Related
While preparing for an exam I came across a question about hash tables.
I am given a table of length 11 with the following hash function:
h(k,i) = ( k mod 13 + i * (1 + k mod 7) ) mod 11
The hash table is then resized to size 12. So the new hash function becomes:
h'(k,i) = ( k mod 13 + i * (1 + k mod 7) ) mod 12
Which problems occur?
The problem is that the hash function becomes worse.
In the first case, the distribution of different combinations of k and i is very even among the 11 hash bins. In the second case, the distribution is not so even - particularly, the number of combinations of k and i for which the result of the hash function will be 0 is noticably higher.
Of course, during an exam, one would probably have to argue why it is this way. It's somehow related to
k mod 13 being a value between 0 and 12
k mod 7 being a value between 0 and 6 (which divides 12)
maybe, somehow: 11 is a prime number and 12 has many divisors...
but (at least for me) it is hard to find a convincing reasoning that goes beyond these trivial insights. Maybe you have another idea based on that.
import java.util.LinkedHashMap;
import java.util.Map;
public class HashTest
{
public static void main(String[] args)
{
int maxK = 30;
int maxI = 30;
System.out.println(computeFrequencies(h0, maxK, maxI));
System.out.println(computeFrequencies(h1, maxK, maxI));
}
private static Map<Integer, Integer> computeFrequencies(
Hash hash, int maxK, int maxI)
{
Map<Integer, Integer> frequencies =
new LinkedHashMap<Integer, Integer>();
for (int k=0; k<maxK; k++)
{
for (int i=0; i<maxI; i++)
{
int value = hash.compute(k, i);
Integer count = frequencies.get(value);
if (count == null)
{
count = 0;
}
frequencies.put(value, count+1);
}
}
return frequencies;
}
private static interface Hash
{
int compute(int k, int i);
}
private static final Hash h0 = new Hash()
{
#Override
public int compute(int k, int i)
{
return ((k % 13) + i * (1 + (k % 7))) % 11;
}
};
private static final Hash h1 = new Hash()
{
#Override
public int compute(int k, int i)
{
return ((k % 13) + i * (1 + (k % 7))) % 12;
}
};
}
I'm working on a game which has tank battles on a tiled map. If a tank is on a cell, that cell is considered unpassable in the A* algorithm, therefore, whenever an unit needs to attack another, I need to plan a path which brings the attacker into range (if range=1, then next to the target).
Currently, I use an iterative approach with increasing radius to find a path to a nearby cell and choose a cell which minimizes the A-Cell-B distance. Unfortunately, this is slow for one unit, not to mention for 50 units.
Is there a way to extract a partial path from a regular A* search data structures?
Just for reference, here is the implementation I have.
Set<T> closedSet = U.newHashSet();
Map<T, T> cameFrom = U.newHashMap();
final Map<T, Integer> gScore = U.newHashMap();
final Map<T, Integer> hScore = U.newHashMap();
final Map<T, Integer> fScore = U.newHashMap();
final Comparator<T> smallestF = new Comparator<T>() {
#Override
public int compare(T o1, T o2) {
int g1 = fScore.get(o1);
int g2 = fScore.get(o2);
return g1 < g2 ? -1 : (g1 > g2 ? 1 : 0);
}
};
Set<T> openSet2 = U.newHashSet();
List<T> openSet = U.newArrayList();
gScore.put(initial, 0);
hScore.put(initial, estimation.invoke(initial, destination));
fScore.put(initial, gScore.get(initial) + hScore.get(initial));
openSet.add(initial);
openSet2.add(initial);
while (!openSet.isEmpty()) {
T current = openSet.get(0);
if (current.equals(destination)) {
return reconstructPath(cameFrom, destination);
}
openSet.remove(0);
openSet2.remove(current);
closedSet.add(current);
for (T loc : neighbors.invoke(current)) {
if (!closedSet.contains(loc)) {
int tentativeScore = gScore.get(current)
+ distance.invoke(current, loc);
if (!openSet2.contains(loc)) {
cameFrom.put(loc, current);
gScore.put(loc, tentativeScore);
hScore.put(loc, estimation.invoke(loc, destination));
fScore.put(loc, gScore.get(loc) + hScore.get(loc));
openSet.add(loc);
Collections.sort(openSet, smallestF);
openSet2.add(loc);
} else
if (tentativeScore < gScore.get(loc)) {
cameFrom.put(loc, current);
gScore.put(loc, tentativeScore);
hScore.put(loc, estimation.invoke(loc, destination));
fScore.put(loc, gScore.get(loc) + hScore.get(loc));
Collections.sort(openSet, smallestF);
}
}
}
}
return Collections.emptyList();
A solution that seems to work (replacing the last return Collections.emptyList();):
// if we get here, there was no direct path available
// find a target location which minimizes initial-L-destination
if (closedSet.isEmpty()) {
return Pair.of(false, Collections.<T>emptyList());
}
T nearest = Collections.min(closedSet, new Comparator<T>() {
#Override
public int compare(T o1, T o2) {
int d1 = trueDistance.invoke(destination, o1);
int d2 = trueDistance.invoke(destination, o2);
int c = U.compare(d1, d2);
if (c == 0) {
d1 = trueDistance.invoke(initial, o1);
d2 = trueDistance.invoke(initial, o2);
c = U.compare(d1, d2);
}
return c;
}
});
return Pair.of(true, reconstructPath(cameFrom, nearest));
Where the trueDistance gives the eucleidian distance of two points. (The base algorithm uses a simpler function yielding 1000 for X-X or YY neightbor, 1414 for XY neighbor).
If you have a recursive function that relies on some other function what is the preferred way to implement that?
1) outside the recursive function
let doSomething n = ...
let rec doSomethingElse x =
match x with
| yourDone -> ...
| yourNotDone -> doSomethingElse (doSomething x)
2) inside the recursive function
let rec doSomethingElse x =
let doSomething n = ...
match x with
| yourDone -> ...
| yourNotDone -> doSomethingElse (doSomething x)
3) encapsulate both inside the a third function
let doSomethingElse x =
let doSomething n = ...
let innerDoSomethingElse =
match x with
| yourDone -> ...
| yourNotDone -> innerDoSomethingElse (doSomething x)
4) something even better?
module Test =
let f x =
let add a b = a + b //inner function
add x 1
let f2 x =
let add a = a + x //inner function with capture, i.e., closure
add x
let outerAdd a b = a + b
let f3 x =
outerAdd x 1
Translates to:
[CompilationMapping(SourceConstructFlags.Module)]
public static class Test {
public static int f(int x) {
FSharpFunc<int, FSharpFunc<int, int>> add = new add#4();
return FSharpFunc<int, int>.InvokeFast<int>(add, x, 1);
}
public static int f2(int x) {
FSharpFunc<int, int> add = new add#8-1(x);
return add.Invoke(x);
}
public static int f3(int x) {
return outerAdd(x, 1);
}
[CompilationArgumentCounts(new int[] { 1, 1 })]
public static int outerAdd(int a, int b) {
return (a + b);
}
[Serializable]
internal class add#4 : OptimizedClosures.FSharpFunc<int, int, int> {
internal add#4() { }
public override int Invoke(int a, int b) {
return (a + b);
}
}
[Serializable]
internal class add#8-1 : FSharpFunc<int, int> {
public int x;
internal add#8-1(int x) {
this.x = x;
}
public override int Invoke(int a) {
return (a + this.x);
}
}
}
The only additional cost for an inner function is new'ing up an instance of FSharpFunc--seems negligible.
Unless you're very performance sensitive, I would go with the scope that makes the most sense, that is, the narrowest scope possible.
Implement Biginteger Multiply
use integer array to store a biginteger
like 297897654 will be stored as {2,9,7,8,9,7,6,5,4}
implement the multiply function for bigintegers
Expamples: {2, 9, 8, 8, 9, 8} * {3,6,3,4,5,8,9,1,2} = {1,0,8,6,3,7,1,4,1,8,7,8,9,7,6}
I failed to implement this class and thought it for a few weeks, couldn't get the answer.
Anybody can help me implement it using C#/Java?
Thanks a lot.
Do you know how to do multiplication on paper?
123
x 456
-----
738
615
492
-----
56088
I would just implement that algorithm in code.
C++ Implementation:
Source Code:
#include <iostream>
using namespace std;
int main()
{
int a[10] = {8,9,8,8,9,2};
int b[10] = {2,1,9,8,5,4,3,6,3};
// INPUT DISPLAY
for(int i=9;i>=0;i--) cout << a[i];
cout << " x ";
for(int i=9;i>=0;i--) cout << b[i];
cout << " = ";
int c[20] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
for(int i=0;i<10;i++)
{
int carry = 0;
for(int j=0;j<10;j++)
{
int t = (a[j] * b[i]) + c[i+j] + carry;
carry = t/10;
c[i+j] = t%10;
}
}
// RESULT DISPLAY
for(int i=19;i>=0;i--) cout << c[i];
cout << endl;
}
Output:
0000298898 x 0363458912 = 00000108637141878976
There is a superb algorithm called Karatsuba algorithm..Here
Which uses divide and conquer startegy..Where you can multiply large numbers..
I have implemented my it in java..
Using some manipulation..
package aoa;
import java.io.*;
public class LargeMult {
/**
* #param args the command line arguments
*/
public static void main(String[] args) throws IOException
{
// TODO code application logic here
BufferedReader br=new BufferedReader(new InputStreamReader(System.in));
System.out.println("Enter 1st number");
String a=br.readLine();
System.out.println("Enter 2nd number");
String b=br.readLine();
System.out.println("Result:"+multiply(a,b));
}
static String multiply(String t1,String t2)
{
if(t1.length()>1&&t2.length()>1)
{
int mid1=t1.length()/2;
int mid2=t2.length()/2;
String a=t1.substring(0, mid1);//Al
String b=t1.substring(mid1, t1.length());//Ar
String c=t2.substring(0, mid2);//Bl
String d=t2.substring(mid2, t2.length());//Br
String s1=multiply(a, c);
String s2=multiply(a, d);
String s3=multiply(b, c);
String s4=multiply(b, d);
long ans;
ans=Long.parseLong(s1)*(long)Math.pow(10,
b.length()+d.length())+Long.parseLong(s3)*(long)Math.pow(10,d.length())+
Long.parseLong(s2)*(long)Math.pow(10, b.length())+Long.parseLong(s4);
return ans+"";
}
else
{
return (Integer.parseInt(t1)*Integer.parseInt(t2))+"";
}
}
}
I hope this helps!!Enjoy..
Give the number you want to multiply in integer type array i.e. int[] one & int[] two.
public class VeryLongMultiplication {
public static void main(String args[]){
int[] one={9,9,9,9,9,9};
String[] temp=new String[100];
int c=0;
String[] temp1=new String[100];
int c1=0;
int[] two={9,9,9,9,9,9};
int car=0,mul=1; int rem=0; int sum=0;
String str="";
////////////////////////////////////////////
for(int i=one.length-1;i>=0;i--)
{
for(int j=two.length-1;j>=0;j--)
{
mul=one[i]*two[j]+car;
rem=mul%10;
car=mul/10;
if(j>0)
str=rem+str;
else
str=mul+str;
}
temp[c]=str;
c++;
str="";
car=0;
}
////////////////////////////////////////
for(int jk=0;jk<c;jk++)
{
for(int l=c-jk;l>0;l--)
str="0"+str;
str=str+temp[jk];
for(int l=0;l<=jk-1;l++)
str=str+"0";
System.out.println(str);
temp1[c1]=str;
c1++;
str="";
}
///////////////////////////////////
String ag="";int carry=0;
System.out.println("========================================================");
for(int jw=temp1[0].length()-1;jw>=0;jw--)
{
for(int iw=0;iw<c1;iw++)
{
int x=temp1[iw].charAt(jw)-'0';
sum+=x;
}
sum+=carry;
int n=sum;
sum=n%10;carry=n/10;
ag=sum+ag;
sum=0;
}
System.out.println(ag);
}
}
Output:
0000008999991
0000089999910
0000899999100
0008999991000
0089999910000
0899999100000
______________
0999998000001
If you do it the long-hand way, you'll have to implement an Add() method too to add up all the parts at the end. I started there just to get the ball rolling. Once you have the Add() down, the Multipy() method gets implemented along the same lines.
public static int[] Add(int[] a, int[] b) {
var maxLen = (a.Length > b.Length ? a.Length : b.Length);
var carryOver = 0;
var result = new List<int>();
for (int i = 0; i < maxLen; i++) {
var idx1 = a.Length - i - 1;
var idx2 = b.Length - i - 1;
var val1 = (idx1 < 0 ? 0 : a[idx1]);
var val2 = (idx2 < 0 ? 0 : b[idx2]);
var addResult = (val1 + val2) + carryOver;
var strAddResult = String.Format("{0:00}", addResult);
carryOver = Convert.ToInt32(strAddResult.Substring(0, 1));
var partialAddResult = Convert.ToInt32(strAddResult.Substring(1));
result.Insert(0, partialAddResult);
}
if (carryOver > 0) result.Insert(0, carryOver);
return result.ToArray();
}
Hint: use divide-and-conquer to split the int into halves, this can effectively reduce the time complexity from O(n^2) to O(n^(log3)). The gist is the reduction of multiplication operations.
I'm posting java code that I wrote. Hope, this will help
import org.junit.Test;
import static org.junit.Assert.*;
import java.math.BigInteger;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
/**
* Created by ${YogenRai} on 11/27/2015.
*
* method multiply BigInteger stored as digits in integer array and returns results
*/
public class BigIntegerMultiply {
public static List<Integer> multiply(int[] num1,int[] num2){
BigInteger first=new BigInteger(toString(num1));
BigInteger result=new BigInteger("0");
for (int i = num2.length-1,k=1; i >=0; i--,k=k*10) {
result = (first.multiply(BigInteger.valueOf(num2[i]))).multiply(BigInteger.valueOf(k)).add(result);
}
return convertToArray(result);
}
private static List<Integer> convertToArray(BigInteger result) {
List<Integer> rs=new ArrayList<>();
while (result.intValue()!=0){
int digit=result.mod(BigInteger.TEN).intValue();
rs.add(digit);
result = result.divide(BigInteger.TEN);
}
Collections.reverse(rs);
return rs;
}
public static String toString(int[] array){
StringBuilder sb=new StringBuilder();
for (int element:array){
sb.append(element);
}
return sb.toString();
}
#Test
public void testArray(){
int[] num1={2, 9, 8, 8, 9, 8};
int[] num2 = {3,6,3,4,5,8,9,1,2};
System.out.println(multiply(num1, num2));
}
}
Are there any examples for a recursive function that calls an other function which calls the first one too ?
Example :
function1()
{
//do something
function2();
//do something
}
function2()
{
//do something
function1();
//do something
}
Mutual recursion is common in code that parses mathematical expressions (and other grammars). A recursive descent parser based on the grammar below will naturally contain mutual recursion: expression-terms-term-factor-primary-expression.
expression
+ terms
- terms
terms
terms
term + terms
term - terms
term
factor
factor * term
factor / term
factor
primary
primary ^ factor
primary
( expression )
number
name
name ( expression )
The proper term for this is Mutual Recursion.
http://en.wikipedia.org/wiki/Mutual_recursion
There's an example on that page, I'll reproduce here in Java:
boolean even( int number )
{
if( number == 0 )
return true;
else
return odd(abs(number)-1)
}
boolean odd( int number )
{
if( number == 0 )
return false;
else
return even(abs(number)-1);
}
Where abs( n ) means return the absolute value of a number.
Clearly this is not efficient, just to demonstrate a point.
An example might be the minmax algorithm commonly used in game programs such as chess. Starting at the top of the game tree, the goal is to find the maximum value of all the nodes at the level below, whose values are defined as the minimum of the values of the nodes below that, whose values are defines as the maximum of the values below that, whose values ...
I can think of two common sources of mutual recursion.
Functions dealing with mutually recursive types
Consider an Abstract Syntax Tree (AST) that keeps position information in every node. The type might look like this:
type Expr =
| Int of int
| Var of string
| Add of ExprAux * ExprAux
and ExprAux = Expr of int * Expr
The easiest way to write functions that manipulate values of these types is to write mutually recursive functions. For example, a function to find the set of free variables:
let rec freeVariables = function
| Int n -> Set.empty
| Var x -> Set.singleton x
| Add(f, g) -> Set.union (freeVariablesAux f) (freeVariablesAux g)
and freeVariablesAux (Expr(loc, e)) =
freeVariables e
State machines
Consider a state machine that is either on, off or paused with instructions to start, stop, pause and resume (F# code):
type Instruction = Start | Stop | Pause | Resume
The state machine might be written as mutually recursive functions with one function for each state:
type State = State of (Instruction -> State)
let rec isOff = function
| Start -> State isOn
| _ -> State isOff
and isOn = function
| Stop -> State isOff
| Pause -> State isPaused
| _ -> State isOn
and isPaused = function
| Stop -> State isOff
| Resume -> State isOn
| _ -> State isPaused
It's a bit contrived and not very efficient, but you could do this with a function to calculate Fibbonacci numbers as in:
fib2(n) { return fib(n-2); }
fib1(n) { return fib(n-1); }
fib(n)
{
if (n>1)
return fib1(n) + fib2(n);
else
return 1;
}
In this case its efficiency can be dramatically enhanced if the language supports memoization
In a language with proper tail calls, Mutual Tail Recursion is a very natural way of implementing automata.
Here is my coded solution. For a calculator app that performs *,/,- operations using mutual recursion. It also checks for brackets (()) to decide the order of precedence.
Flow:: expression -> term -> factor -> expression
Calculator.h
#ifndef CALCULATOR_H_
#define CALCULATOR_H_
#include <string>
using namespace std;
/****** A Calculator Class holding expression, term, factor ********/
class Calculator
{
public:
/**Default Constructor*/
Calculator();
/** Parameterized Constructor common for all exception
* #aparam e exception value
* */
Calculator(char e);
/**
* Function to start computation
* #param input - input expression*/
void start(string input);
/**
* Evaluates Term*
* #param input string for term*/
double term(string& input);
/* Evaluates factor*
* #param input string for factor*/
double factor(string& input);
/* Evaluates Expression*
* #param input string for expression*/
double expression(string& input);
/* Evaluates number*
* #param input string for number*/
string number(string n);
/**
* Prints calculates value of the expression
* */
void print();
/**
* Converts char to double
* #param c input char
* */
double charTONum(const char* c);
/**
* Get error
*/
char get_value() const;
/** Reset all values*/
void reset();
private:
int lock;//set lock to check extra parenthesis
double result;// result
char error_msg;// error message
};
/**Error for unexpected string operation*/
class Unexpected_error:public Calculator
{
public:
Unexpected_error(char e):Calculator(e){};
};
/**Error for missing parenthesis*/
class Missing_parenthesis:public Calculator
{
public:
Missing_parenthesis(char e):Calculator(e){};
};
/**Error if divide by zeros*/
class DivideByZero:public Calculator{
public:
DivideByZero():Calculator(){};
};
#endif
===============================================================================
Calculator.cpp
//============================================================================
// Name : Calculator.cpp
// Author : Anurag
// Version :
// Copyright : Your copyright notice
// Description : Calculator using mutual recursion in C++, Ansi-style
//============================================================================
#include "Calculator.h"
#include <iostream>
#include <string>
#include <math.h>
#include <exception>
using namespace std;
Calculator::Calculator():lock(0),result(0),error_msg(' '){
}
Calculator::Calculator(char e):result(0), error_msg(e) {
}
char Calculator::get_value() const {
return this->error_msg;
}
void Calculator::start(string input) {
try{
result = expression(input);
print();
}catch (Unexpected_error e) {
cout<<result<<endl;
cout<<"***** Unexpected "<<e.get_value()<<endl;
}catch (Missing_parenthesis e) {
cout<<"***** Missing "<<e.get_value()<<endl;
}catch (DivideByZero e) {
cout<<"***** Division By Zero" << endl;
}
}
double Calculator::expression(string& input) {
double expression=0;
if(input.size()==0)
return 0;
expression = term(input);
if(input[0] == ' ')
input = input.substr(1);
if(input[0] == '+') {
input = input.substr(1);
expression += term(input);
}
else if(input[0] == '-') {
input = input.substr(1);
expression -= term(input);
}
if(input[0] == '%'){
result = expression;
throw Unexpected_error(input[0]);
}
if(input[0]==')' && lock<=0 )
throw Missing_parenthesis(')');
return expression;
}
double Calculator::term(string& input) {
if(input.size()==0)
return 1;
double term=1;
term = factor(input);
if(input[0] == ' ')
input = input.substr(1);
if(input[0] == '*') {
input = input.substr(1);
term = term * factor(input);
}
else if(input[0] == '/') {
input = input.substr(1);
double den = factor(input);
if(den==0) {
throw DivideByZero();
}
term = term / den;
}
return term;
}
double Calculator::factor(string& input) {
double factor=0;
if(input[0] == ' ') {
input = input.substr(1);
}
if(input[0] == '(') {
lock++;
input = input.substr(1);
factor = expression(input);
if(input[0]==')') {
lock--;
input = input.substr(1);
return factor;
}else{
throw Missing_parenthesis(')');
}
}
else if (input[0]>='0' && input[0]<='9'){
string nums = input.substr(0,1) + number(input.substr(1));
input = input.substr(nums.size());
return stod(nums);
}
else {
result = factor;
throw Unexpected_error(input[0]);
}
return factor;
}
string Calculator::number(string input) {
if(input.substr(0,2)=="E+" || input.substr(0,2)=="E-" || input.substr(0,2)=="e-" || input.substr(0,2)=="e-")
return input.substr(0,2) + number(input.substr(2));
else if((input[0]>='0' && input[0]<='9') || (input[0]=='.'))
return input.substr(0,1) + number(input.substr(1));
else
return "";
}
void Calculator::print() {
cout << result << endl;
}
void Calculator::reset(){
this->lock=0;
this->result=0;
}
int main() {
Calculator* cal = new Calculator;
string input;
cout<<"Expression? ";
getline(cin,input);
while(input!="."){
cal->start(input.substr(0,input.size()-2));
cout<<"Expression? ";
cal->reset();
getline(cin,input);
}
cout << "Done!" << endl;
return 0;
}
==============================================================
Sample input-> Expression? (42+8)*10 =
Output-> 500
Top down merge sort can use a pair of mutually recursive functions to alternate the direction of merge based on level of recursion.
For the example code below, a[] is the array to be sorted, b[] is a temporary working array. For a naive implementation of merge sort, each merge operation copies data from a[] to b[], then merges b[] back to a[], or it merges from a[] to b[], then copies from b[] back to a[]. This requires n ยท ceiling(log2(n)) copy operations. To eliminate the copy operations used for merging, the direction of merge can be alternated based on level of recursion, merge from a[] to b[], merge from b[] to a[], ..., and switch to in place insertion sort for small runs in a[], as insertion sort on small runs is faster than merge sort.
In this example, MergeSortAtoA() and MergeSortAtoB() are the mutually recursive functions.
Example java code:
static final int ISZ = 64; // use insertion sort if size <= ISZ
static void MergeSort(int a[])
{
int n = a.length;
if(n < 2)
return;
int [] b = new int[n];
MergeSortAtoA(a, b, 0, n);
}
static void MergeSortAtoA(int a[], int b[], int ll, int ee)
{
if ((ee - ll) <= ISZ){ // use insertion sort on small runs
InsertionSort(a, ll, ee);
return;
}
int rr = (ll + ee)>>1; // midpoint, start of right half
MergeSortAtoB(a, b, ll, rr);
MergeSortAtoB(a, b, rr, ee);
Merge(b, a, ll, rr, ee); // merge b to a
}
static void MergeSortAtoB(int a[], int b[], int ll, int ee)
{
int rr = (ll + ee)>>1; // midpoint, start of right half
MergeSortAtoA(a, b, ll, rr);
MergeSortAtoA(a, b, rr, ee);
Merge(a, b, ll, rr, ee); // merge a to b
}
static void Merge(int a[], int b[], int ll, int rr, int ee)
{
int o = ll; // b[] index
int l = ll; // a[] left index
int r = rr; // a[] right index
while(true){ // merge data
if(a[l] <= a[r]){ // if a[l] <= a[r]
b[o++] = a[l++]; // copy a[l]
if(l < rr) // if not end of left run
continue; // continue (back to while)
while(r < ee){ // else copy rest of right run
b[o++] = a[r++];
}
break; // and return
} else { // else a[l] > a[r]
b[o++] = a[r++]; // copy a[r]
if(r < ee) // if not end of right run
continue; // continue (back to while)
while(l < rr){ // else copy rest of left run
b[o++] = a[l++];
}
break; // and return
}
}
}
static void InsertionSort(int a[], int ll, int ee)
{
int i, j;
int t;
for (j = ll + 1; j < ee; j++) {
t = a[j];
i = j-1;
while(i >= ll && a[i] > t){
a[i+1] = a[i];
i--;
}
a[i+1] = t;
}
}