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Target sum dp algorithm when element can be zero

Target sum prompt:
You are given a set of positive numbers and a target sum ‘S’. Each number should be assigned either a ‘+’ or ‘-’ sign. We need to find the total ways to assign symbols to make the sum of the numbers equal to the target ‘S’.
Input: {1, 1, 2, 3}, S=1
Output: 3
Explanation: The given set has '3' ways to make a sum of '1': {+1-1-2+3} & {-1+1-2+3} & {+1+1+2-3}
let’s say ‘Sum(s1)’ denotes the total sum of set ‘s1’, and ‘Sum(s2)’ denotes the total sum of set ‘s2’. Add negative sign to set 's2'
This equation can be reduced to the subset sum problem target + sum(nums)/2
sum(s1) - sum(s2) = target
sum(s1) + sum(s2) = sum(nums)
2 * sum(s1) = target + sum(nums)
sum(s1) = target + sum(nums) / 2
def findTargetSumWays(nums, S):
"""
:type nums: List[int]
:type S: int
:rtype: int
"""
if (sum(nums) + S) % 2 == 1 or sum(nums) < S:
return 0
ssum = (sum(nums) + S) // 2
dp = [[0 for _ in range(ssum + 1)] for _ in range(len(nums))]
# col == 0
for i in range(len(nums)):
# [] or [0]
if i == 0 and nums[i] == 0:
dp[i][0] = 2
# [] or [0] from previous
elif nums[i] == 0:
dp[i][0] = 2 * dp[i-1][0]
else: # empty set only
dp[i][0] = 1
# take 1st element nums[0] in s == nums[0]
for s in range(1, ssum + 1):
if nums[0] == s:
dp[0][s] = 1
for i in range(1, len(nums)):
for s in range(1, ssum + 1):
if nums[i] != 0:
# skip element at i
dp[i][s] = dp[i - 1][s]
# include element at i
if s >= nums[i]:
dp[i][s] += dp[i - 1][s - nums[i]]
else: # nums[i] = 0
dp[i][s] = dp[i-1][s] * 2
return dp[len(nums) - 1][ssum]
I've spent a few hours on this prompt but still couldn't pass the following example
[7,0,3,9,9,9,1,7,2,3]
6
expected: 50
output: 43 (using my algorithm)
I've also looked through other people's answers here, they all makes sense but I just want to know where could I have possibly missed in my algorithm here?

You can do it like this:
from itertools import product
def findTargetSumWays(nums, S):
a = [1,-1]
result=[np.multiply(nums,i) for i in list(product(a, repeat=len(nums))) if sum(np.multiply(nums,i))==S]
return(len(result))
findTargetSumWays(inputs,6)
50
Basically I get all possible combinations of -1,1 in tuples with the size the same as input elements and then I'm multiplying these tuples with input.

I ran into this same issue when handling zeroes but I did this on C++ where I handled zeroes seperately.
Make sure that in the knapsack approach skip zeroes i.e.
if(a[i-1] == 0)
dp[i][j] = dp[i-1][j];
We can handle zeroes seperately by simply counting the zero occurences and we can put them in either S1 or S2. So, for each zero it is 2*(answer) and for n zeroes its 2^n * (answer) i.e.
answer = pow(2, num_zero) * answer;
Also, don't forget to simply return zero if sum(nums) + target is odd as S1 can't be fractional or target is greater than sum(nums) i.e.
if(sum < target || (sum+target)%2 == 1)
return 0;
The overall code looks like this:
int subsetSum(int a[], int n, int sum) {
int dp[n+1][sum+1];
for(int i = 0; i<sum+1; i++)
dp[0][i] = 0;
for(int i = 0; i<n+1; i++)
dp[i][0] = 1;
for(int i = 1; i<n+1; i++) {
for(int j = 1; j<sum+1; j++) {
if(a[i-1] == 0)
dp[i][j] = dp[i-1][j];
else if(a[i-1]<=j)
dp[i][j] = dp[i-1][j-a[i-1]] + dp[i-1][j];
else
dp[i][j] = dp[i-1][j];
}
}
return dp[n][sum]; }
int findTargetSumWays(int a[], int target) {
int sum = 0;
int num_zero = 0;
for(int i = 0; i<a.size(); i++) {
sum += a[i];
if(a[i] == 0)
num_zero++;
}
if(sum < target || (sum+target)%2 == 1)
return 0;
int ans = subsetSum(a, a.size(), (sum + target)/2);
return pow(2, num_zero) * ans;
}

The source of the problem is this part, initializing col == 0:
# col == 0
for i in range(len(nums)):
# [] or [0]
if i == 0 and nums[i] == 0:
dp[i][0] = 2
# [] or [0] from previous
elif nums[i] == 0:
dp[i][0] = 2 * dp[i-1][0]
else: # empty set only
dp[i][0] = 1
This code treats zeros differently depending on how the list is ordered (it resets the value to 1 if it hits a nonzero value). It should instead look like this:
# col == 0
for i in range(len(nums)):
# [] or [0]
if i == 0 and nums[i] == 0:
dp[i][0] = 2
elif i == 0:
dp[i][0] = 1
# [] or [0] from previous
elif nums[i] == 0:
dp[i][0] = 2 * dp[i-1][0]
else: # empty set only
dp[i][0] = dp[i - 1][0]
This way, the first value is set to either 2 or 1 depending on whether or not it's zero, and nonzero values later in the list don't reset the value to 1. This outputs 50 in your sample case.
You can also remove room for error by giving simpler initial conditions:
def findTargetSumWays(nums, S):
"""
:type nums: List[int]
:type S: int
:rtype: int
"""
if (sum(nums) + S) % 2 == 1 or sum(nums) < S:
return 0
ssum = (sum(nums) + S) // 2
dp = [[0 for _ in range(ssum + 1)] for _ in range(len(nums) + 1)]
# col == 0
dp[0][0] = 1
for i in range(len(nums)):
for s in range(ssum + 1):
dp[i + 1][s] = dp[i][s]
if s >= nums[i]:
dp[i + 1][s] += dp[i][s - nums[i]]
return dp[len(nums)][ssum]
This adds an additional row to represent the state before you add any numbers (just a 1 in the top left corner), and it runs your algorithm on the rest of the rows. You don't need to initialize anything else or treat zeros differently, and this way it should be easier to reason about the code.

The issue with your function is related to the way you manage zero values in the list. Perhaps a simpler way for you to handle the zero values would be to exclude them from the process and then multiply your resulting count by 2**Z where Z is the number of zero values.
While trying to find the problem, I did a bit of simplification on your code and ended up with this: (which gives the right answer, even with zeroes in the list).
ssum = (sum(nums) + S) // 2
dp = [1]+[0]*ssum # number of sets that produce each sum from 0 to ssum
for num in nums:
for s in reversed(range(num,ssum + 1)):
dp[s] += dp[s-num]
return dp[ssum]
What I did was:
Eliminate a dimension in dp because you don't need to keep all the previous set counts. Only the current and next one. Actually it can work using only the current set counts if you process the sum values backwards from ssum down to zero (which i did).
The condition s >= nums[i]was eliminated by starting the s range from the current num value so that the index s - num can never be negative.
With that done, there was no need for an index on nums, I could simply go through the values directly.
Then I got rid of all the special conditions on zero values by initializing dp with 1 for the zero sum (i.e. initially an empty set is the one solution to obtain a sum of zero, then increments proceed from there).
Starting with the empty set baseline allows the progressive accumulation of set counts to produce the right result for all values without requiring any special treatment of zeroes. When num is zero it will naturally double all the current set counts because dp[s] += dp[s-0] is the same as dp[s] = 2 * dp[s]. If the list starts out with a zero then the set count for a sum of zero (dp[0]) will be doubled and all subsequent num values will have a larger starting count (because they start out from the dp[0] value initialized with 1 for the empty set).
With that last change, the function started to give the right result.
My assertion is that, because your solution was not starting from the "empty set" baseline, the zero handling logic was interfering with the natural progression of set counts. I didn't try to fine tune the zero conditions because they weren't needed and it seemed pointless to get them to arrive at the same states that a mere initialization "one step earlier" would produce
From there, the logic can be further optimized by avoiding assignments do dp[s] outside the range of minimum and maximum possible sums (which "slides" forward as we progress through the nums list):
ssum = (sum(nums) + S) // 2
dp = [1]+[0]*ssum
maxSum = 0
minSum = S - ssum # equivalent to: ssum - sum(nums)
for num in nums:
maxSum += num
minSum += num
for s in reversed(range(max(num,minSum),min(ssum,maxSum)+1)):
dp[s] += dp[s-num]
return dp[ssum]

DP solution to find the maximum length of a contiguous subarray with equal number of 0 and 1

The question is from here https://leetcode.com/problems/contiguous-array/
Actually, I came up with a DP solution for this question.
However, It won't pass one test case.
Any thought?
DP[i][j] ==1 meaning from substring[i] to substring[j] is valid
Divide the question into smaller
DP[i][j]==1
- if DP[i+2][j]==1 and DP[i][i+1]==1
- else if DP[i][j-2]==1 and DP[j-1][j]==1
- else if num[i],num[j] == set([0,1]) and DP[i+1][j-1]==1
```
current_max_len = 0
if not nums:
return current_max_len
dp = [] * len(nums)
for _ in range(len(nums)):
dp.append([None] * len(nums))
for thisLen in range(2, len(nums)+1, 2):
for i in range(len(nums)):
last_index = i + thisLen -1
if i + thisLen > len(nums):
continue
if thisLen==2:
if set(nums[i:i+2]) == set([0, 1]):
dp[i][last_index] = 1
elif dp[i][last_index-2] and dp[last_index-1][last_index]:
dp[i][last_index] = 1
elif dp[i][i + 1] and dp[i + 2][last_index]:
dp[i][last_index] = 1
elif dp[i + 1][last_index-1] and set([nums[i], nums[last_index]]) == set([0, 1]):
dp[i][last_index] = 1
else:
dp[i][last_index] = 0
if dp[i][last_index] == 1:
current_max_len = max(current_max_len, thisLen)
return current_max_len
```

Here is a counter example [1, 1, 0, 0, 0, 0, 1, 1]. The problem with you solution that it requires a list to be composed of smaller valid lists of size n-1 or n-2 in this counter example it's two lists of length 4 or n-2 . -- SPOILER ALERT -- You can solve the problem by using other dp technique basically for every i,j you can find the number of ones and zeroes between them in constant time to do that just store the number of ones from the start of the list to every index i

here is python code
def func( nums):
track,has=0,{0:-1}
length=len(nums);
ress_max=0;
for i in range(0,length):
track += (1 if nums[i]==1 else -1)
if track not in has:
has[track]=i
elif ress_max <i-has[track]:
ress_max = i-has[track]
return ress_max
l = list(map(int,input().strip().split()))
print(func(l))

Since given length of binary string may be at most 50000. So, running O(n * n) algorithm may lead to time limit exceed. I would like to suggest you to solve it in O(n) time and space complexity. The idea is :
If we take any valid contiguous sub-sequence and perform summation of numbers treating 0 as -1 then, total summation should be zero always.
If we keep track of prefix summation then we can get zero summation in the range L to R, if prefix summation up to L - 1 and prefix summation up to R are equal.
Since we are looking for maximum length, we will always treat index of newly found summation as a first one and put it into hash map with value as current index and which will persist forever for that particular summation.
Every time we calculate cumulative summation, we look whether it has any previous occurrence. If it has previous occurrence we calculate length and try to maximize , otherwise it will be the first one and will persist forever in hash map with value as current index.
Note: To calculate pure prefix, we must treat summation 0 is already in map and paired with value -1 as index.
The sample code in C++ is as follow:
int findMaxLength(vector<int>& nums) {
unordered_map<int,int>lastIndex;
lastIndex[0] = -1;
int cumulativeSum = 0;
int maxLen = 0;
for (int i = 0; i < nums.size(); ++i) {
cumulativeSum += (nums[i] == 0 ? -1 : 1);
if (lastIndex.find(cumulativeSum) != lastIndex.end()) {
maxLen = max(maxLen, i - lastIndex[cumulativeSum]);
} else {
lastIndex[cumulativeSum] = i;
}
}
return maxLen;
}

Speeding up Python code that has to go through entire list

I have a problem where I need to (pretty sure at least) go through the entire list to solve. The question is to figure out the largest number of consecutive numbers in a list that add up to another (greater) element in that list. If there aren't any then we just take the largest value in the list as the candidate summation and 1 as the largest consecutive number of elements.
My general code works, but not too well for large lists (>500,000 elements). I am just looking for tips as to how I could approach the problem differently. My current approach:
L = [1,2,3,4,5,6,7,8,9,10]
candidate_sum = L[-1]
largest_count = 1
N = len(L)
i = 0
while i < N - 1:
s = L[i]
j = 0
while s <= (N - L[i + j + 1]):
j += 1
s += L[i+j]
if s in L and (j+1) > largest_count:
largest_count = j+1
candidate_sum = s
i+=1
In this case, the answer would be [1,2,3,4] as they add up to 10 and the length is 4 (obviously this example L is a very simple example).
I then made it faster by changing the initial while loop condition to:
while i < (N-1)/largest_count
Not a great assumption, but basic thinking that the distribution of numbers is somewhat uniform, so two numbers on the second half of the list are on average bigger than the final number in the list, and therefore are disqualified.
I'm just looking for:
possible bottlenecks
suggestions as to different approaches to try

Strictly ascending: no duplication of elements or subsequences, single possible solution
Arbitrary-spaced: no arithmetical shortcuts, has to operate brute-force
Efficient C implementation using pointer arithmetic, quasi polymorphic over numeric types:
#define TYPE int
int max_subsum(TYPE arr [], int size) {
int max_length = 1;
TYPE arr_fst = * arr;
TYPE* num_ptr = arr;
while (size --) {
TYPE num = * num_ptr++;
TYPE* lower = arr;
TYPE* upper = arr;
TYPE sum = arr_fst;
int length = 1;
for (;;) {
if (sum > num) {
sum -= * lower++;
-- length;
}
else if (sum < num) {
sum += * ++upper;
++ length;
}
else {
if (length > max_length) {
max_length = length;
}
break;
}
}
}
return max_length;
}
The main loop over the nums is parallelizable. Relatively straight-forward translation into Python 3 using the dynamic-array list type for arr and the for each loop:
def max_subsum(arr):
max_len = 1
arr_fst = arr[0]
for n in arr:
lower = 0
upper = 0
sum = arr_fst
while True:
if sum > n:
sum -= arr[lower]
lower += 1
elif sum < n:
upper += 1
sum += arr[upper]
else:
sum_len = upper - lower + 1
if sum_len > max_len:
max_len = sum_len
break
return max_len
This max_subsum is a partial function; Python lists can be empty. The algorithm is appropriate for C-like compiled imperative languages offering fast indexing and statically typed arithmetic. Both are comparatively expensive in Python. A (totally defined) algorithm rather similar to yours, using the set data type for more performant universal quantification, and avoiding Python's dynamically typed arithmetic, can be more efficiently interpreted:
def max_subsum(arr):
size = len(arr)
max_len = 0
arr_set = set(arr)
for i in range(size):
sum = 0
sum_len = 0
for j in range(i, size):
sum_mem = sum + arr[j]
if num_mem not in arr_set:
break
sum = sum_mem
sum_len += 1
if sum_len > max_len:
max_len = sum_len
return max_len

I'm going to ignore the possibility of a changing target value, and let you figure that out, but to answer your question "is there a faster way to do it?" Yes: by using cumulative sums and some math to eliminate one of your loops.
import numpy as np
L = np.random.randint(0,100,100)
L.sort()
cum_sum = np.cumsum(L)
start = 0
end = 0
target = 200
while 1:
total = cum_sum [end-1] - (cum_sum [start-1] if start else 0)
if total == target:
break
elif total < target:
end += 1
elif total > target:
start += 1
if end >= len(L):
raise ValueError('something informative')

Find next highest lexicgraphic permutation of a string [duplicate]

This question already has answers here:
How to get the next lexicographically bigger string in a sorted list by using itertools module?
(5 answers)
Closed 6 years ago.
given a string W, what i want to achieve its next string lexicographically greater.
eg 1:
givenstring = "hegf"
nexthighest = "hefg"
what i have tried till now is here,
from itertools import permutations
q = int(input())
for i in range(q):
s = input()
if s == s[::-1]:
print("no answer")
else:
x = ["".join(p) for p in list(permutations(s))]
x.sort()
index = x.index(s)
print(x[index+1])
since this is not the efficient way to solve this. can u please suggest me better way to solve this problem

here is another way to solve this problem
def NextHighestWord(string):
S = [ord(i) for i in string]
#find non-incresing suffix from last
i = len(S) - 1
while i > 0 and S[i-1] >= S[i]:
i = i - 1
if i <= 0:
return False
#next element to highest is pivot
j = len(S) - 1
while S[j] <= S[i -1]:
j = j - 1
S[i-1],S[j] = S[j],S[i-1]
#reverse the suffix
S[i:] = S[len(S) - 1 : i-1 : -1]
ans = [chr(i) for i in S]
ans = "".join(ans)
print(ans)
return True
test = int(input())
for i in range(test):
s = input()
val = NextHighestWord(s)
if val:
continue
else:
print("no answer")

One classic algorithm to generate next permutation is:
Step 1: Find the largest index k, such that A[k] < A[k + 1].
If not exist, this is the last permutation. (in this problem just reverse the vector and return.)
Step 2: Find the largest index l, such that A[l] > A[k] and l > k.
Step 3: Swap A[k] and A[l].
Step 4: Reverse A[k + 1] to the end.
Here is my C++ snippet of above algorithm. Though its not python, the syntax is simple and pseudo-code alike, hope you will get the idea.
void nextPermutation(vector<int> &num) {
int k = -1;
int l;
//step1
for (int i = num.size() - 1; i > 0; --i) {
if (num[i - 1] < num[i]) {
k = i - 1;
break;
}
}
if (k == -1) {
reverse(num.begin(), num.end());
return;
}
//step2
for (int i = num.size() - 1; i > k; --i) {
if (num[i] > num[k]) {
l = i;
break;
}
}
//step3
swap(num[l], num[k]);
//step4
reverse(num.begin() + k + 1, num.end());
}

How does one iteratively write merge sort?

I have written a recursive version of merge sort. It makes use of a separate merge routine:
def merge(lst1, lst2):
i = j = 0
merged = []
while i < len(lst1) and j < len(lst2):
if lst1[i] <= lst2[j]:
merged.append(lst1[i])
i += 1
else:
merged.append(lst2[j])
j += 1
merged.extend(lst1[i:])
merged.extend(lst2[j:])
return merged
def merge_sort(lst):
if len(lst) < 2:
return lst
else:
middle = len(lst) / 2
return merge(merge_sort(lst[:middle]), merge_sort(lst[middle:]))
To conserve stack space (and for kicks/the sheer joy of learning algorithms), I am trying to write this function in an iterative manner. However, I find this difficult because I am not sure how to combine disparate lists in the very end.
Thank you!

You will need a merge function (the same or almost same merge function) which will be called repeatedly. So, you don't need to change the merge function.
This is a multiple pass solution. Start with a chunk size of 2 and double the chunk size in every pass.
In every pass, partition the list into non-overlapping chunks of size whatever. Split every chunk into 2 and call merge on the 2 parts.
This is a bottom up version.

I expanded based on Divya's description (added a test function for verification as well). The below code may be optimized by eliminating sub-arrays(data_1 and data_2) and sorting in place.
def merge_sort_iterative(data):
""" gets the data using merge sort and returns sorted."""
for j in range(1, len(data)):
j *= 2
for i in range(0,len(data),j):
data_1 = data[i:i+(j/2)]
data_2 = data[i+(j/2):j-i]
l = m = 0
while l < len(data_1) and m < len(data_2):
if data_1[l] < data_2[m]:
m += 1
elif data_1[l] > data_2[m]:
data_1[l], data_2[m] = data_2[m], data_1[l]
l += 1
data[i:i+(j/2)], data[i+(j/2):j-i] = data_1, data_2
return data
def test_merge_sort():
"""test function for verifying algorithm correctness"""
import random
import time
sample_size = 5000
sample_data = random.sample(range(sample_size*5), sample_size)
print 'Sample size: ', sample_size
begin = time.time()
sample_data = [5,4,3,2,1]
result = merge_sort_iterative(sample_data)
end = time.time()
expected = sorted(sample_data)
print 'Sorting time: %f \'secs'%(end-begin)
assert result == expected, 'Algorithm failed'
print 'Algorithm correct'
if __name__ == '__main__':
test_merge_sort()

Here is Java Implementation
public static <T extends Comparable<? super T>> void iterativeMergeSort(T[] seed) {
for (int i = 1; i <seed.length; i=i+i)
{
for (int j = 0; j < seed.length - i; j = j + i+i)
{
inPlaceMerge(seed, j, j + i-1, Math.min(j+i + i -1, seed.length -1));
}
}
}
public static <T extends Comparable<? super T>> void inPlaceMerge(T[] collection, int low, int mid, int high) {
int left = low;
int right = mid + 1;
if(collection[mid].equals(collection[right])) {
return ;//Skip the merge if required
}
while (left <= mid && right <= high) {
// Select from left: no change, just advance left
if (collection[left].compareTo(collection[right]) <= 0) {
left ++;
} else { // Select from right: rotate [left..right] and correct
T tmp = collection[right]; // Will move to [left]
rotateRight(collection, left, right - left);
collection[left] = tmp;
// EVERYTHING has moved up by one
left ++; right ++; mid ++;
}
}
}
Here is the unit test
private Integer[] seed;
#Before
public void doBeforeEachTestCase() {
this.seed = new Integer[]{4,2,3,1,5,8,7,6};
}
#Test
public void iterativeMergeSortFirstTest() {
ArrayUtils.<Integer>iterativeMergeSort(seed);
Integer[] result = new Integer[]{1,2,3,4,5,6,7,8};
assertThat(seed, equalTo(result));
}

Recursion is more intuitive hence I prefer the same except in some situations when I want to avoid a significant stack depth (e.g while consuming certain co-routine implementations). In case of Merge sort however the iterative version is actually easier to follow (at least the pseudo code).
All that's needed is a nested loop with the inner loop performing merges on pairs of 2^k elements with the outer loop responsible for incrementing k.
An additional step that is required is to merge any unpaired group with the previous merged group. An unpaired group will be encountered if the number of elements is not a power of 2. An unpaired group will always be at the end of the iteration.
e.g.
[5, 7, 3, 4, 1, 9] -> [5, 7] [3, 4] [1, 9] -> [3, 4, 5, 7] [1, 9] -> [1, 3, 4, 5, 7, 9]
In the above example [1, 9] is a group that did not have another group to be merged with initially. Thus it was merged with the previous group (which had been merged and sorted already)
Here is a python implementation:
from MergeSort import merge
def sort(arr):
n = len(arr) - 1
c = 1
start = 0
mid = 0
end = 0
while c <= n:
while end < n:
mid = start + c//2
end = start + c
if (start < n) and (end <= n):
merge(arr, start, mid, end)
start = end + 1
else:
merge(arr, start - c - 1, start-1, n)
c = 2*c + 1
start = 0
mid = 0
end = 0
I used the merge function from the regular (recursive) version. While the above code is not the most elegant, but it works and has the same complexity as the recursive version. (I have not checked thoroughly but it seems so to me from a quick glance)
Here is a unit test:
def test_merge_sort_iterative(self):
for i in range(1, 100):
length = randint(10, 5000)
data = [randint(1, 10000) for x in range(1, length)]
IterativeMergeSort.sort(data)
issorted = True
i = 0
while (i < len(data) - 1) & issorted:
if data[i] > data[i + 1]:
issorted = False
i += 1
self.assertTrue(issorted, data)
return