hula 0.0.0.1

Experimental learning methods. The first appearance of these methods.

hula-0.0.0.0

What is hula?

Hula is a set of unconventional machine learning modules. Hula is designed to unify the desired aspects of most machine learning capabilities out there.

Within hula are a few machine learning algorithms which I've coined as the following:

• Deep Recursive Learning
• Reinforcement via Similarity
• Comprehensive Learning

Respectively, the modules are named:

• RecursiveL
• ReinforcementL
• ComprehensiveNet

I will be working on a blog to explain the workings behind each algorithm.

Documentation

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hula.ComprehensiveNet.CNET(design)

Generates an Artificial Neural Network comprised of Memory Activation nodes corresponding to the dimensions of design

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hula.ComprehensiveNet.CNET.activate(X)

Feeds X through the network and returns the output of the last layer.

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hula.ComprehensiveNet.CNET.act(alpha)

Generates a limbo-action proportional to alpha for each memory node in the network.

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hula.ComprehensiveNet.CNET.score(score)

Retroactively scores each limbo-action and turns them into state-actions. Higher scores are favored.

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hula.ComprehensiveNet.CNET.train(alpha)

Finds the lowest distance to each action's state minus the score of that action.

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hula.ComprehensiveNet.CNET.trim(perc)

removes perc percent of state-actions from the state-action tree

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