val result = Pregel(graph.mapVertices((_, _) => List[Long]()), List[Long](), activeNodes)(这一行有问题，activeNodes不能作为参数，提示我需要填一个Int类型的参数

根据你的描述，我可以看出这里的问题是activeNodes参数的类型不正确。根据Pregel的文档，activeNodes应该是一个Int类型的参数。

你需要检查一下你的代码，看看是否在调用Pregel函数时，将activeNodes参数错误地传递为了List[Long]类型，而不是Int类型。如果是这样的话，只需要将activeNodes的类型改为Int即可：

val result = Pregel(graph.mapVertices((_, _) => List[Long]()), List[Long](), activeNodes.toInt)(...))

相关问题

修改class arcnode: def __init__(self, adjvex, weight, link=None): self.adjvex = adjvex self.weight = weight self.link = link class vexnode: def __init__(self, data, first_arc=None): self.data = data self.first_arc = first_arc class Graph: def __init__(self): self.vex_list = [] self.vex_num = 0 self.edge_num = 0 # 请在这里填写答案 def addVertex(self, vex_val): new_vertex = vexnode(vex_val) self.vex_list.append(new_vertex) self.vex_num += 1 def addEdge(self, f, t, cost=0): if f not in self.vex_list: nv = self.addVertex(f) # 如果起始顶点不存在，则将其添加到图中 if t not in self.vex_list: nv = self.addVertex(t) # 如果目标顶点不存在，则将其添加到图中 # 无向图添加双向边 self.vex_list[f].addNeighbor(self.vex_list[t], cost) # 将目标顶点及其权重添加到起始顶点的 connectedTo 字典中 self.vex_list[t].addNeighbor(self.vex_list[f], cost) # 有向图只添加一条边 # 请在这里填写答案 def print_graph(self): for i in range(self.vex_num): print(self.vex_list[i].data, end="->") cur = self.vex_list[i].first_arc while cur: print("adj:{},weight:{}".format(cur.adjvex, cur.weight), end="->") cur = cur.link print('None') if __name__ == "__main__": g = Graph() s = input() for vertex in s: g.addVertex(vertex) g.addEdge(0, 1, 11) g.addEdge(0, 2, 55) g.addEdge(2, 3, 88) g.addEdge(0, 3, 33) g.addEdge(1, 2, 44) g.print_graph()

class arcnode:
def __init__(self, adjvex, weight, link=None):
self.adjvex = adjvex
self.weight = weight
self.link = link

class vexnode:
def __init__(self, data, first_arc=None):
self.data = data
self.first_arc = first_arc

class Graph:
def __init__(self):
self.vex_list = []
self.vex_num = 0
self.edge_num = 0

def addVertex(self, vex_val):
new_vertex = vexnode(vex_val)
self.vex_list.append(new_vertex)
self.vex_num += 1

def addEdge(self, f, t, cost=0):
if f not in range(self.vex_num):
raise ValueError("Invalid Starting Vertex")
if t not in range(self.vex_num):
raise ValueError("Invalid Ending Vertex")
new_arc = arcnode(t, cost)
new_arc.link = self.vex_list[f].first_arc
self.vex_list[f].first_arc = new_arc
self.edge_num += 1

def print_graph(self):
for i in range(self.vex_num):
print(self.vex_list[i].data, end="->")
cur = self.vex_list[i].first_arc
while cur:
print("adj:{},weight:{}".format(cur.adjvex, cur.weight), end="->")
cur = cur.link
print('None')

if __name__ == "__main__":
g = Graph()
s = input()
for vertex in s:
g.addVertex(vertex)
g.addEdge(0, 1, 11)
g.addEdge(0, 2, 55)
g.addEdge(2, 3, 88)
g.addEdge(0, 3, 33)
g.addEdge(1, 2, 44)
g.print_graph()

scaler = MinMaxScaler() X_train = scaler.fit_transform(X_train) X_val = scaler.transform(X_val) X_test = scaler.transform(X_test) y_train = scaler.fit_transform(y_train) y_val = scaler.transform(y_val) y_test = scaler.transform(y_test根据这段代码写出反归一化

假设你用的是 MinMaxScaler 进行归一化处理，反归一化的代码如下：

# 定义反归一化函数
def inverse_min_max_scaler(data, scaler):
    min_val = scaler.data_min_
    max_val = scaler.data_max_
    return data * (max_val - min_val) + min_val

# 对训练集进行反归一化处理
X_train_inverse = inverse_min_max_scaler(X_train, scaler)
y_train_inverse = inverse_min_max_scaler(y_train, scaler)

# 对验证集进行反归一化处理
X_val_inverse = inverse_min_max_scaler(X_val, scaler)
y_val_inverse = inverse_min_max_scaler(y_val, scaler)

# 对测试集进行反归一化处理
X_test_inverse = inverse_min_max_scaler(X_test, scaler)
y_test_inverse = inverse_min_max_scaler(y_test, scaler)

需要注意的是，反归一化的过程中需要传入之前用于归一化的 Scaler 对象，以便正确计算反归一化值。