本文整理汇总了Python中PriorityQueue.PriorityQueue.append方法的典型用法代码示例。如果您正苦于以下问题：Python PriorityQueue.append方法的具体用法？Python PriorityQueue.append怎么用？Python PriorityQueue.append使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类PriorityQueue.PriorityQueue的用法示例。
在下文中一共展示了PriorityQueue.append方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: fast_marching_method
# 需要导入模块: from PriorityQueue import PriorityQueue [as 别名]
# 或者: from PriorityQueue.PriorityQueue import append [as 别名]
def fast_marching_method(graph,start):
h = 1
def calculus_distance(node,graph,weights):
neighbours = graph.get_neighbours(node);
if 'y-1' in neighbours :
if 'y+1' in neighbours:
x1 = min(weights[neighbours['y-1']],weights[neighbours['y+1']]);
else :
x1 = weights[neighbours['y-1']];
else :
if 'y+1' in neighbours:
x1 = weights[neighbours['y+1']];
if 'x-1' in neighbours:
if 'x+1' in neighbours:
x2 = min(weights[neighbours['x-1']],weights[neighbours['x+1']]);
else :
x2 = weights[neighbours['x-1']];
else :
if 'x+1' in neighbours:
x2 = weights[neighbours['x+1']];
if 2*h**2-(x1-x2)**2>=0:
return (x1+x2+(2*h**2-(x1-x2)**2)**0.5)/2
else:
return min(x1,x2)+h
frontier = PriorityQueue();
weights = graph.distances;
explored = []
goals = numpy.where(graph.indicator_map==2)
goals_x = goals[0]
goals_y = goals[1]
for i in range(goals_x.size):
frontier.append([0,(goals_x[i],goals_y[i])])
weights[(goals_x[i],goals_y[i])] = 0
while frontier:
node = frontier.pop();
explored.append(node[1])
#if node[1]==start:
# return weights
neighbours = graph.get_neighbours(node[1]);
for neighbour in neighbours.itervalues():
if neighbour not in explored and graph.indicator_map[neighbour]:
if not neighbour in frontier:
frontier.append([calculus_distance(neighbour,graph,weights),neighbour])
weights[neighbour]=calculus_distance(neighbour,graph,weights)
elif weights[neighbour] > calculus_distance(neighbour,graph,weights):
frontier[neighbour][0]=calculus_distance(neighbour,graph,weights)
weights[neighbour]=calculus_distance(neighbour,graph,weights)
graph.distances = weights