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import sys
def alpha_number(char):
if char == "0" or char == "1":
return int(char)
return ord(char) - 97
def reverse(string):
return string[::-1]
def compare(string, i, end):
for j in range(end):
if i+j == end or string[i+j] != string[j]:
return j
def condense(binary, offset=0, size=2):
out = ""
for i in range(offset, len(binary)-offset, size):
slice = binary[i:i+size]
if len(slice) == size:
out += chr(97 + int(slice, 2))
return out
def gusfield(string):
z = [0 for _ in string]
z[0] = len(string)
r = 0
l = 0
for i in range(1, len(string)):
if i == 1: # base case
z[1] = compare(string, i, len(string))
if z[1] > 0:
r = z[1] + 1
l = 1
elif i > r: # Case 1
z[i] = compare(string, i, len(string))
if z[i] > 0:
q = i + z[i]
r = q - 1
l = i
elif i <= r: # Case 2
if z[i-l] < r-i: # Case 2a
z[i] = z[i-l]
else: # Case 2b
q = compare(string, i, len(string))
z[i] = q
r = q
l = i
return z
def gen_jump_table(pat):
m = len(pat)
R = [[-1 for __ in range(m)] for _ in range(0, 4)] # A space for a, b, c and d used in groupings
for j in range(m):
for i in range(j+1):
R[alpha_number(pat[i])][j] = i
return R
def gen_z_suffix(pat):
return reverse(gusfield(reverse(pat)))+[0]
def gen_good_suffix(pat, Z):
m = len(pat)
good_suffix = [0 for _ in range(0, m + 1)]
for i in range(m):
j = m - Z[i]
good_suffix[j] = i+1
return good_suffix
def gen_matched_prefix(pat):
m = len(pat)
matched_prefix = gusfield(pat)+[0]
for i in range(m-1, -1, -1):
matched_prefix[i] = max(matched_prefix[i], matched_prefix[i+1])
return matched_prefix
def preprocess(pat):
R = gen_jump_table(pat)
Z = gen_z_suffix(pat)
good_suffix = gen_good_suffix(pat, Z)
matched_prefix = gen_matched_prefix(pat)
return R, good_suffix, matched_prefix
def boyer_moore(pat, txt):
R, good_suffix, matched_prefix = preprocess(pat)
m = len(pat)
n = len(txt)
i = m-1
j = 0
occurrences = []
comps = 0
galil = False
start = 0
stop = 0
while j <= n-m:
match = pat[i] == txt[j+i] # The comparison
comps += 1
if match:
if galil and stop >= i > start: # Able to perform Galil
i = max(start-1, 0)
galil = False
if i == 0: # A full match of pat
good_suffix_shift = m - matched_prefix[1]
j += good_suffix_shift
occurrences.append(j)
i = m-1
else:
i -= 1 # A match, but not a full match yet
else:
mismatched = txt[j + i]
bad_char_shift = i - R[alpha_number(mismatched)][i] # Bad character shift
good_suffix_shift = 1
if good_suffix[i+1] > 0: # Good Suffix case 1a
good_suffix_shift = m - good_suffix[i+1]
start = good_suffix[i+1] - m + i + 1
stop = good_suffix[i+1]
elif good_suffix[i+1] == 0: # Good Suffix case 1b (i.e. matched prefix)
good_suffix_shift = m - matched_prefix[i+1]
start = 0
stop = matched_prefix[i + 1]
best_shift = max(good_suffix_shift, bad_char_shift) # Choose the best shift
j += best_shift
galil = best_shift == good_suffix_shift
i = m-1
return comps, occurrences
def two_to_the(n):
return 1 << n
def chunky_search(pat, txt):
# I settled on grouping in 2s, since higher groupings took more runs of BMs than comparisons it saved
# tl;dr: 2 was the fastest for me
factor = 2
occurrences = []
comps = 0
# Do two separate BMs for each binary offset of the grouped bits in txt (i.e. offset by 0 and 1 bits to the right)
for offset in range(two_to_the(factor-1)):
padding = offset if len(pat) % factor else "" # Padding to ensure each BM matches the correct groups
augmented_pat = f"{pat}{padding}"
c, o = boyer_moore(condense(augmented_pat, 0, factor), condense(txt, offset, factor))
comps += c
# Convert grouped match indices to normal ones with this O(n) operation
occurrences += list(map(lambda x: x*factor+offset, o))
return comps, occurrences
def read_args():
with open(sys.argv[1], "r") as txt_file:
txt = txt_file.read()
with open(sys.argv[2], "r") as pat_file:
pat = pat_file.read()
return txt, pat
def output_matches(occurrences):
with open("output_binary_boyermoore.txt", "w") as file:
for o in occurrences:
file.write(f"{o}\n")
def main():
if len(sys.argv) < 3:
print("Not enough arguments!")
else:
txt, pat = read_args()
comps, occurrences = chunky_search(pat, txt)
print(comps)
output_matches(occurrences)
main()
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