def calculate_m(self, current_segment):
results = dict()
for neighbor in self.hypercube.backward_neighbors(current_segment):
# calculate M + S for each neighbor
M = neighbor.get_distance()
indices_changed = Hypercube.compare_indices(current_segment.index, neighbor.index)
sequence = list()
for i in range(0, len(indices_changed)):
if indices_changed[i]:
sequence.append(self.hypercube.sequences[i][current_segment.index[i]])
else:
sequence.append('-')
result = (M + self.calculate_s(sequence), neighbor.index)
if result[0] not in results:
results[result[0]] = list()
results[result[0]].append(result[1])
else:
results[result[0]].append(result[1])
if len(results) <= 0:
return None
max_result = max(results.keys())
backtrack_index = results[max_result]
return (max_result, backtrack_index)
def generate_output(self, traceback_path):
output_sequences = list()
while len(output_sequences) < self.hypercube.dimension_count:
output_sequences.append(list())
sequence_indices = list(self.hypercube.dimensions)
previous_step = traceback_path[0]
for current_step in traceback_path[1:]:
indices_changed = Hypercube.compare_indices(previous_step, current_step)
previous_step = tuple(current_step)
for i in range(0, len(indices_changed)):
if indices_changed[i]:
sequence_indices[i] -= 1
if sequence_indices[i] >= 0:
output_sequences[i].insert(0, self.hypercube.sequences[i][sequence_indices[i]])
else:
output_sequences[i].insert(0, '-')
else:
output_sequences[i].insert(0, '-')
return output_sequences