Hidden relationships with javascript machine learning

Question

I have a long array of objects that are created to track daily actions.

Example:

[
    {name:'workout', duration:'120', enjoy: true, time:1455063275, tags:['gym', 'weights']},
    {name:'lunch', duration:'45', enjoy: false, time:1455063275, tags:['salad', 'wine']},
    {name:'sleep', duration:'420', enjoy: true, time:1455063275, tags:['bed', 'romance']}
]

I'm having a hard time understanding how to use this data in a neural network to predict if future actions would be enjoyable. Additionally, I want to find hidden relationships between various activities.

Not sure how to get the rubber on the road. How do I feed the network my array of objects and read the results?

If anyone can answer this within the context of https://github.com/cazala/synaptic that would be great. It's also super if the answer is a straight machine learning lesson.

Thanks all!

Answer 1

I've written some JavaScript libraries to some basic machine learning, if you're still looking to try some stuff out with your data set. Though the libraries meet the requirements of ML in JavaScript, they aren't ANNs.

Naive Bayes Model: https://github.com/csbuja/NaiveBayesJS

K Nearest Neighbors Model: https://github.com/csbuja/KNNJS