def pattern_matching_mismatches_copy_pseudocode(text, k, d):
frequent_patterns = set()
close = []
frequency_array = []
for i in range(4**k):
close.append(False)
frequency_array.append(0)
for i in range(len(text) - (k)):
neighbourhood = neighbours(text[i:i+k], d)
for pattern in neighbourhood:
index = pattern_to_number(pattern)
close[index] = True
for i in range(4**k):
if close[i]:
pattern = number_to_pattern(i, k)
frequency_array[i] = approx_pattern_count(text, pattern, d)
max_count = max(frequency_array)
for i in range(4**k):
if frequency_array[i] == max_count:
pattern = number_to_pattern(i, k)
frequent_patterns.add(pattern)
return frequent_patterns
def median_string(dna_list, k):
median = None
distance = None
for i in xrange((4 ** k) - 1):
pattern = number_to_pattern(i, k)
dist = distance_between_pattern_and_strings(pattern, dna_list)
if distance is None or dist < distance:
distance = dist
median = pattern
return median
