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VisualizaçõesUnderstanding what's happening in the Kadane Algorithm (Python)
I'm having a difficult time understanding what's happening in these two examples I found of the Kadane Algorithm. I'm new to Python and I'm hoping understanding this complex algo will help me see/read programs better.
Why would one example be better than the other, is it just List vs Range? Is there something else that makes one of the examples more efficient? Also, some questions about what's happening in the calculations. (questions inside the examples)
I've used PythonTutor to help me get a visual on what exactly is happening step by step.
Example 1:
In PythonTuter, when you select next step in the screen shot provided, The value of so_far turns to 1. How is this? Giving the sum, I've thought its adding -2 + 1 which is -1, so when so_far turns to 1, how is this?
def max_sub(nums):
max_sum = 0
so_far = nums[0]
for x in nums[1:]:
so_far = max(x, x + so_far)
max_sum = max(so_far, max_sum)
return max_sum
nums = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
max_sub(nums)
6
Example 2:
Similar question for this one, when I select NEXT step, the max_sum turns from -2 to 4... but how so if it's adding the element in the 2 (which is 4). To me, that would be -2 + 4 = 2 ?
def maxSubArraySum(a,size):
max_so_far =a[0]
curr_max = a[0]
for i in range(1,size):
curr_max = max(a[i], curr_max + a[i])
max_so_far = max(max_so_far,curr_max)
return max_so_far
a = [-2, -3, 4, -1, -2, 1, 5, -3]
print("Maximum contiguous sum is" , maxSubArraySum(a,len(a)))
Maximum contiguous sum is 7
So, this would be a 2 part question than:
[1]Based on understandings, why would one be more pythonic and more efficient than the other?
[2]How can I better understand the calculations happening in the examples?
2 Respostas
Responde à pergunta0
Simply watch each step and you could figure out this problem: [Notes] this program seems to work based on the assumption of mixed integer numbers? only positive and negatives.
# starting
so_far = -2 # init. to nums[0]
max_sum = 0
# in the for-loop:
x = 1 # starting with nums[1:]
so_far = max(1, -1) -> 1 (x is 1, -2 + 1)
max_sum = max(0, 1) -> 1
..... continue .... each step is to find the max accumulated numbers sum, as it's evident in the max( ) statement. *There is no `sum` involved, except it tried to determine the current x is good (not negative) then so add it to the so_far.
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More performance measurement data points to compare these two different approaches shown that first example is definitely faster ~22-24% faster than 2nd one with input size of 2k.
if __name__ == '__main__':
L = list(range(-1_000, 1_000, 1))
random.shuffle(L)
baseTestCase = partial(max_sub, nums=L)
print(timeit.timeit(baseTestCase, number=100_000)) # 86.0588067
rangeTestCase = partial(max_SubArraySum, a=L, size=len(L))
print(timeit.timeit(rangeTestCase, number=100_000)) # 105.415955