seracio / kohonen Goto Github PK

As we use the same datas in the constructor, training and mapping phase...

We can improve performance by removing sqrt

Replace this line to import { PCA } from 'ml-pca'; please.
mljs/pca uses named export

I'm just trying out this library (thanks for sharing!), and would like to create a demonstrator of the algorithm.

For this I would like to be able to "pause" every few steps and display the current state of the system. log() seems to get the state of the neurons only.

Here's a (very simple and unfinished) demo
http://bl.ocks.org/Fil/ae11126ae728cb2af627db6a3dfa756b

First tests and all datas are converging to the same neuron...

The API must be more modular and optional,
As @cbanbury noted in #43, this library is probably buggy, it's time to reconsider the design options

The weights of the neurons are initialized either to small random values or sampled evenly from the subspace spanned by the two largest principal component eigenvectors

https://en.wikipedia.org/wiki/Principal_component_analysis

Iterate over the given datas will be more efficient than attempting to generate close data

Add this as a resource

Hi,

Are you still actively working on this? I've been playing with this package using spectral data and seems to hang with very large arrays (3000).

How far would you reasonably expect this to scale?

in v0.7.0, we've fixed an issue, but this deserves more tests

I've been playing with this a bit more and it works well for the canonical example of mapping colours. However, when I feed data with more variables (~40) into the SOM, all of the inputs tend to converge on a single neuron.

You seem to have had this issue before with: #17, I'm wondering if it is again related to normalisation?

Should probably have:

• options for normalisation and re evaluate normalisation strategy
• more extensive tests for data with larger vectors

• with hexagons

Actually, you can use findBestMatchingUnit, but it assums that your vector is already normalized...

This will be useful to get vector positions from data others than the training set

The API should be more modular, basically, I think that every process step should be overridable thanks to a sub class

We should use a [0,1] normalization I guess