رفع ارور از کد پایتون

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شرح پروژه

رفع ارور از کد پایتون

توضیحات برای برنامه نویس پیوست میشود

import random

def ProfileMostProbablePattern(Text, k, Profile):

    n = len(Text)

    maximum = -1

    probable_pattern = ''

    for i, letter in enumerate(Text):

        for i in range(n-k+1):

            pattern = Text[i:i+k]

            probability = Pr(pattern,Profile)

            if (probability > maximum):

                maximum = probability

                probable_pattern = pattern

    if maximum == -1:

        return Text[0:0+k]

    else:

        return probable_pattern

def CountWithPseudocounts(Motifs):

    t = len(Motifs)

    k = len(Motifs[0])

    count = {}

    for symbol in "ACGT":

        count[symbol] = []

        for j in range(k):

            count[symbol].append(1)

    for i in range(t):

        for j in range(k):

            symbol = Motifs[i][j]

            count[symbol][j] += 1

    return count

def ProfileWithPseudocounts(Motifs):

    t = len(Motifs)

    k = len(Motifs[0])

    profile = {}

    count = CountWithPseudocounts(Motifs)

    for key, motif_lists in sorted(count.items()):

        profile[key] = motif_lists

        for motif_list, number in enumerate(motif_lists):

            motif_lists[motif_list] = number/(float(t+4))

    return profile

def GreedyMotifSearchWithPseudocounts(Dna, k, t):

    BestMotifs = []

    for i in range(0, t):

        BestMotifs.append(Dna[i][0:k])

    n = len(Dna[0])

    for i in range(n-k+1):

        Motifs = []

        Motifs.append(Dna[0][i:i+k])

        for j in range(1, t):

            P = ProfileWithPseudocounts(Motifs[0:j])

            Motifs.append(ProfileMostProbablePattern(Dna[j], k, P))

        if Score(Motifs) < Score(BestMotifs):

            BestMotifs = Motifs

    return BestMotifs

def Consensus(Motifs):

    k = len(Motifs[0])

    profile = ProfileWithPseudocounts(Motifs)

    consensus = ""

    for j in range(k):

        maximum = 0

        frequentSymbol = ""

        for symbol in "ACGT":

            if profile[symbol][j] > maximum:

                maximum = profile[symbol][j]

                frequentSymbol = symbol

        consensus += frequentSymbol

    return consensus

def Score(Motifs):

    count = 0

    consensus = Consensus(Motifs)

    for motif in Motifs:

        for index, letter in enumerate(motif):

            if letter != consensus[index]:

                count += 1

    return count

def Pr(Text, Profile):

    p = 1

    for index,char in enumerate(Text):

        for key, profile_lists in sorted(Profile.items()):

            if char == key:

                p *= profile_lists[index]

    return p

k = 3

t = 5

Dna = ["GGCGTTCAGGCA", "AAGAATCAGTCA", "CAAGGAGTTCGC", "CACGTCAATCAC", "CAATAATATTCG"]

print('\n'.join(map(str,GreedyMotifSearchWithPseudocounts(Dna, k, t ))))

def BuildProfileMatrix(dnamatrix):

    ProfileMatrix = [[1 for x in xrange(len(dnamatrix[0]))] for x in xrange(4)]

    indices = {'A':0, 'C':1, 'G': 2, 'T':3}

    for seq in dnamatrix:

    for i in xrange(len(dnamatrix[0])):            

        ProfileMatrix[indices[seq[i]]][i] += 1

    ProbMatrix = [[float(x)/sum(zip(*ProfileMatrix)[0]) for x in y] for y in ProfileMatrix]

    return ProbMatrix

def ProfileRandomGenerator(profile, dna, k, i):

    indices = {'A':0, 'C':1, 'G': 2, 'T':3}

    score_list = []

    for x in xrange(len(dna[i]) - k + 1):

        probability = 1

        window = dna[i][x : k + x]

    for y in xrange(k):

        probability *= profile[indices[window[y]]][y]

    score_list.append(probability)

    rnd = uniform(0, sum(score_list))

    current = 0

    for z, bias in enumerate(score_list):

        current += bias

        if rnd <= current:

            return dna[i][z : k + z]

def score(motifs):

    ProfileMatrix = [[0 for x in xrange(len(motifs[0]))] for x in xrange(4)]

    indices = {'A':0, 'C':1, 'G': 2, 'T':3}

    for seq in motifs:

        for i in xrange(len(motifs[0])):            

            ProfileMatrix[indices[seq[i]]][i] += 1

    score = len(motifs)*len(motifs[0]) - sum([max(x) for x in zip(*ProfileMatrix)])

    return score

from random import randint, uniform    

def GibbsSampler(k, t, N):

     dna = ['CGCCCCTCTCGGGGGTGTTCAGTAACCGGCCA',

    'GGGCGAGGTATGTGTAAGTGCCAAGGTGCCAG',

    'TAGTACCGAGACCGAAAGAAGTATACAGGCGT',

    'TAGATCAAGTTTCAGGTGCACGTCGGTGAACC',

    'AATCCACCAGCTCCACGTGCAATGTTGGCCTA']

    Motifs = []

    for i in [randint(0, len(dna[0])-k) for x in range(len(dna))]:

        j = 0

        kmer = dna[j][i : k+i]

        j += 1

        Motifs.append(kmer)

    BestMotifs = []

    s_best = float('inf')

    for i in xrange(N):

        x = randint(0, t-1)

    Motifs.pop(x)

    profile = BuildProfileMatrix(Motifs)

    Motif = ProfileRandomGenerator(profile, dna, k, x)

    Motifs.insert(x, Motif)

    s_motifs = score(Motifs)

    if s_motifs < s_best:

        s_best = s_motifs

        BestMotifs = Motifs

return [s_best, BestMotifs]

k, t, N =8, 5, 100            

best_motifs = [float('inf'), None]

# Repeat the Gibbs sampler search 20 times.

for repeat in xrange(20):

    current_motifs = GibbsSampler(k, t, N)

    if current_motifs[0] < best_motifs[0]:

        best_motifs = current_motifs

# Print and save the answer.

print '\n'.join(best_motifs[1])

این پروژه شامل 4 فایل مهم است، لطفا قبل از ارسال پیشنهاد حتما نسبت به بررسی این فایل اقدام فرمایید.

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