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def yield_sequences(starting_position, num_hops, sequence=None):
if sequence is None:
sequence = [starting_position]
if num_hops == 0:
yield sequence
return
for neighbor in neighbors(starting_position):
yield from yield_sequences(
from neighbors import neighbors
def count_sequences(start_position, num_hops):
if num_hops == 0:
return 1
num_sequences = 0
for position in neighbors(start_position):
num_sequences += count_sequences(position, num_hops - 1)
return num_sequences
def count_sequences(start_position, num_hops):
cache = {}
def helper(position, num_hops):
if (position, num_hops) in cache:
return cache[ (position, num_hops) ]
if num_hops == 0:
return 1
NEIGHBORS_MAP = {
1: (6, 8),
2: (7, 9),
3: (4, 8),
4: (3, 9, 0),
5: tuple(), # 5 has no neighbors
6: (1, 7, 0),
7: (2, 6),
8: (1, 3),
9: (2, 4),
def count_sequences(start_position, num_hops):
prior_case = [1] * 10
current_case = [0] * 10
current_num_hops = 1
while current_num_hops <= num_hops:
current_case = [0] * 10
current_num_hops += 1
for position in range(0, 10):
def matrix_multiply(A, B):
A_rows, A_cols = len(A), len(A[0])
B_rows, B_cols = len(B), len(B[0])
result = list(map(lambda i: [0] * B_cols, range(A_rows)))
for row in range(A_rows):
for col in range(B_cols):
for i in range(B_rows):
result[row][col] += A[row][i] * B[i][col]
NEIGHBORS_MAP = {
0: (4, 6),
1: (6, 8),
2: (7, 9),
3: (4, 8),
4: (3, 9, 0),
5: tuple(), # 5 has no neighbors
6: (1, 7, 0),
7: (2, 6),
8: (1, 3),
NEIGHBORS_MAP = {
0: (0, 0, 0, 0, 1, 0, 1, 0, 0, 0),
1: (0, 0, 0, 0, 0, 0, 1, 0, 1, 0),
2: (0, 0, 0, 0, 0, 0, 0, 1, 0, 1),
3: (0, 0, 0, 1, 0, 0, 0, 0, 1, 0),
4: (1, 0, 0, 1, 0, 0, 0, 0, 0, 1),
5: (0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
6: (1, 1, 0, 0, 0, 0, 0, 1, 0, 0),
7: (0, 0, 1, 0, 0, 0, 1, 0, 0, 0),
8: (0, 1, 0, 1, 0, 0, 0, 0, 0 ,0),
SYNONYMS = [
('rate', 'ratings'),
('approval', 'popularity'),
]
QUERIES = [
('obama approval rate', 'obama popularity ratings'),
('obama approval rates', 'obama popularity ratings'),
('obama approval rate', 'popularity ratings obama')
]
...
elif (w1, w2) in synonym_words:
continue
...