brain - neural network for STATA

brain is a no frills implementation of a backpropagation algorithm designed for a hassle free setup of multi-layered neural networks. After training the whole network can be saved/loaded using so called brain-files (default postfix .brn). The network is represented by a set of reserved matrices to provide transparent access to all components and to support older Stata versions. Additional functions facilitate the calculation of pseudo-marginal effects or signal through-put, but the main utility is of course prediction, i.e. for propensity scores or classification.

Prerequisites

STATA

Getting started

• Copy brain.ado, brain.sthlp, brainwin.plugin, brainunix.plugin and brainmac.plugin files into your ADO file directory (typically c:\ado)
• Call the help file within STATA: help brain
• Try out the examples provided by the help document by copying them into do-files

Unix and Mac plugins

The UNIX and MAC plugins do not support multiprocessing because of the erratic support of openmp among the distributions. To activate openmp the plugins need to be locally compiled (see: plugin/build.txt for instructions). The brain.c source code contains MP support, while brainsp.c renounces any openmp references.

NOTICE: The MAC plugin is not yet tested.

Version history

2024.05.17

• Correction of the help file pertaining the "brain fit" command
• small improvements of the code examples in the help file

2023.05.12

• Recompiled and tested the MAC plugin on a M1 machine (still no MP support for MAC).

2021.05.18

• The new norm command allows for specific normalization of groups of variables.
• The nonorm option of the define command skips normalization and testing.
• New command reset re-initilializes the weights without redefining the normalization.
• Default spread set from 0.5 to 0.25, as specified in the help file.

2021.02.16

• Recompiled the MAC plugin to exclude openmp libraries because of incompatible distributions.

2020.12.08

• Improved compatibility with older STATA versions (below 15) in terms of matrix sizes
• Fixed a bug that prevented random weight initialization
• Recompiled the UNIX plugin to exclude openmp libraries because of incompatible distributions.

2020.11.30

• Fixed a bug that prevented the usage of the fit command without a second variable.
• The fit function allows the specification of a threshold for binary one.

2020.10.07

• The define paramter raw prevents automatic normalization for already normalized data.

2020.04.28

• Revised documentation and program in regard of proper usage of weights.

2020.03.11

• All matrices can exceed matsize limitations by taking a detour over mata.
• The brain matrix fragmentation of the former solution (2020.03.02) is obsolete.
• The brain, load and save commands additionally verify the integrity of the matrix structure.

2020.03.02

• Replaced all mata components with C plugins supporting multiprocessing.
• The network can exceed the maximum matrix size of Stata.
• Syntax errors are now more consistent.
• New sp option deactivates multiprocessing if necessary.

2019.11.13

• The commands train and error support weights.
• New command fit calculates recall and precision for binary output.

2019.03.28

• Implementation of batch training.
• Changed training ouput to interval reports of real absolute errors instead of intermediate errors.
• nosort is now called noshuffle as shuffling is now applied before every iteration.
• The best option alway picks the best intermediate result in case of alternating errors.
• New command error reports the overall absolute error instead of using the train command with iter(0).
• signal now works as intended.

2018.11.21 (scc repository version)

• Initial version

Author

• Thorsten Doherr - ZEW