| (defn words [text] (re-seq #"[a-z]+" (.toLowerCase text))) | |
| (defn train [features] | |
| (reduce (fn [model f] (assoc model f (inc (get model f 1)))) {} features)) | |
| (def *nwords* (train (words (slurp "big.txt")))) | |
| (defn edits1 [word] | |
| (let [alphabet "abcdefghijklmnopqrstuvwxyz", n (count word)] | |
| (distinct (concat |
| #!/usr/bin/env python | |
| """ | |
| Shows git branches sorted by last commit date, noting when branch has been | |
| merged: | |
| $ git blast | |
| * master 33 minutes ago | |
| david 4 days ago [M] | |
| unholy-david-payments 4 days ago | |
| handsontable-2 5 days ago |
duplicates = multiple editions
A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen
A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen
| ;; Based on https://clojuredocs.org/clojure.core/condp#example-542692cbc026201cdc326bea | |
| (defn parse-arg [s] | |
| (condp (comp next re-matches) s | |
| #"([+-])(\w+)" :>> (fn [[f k]] [(keyword k) (= f "+")]) | |
| #"(\w+)=(\w+)" :>> (fn [[k v]] [(keyword k) v]) | |
| (throw (IllegalArgumentException. s)))) | |
| (parse-arg "+foo") ;=> [:foo true] | |
| (parse-arg "-foo") ;=> [:foo false] |
| Models | Examples |
|---|---|
| Display ads | Yahoo! |
| Search ads |
| Latency Comparison Numbers | |
| -------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns | |
| Send 1K bytes over 1 Gbps network 10,000 ns 0.01 ms | |
| Read 4K randomly from SSD* 150,000 ns 0.15 ms |
As configured in my dotfiles.
start new:
tmux
start new with session name:
A friend asked me for a few pointers to interesting, mostly recent papers on data warehousing and "big data" database systems, and I figured I'd share the list. This is biased and rather incomplete but maybe of interest to someone. While many are obvious choices, I think there are a few underappreciated gems.
###Dataflow/Stream Processing Engines:
Dryad--general-purpose distributed parallel dataflow engine
http://research.microsoft.com/en-us/projects/dryad/eurosys07.pdf
Google Dremel--columnar storage for fast queries (c.f. Impala)
http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/pubs/archive/36632.pdf