Free training software for linux

gtrainer is a simple tool for manage sports trainings.
Program written in Gtkmm to manage sports trainings. It can store differents records about your daily training:
- Daily weight
- Characteristics of your exercise like: kilometers a day, hours a day, temperature, the route, etc.
- Specific training sessions: series, recoveries, heart rate, etc.
With the routes interface you can save your favourites routes as well. Find the largest route, or the hardest one easily.
You can save these records locally (sqlite3) or remotely (PostgreSQL, MySQL soon). You will know exactly how your training goes, with different kinds of charts and monthly reports, in order to improve your results or just as a curiosity.

Spam Trainer project makes it easier for GNOME users to train spam filtering software (e.g. SpamBayes or SpamAssassin) that "learns" how to tell the difference between real email (ham) and unsolicited messages (spam).

Not all email clients have spam filtering (and training) built in, and training external filtering programs can be fiddly.

Spam Trainer was written to provide easy (drag and drop) training from the Evolution mail client, and should work just as well with any email client that supports the drag and drop protocol implemented by the GTK+ toolkit.

Rally Point is a community collaboration site designed for homeowners associations (HOAs), apartment complexes, condominiums or any other group of people who need to share files, notes, FAQS, log information, financials or car registration information. The project is really simple, allowing you to get everyone up-to-speed without training or confusion.
Main features:
- Reduce costs by communicating by the web instead of mail
- Increase collaborative activities by getting everyone involved
- Solve parking problems
- Post financial information for each unit
- Post files
- Provide a convenient place to post frequently asked questions
- Update everyone automatically with RSS
- Extract the information in Rally Point for other uses with CSV and XML

Vocabulary Trainer project is a flashcard vocabulary training system.
You can use language modules to change the language of its interface and can choose one of three learning modes (Random, Often-unknown words, and Seldom-asked words).
Main features:
- Inquiry after foreign word or meaning
- Optional Attention of upper- and lowercase
- Limitation of time to simulate tests (optional)
- Saving of right, wrong and prompted vocabularies
- Inquiry after: Random order, as given, often unknown vocabularies, seldom asked vocabularies
- Multilingual Interface, changeable with languagefiles
- This program is Open Source, the Delphi-sourcecode kann be downloaded under "Downloads"
- Free of charge
- No further restrictions

Statistics::LTU is an implementation of Linear Threshold Units.

SYNOPSIS

use Statistics::LTU;

my $acr_ltu = new Statistics::LTU::ACR(3, 1); # 3 attributes, scaled

$ltu->train([1,3,2], $LTU_PLUS);
$ltu->train([-1,3,0], $LTU_MINUS);
...
print "LTU looks like this:n";
$ltu->print;

print "[1,5,2] is in class ";
if ($ltu->test([1,5,2]) > $LTU_THRESHOLD) { print "PLUS" }
else { print "MINUS" };

$ltu->save("ACR.saved") or die "Save failed!";
$ltu2 = restore Statistics::LTU("ACR.saved");

EXPORTS

For readability, LTU.pm exports three scalar constants: $LTU_PLUS (+1), $LTU_MINUS (-1) and $LTU_THRESHOLD (0).

Statistics::LTU defines methods for creating, destroying, training and testing Linear Threshold Units. A linear threshold unit is a 1-layer neural network, also called a perceptron. LTUs are used to learn classifications from examples.
An LTU learns to distinguish between two classes based on the data given to it. After training on a number of examples, the LTU can then be used to classify new (unseen) examples. Technically, LTUs learn to distinguish two classes by fitting a hyperplane between examples; if the examples have n features, the hyperplane will have n dimensions. In general, the LTUs weights will converge to a define the separating hyperplane.

The LTU.pm file defines an uninstantiable base class, LTU, and four other instantiable classes built on top of LTU. The four individual classes differs in the training rules used:

ACR - Absolute Correction Rule
TACR - Thermal Absolute Correction Rule (thermal annealing)
LMS - Least Mean Squares rule
RLS - Recursive Least Squares rule

Each of these training rules behaves somewhat differently. Exact details of how these work are beyond the scope of this document; see the additional documentation file (ltu.doc) for discussion.

AI::NNFlex::Reinforce is a very simple experimental NN module.

SYNOPSIS

use AI::NNFlex::Reinforce;

my $network = AI::NNFlex::Reinforce->new(config parameter=>value);

$network->add_layer(nodes=>x,activationfunction=>function);

$network->init();



use AI::NNFlex::Dataset;

my $dataset = AI::NNFlex::Dataset->new([
[INPUTARRAY],[TARGETOUTPUT],
[INPUTARRAY],[TARGETOUTPUT]]);

my $sqrError = 10;

for (1..100)

{

$dataset->learn($network);

}

$network->lesion({nodes=>PROBABILITY,connections=>PROBABILITY});

$network->dump_state(filename=>badgers.wts);

$network->load_state(filename=>badgers.wts);

my $outputsRef = $dataset->run($network);

my $outputsRef = $network->output(layer=>2,round=>1);

Reinforce is a very simple NN module. Its mainly included in this distribution to provide an example of how to subclass AI::NNFlex to write your own NN modules. The training method strengthens any connections that are active during the run pass.