truncs

static inline int ext2_inode_is_fast_symlink(struct inode *inode)
  {
          int ea_blocks = EXT2_I(inode)->i_file_acl ?
                  (inode->i_sb->s_blocksize >> 9) : 0;
  
          return (S_ISLNK(inode->i_mode) &&
                  inode->i_blocks - ea_blocks == 0);
  }

truncs

#!/bin/sh
feh --bg-scale /home/prudhvi/Downloads/wallpaper.jpg

while true; do
        xsetroot -name "`date '+%a %Y-%m-%d %H:%M:%S'`"
        sleep 1
done &
exec /usr/bin/dwm

truncs

bin/mallet import-dir --input /opt/data/* --output ~/obama.mallet


 bin/mallet train-classifier --input ~/media_elections.mallet --trainer MaxEnt --output-classifier ~/media_elections.classifier --training-portion 0.9


bin/mallet classify-dir --input /opt/data --output - --classifier ~/obama.classifier

truncs

#! /usr/bin/python

def merge(a, size):
    for i in range(0, len(a), size):
        tmp = a[i:size + i];print tmp;
        newtmp = []
        for j in range(0, len(tmp)):
            newtmp.append(tmp.pop(tmp.index(min(tmp))))
            print newtmp
        a[i:size + i] = newtmp

truncs

#! /usr/bin/python 

import unittest

class Trie(object):
    """ Trie implementation in python 
    """
    
    def __init__(self, ):
        """ So we use a dictionary at each level to represent a level in the hashmap

truncs

#include <iostream>
#include <vector>

using namespace std;

template <class T>
class Heap {
  vector<T> list;

  void bubbleUp();

truncs

#include <iostream>
#include <algorithm>
#include <string>

using namespace std;


int main(){
  int n;
  

truncs

#include <iostream>
#include <algorithm>
#include <string>
#include <cstdlib>

using namespace std;


int main(){
  int n;

truncs

#include <iostream>
#include <algorithm>
#include <string>
#include <cstdlib>

using namespace std;


int main(){
  int n;

truncs

# Transform days spent to log(1 + x)
y1_data$DaysInHospital_Y2 <- log1p(y1_data$DaysInHospital_Y2)

# Divide the set, 80% for train and 20% for test
indexes <- sample(1:nrow(y1_data), size=0.2*nrow(y1_data))
test <- y1_data[indexes,]
train <- y1_data[-indexes,]

# Remove unwanted features from both the sets
train <- subset(train, select=-c(MemberID_t, YEAR_t, DaysInHospital, trainset,  DaysInHospital_Y3, age_05, PayDelay_max, PayDelay_min, PayDelay_stdev, LOS_max, LOS_min,