Solution #f1c25843-047d-4c09-82ca-9b30eace04bb

failed

Score

0% (0/5)

Runtime

1.13ms

Delta

-100.0% vs parent

-100.0% vs best

Regression from parent

Solution Lineage

Current0%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

    # Implementing LZ77 Compression Algorithm
    def lz77_compress(uncompressed):
        compressed = []
        i = 0
        while i < len(uncompressed):
            match = ""
            best_match_distance = 0
            best_match_length = 0
            
            # Look back at most 255 characters (arbitrary small sliding window)
            start = max(0, i - 255)
            for j in range(start, i):
                length = 0
                while (i + length < len(uncompressed) and 
                       uncompressed[j + length] == uncompressed[i + length]):
                    length += 1
                    if j + length >= i:
                        break
                if length > best_match_length:
                    best_match_distance = i - j
                    best_match_length = length
            
            if best_match_length > 0:
                compressed.append((best_match_distance, best_match_length, uncompressed[i + best_match_length]))
                i += best_match_length + 1
            else:
                compressed.append((0, 0, uncompressed[i]))
                i += 1

        return compressed

    def lz77_decompress(compressed):
        decompressed = []
        for distance, length, next_char in compressed:
            start = len(decompressed) - distance
            decompressed.extend(decompressed[start:start + length])
            decompressed.append(next_char)
        return ''.join(decompressed)

    compressed_data = lz77_compress(data)
    decompressed_data = lz77_decompress(compressed_data)

    if decompressed_data != data:
        return 999.0

    # Calculating the compression ratio
    compressed_size = sum(2 + 1 for _ in compressed_data)  # each (distance, length, char) can be assumed to take 3 bytes
    original_size = len(data)

    return 1.0 - (compressed_size / float(original_size))