有人可以更正代码吗？它说 NameError：名称“Node”未定义

Question

# Also the terms Node, DiscreteDistribution, ConditionalProbabilityTable, distribution, BayesianNetwork(), add_edge, bake are not being recognised.

from pomegranate import \*

# Rain node has no parents

rain = Node(DiscreteDistribution({"none": 0.7,"light": 0.2,"heavy": 0.1}), name="rain")

# Track maintenance node is conditional on rain

maintenance = Node(ConditionalProbabilityTable(\[\["none", "yes", 0.4\],\["none", "no", 0.6\],\["light", "yes", 0.2\],\["light", "no", 0.8\],\["heavy", "yes", 0.1\],\["heavy", "no", 0.9\]\], \[rain.distribution\]), name="maintenance")

# Train node is conditional on rain and maintenance

train = Node(ConditionalProbabilityTable(\[\["none", "yes", "on time", 0.8\],\["none", "yes", "delayed", 0.2\],\["none", "no", "on time", 0.9\],\["none", "no", "delayed", 0.1\],\["light", "yes", "on time", 0.6\],
\["light", "yes", "delayed", 0.4\],\["light", "no", "on time", 0.7\],\["light", "no", "delayed", 0.3\],
\["heavy", "yes", "on time", 0.4\],\["heavy", "yes", "delayed", 0.6\],\["heavy", "no", "on time", 0.5\],\["heavy", "no", "delayed", 0.5\],\], \[rain.distribution, maintenance.distribution\]), name="train")

# Appointment node is conditional on train

appointment = Node(ConditionalProbabilityTable(\[\["on time", "attend", 0.9\],\["on time", "miss", 0.1\],\["delayed", "attend", 0.6\],\["delayed", "miss", 0.4\]\], \[train.distribution\]), name="appointment")

# Create a Bayesian Network and add states

model = BayesianNetwork()
model.add_states(rain, maintenance, train, appointment)

# Add edges connecting nodes

model.add_edge(rain, maintenance)
model.add_edge(rain, train)
model.add_edge(maintenance, train)
model.add_edge(train, appointment)

# Finalize model

model.bake()

这是CS50课程的一个程序。请有人解释一下为什么它没有在我的系统上运行。

https://learning.edx.org/course/course-v1:HarvardX+CS50AI+1T2020/block-v1:HarvardX+CS50AI+1T2020+type@sequential+block@c62f675bf7f94f0e91b408cacda56451/block-v1:HarvardX+CS50AI+ 1T2020+类型@垂直+块@1bf36eea064644718f0c5d4f44fc3e29?_gl=1%2A1jbevuj%2A_ga%2AOTI5MTM1MzM4LjE2NjY0MzEzMDI.%2A_ga_D3KS4KMDT0%2AMTY4Nzg4Njc2Ny 4xMC4wLjE2ODc4ODY3NjcuNjAuMC4w

Answer 1

尝试

from pomegranate import Node, DiscreteDistribution, ConditionalProbabilityTable, BayesianNetwork

# Rain node has no parents
rain = Node(DiscreteDistribution({"none": 0.7, "light": 0.2, "heavy": 0.1}), name="rain")

# Track maintenance node is conditional on rain
maintenance = Node(ConditionalProbabilityTable([["none", "yes", 0.4], ["none", "no", 0.6], ["light", "yes", 0.2], ["light", "no", 0.8], ["heavy", "yes", 0.1], ["heavy", "no", 0.9]], [rain.distribution]), name="maintenance")

# Train node is conditional on rain and maintenance
train = Node(ConditionalProbabilityTable([["none", "yes", "on time", 0.8], ["none", "yes", "delayed", 0.2], ["none", "no", "on time", 0.9], ["none", "no", "delayed", 0.1], ["light", "yes", "on time", 0.6],
["light", "yes", "delayed", 0.4], ["light", "no", "on time", 0.7], ["light", "no", "delayed", 0.3],
["heavy", "yes", "on time", 0.4], ["heavy", "yes", "delayed", 0.6], ["heavy", "no", "on time", 0.5], ["heavy", "no", "delayed", 0.5]], [rain.distribution, maintenance.distribution]), name="train")

# Appointment node is conditional on train
appointment = Node(ConditionalProbabilityTable([["on time", "attend", 0.9], ["on time", "miss", 0.1], ["delayed", "attend", 0.6], ["delayed", "miss", 0.4]], [train.distribution]), name="appointment")

# Create a Bayesian Network and add states
model = BayesianNetwork()
model.add_states(rain, maintenance, train, appointment)

# Add edges connecting nodes
model.add_edge(rain, maintenance)
model.add_edge(rain, train)
model.add_edge(maintenance, train)
model.add_edge(train, appointment)

# Finalize model
model.bake()

类 Node、DiscreteDistribution、ConditionalProbabilityTable 和 BayesianNetwork 已从石榴模块中正确导入。此外，

add_states()

和

add_edge()

方法用于定义网络结构。

在运行代码之前，请确保您的环境中安装了石榴库。