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来自杭州电子科技大学自动化专业的大四理工男一枚。

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)

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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)

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"""

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)

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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)