rbaron

#define N_SAMPLES 64

void setup()
{
  Serial.begin(9600);
  while (!Serial) delay(10);
}

void loop()
{

rbaron

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import collections\n",

rbaron

Md

rbaron

"""
This script is an example of learning parameters of a Bayesian network
with hidden variables. The following Bayesian net is analyzed:


 (hidden)     (observable)
    K -----------> A


Where K (knowledge) is not observable and A (answers), which depends on K, is.

rbaron

Keybase proof

I hereby claim:

• I am rbaron on github.
• I am rbaron (https://keybase.io/rbaron) on keybase.
• I have a public key whose fingerprint is B22D 7692 7828 E780 B3C4 8A20 28F6 64EB A5ED B223

To claim this, I am signing this object:

rbaron

import itertools 
 
def take(n, iterable): 
    return list(itertools.islice(iterable, n)) 
 
def enum_integers(start): 
    return itertools.count(start) 
 
def is_not_divisible_by(n, which): 
    return n%which != 0 

rbaron

trait Indiv[V <: Indiv[V]] {
  val fitness: Double
  def reproduce(other: V): V
}

class RealIndiv[V](override val fitness: Double) extends Indiv[V] {
  override def reproduce(other: V) = new RealIndiv(fitness + other.fitness)
                                                                  // ^ Cannot resolve symbol fitness (on `other.fitness`)
}

rbaron

trait Individual[T] {
  def mate(other: Individual[T]): Individual[T]
}

class Person[T] extends Individual[T] {

  // This "overrides nothing":
  override def mate(other: Person[T]): Person[T] = new Person[T]()
}

rbaron

package objsets

import common._
import TweetReader._

/**
 * A class to represent tweets.
 */
class Tweet(val user: String, val text: String, val retweets: Int) {
  override def toString: String =