projects

Prediction in Projection Using Google Search Trends MAY 2017

Using state-space reconstruction on a scalar time series, can Google search trends be predicted? Check out the Github repository or the project write-up.

 


Who are the scientists who oppose Trump’s travel ban? MAR 2017

I wrote an article about the scientists who have signed the online petition "Academics United Against Immigration Executive Order". Analysis done in Python using signatures and U.S. census data. Visualizations done in D3.

 


Bifurcation Diagram in D3 JAN 2017

I'm learning D3 in my information visualization course, and bifurcation diagrams in my chaotic dynamics course. Below is the bifurcation diagram for the logistic map. Drag a box around a portion of the plot you'd like to zoom in on. The code is based on this D3 example. Check it out on Github!

 


Flappy BI/O NOV-DEC 2016

Inspired by Seth Bling's (super awesome) MarI/O, we implemented Kenneth Stanley and Risto Miikkulainen's NeuroEvolution of Augmenting Topologies (NEAT) algorithm to try to win Flappy Bird. NEAT is a genetic algorithm for evolving neural networks, and it relies on three key principles: (1) tracking the history of genes to determine suitable networks to mate (called crossover), (2) evolving successful networks further (called speciation), and (3) starting from the simplest neural networks possible and complexifying only out of necessity.

Unlike Super Mario World, Flappy Bird navigates a randomly determined playing field, posing an interesting challenge for NEAT. Our code was built on top of an existing Flappy Bird pygame, and that's about it. We've implemented our own neural networks.

You can follow the steps below to watch our implementation of NEAT in action. You must install Python, pygame and git.

    git clone https://github.com/Brennan-M/5622_PacMan_NN.git
    git checkout NEAT_Master
    python flappy_driver.py

If you'd like to learn more, here is a video of our final presentation:

 


Anomalyzer SEP-NOV 2014

Probabilistic anomaly detection for time series written in Go. Blog post about the work featured on front-page of Hacker News August 13th.

Example

    conf := &anomalyzer.AnomalyzerConf{
        Sensitivity: 0.1,
        UpperBound:  5,
        LowerBound:  anomalyzer.NA, // ignore the lower bound
        ActiveSize:  1,
        NSeasons:    4,
        Methods:     []string{"diff", "fence", "highrank", "lowrank", "magnitude"},
    }

    // initialize with empty data or an actual slice of floats
    data := []float64{0.1, 2.05, 1.5, 2.5, 2.6, 2.55}
    anom, _ := anomalyzer.NewAnomalyzer(conf, data)

    // `Push(point)` automatically triggers a recalcuation of the
    // anomalous probability.  recalculation can also be triggered
    // by a call to `Eval()`.
    prob := anom.Push(8.0)
    fmt.Println("anomalous probability:", prob)

 


Multiclass Naive Bayesian Classification NOV-DEC 2014

Often in document classification, a document may have more than one relevant classification -- a question on stackoverflow might have tags go, map, and interface. While multinomial Bayesian classification offers a one-of-many classification, multibayes offers tools for many-of-many classification.

A simple use case for our naive Bayesian classifier is decribed in "Catching Clickbait: Using a Naive Bayesian Classifier in Go". Inspired by Paul Graham's "Plan for Spam", I scraped 10,000 headlines to train our classifier to recognize clickbait (e.g. “17 Facts You Won’t Believe Are True”, “18 Pugs Who Demand To Be Taken Seriously”, etc). At the end of this article is a fun interactive classifier where you can find posterior probability of a new headline being clickbait.

Example

documents := []struct {
    Text    string
    Classes []string
}{
    {
        Text:    "My dog has fleas.",
        Classes: []string{"vet"},
    },
    {
        Text:    "My cat has ebola.",
        Classes: []string{"vet", "cdc"},
    },
    {
        Text:    "Aaron has ebola.",
        Classes: []string{"cdc"},
    },
}

classifier := NewClassifier()

// train the classifier
for _, document := range documents {
    classifier.Add(document.Text, document.Classes)
}

// predict new classes
probs := classifier.Posterior("Aaron's dog has fleas.")
fmt.Printf("posterior probabilities: %+v\n", probs)

// posterior probabilities: map[vet:0.8571 cdc:0.2727]

 


Musical Staircase APR-MAY 2013

Built a musical staircase using an Arduino Uno and 16 pairs of lasers and photoresistors. Featured in Reed Magazine.