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I have an array of numbers and I want to figure out the maximum length of a continuous subarray of 2 unique numbers repeating.
For example, [2, 3, 4, 3, 2, 2, 4] would return 3 since [3, 2, 2] is of length 3.
[2, 4, 2, 5, 1, 5, 4, 2] would return 3.
[7, 8, 7, 8, 7] would return 5.
Edit: I have considered an O(n^2) solution where I start at each value in the array and iterate until I see a third unique value.
for item in array:
iterate until third unique element
if length of this iteration is greater than existing max, update the max length
return maxlength
I do not, however, think this is an efficient solution.
It can be done O(n). The code is in python3. o and t are one and two respectively. m is the max and c is the current count variable.
a = [7, 8, 7, 8, 7]
m = -1
o = a[0]
t = a[1]
# in the beginning one and two are the first 2 numbers
c = 0
index = 0
for i in a:
if i == o or i == t:
# if current element is either one or two current count is increased
c += 1
else:
# if current element is neither one nor two then they are updated accordingly and max is updated
o = a[index - 1]
t = a[index]
m = max(m, c)
c = 2
index += 1
m = max(m, c)
print(m)
We can use two pointer technique to solve this problem in O(n) run time complexity. These two pointer for example startPtr and endPtr will represent the range in the array. We will maintain this range [startPtr, endPtr] in such way that it contains no more than 2 unique number. We can do this by keeping track of position of the 2 unique number. My implement in C++ is given below:
int main()
{
int array[] = {1,2,3,3,2,3,2,3,2,2,2,1,3,4};
int startPtr = 0;
int endPtr = 0;
// denotes the size of the array
int size= sizeof(array)/sizeof(array[0]);
// contain last position of unique number 1 in the range [startPtr, endPtr]
int uniqueNumPos1 = -1; // -1 value represents it is not set yet
// contain last position of unique number 2 in the range [startPtr, endPtr]
int uniqueNumPos2 = -1; // -1 value represents it is not set yet
// contains length of maximum continuous subarray with 2 unique numbers
int ans = 0;
while(endPtr < size) {
if(uniqueNumPos1 == -1 || array[endPtr] == array[uniqueNumPos1]) {
uniqueNumPos1 = endPtr;
}
else {
if(uniqueNumPos2 == -1 || array[endPtr] == array[uniqueNumPos2]) {
uniqueNumPos2 = endPtr;
}
else {
// for this new third unique number update startPtr with min(uniqueNumPos1, uniqueNumPos2) + 1
// to ensure [startPtr, endPtr] does not contain more that two unique
startPtr = min(uniqueNumPos1, uniqueNumPos2) + 1;
// update uniqueNumPos1 and uniqueNumPos2
uniqueNumPos1 = endPtr -1;
uniqueNumPos2 = endPtr;
}
}
// this conditon is to ensure the range contain exactly two unique number
// if you are looking for the range containing less than or equal to two unique number, then you can omit this condition
if (uniqueNumPos1 != -1 && uniqueNumPos2 !=-1) {
ans = max( ans, endPtr - startPtr + 1);
}
endPtr++;
}
printf("%d\n", ans);
}
Thanks #MBo for pointing out the mistakes.
import java.util.Arrays;
import static java.lang.System.out;
class TestCase{
int[] test;
int answer;
TestCase(int[] test,int answer){
this.test = test;
this.answer = answer;
}
}
public class Solution {
public static void main(String[] args) {
TestCase[] tests = {
new TestCase(new int[]{2, 3, 4, 3, 2, 2, 4},3),
new TestCase(new int[]{2, 3, 3, 3, 3, 4, 3, 3, 2, 2, 4},7),
new TestCase(new int[]{1,2,3,3,4,2,3,2,3,2,2,2,1,3,4},7),
new TestCase(new int[]{2, 7, 8, 7, 8, 7},5),
new TestCase(new int[]{-1,2,2,2,2,2,2,2,2,2,2,-1,-1,4},13),
new TestCase(new int[]{1,2,3,4,5,6,7,7},3),
new TestCase(new int[]{0,0,0,0,0},0),
new TestCase(new int[]{0,0,0,2,2,2,1,1,1,1},7),
new TestCase(new int[]{},0)
};
for(int i=0;i<tests.length;++i){
int ans = maxContiguousArrayWith2UniqueElements(tests[i].test);
out.println(Arrays.toString(tests[i].test));
out.println("Expected: " + tests[i].answer);
out.println("Returned: " + ans);
out.println("Result: " + (tests[i].answer == ans ? "ok" : "not ok"));
out.println();
}
}
private static int maxContiguousArrayWith2UniqueElements(int[] A){
if(A == null || A.length <= 1) return 0;
int max_subarray = 0;
int first_number = A[0],second_number = A[0];
int start_index = 0,same_element_run_length = 1;
for(int i=1;i<A.length;++i){
if(A[i] != A[i-1]){
if(first_number == second_number){
second_number = A[i];
}else{
if(A[i] != first_number && A[i] != second_number){
max_subarray = Math.max(max_subarray,i - start_index);
start_index = i - same_element_run_length;
first_number = A[i-1];
second_number = A[i];
}
}
same_element_run_length = 1;
}else{
same_element_run_length++;
}
}
return first_number == second_number ? max_subarray : Math.max(max_subarray,A.length - start_index);
}
}
OUTPUT:
[2, 3, 4, 3, 2, 2, 4]
Expected: 3
Returned: 3
Result: ok
[2, 3, 3, 3, 3, 4, 3, 3, 2, 2, 4]
Expected: 7
Returned: 7
Result: ok
[1, 2, 3, 3, 4, 2, 3, 2, 3, 2, 2, 2, 1, 3, 4]
Expected: 7
Returned: 7
Result: ok
[2, 7, 8, 7, 8, 7]
Expected: 5
Returned: 5
Result: ok
[-1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, -1, -1, 4]
Expected: 13
Returned: 13
Result: ok
[1, 2, 3, 4, 5, 6, 7, 7]
Expected: 3
Returned: 3
Result: ok
[0, 0, 0, 0, 0]
Expected: 0
Returned: 0
Result: ok
[0, 0, 0, 2, 2, 2, 1, 1, 1, 1]
Expected: 7
Returned: 7
Result: ok
[]
Expected: 0
Returned: 0
Result: ok
Algorithm:
So, we maintain 2 variables first_number and second_number which will hold those 2 unique numbers.
As you know, there could be many possible subarrays we have to consider to get the max subarray length which has 2 unique elements. Hence, we need a pointer variable which will point to start of a subarray. In this, that pointer is start_index.
Any subarray breaks when we find a third number which is not equal to first_number or second_number. So, now, we calculate the previous subarray length(which had those 2 unique elements) by doing i - start_index.
Tricky part of this question is how to get the start_index of the next subarray.
If you closely observe, second_number of previous subarray becomes first_number of current subarray and third number we encountered just now becomes second_number of this current subarray.
So, one way to calculate when this first_number started is to run a while loop backwards to get that start_index. But that would make the algorithm O(n^2) if there are many subarrays to consider(which it will be).
Hence, we maintain a variable called same_element_run_length which just keeps track of the length or frequency of how many times the number got repeated and gets updated whenever it breaks. So, start_index for the next subarray after we encounter the third number becomes start_index = i - same_element_run_length.
Rest of the computation done is self-explanatory.
Time Complexity: O(n), Space Complexity : O(1).
It was one of my interview question, and I could not think of the good way to get number N. (plus, I did not understand the American football scoring system as well)
6 points for the touchdown
1 point for the extra point (kicked)
2 points for a safety or a conversion (extra try after a touchdown)
3 points for a field goal
What would be an efficient algorithm to get all combinations of point-accumulations necessary to get a certain score N?
Assuming here you are looking for a way to get number of possibilities and not the actual possibilities.
First let's find a recursive function:
f(n) = (f(n-6) >= 0? f(n-6) : 0) + (f(n-1) >= 0 ? f(n-1) : 0) + (f(n-2) >= 0 ? f(n-2) : 0) + (f(n-3) >= 0 ? f(n-3) : 0)
base: f(0) = 1 and f(n) = -infinity [n<0]
The idea behind it is: You can always get to 0, by a no scoring game. If you can get to f(n-6), you can also get to f(n), and so on for each possibility.
Using the above formula one can easily create a recursive solution.
Note that you can even use dynamic programming with it, initialize a table with [-5,n], init f[0] = 0 and f[-1] = f[-2] = f[-3] = f[-4] = f[-5] = -infinity and iterate over indexes [1,n] to achieve the number of possibilities based on the the recursive formula above.
EDIT:
I just realized that a simplified version of the above formula could be:
f(n) = f(n-6) + f(n-1) + f(n-2) + f(n-3)
and base will be: f(0) = 1, f(n) = 0 [n<0]
The two formulas will yield exactly the same result.
This is identical to the coin change problem, apart from the specific numbers used. See this question for a variety of answers.
You could use dynamic programming loop from 1 to n, here is some pseudo code:
results[1] = 1
for i from 1 to n :
results[i+1] += results[i]
results[i+2] += results[i]
results[i+3] += results[i]
results[i+6] += results[i]
this way complexity is O(N), instead of exponential complexity if you compute recursively by subtracting from the final score... like computing a Fibonacci series.
I hope my explanation is understandable enough..
I know this question is old, but all of the solutions I see help calculate the number of scoring permutations rather than the number of scoring combinations. (So I think either something like this should be an answer or the question title should be changed.)
Some code such as the following (which could then be converted into a dp) will calculate the number of possible combinations of different scores:
int getScoreCombinationCount(int score, int scoreVals[], int scoreValIndex) {
if (scoreValIndex < 0)
return 0;
if (score == 0)
return 1;
if (score < 0)
return 0;
return getScoreCombinationCount(score - scoreVals[scoreValIndex], scoreVals, scoreValIndex) +
getScoreCombinationCount(score, scoreVals, scoreValIndex - 1);
}
This solution, implemented based on a solution in the book Elements of Programming Interviews seems to be correct for counting the number of 'combinations' (no duplicate sets) for a set of score points.
For example, if points = {7, 3, 2}, there are 2 combinations for a total score of 7:
{7} and {3, 2, 2}.
public static int ScoreCombinationCount(int total, int[] points)
{
int[] combinations = new int[total + 1];
combinations[0] = 1;
for (var i = 0; i < points.Length; i++)
{
int point = points[i];
for (var j = point; j <= total; j++)
{
combinations[j] += combinations[j - point];
}
}
return combinations[total];
}
I am not sure I understand the logic though. Can someone explain?
The answer to this question depends on whether or not you allow the total number of combinations to include duplicate unordered combinations.
For example, in American football, you can score 2, 3, or 7 points (yes, I know you can miss the extra point on a touchdown, but let's ignore 1 point).
Then if your target N is 5, then you can reach it with {2, 3} or {3, 2}. If you count that as two combinations, then the Dynamic Programming solution by #amit will work. However, if you count those two combinations as one combination, then the iterative solution by #Maximus will work.
Below is some Java code, where findWays() corresponds to counting all possible combinations, including duplicates, and findUniqueWays() corresponds to counting only unique combinations.
// Counts the number of non-unique ways to reach N.
// Note that this algorithm counts {1,2} separately from {2,1}
// Applies a recurrence relationship. For example, with values={1,2}:
// cache[i] = cache[i-1] + cache[i-2]
public static long findWays(int N, int[] values) {
long cache[] = new long[N+1];
cache[0] = 1;
for (int i = 1; i <= N; i++) {
cache[i] = 0;
for (int value : values) {
if (value <= i)
cache[i] += cache[i-value];
}
}
return cache[N];
}
// Counts the number of unique ways to reach N.
// Note that this counts truly unique combinations: {1,2} is the same as {2,1}
public static long findUniqueWays(int N, int[] values) {
long [] cache = new long[N+1];
cache[0] = 1;
for (int i = 0; i < values.length; i++) {
int value = values[i];
for (int j = value; j <= N; j++) {
cache[j] += cache[j-value];
}
}
return cache[N];
}
Below is a test case where the possible points are {2,3,7}.
private static void testFindUniqueWaysFootball() {
int[] points = new int[]{2, 3, 7}; // Ways of scoring points.
int[] NValues = new int[]{5, 7, 10}; // Total score.
long result = -1;
for (int N : NValues) {
System.out.printf("\nN = %d points\n", N);
result = findWays(N, points);
System.out.printf("findWays() result = %d\n", result);
result = findUniqueWays(N, points);
System.out.printf("findUniqueWays() result = %d\n", result);
}
}
The output is:
N = 5 points
findWays() result = 2
findUniqueWays() result = 1
N = 7 points
findWays() result = 4
findUniqueWays() result = 2
N = 10 points
findWays() result = 9
findUniqueWays() result = 3
The results above show that to reach N=7 points, then there 4 non-unique ways to do so (those ways are {7}, {2,2,3}, {2,3,2}, {3,2,2}). However, there are only 2 unique ways (those ways are {7} and {2,2,3}). However, .
Below is a python program to find all combinations ignoring the combination order (e.g. 2,3,6 and 3,2,6 are considered one combination). This is a dynamic programming solution with order(n) time. Scores are 2,3,6,7.
We traverse from row score 2 to row score 7 (4 rows). Row score 2 contains the count if we only consider score 2 in calculating the number of combinations. Row score 3 produces each column by taking the count in row score 2 for the same final score plus the previous 3 count in its own row (current position minus 3). Row score 6 uses row score 3, which contains counts for both 2,3 and adds in the previous 6 count (current position minus 6). Row score 7 uses row score 6, which contains counts for row scores 2,3,6 plus the previous 7 count.
For example, numbers[1][12] = numbers[0][12] + numbers[1][9] (9 = 12-3) which results in 3 = 1 + 2; numbers[3][12] = numbers[2][12] + numbers[3][9] (9 = 12-3) which results in 7 = 6 + 1;
def cntMoney(num):
mSz = len(scores)
numbers = [[0]*(1+num) for _ in range(mSz)]
for mI in range(mSz): numbers[mI][0] = 1
for mI,m in enumerate(scores):
for i in range(1,num+1):
numbers[mI][i] = numbers[mI][i-m] if i >= m else 0
if mI != 0: numbers[mI][i] += numbers[mI-1][i]
print('m,numbers',m,numbers[mI])
return numbers[mSz-1][num]
scores = [2,3,6,7]
num = 12
print('score,combinations',num,cntMoney(num))
output:
('m,numbers', 2, [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1])
('m,numbers', 3, [1, 0, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 3])
('m,numbers', 6, [1, 0, 1, 1, 1, 1, 3, 1, 3, 3, 3, 3, 6])
('m,numbers', 7, [1, 0, 1, 1, 1, 1, 3, 2, 3, 4, 4, 4, 7])
('score,combinations', 12, 7)
Below is a python program to find all ordered combinations (e.g. 2,3,6 and 3,2,6 are considered two combinations). This is a dynamic programming solution with order(n) time. We build up from the start, adding the combinations calculated from previous score numbers, for each of the scores (2,3,6,7).
'vals[i] += vals[i-s]' means the current value equals the addition of the combinations from the previous values for the given scores. For example, for column vals[12] = the addition of scores 2,3,6,7: 26 = 12+9+3+2 (i-s = 10,9,6,5).
def allSeq(num):
vals = [0]*(num+1)
vals[0] = 1
for i in range(num+1):
for s in scores:
if i-s >= 0: vals[i] += vals[i-s]
print(vals)
return vals[num]
scores = [2,3,6,7]
num = 12
print('num,seqsToNum',num,allSeq(num))
Output:
[1, 0, 1, 1, 1, 2, 3, 4, 6, 9, 12, 18, 26]
('num,seqsToNum', 12, 26)
Attached is a program that prints the sequences for each score up to the given final score.
def allSeq(num):
seqs = [[] for _ in range(num+1)]
vals = [0]*(num+1)
vals[0] = 1
for i in range(num+1):
for sI,s in enumerate(scores):
if i-s >= 0:
vals[i] += vals[i-s]
if i == s: seqs[i].append(str(s))
else:
for x in seqs[i-s]:
seqs[i].append(x + '-' + str(s))
print(vals)
for sI,seq in enumerate(seqs):
print('num,seqsSz,listOfSeqs',sI,len(seq),seq)
return vals[num],seqs[num]
scores = [2,3,6,7]
num = 12
combos,seqs = allSeq(num)
Output:
[1, 0, 1, 1, 1, 2, 3, 4, 6, 9, 12, 18, 26]
('num,seqsSz,listOfSeqs', 0, 0, [])
('num,seqsSz,listOfSeqs', 1, 0, [])
('num,seqsSz,listOfSeqs', 2, 1, ['2'])
('num,seqsSz,listOfSeqs', 3, 1, ['3'])
('num,seqsSz,listOfSeqs', 4, 1, ['2-2'])
('num,seqsSz,listOfSeqs', 5, 2, ['3-2', '2-3'])
('num,seqsSz,listOfSeqs', 6, 3, ['2-2-2', '3-3', '6'])
('num,seqsSz,listOfSeqs', 7, 4, ['3-2-2', '2-3-2', '2-2-3', '7'])
('num,seqsSz,listOfSeqs', 8, 6, ['2-2-2-2', '3-3-2', '6-2', '3-2-3', '2-3-3', '2-6'])
('num,seqsSz,listOfSeqs', 9, 9, ['3-2-2-2', '2-3-2-2', '2-2-3-2', '7-2', '2-2-2-3', '3-3-3', '6-3', '3-6', '2-7'])
('num,seqsSz,listOfSeqs', 10, 12, ['2-2-2-2-2', '3-3-2-2', '6-2-2', '3-2-3-2', '2-3-3-2', '2-6-2', '3-2-2-3', '2-3-2-3', '2-2-3-3', '7-3', '2-2-6', '3-7'])
('num,seqsSz,listOfSeqs', 11, 18, ['3-2-2-2-2', '2-3-2-2-2', '2-2-3-2-2', '7-2-2', '2-2-2-3-2', '3-3-3-2', '6-3-2', '3-6-2', '2-7-2', '2-2-2-2-3', '3-3-2-3', '6-2-3', '3-2-3-3', '2-3-3-3', '2-6-3', '3-2-6', '2-3-6', '2-2-7'])
('num,seqsSz,listOfSeqs', 12, 26, ['2-2-2-2-2-2', '3-3-2-2-2', '6-2-2-2', '3-2-3-2-2', '2-3-3-2-2', '2-6-2-2', '3-2-2-3-2', '2-3-2-3-2', '2-2-3-3-2', '7-3-2', '2-2-6-2', '3-7-2', '3-2-2-2-3', '2-3-2-2-3', '2-2-3-2-3', '7-2-3', '2-2-2-3-3', '3-3-3-3', '6-3-3', '3-6-3', '2-7-3', '2-2-2-6', '3-3-6', '6-6', '3-2-7', '2-3-7'])
~
I just did a Top Coder SRM where there was a question I had problem solving. I am trying to searching online for details of the algorithm but I can't seem to find it.
The question went around the lines of:
You have an array, for example [12, 10, 4]
Each round, you can apply any permutation of 9,3,1 to subtract from [12,10,4]
Return the minimum amount of permutations needed to be applied to get to 0 for all numbers in the array.
Any help?
Edit: Let me be somewhat more descriptive so that the question can be understood better.
One question would be
input: [12 10 4]
output: 2 (minimum rounds)
How it would work:
[12 10 4] - [9 3 1] = [3 7 3]
[12 10 4] - [9 1 3] = [3 9 1]
[12 10 4] - [3 9 1] = ..
[12 10 4] - [3 1 9] = ..
[12 10 4] - [1 3 9] =
[12 10 4] - [1 9 3] =
[3 7 3] - [3 9 1] = ...
..
[9 1 3] - [9 1 3] = [0 0 0] <-- achieved with only two permutation subtractions
Edit2:
Here is the topcoder question:
http://community.topcoder.com/stat?c=problem_statement&pm=13782
Edit3:
Can anyone also explain the Overlapping Subproblem and Optimal Substructure if they believe a solution is with dynamic programming?
EDIT :
after seeing the original question, here is the solution which gave correct output for every testcase provided in above question link, if u want give input {60} , u shud give as {60,0,0} .
Basic idea is , u need to get all of them less than or equal to zero atlast , so if number is divisible by 3 , u can get zero by subtracting 9 or 3 and if not divisible, subtract by 1 to make it divisible by 3
first , sort the given triplet , then
check the largest number is divisible by 3, if so, subtract 9 and the number still can be divisible by 3
now check the next largest number , if it is divisible by 3, subtract 3 else subtract 1
if largest number is not divisible by 3, subtract 1 so that it may be divisible by 3
now subtract next largest number by 9 and the other one by 3
here is the implementation
#include <iostream>
#include <algorithm>
#include<cmath>
using namespace std;
int f(int* a,int k){
sort(a,a+3);
if(a[2]<=0 )
{cout << k<< endl ;
return 0;}
if(a[1]<=0){
cout << k+ceil((double)a[2]/9) << endl;
return 0;
}
if(a[2]%3==0 ){
a[2]=a[2]-9;
if(a[1]%3==0 )
{
a[1] = a[1] -3;
a[0] = a[0] -1;
}
else{
if(a[2]%2==0 && (a[1]-1)%2==0 && a[1]!=0){
a[2]=a[2] +9 -3;
a[1] = a[1] -9;
a[0] = a[0]-1;
}
else{
a[1] = a[1] -1;
a[0] = a[0] - 3;}
}
return f(a,++k);
}
else{
a[2] = a[2] -1;
a[1] = a[1] -9;
a[0]=a[0] -3;
return f(a,++k);
}
}
int main() {
int a[] = {54,18,6};
f(a,0);
return 0;
}
hope this is helpful
Example:
input is [12,4,10]
sort it [12,4,10] -> [12,10,4]
now check if largest number is divisible by 3, here Yes
so
[12-9,10,4] -> [3,10,4]
now check next largest number divisible by 3, here N0
so
[3,10-1,4-3] ->[3,9,1]
now increment count and pass this to function (recursive)
input -> [3,9,1]
sort [3,9,1] -> [9,3,1]
now check if largest number is divisible by 3, here Yes
so [9-9,3,1] -> [0,3,1]
now check next largest number divisible by 3, here Yes
so [0,3-3,1-1] -> [0,0,0]
now increment the count and pass the array
as largest element is 0 , we will print count and that is 2
Let the number of elements be n, let p1 to pn! be the permutations of the array you want to subtract, and let A be your original array. Then you want the natural numbers solutions of the linear system
A - k1*p1 - ... - kn!*pn! = 0
which is equivalent to
A = k1*p1 + ... + kn!*pn!
where 0 is the n-item array with all zeroes. You can't figure that out using the obvious linear algebra solution since ℕ^n is not an ℕ-vector space; actually, finding solutions to linear systems over natural numbers in general is NP-complete by a reduction from subset sum. I couldn't adapt the reduction to your version of the problem ad hoc, so maybe think about that for a bit. It should remain NP-hard, at least that's what I would expect.
It can be solved using dynamic programming. If you look at common solutions to dynamic programming problems, they follow the same general structure. We use an array to store the minimum number of permutations needed to reach each value, and update each value in turn using nested loops. The answer will end up in d[0][0][0] which is the number of permutations required to get to [0, 0, 0].
public static int count(int a, int b, int c) {
int[][] permutations = {{9, 3, 1}, {9, 1, 3}, {1, 9, 3}, {1, 3, 9}, {3, 9, 1}, {3, 1, 9}};
int[][][] d = new int[a + 1][b + 1][c + 1];
// Set initial values to high value to represent no solution.
for(int x = 0; x <= a; x++) {
for(int y = 0; y <= b; y++) {
for(int z = 0; z <= c; z++) {
d[x][y][z] = Integer.MAX_VALUE / 2;
}
}
}
// Set number of permutations for initial value to 0.
d[a][b][c] = 0;
// Update all values.
for(int x = a; x >= 0; x--) {
for(int y = b; y >= 0; y--) {
for(int z = c; z >= 0; z--) {
for(int[] p:permutations) {
// Update count from [x, y, z] -> [nx, ny, nz] using permutation p.
int nx = x - p[0];
int ny = y - p[1];
int nz = z - p[2];
if(nx >= 0 && ny >= 0 && nz >= 0) {
d[nx][ny][nz] = Math.min(d[nx][ny][nz], d[x][y][z] + 1);
}
}
}
}
}
// Return final answer.
return d[0][0][0];
}
This question was asked in an interview.
For a given integer n >= 3 return an array of size 2n such that every number k from 1 to n is occurring exactly twice and every number and its repetition is separated by a distance equal to the number.
Function signature:
int* buildArray(int n)
For example, for n = 3:
3, 1, 2, 1, 3, 2
Number 2: 1st position 3 and 2nd position 6, so distance 6 - 3 - 1 = 2.
Number 3: First 3 at position 1 and 2nd 3 at position 5, so distance 5 - 1 - 1 = 3.
For n = 4:
4, 1, 3, 1, 2, 4, 3, 2
This is an exact cover problem, which you can solve with Algorithm X. (And it's a nicer, simpler example than Sudoku.) You have the following constraints:
each number must be used exaclty twice and
each slot in the array can only be occupied by one number
For your problem with n = 3, you get the following matrix:
[0] [1] [2] [3] [4] [5] 1 2 3
--- --- --- --- --- --- --- --- ---
#0 X X X
#1 X X X
#2 X X X
#3 X X X
#4 X X X
#5 X X X
#6 X X X
#7 X X X
#8 X X X
The columns [x] mean that slot x is used; plain x means that the digit x has been placed. The rows #0 to #3 describe the possible placements of ones, #4 to #6 the placements of twos and #7 and #8 the twi possibilities to place the threes. This will yield the two (mirrored) solutions:
2 3 1 2 1 3 (#2 + #4 + #8)
3 1 2 1 3 2 (#1 + #6 + #7)
Not all n yield solutions, there are no solutions for 5 and 6, for example.
It's a Langford's problem/sequence.
There is a topic about the same problem on SO with implementation already.
Langford sequence implementation Haskell or C
It's NP-complete problem.
However, it can be fairly easily coded-up using recursion and backtracking, making it suitable solution for an interview. It's similar to, for example, N queens puzzle backtracking solution (which was my inspiration).
Ready-to-run code in Java:
import java.util.Arrays;
public class Test {
public static void main(String[] args) {
for (int i = 3; i < 13; i++) {
int[] answer = buildArray(i);
if (answer[0] != 0) {
System.out.println(i + " " + Arrays.toString(answer));
}
}
}
public static int[] buildArray(int n) {
int[] answer = new int[2 * n];
put(answer, n); // start with placing n, later (n - 1), (n - 2), ..., 1
return answer;
}
private static boolean put(int[] answer, int k) {
for (int i = 0; i + k + 1 < answer.length; i++) { // try every posiiton
if (answer[i] == 0 && answer[i + k + 1] == 0) {
answer[i] = k;
answer[i + k + 1] = k;
if (k == 1) {
return true; // we found a solution, escape!
}
if (put(answer, k - 1)) {
return true; // we found a solution, escape!
}
answer[i] = 0; // step back and erase this placement
answer[i + k + 1] = 0;
}
}
return false; // still not full solution, continue
}
}
Output:
3 [3, 1, 2, 1, 3, 2]
4 [4, 1, 3, 1, 2, 4, 3, 2]
7 [7, 3, 6, 2, 5, 3, 2, 4, 7, 6, 5, 1, 4, 1]
8 [8, 3, 7, 2, 6, 3, 2, 4, 5, 8, 7, 6, 4, 1, 5, 1]
11 [11, 6, 10, 2, 9, 3, 2, 8, 6, 3, 7, 5, 11, 10, 9, 4, 8, 5, 7, 1, 4, 1]
12 [12, 10, 11, 6, 4, 5, 9, 7, 8, 4, 6, 5, 10, 12, 11, 7, 9, 8, 3, 1, 2, 1, 3, 2]
It was one of my interview question, and I could not think of the good way to get number N. (plus, I did not understand the American football scoring system as well)
6 points for the touchdown
1 point for the extra point (kicked)
2 points for a safety or a conversion (extra try after a touchdown)
3 points for a field goal
What would be an efficient algorithm to get all combinations of point-accumulations necessary to get a certain score N?
Assuming here you are looking for a way to get number of possibilities and not the actual possibilities.
First let's find a recursive function:
f(n) = (f(n-6) >= 0? f(n-6) : 0) + (f(n-1) >= 0 ? f(n-1) : 0) + (f(n-2) >= 0 ? f(n-2) : 0) + (f(n-3) >= 0 ? f(n-3) : 0)
base: f(0) = 1 and f(n) = -infinity [n<0]
The idea behind it is: You can always get to 0, by a no scoring game. If you can get to f(n-6), you can also get to f(n), and so on for each possibility.
Using the above formula one can easily create a recursive solution.
Note that you can even use dynamic programming with it, initialize a table with [-5,n], init f[0] = 0 and f[-1] = f[-2] = f[-3] = f[-4] = f[-5] = -infinity and iterate over indexes [1,n] to achieve the number of possibilities based on the the recursive formula above.
EDIT:
I just realized that a simplified version of the above formula could be:
f(n) = f(n-6) + f(n-1) + f(n-2) + f(n-3)
and base will be: f(0) = 1, f(n) = 0 [n<0]
The two formulas will yield exactly the same result.
This is identical to the coin change problem, apart from the specific numbers used. See this question for a variety of answers.
You could use dynamic programming loop from 1 to n, here is some pseudo code:
results[1] = 1
for i from 1 to n :
results[i+1] += results[i]
results[i+2] += results[i]
results[i+3] += results[i]
results[i+6] += results[i]
this way complexity is O(N), instead of exponential complexity if you compute recursively by subtracting from the final score... like computing a Fibonacci series.
I hope my explanation is understandable enough..
I know this question is old, but all of the solutions I see help calculate the number of scoring permutations rather than the number of scoring combinations. (So I think either something like this should be an answer or the question title should be changed.)
Some code such as the following (which could then be converted into a dp) will calculate the number of possible combinations of different scores:
int getScoreCombinationCount(int score, int scoreVals[], int scoreValIndex) {
if (scoreValIndex < 0)
return 0;
if (score == 0)
return 1;
if (score < 0)
return 0;
return getScoreCombinationCount(score - scoreVals[scoreValIndex], scoreVals, scoreValIndex) +
getScoreCombinationCount(score, scoreVals, scoreValIndex - 1);
}
This solution, implemented based on a solution in the book Elements of Programming Interviews seems to be correct for counting the number of 'combinations' (no duplicate sets) for a set of score points.
For example, if points = {7, 3, 2}, there are 2 combinations for a total score of 7:
{7} and {3, 2, 2}.
public static int ScoreCombinationCount(int total, int[] points)
{
int[] combinations = new int[total + 1];
combinations[0] = 1;
for (var i = 0; i < points.Length; i++)
{
int point = points[i];
for (var j = point; j <= total; j++)
{
combinations[j] += combinations[j - point];
}
}
return combinations[total];
}
I am not sure I understand the logic though. Can someone explain?
The answer to this question depends on whether or not you allow the total number of combinations to include duplicate unordered combinations.
For example, in American football, you can score 2, 3, or 7 points (yes, I know you can miss the extra point on a touchdown, but let's ignore 1 point).
Then if your target N is 5, then you can reach it with {2, 3} or {3, 2}. If you count that as two combinations, then the Dynamic Programming solution by #amit will work. However, if you count those two combinations as one combination, then the iterative solution by #Maximus will work.
Below is some Java code, where findWays() corresponds to counting all possible combinations, including duplicates, and findUniqueWays() corresponds to counting only unique combinations.
// Counts the number of non-unique ways to reach N.
// Note that this algorithm counts {1,2} separately from {2,1}
// Applies a recurrence relationship. For example, with values={1,2}:
// cache[i] = cache[i-1] + cache[i-2]
public static long findWays(int N, int[] values) {
long cache[] = new long[N+1];
cache[0] = 1;
for (int i = 1; i <= N; i++) {
cache[i] = 0;
for (int value : values) {
if (value <= i)
cache[i] += cache[i-value];
}
}
return cache[N];
}
// Counts the number of unique ways to reach N.
// Note that this counts truly unique combinations: {1,2} is the same as {2,1}
public static long findUniqueWays(int N, int[] values) {
long [] cache = new long[N+1];
cache[0] = 1;
for (int i = 0; i < values.length; i++) {
int value = values[i];
for (int j = value; j <= N; j++) {
cache[j] += cache[j-value];
}
}
return cache[N];
}
Below is a test case where the possible points are {2,3,7}.
private static void testFindUniqueWaysFootball() {
int[] points = new int[]{2, 3, 7}; // Ways of scoring points.
int[] NValues = new int[]{5, 7, 10}; // Total score.
long result = -1;
for (int N : NValues) {
System.out.printf("\nN = %d points\n", N);
result = findWays(N, points);
System.out.printf("findWays() result = %d\n", result);
result = findUniqueWays(N, points);
System.out.printf("findUniqueWays() result = %d\n", result);
}
}
The output is:
N = 5 points
findWays() result = 2
findUniqueWays() result = 1
N = 7 points
findWays() result = 4
findUniqueWays() result = 2
N = 10 points
findWays() result = 9
findUniqueWays() result = 3
The results above show that to reach N=7 points, then there 4 non-unique ways to do so (those ways are {7}, {2,2,3}, {2,3,2}, {3,2,2}). However, there are only 2 unique ways (those ways are {7} and {2,2,3}). However, .
Below is a python program to find all combinations ignoring the combination order (e.g. 2,3,6 and 3,2,6 are considered one combination). This is a dynamic programming solution with order(n) time. Scores are 2,3,6,7.
We traverse from row score 2 to row score 7 (4 rows). Row score 2 contains the count if we only consider score 2 in calculating the number of combinations. Row score 3 produces each column by taking the count in row score 2 for the same final score plus the previous 3 count in its own row (current position minus 3). Row score 6 uses row score 3, which contains counts for both 2,3 and adds in the previous 6 count (current position minus 6). Row score 7 uses row score 6, which contains counts for row scores 2,3,6 plus the previous 7 count.
For example, numbers[1][12] = numbers[0][12] + numbers[1][9] (9 = 12-3) which results in 3 = 1 + 2; numbers[3][12] = numbers[2][12] + numbers[3][9] (9 = 12-3) which results in 7 = 6 + 1;
def cntMoney(num):
mSz = len(scores)
numbers = [[0]*(1+num) for _ in range(mSz)]
for mI in range(mSz): numbers[mI][0] = 1
for mI,m in enumerate(scores):
for i in range(1,num+1):
numbers[mI][i] = numbers[mI][i-m] if i >= m else 0
if mI != 0: numbers[mI][i] += numbers[mI-1][i]
print('m,numbers',m,numbers[mI])
return numbers[mSz-1][num]
scores = [2,3,6,7]
num = 12
print('score,combinations',num,cntMoney(num))
output:
('m,numbers', 2, [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1])
('m,numbers', 3, [1, 0, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 3])
('m,numbers', 6, [1, 0, 1, 1, 1, 1, 3, 1, 3, 3, 3, 3, 6])
('m,numbers', 7, [1, 0, 1, 1, 1, 1, 3, 2, 3, 4, 4, 4, 7])
('score,combinations', 12, 7)
Below is a python program to find all ordered combinations (e.g. 2,3,6 and 3,2,6 are considered two combinations). This is a dynamic programming solution with order(n) time. We build up from the start, adding the combinations calculated from previous score numbers, for each of the scores (2,3,6,7).
'vals[i] += vals[i-s]' means the current value equals the addition of the combinations from the previous values for the given scores. For example, for column vals[12] = the addition of scores 2,3,6,7: 26 = 12+9+3+2 (i-s = 10,9,6,5).
def allSeq(num):
vals = [0]*(num+1)
vals[0] = 1
for i in range(num+1):
for s in scores:
if i-s >= 0: vals[i] += vals[i-s]
print(vals)
return vals[num]
scores = [2,3,6,7]
num = 12
print('num,seqsToNum',num,allSeq(num))
Output:
[1, 0, 1, 1, 1, 2, 3, 4, 6, 9, 12, 18, 26]
('num,seqsToNum', 12, 26)
Attached is a program that prints the sequences for each score up to the given final score.
def allSeq(num):
seqs = [[] for _ in range(num+1)]
vals = [0]*(num+1)
vals[0] = 1
for i in range(num+1):
for sI,s in enumerate(scores):
if i-s >= 0:
vals[i] += vals[i-s]
if i == s: seqs[i].append(str(s))
else:
for x in seqs[i-s]:
seqs[i].append(x + '-' + str(s))
print(vals)
for sI,seq in enumerate(seqs):
print('num,seqsSz,listOfSeqs',sI,len(seq),seq)
return vals[num],seqs[num]
scores = [2,3,6,7]
num = 12
combos,seqs = allSeq(num)
Output:
[1, 0, 1, 1, 1, 2, 3, 4, 6, 9, 12, 18, 26]
('num,seqsSz,listOfSeqs', 0, 0, [])
('num,seqsSz,listOfSeqs', 1, 0, [])
('num,seqsSz,listOfSeqs', 2, 1, ['2'])
('num,seqsSz,listOfSeqs', 3, 1, ['3'])
('num,seqsSz,listOfSeqs', 4, 1, ['2-2'])
('num,seqsSz,listOfSeqs', 5, 2, ['3-2', '2-3'])
('num,seqsSz,listOfSeqs', 6, 3, ['2-2-2', '3-3', '6'])
('num,seqsSz,listOfSeqs', 7, 4, ['3-2-2', '2-3-2', '2-2-3', '7'])
('num,seqsSz,listOfSeqs', 8, 6, ['2-2-2-2', '3-3-2', '6-2', '3-2-3', '2-3-3', '2-6'])
('num,seqsSz,listOfSeqs', 9, 9, ['3-2-2-2', '2-3-2-2', '2-2-3-2', '7-2', '2-2-2-3', '3-3-3', '6-3', '3-6', '2-7'])
('num,seqsSz,listOfSeqs', 10, 12, ['2-2-2-2-2', '3-3-2-2', '6-2-2', '3-2-3-2', '2-3-3-2', '2-6-2', '3-2-2-3', '2-3-2-3', '2-2-3-3', '7-3', '2-2-6', '3-7'])
('num,seqsSz,listOfSeqs', 11, 18, ['3-2-2-2-2', '2-3-2-2-2', '2-2-3-2-2', '7-2-2', '2-2-2-3-2', '3-3-3-2', '6-3-2', '3-6-2', '2-7-2', '2-2-2-2-3', '3-3-2-3', '6-2-3', '3-2-3-3', '2-3-3-3', '2-6-3', '3-2-6', '2-3-6', '2-2-7'])
('num,seqsSz,listOfSeqs', 12, 26, ['2-2-2-2-2-2', '3-3-2-2-2', '6-2-2-2', '3-2-3-2-2', '2-3-3-2-2', '2-6-2-2', '3-2-2-3-2', '2-3-2-3-2', '2-2-3-3-2', '7-3-2', '2-2-6-2', '3-7-2', '3-2-2-2-3', '2-3-2-2-3', '2-2-3-2-3', '7-2-3', '2-2-2-3-3', '3-3-3-3', '6-3-3', '3-6-3', '2-7-3', '2-2-2-6', '3-3-6', '6-6', '3-2-7', '2-3-7'])
~