class EAMotifsReal (EvolAlgorithm):
def __init__(self, popsize, numits, noffspring, filename):
self.motifs = MotifFinding()
self.motifs.readFile(filename, "dna")
indsize = self.motifs.motifSize * len(self.motifs.alphabet)
EvolAlgorithm.__init__(self, popsize, numits, noffspring, indsize)
def initPopul(self, indsize):
maxvalue = self.motifs.seqSize(0) - self.motifs.motifSize
self.popul = PopulReal(self.popsize, indsize,
maxvalue, [])
def vector_to_PWM(self, v): #v -> vetor de números reais
self.pwm = createMatZeros(len(self.mtoifs.alphabet), self.motifs.motifSize)
for i in range(0, len(v), self.motifs.alphabet):
col_idx = i / len(self.motifs.alphabet)
col = v[i : i + len(self.motifs.alphabet)]
soma = sum(col)
for j in range(len(self.motifs.alphabet)):
self.pwm[j][col_idx] = col[j] / soma
return pwm
def evaluate(self, indivs):
for i in range(len(indivs)):
ind = indivs[i]
sol = ind.getGenes()
self.motifs.pwm = self.vector_to_PWM(sol)
s = []
for seq in self.motifs.seqs:
p = self.motifs.mostProbableSeq(seq)
s.append(p)
## TPC - ussar score multiplicativo sem atualizar a PWM ##
fit = self.motifs.score(sol)
ind.setFitness(fit)
class EAMotifsReal (EvolAlgorithm):
def __init__(self, popsize, numits, noffspring, filename):
self.motifs = MotifFinding()
self.motifs.readFile(filename, "dna")
indsize = self.motifs.motifSize * len(self.motifs.alphabet)
EvolAlgorithm.__init__(self, popsize, numits, noffspring, indsize)
def initPopul(self, indsize):
maxvalue = self.motifs.seqSize(0) - self.motifs.motifSize
self.popul = PopulReal(self.popsize, indsize, 0,
maxvalue, [])
def vec_to_pwm(self,v):
n_alph = len(self.motifs.alphabet)
n_motif = self.motifs.motifSize
pwm = createMatZeros(n_alph, n_motif)
for i in range(0,len(v),n_alph):
col_idx = int(i / n_alph)
col = v[i:i+n_alph]
soma = sum(col)
for j in range(n_alph):
pwm[j][col_idx] = col[j] / soma
return pwm
def probabSeq(self, seq):
res = 1.0
for i in range(self.motifs.motifSize):
lin = self.motifs.alphabet.index(seq[i])
res *= self.motifs.pwm[lin][i]
return res
def mostProbableSeq(self, seq):
maximo = -1.0
maxind = -1
for k in range(len(seq)-self.motifs.motifSize):
p = self.probabSeq(seq[k:k + self.motifs.motifSize])
if(p > maximo):
maximo = p
maxind = k
return maxind
def evaluate(self, indivs):
for i in range(len(indivs)):
ind = indivs[i]
sol = ind.getGenes()
pwm = self.vec_to_pwm(sol)
mtf = MyMotifs(pwm=pwm, alphabet=self.motifs_finding.alphabet)
s = []
for j in range(len(self.motifs_finding.seqs)):
seq = self.motifs_finding.seqs[j].seq
p = mtf.mostProbableSeq(seq)
s.append(p)
fit1 = self.motifs.scoreMult(s,self.motifs.pwm) # com scoreMult
fit = self.motifs.score(s)
ind.setFitness(fit)
ind.setmultFitness(fit1)
class EAMotifsReal(EvolAlgorithm):
def __init__(self, popsize, numits, noffspring, filename):
self.motifs = MotifFinding()
self.motifs.readFile(filename, 'dna')
indsize = self.motifs.motifSize * len(self.motifs.alphabet)
EvolAlgorithm.__init__(self, popsize, numits, noffspring, indsize)
def initPopul(self, indsize):
maxvalue = self.motifs.seqSize(0) - self.motifs.motifSize
self.popul = PopulReal(self.popsize, indsize, ub=maxvalue, indivs=[])
def vec_to_pwm(self, v):
pwm = createMatZeros(len(self.motifs.alphabet), self.motifs.motifSize)
for i in range(0, len(v), len(self.motifs.alphabet)):
col_idx = int(i / len(self.motifs.alphabet))
col = v[i:i + len(self.motifs.alphabet)]
soma = sum(col)
for j in range(len(self.motifs.alphabet)):
pwm[i][col_idx] = col[j] / soma
return pwm
def evaluate(self, indivs):
for i in range(len(indivs)):
ind = indivs[i]
sol = ind.getGenes()
pwm = self.vec_to_pwm(sol)
s = []
my_motif = MyMotifs(pwm, self.motifs.alphabet)
for seq in self.motifs.seqs:
p = my_motif.mostProbableSeq(seq)
s.append(p)
fit = self.motifs.scoreMult(sol, pwm)
ind.setFitness(fit)
class EAMotifsReal(EvolAlgorithm):
def __init__(self, popsize, numits, noffspring, filename):
self.motifs = MotifFinding()
self.motifs.readFile(filename, "dna")
indsize = self.motifs.motifSize * len(self.motifs.alphabet)
EvolAlgorithm.__init__(self, popsize, numits, noffspring, indsize)
def initPopul(self, indsize):
maxvalue = self.motifs.seqSize(0) - self.motifs.motifSize
self.popul = PopulReal(self.popsize, indsize, maxvalue, [])
def vec_to_pwm(self, v):
n_alph = len(self.motifs.alphabet)
n_motif = self.motifs.motifSize
pwm = createMatZeros(len(n_alph), n_motif)
for i in range(0, len(v), n_alph): #
col_idx = i / n_alph
col = v[i:i + n_alph]
soma = sum(col)
for j in range(n_alph):
pwm[j][col_idx] = col[j] / soma
#EXEMPLO:
#[1,2,3,4,5,6,7,8,9,10,....]
#A 1
#C 2
#T 3
#G 4
#A 1 2 3 4
#C
def evaluate(self, indivs):
for i in range(len(indivs)):
ind = indivs[i]
sol = ind.getGenes()
#criar um MyMotif a partir da PWM
pwm = self.vec_to_pwm(sol)
mtf = MyMotifs(pwm=pwm, alphabet=self.motifs_finding.alphabet)
s = []
for j in range(len(self.motifs_finding.seqs)):
seq = self.motifs_finding.seqs[j].seq
p = mtf.motifs.mostProbableSeq(seq)
s.append(p)
## TPC - ussar score multiplicativo sem atualizar a PWM ##
fit = self.motifs_finding.score(s)
ind.setFitness(fit)
def __init__(self, popsize, numits, noffspring, filename):
self.mottifes = MotifFinding() #iniciar o MotifFiding
self.motifs.readFile(filename, "dna") #guarda as seqs
indsize = self.motifs.motifSize * len(
self.motifs.alphabet
) #tamanho vai ser um vetor da pwm, linhas( tamanho alfabeto) e colunas(tamanho motif)
EvolAlgorithm.__init__(self, popsize, numits, noffspring, indsize)
class EAMotifsInt(EvolAlgorithm):
def __init__(self, popsize, numits, noffspring, filename):
self.motifs = MotifFinding()
self.motifs.readFile(filename, "dna")
indsize = len(self.motifs)
EvolAlgorithm.__init__(self, popsize, numits, noffspring, indsize)
def initPopul(self, indsize):
maxvalue = self.motifs.seqSize(0) - self.motifs.motifSize
self.popul = PopulInt(self.popsize, indsize, maxvalue, [])
def evaluate(self, indivs):
for i in range(len(indivs)):
ind = indivs[i]
sol = ind.getGenes()
fit = self.motifs.score(sol)
ind.setFitness(fit)
class EAMotifsInt(EvolAlgorithm):
def __init__(self, popsize, numits, noffspring, filename):
self.motifs = MotifFinding()
self.motifs.readFile(filename, "dna")
indsize = len(self.motifs)
EvolAlgorithm.__init__(self, popsize, numits, noffspring, indsize)
def initPopul(self, indsize):
maxvalue = self.motifs.seqSize(0) - self.motifs.motifSize
self.popul = PopulInt(self.popsize, indsize, maxvalue, [])
def evaluate(self, indivs): #mudar a funcao de avalicao -> usamos o score
for i in range(len(indivs)):
ind = indivs[i] #cada vetor de posicoes
sol = ind.getGenes()
fit = self.motifs.score(
sol
) #avaliar o score que sera a fit para cada vetor de posicoes iniciais
ind.setFitness(fit)
class EAMotifsInt(EvolAlgorithm):
def __init__(self, popsize, numits, noffspring, filename):
self.motifs = MotifFinding()
self.motifs.readFile(filename, "dna")
indsize = len(
self.motifs) #numero de seqs a analisar aka numero de individuos
EvolAlgorithm.__init__(self, popsize, numits, noffspring, indsize)
def initPopul(self, indsize):
maxvalue = self.motifs.seqSize(
0
) - self.motifs.motifSize #upper bound é o indice maximo que o vetor das pos pode ter (tamanho da seq - tamanho do motif)
self.popul = PopulInt(self.popsize, indsize, maxvalue, [])
def evaluate(self, indivs):
for i in range(len(indivs)):
ind = indivs[i]
sol = ind.getGenes()
fit = self.motifs.score(sol)
multifit = self.motifs.scoreMult(sol)
ind.setFitness(fit)
ind.setMultiFitness(multifit)
def __init__(self, popsize, numits, noffspring, filename):
self.motifs_finding = MotifFinding()
self.motifs.readFile(filename, "dna")
indsize = self.motifs.motifSize * len(self.motifs.alphabet)
EvolAlgorithm.__init__(self, popsize, numits, noffspring, indsize)
def __init__(self, popsize, numits, noffspring, filename):
self.motifs = MotifFinding()
self.motifs.readFile(filename, "dna")
indsize = len(self.motifs)
EvolAlgorithm.__init__(self, popsize, numits, noffspring, indsize)
def __init__(self, popsize, numits, noffspring, filename):
self.motifs = MotifFinding()
self.motifs.readFile(filename, "dna")
indsize = len(
self.motifs) #numero de seqs a analisar aka numero de individuos
EvolAlgorithm.__init__(self, popsize, numits, noffspring, indsize)
