Return maximum value of the integer at index k in array - algorithm

This question was asked during Microsoft interview for intern position, I have no idea how to even approach this.
Array has n positive integers, sum of all elements in the array is at most max_sum, absolute difference between any two consecutive elements in the array is at most 1.
Return maximum value of the integer at index k in array.
Input : n = 3, max_sum = 7, k = 1
Output: 3
In this case let's say array is [2,3,2]
Input: n = 4, max_sum = 6, k = 2
output = 2
In this case let's say array is [1,1,2,1]


This is a brute-force approach. A better solution would be to calculate the values, but I'll leave it to you do figure that out. This is your challenge to get the job, right?
Input: n = 7, max_sum = 34, k = 4
Set all values to 0.
// ↓ k
array = { 0, 0, 0, 0, 0, 0, 0 }, sum = 0
Since we want maximum value at k, with lowest sum, just increment the value to 1.
// ↓ k
array = { 0, 0, 0, 0, 1, 0, 0 }, sum = 1 (+1)
Since consecutive elements must be at most 1 apart, when we increment value at k again, we need to increment the neighboring values too.
// ↓ k
array = { 0, 0, 0, 1, 2, 1, 0 }, sum = 4 (+3)
Repeat, repeatedly.
// ↓ k
array = { 0, 0, 1, 2, 3, 2, 1 }, sum = 9 (+5)
array = { 0, 1, 2, 3, 4, 3, 2 }, sum = 15 (+6)
array = { 1, 2, 3, 4, 5, 4, 3 }, sum = 22 (+7)
array = { 2, 3, 4, 5, 6, 5, 4 }, sum = 29 (+7)
We can't repeat again, because we'd get sum = 29 + 7 = 36 if we did, and that would exceed max_sum = 34.
Result: Max value at k is 6.
There are many ways to distribute the remaining 5 points, to get the exact sum, but showing a solution with the exact sum isn't the goal, so we don't need to do anything about the 5 extra points.


Let's define a as average array value:
a = max_sum / n
Let's find the maximum for k = 0:
max(0) = a + n/2
In this case, all other values of array would decrease, so the last value will be
a - n/2
for k = 1 we can see that maximum will not exceed max(0)-1, so
max(1) = a + n/2 - 1
and so on until k = n/2. for k > n/2 the max value will increase up to a + n/2 at k = n-1, so we have "V"-like curve with minimum at k=n/2, equal to a.
The only thing rest is to properly process border conditions, odd or even n and so on. I hope you got the idea.

Related

Dynamic programming (Solve combination of scores) [duplicate]

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'])
~

Display all positive solutions of equation x1+x2+...+xk = n

Below is the program that displays all positive solutions of equation x1+x2+...+xk = n, where k and n are positive integers:
func solution(k: Int, n: Int) {
if k > n || k <= 0 {
print("No solution")
} else
if k==1 {
print(n)
} else {
for i in 1...(n-k+1) {
print(i, terminator:"")
solution(k-1, n: n-i)
print("")
}
}
}
solution(4, n: 4)
This program runs well with n = 4 and k = 1,2,4, but it displays incorrectly when k = 3. Can somebody helps find the mistake?

The problem is for n = 4 and case k = 1, 2, 4, there is only one solution for each i, so your print(i, terminator:"") work correctly.
However, for case k = 3, for example, after printing 1 at k = 3, so there are more than one correct cases: (1 , 2, 1) or ( 1, 1, 2), which means, just one command print(1, terminator:"") at k = 1 will not be sufficient.
Image the printing routine will be smt like:
at k = 3, i = 1, print 1
at k = 2, i = 1, print 1
at k = 1, i = 2, print 2
So, at this time, we have (1, 1, 2), looks good.
However, when we backtrack to k = 2, i = 2, print 2
at k = 1, i = 1, print 1,
So, we only have (2, 1), which is not correct.
One simple way to fix this is rather than printing at each recursive step, you just store all result in one array, and print this array when k reaches 0

How to count children in a tree

I have a list of numbers:
[1, 2, 3, 4, 5, 6, 7]
I'm interested in finding an algorithm that can sum the total children in this list if the list where a tree:
1
/ \
2 3
/ \ / \
4 5 6 7
I'm looking for an algorithm that would give:
[6, 2, 2, 0, 0, 0, 0]
A = 6
B = 2
C = 2
D = 0
E = 0
F = 0
G = 0
Each node (except the leaves) has two children. The only exception is if the list if even:
1
/ \
2 3
/ \ /
4 5 6
I would like to avoid building a tree and then counting the number of children at each node. There must be a simple mathematical way to count the number of children from a list?

1-indexed the array.
Then for node with index i, the left son is with the index 2*i, and right is the 2*i+1.
Then go through the array from the end, for the node now:
if index of his (left or right)son is out of bound of array, then he has no (left or right)son.
If not, then you can know the number of the children of his son(we go through the array from then end).The result = number of children of now's son + number of now's son.
For example:
[1, 2, 3, 4, 5, 6, 7]
A is the result array.
1.A=[0, 0, 0, 0, 0, 0, 0],now(now is a index) = 7(1-indexed) since 7*2>7, a[7]=0
2.A=[0, 0, 0, 0, 0, 0, 0],now = 6,since 6*2>7, a[6]=0
3.A=[0, 0, 0, 0, 0, 0, 0],now = 5,since 5*2>7, a[5]=0
4.A=[0, 0, 0, 0, 0, 0, 0],now = 4,since 4*2>7, a[4]=0
5.A=[0, 0, 2, 0, 0, 0, 0],now = 3,since 3*2<7 and 3*2+1<7, a[3]=2+a[6]+a[7]=2
6.A=[0, 2, 2, 0, 0, 0, 0],now = 2,since 2*2<7 and 2*2+1<7, a[2]=2+a[4]+a[5]=2
7.A=[6, 2, 2, 0, 0, 0, 0],now = 1,since 1*2<7 and 1*2+1<7, a[1]=2+a[2]+a[3]=6

For the case where the tree is balanced (i.e. the number of elements in the input list is odd), this can be calculated with:
n = length of elements in input list
Then for element i in the output list:
d = depth of element in tree = floor(log2(i+1))+1
Then the number of children below that element in the tree is:
n_children = n - ((2^d)-1) / 2^(d-1)
So for your example of [1,2,3,4,5,6,7]:
n = 7
For array position 0 (i.e. node 1):
d = depth = floor(log2(1))+1 = 1
n_children = (7 - ((2^1)-1)) / 2^(1-1)
= (7 - 1) / 2^0
= 6 / 1
= 6
Then for then array position 1, (i.e. node 2):
d = depth = floor(log2(2))+1 = 2
n_children = (7 - ((2^2)-1)) / 2^(2-1)
= (7 - 3) / 2
= 2
Carrying on with this gives [6, 2, 2, 0, 0, 0, 0] for i=0 to i=6.
Python code for this would look like:
import math
def n_children(list_size, i):
depth = math.floor(math.log(i+1,2)) + 1
return (list_size - ((2**depth)-1)) / 2**(depth-1)
print [n_children(7, i) for i in range(7)]
This outputs [6.0, 2.0, 2.0, 0.0, 0.0, 0.0, 0.0].
It'd need some modification to deal with even numbered input arrays though (easiest way might be to round array size up to nearest odd number, then subtract 1 from any odd numbered values of i, or similar).

Interpret the first array as a heap, in which the children of node n are at 2*n+1 and 2*n+2, then recursively travel the tree:
def children(t, n):
if 2 * n + 1 >= t:
return 0
elif 2 * n + 2 >= t:
return 1
else:
return 2 + children(t, 2 * n + 1) + children(t, 2 * n + 2)
size = 7
childcounts = [ children(size, i) for i in range(size) ]
print(childcounts)
This will print:
[6, 2, 2, 0, 0, 0, 0]

Like we do in heap,
children[i] = sum of children of all its child + number of child
Like for 0th element, a[0] = number of children of its left child + number of children of its right child + number of its child
so a[0] = 2 + 2 + 2
for(int i=n-1;i>=0;i--) {
if(i*2+2 < n)
a[i]+=a[i*2+2]+1;
if(i*2+1 < n)
a[i]+=a[i*2+1]+1;
}

Pseudocode to find the longest run within an array

I know that A run is a sequence of adjacent repeated values , How would you write pseudo code for computing the length of the longest run in an array e.g.
5 would be the longest run in this array of integers.
1 2 4 4 3 1 2 4 3 5 5 5 5 3 6 5 5 6 3 1
Any idea would be helpful.

def longest_run(array):
result = None
prev = None
size = 0
max_size = 0
for element in array:
if (element == prev):
size += 1
if size > max_size:
result = element
max_size = size
else:
size = 0
prev = element
return result
EDIT
Wow. Just wow! This pseudocode is actually working:
>>> longest_run([1,2,4,4,3,1,2,4,3,5,5,5,5,3,6,5,5,6,3,1])
5

max_run_length = 0;
current_run_length = 0;
loop through the array storing the current index value, and the previous index's value
if the value is the same as the previous one, current_run_length++;
otherwise {
if current_run_length > max_run_length : max_run_length = current_run_length
current_run_length = 1;
}

Here a different functional approach in Python (Python looks like Pseudocode). This code works only with Python 3.3+. Otherwise you must replace "return" with "raise StopIteration".
I'm using a generator to yield a tuple with quantity of the element and the element itself. It's more universal. You can use this also for infinite sequences. If you want to get the longest repeated element from the sequence, it must be a finite sequence.
def group_same(iterable):
iterator = iter(iterable)
last = next(iterator)
counter = 1
while True:
try:
element = next(iterator)
if element is last:
counter += 1
continue
else:
yield (counter, last)
counter = 1
last = element
except StopIteration:
yield (counter, last)
return
If you have a list like this:
li = [0, 0, 2, 1, 1, 1, 1, 1, 5, 5, 6, 7, 7, 7, 12, 'Text', 'Text', 'Text2']
Then you can make a new list of it:
list(group_same(li))
Then you'll get a new list:
[(2, 0),
(1, 2),
(5, 1),
(2, 5),
(1, 6),
(3, 7),
(1, 12),
(2, 'Text'),
(1, 'Text2')]
To get longest repeated element, you can use the max function.
gen = group_same(li) # Generator, does nothing until iterating over it
grouped_elements = list(gen) # iterate over the generator until it's exhausted
longest = max(grouped_elements, key=lambda x: x[0])
Or as a one liner:
max(list(group_same(li)), key=lambda x: x[0])
The function max gives us the biggest element in a list. In this case, the list has more than one element. The argument key is just used to get the first element of the tuple as max value, but you'll still get back the tuple.
In : max(list(group_same(li)), key=lambda x: x[0])
Out: (5, 1)
The element 1 occurred 5 times repeatedly.

int main()
{
int a[20] = {1, 2, 4, 4, 3, 1, 2, 4, 3, 5, 5, 5, 5, 3, 6, 5, 5, 6, 3, 1};
int c=0;
for (int i=0;i<19;i++)
{
if (a[i] == a[i+1])
{
if (i != (i+1))
{
c++;
}
}
}
cout << c-1;
return 0;
}

Algorithm to get all combinations of (American) football point-accumulations necessary to get a certain score N

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'])
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