cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5这句代码什么意思

这行代码的作用是将二维数组 cost_matrix 中大于 max_distance 的元素赋值为 max_distance + 1e-5，其中 1e-5 是一个非常小的数，用于避免出现精度问题。

换句话说，这行代码的目的是将 cost_matrix 中所有大于 max_distance 的元素都设置为 max_distance，以保证在后续的计算中这些元素不会对结果产生影响。

相关问题

gating_distance = kf.gating_distance( track.mean, track.covariance, measurements, only_position) cost_matrix[row, gating_distance > gating_threshold] = gated_cost这两句代码什么意思

这两句代码是用于多目标跟踪（multi-target tracking）的卡尔曼滤波器中的一部分。

首先，kf.gating_distance()函数计算目标的状态（mean和covariance）和测量值（measurements）之间的马氏距离。如果only_position参数为True，则只考虑状态向量的前两个元素（即位置），否则将考虑整个状态向量。这个距离度量允许我们将测量值分配给目标，以便进行下一步的更新。

接下来，对于距离大于门限值（gating_threshold）的测量，将其对应的代价设置为无穷大，以表示这些测量值与目标之间的不可能关系。这个过程被称为“gating”，它可以排除一些明显不可能的测量结果，提高跟踪的准确性。这些代价将在卡尔曼滤波器的下一步更新中使用。

翻译代码：#计算代价 def calTravelCost(route_list,model): timetable_list=[] distance_of_routes=0 time_of_routes=0 obj=0 for route in route_list: timetable=[] vehicle=model.vehicle_dict[route[0]] travel_distance=0 travel_time=0 v_type = route[0] free_speed=vehicle.free_speed fixed_cost=vehicle.fixed_cost variable_cost=vehicle.variable_cost for i in range(len(route)): if i == 0: next_node_id=route[i+1] travel_time_between_nodes=model.distance_matrix[v_type,next_node_id]/free_speed departure=max(0,model.demand_dict[next_node_id].start_time-travel_time_between_nodes) timetable.append((int(departure),int(departure))) elif 1<= i <= len(route)-2: last_node_id=route[i-1] current_node_id=route[i] current_node = model.demand_dict[current_node_id] travel_time_between_nodes=model.distance_matrix[last_node_id,current_node_id]/free_speed arrival=max(timetable[-1][1]+travel_time_between_nodes,current_node.start_time) departure=arrival+current_node.service_time timetable.append((int(arrival),int(departure))) travel_distance += model.distance_matrix[last_node_id, current_node_id] travel_time += model.distance_matrix[last_node_id, current_node_id]/free_speed+\ + max(current_node.start_time - arrival, 0) else: last_node_id = route[i - 1] travel_time_between_nodes = model.distance_matrix[last_node_id,v_type]/free_speed departure = timetable[-1][1]+travel_time_between_nodes timetable.append((int(departure),int(departure))) travel_distance += model.distance_matrix[last_node_id,v_type] travel_time += model.distance_matrix[last_node_id,v_type]/free_speed distance_of_routes+=travel_distance time_of_routes+=travel_time if model.opt_type==0: obj+=fixed_cost+travel_distance*variable_cost else: obj += fixed_cost + travel_time *variable_cost timetable_list.append(timetable) return timetable_list,time_of_routes,distance_of_routes,obj

# Calculate the cost
def calTravelCost(route_list, model):
timetable_list = []
distance_of_routes = 0
time_of_routes = 0
obj = 0

# Loop through each route
for route in route_list:
timetable = []
vehicle = model.vehicle_dict[route[0]]
travel_distance = 0
travel_time = 0
v_type = route[0]
free_speed = vehicle.free_speed
fixed_cost = vehicle.fixed_cost
variable_cost = vehicle.variable_cost

# Loop through each node in the route
for i in range(len(route)):
if i == 0:
next_node_id = route[i+1]
travel_time_between_nodes = model.distance_matrix[v_type, next_node_id] / free_speed
departure = max(0, model.demand_dict[next_node_id].start_time - travel_time_between_nodes)
timetable.append((int(departure), int(departure)))
elif 1 <= i <= len(route)-2:
last_node_id = route[i-1]
current_node_id = route[i]
current_node = model.demand_dict[current_node_id]
travel_time_between_nodes = model.distance_matrix[last_node_id, current_node_id] / free_speed
arrival = max(timetable[-1][1] + travel_time_between_nodes, current_node.start_time)
departure = arrival + current_node.service_time
timetable.append((int(arrival), int(departure)))
travel_distance += model.distance_matrix[last_node_id, current_node_id]
travel_time += model.distance_matrix[last_node_id, current_node_id] / free_speed + \
max(current_node.start_time - arrival, 0)
else:
last_node_id = route[i - 1]
travel_time_between_nodes = model.distance_matrix[last_node_id, v_type] / free_speed
departure = timetable[-1][1] + travel_time_between_nodes
timetable.append((int(departure), int(departure)))
travel_distance += model.distance_matrix[last_node_id, v_type]
travel_time += model.distance_matrix[last_node_id, v_type] / free_speed

# Calculate the cost for this route
distance_of_routes += travel_distance
time_of_routes += travel_time
if model.opt_type == 0:
obj += fixed_cost + travel_distance * variable_cost
else:
obj += fixed_cost + travel_time * variable_cost
timetable_list.append(timetable)

return timetable_list, time_of_routes, distance_of_routes, obj