An interactive version of a Reingold–Tilford tree. Click on the nodes to expand or collapse.
Pan and Zoom (mousewheel) to explore the randomized data.
This experiment uses d3.js, dpl.js and pyfy.js to simulate geometric brownian motion with brownian bridge between any "known points" (either given values or already-randomized values). Active caching is utilized to ensure that any base random number stays in place until the world is re-randomized (i.e. I keep filling in missing information). This means that when volatility or drift is changed, the path is recalculated using the previously established Gaussian random outcomes.
The blue dots are known monthly closing values for APPL and the gray line is the simulated brownian motion Pan and zoom to look deeper and further. As you move around the cache will grow larger, but pressing "randomize" will reset the world to a new initial vector.
license: gpl-3.0 |
license: gpl-3.0 |
license: gpl-3.0 |
Based on Sara Quigley's Curriculum Exploration
A random sample of 750 Abalone measurements from UCI Machine Learning Repository.
Name Data Type Meas. Description
---- --------- ----- -----------
Sex nominal M, F, and I (infant)
Length continuous mm Longest shell measurement
Diameter continuous mm perpendicular to length
Space station orbit data in Two-Element Format. Data collected at 1:40am PDT August 8, 2015.