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Problem solution · Python

String Transformation

String Transformation: a Python solution using dynamic programming. Learn the idea, check the complexity, and read the full code, with credit to walkccc LeetCode Solutions.

Technique
Dynamic programming
Source
walkccc LeetCode Solutions
Length
53 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Dynamic programming

For String Transformation, the implementation records answers for smaller states and reuses them to build the requested result without repeating work.

  1. Define precisely what one DP state represents.
  2. Establish the base cases before transitions are evaluated.
  3. Process states in dependency order and combine only already-known values.

Code notes

  • 53 lines of Python from the credited upstream file 2851.py.
  • The implementation visibly relies on sequence storage, cached states.
  • No explicit loop blocks detected.

Complexity

Multiply the number of reachable states by the work performed for each transition, then include the stored state table in memory usage.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from walkccc LeetCode Solutions by P.-Y. Chen (walkccc) and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeString Transformation · PythonPython
Use this to learn the idea, then write your own version.
class Solution:  # This dynamic programming table dp[k][i] represents the number of ways to  # rearrange the String s after k steps such that it starts with s[i].  # A String can be rotated from 1 to n - 1 times. The transition rule is  # dp[k][i] = sum(dp[k - 1][j]) for all j != i. For example, when n = 4 and  # k = 3, the table looks like this:  #  # -----------------------------------------------------------  # |       | i = 0 | i = 1 | i = 2 | i = 3 | sum = (n - 1)^k |  # -----------------------------------------------------------  # | k = 0 |   1   |   0   |   0   |   0   |        1        |  # | k = 1 |   0   |   1   |   1   |   1   |        3        |  # | k = 2 |   3   |   2   |   2   |   2   |        9        |  # | k = 3 |   6   |   7   |   7   |   7   |       27        |  # -----------------------------------------------------------  #  # By observation, we have  #   * dp[k][!0] = ((n - 1)^k - (-1)^k) / n  #   * dp[k][0] = dp[k][!0] + (-1)^k  def numberOfWays(self, s: str, t: str, k: int) -> int:    MOD = 1_000_000_007    n = len(s)    negOnePowK = 1 if k % 2 == 0 else -1  # (-1)^k    z = self._zFunction(s + t + t)    # indices in `s` s.t. for each `i` in the returned indices,    # `s[i..n) + s[0..i) = t`.    indices = [i - n for i in range(n, n + n) if z[i] >= n]    dp = [0] * 2  # dp[0] := dp[k][0]; dp[1] := dp[k][!0]    dp[1] = (pow(n - 1, k, MOD) - negOnePowK) * pow(n, MOD - 2, MOD)    dp[0] = dp[1] + negOnePowK    return sum(dp[0] if index == 0 else dp[1] for index in indices) % MOD   def _zFunction(self, s: str) -> list[int]:    """    Returns the z array, where z[i] is the length of the longest prefix of    s[i..n) which is also a prefix of s.     https://cp-algorithms.com/string/z-function.html#implementation    """    n = len(s)    z = [0] * n    l = 0    r = 0    for i in range(1, n):      if i < r:        z[i] = min(r - i, z[i - l])      while i + z[i] < n and s[z[i]] == s[i + z[i]]:        z[i] += 1      if i + z[i] > r:        l = i        r = i + z[i]    return z

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