dlwh

  private[hadoop] def runMapReduce[K1,V1,K2,V2,K3,V3](paths : Array[Path],
    m: Mapper[K1,V1,K2,V2],
    r: Reduce[K2,V2,K3,V3])
   (implicit mk2:Manifest[K2], mv2:Manifest[V2],
             mk3:Manifest[K3], mv3:Manifest[V3]) = {
    implicit val jobConf = new JobConf(conf, m.getFunClass); 

    val outputPath = genDir;

    jobConf.setJobName("SMR-"+outputPath.getName);

dlwh

A gnostic was seated before a grammarian. The grammarian
said, ‘A word must be one of three things: either it is a noun, a
verb, or a particle.’ The gnostic tore his robe and cried, “Alas!
Twenty years of my life and striving and seeking have gone to the
winds, for I laboured greatly in the hope that there was another
word outside of this. Now you have destroyed my hope.’ Though
the gnostic had already attained the word which was his purpose,
he spoke thus in order to arouse the grammarian.
 -- Rumi, trans. A.J. Arberry

dlwh

implicit class RichString(s: String) extends RandomAccessSeq[Char] {
  def apply(x: Int) = s.charAt(x); 
  // whatever
 
  var foo = 3;
  def twiddle = { foo += 1; foo }
}

yields

dlwh

implicit class RichString(s: String) extends RandomAccessSeq[Char] {
  def apply(x: Int) = s.charAt(x);
  // whatever
 
  var foo = 3;
  def twiddle = { foo += 1; foo }
}
 
yields
 

dlwh

def producerToIterator[L](prod : =>L):Iterator[L] = new Iterator[L] {
  private var current: Option[L] = None
  def hasNext = current != None || {
    current = prod match {
      case null => None
      case x => Some(x);
    };
    current != None;
  }
  

dlwh

def improve(image: Image, desc: String) = for {
  page = generateImprovementPage(image,desc);
  improvement <- runHits(page)
} yield improvement;

def vote(improvement: Improvement) = for {
  page = generateVotingPage(improvement);
  v <- runHits(page);
} yield v;

dlwh

>>I am getting stuck in interpreting joint and conditional probabilities while trying to understand some 
>>language modeling stuff.  Given w (the words in a corpus of documents), and z (the topics), and T (the number 
>>of topics), how would you interpret the following: (1) P(*w*|T), (2) P(*w*|*z*, T) and (3) P(*z*|*w*, T)? 
>>Thanks!

First, bayes rule:

p(z|w,t) = p(w|z,t)p(z|t)/p(w|t)

"the posterior probability is proportional to the prior probability (p(z|T)) times conditional likelihood (p(w|z,T))" The other term makes it normalize.

dlwh

package implicitfuck;
import metascala.Nats._;
import metascala.TLists._;

object ImplicitFuck {
  type < = _0 // Left
  type > = _1 // Right
  type |# = _2 // while
  type #| = _3 // end while
  type + = _4 // inc

dlwh

import java.util.*;

import it.unimi.dsi.fastutil.ints.IntOpenHashSet;

public class Stupid {
	
	public static void run() {	
     for(int N = 32; N<=1048576;N*=2) {
		int[] x = new int[N];
		Random rand = new Random();

dlwh

import scalala.Scalala._;
import scalala.tensor._;
import scalala.tensor.dense_;

object KMeans {
  // Returns the centers of the k clusters, and the clusters themselves
  def cluster(data: Seq[Vector], k: Int): Seq[Vector] = {
    var means = data.take(k);
    var converged = false;