Eleonore9

(custom-set-faces
 ;; custom-set-faces was added by Custom.
 ;; If you edit it by hand, you could mess it up, so be careful.
 ;; Your init file should contain only one such instance.
 ;; If there is more than one, they won't work right.
 '(rainbow-blocks-depth-1-face ((t (:foreground "SpringGreen4"))))
 '(rainbow-blocks-depth-2-face ((t (:foreground "dark cyan"))))
 '(rainbow-blocks-depth-3-face ((t (:foreground "deep sky blue"))))
 '(rainbow-blocks-depth-4-face ((t (:foreground "slate blue"))))
 '(rainbow-blocks-depth-5-face ((t (:foreground "medium orchid"))))

jarohen

;; We can write map and filter in terms of reduce

(defn my-map [f coll]
  (reduce (fn [acc el]
            (conj acc (f el)))
          []
          coll))

(defn my-filter [pred coll]
  (reduce (fn [acc el]

cgrand

(ns foami.core
  "FOreign Asynchronous Mechanism Interop"
  (:require [clojure.core.async :as async]))

(def ^:private abandon (doto (async/chan) async/close!))

(def ^:private pending-writes (async/chan))

(defn put!
  "Tries to put a val into chan, returns either: true if put succeeded, false if chan is

avescodes

Check out README.md to get started editing Clojure with Emacs.

hugoduncan

  <appender name="SIFT" class="ch.qos.logback.classic.sift.SiftingAppender">
    <discriminator>
      <key>target</key>
      <defaultValue>unspecified</defaultValue>
    </discriminator>
    <sift>
      <appender name="FILE-${target}" class="ch.qos.logback.core.FileAppender">
        <file>logs/target-${target}.log</file>
        <append>false</append>
        <encoder>

mattb

Here are the areas I've been researching, some things I've read and some open source packages...

Nearly all text processing starts by transforming text into vectors: http://en.wikipedia.org/wiki/Vector_space_model

Often it uses transforms such as TFIDF to normalise the data and control for outliers (words that are too frequent or too rare confuse the algorithms): http://en.wikipedia.org/wiki/Tf%E2%80%93idf

Collocations is a technique to detect when two or more words occur more commonly together than separately (e.g. "wishy-washy" in English) - I use this to group words into n-gram tokens because many NLP techniques consider each word as if it's independent of all the others in a document, ignoring order: http://matpalm.com/blog/2011/10/22/collocations_1/

kelsey-sorrels

(ns overtone.examples.internal-sequencer
  (:use [overtone.live]))

;; A fully server-side sample sequencer.
;; =====================================

;; This example demonstrates some of the benefits of moving all synth
;; triggers inside the server itself. For example, it allows you to
;; modify the synthesis with *immediate* effect (rather than waiting for
;; the next bar/chunk to be scheduled) and you can use a global pulse to

sritchie

package forma;

import forma.WholeFileInputFormat;
import cascading.scheme.Scheme;
import cascading.tap.Tap;
import cascading.tuple.Fields;
import cascading.tuple.Tuple;
import cascading.tuple.TupleEntry;
import java.io.IOException;
import org.apache.hadoop.mapred.JobConf;

noidi

      <plugin>
        <artifactId>maven-assembly-plugin</artifactId>
        <executions>
          <execution>
            <id>jar-with-dependencies</id>
            <phase>package</phase>
            <goals>
              <goal>single</goal>
            </goals>
            <configuration>