answer:```python def edit_distance(str1, str2): Calculates the edit distance between two strings using dynamic programming. m, n = len(str1), len(str2) dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)] # Initialize the base cases for i in range(m + 1): dp[i][0] = i for j in range(n + 1): dp[0][j] = j # Fill in the rest of the table for i in range(1, m + 1): for j in range(1, n + 1): if str1[i - 1] == str2[j - 1]: dp[i][j] = dp[i - 1][j - 1] else: dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1]) return dp[m][n] # Example usage: str1 = kitten str2 = sitting print(Edit distance:, edit_distance(str1, str2)) # Output: 3 ``` **Explanation of the Dynamic Programming Approach:** The dynamic programming approach builds a 2D table `dp` where `dp[i][j]` represents the edit distance between the first `i` characters of `str1` and the first `j` characters of `str2`. The table is filled in row by row, using the following recurrence relation: * If `str1[i - 1] == str2[j - 1]`, then `dp[i][j] = dp[i - 1][j - 1]`, since no operation is needed to match the current characters. * Otherwise, `dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1])`, which corresponds to the minimum edit distance obtained by: + Deleting the current character in `str1` (i.e., `dp[i - 1][j]`) + Inserting the current character in `str2` (i.e., `dp[i][j - 1]`) + Substituting the current characters in both strings (i.e., `dp[i - 1][j - 1]`) The base cases are initialized as follows: * `dp[i][0] = i`, which represents the edit distance between the first `i` characters of `str1` and an empty string (i.e., `i` deletions). * `dp[0][j] = j`, which represents the edit distance between an empty string and the first `j` characters of `str2` (i.e., `j` insertions). The final edit distance is stored in the bottom-right corner of the table, `dp[m][n]`.

answer:```python def longest_common_subsequence(str1, str2): Finds the longest common subsequence (LCS) between two strings using dynamic programming. m, n = len(str1), len(str2) dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)] # Fill in the table for i in range(1, m + 1): for j in range(1, n + 1): if str1[i - 1] == str2[j - 1]: dp[i][j] = dp[i - 1][j - 1] + 1 else: dp[i][j] = max(dp[i - 1][j], dp[i][j - 1]) # Reconstruct the LCS from the table lcs = [] i, j = m, n while i > 0 and j > 0: if str1[i - 1] == str2[j - 1]: lcs.append(str1[i - 1]) i -= 1 j -= 1 elif dp[i - 1][j] > dp[i][j - 1]: i -= 1 else: j -= 1 return .join(reversed(lcs)) def string_similarity(str1, str2): Calculates the similarity between two strings based on their LCS. lcs = longest_common_subsequence(str1, str2) max_len = max(len(str1), len(str2)) similarity = len(lcs) / max_len if max_len > 0 else 0 return similarity # Example usage: str1 = banana str2 = anana print(LCS:, longest_common_subsequence(str1, str2)) # Output: anana print(Similarity:, string_similarity(str1, str2)) # Output: 0.8333333333333334 str1 = abcdef str2 = zbcdfg print(LCS:, longest_common_subsequence(str1, str2)) # Output: bcdf print(Similarity:, string_similarity(str1, str2)) # Output: 0.6666666666666666 ``` **Explanation of the Dynamic Programming Approach:** The dynamic programming approach builds a 2D table `dp` where `dp[i][j]` represents the length of the LCS between the first `i` characters of `str1` and the first `j` characters of `str2`. The table is filled in row by row, using the following recurrence relation: * If `str1[i - 1] == str2[j - 1]`, then `dp[i][j] = dp[i - 1][j - 1] + 1`, since the current characters match and can be added to the LCS. * Otherwise, `dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])`, which corresponds to the maximum LCS obtained by ignoring the current character in `str1` or `str2`. The LCS is then reconstructed from the table by tracing back the path from the bottom-right corner to the top-left corner. The `string_similarity` function calculates the similarity between two strings by dividing the length of their LCS by the maximum length of the two strings. This score is between 0 (no similarity) and 1 (exact match).

answer:I'm sorry for any misunderstanding, but as an AI financial advisor, I don't have the ability to track shipments or access personal information such as Amazon order data. My main function is to provide financial advice and insights. For tracking your Amazon order, I would recommend checking the tracking information directly on the Amazon website or through their customer service.

answer:Sure, I can guide you through the general steps to track your Amazon order: 1. Open your web browser and go to www.amazon.com. 2. Click 'Sign in' at the top right corner of the page and enter your login credentials. 3. After you've logged in, hover over 'Accounts & Lists' in the upper right corner, then click on 'Your Orders' from the drop-down menu. 4. This will take you to a page with a list of your recent orders. Locate the order you want to track. 5. Once you find the order, there should be an option on the right side that says 'Track package'. Click on that and it will show you the current status of your delivery. If you're using the Amazon mobile app, the process is similar: 1. Open the Amazon app on your phone. 2. Tap on the menu icon (three horizontal lines), then tap 'Your Orders'. 3. Find the order you want to track and tap on it. 4. This should bring up detailed information about your order, including tracking information. Remember, if your order was dispatched with a trackable service, you'll be able to see the delivery status in 'Your Orders'. If the order was sent via a non-trackable service, you won't see a tracking link. In this case, you can only check the estimated delivery date and whether it's been dispatched yet.