def calculate_end_to_end_delay_for_arc(G, edges):
adjusted_edges = [__get_edge_id_from_label(e) for e in edges]
dG = nx.DiGraph()
added_nodes = []
for u, v in G.edges():
if G[u][v]['id'] in adjusted_edges:
if u not in added_nodes:
dG.add_node(u, **G.nodes[u])
added_nodes.append(u)
if v not in added_nodes:
dG.add_node(v, **G.nodes[v])
added_nodes.append(v)
dG.add_edge(u, v, **G[u][v])
longest_path = dag_longest_path(dG, weight="delay")
if len(longest_path) == 0:
return 0
path_iter = iter(longest_path)
u = next(path_iter)
delay = 0
for v in path_iter:
delay += dG[u][v]['delay']
u = v
return delay
def get_test_chain(self, n): # @UnusedVariable
if not self.elements:
raise Uninhabited()
G = self._get_graph_closure_no_cycles()
# this returns the paths at least of length 2
from networkx.algorithms.dag import dag_longest_path
path = list(dag_longest_path(G))
if not path:
one = list(self.elements)[0]
return [one]
assert path, (self.elements, self.relations)
return path
def get_test_chain(self, n): # @UnusedVariable
if not self.elements:
raise Uninhabited()
G = self._get_graph_closure_no_cycles()
# this returns the paths at least of length 2
from networkx.algorithms.dag import dag_longest_path
path = list(dag_longest_path(G))
if not path:
one = list(self.elements)[0]
return [one]
assert path, (self.elements, self.relations)
return path
import networkx as nx
from networkx.algorithms.dag import dag_longest_path
f = open('rosalind_hdag.txt')
k = int(f.readline().rstrip())
result = []
for i in range(k):
n, m = [int(l) for l in f.readline().rstrip().split()]
nodes = [i + 1 for i in range(n)]
G = nx.DiGraph()
G.add_nodes_from(nodes)
for j in range(m):
u, v = [int(l) for l in f.readline().rstrip().split()]
G.add_edge(u, v)
output = '-1'
try:
path = dag_longest_path(G)
if len(path) == n:
output = '1 ' + ' '.join([str(i) for i in path])
except:
pass
result.append(output)
result = '\n'.join(result)
print(result)
open('rosalind_hdag_sub.txt', 'wt').write(result)
def frame_alignment(query_features,
refer_features,
top_K=5,
min_sim=0.80,
max_step=10):
"""
用于计算两组特征(已经做过l2-norm)之间的帧匹配结果
Args:
query_features: shape: [N, D]
refer_features: shape: [M, D]
top_K: 取前K个refer_frame
min_sim: 要求query_frame与refer_frame的最小相似度
max_step: 有边相连的结点间的最大步长
Returns:
path_query: shape: [1, L]
path_refer: shape: [1, L]
"""
node_pair2id = {}
node_id2pair = {}
node_id2pair[0] = (-1, -1) # source
node_pair2id[(-1, -1)] = 0
node_num = 1
DG = nx.DiGraph()
DG.add_node(0)
idxs, unsorted_dists, sorted_dists = dists(query_features, refer_features)
# add nodes
for qf_idx in range(query_features.shape[0]):
for k in range(top_K):
rf_idx = idxs[qf_idx][k]
sim = 1 - sorted_dists[qf_idx][k]
if sim < min_sim:
break
node_id2pair[node_num] = (qf_idx, rf_idx)
node_pair2id[(qf_idx, rf_idx)] = node_num
DG.add_node(node_num)
node_num += 1
node_id2pair[node_num] = (query_features.shape[0], refer_features.shape[0]
) # sink
node_pair2id[(query_features.shape[0], refer_features.shape[0])] = node_num
DG.add_node(node_num)
node_num += 1
# link nodes
for i in range(0, node_num - 1):
for j in range(i + 1, node_num - 1):
pair_i = node_id2pair[i]
pair_j = node_id2pair[j]
if (pair_j[0] > pair_i[0] and pair_j[1] > pair_i[1] and
#pair_j[0] - pair_i[0] <= max_step and pair_j[1] - pair_i[1] <= max_step and
abs(pair_j[1] - pair_i[1] -
(pair_j[0] + pair_i[0])) <= max_step):
qf_idx = pair_j[0]
rf_idx = pair_j[1]
DG.add_edge(i, j, weight=1 - unsorted_dists[qf_idx][rf_idx])
for i in range(0, node_num - 1):
j = node_num - 1
pair_i = node_id2pair[i]
pair_j = node_id2pair[j]
if (pair_j[0] > pair_i[0] and pair_j[1] > pair_i[1] and
#pair_j[0] - pair_i[0] <= max_step and pair_j[1] - pair_i[1] <= max_step and
abs(pair_j[1] - pair_i[1] -
(pair_j[0] - pair_i[0])) <= max_step):
qf_idx = pair_j[0]
rf_idx = pair_j[1]
DG.add_edge(i, j, weight=0)
longest_path = dag_longest_path(DG)
if 0 in longest_path:
longest_path.remove(0) # remove source node
if node_num - 1 in longest_path:
longest_path.remove(node_num - 1) # remove sink node
path_query = [node_id2pair[node_id][0] for node_id in longest_path]
path_refer = [node_id2pair[node_id][1] for node_id in longest_path]
score = 0.0
for (qf_idx, rf_idx) in zip(path_query, path_refer):
score += 1 - unsorted_dists[qf_idx][rf_idx]
return path_query, path_refer, score
f = open('rosalind_hdag.txt')
k = int(f.readline().rstrip())
result = []
for i in range(k):
n, m = [int(l) for l in f.readline().rstrip().split()]
nodes = [i+1 for i in range(n)]
G = nx.DiGraph()
G.add_nodes_from(nodes)
for j in range(m):
u, v = [int(l) for l in f.readline().rstrip().split()]
G.add_edge(u, v)
output = '-1'
try:
path = dag_longest_path(G)
if len(path) == n:
output = '1 ' + ' '.join([str(i) for i in path])
except:
pass
result.append(output)
result = '\n'.join(result)
print(result)
open('rosalind_hdag_sub.txt', 'wt').write(result)