Solution #1734c297-77a2-4f19-b5bd-105699bde61a

failed

Score

0% (0/5)

Runtime

84μs

Delta

New score

-100.0% vs best

Same as parent

Solution Lineage

Current0%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 a simple dictionary-based compression
    def dictionary_compress(data):
        dictionary = {}
        compressed_data = []
        buffer = ""
        dict_size = 256

        for char in data:
            buffer_plus_char = buffer + char
            if buffer_plus_char in dictionary:
                buffer = buffer_plus_char
            else:
                if buffer:
                    compressed_data.append(dictionary[buffer])
                else:
                    compressed_data.append(ord(char))
                dictionary[buffer_plus_char] = dict_size
                dict_size += 1
                buffer = char

        if buffer:
            compressed_data.append(dictionary[buffer])

        return compressed_data

    def dictionary_decompress(compressed_data):
        dictionary = {i: chr(i) for i in range(256)}
        dict_size = 256
        result = []

        w = chr(compressed_data[0])
        result.append(w)

        for k in compressed_data[1:]:
            if k in dictionary:
                entry = dictionary[k]
            elif k == dict_size:
                entry = w + w[0]
            else:
                return None

            result.append(entry)

            dictionary[dict_size] = w + entry[0]
            dict_size += 1

            w = entry

        return ''.join(result)

    compressed_data = dictionary_compress(data)
    decompressed_data = dictionary_decompress(compressed_data)

    if decompressed_data != data:
        return 999.0

    compressed_size = len(compressed_data) * 2  # Assume each entry takes 2 bytes
    original_size = len(data)

    return 1.0 - (compressed_size / float(original_size))