The 2 statements that correctly describe the time complexity of data structures with N data are:
- The average time complexity of data structures with N data is O(N).
- The average time complexity of inserting data into a heap is O(logN)
<h3>What is Time Complexity in the above case/</h3>
This is known to be the amount of times a specific instruction set is executed instead of the total time is taken.
Note that The 2 statements that correctly describe the time complexity of data structures with N data are:
- The average time complexity of data structures with N data is O(N).
- The average time complexity of inserting data into a heap is O(logN)
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it mean for some people that have technology like a computer and there are some who don't have access to technology like phones, and tv's
Answer:
Type of Business/entity and User Location are True.
User Intent and your judgment are False.
Explanation:
The following are the factors that treated at the time of deciding the following business is based on the too far query and the location of the user. So, That's why the first two factors are applied by the type of business or entity and the location of the user but the last two factors are not applied because it is not about the user, it is related to the firm or the business.
Answer:
Explanation:
The following code is written in Python, the function creates various nested loops to loop through each individual string comparing letter by letter in order to find the longest common substring. Finally, returning the substring itself.
def shared_motif(dna_list):
substr = ''
if len(dna_list) > 1 and len(dna_list[0]) > 0:
for x in range(len(dna_list[0])):
for i in range(len(dna_list[0]) - x + 1):
if i > len(substr) and all(dna_list[0][x:x + i] in y for y in dna_list):
substr = dna_list[0][x:x + i]
return substr
arr = ["GATTACA", "TAGACCA", "ATACA"]
stems = shared_motif(arr)
print(stems)
