yurivish

import Base: isempty, start, next, done, length, getindex, minimum, maximum, search, show

export Treap, TreapNode

type TreapNode{K}
	priority::Float64
	size::Int
	key::K
	left::TreapNode{K}
	right::TreapNode{K}

owainlewis

import Control.Monad
import Data.Char
import qualified Data.List.Key as K
import System.Random
 
data Treap k a = Nil | Node Int k a (Treap k a) (Treap k a)
 
priority :: Treap k a -> Int
priority Nil = -1
priority (Node p _ _ _ _) = p

sbos

module HMM

using Distributions

import Distributions.rand
import Distributions.fit

immutable HiddenMarkovModel{TP, K}
    theta::Vector{TP}
    A::Matrix{Float64}

lqdc

# Author: Roman Sinayev
# License: BSD Style.
# Viterbi algorithm implementation and example written in Julia Language
#
# The idea is to decode the most probable order of a hidden markov model 
# based on indirect observations.

# In the example below, we attempt to determine where a person is based on
# their cell phone service strength.  We also have fake probabilities associated
# with each state(location) and with transitions between locations and with

dvanhorn

#lang racket

;; 0CFA in the AAM style on some hairy Church numeral churning

;; Moral: per machine-state store polyvariance is not viable,
;; but with widening it's not so bad.

;; (X -> Set X) -> (Set X) -> (Set X)
(define ((appl f) s)
  (for/fold ([i (set)])