我正在研究算术编码和解码算法的自适应实现，并且已经实现了python，但是对于某些字符串，我得到了正确的答案，但对于其他字符串，我得到了正确的答案。

程序首次启动时，会提供一个参数来决定符号概率的更改频率。 例如，如果参数为10，则在发送/接收10个符号之后，概率表会根据到目前为止发送/接收的所有符号进行更改。 因此，域分配也被更改。 最初，我具有1/26概率的均匀分布[az]。

它不适用于“ heloworldheloworld”和许多其他情况。

另外，我已经了解了下溢问题，但是如何解决该问题。 

import sys
import random
import string


def encode(encode_str, N):
    count = dict.fromkeys(string.ascii_lowercase, 1)                                        # probability table
    cdf_range = dict.fromkeys(string.ascii_lowercase, 0)
    pdf = dict.fromkeys(string.ascii_lowercase, 0)

    low = 0
    high = float(1)/float(26)

    for key, value in sorted(cdf_range.iteritems()):
        cdf_range[key] = [low, high]
        low = high
        high += float(1)/float(26)

    for key, value in sorted(pdf.iteritems()):
        pdf[key] = float(1)/float(26)

    # for key, value in sorted(cdf_range.iteritems()):
    #   print key, value

    # for key, value in sorted(pdf.iteritems()):
    #   print key, value

    i = 26

    lower_bound = 0                                                                     # upper bound
    upper_bound = 1                                                                     # lower bound

    u = 0

    # go thru every symbol in the string
    for sym in encode_str:
        i += 1
        u += 1
        count[sym] += 1

        curr_range = upper_bound - lower_bound                                          # current range
        upper_bound = lower_bound + (curr_range * cdf_range[sym][1])                    # upper_bound
        lower_bound = lower_bound + (curr_range * cdf_range[sym][0])                    # lower bound

        # update cdf_range after N symbols have been read
        if (u == N):
            u = 0

            for key, value in sorted(pdf.iteritems()):
                pdf[key] = float(count[key])/float(i)

            low = 0
            for key, value in sorted(cdf_range.iteritems()):
                high = pdf[key] + low
                cdf_range[key] = [low, high]
                low = high

    return lower_bound

def decode(encoded, strlen, every):
    decoded_str = ""

    count = dict.fromkeys(string.ascii_lowercase, 1)                                        # probability table
    cdf_range = dict.fromkeys(string.ascii_lowercase, 0)
    pdf = dict.fromkeys(string.ascii_lowercase, 0)

    low = 0
    high = float(1)/float(26)

    for key, value in sorted(cdf_range.iteritems()):
        cdf_range[key] = [low, high]
        low = high
        high += float(1)/float(26)

    for key, value in sorted(pdf.iteritems()):
        pdf[key] = float(1)/float(26)


    lower_bound = 0                                                                     # upper bound
    upper_bound = 1                                                                     # lower bound

    k = 0

    while (strlen != len(decoded_str)):
        for key, value in sorted(pdf.iteritems()):

            curr_range = upper_bound - lower_bound                                      # current range
            upper_cand = lower_bound + (curr_range * cdf_range[key][1])                 # upper_bound
            lower_cand = lower_bound + (curr_range * cdf_range[key][0])                 # lower bound

            if (lower_cand <= encoded < upper_cand):
                k += 1
                decoded_str += key

                if (strlen == len(decoded_str)):
                    break

                upper_bound = upper_cand
                lower_bound = lower_cand

                count[key] += 1

                if (k == every):
                    k = 0
                    for key, value in sorted(pdf.iteritems()):
                        pdf[key] = float(count[key])/float(26+len(decoded_str))

                    low = 0
                    for key, value in sorted(cdf_range.iteritems()):
                        high = pdf[key] + low
                        cdf_range[key] = [low, high]
                        low = high

    print decoded_str

def main():
    count = 10
    encode_str = "yyyyuuuuyyyy"
    strlen = len(encode_str)
    every = 3
    encoded = encode(encode_str, every)
    decoded = decode(encoded, strlen, every)

if __name__ == '__main__':
    main()

===============>>#1 票数：1

错误出现在大约12个字符长的字符串上。 这接近python使用的双精度，可能会引起您的问题。

我对BigFloat库（具有任意精度）进行了快速测试，并获得了正确的答案： 

import sys
import random
import string
from bigfloat import *

factor = BigFloat(1)/BigFloat(26)

def encode(encode_str, N):
    count = dict.fromkeys(string.ascii_lowercase, 1)                                        # probability table
    cdf_range = dict.fromkeys(string.ascii_lowercase, 0)
    pdf = dict.fromkeys(string.ascii_lowercase, 0)

    with precision(200) + RoundTowardZero:
        low = 0
        high = factor

        for key, value in sorted(cdf_range.iteritems()):
            cdf_range[key] = [low, high]
            low = high
            high += factor

        for key, value in sorted(pdf.iteritems()):
            pdf[key] = factor

        # for key, value in sorted(cdf_range.iteritems()):
        #   print key, value

        # for key, value in sorted(pdf.iteritems()):
        #   print key, value

        i = 26

        lower_bound = 0                         # upper bound
        upper_bound = 1                         # lower bound

        u = 0

        # go thru every symbol in the string
        for sym in encode_str:
            i += 1
            u += 1
            count[sym] += 1

            curr_range = upper_bound - lower_bound                                          # current range
            upper_bound = lower_bound + (curr_range * cdf_range[sym][1])                    # upper_bound
            lower_bound = lower_bound + (curr_range * cdf_range[sym][0])                    # lower bound

            # update cdf_range after N symbols have been read
            if (u == N):
                u = 0

                for key, value in sorted(pdf.iteritems()):
                    pdf[key] = BigFloat(count[key])/BigFloat(i)

                low = 0
                for key, value in sorted(cdf_range.iteritems()):
                    high = pdf[key] + low
                    cdf_range[key] = [low, high]
                    low = high

    return lower_bound

def decode(encoded, strlen, every):
    decoded_str = ""

    count = dict.fromkeys(string.ascii_lowercase, 1)                                        # probability table
    cdf_range = dict.fromkeys(string.ascii_lowercase, 0)
    pdf = dict.fromkeys(string.ascii_lowercase, 0)


    with precision(200) + RoundTowardZero:
        low = 0
        high = factor

        for key, value in sorted(cdf_range.iteritems()):
            cdf_range[key] = [low, high]
            low = high
            high += factor

        for key, value in sorted(pdf.iteritems()):
            pdf[key] = factor


        lower_bound = BigFloat(0)                           # upper bound
        upper_bound = BigFloat(1)                           # lower bound

        k = 0

        while (strlen != len(decoded_str)):
            for key, value in sorted(pdf.iteritems()):

                curr_range = upper_bound - lower_bound                                      # current range
                upper_cand = lower_bound + (curr_range * cdf_range[key][1])                 # upper_bound
                lower_cand = lower_bound + (curr_range * cdf_range[key][0])                 # lower bound

                if (lower_cand <= encoded < upper_cand):
                    k += 1
                    decoded_str += key

                    if (strlen == len(decoded_str)):
                        break

                    upper_bound = upper_cand
                    lower_bound = lower_cand

                    count[key] += 1

                    if (k == every):
                        k = 0
                        for key, value in sorted(pdf.iteritems()):
                            pdf[key] = BigFloat(count[key])/BigFloat(26+len(decoded_str))

                        low = 0
                        for key, value in sorted(cdf_range.iteritems()):
                            high = pdf[key] + low
                            cdf_range[key] = [low, high]
                            low = high

        print decoded_str

def main():
    count = 10
    encode_str = "heloworldheloworld"
    strlen = len(encode_str)
    every = 3
    encoded = encode(encode_str, every)
    decoded = decode(encoded, strlen, every)

if __name__ == '__main__':
    main()

===============>>#2 票数：1

发生这种情况是因为Python float具有53位精度。 您不能编码很长的字符串。

您可能需要使用decimal而不是floats来获得任意精度 

import sys
import random
import string

import decimal
from decimal import Decimal

decimal.getcontext().prec=100

def encode(encode_str, N):
    count = dict.fromkeys(string.ascii_lowercase, 1)                                        # probability table
    cdf_range = dict.fromkeys(string.ascii_lowercase, 0)
    pdf = dict.fromkeys(string.ascii_lowercase, 0)

    low = 0
    high = Decimal(1)/Decimal(26)

    for key, value in sorted(cdf_range.iteritems()):
        cdf_range[key] = [low, high]
        low = high
        high += Decimal(1)/Decimal(26)

    for key, value in sorted(pdf.iteritems()):
        pdf[key] = Decimal(1)/Decimal(26)

    # for key, value in sorted(cdf_range.iteritems()):
    #   print key, value

    # for key, value in sorted(pdf.iteritems()):
    #   print key, value

    i = 26

    lower_bound = 0                                                                     # upper bound
    upper_bound = 1                                                                     # lower bound

    u = 0

    # go thru every symbol in the string
    for sym in encode_str:
        i += 1
        u += 1
        count[sym] += 1

        curr_range = upper_bound - lower_bound                                          # current range
        upper_bound = lower_bound + (curr_range * cdf_range[sym][1])                    # upper_bound
        lower_bound = lower_bound + (curr_range * cdf_range[sym][0])                    # lower bound

        # update cdf_range after N symbols have been read
        if (u == N):
            u = 0

            for key, value in sorted(pdf.iteritems()):
                pdf[key] = Decimal(count[key])/Decimal(i)

            low = 0
            for key, value in sorted(cdf_range.iteritems()):
                high = pdf[key] + low
                cdf_range[key] = [low, high]
                low = high

    return lower_bound

def decode(encoded, strlen, every):
    decoded_str = ""

    count = dict.fromkeys(string.ascii_lowercase, 1)                                        # probability table
    cdf_range = dict.fromkeys(string.ascii_lowercase, 0)
    pdf = dict.fromkeys(string.ascii_lowercase, 0)

    low = 0
    high = Decimal(1)/Decimal(26)

    for key, value in sorted(cdf_range.iteritems()):
        cdf_range[key] = [low, high]
        low = high
        high += Decimal(1)/Decimal(26)

    for key, value in sorted(pdf.iteritems()):
        pdf[key] = Decimal(1)/Decimal(26)


    lower_bound = 0                                                                     # upper bound
    upper_bound = 1                                                                     # lower bound

    k = 0

    while (strlen != len(decoded_str)):
        for key, value in sorted(pdf.iteritems()):

            curr_range = upper_bound - lower_bound                                      # current range
            upper_cand = lower_bound + (curr_range * cdf_range[key][1])                 # upper_bound
            lower_cand = lower_bound + (curr_range * cdf_range[key][0])                 # lower bound

            if (lower_cand <= encoded < upper_cand):
                k += 1
                decoded_str += key

                if (strlen == len(decoded_str)):
                    break

                upper_bound = upper_cand
                lower_bound = lower_cand

                count[key] += 1

                if (k == every):
                    k = 0
                    for key, value in sorted(pdf.iteritems()):
                        pdf[key] = Decimal(count[key])/Decimal(26+len(decoded_str))

                    low = 0
                    for key, value in sorted(cdf_range.iteritems()):
                        high = pdf[key] + low
                        cdf_range[key] = [low, high]
                        low = high

    print decoded_str

def main():
    count = 10
    encode_str = "heloworldheloworld"
    strlen = len(encode_str)
    every = 3
    encoded = encode(encode_str, every)
    decoded = decode(encoded, strlen, every)

if __name__ == '__main__':
    main()

  ask by Pete translate from so

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