def test_flip_sebastienfilion():
# add :: Number → [Number] → Number
add = reduce(lambda a, b: a + b)
# multiply :: Number → [Number] → Number
multiply = reduce(lambda a, b: a * b)
total = compose(flip(multiply)([1, 2, 3]), flip(add)([1, 2, 3]))(0)
assert total == 36
def edges_to_functions(edges):
result = []
for edge in edges:
# z założenia są monotoniczne
min_e = R.reduce(R.min_by(lambda x: x[0]), [1e9, 0], edge)
max_e = R.reduce(R.max_by(lambda x: x[0]), [0, 0], edge)
a = (max_e[1] - min_e[1]) / (max_e[0] - min_e[0])
b = min_e[1] - a * min_e[0]
result.append([a, b])
return result
def stats_to_matrix(pair_stats, authors={}):
result_stats = []
pairs = ramda.to_pairs(pair_stats)
_pair_list = ramda.reduce(stats_to_matrix_prep, [], pairs)
pair_list = ramda.reduce(lambda acc, cur: [*acc, *cur], [], _pair_list)
authors_emails = sorted(ramda.uniq(ramda.pluck(0, pair_list)))
for author_a in [*authors_emails]:
author_a_pair_stats = []
for author_b in [*authors_emails]:
if author_a == author_b:
author_a_pair_stats.append(None)
else:
stat = ramda.find(
lambda pair: pair[0] == author_a and pair[1] == author_b,
pair_list)
author_a_pair_stats.append(stat[2])
result_stats.append({
'author': authors.get(author_a, {}),
'matrix': author_a_pair_stats,
})
return result_stats
def apply_to_resource(self, coll, fn):
return r.reduce(fn, self, r.to_pairs(coll))
# im2 = copy.deepcopy(im)
edges1 = feature.canny(im)
edges = break_edges(edges1)
functions = edges_to_functions(edges)
errors = blad_srednikwadratowy(edges, functions)
print(errors)
# angles_between_functions(functions)
neighbour_angles = angles_with_neighbouring_functions(functions)
print(neighbour_angles)
neighbour_angles_sum = R.map(sum, neighbour_angles)
print(neighbour_angles_sum)
max_index = lambda my_list: R.reduce(
lambda acc, x: acc if acc[1] >= x[1] else x, [0, 0],
R.zip(range(len(my_list)), my_list))
e = max_index(errors)
print(e)
a = max_index(neighbour_angles_sum)
print(a)
#wg błędów
index_podstawy = (e[0] + 2) % 4
print('wg błędów', index_podstawy, functions[index_podstawy])
[a1, _] = functions[index_podstawy]
#wg kątów
index_podstawy2 = a[0]
print('wg kątów', index_podstawy2, functions[index_podstawy2])
[a2, _] = functions[index_podstawy2]
def accumulate_pair_stats(self):
self.pair_stats = ramda.reduce(
lambda a, b: ramda.merge_with(lambda _a, _b: _a + _b, a, b),
{},
self._pair_stats,
)