madaan

from nltk.corpus import brown
from nltk.lm import MLE
from nltk import bigrams
from nltk.lm.preprocessing import padded_everygram_pipeline

def get_model(order=2):
  lm = MLE(order)
  train, vocab = padded_everygram_pipeline(order=2, text=brown.sents())
  lm.fit(train, vocab)
  return lm

madaan

Castle Bill Split

• We have 8 people living in the castle. Thus, the bill gets split in 8 parts.

• Each part is thus 1/8

• 4 of these parts are paid by AS. Thus, AS pays 4/8th.

• Because AS pays 4/8th of the bill, we are left with 4/8th of the bill to pay.

madaan

1A
1B
1C
1D
2A
2B
2C
3
3
4A

madaan

madaan

/*
		 * Now start the agreeing k 
		 */
		//Different deltas for different relations
double delta = GoldDbInference.marginMap.get(lrg.relation);
int leastFlips = Integer.MAX_VALUE;
double bestCentralValue = -1;
	/**
	 * Find the optimal central value
	 */

madaan

rrb lrb xinhua aug tuesday wednesday dec official hail
afp lrb rrb 2009 2010 thursday dec friday urgent
particulate level 12 china pollution city reading air quality
stock percent point close share market index exchange higher
say deal source buy march arm asian 35 like
2010 rrb afp saturday lrb feb jan defend 25
year month 15 investment 10 direct foreign consumer 28
guinea country civilian rule african west junta attempt leader
late member early state president 1998 people away counterpart
japan japanese tokyo naoto kan honda yen shed expand

madaan

#include <SDL.h>
#include <iostream>
using namespace std;
int main(int argc, char ** argv)
{

    bool quit = false;
    SDL_Event event;
    int x = 288;
    int y = 208;

madaan

import java.io.File;
import java.io.IOException;
import java.util.Collection;
import java.util.Iterator;
import java.util.List;
import java.util.Properties;

import org.apache.commons.io.FileUtils;

import edu.stanford.nlp.ling.CoreAnnotations.SentencesAnnotation;

madaan

#sg
#This script iterates over documents in a directory and strips tags
#Thanks to http://stackoverflow.com/questions/753052/strip-html-from-strings-in-python

from HTMLParser import HTMLParser

class MLStripper(HTMLParser):
    def __init__(self):
        self.reset()
        self.fed = []

madaan

{
  "corpusPath" :
"jdbc:derby:/mnt/a99/d0/aman/multir-exp-second/MultirExperiments-master/data/FullCorpus-First100Sentences",
  "train" : "true",
  "testDocumentsFile" :
"data/emptyFile",
  "fg" :
"edu.washington.multirframework.featuregeneration.DefaultFeatureGenerator",
  "ai" :