| 模型 | 模型答案 | 测试结果 |
|---|---|---|
| 金声玉亮2.0 | ```python def is_samepatterns(lst1, lst2): if len(lst1) != len(lst2): return False pattern1 = {} pattern2 = {} for i, (item1, item2) in enumerate(zip(lst1, lst2)): if item1 not in pattern1: pattern1[item1] = i if item2 not in pattern2: pattern2[item2] = i if pattern1[item1] != pattern2[item2]: return False return True ``` [DONE] |
pass 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| deepseekr1 | To solve this problem, we need to determine if two given lists follow the same sequence pattern. The sequence pattern is defined by the order in which unique elements appear. For example, if the first list has a structure where elements repeat in a certain order, the second list must follow the same repetition structure, even if the actual elements are different. ### Approach 1. **Check Lengths**: If the two lists have different lengths, they cannot follow the same pattern, so return False. 2. **Generate Patterns**: Convert each list into a normalized pattern where each element is replaced by an index representing the order of its first appearance. For example, the first unique element is 0, the second unique element is 1, and so on. 3. **Compare Patterns**: Check if the normalized patterns of the two lists are identical. If they are, the lists follow the same sequence pattern. ### Solution Code ```python def is_samepatterns(list1, list2): def get_pattern(arr): pattern = [] element_map = {} index = 0 for element in arr: if element not in element_map: element_map[element] = index index += 1 pattern.append(element_map[element]) return pattern if len(list1) != len(list2): return False return get_pattern(list1) == get_pattern(list2) ``` ### Explanation 1. **Helper Function (get_pattern)**: This function converts a list into a normalized pattern. It uses a dictionary to track each element's first occurrence index. As we iterate through the list, each element is replaced by its corresponding index based on its first appearance. 2. **Check Lengths**: The function first checks if the two input lists have the same length. If not, it immediately returns False. 3. **Pattern Comparison**: By generating the normalized patterns for both lists and comparing them, we determine if they follow the same sequence structure. If the patterns match, the function returns True; otherwise, it returns False. This approach efficiently checks for sequence patterns by converting elements into indices based on their order of appearance, ensuring that the structure rather than the actual values is compared. |
pass 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| gpto1 | 'def is_samepatterns(items, patterns): if len(items) != len(patterns): return False pattern_to_item = {} item_to_pattern = {} for item, pattern in zip(items, patterns): if pattern in pattern_to_item: if pattern_to_item[pattern] != item: return False else: pattern_to_item[pattern] = item if item in item_to_pattern: if item_to_pattern[item] != pattern: return False else: item_to_pattern[item] = pattern return True' |
failed 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| GPT-4o | To solve the task of checking whether a sequence follows the given pattern array, we can map each element of the first list to the corresponding element of the second list. If the same mapping can be consistently applied throughout the entire list, then the pattern holds. Here is the function that performs this check: ```python def is_samepatterns(arr, pattern): if len(arr) != len(pattern): return False mapping = {} reverse_mapping = {} for a, p in zip(arr, pattern): if p in mapping: if mapping[p] != a: return False else: if a in reverse_mapping: return False mapping[p] = a reverse_mapping[a] = p return True # Tests assert is_samepatterns(["red", "green", "green"], ["a", "b", "b"]) == True assert is_samepatterns(["red", "green", "greenn"], ["a", "b", "b"]) == False assert is_samepatterns(["red", "green", "greenn"], ["a", "b"]) == False ``` ### Explanation - First, we check if the lengths of `arr` and `pattern` are the same. If they are not, we return `False` immediately. - We create two dictionaries, `mapping` to store the mapping from elements in `pattern` to elements in `arr`, and `reverse_mapping` to ensure that the mapping is one-to-one. - We iterate through both `arr` and `pattern` simultaneously using `zip()`. - For each pair `(a, p)`, we check if `p` is already in `mapping`: - If it is and the corresponding mapped value is not `a`, we return `False` because the pattern is not consistent. - If `p` is not in `mapping`, we check if `a` is already in `reverse_mapping`: - If it is, we return `False` because it means `a` is already mapped to a different pattern. - Otherwise, we add the new mapping `p -> a` and `a -> p` to `mapping` and `reverse_mapping` respectively. - If we complete the iteration without conflicts, we return `True`. This approach ensures that the `arr` follows the sequence given in the `pattern` array. |
pass 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| llama3.1-70B-Instruct | def is_samepatterns(patterns, colors): if len(patterns) != len(colors): return False pattern_dict = {} for pattern, color in zip(patterns, colors): if pattern in pattern_dict: if pattern_dict[pattern] != color: return False else: if color in pattern_dict.values(): return False pattern_dict[pattern] = color return True |
pass 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| llama3.1-405B-Instruct | def is_samepatterns(patterns, words): if len(patterns) != len(words): return False pattern_map = {} for pattern, word in zip(patterns, words): if pattern in pattern_map: if pattern_map[pattern] != word: return False else: if word in pattern_map.values(): return False pattern_map[pattern] = word return True |
pass 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| DeepSeek-Coder-V2 | ```python def is_samepatterns(lst1, lst2): if len(lst1) != len(lst2): return False pattern1 = {} pattern2 = {} for i, (item1, item2) in enumerate(zip(lst1, lst2)): if item1 not in pattern1: pattern1[item1] = i if item2 not in pattern2: pattern2[item2] = i if pattern1[item1] != pattern2[item2]: return False return True ``` [DONE] |
pass 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| Qwen2-72B-Instruct | 'def is_samepatterns(str_arr, patterns): pattern_dict = {} if len(str_arr) != len(patterns): return False for i in range(len(str_arr)): if str_arr[i] not in pattern_dict: if patterns[i] in pattern_dict.values(): return False pattern_dict[str_arr[i]] = patterns[i] elif pattern_dict[str_arr[i]] != patterns[i]: return False return True' [DONE] |
pass 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| baichuan4 | To complete your task, we can use the `zip` function to pair up elements from the two input lists and then compare them. Here's a possible implementation: ```python def is_samepatterns(colors, patterns): # Pair up elements from both lists using zip paired_lists = zip(colors, patterns) # Check if each pair matches the required pattern for color, pattern in paired_lists: if color != pattern: return False # If all pairs match and lengths are equal, return True return len(colors) == len(patterns) ``` Let's run the tests you provided: ```python assert is_samepatterns(["red","green","green"], ["a", "b", "b"]) == True assert is_samepatterns(["red","green","greenn"], ["a","b","b"]) == False assert is_samepatterns(["red","green","greenn"], ["a","b"]) == False ``` All tests should pass with this implementation. |
wrong_answer 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| doubao | def is_samepatterns(pattern1, pattern2): if len(pattern1)!= len(pattern2): return False for i in range(len(pattern1)): if pattern1[i]!= pattern2[i]: return False return True [DONE] |
wrong_answer 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| minimax | ```python def is_samepatterns(colors, patterns): if len(colors) != len(patterns): return False color_to_pattern = {} pattern_to_color = {} for color, pattern in zip(colors, patterns): if color in color_to_pattern and color_to_pattern[color] != pattern: return False if pattern in pattern_to_color and pattern_to_color[pattern] != color: return False color_to_pattern[color] = pattern pattern_to_color[pattern] = color return True # Test cases assert is_samepatterns(["red", "green", "green"], ["a", "b", "b"]) == True assert is_samepatterns(["red", "green", "greenn"], ["a", "b", "b"]) == False assert is_samepatterns(["red", "green", "greenn"], ["a", "b"]) == False ``` This function `is_samepatterns` takes two lists, `colors` and `patterns`, and checks if there is a one-to-one mapping between the elements of the two lists, meaning each color is associated with exactly one pattern and vice versa. If the lengths of the lists are different, it immediately returns `False`. Otherwise, it uses two dictionaries to track the mappings from colors to patterns and from patterns to colors, ensuring that no color or pattern is associated with more than one element from the other list. If any inconsistency is found, it returns `False`; otherwise, it returns `True`. |
pass 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| kimi | Sure, let's tackle each task one by one. 1. Function to find similar elements from two tuple lists: ```python def similar_elements(test_tup1, test_tup2): res = tuple(sorted(set(test_tup1) & set(test_tup2))) return res ``` 2. Function to identify non-prime numbers: ```python import math def is_not_prime(n): if n <= 1: return True for i in range(2, int(math.sqrt(n)) + 1): if n % i == 0: return True return False ``` 3. Function to find the largest integers from a given list of numbers using heap queue algorithm: ```python import heapq def heap_queue_largest(nums, n): largest_nums = heapq.nlargest(n, nums) return largest_nums ``` 4. Function to check whether it follows the sequence given in the patterns array: ```python def is_samepatterns(pattern, sequence): return len(pattern) == len(sequence) and all(p == s for p, s in zip(pattern, sequence)) ``` Let's test each function with the provided test cases: ```python # Test case for similar_elements assert similar_elements((3, 4, 5, 6),(5, 7, 4, 10)) == (4, 5) assert similar_elements((1, 2, 3, 4),(5, 4, 3, 7)) == (3, 4) assert similar_elements((11, 12, 14, 13),(17, 15, 14, 13)) == (13, 14) # Test case for is_not_prime assert is_not_prime(2) == False assert is_not_prime(10) == True assert is_not_prime(35) == True # Test case for heap_queue_largest assert heap_queue_largest([25, 35, 22, 85, 14, 65, 75, 22, 58], 3) == [85, 75, 65] assert heap_queue_largest([25, 35, 22, 85, 14, 65, 75, 22, 58], 2) == [85, 75] assert heap_queue_largest([25, 35, 22, 85, 14, 65, 75, 22, 58], 5) == [85, 75, 65, 58, 35] # Test case for is_samepatterns assert is_samepatterns(["red","green","green"], ["a", "b", "b"]) == True assert is_samepatterns(["red","green","greenn"], ["a","b","b"]) == False assert is_samepatterns(["red","green","greenn"], ["a","b"]) == False ``` Each function should pass its respective test cases without any assertion errors. |
failed 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| wenxinyiyan | Here's the solution for the `similar_elements` function, which finds the similar elements from two given tuple lists: ```python def similar_elements(test_tup1, test_tup2): res = tuple(set(test_tup1) & set(test_tup2)) # To ensure the order of elements is consistent with the tests, we sort the tuple return tuple(sorted(res)) ``` Note: In the original code you provided, the returned tuple `res` was not sorted. Since sets do not maintain order, sorting the resulting tuple ensures a consistent output order for comparison with the test cases. Now, moving on to the `is_not_prime` function, which identifies non-prime numbers: ```python import math def is_not_prime(n): if n < 2: # Numbers less than 2 are not prime return True for i in range(2, int(math.sqrt(n)) + 1): if n % i == 0: return True return False ``` The `is_not_prime` function checks if a number `n` is not prime. It returns `True` if `n` is less than 2 or if it is divisible by any number from 2 to the square root of `n`. Otherwise, it returns `False`, indicating that the number is prime. Next, the `heap_queue_largest` function, which finds the largest integers from a given list of numbers using the heap queue algorithm: ```python import heapq as hq def heap_queue_largest(nums, n): largest_nums = hq.nlargest(n, nums) return largest_nums ``` The `heap_queue_largest` function uses the `heapq.nlargest()` function from the `heapq` module to find the `n` largest numbers in the given list `nums`. Finally, for the `is_samepatterns` function, which checks whether a sequence follows the pattern given in the patterns array: ```python def is_samepatterns(colors, patterns): if len(colors) != len(patterns): return False patterns_dict = {} for color, pattern in zip(colors, patterns): if pattern not in patterns_dict: patterns_dict[pattern] = color elif patterns_dict[pattern] != color: return False return True ``` The `is_samepatterns` function compares two sequences, `colors` and `patterns`, to check if they follow the same pattern. It creates a dictionary `patterns_dict` to store the mapping between patterns and colors. If a pattern is encountered again with a different color, the function returns `False`. If all patterns match their corresponding colors, the function returns `True`. Note: The provided test cases for `is_samepatterns` assume that the function should check if the patterns in the second list (`patterns`) correspond to the same relative ordering of elements in the first list (`colors`), regardless of the actual values. The implementation provided above fulfills this requirement. If the intention is to check for exact matches between the elements of the two lists, the implementation would need to be adjusted accordingly. |
failed 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| zhipu | To solve this task, we need to write a function `is_samepatterns` that checks if the sequence of strings in the first list follows the same pattern as the sequence of strings in the second list. The pattern is defined by the repetition and order of the elements in the second list. Here's the implementation of the function: ```python def is_samepatterns(colors, patterns): if len(colors) != len(patterns): return False color_to_pattern = {} pattern_to_color = {} for color, pattern in zip(colors, patterns): if color in color_to_pattern: if color_to_pattern[color] != pattern: return False else: color_to_pattern[color] = pattern if pattern in pattern_to_color: if pattern_to_color[pattern] != color: return False else: pattern_to_color[pattern] = color return True # Test cases assert is_samepatterns(["red", "green", "green"], ["a", "b", "b"]) == True assert is_samepatterns(["red", "green", "greenn"], ["a", "b", "b"]) == False assert is_samepatterns(["red", "green", "greenn"], ["a", "b"]) == False ``` ### Explanation: 1. **Length Check**: First, we check if the lengths of the two lists are the same. If not, they cannot follow the same pattern, so we return `False`. 2. **Mapping**: We use two dictionaries: - `color_to_pattern`: Maps each color to its corresponding pattern. - `pattern_to_color`: Maps each pattern to its corresponding color. 3. **Pattern Matching**: - For each pair of elements from `colors` and `patterns`, we check: - If the color is already mapped to a different pattern, return `False`. - If the pattern is already mapped to a different color, return `False`. - If neither mapping exists, we create the mapping. 4. **Return True**: If all checks pass, the sequences follow the same pattern, so we return `True`. This function should pass all the given test cases. |
pass 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| gpto1 | 'def is_samepatterns(items, patterns): if len(items) != len(patterns): return False pattern_to_item = {} item_to_pattern = {} for item, pattern in zip(items, patterns): if pattern in pattern_to_item: if pattern_to_item[pattern] != item: return False else: pattern_to_item[pattern] = item if item in item_to_pattern: if item_to_pattern[item] != pattern: return False else: item_to_pattern[item] = pattern return True' |
failed 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |
| deepseekv3 | To solve the problem of checking whether two sequences follow the same pattern, we need to ensure that the mapping between the elements of the two sequences is consistent. If the elements in the first sequence match the elements in the second sequence in the same order, then the sequences follow the same pattern. Here's a Python function that accomplishes this: ```python def is_samepatterns(sequence1, sequence2): # If the lengths of the sequences are different, they can't follow the same pattern if len(sequence1) != len(sequence2): return False # Create a mapping from the first sequence to the second sequence mapping = {} for i in range(len(sequence1)): elem1 = sequence1[i] elem2 = sequence2[i] # If elem1 is already mapped but to a different element in sequence2 if elem1 in mapping: if mapping[elem1] != elem2: return False else: # If elem2 is already mapped to a different element in sequence1 if elem2 in mapping.values(): return False mapping[elem1] = elem2 return True # Test cases assert is_samepatterns(["red","green","green"], ["a", "b", "b"]) == True assert is_samepatterns(["red","green","greenn"], ["a","b","b"]) == False assert is_samepatterns(["red","green","greenn"], ["a","b"]) == False ``` ### Explanation: 1. **Length Check**: First, we check if the lengths of the two sequences are different. If they are, they can't follow the same pattern, so we return `False`. 2. **Mapping**: We then create a dictionary `mapping` to store the relationship between elements in `sequence1` and `sequence2`. 3. **Consistency Check**: For each element in `sequence1` and `sequence2`: - If the element from `sequence1` is already in the `mapping`, we check if it maps to the correct element in `sequence2`. If not, we return `False`. - If the element from `sequence1` is not in the `mapping`, we check if the corresponding element from `sequence2` is already mapped to another element in `sequence1`. If it is, we return `False`. - If both checks pass, we add the mapping to the dictionary. 4. **Return True**: If all elements pass the consistency check, we return `True`. This function ensures that the sequences follow the same pattern by maintaining a consistent mapping between their elements. |
pass 测试用例：assert is_samepatterns(["red","green","green"], ["a", "b", "b"])==True assert is_samepatterns(["red","green","greenn"], ["a","b","b"])==False assert is_samepatterns(["red","green","greenn"], ["a","b"])==False |