swannodette

--- src/clj/cljs/analyzer.clj	2012-11-17 01:01:55.712323358 -0500
+++ src/cljs/cljs/analyzer.cljs	2012-11-17 11:51:47.030558150 -0500
@@ -6,14 +6,23 @@
 ;   the terms of this license.
 ;   You must not remove this notice, or any other, from this software.
 
-(set! *warn-on-reflection* true)
+;; (set! *warn-on-reflection* true)
 
 (ns cljs.analyzer

swannodette

;;I'd like to use core.logic to rewrite "colloquial" plot specifications into full, correct specs from which the C2PO compiler can generate graphics.
;;A full C2PO plot spec looks like:

{:data [...] ;;A seq of maps
 :group :identity
 :stat :identity
 :mapping {} ;;A map from a geom's aesthetic to a key in the data maps, e.g., {:x :weight, :y :miles-per-gallon}
 :geom :point
 :scales {} ;;A map of aesthetic keywords to scales, e.g., {:x :linear}
 }

lynaghk

(rewrite-layer
 {:mapping {:y :mpg}
  :stat (stat/quantiles)})

;; =>

{:mapping {:upper :q75, :min :min, :lower :q25, :max :max, :middle :q50, :y :mpg}
 :stat #com.keminglabs/c2po$stat$quantiles {:dimension :mpg}
 :geom #com.keminglabs/c2po$geom$boxplot {}}

dvanhorn

#lang racket

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

;; + compilation phase
;; + lazy non-determinism
;; + specialized step & iterator 0m34.248s vs 0m16.339s
;; + compute store ∆s 0m16.339s vs 0m1.065s (!!!)

;; An Exp is one of:

Pet3ris

% Two dices are thrown and the results added up.
% We sample when the sum is conditioned on the first dice landing on 2.

output(S) :-
  randvar(a, dice(X), X),
  dice(Y),
  S is X + Y.

dice(X) :-
  X is random(6) + 1.

ninjudd

fallout:~ master$ time lein version
Leiningen 2.0.0-SNAPSHOT on Java 1.6.0_29 Java HotSpot(TM) 64-Bit Server VM

real	0m2.041s
user	0m5.020s
sys	0m0.189s

fallout:~ master$ time LEIN_JAVA_CMD=decaf lein version
Leiningen 2.0.0-SNAPSHOT on Java 1.6.0_29 Java HotSpot(TM) 64-Bit Server VM

dvanhorn

Memoization is fundamentally a top-down computation and dynamic
programming is fundamentally bottom-up.  In memoization, we observe
that a computational *tree* can actually be represented as a
computational *DAG* (the single most underrated data structure in
computer science); we then use a black-box to turn the tree into a
DAG.  But it allows the top-down description of the problem to remain
unchanged.

In dynamic programming, we make the same observation, but construct
the DAG from the bottom-up.  That means we have to rewrite the

swannodette

; y has upper bound x, z has upper bound (Seqable y)
typed.core=> (ann foo (All [x [y :< x] [z :< (Seqable y)]] 
                           [x y z -> Any]))
[typed.core/foo (All [x [y :< x] [z :< (clojure.lang.Seqable y)]] (Fn [x y z -> Any]))]
typed.core=> (declare foo)
#'typed.core/foo

;cf = check-form
typed.core=> (cf (foo 2 2 [2]))
Any

martintrojer

(defne all-distinctfd [l]
  ([()])
  ([[h . t]]
     (distinctfd h)
     (all-distinctfd t)))

(run 1 [q]
     (fresh [a1 a2 a3 a4
             b1 b2 b3 b4
             c1 c2 c3 c4

terjesb

(ns lucenalog.core
    "Lucenalog = Datalog interface to Lucene in 10 lines.

Simple but powerful.

Use

  (db/add (index) {:a \"foo\" :b \"bar\"})

to index a map with Lucene.  Then you can use the relation