def approximate_pattern_match(pattern,text,d): idx = [] k = len(pattern) for i in range(len(text)-k+1): if hd.hamming_distance(text[i:i+k],pattern) <= d: idx.append(i) return idx
def approximate_pattern_match(pattern, text, d):
idx = []
k = len(pattern)
for i in range(len(text) - k + 1):
if hd.hamming_distance(text[i:i + k], pattern) <= d:
idx.append(i)
return idx
def neighbors(pattern, d):
if d == 0:
return pattern
if len(pattern) == 1:
return ["A", "C", "G", "T"]
neighborhood = []
suffixNeighbors = neighbors(pattern[1:], d)
for text in suffixNeighbors:
if hd.hamming_distance(pattern[1:], text) < d:
for x in ["A", "C", "G", "T"]:
neighborhood.append(x + text)
else:
neighborhood.append(pattern[:1] + text)
neighborhood = list(set(neighborhood))
return neighborhood
def neighbors(pattern, d):
if d == 0:
return pattern
if len(pattern) == 1:
return ['A', 'C', 'G', 'T']
neighborhood = []
suffixNeighbors = neighbors(pattern[1:], d)
for text in suffixNeighbors:
if hd.hamming_distance(pattern[1:], text) < d:
for x in ['A', 'C', 'G', 'T']:
neighborhood.append(x + text)
else:
neighborhood.append(pattern[:1] + text)
neighborhood = list(set(neighborhood))
return neighborhood
def approximatePatternCount(text, pattern, d):
count = 0
for i in range(len(text) - len(pattern) + 1):
if hd.hamming_distance(text[i : i + len(pattern)], pattern) <= d:
count += 1
return count
def approximatePatternCount(pattern, text, d):
count = 0
for i in range(len(text) - len(pattern) + 1):
if hd.hamming_distance(text[i:i + len(pattern)], pattern) <= d:
count += 1
return count