Hellinger Distance for ALEC: Calculate distance.

Implement Hellinger Distance calculation using dummy data. This is a POC step to learn how to calculate it. Once this is achieved, we can proceed to train with real data and implement into ALEC for comparison purposes.

GOALS:

• Learn the correct design of the neural network that helps fit timestamps to a distribution.
• Use the parameters learnt by the neural network to calculate Hellinger Distance.

DONE CRITERIA:

• Generate dummy data.
• Write and test Neural network that learns values necessary to calculate Hellinger Distance.
• Calculate Hellinger Distance using with dummy data.

This doesn't include:

• Training with "real" data.
• ALEC implementation.
• Comparison with current distance measures for either clustering (DBSCAN) or classification (Deep Learning) engines.