Simply put, destructuring in Clojure is a way extract values from a datastructure and bind them to symbols, without having to explicitly traverse the datstructure. It allows for elegant and concise Clojure code.
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;; An (imaginary) actor macro takes an initial state and callback fns. | |
;; (actor {} (fn1 [state message]) (fn2 [state message) ...) | |
;; The most obvious callback fn is (receive [state message) that is called when a | |
;; message is consumed from the agents mailbox. receive is called with the old state | |
;; and the message as parameters -- it returns the new state. | |
;; sender is bound to the senders pid. | |
;; other callbacks are stuff broken link detection etc. |
Inspired by "Parsing CSS with Parsec".
Just quick notes and code that you can play with in REPL.
By @kachayev
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// Use Gists to store code you would like to remember later on | |
console.log(window); // log the "window" object to the console |
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(comment ; Fun with transducers, v2 | |
;; Still haven't found a brief + approachable overview of Clojure 1.7's new | |
;; transducers in the particular way I would have preferred myself - so here goes: | |
;;;; Definitions | |
;; Looking at the `reduce` docstring, we can define a 'reducing-fn' as: | |
(fn reducing-fn ([]) ([accumulation next-input])) -> new-accumulation | |
;; (The `[]` arity is actually optional; it's only used when calling | |
;; `reduce` w/o an init-accumulator). |
Inspired by "Parsing CSS with Parsec".
Just quick notes and code that you can play with in REPL.
By @kachayev
- General Background and Overview
- Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
- Models and Issues in Data Stream Systems
- Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
- Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
- [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep