2_2.3.4.zip(r-4.5)ANN2_2.3.4.zip(r-4.4)ANN2_2.3.4.zip(r-4.3)
ANN2_2.3.4.tgz(r-4.4-x86_64)ANN2_2.3.4.tgz(r-4.4-arm64)ANN2_2.3.4.tgz(r-4.3-x86_64)ANN2_2.3.4.tgz(r-4.3-arm64)
ANN2_2.3.4.tar.gz(r-4.5-noble)ANN2_2.3.4.tar.gz(r-4.4-noble)
ANN2_2.3.4.tgz(r-4.4-emscripten)ANN2_2.3.4.tgz(r-4.3-emscripten)
ANN2.pdf |ANN2.html✨
ANN2/json (API)
| # Install 'ANN2' in R: |
| install.packages('ANN2', repos = c('https://bflammers.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bflammers/ann2/issues
anomaly-detectionartificial-neural-networksautoencodersneural-networksrobust-statistics
Last updated 4 years agofrom:be9809f7de. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 28 2024 |
| R-4.5-win-x86_64 | NOTE | Aug 28 2024 |
| R-4.5-linux-x86_64 | NOTE | Aug 28 2024 |
| R-4.4-win-x86_64 | NOTE | Aug 28 2024 |
| R-4.4-mac-x86_64 | NOTE | Aug 28 2024 |
| R-4.4-mac-aarch64 | NOTE | Aug 28 2024 |
| R-4.3-win-x86_64 | NOTE | Aug 28 2024 |
| R-4.3-mac-x86_64 | NOTE | Aug 28 2024 |
| R-4.3-mac-aarch64 | NOTE | Aug 28 2024 |
Exports:autoencodercompression_plotdecodeencodeneuralnetworkread_ANNreconstructreconstruction_plottrainwrite_ANN
Dependencies:briocallrclicolorspacecrayondescdiffobjdigestevaluatefansifarverfsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgbuildpkgconfigpkgloadplyrpraiseprocessxpsR6RColorBrewerRcppRcppArmadillorematch2reshape2rlangrprojrootscalesstringistringrtestthattibbleutf8vctrsviridisLitewaldowithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Rcpp module exposing C++ class ANN | ANN Rcpp_ANN-class |
| Train an Autoencoding Neural Network | autoencoder |
| Compression plot | compression_plot compression_plot.ANN |
| Decoding step | decode decode.ANN |
| Encoding step | encode encode.ANN |
| Train a Neural Network | neuralnetwork |
| Plot training and validation loss | plot.ANN |
| Make predictions for new data | predict.ANN |
| Print ANN | print.ANN |
| Read ANN object from file | read_ANN |
| Reconstruct data using trained ANN object of type autoencoder | reconstruct |
| Reconstruction plot | reconstruction_plot reconstruction_plot.ANN |
| Continue training of a Neural Network | train |
| Write ANN object to file | write_ANN |
