def get_results(self):
for f in self.parent.upper_frame.right_frame.list_files:
structure = read_file(join("Data/all", f), f in listFiles)
if self.parent.upper_frame.left_frame.algo.get() == 0:
preferences = structure["preferences"]
if self.parent.upper_frame.left_frame.filtered.get():
preferences, nb_voters, nb_unique = remove_last_ballots(
preferences)
candidates = structure["candidates"]
ensemble, best = bnb(len(preferences), preferences, candidates)
axes, card = find_axes2(best[0][0], candidates)
axes = filter_symmetric_axes(axes)
for axis in axes:
self.results.append((f, axis))
else:
if self.parent.upper_frame.left_frame.dissimilarity.get() == 0:
dissimilarity_function = dissimilarity_and_n
elif self.parent.upper_frame.left_frame.dissimilarity.get(
) == 1:
dissimilarity_function = dissimilarity_and_or
else:
dissimilarity_function = dissimilarity_over_over
if f in listFiles:
t, permutations = find_axis_from_structure(
structure, dissimilarity_function,
self.parent.upper_frame.left_frame.weighted.get())
else:
t, permutations = find_axis_from_structure(
structure,
dissimilarity_function,
self.parent.upper_frame.left_frame.weighted.get(),
unwanted_candidates=[2, 3, 7, 11])
permutations = filter_symmetric_axes(permutations[1])
for permutation in permutations:
self.results.append((f, permutation))
def calculate_bnb(self, file, vrow, filtered):
structure = read_file("Data/all/" + str(file), file in listFiles)
if not filtered:
preferences = structure["preferences"]
candidates = structure["candidates"]
Label(self, text=str(structure["nb_voters"])).grid(row=vrow, column=3)
Label(self, text=str(structure["nb_unique_orders"])).grid(row=vrow,
column=5)
else:
preferences_bis = structure["preferences"]
preferences, nb_voters, nb_unique = remove_last_ballots(
preferences_bis)
candidates = structure["candidates"]
Label(self, text=str(nb_voters)).grid(row=vrow, column=3)
Label(self, text=str(nb_unique)).grid(row=vrow, column=5)
t1 = time()
ensemble, best = bnb(len(preferences), preferences, candidates)
t2 = time()
Label(self, text=str(best[1])).grid(row=vrow, column=7)
Label(self, text=str(len(best[0][0]))).grid(row=vrow, column=9)
if not filtered:
Label(self, text=str(best[1] * 100.0 / structure["nb_voters"])).grid(
row=vrow, column=11)
else:
Label(self, text=str(best[1] * 100.0 / nb_voters)).grid(row=vrow,
column=11)
axes, card = find_axes2(best[0][0], candidates)
Label(self, text=str(len(axes))).grid(row=vrow, column=13)
Label(self, text=str(t2 - t1)).grid(row=vrow, column=15)
def get_results(self):
for f in self.parent.upper_frame.right_frame.list_files:
structure = read_file(join("Data/all", f), f in listFiles)
if self.parent.upper_frame.left_frame.algo.get() == 0:
preferences = structure["preferences"]
if self.parent.upper_frame.left_frame.filtered.get():
preferences, nb_voters, nb_unique = remove_last_ballots(preferences)
candidates = structure["candidates"]
ensemble, best = bnb(len(preferences), preferences, candidates)
axes, card = find_axes2(best[0][0], candidates)
axes = filter_symmetric_axes(axes)
for axis in axes:
self.results.append((f, axis))
else:
if self.parent.upper_frame.left_frame.dissimilarity.get() == 0:
dissimilarity_function = dissimilarity_and_n
elif self.parent.upper_frame.left_frame.dissimilarity.get() == 1:
dissimilarity_function = dissimilarity_and_or
else:
dissimilarity_function = dissimilarity_over_over
if f in listFiles:
t, permutations = find_axis_from_structure(structure, dissimilarity_function,
self.parent.upper_frame.left_frame.weighted.get())
else:
t, permutations = find_axis_from_structure(structure, dissimilarity_function,
self.parent.upper_frame.left_frame.weighted.get(),
unwanted_candidates=[2, 3, 7, 11])
permutations = filter_symmetric_axes(permutations[1])
for permutation in permutations:
self.results.append((f, permutation))
def calculate_bnb(self, file, vrow, filtered):
structure = read_file("Data/all/" + str(file), file in listFiles)
if not filtered:
preferences = structure["preferences"]
candidates = structure["candidates"]
Label(self, text=str(structure["nb_voters"])).grid(row=vrow, column=3)
Label(self, text=str(structure["nb_unique_orders"])).grid(row=vrow, column=5)
else:
preferences_bis = structure["preferences"]
preferences, nb_voters, nb_unique = remove_last_ballots(preferences_bis)
candidates = structure["candidates"]
Label(self, text=str(nb_voters)).grid(row=vrow, column=3)
Label(self, text=str(nb_unique)).grid(row=vrow, column=5)
t1 = time()
ensemble, best = bnb(len(preferences), preferences, candidates)
t2 = time()
Label(self, text=str(best[1])).grid(row=vrow, column=7)
Label(self, text=str(len(best[0][0]))).grid(row=vrow, column=9)
if not filtered:
Label(self, text=str(best[1] * 100.0 / structure["nb_voters"])).grid(row=vrow, column=11)
else:
Label(self, text=str(best[1] * 100.0 / nb_voters)).grid(row=vrow, column=11)
axes, card = find_axes2(best[0][0], candidates)
Label(self, text=str(len(axes))).grid(row=vrow, column=13)
Label(self, text=str(t2 - t1)).grid(row=vrow, column=15)
