modeling tabular data using conditional gan

Modeling Tabular Data Using Conditional GAN

Introduction

In recent years, the generation and analysis of tabular data has gained substantial attention in the field of machine learning and data science. Tabular data, which consists of rows and columns, is commonly used to represent structured information such as customer records, financial transactions, and sensor readings. Traditional statistical and machine learning models have been widely used for modeling and analyzing tabular data. However, these models often assume linear relationships and have limitations in capturing complex patterns and dependencies within the data. In this article, we will explore the use of Conditional Generative Adversarial Networks (CGANs) for modeling tabular data, providing a powerful and flexible approach to simulate, generate, and analyze structured data.

What is a Generative Adversarial Network (GAN)?

A Generative Adversarial Network (GAN) is a type of deep learning model consisting of two components: a generator and a discriminator. The generator takes random noise as input and generates synthetic data, while the discriminator evaluates whether the generated data is real or fake. The generator and discriminator are trained together in a competitive manner, with the generator aiming to fool the discriminator and the discriminator trying to distinguish between real and fake data. Through this adversarial training process, the generator gradually improves its ability to generate realistic data that is

indistinguishable from the real data.

Conditional Generative Adversarial Networks (CGANs)

Conditional Generative Adversarial Networks (CGANs) extend the basic GAN architecture by introducing conditional information. In the case of tabular data, this conditional information could be additional

attributes or labels associated with each data point. By conditioning the generator on specific input conditions, CGANs are capable of

generating data that corresponds to the given conditions. This makes CGANs well-suited for tasks such as data augmentation, missing value imputation, and synthetic data generation.

Architecture of CGAN

Generator

The generator in a CGAN takes both random noise and conditional information as input. It consists of multiple layers of neural networks, typically implemented using fully connected layers. The last layer of the generator outputs the generated data, which should match the structure and distribution of the real data. To ensure the generated data is conditioned on the given input, the conditional information is usually concatenated with the noise input at each layer of the generator.

Discriminator

The discriminator in a CGAN also takes both real and generated data as input, along with the corresponding conditional information. The discriminator’s role is to distinguish real data from generated data. Similar to the generator, the discriminator is implemented using neural networks with fully connected layers. The last layer of the

discriminator outputs a probability score, indicating the likelihood of the input being real or fake. The discriminator is trained using both real and generated data, adjusting its parameters to improve its ability to distinguish between the two.

Training Procedure

The training of a CGAN involves an adversarial process where the generator and discriminator are trained iteratively. The generator first generates synthetic data conditioned on the input conditions. This generated data, along with the real data and corresponding conditions, are fed into the discriminator for evaluation. The generator’s

objective is to produce synthetic data that the discriminator cannot distinguish from real data. Conversely, the discriminator’s objective

is to accurately classify real and fake data. This adversarial training

process continues until both the generator and discriminator reach a stable equilibrium.

Applications of CGANs for Tabular Data Modeling

Data Augmentation

One of the main applications of CGANs in tabular data modeling is data augmentation. Data augmentation refers to the process of expanding the size of the training dataset by generating new synthetic samples. By conditioning the generator on the existing data and additional attribute or label information, CGANs can generate realistic synthetic data that captures the underlying patterns and distributions of the original data. This augmented data can then be used to improve the performance of traditional machine learning models by providing more diverse and representative examples.

Missing Value Imputation

In many real-world datasets, missing values are a common issue that can affect the performance of machine learning models. CGANs can be leveraged to impute missing values by conditioning the generator on the available attributes or labels and generating synthetic values for the missing entries. By capturing the dependencies and patterns present in the data, CGANs can generate realistic imputations that preserve the overall characteristics of the dataset. This allows for more accurate analysis and modeling of the data.

Synthetic Data Generation

CGANs can also be used to generate entirely synthetic datasets that follow the same patterns and distributions as the real data. This can be particularly useful in scenarios where access to real data is limited due to privacy concerns or other constraints. By conditioning the generator on the desired attributes or labels, CGANs can produce synthetic datasets that mimic the characteristics of the real data. These synthetic datasets can then be used for various purposes, such as training machine learning models, conducting simulations, or performing sensitivity analyses.

Outlier Detection

Another application of CGANs in tabular data modeling is outlier detection. Outliers are data points that deviate significantly from the majority of the data and can have a substantial impact on the results of data analysis and modeling. CGANs can be trained to learn the underlying patterns and distributions of the data, making them capable of identifying unusual patterns or data points that do not conform to these patterns. By comparing the generated data with the real data, the discriminator can help identify potential outliers or anomalous data points.

Advantages and Limitations of CGANs for Tabular Data Modeling

Advantages

•CGANs provide a flexible and powerful approach to model and analyze tabular data, capturing complex patterns and dependencies that traditional models may struggle to capture.

•By conditioning the generator on specific input conditions, CGANs can generate data that corresponds to those conditions, enabling

various applications such as data augmentation, missing value

imputation, and synthetic data generation.

•CGANs can generate realistic synthetic data that can be used to expand the training dataset, improve model performance, and

address privacy concerns.

•The adversarial training process of CGANs allows for the generation of data that closely matches the distribution and

characteristics of the real data, leading to more accurate

analysis and modeling results.

Limitations

•CGANs require large amounts of data for training, as well as careful tuning of hyperparameters to ensure stable and meaningful results.

•The quality and fidelity of the generated data heavily depend on the architecture and training procedure of the CGAN. If the

generator or discriminator are not well-designed, the generated

data may not accurately represent the real data.

•CGANs may struggle with generating rare or uncommon data points, as they are not often observed in the training data and may not be well-represented in the generated samples.

•The interpretation of the generated data can be challenging, as it does not originate from real observations and may not reflect the true underlying processes and mechanisms present in the data.

Conclusion

Conditional Generative Adversarial Networks (CGANs) provide a powerful and flexible approach for modeling and analyzing tabular data. By conditioning the generator on specific input conditions, CGANs can generate realistic synthetic data that captures the underlying patterns and dependencies of the real data. This opens up various applications in data augmentation, missing value imputation, synthetic data generation, and outlier detection. Despite some limitations, CGANs offer a promising solution to the challenges associated with modeling tabular data and can provide valuable insights for decision-making and analysis in various domains.

ansys Workbench菜单选项中英文对照翻译

ansys Workbench;菜单选项中英文对照 1、ANSYS12.1 Workbench 界面相关分析系统和组件说明 【Analysis Systems】分析系统【Component Systems】组件系统】【CustomSystems 】自定义系统【Design Exploration】设计优化分析类型 Electric (ANSYS) Explicit Dynamics (ANSYS) Fluid Flow (CFX) Fluid Flow (Fluent) Hamonic Response (ANSYS) Linear Buckling (ANSYS) Magnetostatic (ANSYS) Modal (ANSYS) Random Vibration (ANSYS) Response Spectrum (ANSYS) Shape Optimization (ANSYS) Static Structural (ANSYS) Steady-State Thermal (ANSYS) Thermal-Electric (ANSYS) Transient Structural(ANSYS) Transient Structural(MBD) Transient Thermal(ANSYS) 说明 ANSYS 电场分析 ANSYS 显式动力学分析 CFX 流体分析 FLUENT 流体分析ANSYS 谐响应分析 ANSYS 线性屈曲 ANSYS 静磁场分析 ANSYS 模态分析 ANSYS 随机振动分析 ANSYS 响应谱分析 ANSYS 形状优化分析 ANSYS 结构静力分析ANSYS 稳态热分析 ANSYS 热电耦合分析 ANSYS 结构瞬态分析 MBD 多体结构动力分析 ANSYS 瞬态热分析 组件类型 AUTODYN BladeGen CFX Engineering Data

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1、ANSYS12.1 Workbench界面相关分析系统和组件说明【Analysis Systems】分析系统【Component Systems】组件系统 【CustomSystems】自定义系统【Design Exploration】设计优化 分析类型说明 Electric (ANSYS) ANSYS电场分析 Explicit Dynamics (ANSYS) ANSYS显式动力学分析 Fluid Flow (CFX) CFX流体分析 Fluid Flow (Fluent) FLUENT流体分析 Hamonic Response (ANSYS) ANSYS谐响应分析 Linear Buckling (ANSYS) ANSYS线性屈曲 Magnetostatic (ANSYS) ANSYS静磁场分析 Modal (ANSYS) ANSYS模态分析 Random Vibration (ANSYS) ANSYS随机振动分析

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ANSYSfluent菜单中英文对照

1、ANSYS12.1Workbench界面相关分析系统和组件说明【AnalysisSystems】分析系统【ComponentSystems】组件系统 【CustomSystems】自定义系统【DesignExploration】设计优化 分析类型说明 Electric(ANSYS)ANSYS电场分析 ExplicitDynamics(ANSYS)ANSYS显式动力学分析 FluidFlow(CFX)CFX流体分析 FluidFlow(Fluent)FLUENT流体分析 HamonicResponse(ANSYS)ANSYS谐响应分析 LinearBuckling(ANSYS)ANSYS线性屈曲 Magnetostatic(ANSYS)ANSYS静磁场分析 Modal(ANSYS)ANSYS模态分析 RandomVibration(ANSYS)ANSYS随机振动分析 ResponseSpectrum(ANSYS)ANSYS响应谱分析 ShapeOptimization(ANSYS)ANSYS形状优化分析 StaticStructural(ANSYS)ANSYS结构静力分析 Steady-StateThermal(ANSYS)ANSYS稳态热分析 Thermal-Electric(ANSYS)ANSYS热电耦合分析 TransientStructural(ANSYS)ANSYS结构瞬态分析 TransientStructural(MBD)MBD多体结构动力分析 TransientThermal(ANSYS)ANSYS瞬态热分析 组件类型说明

AUTODYNAUTODYN非线性显式动力分析 BladeGen涡轮机械叶片设计工具 CFXCFX高端流体分析工具 EngineeringData工程数据工具 ExplicitDynamicLS-DYNALS-DYNA显式动力分析FiniteElementModelerFEM有限元模型工具 FLUNETFLUNET流体分析Geometry 几何建模工具 MechanicalAPDL机械APDL命令 MechanicalModel机械分析模型 Mesh网格划分工具 Results结果后处理工具 TurboGrid涡轮叶栅通道网格生成工具 VistaTF叶片二维性能评估工具 2、主菜单【File】文件操作【View】窗口显示【Tools】提供工具【Units】单位制【Help】帮助信息 3、基本工具条【New】新建文件【Open】打开文件【Save】保存文件【SaveAs】另存为文件【Import】导入模型【CompactMode】紧凑视图模式【ShadeExteriorandEdges】轮廓线显示【Wireframe】线框显示【Ruler】显示标尺 【Legend】显示图例【Triad】显示坐标图示【ExpandAll】展开结构树【CollapseEnvironments】折叠结构树

WinNonlin Features

https://www.sodocs.net/doc/0e19508930.html,/products/prod_winnonlin_feature.php WinNonlin? Features Key features of WinNonlin? Professional and Enterprise editions include the following: ?Quick data export to NONMEM?, SAS, S-PLUS?, SigmaPlot? and Excel ?Extensive built-in library of predefined models ?Support for user-defined models ?Easy data management via Excel compatible workbooks, including formulas, functions, data import/export, missing value codes, sort, transformations and worksheet merge ?One-click export of results, plots, and tables to Microsoft Word? ?Dynamic memory management for large data sets and complex models ?Descriptive statistics, including weighted descriptive statistics ?Units definition and conversions, including the ability to specify preferred output units, units for a dosing regimen, and unit conversions ?Noncompartmental analysis of both blood and urine data using methods for rich or sparse datasets ?Three methods of AUC calculation: linear trapezoidal, linear/log trapezoidal, and linear up/log down ?Support for Excel, SAS Transport, and NONMEM file formats ?Toolbox functions for deconvolution, semicompartmental modeling, nonparametric superposition and crossover design ?Automatic determination of initial estimates for most library models ?Analysis using general linear modeling approaches, including bioequivalence tests as appropriate ?Built-in scripting language ?Analysis can be automated or controlled from any VBA-capable tool, such as VB, Word, or Excel WinNonlin Enterprise edition offers additional data connectivity: ?Full integration with the PKS? Suite of data management, automation and reporting software ? ?Custom Query Builder wizard for access to LIMS and other data sources ? A software development kit (SDK) for your IT/IS staff to use in building and installing additional Custom Query Builder wizards for data management systems not supported by Pharsight? Supported data sources for the WinNonlin Enterprise include: ?Any row-oriented data source that has a Microsoft’s? Open Database Connectivity (ODBC) driver accessed via the latest version of ODBC API ?Remote Oracle databases via an Oracle-supplied ODBC driver and Oracle data connectivity software ?SAS via a SAS-supplied ODBC driver for both remote and local access

ANSYS-Workbench菜单中英文对照

1、ANSYS12。1 Workbench界面相关分析系统和组件说明 【Analysis Systems】分析系统【Component Systems】组件系统【CustomSystems】自定义系统【Design Exploration】设计优化 分析类型说明 Electric （ANSYS) ANSYS电场分析 Explicit Dynamics （ANSYS）ANSYS显式动力学分析Fluid Flow (CFX）CFX流体分析 Fluid Flow (Fluent）FLUENT流体分析Hamonic Response (ANSYS）ANSYS谐响应分析 Linear Buckling （ANSYS) ANSYS线性屈曲Magnetostatic (ANSYS) ANSYS静磁场分析 Modal (ANSYS）ANSYS模态分析 Random Vibration (ANSYS）ANSYS随机振动分析Response Spectrum (ANSYS）ANSYS响应谱分析 Shape Optimization （ANSYS）ANSYS形状优化分析Static Structural （ANSYS）ANSYS结构静力分析Steady—State Thermal （ANSYS）ANSYS稳态热分析Thermal—Electric （ANSYS）ANSYS热电耦合分析Transient Structural（ANSYS) ANSYS结构瞬态分析Transient Structural（MBD）MBD 多体结构动力分析Transient Thermal(ANSYS）ANSYS瞬态热分析 组件类型说明 AUTODYN AUTODYN非线性显式动力分析BladeGen 涡轮机械叶片设计工具 CFX CFX高端流体分析工具

ANSYS fluent菜单中英文对照

1、Workbench界面相关分析系统和组件说明【Analysis Systems】分析系统【Component Systems】组件系统 【CustomSystems】自定义系统【Design Exploration】设计优化 分析类型说明 Electric (ANSYS) ANSYS电场分析 Explicit Dynamics (ANSYS) ANSYS显式动力学分析 Fluid Flow (CFX) CFX流体分析 Fluid Flow (Fluent) FLUENT流体分析 Hamonic Response (ANSYS) ANSYS谐响应分析 Linear Buckling (ANSYS) ANSYS线性屈曲 Magnetostatic (ANSYS) ANSYS静磁场分析 Modal (ANSYS) ANSYS模态分析 Random Vibration (ANSYS) ANSYS随机振动分析

Response Spectrum (ANSYS) ANSYS响应谱分析Shape Optimization (ANSYS) ANSYS形状优化分析Static Structural (ANSYS) ANSYS结构静力分析Steady-State Thermal (ANSYS) ANSYS稳态热分析Thermal-Electric (ANSYS) ANSYS热电耦合分析Transient Structural(ANSYS) ANSYS结构瞬态分析Transient Structural(MBD) MBD 多体结构动力分析Transient Thermal(ANSYS) ANSYS瞬态热分析 组件类型说明 AUTODYN AUTODYN非线性显式动力分析BladeGen 涡轮机械叶片设计工具CFX CFX高端流体分析工具

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土木工程专业英语(带翻译)

State-of-the-art report of bridge health monitoring Abstract The damage diagnosis and healthmonitoring of bridge structures are active areas of research in recent years. Comparing with the aerospace engineering and mechanical engineering, civil engineering has the specialities of its own in practice. For example, because bridges, as well as most civil engineering structures, are large in size, and have quite lownatural frequencies and vibration levels, at low amplitudes, the dynamic responses of bridge structure are substantially affected by the nonstructural components, unforeseen environmental conditions, and changes in these components can easily to be confused with structural damage.All these give the damage assessment of complex structures such as bridges a still challenging task for bridge engineers. This paper firstly presents the definition of structural healthmonitoring system and its components. Then, the focus of the discussion is placed on the following sections:①the laboratory and field testing research on the damage assessment;②analytical developments of damage detectionmethods, including (a) signature analysis and pattern recognition approaches, (b) model updating and system identification approaches, (c) neural networks approaches; and③sensors and their optimum placements. The predominance and shortcomings of each method are compared and analyzed. Recent examples of implementation of structural health monitoring and damage identification are summarized in this paper. The key problem of bridge healthmonitoring is damage automatic detection and diagnosis, and it is the most difficult problem. Lastly, research and development needs are addressed. 1 Introduction Due to a wide variety of unforeseen conditions and circumstance, it will never be possible or practical to design and build a structure that has a zero percent probability of failure. Structural aging, environmental conditions, and reuse are examples of circumstances that could affect the reliability and the life of a structure. There are needs of periodic inspections to detect deterioration resulting from normal operation and environmental attack or inspections following extreme events, such as strong-motion earthquakes or hurricanes. To quantify these system performance measures requires some means to monitor and evaluate the integrity of civil structureswhile in service. Since the Aloha Boeing 737 accident that occurred on April

modeling tabular data using conditional gan

modeling tabular data using conditional gan 在数据科学和机器学习中，表格数据是一类常见的数据类型。在许多实际应用中，我们需要对这些数据进行建模和分析。本文将介绍一种新的建模表格数据的方法——使用条件生成对抗网络（Conditional Generative Adversarial Network，CGAN）。 传统的建模方法通常使用统计学方法或机器学习方法，如线性回归、决策树和随机森林等。这些方法对于简单的数据集效果良好，但对于复杂的数据集则存在一定的限制。随着深度学习的发展，生成对抗网络（GAN）已经成为一种流行的建模方法。GAN通过让两个神经网络相互竞争，从而学习生成与真实数据相似的数据。然而，传统的GAN并不适用于表格数据，因为表格数据通常是高维、离散和带有结构的。因此，CGAN应运而生。 CGAN是一种将条件信息（如标签或特征向量）嵌入生成器和判别器中的GAN。通过加入条件信息，CGAN可以生成与特定条件相关的数据。对于表格数据，条件信息可以是一些特征，例如数据集中的某些列。因此，CGAN可以学习生成与指定特征相关的数据。 如何使用CGAN建模表格数据？首先，我们需要定义生成器和判别器的结构。生成器通常由一些全连接层、卷积层和反池化层组成。生成器将一些随机噪声作为输入，并生成与条件信息相关的数据。判别器则通常由一些卷积层和全连接层组成。判别器将真实数据和生成器生成的数据作为输入，并输出一个二进制值，表示输入数据的真伪。CGAN的目标是让判别器无法区分真实数据和生成的数据，同时让生

成器生成与条件信息相关的数据。 其次，我们需要准备数据集。数据集应包括表格数据和条件信息。表格数据可以是一个二维矩阵，其中每一行代表一个样本，每一列代表一个特征。条件信息可以是一些列或特征，例如数据集中的某些列或某些标签。在训练CGAN时，我们需要将表格数据和条件信息作为 输入传递给生成器和判别器。 最后，我们可以训练CGAN模型，并使用模型生成新的表格数据。一般来说，我们可以使用反向传播算法更新生成器和判别器的参数。在每个训练步骤中，我们将一些真实数据和生成的数据输入给判别器，并使用判别器的输出来更新生成器和判别器的参数。通过多次迭代训练，生成器可以学习生成与条件信息相关的表格数据。 总之，CGAN是一种有效的建模表格数据的方法。通过将条件信 息嵌入GAN中，CGAN可以生成与指定特征相关的数据。在实际应用中，CGAN可以用于数据增强、数据合成和数据转换等任务。

英文文章模板1

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Ansysworbench菜单说明

Ansysworbench菜单说明

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Static Structural (ANSYS) ANSYS结构静力分析 Steady-State Thermal (ANSYS) ANSYS稳态热分析 Thermal-Electric (ANSYS) ANSYS热电耦合分析 Transient Structural(ANSYS) ANSYS结构瞬态分析 Transient Structural(MBD) MBD 多体结构动力分析 Transient Thermal(ANSYS) ANSYS 瞬态热分析 组件类型说明 AUTODYN AUTODYN非 线性显式动力分析 BladeGen 涡轮机 械叶片设计工具 CFX CFX高端 流体分析工具 Engineering Data 工程 数据工具