from pybrain.datasets import SupervisedDataSet
from pybrain.structure import FeedForwardNetwork, LinearLayer, SigmoidLayer, FullConnection
from pybrain.supervised import BackpropTrainer

ds = SupervisedDataSet(, )
ds.addSample((, ), (,))
ds.addSample((, ), (,))
ds.addSample((, ), (,))
ds.addSample((, ), (,))

net = FeedForwardNetwork()
inLayer = LinearLayer(, name='in')
hiddenLayer = SigmoidLayer(, name='hidden')  # 3个神经元的隐藏层
outLayer = LinearLayer(, name='out')

net.addInputModule(inLayer)
net.addModule(hiddenLayer)
net.addOutputModule(outLayer)

in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)

net.addConnection(in_to_hidden)
net.addConnection(hidden_to_out)
net.sortModules()  # 排序模块，确保网络结构正确

trainer = BackpropTrainer(net, ds, learningrate=0.01, momentum=0.9)
for i in range():
    trainer.train()
    if i %  == :
        print(f"第{i}次训练，误差：{trainer.testOnData()}")

print(net.activate((,)))  # 应该接近0
print(net.activate((,)))  # 应该接近1

import csv
ds = SupervisedDataSet(, )  # 假设4个特征，1个输出
with open('data.csv', 'r') as f:
    reader = csv.reader(f)
    next(reader)  # 跳过表头
    for row in reader:
        inputs = [float(x) for x in row[:-]]
        target = [float(row[-])]
        ds.addSample(inputs, target)

from pybrain.tools.shortcuts import buildNetwork
from pybrain.tools.validation import Validator
from pybrain.tools.data import normalizeDataset

normalizeDataset(ds)  # 对整个数据集进行归一化

label_map = {'cat': , 'dog': , 'bird': }
ds = SupervisedDataSet(, )  # 3个特征，分类标签为1个数字
for data in raw_data:
    features = data[:]
    label = label_map[data[]]
    ds.addSample(features, [label])

from pybrain.datasets import SequentialDataSet

ds = SequentialDataSet(, )  # 2个输入特征，1个输出
for t in range(len(time_series)):
    ds.addSample(time_series[t][:], time_series[t][])
ds.assignSequenceNumbers()  # 标记序列位置

from pybrain.structure import RecurrentNetwork

net = RecurrentNetwork()
# 添加输入层、隐藏层、输出层（代码同上）
# 添加循环连接
hidden_to_hidden = FullConnection(hiddenLayer, hiddenLayer)
net.addConnection(hidden_to_hidden)
net.sortModules()

from pybrain.structure.networks.rbfn import RBFNetwork

net = RBFNetwork(inputdim=, numcenters=, outdim=)
# 初始化中心和宽度
net._setParameters(centers, widths, weights)

from pybrain.tools.shortcuts import buildNetwork
from pybrain.unsupervised.trainers import SOMTrainer

net = buildNetwork(, , , hiddenclass=SigmoidLayer)  # 输入4维，输出2维（降维）
trainer = SOMTrainer(net, ds)
trainer.train()

trainer = BackpropTrainer(net, ds, learningrate=0.01, momentum=0.9)

# 动态调整学习率
for i in range():
    if i %  ==  and trainer.learningrate > 0.001:
        trainer.learningrate *= 0.5  # 每10轮衰减一半
    trainer.trainEpochs()

trainer = BackpropTrainer(net, ds, weightdecay=0.0001)

train_data, test_data = ds.splitWithProportion(0.7)  # 70%训练，30%测试
trainer = BackpropTrainer(net, train_data)
trainer.trainUntilConvergence(maxEpochs=)
# 在测试集上评估
error = Validator.MSE(net, test_data)
print(f"测试集误差：{error}")

from pybrain.rl.environments import Environment

class MazeEnvironment(Environment):
    def __init__(self, maze):
        self.maze = maze  # 迷宫地图，0表示通路，1表示墙壁
        self.agent_pos = (, )  # 智能体初始位置
        self.goal_pos = (, )  # 终点位置
        self.actions = [, , , ]  # 上下左右四个动作

    def performAction(self, action):
        # 根据动作移动智能体，检查是否撞墙
        # 更新位置并返回奖励
        if action == :  # 上
            new_pos = (self.agent_pos[]-, self.agent_pos[])
        # 其他动作类似...
        if self.maze[new_pos] == :  # 撞墙
            return -  # 负奖励
        elif new_pos == self.goal_pos:  # 到达终点
            self.agent_pos = new_pos
            return   # 正奖励
        else:
            self.agent_pos = new_pos
            return -  # 每步小惩罚，鼓励快速到达

from pybrain.rl.agents import LearningAgent
from pybrain.rl.learners import QLearner
from pybrain.rl.explorers import EpsilonGreedyExplorer

# 创建Q学习器
learner = QLearner()
# 添加探索策略（ε-贪婪策略，兼顾探索和利用）
explorer = EpsilonGreedyExplorer(epsilon=0.3)  # 30%概率随机探索
agent = LearningAgent(learner, explorer)

from pybrain.rl.tasks import Task

class MazeTask(Task):
    def __init__(self, environment):
        self.env = environment
        self.lastaction = None

    def getObservation(self):
        # 返回当前状态（智能体位置）
        return self.env.agent_pos

    def performAction