def find_center_string_fast(S):
# Contains pairwise distances from s to s
score_matrix = np.full((len(S), len(S)), None)
# Distances from s to s is 0
for i in range(len(S)):
score_matrix[i, i] = 0
# Iterate through possible centers, S[i]
for i in range(len(S)):
# Score for S[i]
sum_scores = 0
# Iterate through all other strings, S[j]
for j in range(len(S)):
# If we have NOT already computed the distance from S[i] to S[j], do this
if(score_matrix[i, j] == None):
score = pa.calculate_alignment_matrix(sub_matrix, gap_cost, S[i], S[j])[len(S[i]), len(S[j])]
# Distance from S[i] to S[j] is equal to the distance from S[j] to S[i]
score_matrix[i, j] = score
score_matrix[j, i] = score
sum_scores += score_matrix[i, j]
# Calculate total scores for S[i]'s
total_scores = [sum(score) for score in score_matrix]
# The min score index
best_score_index = np.argmin(total_scores)
# Return string with min score (= center)
return S[best_score_index]
def multiple_align(S, center):
M = []
S.remove(center)
for s in S:
A_matrix = pa.calculate_alignment_matrix(sub_matrix, gap_cost, center, s)
# optimal alignment
A = pa.backtrack_nonrec(A_matrix, center, s, sub_matrix, gap_cost, "", "", len(center), len(s))
if(s != S[0]):
M = extend_M_with_A(M, A)
else:
M = A
return M
def find_center_string(S):
print("Finding center string...")
sum_scores_list = []
for pos_cen in S: # possible center
sum_scores = 0
for s in S:
sum_scores += pa.calculate_alignment_matrix(sub_matrix, gap_cost, pos_cen, s)[len(pos_cen), len(s)]
#print("sum_scores: ", sum_scores)
sum_scores_list.append(sum_scores)
index_of_min_score = np.argmin(sum_scores_list)
return S[index_of_min_score]