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Functional Data Analysis(Second Edition)

Functional

Data

Analysis

函数数据

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Functional Data Analysis(Second Edition)，J.O. Ramsay，B.W. Silverman， Springer Series in Statistics

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Springer Series in Statistics

Advisors:

P. Bickel, P. Diggle, S. Fienberg, U. Gather,

I. Olkin, S. Zeger

J.O. Ramsay

B.W. Silverman

Functional Data Analysis

Second Edition

With 151 Illustrations

J.O. Ramsay

Department of Psychology

University of Montreal

Montreal, Quebec H3A 1B1

Canada

ramsay@psych.mcgill.ca

B.W. Silverman

St. Peter’s College

Oxford OX1 2DL

United Kingdom

bernard.silverman@spc.ox.ac.uk

Library of Congress Control Number: 2005923773

ISBN-10: 0-387-40080-X Printed on acid-free paper.

ISBN-13: 978-0387-40080-8

written permission of the publisher (Springer Science+Business Media, Inc., 233 Spring St., New

York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis.

Use in connection with any form of information storage and retrieval, electronic adaptation, com-

puter software, or by similar or dissimilar methodology now known or hereafter developed is for-

bidden.

The use in this publication of trade names, trademarks, service marks, and similar terms, even if

they are not identified as such, is not to be taken as an expression of opinion as to whether or not

they are subject to proprietary rights.

Printed in the United States of America. (MP)

987654321

springeronline.com

J.O. Ramsay

Department of Psychology

McGill University

Montreal, Quebec H4A 1B1

Canada

ramsay@psych.mcgill.ca

B.W. Silverman

St. Peter’s College

Oxford OX1 2DL

United Kingdom

bernard.silverman@spc.ox.ac.uk

Library of Congress Control Number: 2005923773

ISBN-10: 0-387-40080-X Printed on acid-free paper.

ISBN-13: 978-0387-40080-8

written permission of the publisher (Springer Science+Business Media, Inc., 233 Spring St., New

York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis.

Use in connection with any form of information storage and retrieval, electronic adaptation, com-

puter software, or by similar or dissimilar methodology now known or hereafter developed is for-

bidden.

The use in this publication of trade names, trademarks, service marks, and similar terms, even if

they are not identified as such, is not to be taken as an expression of opinion as to whether or not

they are subject to proprietary rights.

Printed in the United States of America. (MP)

987654321

springeronline.com

Preface to the Second Edition

This book continues in the footsteps of the First Edition in being a snap-

shot of a highly social, and therefore decidedly unpredictable, process. The

combined personal view of functional data analysis that it presents has

emerged over a number of years of research and contact, and has been

greatly nourished by delightful collaborations with many friends. We hope

that readers will enjoy the book as much as we have enjoyed writing it,

whether they are our colleagues as researchers or applied data analysts

reading the book as a research monograph, or students using it as a course

text.

As in the First Edition, live data are used throughout for both motivation

and illustration, showing how functional approaches allow us to see new

things, especially by exploiting the smoothness of the processes generating

the data. The data sets exemplify the wide scope of functional data analysis;

they are drawn from growth analysis, meteorology, biomechanics, equine

science, economics and medicine.

“Back to the data” was the heading to the last section of the First Edi-

tion. We did not know then how well those words would predict the next

eight years. Since then we have seen functional data applications in more

scientiﬁc and industrial settings than we could have imagined, and so we

wanted an opportunity to make this new ﬁeld accessible to a wider reader-

ship than the the ﬁrst volume seemed to permit. Our book of case studies,

Ramsay and Silverman (2002), was our ﬁrst response, but we have known

for some time that a new edition of our original volume was also required.

We have added a considerable amount of new material, and considered

carefully how the original material should be presented. One main objec-

剩余430页未读，继续阅读

xuzhenhuaxu

2012-05-17

这本书是介绍函数型数据分析的权威著作。

修改一下这段代码在pycharm中的实现，import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim #from torchvision import datasets,transforms import torch.utils.data as data #from torch .nn:utils import weight_norm import matplotlib.pyplot as plt from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import cohen_kappa_score data_ = pd.read_csv(open(r"C:\Users\zhangjinyue\Desktop\rice.csv"),header=None) data_ = np.array(data_).astype('float64') train_data =data_[:,:520] train_Data =np.array(train_data).astype('float64') train_labels=data_[:,520] train_labels=np.array(train_data).astype('float64') train_data,train_data,train_labels,train_labels=train_test_split(train_data,train_labels,test_size=0.33333) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) start_epoch=1 num_epoch=1 BATCH_SIZE=70 Ir=0.001 classes=('0','1','2','3','4','5') device=torch.device("cuda"if torch.cuda.is_available()else"cpu") torch.backends.cudnn.benchmark=True best_acc=0.0 train_dataset=data.TensorDataset(train_data,train_labels) test_dataset=data.TensorDataset(train_data,train_labels) train_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True) test_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True)

import torch.nn.functional as F import torch.optim as optim import torch.utils.data as data import matplotlib.pyplot as plt from sklearn.metrics import precision_score, recall_score, f1_score, cohen_...

import torch import torch.nn.functional as F import torch.optim as optim from torchvision import transforms from torchvision import datasets from torch.utils. data import DataI oader 补全代码

import torch.nn.functional as F import torch.optim as optim from torchvision import transforms from torchvision import datasets from torch.utils.data import DataLoader # 定义数据预处理 transform = ...

Error using dir Invalid path. The path must not contain a null character. Error in compute_rest_functional_connectivity (line 17) Dir_Data = dir(fullfile(Data_Dir,'*.set'));

这个错误的原因是你指定的 Data_Dir 目录包含了空字符，而 dir 函数不支持包含空字符的路径。你需要检查一下 Data_Dir 的值是否正确，并确保其中没有包含空字符。你可以尝试使用 MATLAB 的 strrep 函数将 Data_Dir ...

import pandas as pd import torch import torch.nn as nn import numpy as np # 读取Excel文件 data_frame = pd.read_excel('zd2.xlsx') # 去掉第一列第一列是时间 data = data_frame.iloc[:, 1:] data = data.values # 按500个时间段划分 new_data = data.reshape(-1, 500, 2) # 修改数据尺寸 new_data = torch.from_numpy(new_data).permute(0,2,1).float() #20, 2, 500 20代表10000个数据划分了20组 2是两个特征 500指500个时间段 class Net(nn.Module): def init(self): super(Net, self).init() self.conv1 = nn.Conv1d(2, 16, kernel_size=3, padding=1) self.conv2 = nn.Conv1d(16, 32, kernel_size=3, padding=1) self.conv3 = nn.Conv1d(32, 64, kernel_size=3, padding=1) self.pool = nn.MaxPool1d(kernel_size=2) # self.fc1 = nn.Linear(64 * 62, 128) def forward(self, x): x = self.conv1(x) x = nn.functional.relu(x) x = self.pool(x) x = self.conv2(x) x = nn.functional.relu(x) x = self.pool(x) x = self.conv3(x) x = nn.functional.relu(x) x = self.pool(x) return x net = Net() after_data = net(new_data) mean = torch.mean(after_data, dim=0, keepdim=True) std = torch.std(after_data, dim=0, keepdim=True) result = (after_data - mean) / std after_data = nn.functional.normalize(after_data, p=1, dim=1)

5. 在神经网络中进行前向传播，得到输出结果 after_data。 6. 对输出结果进行归一化处理，计算均值和标准差，并将数据减去均值再除以标准差得到归一化后的结果 result。 7. 使用 L1 归一化函数对 after_data 进行...

DMA functional description

The functional description of DMA involves the following steps: 1. Initialization: The DMA controller is initialized with the source and destination addresses, transfer size, and other parameters ...

import os, yaml, pickle, shutil, tarfile, glob import cv2 import albumentations import PIL import numpy as np import torchvision.transforms.functional as TF from omegaconf import OmegaConf from functools import partial from PIL import Image from tqdm import tqdm from torch.utils.data import Dataset, Subset import taming.data.utils as tdu from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve from taming.data.imagenet import ImagePaths from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light 解释

- torchvision.transforms.functional：提供了图像变换的功能，如旋转、裁剪、翻转等。 - OmegaConf：提供了配置文件的读取和解析功能。 - partial：提供了创建一个新函数的功能，该新函数是原函数的一个部分应用。 ...

检查一下：import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from sklearn.metrics import roc_auc_score # 定义神经网络模型 class Net(nn.Module): def init(self): super(Net, self).init() self.fc1 = nn.Linear(10, 64) self.fc2 = nn.Linear(64, 32) self.fc3 = nn.Linear(32, 1) self.sigmoid = nn.Sigmoid() def forward(self, x): x = self.fc1(x) x = nn.functional.relu(x) x = self.fc2(x) x = nn.functional.relu(x) x = self.fc3(x) x = self.sigmoid(x) return x # 加载数据集 data = torch.load('data.pt') x_train, y_train, x_test, y_test = data train_dataset = TensorDataset(x_train, y_train) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) test_dataset = TensorDataset(x_test, y_test) test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) # 定义损失函数和优化器 criterion = nn.BCELoss() optimizer = optim.Adam(net.parameters(), lr=0.01) # 训练模型 net = Net() for epoch in range(10): running_loss = 0.0 for i, data in enumerate(train_loader): inputs, labels = data optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() # 在测试集上计算AUC y_pred = [] y_true = [] with torch.no_grad(): for data in test_loader: inputs, labels = data outputs = net(inputs) y_pred += outputs.tolist() y_true += labels.tolist() auc = roc_auc_score(y_true, y_pred) print('Epoch %d, loss: %.3f, test AUC: %.3f' % (epoch + 1, running_loss / len(train_loader), auc))

data = torch.load('data.pt') x_train, y_train, x_test, y_test = data train_dataset = TensorDataset(x_train, y_train) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) test_dataset ...

C:\Users\conservator\Desktop\mqttToAliyun\mqttToAliyun.ino: In function 'void mqttIntervalPost()': C:\Users\conservator\Desktop\mqttToAliyun\mqttToAliyun.ino:80:50: error: 'data' was not declared in this scope; did you mean 'std::data'? 80 | sprintf(param, "{\"CurrentTemperature\":%d}",data); | ^~ | std::data In file included from c:\users\conservator\appdata\local\arduino15\packages\esp8266\tools\xtensa-lx106-elf-gcc\3.1.0-gcc10.3-e5f9fec\xtensa-lx106-elf\include\c++\10.3.0\array:41, from c:\users\conservator\appdata\local\arduino15\packages\esp8266\tools\xtensa-lx106-elf-gcc\3.1.0-gcc10.3-e5f9fec\xtensa-lx106-elf\include\c++\10.3.0\tuple:39, from c:\users\conservator\appdata\local\arduino15\packages\esp8266\tools\xtensa-lx106-elf-gcc\3.1.0-gcc10.3-e5f9fec\xtensa-lx106-elf\include\c++\10.3.0\functional:54, from c:\users\conservator\appdata\local\arduino15\packages\esp8266\tools\xtensa-lx106-elf-gcc\3.1.0-gcc10.3-e5f9fec\xtensa-lx106-elf\include\c++\10.3.0\pstl\glue_algorithm_defs.h:13, from c:\users\conservator\appdata\local\arduino15\packages\esp8266\tools\xtensa-lx106-elf-gcc\3.1.0-gcc10.3-e5f9fec\xtensa-lx106-elf\include\c++\10.3.0\algorithm:74, from C:\Users\conservator\AppData\Local\Arduino15\packages\esp8266\hardware\esp8266\3.1.2\cores\esp8266/Arduino.h:229, from C:\Users\conservator\AppData\Local\Temp\arduino\sketches\304EB848CF381B8A7A8E84D646752A81\sketch\mqttToAliyun.ino.cpp:1: c:\users\conservator\appdata\local\arduino15\packages\esp8266\tools\xtensa-lx106-elf-gcc\3.1.0-gcc10.3-e5f9fec\xtensa-lx106-elf\include\c++\10.3.0\bits\range_access.h:319:5: note: 'std::data' declared here 319 | data(initializer_list<_Tp> __il) noexcept | ^~ exit status 1 Compilation error: 'data' was not declared in this scope; did you mean 'std::data'?

根据错误信息，你在代码中使用了未声明的变量`data`，并且编译器建议你是否指的是`std::data`。为了解决这个问题，你需要在适当的位置声明并初始化`data`变量。以下是一个示例，假设你要将温度数据存储在`data`...

super100

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import torch import torch.nn.functional as F import torch.optim as optim from torchvision import transforms from torchvision import datasets from torch.utils. data import DataI oader 补全代码

Error using dir Invalid path. The path must not contain a null character. Error in compute_rest_functional_connectivity (line 17) Dir_Data = dir(fullfile(Data_Dir,'*.set'));

DMA functional description

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