t20100

MLWave

# Author: HJ van Veen <info@mlwave.com>
# Description: Experiment to learn a tSNE transformer for new 
#              test data with a multi-output GBM
#
# Idea first seen at lvdmaaten.github.io/tsne
# > [...] it is not possible to embed test points in an existing 
# > map [...]
# > A potential approach to deal with this would be to train 
# > a multivariate regressor to predict the map location from 
# > the input data.

zsxwing

package rx.android.observables;

import rx.Observable;
import rx.Observable.OnSubscribe;
import rx.Subscriber;
import rx.android.subscriptions.AndroidSubscriptions;
import rx.functions.Action0;
import android.content.BroadcastReceiver;
import android.content.Context;
import android.content.Intent;

emeeks

The range sliders at the top change the values for the force-directed algorithm and the buttons load new graphs and apply various techniques. This will hopefully serve as a tool for teaching network analysis and visualization principles during my Gephi courses and general Networks in the Humanities presentations.

Notice this includes a pretty straightforward way to load CSV node and edge lists as exported from Gephi.

It also includes a pathfinding algorithm built for the standard data structure of force-directed networks in D3. This requires the addition of .id attributes for the nodes, however.

Now with Clustering Coefficients!

Also, it loads images for nodes but the images are not in the gist. The code also refers to different network types but the data files on Gist only refer to the transportation network.