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question:Data Cleanup and Reconciler You are given a list of product records from two different databases. Each record contains a product ID and a product name, and you need to reconcile discrepancies between these databases. Sometimes, two records from the two databases may represent the same product but are listed with slightly different names or small typographical errors. Task Your task is to implement a function `reconcile_products` that takes two lists of product records and returns a list of reconciled product IDs. For the purpose of this task, assume that two product names are considered a match if their Levenshtein distance is less than or equal to 2. The Levenshtein distance between two strings is a measure of the difference between them, with smaller values indicating more similar strings. It is defined as the minimum number of single-character edits (insertions, deletions or substitutions) required to change one string into the other. Function Signature ```python def reconcile_products(db1: List[Tuple[int, str]], db2: List[Tuple[int, str]]) -> List[int]: ``` Input - `db1`: List of tuples representing products in the first database. Each tuple contains an integer product ID and a string product name. - `db2`: List of tuples representing products in the second database. Each tuple contains an integer product ID and a string product name. The elements of db1 and db2 are as follows: ```python db1: [(int, str)] db2: [(int, str)] ``` Output - Returns a list of product IDs from `db1` that have a matching product in `db2` based on the aforementioned criterion. Example ```python db1 = [(1, "apple"), (2, "banana"), (3, "orange")] db2 = [(4, "appl"), (5, "banan"), (6, "oraneg")] output = reconcile_products(db1, db2) print(output) # [1, 2, 3] ``` In the example, "apple" in db1 and "appl" in db2 match because their Levenshtein distance is 1. Similarly, "banana" and "banan" (distance 1) and "orange" and "oraneg" (distance 1) match. Good luck!
answer:from typing import List, Tuple import numpy as np def levenshtein_distance(a: str, b: str) -> int: if a == b: return 0 if len(a) < len(b): a, b = b, a previous_row = range(len(b) + 1) for i, ca in enumerate(a): current_row = [i + 1] for j, cb in enumerate(b): insertions = previous_row[j + 1] + 1 deletions = current_row[j] + 1 substitutions = previous_row[j] + (ca != cb) current_row.append(min(insertions, deletions, substitutions)) previous_row = current_row return previous_row[-1] def reconcile_products(db1: List[Tuple[int, str]], db2: List[Tuple[int, str]]) -> List[int]: matching_ids = [] for id1, name1 in db1: for id2, name2 in db2: if levenshtein_distance(name1, name2) <= 2: matching_ids.append(id1) break # no need to continue, we found a match for this id1 return matching_ids
question:Develop a function `isPerfect` that determines whether a given string of digits contains a perfect square within a certain range of lengths. A perfect square is a number that is the square of an integer. For example, 16 is a perfect square because it is 4 squared (4 * 4 = 16). The function will take three parameters: 1. `s` (string) - the string of digits to examine. 2. `minLength` (integer) - the minimum length of the substring to consider. 3. `maxLength` (integer) - the maximum length of the substring to consider. The function `isPerfect` should return `True` if there exists at least one perfect square among the substrings of lengths between `minLength` and `maxLength` (inclusive) within the given string. Otherwise, it returns `False`. # Example ```python isPerfect("14432116", 2, 3) # This would return True, since 16 and 144 are perfect squares. isPerfect("123456789", 2, 2) # This would return False, since no 2-digit substrings are perfect squares. isPerfect("2125263649", 1, 2) # This would return True, since 4 and 9 (both single digits) are perfect squares. ``` # Note - The function should search for all possible substrings within the specified length range. - Consider leading zeros in substrings. For example, "04" is valid, but is ignored for being not a perfect square of any integer. # Constraints - The length of the string `s` is between 1 and 1000. - `minLength` and `maxLength` will satisfy 1 <= minLength <= maxLength <= length of `s`. # Implementation ```python def isPerfect(s, minLength, maxLength): def is_perfect_square(n): if n < 0: return False root = int(n ** 0.5) return root * root == n for length in range(minLength, maxLength + 1): for i in range(len(s) - length + 1): sub = s[i:i + length] if is_perfect_square(int(sub)): return True return False # Test cases print(isPerfect("14432116", 2, 3)) # True print(isPerfect("123456789", 2, 2)) # False print(isPerfect("2125263649", 1, 2)) # True ```
answer:def isPerfect(s, minLength, maxLength): Determines if there exists a perfect square among the substrings of a given string of digits within the specified range of lengths. Parameters: s (str): The string of digits to examine. minLength (int): The minimum length of the substring to consider. maxLength (int): The maximum length of the substring to consider. Returns: bool: True if there exists a perfect square among the substrings, False otherwise. def is_perfect_square(n): if n < 0: return False root = int(n ** 0.5) return root * root == n for length in range(minLength, maxLength + 1): for i in range(len(s) - length + 1): sub = s[i:i + length] if sub[0] == '0': continue if is_perfect_square(int(sub)): return True return False
question:Determine Dependent Variable You will be given a dataset represented as a list of tuples, where each tuple contains pairs of values that follow a certain linear relationship. Your task is to determine the dependent variable (i.e., the y-values) given the independent values (i.e., the x-values), the slope `m`, and the y-intercept `b` of the linear relationship. Return the calculated list of dependent variables. For example: ```python def calculate_y_values(data, m, b): return [m * x + b for x in data] # Test cases print(calculate_y_values([0, 1, 2, 3], 2, 1)) # should return [1, 3, 5, 7] print(calculate_y_values([-1, 0, 1, 2], -3, 4)) # should return [7, 4, 1, -2] print(calculate_y_values([10, 20, 30], 0.5, -2)) # should return [3, 8, 13] ``` NOTES: * The dataset of x-values and the constants `m` (slope) and `b` (y-intercept) will be provided as input. * The dataset will contain at least one value and at most 100 values. * The slope and y-intercept will be real numbers and can be negative or positive. * Your function should handle floating-point arithmetic with reasonable precision.
answer:def calculate_y_values(data, m, b): Calculate the dependent variable (y-values) given the independent variable (x-values), slope (m), and y-intercept (b). Parameters: data (list of float): The list of independent variable values. m (float): The slope of the linear relationship. b (float): The y-intercept of the linear relationship. Returns: list of float: The calculated dependent variable values. return [m * x + b for x in data]
question:Anna and her friends are playing with stacks of coins and they need your help to keep the game fair. They start with an empty stack and perform m operations. Each operation can be one of the following: 1. Add x_{i} coins to the stack. 2. Remove the top y_{i} coins from the stack. This operation can only be performed if the stack has at least y_{i} coins in it. 3. Get the maximum number of coins in the stack at any point in time after every operation. Your task is to help Anna and her friends track the maximum number of coins in the stack after each operation and output this value. -----Input----- The first line contains a single integer m (1 ≤ m ≤ 2·10^5) — the number of operations. The next m lines describe the operations. Each line will start with an integer t_{i} (1 ≤ t_{i} ≤ 3), denoting the type of the operation (see above). If t_{i} = 1, it will be followed by a single integer x_{i} (1 ≤ x_{i} ≤ 10^3). If t_{i} = 2, it will be followed by a single integer y_{i} (1 ≤ y_{i} ≤ 10^3; y_{i} ≤ current number of coins in the stack). It is guaranteed that all operations are correct (y_{i} ≤ current number of coins in the stack). -----Output----- Output m lines each containing the maximum number of coins in the stack after the corresponding operation. -----Examples----- Input 5 1 100 1 50 2 30 1 200 3 Output 100 150 120 320 320 Input 4 1 10 1 5 2 10 3 Output 10 15 5 5 -----Note----- In the first sample, the stack changes as follows: - Add 100 coins: [100] (maximum = 100) - Add 50 coins: [100, 50] (maximum = 150) - Remove 30 coins: [100, 20] (maximum = 120) - Add 200 coins: [100, 20, 200] (maximum = 320) - Get maximum: 320 In the second sample, the stack changes as follows: - Add 10 coins: [10] (maximum = 10) - Add 5 coins: [10, 5] (maximum = 15) - Remove 10 coins: [5] (maximum = 5) - Get maximum: 5
answer:def track_max_coins(m, operations): Returns a list of the maximum number of coins in the stack after each operation. Args: m (int): The number of operations. operations (list of tuples): Each tuple contains the operation code and the corresponding value. Returns: list: A list containing the maximum number of coins in the stack after each operation. stack = [] max_coins = 0 result = [] for operation in operations: if operation[0] == 1: # Add operation coins = operation[1] stack.append(coins) max_coins += coins elif operation[0] == 2: # Remove operation coins = operation[1] stack[-1] -= coins if stack[-1] == 0: stack.pop() max_coins -= coins elif operation[0] == 3: # Get maximum operation result.append(max_coins) if operation[0] != 3: # For non-max operations, always append the current max_coins result.append(max_coins) return result