Pancake's Personal Website

特色

个人简介

杭州电子科技大学研究所

喜欢的游戏：明日方舟、csgo……

想联系我也可以加QQ：2114496089，加好友都请备注来意。

圣光机联合学院毕业论文Latex参考

因为字体文件较大，win和mac没必要使用下载的字体，所以推荐使用no-fonts分支

包含字体文件：

git clone -b main –depth=1 http://git.pancake2021.work/pancake/GLT.git

或

http://git.pancake2021.work/pancake/GLT/archive/main.zip

不含字体文件：

git clone -b no-fonts –depth=1 http://git.pancake2021.work/pancake/GLT.git

或

http://git.pancake2021.work/pancake/GLT/archive/no-fonts.zip

LSTM简易通用版本

下载链接

因为在数学建模中用的很麻烦，写了个简易易使用版本。

以下代码为最终成品代码 demo 版本，不推荐使用，最新版本可点下载链接或可见 csdn。

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import warnings
import pandas as pd
import scipy.stats as st

# 设定随机种子，以确保实验的可重复性
torch.manual_seed(1)
np.random.seed(1)


# 定义LSTM模型
class LSTM(nn.Module):
    hidden = None

    def __init__(self, input_size, hidden_size, output_size, num_layers):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.train_on_gpu = torch.cuda.is_available()
        # As batch_first=True, input: (batch_size, sequence_length, input_size)
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.lstm2 = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, hidden_size)
        self.fcOut = nn.Linear(hidden_size, output_size)
        self.drop = nn.Dropout(0.5)
        # 可选操作，可以把下一行注释
        self.apply(LSTM.init_weights)

    def forward(self, x):
        # 防止 loss.backward 报错
        hidden = [each.data for each in self.hidden]
        x, hidden = self.lstm(x, hidden)
        x, self.hidden = self.lstm2(x, hidden)
        x = x[:, -1, :]
        x = F.relu(self.fc(x))
        x = self.drop(x)
        x = self.fcOut(x)
        return x

    def init_hidden(self, batch_size):
        weight = next(self.parameters()).data

        if self.train_on_gpu:
            self.hidden = (weight.new(self.num_layers, batch_size, self.hidden_size).zero_().cuda(),
                           weight.new(self.num_layers, batch_size, self.hidden_size).zero_().cuda())
        else:
            self.hidden = (weight.new(self.num_layers, batch_size, self.hidden_size).zero_(),
                           weight.new(self.num_layers, batch_size, self.hidden_size).zero_())

    @staticmethod
    def init_weights(m):
        if type(m) == nn.LSTM:
            for name, param in m.named_parameters():
                if 'weight_ih' in name:
                    torch.nn.init.orthogonal_(param.data)
                elif 'weight_hh' in name:
                    torch.nn.init.orthogonal_(param.data)
                elif 'bias' in name:
                    param.data.fill_(0)
        elif type(m) == nn.Conv1d or type(m) == nn.Linear:
            torch.nn.init.orthogonal_(m.weight)
            m.bias.data.fill_(0)


class CustomLSTM:
    X, Y, data, model, optimizer, criterion, result, result_, window, losses = [None] * 10

    def __init__(self, data: np.array, window):
        self.data = data
        self.input_dim = self.init_data()
        self.output_dim = self.input_dim
        self.slice(window)

    # data 可为一维数组或二维数组，强制把一维数组转化为二维数组
    def init_data(self):
        assert (length := len(self.data.shape)) in [1, 2]
        if length == 1:
            self.data = self.data[:, np.newaxis]
        return len(self.data[0])

    # 检查总数据大小是否能整除 batch
    def check_batch(self, batch_size):
        length = self.X.shape[0]
        # 保证 batch_size 比总长度小
        assert length >= batch_size
        if batch_size * (length // batch_size) != length:
            warnings.warn(f'数据大小为{length}, batch大小为{batch_size},无法整除,会损失{(length % batch_size) / length * 100}%数据', DeprecationWarning)

    # 以 window 为窗口大小切片形成整个batch
    def slice(self, window):
        self.window = window
        X, Y = [], []
        for i in range(len(self.data) - window):
            X.append(self.data[i:i + window])
            Y.append(self.data[i + window])
        X = np.array(X)
        Y = np.array(Y)
        X = torch.from_numpy(X).float()  # (batch_size, sequence_length, input_size)
        Y = torch.from_numpy(Y).float()
        print(f"数据格式:X = {X.shape}, Y = {Y.shape}")
        self.X = X
        self.Y = Y

    def re_slice(self, window):
        self.X, self.Y, self.model, self.optimizer, self.criterion, self.result, self.result_, self.losses = [None] * 9
        self.slice(window)

    # 初始化 LSTM model
    def init_lstm(self, hidden=64, lr=0.001, num_layers=1):
        self.model = LSTM(self.input_dim, hidden, self.output_dim, num_layers)
        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)
        self.criterion = nn.MSELoss()
        if torch.cuda.is_available():
            self.model.cuda()
            self.X.cuda()
            self.Y.cuda()

    # 总数据产生 batch 并可以进行 shuffle
    @staticmethod
    def iterate_batches(inputs, targets, batchsize, shuffle=True):
        assert len(inputs) == len(targets)
        if shuffle:
            indices = np.arange(len(inputs))
            np.random.shuffle(indices)
        for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
            if shuffle:
                excerpt = indices[start_idx:start_idx + batchsize]
            else:
                excerpt = slice(start_idx, start_idx + batchsize)
            yield inputs[excerpt], targets[excerpt]

    # 开始训练
    def train(self, num_epochs=100, batch_size=128, max_batch=False):
        losses = []
        if self.model is None:
            raise ValueError("请先使用CustomLSTM.init_lstm初始化网络")
        if max_batch:
            batch_size = self.X.shape[0]
        else:
            self.check_batch(batch_size)
        for epoch in range(num_epochs):
            loss_all = 0
            self.model.init_hidden(batch_size)
            for index, (batch_x, batch_y) in enumerate(CustomLSTM.iterate_batches(self.X, self.Y, batch_size, shuffle=True)):
                self.optimizer.zero_grad()
                outputs = self.model(batch_x)
                loss = self.criterion(outputs, batch_y)
                loss.backward()
                self.optimizer.step()
                loss_all += loss.detach().cpu()
            losses.append(loss_all / (index + 1))
            if epoch % 20 == 0:
                print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, loss_all / (index + 1)))
        self.losses = losses
        self.predicted()

    def plot_loss(self):
        if self.losses is None:
            raise ValueError("loss 不存在，请先进行训练")
        plt.figure(figsize=(12, 6))
        plt.plot(self.losses)
        plt.xlabel("Epoch")
        plt.ylabel("Loss")
        plt.show()

    # 预测之后 n 天的值
    def predict(self, n):
        # self.predicted()
        self.model.init_hidden(1)
        data = self.data[-self.window:, :].tolist()
        y = []
        for i in range(n):
            x = torch.tensor(data).float().unsqueeze(0)
            result = self.model.cpu()(x).tolist()
            y.append(result[0])
            data.append(result[0])
            data.pop(0)
        self.result = np.array(y)
        return self.result

    # 预测从 window_size 到结束数据的预测值，以便与真实值做比较
    def predicted(self):
        self.model.eval()
        self.model.init_hidden(len(self.X))
        with torch.no_grad():
            predicted = self.model(self.X)
            predicted = predicted.detach().cpu().numpy()
        self.result_ = predicted
        return self.result_

    # 因为可以为二维数组即对多个变量进行预测，names即为每个变量的名字
    def plot(self, names=None):
        if self.result is None:
            raise ValueError("请先使用CustomLSTM.predict")
        if names is None:
            names = [None] * len(self.data[0])
        # further = self.predict(n)
        x = np.arange(len(self.data))
        x_further = np.arange(len(self.data), len(self.data) + len(self.result))
        plt.figure(figsize=(12, 6))
        for i in range(len(self.data[0])):
            plt.plot(x, self.data, label=f'{names[0]} True Values')
            plt.plot(x[self.window:], self.result_[:, i], label=f'{names[0]} Predictions')
            plt.plot(x_further, self.result[:, i], label=f"{names[0]} Further Predictions")
        plt.show()

    # 画置信区间，demo 版本
    # ToDo
    def plot_confidence(self, index=0, alpha=0.05):
        if self.result is None:
            raise ValueError("请先使用CustomLSTM.predict")
        plt.figure(figsize=(12, 6))
        x = np.arange(len(self.data))
        x_further = np.arange(len(self.data), len(self.data) + len(self.result))
        y_true = self.data[self.window:, index].tolist()
        plt.plot(y_true, label='True Values')
        plt.plot(x_further, self.result[:, index], label="Further Predictions")
        plt.plot(x[self.window:], self.result_[:, index], label='Predictions')

        y_pred = self.result[:, index]
        lower, upper = Utils.ci(y_true, y_pred, alpha=alpha)
        plt.plot(y_pred, label='Predictions')
        plt.fill_between(np.arange(), lower, upper, alpha=0.2, label='Confidence interval')
        plt.legend()
        plt.show()

    # 打印 summary， 即 r^2 评价函数等
    def summary(self):
        if self.model is None:
            raise ValueError("请先进行训练")
        print("==========Summary Begin===========")
        print("R2   =", score_r2 := Utils.r2(self.result_, self.data[self.window:, :]))
        print("MSE  =", score_mse := Utils.mse(self.result_, self.data[self.window:, :]))
        print("RMSE =", score_rmse := np.sqrt(score_mse))
        print("MAE  =", score_mae := Utils.mae(self.result_, self.data[self.window:, :]))
        print("===========Summary end============")
        return score_r2, score_mse, score_rmse, score_mae


# 工具
class Utils:
    # 置信区间，demo
    # ToDo
    @staticmethod
    def ci(y_true, y_pred, alpha=0.05):
        residuals = y_true - y_pred
        n = len(residuals)
        df = n - 1
        t_value = st.norm.ppf(1 - alpha / 2, df)
        std_err = np.std(residuals, ddof=1) / np.sqrt(n)
        upper = residuals + t_value * std_err
        lower = residuals - t_value * std_err
        return lower, upper

    # r2 评价函数
    @staticmethod
    def r2(y_pred, y_true):
        return 1 - ((y_pred - y_true) ** 2).sum(axis=0) / ((y_true.mean(axis=0) - y_true) ** 2).sum(axis=0)

    @staticmethod
    def mse(y_pred, y_true):
        return ((y_true - y_pred) ** 2).sum(axis=0) / len(y_pred)

    @staticmethod
    def rmse(y_pred, y_true):
        return np.sqrt(((y_true - y_pred) ** 2).sum(axis=0) / len(y_pred))

    @staticmethod
    def mae(y_pred, y_true):
        return (np.absolute(y_true - y_pred)).sum(axis=0) / len(y_true)

    # 添加快捷打开文件操作
    @staticmethod
    def openfile(name):
        file_type = name.split(".")[-1]
        if file_type == "csv":
            df = pd.read_csv(name, encoding='GBK')
        elif file_type == "xlsx" or file_type == "xls":
            df = pd.read_excel(name)
        else:
            raise TypeError(f"{name} 类型不是 csv, xls, xlsx")
        # df = df[["列名字1", "列名字2"]]
        print(df)
        return np.array(df)


def load_data():
    # 以下注释代码为快捷打开操作
    # return Utils.openfile(file_location)
    data = np.sin(np.arange(100) * np.pi / 50) + np.random.randn(100) * 0.1
    return data


if __name__ == "__main__":
    # 加载数据
    data = load_data()

    # 初始化网络
    window_size = 10
    batch = 100
    lstm = CustomLSTM(data, window_size)
    lstm.init_lstm(hidden=64, lr=0.001, num_layers=1)

    # 训练网络
    # max_batch 表示是否以整一个数据作为 batch 不做分割
    lstm.train(num_epochs=1000, max_batch=True)
    # lstm.train(num_epochs=50, batch_size=30, max_batch=False)

    # 调整窗口大小重新训练
    # lstm.re_slice(20)
    # lstm.init_lstm(hidden=64, lr=0.001, num_layers=1)
    # lstm.train(num_epochs=50, batch_size=40, max_batch=False)

    # 打印 summary
    r2, mse, rmse, mae = lstm.summary()

    # 预测之后 100 步数据
    lstm.predict(100)

    # 画图
    lstm.plot_loss()
    lstm.plot(['data'])
    # lstm.plot_confidence(index=0)

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

import torch

import torch.nn as nn

import numpy as np

import matplotlib.pyplot as plt

import torch.nn.functional as F

import warnings

import pandas as pd

import scipy.stats as st

# 设定随机种子，以确保实验的可重复性

torch.manual_seed(1)

np.random.seed(1)

# 定义LSTM模型

class LSTM(nn.Module):

hidden = None

def __init__(self, input_size, hidden_size, output_size, num_layers):

super().__init__()

self.hidden_size = hidden_size

self.num_layers = num_layers

self.train_on_gpu = torch.cuda.is_available()

# As batch_first=True, input: (batch_size, sequence_length, input_size)

self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)

self.lstm2 = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)

self.fc = nn.Linear(hidden_size, hidden_size)

self.fcOut = nn.Linear(hidden_size, output_size)

self.drop = nn.Dropout(0.5)

# 可选操作，可以把下一行注释

self.apply(LSTM.init_weights)

def forward(self, x):

# 防止 loss.backward 报错

hidden = [each.data for each in self.hidden]

x, hidden = self.lstm(x, hidden)

x, self.hidden = self.lstm2(x, hidden)

x = x[:, -1, :]

x = F.relu(self.fc(x))

x = self.drop(x)

x = self.fcOut(x)

return x

def init_hidden(self, batch_size):

weight = next(self.parameters()).data

if self.train_on_gpu:

self.hidden = (weight.new(self.num_layers, batch_size, self.hidden_size).zero_().cuda(),

weight.new(self.num_layers, batch_size, self.hidden_size).zero_().cuda())

else:

self.hidden = (weight.new(self.num_layers, batch_size, self.hidden_size).zero_(),

weight.new(self.num_layers, batch_size, self.hidden_size).zero_())

@staticmethod

def init_weights(m):

if type(m) == nn.LSTM:

for name, param in m.named_parameters():

if 'weight_ih' in name:

torch.nn.init.orthogonal_(param.data)

elif 'weight_hh' in name:

torch.nn.init.orthogonal_(param.data)

elif 'bias' in name:

param.data.fill_(0)

elif type(m) == nn.Conv1d or type(m) == nn.Linear:

torch.nn.init.orthogonal_(m.weight)

m.bias.data.fill_(0)

class CustomLSTM:

X, Y, data, model, optimizer, criterion, result, result_, window, losses = [None] * 10

def __init__(self, data: np.array, window):

self.data = data

self.input_dim = self.init_data()

self.output_dim = self.input_dim

self.slice(window)

# data 可为一维数组或二维数组，强制把一维数组转化为二维数组

def init_data(self):

assert (length := len(self.data.shape)) in [1, 2]

if length == 1:

self.data = self.data[:, np.newaxis]

return len(self.data[0])

# 检查总数据大小是否能整除 batch

def check_batch(self, batch_size):

length = self.X.shape[0]

# 保证 batch_size 比总长度小

assert length >= batch_size

if batch_size * (length // batch_size) != length:

warnings.warn(f'数据大小为{length}, batch大小为{batch_size},无法整除,会损失{(length % batch_size) / length * 100}%数据', DeprecationWarning)

# 以 window 为窗口大小切片形成整个batch

def slice(self, window):

self.window = window

X, Y = [], []

for i in range(len(self.data) - window):

X.append(self.data[i:i + window])

Y.append(self.data[i + window])

X = np.array(X)

Y = np.array(Y)

X = torch.from_numpy(X).float() # (batch_size, sequence_length, input_size)

Y = torch.from_numpy(Y).float()

print(f"数据格式:X = {X.shape}, Y = {Y.shape}")

self.X = X

self.Y = Y

def re_slice(self, window):

self.X, self.Y, self.model, self.optimizer, self.criterion, self.result, self.result_, self.losses = [None] * 9

self.slice(window)

# 初始化 LSTM model

def init_lstm(self, hidden=64, lr=0.001, num_layers=1):

self.model = LSTM(self.input_dim, hidden, self.output_dim, num_layers)

self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)

self.criterion = nn.MSELoss()

if torch.cuda.is_available():

self.model.cuda()

self.X.cuda()

self.Y.cuda()

# 总数据产生 batch 并可以进行 shuffle

@staticmethod

def iterate_batches(inputs, targets, batchsize, shuffle=True):

assert len(inputs) == len(targets)

if shuffle:

indices = np.arange(len(inputs))

np.random.shuffle(indices)

for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):

if shuffle:

excerpt = indices[start_idx:start_idx + batchsize]

else:

excerpt = slice(start_idx, start_idx + batchsize)

yield inputs[excerpt], targets[excerpt]

# 开始训练

def train(self, num_epochs=100, batch_size=128, max_batch=False):

losses = []

if self.model is None:

raise ValueError("请先使用CustomLSTM.init_lstm初始化网络")

if max_batch:

batch_size = self.X.shape[0]

else:

self.check_batch(batch_size)

for epoch in range(num_epochs):

loss_all = 0

self.model.init_hidden(batch_size)

for index, (batch_x, batch_y) in enumerate(CustomLSTM.iterate_batches(self.X, self.Y, batch_size, shuffle=True)):

self.optimizer.zero_grad()

outputs = self.model(batch_x)

loss = self.criterion(outputs, batch_y)

loss.backward()

self.optimizer.step()

loss_all += loss.detach().cpu()

losses.append(loss_all / (index + 1))

if epoch % 20 == 0:

print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, loss_all / (index + 1)))

self.losses = losses

self.predicted()

def plot_loss(self):

if self.losses is None:

raise ValueError("loss 不存在，请先进行训练")

plt.figure(figsize=(12, 6))

plt.plot(self.losses)

plt.xlabel("Epoch")

plt.ylabel("Loss")

plt.show()

# 预测之后 n 天的值

def predict(self, n):

# self.predicted()

self.model.init_hidden(1)

data = self.data[-self.window:, :].tolist()

y = []

for i in range(n):

x = torch.tensor(data).float().unsqueeze(0)

result = self.model.cpu()(x).tolist()

y.append(result[0])

data.append(result[0])

data.pop(0)

self.result = np.array(y)

return self.result

# 预测从 window_size 到结束数据的预测值，以便与真实值做比较

def predicted(self):

self.model.eval()

self.model.init_hidden(len(self.X))

with torch.no_grad():

predicted = self.model(self.X)

predicted = predicted.detach().cpu().numpy()

self.result_ = predicted

return self.result_

# 因为可以为二维数组即对多个变量进行预测，names即为每个变量的名字

def plot(self, names=None):

if self.result is None:

raise ValueError("请先使用CustomLSTM.predict")

if names is None:

names = [None] * len(self.data[0])

# further = self.predict(n)

x = np.arange(len(self.data))

x_further = np.arange(len(self.data), len(self.data) + len(self.result))

plt.figure(figsize=(12, 6))

for i in range(len(self.data[0])):

plt.plot(x, self.data, label=f'{names[0]} True Values')

plt.plot(x[self.window:], self.result_[:, i], label=f'{names[0]} Predictions')

plt.plot(x_further, self.result[:, i], label=f"{names[0]} Further Predictions")

plt.show()

# 画置信区间，demo 版本

# ToDo

def plot_confidence(self, index=0, alpha=0.05):

if self.result is None:

raise ValueError("请先使用CustomLSTM.predict")

plt.figure(figsize=(12, 6))

x = np.arange(len(self.data))

x_further = np.arange(len(self.data), len(self.data) + len(self.result))

y_true = self.data[self.window:, index].tolist()

plt.plot(y_true, label='True Values')

plt.plot(x_further, self.result[:, index], label="Further Predictions")

plt.plot(x[self.window:], self.result_[:, index], label='Predictions')

y_pred = self.result[:, index]

lower, upper = Utils.ci(y_true, y_pred, alpha=alpha)

plt.plot(y_pred, label='Predictions')

plt.fill_between(np.arange(), lower, upper, alpha=0.2, label='Confidence interval')

plt.legend()

plt.show()

# 打印 summary，即 r^2 评价函数等

def summary(self):

if self.model is None:

raise ValueError("请先进行训练")

print("==========Summary Begin===========")

print("R2 =", score_r2 := Utils.r2(self.result_, self.data[self.window:, :]))

print("MSE =", score_mse := Utils.mse(self.result_, self.data[self.window:, :]))

print("RMSE =", score_rmse := np.sqrt(score_mse))

print("MAE =", score_mae := Utils.mae(self.result_, self.data[self.window:, :]))

print("===========Summary end============")

return score_r2, score_mse, score_rmse, score_mae

# 工具

class Utils:

# 置信区间，demo

# ToDo

@staticmethod

def ci(y_true, y_pred, alpha=0.05):

residuals = y_true - y_pred

n = len(residuals)

df = n - 1

t_value = st.norm.ppf(1 - alpha / 2, df)

std_err = np.std(residuals, ddof=1) / np.sqrt(n)

upper = residuals + t_value * std_err

lower = residuals - t_value * std_err

return lower, upper

# r2 评价函数

@staticmethod

def r2(y_pred, y_true):

return 1 - ((y_pred - y_true) ** 2).sum(axis=0) / ((y_true.mean(axis=0) - y_true) ** 2).sum(axis=0)

@staticmethod

def mse(y_pred, y_true):

return ((y_true - y_pred) ** 2).sum(axis=0) / len(y_pred)

@staticmethod

def rmse(y_pred, y_true):

return np.sqrt(((y_true - y_pred) ** 2).sum(axis=0) / len(y_pred))

@staticmethod

def mae(y_pred, y_true):

return (np.absolute(y_true - y_pred)).sum(axis=0) / len(y_true)

# 添加快捷打开文件操作

@staticmethod

def openfile(name):

file_type = name.split(".")[-1]

if file_type == "csv":

df = pd.read_csv(name, encoding='GBK')

elif file_type == "xlsx" or file_type == "xls":

df = pd.read_excel(name)

else:

raise TypeError(f"{name} 类型不是 csv, xls, xlsx")

# df = df[["列名字1", "列名字2"]]

print(df)

return np.array(df)

def load_data():

# 以下注释代码为快捷打开操作

# return Utils.openfile(file_location)

data = np.sin(np.arange(100) * np.pi / 50) + np.random.randn(100) * 0.1

return data

if __name__ == "__main__":

# 加载数据

data = load_data()

# 初始化网络

window_size = 10

batch = 100

lstm = CustomLSTM(data, window_size)

lstm.init_lstm(hidden=64, lr=0.001, num_layers=1)

# 训练网络

# max_batch 表示是否以整一个数据作为 batch 不做分割

lstm.train(num_epochs=1000, max_batch=True)

# lstm.train(num_epochs=50, batch_size=30, max_batch=False)

# 调整窗口大小重新训练

# lstm.re_slice(20)

# lstm.init_lstm(hidden=64, lr=0.001, num_layers=1)

# lstm.train(num_epochs=50, batch_size=40, max_batch=False)

# 打印 summary

r2, mse, rmse, mae = lstm.summary()

# 预测之后 100 步数据

lstm.predict(100)

# 画图

lstm.plot_loss()

lstm.plot(['data'])

# lstm.plot_confidence(index=0)

2022小美赛C题

主要讲解团队对2022小美赛C题的讲解，包含讲解与代码，赛题data。全部为python代码，基本不会提供代码注释及逻辑。

该题主要为时间序列分类，团队尝试的方法如下：

决策树、随机森林、极限森林分类（DecisionTreeClassifier、 RandomForestClassifier、 ExtraTreesClassifier）在文章中将简写为DTC、RFC、ETC
带有时间序列的决策树分类（MultivariateClassifier、 TimeSeriesForest）在文章中TimeSeriesForestClassifier将简写为TSFC
LSTM 团队采用 DeepConvLSTM在文章中将简写为DCLSTM
NSGA2剪枝优化

讲解顺序：python库→数据预处理：3\sigma与滤波→数据处理与导入（不含前一步的处理）→决策树等分类→TSFC→DCLSTM→GA决策树剪枝优化。总体结论：极限森林在赛题数据分类效果最好。

文章思路：数据处理→决策树分类→DCLSTM→算法对比→NSGA2对极限森林过拟合优化。

代码有前后关联性，后面代码没有的函数请到前面找。代码非常庞大，请耐心观看。

1.python库

python库

pip3 install numpy matplotlib pandas
pip3 isntall sklearn
pip3 install torch-对应gpu版本
pip3 install pyts
pip3 install geatpy

pip3 install numpy matplotlib pandas

pip3 isntall sklearn

pip3 install torch-对应gpu版本

pip3 install pyts

pip3 install geatpy

# math
import math
# plot
import matplotlib.pyplot as plt
# warning
import warnings
# system process
import os
# numpy
import numpy as np
# pandas
import pandas as pd
# data loader
import pickle
# csv
import csv

# math

import math

# plot

import matplotlib.pyplot as plt

# warning

import warnings

# system process

import os

# numpy

import numpy as np

# pandas

import pandas as pd

# data loader

import pickle

# csv

import csv

# cross score
from sklearn.model_selection import cross_val_score
# classifier for trees
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.tree import DecisionTreeClassifier
# train test split
from sklearn.model_selection import train_test_split
# nomolize
from sklearn.preprocessing import normalize
# scores
from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score, roc_auc_score, roc_curve, auc, confusion_matrix

# cross score

from sklearn.model_selection import cross_val_score

# classifier for trees

from sklearn.ensemble import RandomForestClassifier

from sklearn.ensemble import ExtraTreesClassifier

from sklearn.tree import DecisionTreeClassifier

# train test split

from sklearn.model_selection import train_test_split

# nomolize

from sklearn.preprocessing import normalize

# scores

from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score, roc_auc_score, roc_curve, auc, confusion_matrix

# draw tree
from sklearn.tree import plot_tree
# classifiers
from pyts.classification import BOSSVS, TimeSeriesForest
# multivarible
from pyts.multivariate.classification import MultivariateClassifier

# draw tree

from sklearn.tree import plot_tree

# classifiers

from pyts.classification import BOSSVS, TimeSeriesForest

# multivarible

from pyts.multivariate.classification import MultivariateClassifier

import torch
from torch import nn
import torch.nn.functional as F

import torch

from torch import nn

import torch.nn.functional as F

import geatpy as ea
from multiprocessing import Pool as ProcessPool

1 2	import geatpy as ea from multiprocessing import Pool as ProcessPool

2. 3\sigma and filter

Preprocess

def three_sigma(col):
    rule = (col.mean() - 3 * col.std() > col) | (col.mean() + 3 * col.std() < col)
    index = np.arange(col.shape[0])[rule]
    return index

def three_sigma(col):

rule = (col.mean() - 3 * col.std() > col) | (col.mean() + 3 * col.std() < col)

index = np.arange(col.shape[0])[rule]

return index

"""
https://www.bilibili.com/read/cv17553128
"""
result = []
for action in range(19):
    filename = "./data/a{}/p1".format(str(action + 1).zfill(2))
    now = None
    for i in range(60):
        f = np.loadtxt(os.path.join(filename, "s.txt".format(str(i + 1).zfill(2))), delimiter=",").T
        if now is None:
            now = f
        else:
            now = np.c_[now, f]
    data = now[:, :300]

    for i in [2]:
        AccX_Value = data[i]
        outs = three_sigma(AccX_Value)
        for out in outs:
            AccX_Value[out] = AccX_Value[out - 5: out + 5].mean()

        AccX_Value = AccX_Value[:, np.newaxis]
        Time = np.linspace(0, 5 * 2.4, int(5 * 2.4 * 25))
        AccX_Variance = 0.01

        # time step
        dt = 1 / 25

        # transition_matrix
        F = [[1, dt, 0.5 * dt ** 2],
             [0, 1, dt],
             [0, 0, 1]]

        # observation_matrix
        H = [0, 0, 1]

        # transition_covariance
        Q = [[0.2, 0, 0],
             [0, 0.1, 0],
             [0, 0, 10e-4]]

        # observation_covariance
        R = AccX_Variance

        # initial_state_mean
        X0 = [0,
              0,
              AccX_Value[0, 0]]

        # initial_state_covariance
        P0 = [[0, 0, 0],
              [0, 0, 0],
              [0, 0, AccX_Variance]]

        n_timesteps = AccX_Value.shape[0]
        n_dim_state = 3
        filtered_state_means = np.zeros((n_timesteps, n_dim_state))
        filtered_state_covariances = np.zeros((n_timesteps, n_dim_state, n_dim_state))

        kf = KalmanFilter(transition_matrices=F,
                          observation_matrices=H,
                          transition_covariance=Q,
                          observation_covariance=R,
                          initial_state_mean=X0,
                          initial_state_covariance=P0)

        # iterative estimation for each new measurement
        for t in range(n_timesteps):
            if t == 0:
                filtered_state_means[t] = X0
                filtered_state_covariances[t] = P0
            else:
                filtered_state_means[t], filtered_state_covariances[t] = (
                    kf.filter_update(
                        filtered_state_means[t - 1],
                        filtered_state_covariances[t - 1],
                        AccX_Value[t, 0]
                    )
                )
        result.append(list(filtered_state_means[:, 2]))
        # plt.plot(Time, AccX_Value)
        # plt.plot(Time, filtered_state_means[:, 2], "r-")
        # plt.title('Acceleration X')
        # plt.ylim(7.5, 8.5)
        # plt.grid()
        # plt.legend()
        # plt.savefig("original.png")
        # plt.show()
f = csv.writer(open("result filter.csv", "w", newline=""))
f.writerows(result)

"""

https://www.bilibili.com/read/cv17553128

"""

result = []

for action in range(19):

filename = "./data/a{}/p1".format(str(action + 1).zfill(2))

now = None

for i in range(60):

f = np.loadtxt(os.path.join(filename, "s.txt".format(str(i + 1).zfill(2))), delimiter=",").T

if now is None:

now = f

else:

now = np.c_[now, f]

data = now[:, :300]

for i in [2]:

AccX_Value = data[i]

outs = three_sigma(AccX_Value)

for out in outs:

AccX_Value[out] = AccX_Value[out - 5: out + 5].mean()

AccX_Value = AccX_Value[:, np.newaxis]

Time = np.linspace(0, 5 * 2.4, int(5 * 2.4 * 25))

AccX_Variance = 0.01

# time step

dt = 1 / 25

# transition_matrix

F = [[1, dt, 0.5 * dt ** 2],

[0, 1, dt],

[0, 0, 1]]

# observation_matrix

H = [0, 0, 1]

# transition_covariance

Q = [[0.2, 0, 0],

[0, 0.1, 0],

[0, 0, 10e-4]]

# observation_covariance

R = AccX_Variance

# initial_state_mean

X0 = [0,

AccX_Value[0, 0]]

# initial_state_covariance

P0 = [[0, 0, 0],

[0, 0, 0],

[0, 0, AccX_Variance]]

n_timesteps = AccX_Value.shape[0]

n_dim_state = 3

filtered_state_means = np.zeros((n_timesteps, n_dim_state))

filtered_state_covariances = np.zeros((n_timesteps, n_dim_state, n_dim_state))

kf = KalmanFilter(transition_matrices=F,

observation_matrices=H,

transition_covariance=Q,

observation_covariance=R,

initial_state_mean=X0,

initial_state_covariance=P0)

# iterative estimation for each new measurement

for t in range(n_timesteps):

if t == 0:

filtered_state_means[t] = X0

filtered_state_covariances[t] = P0

else:

filtered_state_means[t], filtered_state_covariances[t] = (

kf.filter_update(

filtered_state_means[t - 1],

filtered_state_covariances[t - 1],

AccX_Value[t, 0]

)

result.append(list(filtered_state_means[:, 2]))

# plt.plot(Time, AccX_Value)

# plt.plot(Time, filtered_state_means[:, 2], "r-")

# plt.title('Acceleration X')

# plt.ylim(7.5, 8.5)

# plt.grid()

# plt.legend()

# plt.savefig("original.png")

# plt.show()

f = csv.writer(open("result filter.csv", "w", newline=""))

f.writerows(result)

# n is the window size
def sliding_window_filter(data_filter, n):
    n -= 1
    length = len(data_filter)
    assert 1 <= n <= length
    filtered_data = np.copy(data_filter).astype(np.float32)
    for i in range(n):
        filtered_data[n:] += data_filter[i: length - n + i]
    filtered_data[n:] *= 1 / (n + 1)
    return filtered_data

# n is the window size

def sliding_window_filter(data_filter, n):

n -= 1

length = len(data_filter)

assert 1 <= n <= length

filtered_data = np.copy(data_filter).astype(np.float32)

for i in range(n):

filtered_data[n:] += data_filter[i: length - n + i]

filtered_data[n:] *= 1 / (n + 1)

return filtered_data

3. 数据处理

其中：num为选择哪些传感器，n_class为选择哪些活动进行分类

Data process

# 标准data类，用于dataloder
class _Data:
    def __init__(self, data, label, length=None):
        self.x = data
        self.y = label
        if length:
            self.len = length
        else:
            self.len = len(self.y)

    def __len__(self):
        return self.len

    def __getitem__(self, item):
        return self.x[item], self.y[item]


# 提取特征
def process(data):
    arange = np.arange(0, len(data))[:, np.newaxis]
    varange = np.sum(arange ** 2)
    mean = data.mean(axis=0)
    std = np.std(data, axis=0)
    k = np.sum((data - mean) * arange / varange, axis=0)
    return np.r_[mean, std, k]

# 以多少step（5s）提取数据
def load_data(step=1, process=None):
    num = [i for i in range(45)]
    dirname = "./data/"
    n_class = np.array(os.listdir(dirname))
    now = []
    label = []
    for y, class_name in enumerate(n_class):
        print(f"now process {y + 1} ")
        dir_a = os.listdir(now_dir_name := os.path.join(dirname, class_name))
        for person in range(len(dir_a)):
            dir_b = os.listdir(now_file_name := os.path.join(now_dir_name, dir_a[person]))
            for segment in range(0, (len(dir_b) + 1) // step * step - 1, step):
                temp = None
                for i in range(step):
                    if temp is None:
                        temp = np.loadtxt(os.path.join(now_file_name, dir_b[i + segment]), delimiter=",")[:, num]
                    else:
                        temp = np.r_[temp, np.loadtxt(os.path.join(now_file_name, dir_b[i + segment]), delimiter=",")[:, num]]
                temp = normalize(temp, axis=0)
                now.append(process(temp) if process is not None else temp)
                label.append(y)
    data = _Data(np.array(now), np.array(label))
    return data


# 切分数据把5s数据进行切分
def load_data_cut(cut=3, process=None):
    num = [i for i in range(45)]
    dirname = "./data/"
    n_class = os.listdir(dirname)
    now = []
    label = []
    for y, class_name in enumerate(n_class[:]):
        print(f"now process {y + 1} ")
        dir_a = os.listdir(now_dir_name := os.path.join(dirname, class_name))
        for person in range(len(dir_a)):
            dir_b = os.listdir(now_file_name := os.path.join(now_dir_name, dir_a[person]))
            for segment in range(0, len(dir_b)):
                file = np.loadtxt(os.path.join(now_file_name, dir_b[segment]), delimiter=",")[:, num]
                number = len(file) // cut
                for i in range(cut):
                    file_cut = normalize(file[number * i: number * (i + 1), :], axis=0)
                    now.append(process(file_cut) if process is not None else file_cut)
                    label.append(y)
    data = _Data(np.array(now), np.array(label))
    return data


# shuffle
def shuffleData(X, y, seed=None):
    import random
    random.seed(seed)
    index = [i for i in range(len(X))]
    random.shuffle(index)
    X = X[index]
    y = y[index]
    return X, y

# 标准data类，用于dataloder

class _Data:

def __init__(self, data, label, length=None):

self.x = data

self.y = label

if length:

self.len = length

else:

self.len = len(self.y)

def __len__(self):

return self.len

def __getitem__(self, item):

return self.x[item], self.y[item]

# 提取特征

def process(data):

arange = np.arange(0, len(data))[:, np.newaxis]

varange = np.sum(arange ** 2)

mean = data.mean(axis=0)

std = np.std(data, axis=0)

k = np.sum((data - mean) * arange / varange, axis=0)

return np.r_[mean, std, k]

# 以多少step（5s）提取数据

def load_data(step=1, process=None):

num = [i for i in range(45)]

dirname = "./data/"

n_class = np.array(os.listdir(dirname))

now = []

label = []

for y, class_name in enumerate(n_class):

print(f"now process {y + 1} ")

dir_a = os.listdir(now_dir_name := os.path.join(dirname, class_name))

for person in range(len(dir_a)):

dir_b = os.listdir(now_file_name := os.path.join(now_dir_name, dir_a[person]))

for segment in range(0, (len(dir_b) + 1) // step * step - 1, step):

temp = None

for i in range(step):

if temp is None:

temp = np.loadtxt(os.path.join(now_file_name, dir_b[i + segment]), delimiter=",")[:, num]

else:

temp = np.r_[temp, np.loadtxt(os.path.join(now_file_name, dir_b[i + segment]), delimiter=",")[:, num]]

temp = normalize(temp, axis=0)

now.append(process(temp) if process is not None else temp)

label.append(y)

data = _Data(np.array(now), np.array(label))

return data

# 切分数据把5s数据进行切分

def load_data_cut(cut=3, process=None):

num = [i for i in range(45)]

dirname = "./data/"

n_class = os.listdir(dirname)

now = []

label = []

for y, class_name in enumerate(n_class[:]):

print(f"now process {y + 1} ")

dir_a = os.listdir(now_dir_name := os.path.join(dirname, class_name))

for person in range(len(dir_a)):

dir_b = os.listdir(now_file_name := os.path.join(now_dir_name, dir_a[person]))

for segment in range(0, len(dir_b)):

file = np.loadtxt(os.path.join(now_file_name, dir_b[segment]), delimiter=",")[:, num]

number = len(file) // cut

for i in range(cut):

file_cut = normalize(file[number * i: number * (i + 1), :], axis=0)

now.append(process(file_cut) if process is not None else file_cut)

label.append(y)

data = _Data(np.array(now), np.array(label))

return data

# shuffle

def shuffleData(X, y, seed=None):

import random

random.seed(seed)

index = [i for i in range(len(X))]

random.shuffle(index)

X = X[index]

y = y[index]

return X, y

# 标准方式如下（5s）
data = load_data(step:int, process)
# 数据切分的更小
data = load_data_cut(cut:int, process)
# 保存数据
f = open("data.data", 'wb')
pickle.dump(data, f)
# 加载数据
f = open("data.data", 'rb')
data = pickle.load(f)
data = _Data(*shuffleData(data.x, data.y, 1))

# 标准方式如下（5s）

data = load_data(step:int, process)

# 数据切分的更小

data = load_data_cut(cut:int, process)

# 保存数据

f = open("data.data", 'wb')

pickle.dump(data, f)

# 加载数据

f = open("data.data", 'rb')

data = pickle.load(f)

data = _Data(*shuffleData(data.x, data.y, 1))

4. 决策树等分类

说明：代码包含k-fold cross-validation，其中注释代码可用作别的用途，具体你要画什么图就用什么代码。

Classifier

f = open("data.data", 'rb')
data = pickle.load(f)
data = _Data(*shuffleData(data.x, data.y, 1))
result = []
K = 10
size = (n := len(data)) // K
# K-fold cross validation
for i in range(10):
    now_silence = [i for i in range(i * size)] + [i for i in range((i + 1) * size, n)]
    now_silence_test = [i for i in range(i * size, (i + 1) * size)]
    X_train, X_test, y_train, y_test = data.x[now_silence], data.x[now_silence_test], data.y[now_silence], data.y[now_silence_test]

    clf3 = ExtraTreesClassifier(max_depth=10,  max_leaf_nodes=100, min_samples_split=2, random_state=0)

    clf3.fit(X_train, y_train)

    out = clf3.predict(X_test)
    result.append([f1_score(y_test, out, average='weighted'), precision_score(y_test, out, average='weighted'), recall_score(y_test, out, average='weighted'),
                   accuracy_score(y_test, out)])

    # clf1 = DecisionTreeClassifier(max_depth=None, min_samples_split=2, random_state=0)
    # clf2 = RandomForestClassifier(n_estimators=10, max_depth=None, min_samples_split=2, bootstrap=True)

    # clf1.fit(x_train, y_train)
    # clf2.fit(x_train, y_train)

    # print(clf1.feature_importances_)
    # print(clf2.feature_importances_)
    # print(clf3.feature_importances_)

    # result.append([clf1.score(x_test, y_test), clf2.score(x_test, y_test), clf3.score(x_test, y_test)])

    # confusion_matrix(y_test, clf3.predict(y_test))

    # scores1 = cross_val_score(clf1, x_train, y_train)
    # scores2 = cross_val_score(clf2, x_train, y_train)
    # scores3 = cross_val_score(clf3, x_train, y_train)
    # print('DecisionTreeClassifier交叉验证准确率为:' + str(scores1.mean()))
    # print('RandomForestClassifier交叉验证准确率为:' + str(scores2.mean()))
    # print('ExtraTreesClassifier交叉验证准确率为:' + str(scores3.mean()))

    print(result)
    print(np.array(result).mean(axis=0))
    f = open("result tree k fold.txt", 'w')
    f.writelines(str(result))

f = open("data.data", 'rb')

data = pickle.load(f)

data = _Data(*shuffleData(data.x, data.y, 1))

result = []

K = 10

size = (n := len(data)) // K

# K-fold cross validation

for i in range(10):

now_silence = [i for i in range(i * size)] + [i for i in range((i + 1) * size, n)]

now_silence_test = [i for i in range(i * size, (i + 1) * size)]

X_train, X_test, y_train, y_test = data.x[now_silence], data.x[now_silence_test], data.y[now_silence], data.y[now_silence_test]

clf3 = ExtraTreesClassifier(max_depth=10, max_leaf_nodes=100, min_samples_split=2, random_state=0)

clf3.fit(X_train, y_train)

out = clf3.predict(X_test)

result.append([f1_score(y_test, out, average='weighted'), precision_score(y_test, out, average='weighted'), recall_score(y_test, out, average='weighted'),

accuracy_score(y_test, out)])

# clf1 = DecisionTreeClassifier(max_depth=None, min_samples_split=2, random_state=0)

# clf2 = RandomForestClassifier(n_estimators=10, max_depth=None, min_samples_split=2, bootstrap=True)

# clf1.fit(x_train, y_train)

# clf2.fit(x_train, y_train)

# print(clf1.feature_importances_)

# print(clf2.feature_importances_)

# print(clf3.feature_importances_)

# result.append([clf1.score(x_test, y_test), clf2.score(x_test, y_test), clf3.score(x_test, y_test)])

# confusion_matrix(y_test, clf3.predict(y_test))

# scores1 = cross_val_score(clf1, x_train, y_train)

# scores2 = cross_val_score(clf2, x_train, y_train)

# scores3 = cross_val_score(clf3, x_train, y_train)

# print('DecisionTreeClassifier交叉验证准确率为:' + str(scores1.mean()))

# print('RandomForestClassifier交叉验证准确率为:' + str(scores2.mean()))

# print('ExtraTreesClassifier交叉验证准确率为:' + str(scores3.mean()))

print(result)

print(np.array(result).mean(axis=0))

f = open("result tree k fold.txt", 'w')

f.writelines(str(result))

5. TFSC

TFSC

dirname = "./data/"
# 具体让那几个东西进行分类
n_class = os.listdir(dirname)
# n_class = [n_class[0], n_class[17]]
# 具体使用那些传感器
num = [i for i in range(45)]
now = []
label = []
for y, class_name in enumerate(n_class):
    print(f"now process {y + 1} ")
    for person, person_name in enumerate(os.listdir(now_dir_name := os.path.join(dirname, class_name))[:]):
        for segment, segment_name in enumerate(os.listdir(now_file_name := os.path.join(now_dir_name, person_name))[:]):
            now.append(np.loadtxt(os.path.join(now_file_name, segment_name), delimiter=",")[:, num].T)
            label.append(y)

data = _Data(np.array(now), np.array(label))
print("data process finished, start TSFC")
X_train, X_test, y_train, y_test = train_test_split(data.x, data.y, test_size=0.1)
clf = MultivariateClassifier(TimeSeriesForest(n_jobs=6))
# print(clf.estimators_[0]._pipeline['rfc'].estimators_)
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))

with open("clf.data", 'wb') as f:
    pickle.dump(clf, f)
    f.close()

dirname = "./data/"

# 具体让那几个东西进行分类

n_class = os.listdir(dirname)

# n_class = [n_class[0], n_class[17]]

# 具体使用那些传感器

num = [i for i in range(45)]

now = []

label = []

for y, class_name in enumerate(n_class):

print(f"now process {y + 1} ")

for person, person_name in enumerate(os.listdir(now_dir_name := os.path.join(dirname, class_name))[:]):

for segment, segment_name in enumerate(os.listdir(now_file_name := os.path.join(now_dir_name, person_name))[:]):

now.append(np.loadtxt(os.path.join(now_file_name, segment_name), delimiter=",")[:, num].T)

label.append(y)

data = _Data(np.array(now), np.array(label))

print("data process finished, start TSFC")

X_train, X_test, y_train, y_test = train_test_split(data.x, data.y, test_size=0.1)

clf = MultivariateClassifier(TimeSeriesForest(n_jobs=6))

# print(clf.estimators_[0]._pipeline['rfc'].estimators_)

clf.fit(X_train, y_train)

print(clf.score(X_test, y_test))

with open("clf.data", 'wb') as f:

pickle.dump(clf, f)

f.close()

with open("clf.data", 'rb') as f:
    clf = pickle.load(f)
    print(clf)
    f.close()

print(len(clf.estimators_[0].feature_importances_))

# now = []
# label = []
# for y, class_name in enumerate(n_class):
#     print(f"now process {y + 1} ")
#     for person, person_name in enumerate(os.listdir(now_dir_name := os.path.join(dirname, class_name))[:]):
#         for segment, segment_name in enumerate(os.listdir(now_file_name := os.path.join(now_dir_name, person_name))[:]):
#             now.append(np.loadtxt(os.path.join(now_file_name, segment_name), delimiter=",")[:, num].T)
#             label.append(y)
# data = _Data(np.array(now), np.array(label))
# print(clf.score(data.x, data.y))

with open("clf.data", 'rb') as f:

clf = pickle.load(f)

print(clf)

f.close()

print(len(clf.estimators_[0].feature_importances_))

# now = []

# label = []

# for y, class_name in enumerate(n_class):

# print(f"now process {y + 1} ")

# for person, person_name in enumerate(os.listdir(now_dir_name := os.path.join(dirname, class_name))[:]):

# for segment, segment_name in enumerate(os.listdir(now_file_name := os.path.join(now_dir_name, person_name))[:]):

# now.append(np.loadtxt(os.path.join(now_file_name, segment_name), delimiter=",")[:, num].T)

# label.append(y)

# data = _Data(np.array(now), np.array(label))

# print(clf.score(data.x, data.y))

6. DCLSTM

DCLSTM

"""
https://github.com/dspanah/Sensor-Based-Human-Activity-Recognition-DeepConvLSTM-Pytorch
"""
def load_dataset(filename, num, step=None):
    dirname = filename
    now = []
    label = []
    for y, class_name in enumerate(os.listdir(dirname)[:]):
        print(f"now process {y + 1} ")
        dir_a = os.listdir(now_dir_name := os.path.join(dirname, class_name))
        for person in range(len(dir_a)):
            dir_b = os.listdir(now_file_name := os.path.join(now_dir_name, dir_a[person]))
            for segment in range(0, (len(dir_b) + 1) // step * step - 1, step):
                temp = None
                for i in range(step):
                    if temp is None:
                        temp = np.loadtxt(os.path.join(now_file_name, dir_b[i + segment]), delimiter=",")[:, num].T
                    else:
                        temp = np.r_[temp, np.loadtxt(os.path.join(now_file_name, dir_b[i + segment]), delimiter=",")[:, num].T]
                now.append(temp)
                label.append(y)
    x = torch.nn.functional.normalize(torch.tensor(torch.from_numpy(np.array(now)), dtype=torch.float32), dim=-1)
    # x = torch.tensor(torch.from_numpy(np.array(now)), dtype=torch.float32)
    data = _Data(x, torch.from_numpy(np.array(label)).long())

    return data


# data1 无滤波归一化 data2滤波无归一化 data3滤波归一化
datafile = "./data.data"
if os.path.exists(datafile):
    with open(datafile, 'rb') as f:
        data = pickle.load(f)
        f.close()
else:
    print("Loading data...")
    num = [i for i in range(45)]
    data = load_dataset('./data/', num, 1)
    print("Done")
    with open(datafile, 'wb') as f:
        pickle.dump(data, f)
        f.close()

X_train, X_test, y_train, y_test = train_test_split(data.x, data.y, test_size=0.3, random_state=1)
# for i in range(len(X_test)):
#     for j in range(-6, 0):
#         X_test[i][j][:] = torch.tensor([0]*125)
print(X_train.shape)
_, SLIDING_WINDOW_LENGTH, NB_SENSOR_CHANNELS = X_train.shape


class HARModel(nn.Module):

    def __init__(self, n_hidden=128, n_layers=1, n_filters=100,
                 n_classes=19, filter_size=1, drop_prob=0.5):
        super(HARModel, self).__init__()
        self.drop_prob = drop_prob
        self.n_layers = n_layers
        self.n_hidden = n_hidden
        self.n_filters = n_filters
        self.n_classes = n_classes
        self.filter_size = (filter_size,)

        self.conv1 = nn.Conv1d(NB_SENSOR_CHANNELS, n_filters, self.filter_size)
        self.conv2 = nn.Conv1d(n_filters, n_filters, self.filter_size)
        self.conv3 = nn.Conv1d(n_filters, n_filters, self.filter_size)
        self.conv4 = nn.Conv1d(n_filters, n_filters, self.filter_size)

        self.lstm1 = nn.LSTM(n_filters, n_hidden, n_layers)
        self.lstm2 = nn.LSTM(n_hidden, n_hidden, n_layers)

        self.fc = nn.Linear(n_hidden, n_classes)

        self.dropout = nn.Dropout(drop_prob)

    def forward(self, x, hidden, batch_size):

        x = x.view(-1, NB_SENSOR_CHANNELS, SLIDING_WINDOW_LENGTH)
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))

        x = x.view(-1, batch_size, self.n_filters)
        # x = x.view(SLIDING_WINDOW_LENGTH, -1, NB_SENSOR_CHANNELS)
        x, hidden = self.lstm1(x, hidden)
        x, hidden = self.lstm2(x, hidden)

        x = x.contiguous().view(-1, self.n_hidden)
        x = self.dropout(x)
        x = self.fc(x)

        out = x.view(batch_size, -1, self.n_classes)[:, -1, :]

        return out, hidden

    def init_hidden(self, batch_size):
        ''' Initializes hidden state '''
        # Create two new tensors with sizes n_layers x batch_size x n_hidden,
        # initialized to zero, for hidden state and cell state of LSTM
        weight = next(self.parameters()).data

        if (train_on_gpu):
            hidden = (weight.new(self.n_layers, batch_size, self.n_hidden).zero_().cuda(),
                      weight.new(self.n_layers, batch_size, self.n_hidden).zero_().cuda())
        else:
            hidden = (weight.new(self.n_layers, batch_size, self.n_hidden).zero_(),
                      weight.new(self.n_layers, batch_size, self.n_hidden).zero_())

        return hidden


net = HARModel()


def init_weights(m):
    if type(m) == nn.LSTM:
        for name, param in m.named_parameters():
            if 'weight_ih' in name:
                torch.nn.init.orthogonal_(param.data)
            elif 'weight_hh' in name:
                torch.nn.init.orthogonal_(param.data)
            elif 'bias' in name:
                param.data.fill_(0)
    elif type(m) == nn.Conv1d or type(m) == nn.Linear:
        torch.nn.init.orthogonal_(m.weight)
        m.bias.data.fill_(0)


net.apply(init_weights)


def iterate_minibatches(inputs, targets, batchsize, shuffle=True):
    assert len(inputs) == len(targets)
    if shuffle:
        indices = np.arange(len(inputs))
        np.random.shuffle(indices)
    for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
        if shuffle:
            excerpt = indices[start_idx:start_idx + batchsize]
        else:
            excerpt = slice(start_idx, start_idx + batchsize)
        yield inputs[excerpt], targets[excerpt]


## check if GPU is available
train_on_gpu = torch.cuda.is_available()
if (train_on_gpu):
    print('Training on GPU!')
else:
    print('No GPU available, training on CPU; consider making n_epochs very small.')


def train(net, epochs=1000, batch_size=400, lr=0.001):
    opt = torch.optim.Adam(net.parameters(), lr=lr)
    criterion = nn.CrossEntropyLoss()

    if (train_on_gpu):
        net.cuda()

    for e in range(epochs):

        # initialize hidden state
        h = net.init_hidden(batch_size)
        train_losses = []
        net.train()
        for batch in iterate_minibatches(X_train, y_train, batch_size):
            inputs, targets = batch

            if (train_on_gpu):
                inputs, targets = inputs.cuda(), targets.cuda()

            # Creating new variables for the hidden state, otherwise
            # we'd backprop through the entire training history
            h = tuple([each.data for each in h])

            # zero accumulated gradients
            opt.zero_grad()

            # get the output from the model
            output, h = net(inputs, h, batch_size)

            loss = criterion(output, targets.long())
            train_losses.append(loss.item())
            loss.backward()
            opt.step()

        val_h = net.init_hidden(batch_size)
        val_losses = []
        accuracy = 0
        f1score = 0
        net.eval()

        with torch.no_grad():
            for batch in iterate_minibatches(X_test, y_test, batch_size):
                inputs, targets = batch

                val_h = tuple([each.data for each in val_h])

                if (train_on_gpu):
                    inputs, targets = inputs.cuda(), targets.cuda()

                output, val_h = net(inputs, val_h, batch_size)
                # print(confusion_matrix(y_train, output))

                val_loss = criterion(output, targets.long())
                val_losses.append(val_loss.item())

                top_p, top_class = output.topk(1, dim=1)
                equals = top_class == targets.view(*top_class.shape).long()
                accuracy += torch.mean(equals.type(torch.FloatTensor))
                f1score += metrics.f1_score(top_class.cpu(), targets.view(*top_class.shape).long().cpu(), average='weighted')

        net.train()  # reset to train mode after iterationg through validation data
        print("Epoch: {}/{}...".format(e + 1, epochs),
              "Train Loss: {:.4f}...".format(np.mean(train_losses)),
              "Val Loss: {:.4f}...".format(np.mean(val_losses)),
              "Val Acc: {:.4f}...".format(accuracy / (len(X_test) // batch_size)),
              "F1-Score: {:.4f}...".format(f1score / (len(X_test) // batch_size)))


train(net)

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

"""

https://github.com/dspanah/Sensor-Based-Human-Activity-Recognition-DeepConvLSTM-Pytorch

"""

def load_dataset(filename, num, step=None):

dirname = filename

now = []

label = []

for y, class_name in enumerate(os.listdir(dirname)[:]):

print(f"now process {y + 1} ")

dir_a = os.listdir(now_dir_name := os.path.join(dirname, class_name))

for person in range(len(dir_a)):

dir_b = os.listdir(now_file_name := os.path.join(now_dir_name, dir_a[person]))

for segment in range(0, (len(dir_b) + 1) // step * step - 1, step):

temp = None

for i in range(step):

if temp is None:

temp = np.loadtxt(os.path.join(now_file_name, dir_b[i + segment]), delimiter=",")[:, num].T

else:

temp = np.r_[temp, np.loadtxt(os.path.join(now_file_name, dir_b[i + segment]), delimiter=",")[:, num].T]

now.append(temp)

label.append(y)

x = torch.nn.functional.normalize(torch.tensor(torch.from_numpy(np.array(now)), dtype=torch.float32), dim=-1)

# x = torch.tensor(torch.from_numpy(np.array(now)), dtype=torch.float32)

data = _Data(x, torch.from_numpy(np.array(label)).long())

return data

# data1 无滤波归一化 data2滤波无归一化 data3滤波归一化

datafile = "./data.data"

if os.path.exists(datafile):

with open(datafile, 'rb') as f:

data = pickle.load(f)

f.close()

else:

print("Loading data...")

num = [i for i in range(45)]

data = load_dataset('./data/', num, 1)

print("Done")

with open(datafile, 'wb') as f:

pickle.dump(data, f)

f.close()

X_train, X_test, y_train, y_test = train_test_split(data.x, data.y, test_size=0.3, random_state=1)

# for i in range(len(X_test)):

# for j in range(-6, 0):

# X_test[i][j][:] = torch.tensor([0]*125)

print(X_train.shape)

_, SLIDING_WINDOW_LENGTH, NB_SENSOR_CHANNELS = X_train.shape

class HARModel(nn.Module):

def __init__(self, n_hidden=128, n_layers=1, n_filters=100,

n_classes=19, filter_size=1, drop_prob=0.5):

super(HARModel, self).__init__()

self.drop_prob = drop_prob

self.n_layers = n_layers

self.n_hidden = n_hidden

self.n_filters = n_filters

self.n_classes = n_classes

self.filter_size = (filter_size,)

self.conv1 = nn.Conv1d(NB_SENSOR_CHANNELS, n_filters, self.filter_size)

self.conv2 = nn.Conv1d(n_filters, n_filters, self.filter_size)

self.conv3 = nn.Conv1d(n_filters, n_filters, self.filter_size)

self.conv4 = nn.Conv1d(n_filters, n_filters, self.filter_size)

self.lstm1 = nn.LSTM(n_filters, n_hidden, n_layers)

self.lstm2 = nn.LSTM(n_hidden, n_hidden, n_layers)

self.fc = nn.Linear(n_hidden, n_classes)

self.dropout = nn.Dropout(drop_prob)

def forward(self, x, hidden, batch_size):

x = x.view(-1, NB_SENSOR_CHANNELS, SLIDING_WINDOW_LENGTH)

x = F.relu(self.conv1(x))

x = F.relu(self.conv2(x))

x = F.relu(self.conv3(x))

x = F.relu(self.conv4(x))

x = x.view(-1, batch_size, self.n_filters)

# x = x.view(SLIDING_WINDOW_LENGTH, -1, NB_SENSOR_CHANNELS)

x, hidden = self.lstm1(x, hidden)

x, hidden = self.lstm2(x, hidden)

x = x.contiguous().view(-1, self.n_hidden)

x = self.dropout(x)

x = self.fc(x)

out = x.view(batch_size, -1, self.n_classes)[:, -1, :]

return out, hidden

def init_hidden(self, batch_size):

''' Initializes hidden state '''

# Create two new tensors with sizes n_layers x batch_size x n_hidden,

# initialized to zero, for hidden state and cell state of LSTM

weight = next(self.parameters()).data

if (train_on_gpu):

hidden = (weight.new(self.n_layers, batch_size, self.n_hidden).zero_().cuda(),

weight.new(self.n_layers, batch_size, self.n_hidden).zero_().cuda())

else:

hidden = (weight.new(self.n_layers, batch_size, self.n_hidden).zero_(),

weight.new(self.n_layers, batch_size, self.n_hidden).zero_())

return hidden

net = HARModel()

def init_weights(m):

if type(m) == nn.LSTM:

for name, param in m.named_parameters():

if 'weight_ih' in name:

torch.nn.init.orthogonal_(param.data)

elif 'weight_hh' in name:

torch.nn.init.orthogonal_(param.data)

elif 'bias' in name:

param.data.fill_(0)

elif type(m) == nn.Conv1d or type(m) == nn.Linear:

torch.nn.init.orthogonal_(m.weight)

m.bias.data.fill_(0)

net.apply(init_weights)

def iterate_minibatches(inputs, targets, batchsize, shuffle=True):

assert len(inputs) == len(targets)

if shuffle:

indices = np.arange(len(inputs))

np.random.shuffle(indices)

for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):

if shuffle:

excerpt = indices[start_idx:start_idx + batchsize]

else:

excerpt = slice(start_idx, start_idx + batchsize)

yield inputs[excerpt], targets[excerpt]

## check if GPU is available

train_on_gpu = torch.cuda.is_available()

if (train_on_gpu):

print('Training on GPU!')

else:

print('No GPU available, training on CPU; consider making n_epochs very small.')

def train(net, epochs=1000, batch_size=400, lr=0.001):

opt = torch.optim.Adam(net.parameters(), lr=lr)

criterion = nn.CrossEntropyLoss()

if (train_on_gpu):

net.cuda()

for e in range(epochs):

# initialize hidden state

h = net.init_hidden(batch_size)

train_losses = []

net.train()

for batch in iterate_minibatches(X_train, y_train, batch_size):

inputs, targets = batch

if (train_on_gpu):

inputs, targets = inputs.cuda(), targets.cuda()

# Creating new variables for the hidden state, otherwise

# we'd backprop through the entire training history

h = tuple([each.data for each in h])

# zero accumulated gradients

opt.zero_grad()

# get the output from the model

output, h = net(inputs, h, batch_size)

loss = criterion(output, targets.long())

train_losses.append(loss.item())

loss.backward()

opt.step()

val_h = net.init_hidden(batch_size)

val_losses = []

accuracy = 0

f1score = 0

net.eval()

with torch.no_grad():

for batch in iterate_minibatches(X_test, y_test, batch_size):

inputs, targets = batch

val_h = tuple([each.data for each in val_h])

if (train_on_gpu):

inputs, targets = inputs.cuda(), targets.cuda()

output, val_h = net(inputs, val_h, batch_size)

# print(confusion_matrix(y_train, output))

val_loss = criterion(output, targets.long())

val_losses.append(val_loss.item())

top_p, top_class = output.topk(1, dim=1)

equals = top_class == targets.view(*top_class.shape).long()

accuracy += torch.mean(equals.type(torch.FloatTensor))

f1score += metrics.f1_score(top_class.cpu(), targets.view(*top_class.shape).long().cpu(), average='weighted')

net.train() # reset to train mode after iterationg through validation data

print("Epoch: {}/{}...".format(e + 1, epochs),

"Train Loss: {:.4f}...".format(np.mean(train_losses)),

"Val Loss: {:.4f}...".format(np.mean(val_losses)),

"Val Acc: {:.4f}...".format(accuracy / (len(X_test) // batch_size)),

"F1-Score: {:.4f}...".format(f1score / (len(X_test) // batch_size)))

train(net)

7. NSGA-ii剪枝优化

NSGA2

# 自定义 GA
class My_nsga(ea.Problem):
    def __init__(self, pools=10):
        # max_depth = 18-30     min_samples_leaf = 10-20   max_features=85-135  max_leaf_nodes = 50-200 ccp_alpha = 0.0001-0.002
        max_depth = [18, 30]
        min_samples_leaf = [10, 20]
        max_feature = [85, 135]
        max_leaf_node = [50, 200]
        ccp_alpha = [0.0001, 0.002]
        name = 'Tree Classifier'
        M = 2
        maxormins = [-1, 1]
        Dim = 5
        varTypes = [1] * 4 + [0]
        lb, ub = list(zip(*[max_depth, min_samples_leaf, max_feature, max_leaf_node, ccp_alpha]))
        lb = list(lb)
        ub = list(ub)
        lbin = [1] * Dim
        ubin = [1] * Dim

        self.ans = {}
        self.max_score = -float("inf")
        self.epoch = 0
        self.pool = ProcessPool(pools)
        ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)

    # 目标函数即神经网络返回值
    # 多线程
    # @cal_time()
    def evalVars(self, Vars):
        global data
        args = []
        for i in range(len(Vars)):
            varibal = list(Vars[i])
            args.append(varibal + [data.x, data.y])
        result = self.pool.starmap_async(get_ans, args)
        result.wait()
        ans = result.get()
        ans = np.array(ans)
        print(f"Epoch: {self.epoch}, Epoch Max: {ans.max(axis=0)}")
        self.epoch += 1
        return ans


def get_ans(max_depth, min_samples_leaf, max_feature, max_leaf_node, ccp_alpha, x, y):
    score = []
    max_depth, min_samples_leaf, max_feature, max_leaf_node = map(int, (max_depth, min_samples_leaf, max_feature, max_leaf_node))
    for i in range(10):
        x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7)
        clf = ExtraTreesClassifier(max_depth=max_depth, min_samples_leaf=min_samples_leaf, max_features=max_feature, max_leaf_nodes=max_leaf_node, ccp_alpha=ccp_alpha, n_estimators=10, min_samples_split=2, bootstrap=False)
        clf.fit(x_train, y_train)
        score1 = float(clf.score(x_test, y_test))
        score2 = float(clf.score(x_train, y_train))
        score.append([score1, abs(score1 - score2)])
    return np.array(score).mean(axis=0)


# 运行 GA
# @cal_time()
def Run_nsga(ndind=10, maxgen=100, pools=10):
    problem = My_nsga(pools)
    encoding = "RI"
    # 查看染色体编码
    # field = ea.crtfld(encoding, problem.varTypes, problem.ranges, problem.borders)
    # print(ea.crtpc(encoding, ndind, field))
    myAlgorithm = ea.moea_NSGA2_templet(problem, ea.Population(Encoding=encoding, NIND=ndind), MAXGEN=maxgen, logTras=1, drawing=1)
    res = ea.optimize(myAlgorithm, seed=1, verbose=True, drawing=1, outputMsg=1, drawLog=1, saveFlag=1, dirName='result')
    print(res)


if __name__ == "__main__":
    with open(r"./Datas/data_tree_{}".format(0), 'rb') as f:
        data = pickle.load(f)
        f.close()

    Run_nsga(30, 100, 14)

# 自定义 GA

class My_nsga(ea.Problem):

def __init__(self, pools=10):

# max_depth = 18-30 min_samples_leaf = 10-20 max_features=85-135 max_leaf_nodes = 50-200 ccp_alpha = 0.0001-0.002

max_depth = [18, 30]

min_samples_leaf = [10, 20]

max_feature = [85, 135]

max_leaf_node = [50, 200]

ccp_alpha = [0.0001, 0.002]

name = 'Tree Classifier'

M = 2

maxormins = [-1, 1]

Dim = 5

varTypes = [1] * 4 + [0]

lb, ub = list(zip(*[max_depth, min_samples_leaf, max_feature, max_leaf_node, ccp_alpha]))

lb = list(lb)

ub = list(ub)

lbin = [1] * Dim

ubin = [1] * Dim

self.ans = {}

self.max_score = -float("inf")

self.epoch = 0

self.pool = ProcessPool(pools)

ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)

# 目标函数即神经网络返回值

# 多线程

# @cal_time()

def evalVars(self, Vars):

global data

args = []

for i in range(len(Vars)):

varibal = list(Vars[i])

args.append(varibal + [data.x, data.y])

result = self.pool.starmap_async(get_ans, args)

result.wait()

ans = result.get()

ans = np.array(ans)

print(f"Epoch: {self.epoch}, Epoch Max: {ans.max(axis=0)}")

self.epoch += 1

return ans

def get_ans(max_depth, min_samples_leaf, max_feature, max_leaf_node, ccp_alpha, x, y):

score = []

max_depth, min_samples_leaf, max_feature, max_leaf_node = map(int, (max_depth, min_samples_leaf, max_feature, max_leaf_node))

for i in range(10):

x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7)

clf = ExtraTreesClassifier(max_depth=max_depth, min_samples_leaf=min_samples_leaf, max_features=max_feature, max_leaf_nodes=max_leaf_node, ccp_alpha=ccp_alpha, n_estimators=10, min_samples_split=2, bootstrap=False)

clf.fit(x_train, y_train)

score1 = float(clf.score(x_test, y_test))

score2 = float(clf.score(x_train, y_train))

score.append([score1, abs(score1 - score2)])

return np.array(score).mean(axis=0)

# 运行 GA

# @cal_time()

def Run_nsga(ndind=10, maxgen=100, pools=10):

problem = My_nsga(pools)

encoding = "RI"

# 查看染色体编码

# field = ea.crtfld(encoding, problem.varTypes, problem.ranges, problem.borders)

# print(ea.crtpc(encoding, ndind, field))

myAlgorithm = ea.moea_NSGA2_templet(problem, ea.Population(Encoding=encoding, NIND=ndind), MAXGEN=maxgen, logTras=1, drawing=1)

res = ea.optimize(myAlgorithm, seed=1, verbose=True, drawing=1, outputMsg=1, drawLog=1, saveFlag=1, dirName='result')

print(res)

if __name__ == "__main__":

with open(r"./Datas/data_tree_{}".format(0), 'rb') as f:

data = pickle.load(f)

f.close()

Run_nsga(30, 100, 14)

量子速写文章生成器网站

https://xiezuocat.com/#/quantum

真的超好用，现在还免费

Python 遗传算法库 Geatpy 深入

为了应对更加复杂的问题，不同的编码方式的问题，对此进行代码讲解，个人觉得这个遗传算法库做的很好，上手比较快，适用性很强，强烈推荐，用的很顺手。

示例说明：
a = [0~30连续数] * _num
b = [1 ~ num num个数排列排序] 即数列 1~num 不重复排列
c = [0~30离散数（整数）] * _num

import geatpy as ea
import numpy as np


# 示例说明：
# a = [0~30连续数] * _num
# b = [1 ~ num num个数排列排序] 即数列 1~num 不重复排列
# c = [0~30离散数（整数）] * _num
def func(a, b, c):
    return np.sin(np.pi * a * b) * np.cos(np.pi * c * b) * np.tan(np.pi * a * b * c)


# 初始化文件num大小
_num = 10


# 自定义 GA
class My_nsga(ea.Problem):
    def __init__(self):
        global _num
        name = 'GEATPY Facilitate Learning'
        # 只优化func一个变量
        M = 1
        # 最大化变量
        maxormins = [-1] * M
        # 一共有 a.size + b.size + c.size 个数
        Dim = _num + _num + _num
        # a 连续， b and c 离散
        varTypes = [0] * _num + [1] * _num + [1] * _num
        # a ∈ [0,30],  b ∈ [1,_num], c ∈ [0,30]
        lb = [0] * _num + [1] * _num + [0] * _num
        ub = [30] * _num + [_num] * _num + [30] * _num
        # 下限可取
        lbin = [1] * Dim
        # 上限可取
        ubin = [1] * Dim
        # 保存最大数据
        self.max_ans = 0
        # epoch
        self.epoch = 0
        # super
        ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)

    # 目标函数即神经网络返回值
    def evalVars(self, Vars):
        # init ans
        ans = np.zeros(len(Vars), dtype=float).reshape(len(Vars), 1)
        for i in range(len(Vars)):
            # 一定要确定区域选择是否正确
            a = Vars[i][:_num]
            # 可以选择int类型防止特定错误，因为正常输出时为float，具体问题可能会出错
            b = list(map(int, Vars[i][_num: _num + _num]))
            c = Vars[i][-_num:]
            score = func(a, b, c)
            ans[i] = score.max()
        self.max_ans = (now_max := ans.max())
        print(f"Epoch: {self.epoch + 1}, Epoch Max: {now_max}, Global Max: {self.max_ans}")
        self.epoch += 1
        return ans


# 运行 GA
def Run_nsga(ndind=10, maxgen=100, *args, **kwargs):
    global _num
    # init problem
    problem = My_nsga()
    # 染色体编码方式为[格雷编码，排列编码，格雷编码]
    encodings = ["RI", "P", "RI"]
    # 设置具体field，一定要多加这一部，geatpy还没有具体到能直接输入encoding: List[str]，需要进行转换才能使用
    # 一定要注意数组范围，报错的话一定要仔细检查！！！
    field1 = ea.crtfld(encodings[0], problem.varTypes[:_num], problem.ranges[:, :_num], problem.borders[:, :_num])
    field2 = ea.crtfld(encodings[1], problem.varTypes[_num: _num + _num], problem.ranges[:, _num: _num + _num], problem.borders[:, _num: _num + _num])
    field3 = ea.crtfld(encodings[2], problem.varTypes[-_num:], problem.ranges[:, -_num:], problem.borders[:, -_num:])
    # 合并field
    fields = [field1, field2, field3]
    # 代入模板
    myAlgorithm = ea.soea_psy_EGA_templet(problem, ea.PsyPopulation(Encodings=encodings, Fields=fields, NIND=ndind), MAXGEN=maxgen, logTras=0)
    # 是否画图
    myAlgorithm.drawing = 0
    # 进行优化
    res = ea.optimize(myAlgorithm, seed=1, verbose=False, drawing=0, outputMsg=True, drawLog=False, saveFlag=False, dirName='result')
    return res['Vars'][0]


if __name__ == "__main__":
    Run_nsga(50, 1000)

import geatpy as ea

import numpy as np

# 示例说明：

# a = [0~30连续数] * _num

# b = [1 ~ num num个数排列排序] 即数列 1~num 不重复排列

# c = [0~30离散数（整数）] * _num

def func(a, b, c):

return np.sin(np.pi * a * b) * np.cos(np.pi * c * b) * np.tan(np.pi * a * b * c)

# 初始化文件num大小

_num = 10

# 自定义 GA

class My_nsga(ea.Problem):

def __init__(self):

global _num

name = 'GEATPY Facilitate Learning'

# 只优化func一个变量

M = 1

# 最大化变量

maxormins = [-1] * M

# 一共有 a.size + b.size + c.size 个数

Dim = _num + _num + _num

# a 连续， b and c 离散

varTypes = [0] * _num + [1] * _num + [1] * _num

# a ∈ [0,30], b ∈ [1,_num], c ∈ [0,30]

lb = [0] * _num + [1] * _num + [0] * _num

ub = [30] * _num + [_num] * _num + [30] * _num

# 下限可取

lbin = [1] * Dim

# 上限可取

ubin = [1] * Dim

# 保存最大数据

self.max_ans = 0

# epoch

self.epoch = 0

# super

ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)

# 目标函数即神经网络返回值

def evalVars(self, Vars):

# init ans

ans = np.zeros(len(Vars), dtype=float).reshape(len(Vars), 1)

for i in range(len(Vars)):

# 一定要确定区域选择是否正确

a = Vars[i][:_num]

# 可以选择int类型防止特定错误，因为正常输出时为float，具体问题可能会出错

b = list(map(int, Vars[i][_num: _num + _num]))

c = Vars[i][-_num:]

score = func(a, b, c)

ans[i] = score.max()

self.max_ans = (now_max := ans.max())

print(f"Epoch: {self.epoch + 1}, Epoch Max: {now_max}, Global Max: {self.max_ans}")

self.epoch += 1

return ans

# 运行 GA

def Run_nsga(ndind=10, maxgen=100, *args, **kwargs):

global _num

# init problem

problem = My_nsga()

# 染色体编码方式为[格雷编码，排列编码，格雷编码]

encodings = ["RI", "P", "RI"]

# 设置具体field，一定要多加这一部，geatpy还没有具体到能直接输入encoding: List[str]，需要进行转换才能使用

# 一定要注意数组范围，报错的话一定要仔细检查！！！

field1 = ea.crtfld(encodings[0], problem.varTypes[:_num], problem.ranges[:, :_num], problem.borders[:, :_num])

field2 = ea.crtfld(encodings[1], problem.varTypes[_num: _num + _num], problem.ranges[:, _num: _num + _num], problem.borders[:, _num: _num + _num])

field3 = ea.crtfld(encodings[2], problem.varTypes[-_num:], problem.ranges[:, -_num:], problem.borders[:, -_num:])

# 合并field

fields = [field1, field2, field3]

# 代入模板

myAlgorithm = ea.soea_psy_EGA_templet(problem, ea.PsyPopulation(Encodings=encodings, Fields=fields, NIND=ndind), MAXGEN=maxgen, logTras=0)

# 是否画图

myAlgorithm.drawing = 0

# 进行优化

res = ea.optimize(myAlgorithm, seed=1, verbose=False, drawing=0, outputMsg=True, drawLog=False, saveFlag=False, dirName='result')

return res['Vars'][0]

if __name__ == "__main__":

Run_nsga(50, 1000)