avibryant

SystemOrganization addCategory: #'Diplomatik-Game'!
SystemOrganization addCategory: #'Diplomatik-Judge'!
SystemOrganization addCategory: #'Diplomatik-Users'!
SystemOrganization addCategory: #'Diplomatik-UI'!

WAComponent subclass: #DAdmin
	instanceVariableNames: 'password'
	classVariableNames: ''
	poolDictionaries: ''
	category: 'Diplomatik-UI'!

avibryant

It had been 13 days since I'd found a spreader, and I was starting to get worried.

The last time my sero levels had been this low, I'd gone down to the arrivals level of the international terminal at the airport. I mean, obviously there wouldn't be any spreaders there unless someone screwed up the pre-flight screening. But they say that low viral loads of unfamiliar strains could still boost you in a pinch, so I scanned the room for the most exotic-looking passengers I could find and tried to cozy up to them. Once, I managed to lick the handles of a few suitcases going around the carousel without too many people noticing.

Anyway, I guess what they say is true, because my sero bounced up. But I think I got lucky; there's no substitute for prolonged contact with someone who's gone full sympto.

I texted my doctor again.

ffs you're literally killing me here

avibryant

val a = Real.latent{a => a.mean(0) && a.stdDev(1)}
val b = Real.latent{b => b.mean(0) && b.stdDev(1)}

val xs: Seq[Double] = ???
val ys: Seq[Long] = ???

Model.observe(xs, ys){(x,y)=> 
  y.mean(a + x*b)
}

avibryant

Positive.median(x)

#Exponential
Positive.mean(x)

Positive.countAndRate(n, x)

#LogNormal
Positive.meanAndStdDevOfLog(x,y)

avibryant

Mean(x).stdDev(y)
Mean(x).stdDev(y).exp
Mean(x).absErr(y)
Mean(x).positive
Median(x).positive
Median(x).iqr(y)
Probability(p).successes(n)
Probability(p).stdDev(y)
Probability(p).stdDev(y).successes(n)

avibryant

attacker = ARGV[0].to_i
defender = ARGV[1].to_i

def roll(k, n)
    (1..k).map{rand(6)}.sort.reverse.first(n)
end

while attacker > 0 && defender > 0
    sleep 1
    attacker_dice = [attacker, 3].min

avibryant

case class DensityPlot(
    nRows: Int = 20,
    nColumns: Int = 80,
    xLabelWidth: Int = 9,
    yLabelWidth: Int = 9,
    yLabelEvery: Int = 5,
    logX: Boolean = false,
    logY: Boolean = false,
    logMarkers: Boolean = false,
    markers: String = "·∘⚬") {

avibryant

//A type-level implementation of the broadcasting rules from NumPy,
//such that incompatible shapes are a compile-time error.
//see eg http://scipy.github.io/old-wiki/pages/EricsBroadcastingDoc
//for more info on what the constraints this is enccoding

//Note: this is only the shape logic. It does not include,
//and is agnostic to, any particular multi-dimensional array implementation.

sealed trait Shape
sealed trait Dimension extends Shape

avibryant

An idiosyncratic guide to teaching yourself practical machine learning, without links:

• Find a binary classification dataset; maybe you have one internally.
• Implement a simple decision tree algorithm, like CART.
• Write some code to validate your model; produce an ROC curve and understand the tradeoff it embodies.
• Compare the ROC for your training set with the ROC for a holdout and understand what it means that they differ.
• Experiment with some hyperparameters: how does the comparison above change as you adjust the depth of the tree or other stopping criteria?
• Combine your decision tree algorithm with bagging to produce a random forest. How does its ROC compare?
• Do the same hyperparameter tuning here. (How many trees?) Reflect on overfitting and on the bias/variance tradeoff.

avibryant

def takeBy(pipe: TypedPipe[(K,V)], max: Int)(fn: V => Double): TypedPipe[(K,V)] = {
  implicit val qtreeSemi = QTreeSemigroup(4) //magic number, determines how much RAM the trees take 
  val qtrees = pipe.map{case (k,v) => k -> QTree(fn(v))}.sumByKey
  val maxV = qtrees.flatMap{case (k,q) =>
    if(q.size > max) {
      val targetQuantile = max.toDouble / q.size
      val (lower, upper) = q.quantileBounds(targetQuantile)
      Some(k -> upper) //this will give us at least max values; use lower to get at most max values
    }
    else