Hello there! Are you ready to solve another engaging problem today? We have a practical task that will enhance your problem-solving skills. It involves critical aspects of programming — dealing with arrays and using techniques such as hashmaps, sorting, and the two-pointer method. So, let's jump in!
Alright, our task is as follows. Suppose you have two equally long arrays, A
and B
, with a length varying from 1 to 1000, with each element being a unique positive integer ranging from 1 up to 10^6. Your challenge is to craft a Python function that identifies the closest number in array B
to 2 * B[i]
for each i
. Once this number is identified, say for the specific i
it is B[j]
, we want to create an array from A[j]
s in the order of increasing i
s.
To illustrate this, let's consider an example. We have:
Python1A = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110] 2B = [4, 12, 3, 9, 6, 1, 5, 8, 37, 25, 100]
After running your function, the resulting array should look like this:
Python1[80, 100, 50, 20, 20, 60, 40, 20, 110, 90, 110]
Let's walk through the first few steps:
The first item in B
is 4
at index=0. Double of this number is 8
. The closest number to 8
in array B
is 8
which is at index=7. The number at the same index in array A
is 80
, so we add 80
to our new array.
The second item in B
is 12
at index=1. Double of this number is 24
. The closest number to 24
in B
is 25
which is at index=9. Corresponding index in A
has the number 100
. So, we add 100
to our new array.
The third item in B
is 3
at index=2. Double of this number is 6
. The closest number to 6
in B
is 6
which is at index=4. Corresponding index in A
has the number 50
. So, we add 50
to our new array.
We continue this process for the rest of the elements in B
.
Let's embark on our solution-building journey by constructing a sorted list for array B
. This list will include pairs of values (val
) and their corresponding indices (idx
) from array B
. Here, val
represents the element in B
, while idx
denotes the index at which val
is found in array B
.
This sorted list will act similar to a hashmap, storing 'value-index' pairs. It not only organizes the data for efficient retrieval but also makes it easier for us to traverse the list. Here's the introductory part of our Python function, including the complete sorted list:
Python1def find_and_replace(A, B): 2 B_sorted = sorted([(val, idx) for idx, val in enumerate(B)], key=lambda x: x[0])
In the above code, we generate a list of tuples comprising the values from 'B' and their respective indices using the enumerate
function. Then, the sorted
function arranges these tuples in ascending order of their values.
Now that our sorted list (or hashmap) is ready, we initiate the right pointer, j
, and result array, res
. The former assists in ensuring our search remains within the boundaries of B_sorted
, while the latter will be our final output containing the replaced elements from array A
. We'll update res
as we progress through the solution.
Python1def find_and_replace(A, B): 2 B_sorted = sorted([(val, idx) for idx, val in enumerate(B)], key=lambda x: x[0]) 3 4 j = 0 # Initialize right pointer 5 res = [0]*len(A) # Initialize the result array
The primary logic of the problem lies in this step. We iterate over each item in B_sorted
using its index i
. For every item, we calculate the target which is double the value of the element at the current index in B_sorted
. We adjust the position of the right pointer, j
, until it points to the closest number that is less than the target (2 * B_sorted[i][0]
). The operation continues as long as j
is within the bound for B_sorted
and the next number in the array is smaller than the target.
Once we've identified a number greater than or equal to the target, we compare it with the previous number to see which one is closer to the target. If the next number is closer, we advance j
by one step.
Python1def find_and_replace(A, B): 2 B_sorted = sorted([(val, idx) for idx, val in enumerate(B)], key=lambda x: x[0]) 3 4 j = 0 # Initialize right pointer 5 res = [0]*len(A) # Initialize the result array 6 7 for i in range(len(B)): 8 target = 2 * B_sorted[i][0] # The target is twice the current number in the sorted B 9 while j < len(B_sorted) - 1 and B_sorted[j+1][0] < target: 10 j += 1 # Move the right pointer to find a number smaller than or equal to the target 11 if j < len(B_sorted) - 1 and abs(B_sorted[j+1][0] - target) < abs(target - B_sorted[j][0]): 12 j += 1 # Move the right pointer one more step if the next number is closer to the target
In this final step, we employ the indices from B_sorted
to alter the appropriate elements in array A
. Based on the position of the right pointer j
, we replace the corresponding element in res
with the element in A
located at the same index.
Python1def find_and_replace(A, B): 2 B_sorted = sorted([(val, idx) for idx, val in enumerate(B)], key=lambda x: x[0]) 3 4 j = 0 # Initialize right pointer 5 res = [0]*len(A) # Initialize the result array 6 7 for i in range(len(B)): 8 target = 2 * B_sorted[i][0] # The target is twice the current number in the sorted B 9 while j < len(B_sorted) - 1 and B_sorted[j+1][0] < target: 10 j += 1 # Move the right pointer to find a number smaller than or equal to the target 11 if j < len(B_sorted) - 1 and abs(B_sorted[j+1][0] - target) < abs(target - B_sorted[j][0]): 12 j += 1 # Move the right pointer one more step if the next number is closer to the target 13 res[B_sorted[i][1]] = A[B_sorted[j][1]] 14 # Collect the corresponding element from A at the same index as the closest number in B_sorted 15 16 return res
And there you have it! You've successfully crafted a Python function capable of producing the required output. This Python function iteratively replaces each element in array A
according to the defined logic and returns the modified array.
It's vital to have an understanding of the computational complexity of our Two-Pointer approach and why it's effective for this problem.
Time Complexity: The main steps of our solution involve sorting the array B
and traversing it with two pointers. Sorting an array of n
elements has a time complexity of $O(n log n)$. The two-pointer traversal of the sorted array adds an $O(n)$ time complexity. Thus, the overall time complexity of our solution is $O(n log n)$ for the sorting operation, which dominates the linear time traversal.
Space Complexity: Apart from the input data, our solution needs space for B_sorted
and res
, both of which are arrays having the same length as the input. Therefore, our solution has a linear space complexity $O(n)$, where n
is the length of the input arrays.
Great job! You've successfully tackled a high-level task that involved manipulating arrays and implementing advanced techniques such as hashmaps, sorting, and the two-pointer method. Through this exercise, you've honed your Python coding skills further and demonstrated your ability to solve a complex, real-world challenge.
Now it's your turn to master these techniques. We encourage you to practice solving similar challenges using the skills you've learned from this lesson. The more you practice, the better your problem-solving skills will become. So, get started and enjoy your journey in coding!