def frequent_words_with_mismatches_and_revcomps(text, k, d):
frequent_patterns = []
close = [0] * (4**k)
frequency_array = [0] * (4**k)
for i in range(0,len(text)-k+1):
neighborhood = neighbors(text[i:i+k], d)
for pattern in neighborhood:
index = pattern_to_number(pattern)
close[index] = 1
for i in range(0,4**k):
if close[i] == 1:
pattern = number_to_pattern(i, k)
frequency_array[i] = approximate_pattern_count(text, pattern, d)
frequency_array[i] += approximate_pattern_count(text, reverse_complement(pattern), d)
max_count = max(frequency_array)
for i in range(0,4**k):
if frequency_array[i] == max_count:
pattern = number_to_pattern(i, k)
if pattern not in frequent_patterns:
frequent_patterns.append(pattern)
return frequent_patterns
def prob_9_6():
lines = open("data/dataset_9_6.txt").read().splitlines()
pattern = lines[0]
text = lines[1]
d = int(lines[2])
fout = open("out.txt", "w")
fout.write(str(approximate_pattern_count(text, pattern, d)))
fout.close()
