没有合适的资源？快使用搜索试试~ 我知道了~

首页Visualizing Data-Ben Fry-可视化数据英文高清版 pdf

Visualizing Data-Ben Fry-可视化数据英文高清版 pdf

可视化,

媒体艺术

5星 · 超过95%的资源需积分: 29 133 下载量 44 浏览量更新于2023-06-07 1 收藏 6.25MB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

试读

384页

Visualizing Data: Exploring and Explaining Data with the Processing Environment Paperback by Ben Fry 这是一本关于计算信息设计的书籍。从如何获取原始数据开始，到如何理解原始数据，本书都作了非常详尽的介绍。书中使用由作者开发的开源编程环境Processing编程，它非常简单易用。对于熟悉Java的程序员来说，本书后面的章节还介绍了如何将Processing和Java结合使用。本书是写给那些拥有一个数据集合，好奇如何探索它，并且考虑如何交流这些数据的人们的。随着我们处理越来越多的信息，需要可视化数据的人的数量增长非常迅速。更重要的是，读者已经超越了某些可视化领域的专家。通过让更大范围的人接触到可视化思想，在接下来的几十年中应该可以看到一些真正让人惊叹的成果。 Ben Fry在麻省理工多媒体实验室Aesthetics + Computation Group获得了博士学位。他是卡内基•梅隆设计学院2006~2007年度Nierenberg Chair of Design的客座教授。他同Casey Reas共同开发了Processing，在2005年获得了Prix Ars Electronica的Gloden Nica奖项。Ben的作品获得了Rockefeller 基金会的New Media奖学金，并且展示在现代艺术博物馆，Ars Electronica，2002年的Whitney Biennial，以及2003年的Cooper Hewitt Design Triennial中。

资源详情

资源推荐

Visualizing Data

Ben Fry

Beijing

•

Cambridge

•

Farnham

•

Köln

•

Paris

•

Sebastopol

•

Taipei

•

Tokyo

Visualizing Data

by Ben Fry

Printed in the United States of America.

Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.

O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions

are also available for most titles (safari.oreilly.com). For more information, contact our

corporate/institutional sales department: (800) 998-9938 or corporate@oreilly.com.

Editor:

Andy Oram

Production Editor:

Loranah Dimant

Copyeditor:

Genevieve d’Entremont

Proofreader:

Loranah Dimant

Indexer:

Ellen Troutman Zaig

Cover Designer:

Karen Montgomery

Interior Designer:

David Futato

Illustrator:

Jessamyn Read

Printing History:

December 2007: First Edition.

Nutshell Handbook, the Nutshell Handbook logo, and the O’Reilly logo are registered trademarks of

O’Reilly Media, Inc. Visualizing Data, the image of an owl, and related trade dress are trademarks of

O’Reilly Media, Inc.

Many of the designations used by manufacturers and sellers to distinguish their products are claimed as

trademarks. Where those designations appear in this book, and O’Reilly Media, Inc. was aware of a

trademark claim, the designations have been printed in caps or initial caps.

While every precaution has been taken in the preparation of this book, the publisher and author assume

no responsibility for errors or omissions, or for damages resulting from the use of the information

contained herein.

This book uses RepKover

™

, a durable and flexible lay-flat binding.

ISBN-10: 0-596-51455-7

ISBN-13: 978-0-596-51455-6

[C]

iii

Table of Contents

Preface

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

vii

1. The Seven Stages of Visualizing Data

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Why Data Display Requires Planning 2

An Example 6

Iteration and Combination 14

Principles 15

Onward 18

2. Getting Started with Processing

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Sketching with Processing 20

Exporting and Distributing Your Work 23

Examples and Reference 24

Functions 27

Sketching and Scripting 28

Ready? 30

3. Mapping

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Drawing a Map 31

Locations on a Map 32

Data on a Map 34

Using Your Own Data 51

Next Steps 53

iv | Table of Contents

4. Time Series

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Milk, Tea, and Coffee (Acquire and Parse) 55

Cleaning the Table (Filter and Mine) 55

A Simple Plot (Represent and Refine) 57

Labeling the Current Data Set (Refine and Interact) 59

Drawing Axis Labels (Refine) 62

Choosing a Proper Representation (Represent and Refine) 73

Using Rollovers to Highlight Points (Interact) 76

Ways to Connect Points (Refine) 77

Text Labels As Tabbed Panes (Interact) 83

Interpolation Between Data Sets (Interact) 87

End of the Series 92

5. Connections and Correlations

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Changing Data Sources 94

Problem Statement 95

Preprocessing 96

Using the Preprocessed Data (Acquire, Parse, Filter, Mine) 111

Displaying the Results (Represent) 118

Returning to the Question (Refine) 121

Sophisticated Sorting: Using Salary As a Tiebreaker (Mine) 126

Moving to Multiple Days (Interact) 127

Smoothing Out the Interaction (Refine) 132

Deployment Considerations (Acquire, Parse, Filter) 133

6. Scatterplot Maps

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

145

Preprocessing 145

Loading the Data (Acquire and Parse) 155

Drawing a Scatterplot of Zip Codes (Mine and Represent) 157

Highlighting Points While Typing (Refine and Interact) 158

Show the Currently Selected Point (Refine) 162

Progressively Dimming and Brightening Points (Refine) 165

Zooming In (Interact) 167

Changing How Points Are Drawn When Zooming (Refine) 177

Deployment Issues (Acquire and Refine) 178

Next Steps 180

剩余383页未读，继续阅读

Visualizing-lidar-data:使用Uber自主可视化系统（AVS）和Jupyter Notebook应用程序可视化激光雷达数据

可视化激光雷达数据使用Uber自主可视化系统（AVS）和Jupyter Notebook应用程序可视化激光雷达数据该项目包含两个不同的应用程序，这些应用程序使用KITTI Vision Benchmark Suite数据集可视化激光雷达数据。 1. Uber AVS自主可视化系统（AVS）-XVIZ（AVS的数据层）快速开始您需要和来运行示例。 # Clone XVIZ $ git clone https://github.com/uber/xviz.git $ cd xviz # Install dependencies $ yarn bootstrap 转换并提供KITTI示例数据： # Download KITTI data $ ./scripts/download-kitti-data.sh # Convert KITTI data if necessary and

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import confusion_matrix, classification_report, accuracy_score # 1. 数据准备 train_data = pd.read_csv('train.csv') test_data = pd.read_csv('test_noLabel.csv') # 填充缺失值 train_data.fillna(train_data.mean(), inplace=True) test_data.fillna(test_data.mean(), inplace=True) # 2. 特征工程 X_train = train_data.drop(['Label', 'ID'], axis=1) y_train = train_data['Label'] X_test = test_data.drop('ID', axis=1) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # 3. 模型建立 model = RandomForestClassifier(n_estimators=100, random_state=42) # 4. 模型训练 model.fit(X_train, y_train) # 5. 进行预测 y_pred = model.predict(X_test) # 6. 保存预测结果 df_result = pd.DataFrame({'ID': test_data['ID'], 'Label': y_pred}) df_result.to_csv('forecast_result.csv', index=False) # 7. 模型评估 y_train_pred = model.predict(X_train) print('训练集准确率：', accuracy_score(y_train, y_train_pred)) print('测试集准确率：', accuracy_score(y_test, y_pred)) print(classification_report(y_test, y_pred)) # 8. 绘制柱形图 feature_importances = pd.Series(model.feature_importances_, index=X_train.columns) feature_importances = feature_importances.sort_values(ascending=False) plt.figure(figsize=(10, 6)) sns.barplot(x=feature_importances, y=feature_importances.index) plt.xlabel('Feature Importance Score') plt.ylabel('Features') plt.title('Visualizing Important Features') plt.show() # 9. 对比类分析 train_data['Label'].value_counts().plot(kind='bar', color=['blue', 'red']) plt.title('Class Distribution') plt.xlabel('Class') plt.ylabel('Frequency') plt.show()

这段代码是一个机器学习模型的完整流程，包括数据准备、特征工程、模型建立、模型训练、预测结果保存、模型评估和可视化分析等步骤。其中包括了绘制柱形图和对比类分析的代码。绘制柱形图的代码如下： ``` ...

valuation Pretrained models from the model zoo can be evaluated using the `eval.py` script. For example, the model on cifar-10 can be evaluated as follows: ```shell python eval.py --config_exp configs/scan/scan_cifar10.yml --model $MODEL_PATH ``` Visualizing the prototype images is easily done by setting the `--visualize_prototypes` flag. For example on cifar-10:

Similarly, you might want to have a look at the clusters found on ImageNet (as shown at the top). First download the model (link in table above) and then execute the following command: ```shell python eval.py --config_exp configs/scan/wjd_eval.yml --model $MODEL_PATH_IMAGENET ```，这段话说了啥，能解释一下吗

这段话是在介绍如何使用预训练模型进行评估和可视化。预训练模型可以从模型库中获取。以CIFAR-10为例，可以使用以下命令进行评估：```shell python eval.py --config_exp configs/scan/scan_cifar10.yml --model $...

粉丝: 0
资源: 1

上传资源快速赚钱

我的内容管理收起

我的资源快来上传第一个资源

我的收益

登录查看自己的收益

我的积分登录查看自己的积分

我的C币登录后查看C币余额

我的收藏

我的下载

下载帮助

会员权益专享

Visualizing Data-Ben Fry-可视化数据 英文高清版 pdf

Visualizing-lidar-data:使用Uber自主可视化系统（AVS）和Jupyter Notebook应用程序可视化激光雷达数据

Data Security in Cloud Computing 无水印原版pdf

数据可视化 ppt 英文

数据结构知识图谱构建与可视化参考文献

sklearn.manifold

请给我一段GitHub上关于vgg16模型特征可视化的项目

请提交一份网上找到的视频或者图片，能够体现数据可视化应用，并根据你提交的内容总结出其展示的数据或信息是什么。

关于人工智能数据可视化的参考文献

深度学习热力图可视化

mmdet3d可视化nuscenes

帮我找一个网站，这个网站可以机器学习中模拟聚类的过程

https://www.idiap.ch/en/dataset/eyediap

给几个可以在线选择卷积核大小的网站

sklearn tsne

visualizing and understanding convolutional networks

r语言tm package

可视化深度学习模型代码地址

影子流形创建图的matlab代码

会员权益专享

最新资源

Visualizing Data-Ben Fry-可视化数据英文高清版 pdf