Solution #7eba7d1a-1fe6-4b13-9a1c-45ff8c9b0834

failed

Score

0% (0/5)

Runtime

777μs

Delta

-100.0% vs parent

-100.0% vs best

Regression from parent

Solution Lineage

Current0%Regression from parent
d969cb8041%Improved from parent
d210ec5619%Regression from parent
2c8087b020%Regression from parent
e74e938420%Improved from parent
4d0aaeef19%Regression from parent
3d4a920597%Improved from parent
f1c258430%Regression from parent
05321f7320%Regression from parent
69815a2320%Improved from parent
f3a4c5bd20%Improved from parent
1734c2970%Same as parent
4f69822f0%Regression from parent
14d0b3da20%Improved from parent
528f38cd10%Regression from parent
0d6c341619%Regression from parent
ae69dbab39%Regression from parent
5a97585772%Improved from parent
5266c9ec0%Regression from parent
da617b596%Regression from parent
06ed21e748%Improved from parent
b618404727%Regression from parent
35f1acec41%Regression from parent
aacb270845%Improved from parent
44170f1439%Improved from parent
d4a144706%Regression from parent
ac75ae0340%Regression from parent
5d1898f963%Improved from parent
669949f251%Regression from parent
cdf35bb558%Improved from parent
1c6ceef237%Regression from parent
a48275e057%Improved from parent
b6016c2857%Improved from parent
5fad927440%Regression from parent
cb4d87e147%Improved from parent
7f265cec45%Improved from parent
2143671f19%Improved from parent
c0d68d5c0%Regression from parent
ae54b0ca54%Regression from parent
e0f66b5554%Improved from parent
465e93a245%Regression from parent
73be1f5e49%Improved from parent
dd5155da19%Improved from parent
a9d69e700%Regression from parent
63acaad058%Improved from parent
1265a3fc48%Improved from parent
693a4dda33%Regression from parent
d5bf925948%Regression from parent
48e560c749%Improved from parent
78afbd2538%Improved from parent
f0098ec50%Same as parent
bb8caee80%Regression from parent
ce53db5152%Improved from parent
9e6f727542%Improved from parent
2c6b742934%Regression from parent
223a455254%Improved from parent
4a54e07352%Improved from parent
99326a1432%Improved from parent
d8629f4919%Regression from parent
0deb287347%Improved from parent
e4b007c347%Improved from parent
32b7128c43%Regression from parent
f209f80655%Improved from parent
9161b31714%Regression from parent
9ab0f66324%Improved from parent
110fbd0b0%Regression from parent
e3d01a5c52%Improved from parent
c6fc252643%Regression from parent
23b4491152%Improved from parent
03aea6db43%Regression from parent
5f1a15ce53%Improved from parent
f22b171153%Same as parent
7b6d9f0953%Improved from parent
0401f74f12%Regression from parent
b96fbcb340%Improved from parent
84cc9d0420%First in chain

Code

def solve(input):
    data = input.get("data", "")
    if not isinstance(data, str) or not data:
        return 999.0

    # Approach: LZ77 with memoization and recursion

    def compress_recursively(s, i, memo):
        if i >= len(s):
            return []

        if i in memo:
            return memo[i]

        best_match_length = 0
        best_match_position = 0

        for j in range(max(0, i - 255), i):
            match_length = 0
            while (i + match_length < len(s)) and (s[j + match_length] == s[i + match_length]):
                match_length += 1
                if match_length > best_match_length:
                    best_match_length = match_length
                    best_match_position = i - j

        if best_match_length > 0:
            result = [(best_match_position, best_match_length, s[i + best_match_length])] + compress_recursively(s, i + best_match_length + 1, memo)
        else:
            result = [(0, 0, s[i])] + compress_recursively(s, i + 1, memo)

        memo[i] = result
        return result

    def decompress_recursively(compressed_data, i, decompressed, memo):
        if i >= len(compressed_data):
            return decompressed

        if i in memo:
            return memo[i]

        position, length, next_char = compressed_data[i]

        if position > 0:
            start = len(decompressed) - position
            for j in range(length):
                decompressed += decompressed[start + j]
        
        decompressed += next_char
        memo[i] = decompress_recursively(compressed_data, i + 1, decompressed, memo)
        return memo[i]

    if len(data) == 0:
        return 999.0

    compressed_data = compress_recursively(data, 0, {})
    decompressed_data = decompress_recursively(compressed_data, 0, "", {})

    if decompressed_data != data:
        return 999.0

    original_size = len(data) * 8  # assuming each char is 1 byte (8 bits)
    compressed_size = sum(8 + 8 + 8 for _ in compressed_data)  # 8 bits each for position, length, and next_char

    if original_size == 0:
        return 999.0

    compression_ratio = compressed_size / original_size
    return 1.0 - compression_ratio