Introduction | Method | Appendix
Package: mactivate 0.6.6
mactivate: Multiplicative Activation
Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models. M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model. M-activation can detect high-order interactions -- a traditionally non-trivial challenge. Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About."
Authors:
mactivate_0.6.6.tar.gz
mactivate_0.6.6.zip(r-4.7-x86_64)mactivate_0.6.6.zip(r-4.7-arm64)mactivate_0.6.6.zip(r-4.6-x86_64)mactivate_0.6.6.zip(r-4.6-arm64)mactivate_0.6.6.zip(r-4.5-x86_64)
mactivate_0.6.6.tgz(r-4.6-x86_64)mactivate_0.6.6.tgz(r-4.6-arm64)mactivate_0.6.6.tgz(r-4.5-x86_64)mactivate_0.6.6.tgz(r-4.5-arm64)
mactivate_0.6.6.tar.gz(r-4.7-arm64)mactivate_0.6.6.tar.gz(r-4.7-x86_64)mactivate_0.6.6.tar.gz(r-4.6-arm64)mactivate_0.6.6.tar.gz(r-4.6-x86_64)
mactivate_0.6.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mactivate/json (API)
| # Install 'mactivate' in R: |
| install.packages('mactivate', repos = c('https://davezes.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- df_hospitals_ortho - Orthopedic Device Sales
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:6c6cfa6126. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 110 | ||
| linux-devel-x86_64 | OK | 95 | ||
| source / vignettes | OK | 166 | ||
| linux-release-arm64 | OK | 98 | ||
| linux-release-x86_64 | OK | 92 | ||
| macos-release-arm64 | OK | 80 | ||
| macos-release-x86_64 | OK | 142 | ||
| macos-oldrel-arm64 | OK | 74 | ||
| macos-oldrel-x86_64 | OK | 239 | ||
| windows-devel-arm64 | OK | 315 | ||
| windows-devel-x86_64 | OK | 110 | ||
| windows-release-arm64 | OK | 367 | ||
| windows-release-x86_64 | OK | 69 | ||
| windows-oldrel-x86_64 | OK | 70 | ||
| wasm-release | OK | 65 |
Exports:f_control_mactivatef_dmss_dWf_fit_gradient_01f_fit_gradient_logistic_01f_fit_hybrid_01f_logit_costf_mactivatepredict.mactivate_fit_gradient_01predict.mactivate_fit_gradient_logistic_01predict.mactivate_fit_hybrid_01
Dependencies:
mactivate-about
Last update: 2021-01-08
Started: 2021-01-08
mactivate-examples-1
Example: Small Data, Categorical Inputs, Numerical Response | Example: Small Data, Categorical Inputs, Dichotomous Response
Last update: 2021-01-08
Started: 2021-01-08
mactivate-examples-2
Example: Medium Data, Numerical Inputs, Numerical Response | Example: Orthopedic Sales Data, Numerical Inputs, Numerical Response, 10-Fold CV | Example: Medium Data, Numerical Inputs, Dichotomous Response
Last update: 2021-01-08
Started: 2021-01-08
mactivate-tutorial-1
Simple Example
Last update: 2021-01-08
Started: 2021-01-08
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| m-activation | mactivate-package mactivate |
| Orthopedic Device Sales | df_hospitals_ortho |
| Set Fitting Hyperparameters | f_control_mactivate |
| Calculate Derivative of Cost Function wrt W | f_dmss_dW |
| Fit Multivariate Regression Model with mactivate Using Gradient Descent | f_fit_gradient_01 |
| Fit Logistic Multivariate Regression Model with mactivate Using Gradient Descent | f_fit_gradient_logistic_01 |
| Fit Multivariate Regression Model with mactivate Using Hybrid Method | f_fit_hybrid_01 |
| Logistic Cost | f_logit_cost |
| Map Activation Layer and Inputs to Polynomial Model Inputs | f_mactivate |
| Predict from Fitted Gradient Model | predict.mactivate_fit_gradient_01 |
| Predict from Fitted Gradient Logistic Model | predict.mactivate_fit_gradient_logistic_01 |
| Predict from Fitted Hybrid Model | predict.mactivate_fit_hybrid_01 |