R语言API详尽参考指南

R语言
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"The R reference index 是一份非常全面的R语言API文档目录，适用于不同熟练程度的R语言用户。这份文档由R核心团队编写，版本为2.15.1，发布于2012年6月22日，包含了R语言的统计计算环境的相关参考索引。该文档遵循GNU General Public License，允许用户复制、分发和修改，但需保留版权信息和许可通知。此外，还特别提到，R软件是免费的，没有任何保修，用户可以在满足特定许可条件的情况下进行翻译和分发。" 在"R: A Language and Environment for Statistical Computing"中，R不仅是一种编程语言，还是一个用于统计计算和图形生成的环境。它提供了广泛的数据分析工具和方法，包括统计模型、数据处理、可视化等。参考索引涵盖了R语言的基础包（base package），这是R安装时默认包含的一组核心功能。 基础包（base-package）是R语言的核心部分，包含了众多基础函数和数据结构。例如： 1. `.Device`：这可能是指R中的图形设备，用于创建和管理图形输出，如屏幕显示器、PDF文件或PNG图片等。 2. `.Machine`: 这个是R中一个内部对象，存储了与当前运行环境相关的数值属性，如浮点数精度、最大整数等，对于理解数值计算的限制和特性很有帮助。 文档的目录结构进一步深入到R的各种功能，如数据操作、控制流程、函数定义、错误处理、输入/输出、统计函数、图形系统等等。这些内容详细介绍了每个函数的用法、参数、返回值以及示例，对于学习和使用R语言进行统计分析来说极其宝贵。 在R语言的学习和实践中，这个参考索引是不可或缺的工具，无论你是初学者还是经验丰富的开发者，都能从中找到解答问题的答案。通过查阅这些文档，用户可以了解到如何有效地进行数据导入、清洗、探索性数据分析、建模以及结果的可视化，从而提升在R环境中的工作效率和质量。同时，由于R语言的强大生态和社区支持，许多额外的包（packages）也能够通过这个基础包作为起点，进一步扩展其功能，满足更复杂的数据科学需求。

The Unified Modeling Language Reference Manual, Second Edition

2009-07-28 上传
Language Reference Manual in Adobe(r) Reader(r) format. It is compatible with Adobe Reader version 5.0 or later, but version 6.0 or later is recommended for best performance. If you do not have Adobe ...

ANSI B92.1-1970(R1993) SAE花键标准.pdf

2020-04-13 上传
Major Diameter (Do, Dri) is the diameter of the major circle. Minor Circle is the circle formed by the innermost surface of the spline. It is the root circle of the external spline or the inside ...

We have seen the cars2018.csv data in the 'Example: Fuel Consumption' Lecture Task 1: Install {keras} (and Tenserflow) on your computer. Make sure its linkage with Python libary is also established. (It is one of the key steps for this assignment. It may take some time if you have not get the right library installed. Here is a reference link. https://keras.rstudio.com/reference/install_keras.html) Task 2: Divide the data randomly into 80% training and 20% test, fit a neural network model on fuel consumption (mpg) with one hidden dense layer. Choose the number of hidden units, and select the right activation function and modelling parameters. Task 3: Fit another neural network with 2 hidden dense layers, i.e. stack another hidden layer on top of the pervious neural net. Choose the number of the hidden units for the second layer. Compare the results with output from Task 2. Task 4: Compare the model performance with the model discussed in the lecture, i.e. the lasso, rpart, random forest, and xgboost models. Task 5: Change the response variable from mpg to Liters/KM (i.e. 235.15/mpg), and re-fit a neural network model. Compare performance as in Task 4.请你提供这个题目的task4的代码

2023-06-06 上传
```R library(caret) library(mlbench) library(glmnet) library(rpart) library(randomForest) library(xgboost) # Load data data("cars2018") # Task 2 - Fit a neural network model with one hidden layer ...

for (int camera_index = 0; camera_index < this->m_safe_camera_list.size(); ++camera_index) { camera* cam = &(this->m_safe_camera_list[camera_index]); if (cam->m_is_exter_calib_check_mark == true) { // as a Internal reference K of the camera, the K-1 is : // 1/ax 0 -px/ax // 0 1/ay -py/ay // 0 0 1 Eigen::Matrix3f invk; invk.setIdentity(); invk(0, 0) = 1.0 / cam->m_inter_calib(0, 0); invk(0, 2) = -1.0 * cam->m_inter_calib(0, 2) / cam->m_inter_calib(0, 0); invk(1, 1) = 1.0 / cam->m_inter_calib(1, 1); invk(1, 2) = -1.0 * cam->m_inter_calib(1, 2) / cam->m_inter_calib(1, 1); Eigen::Vector3f tmp_t_verts = cam->m_exter_calib.topRightCorner(3, 1); Eigen::Matrix3f tmp_inv_r_mat= cam->m_exter_calib.topLeftCorner(3, 3).transpose(); Eigen::Vector3f tmp_root_point = -tmp_inv_r_mat * tmp_t_verts; for (int pose_index = 0; pose_index < cam->m_2D_pose_list.size(); ++pose_index) { Eigen::MatrixXf pose = cam->m_2D_pose_list[pose_index]; // check the pose's Confidence, if all the joints's confidiance is smaller than the gain, drop out float confidence = pose.row(2).maxCoeff(); if (confidence < this->m_joint_confidence_gian) { continue; }; my_radials tmpradials; tmpradials.m_2d_pose = pose; tmpradials.m_root_point = tmp_root_point; tmpradials.m_radials_points = (invk * pose.topRows(2).colwise().homogeneous()).colwise().normalized(); tmpradials.m_radials_points = tmp_inv_r_mat * tmpradials.m_radials_points; tmpradials.m_3d_pose_ID = -1; tmpradials.m_camera_index = camera_index; tmpradials.m_poes_index = pose_index; tmpradials.m_pose_confidence = pose.row(2).leftCols(7).sum(); this->m_3d_radials.push_back(tmpradials); } } }

2023-06-13 上传
2. 计算相机的外参矩阵 m_exter_calib 的逆矩阵的转置矩阵 tmp_inv_r_mat； 3. 根据 tmp_inv_r_mat 和 m_exter_calib 中的平移向量计算相机在世界坐标系下的位置 tmp_root_point； 4. 遍历相机 m_2D_pose_list 中的...

Author index

2021-02-06 上传
Based on the given "Author Index" content, we can infer that this is an index of authors who have contributed to Volume 5 (2012) of a specific scientific journal. The references listed under each ...

The Princeton Companion to Mathematics

2016-08-23 上传
III.83 The Schrödinger Equation 285 III.84 The Simplex Algorithm 288 III.85 Special Functions 290 III.86 The Spectrum 294 III.87 Spherical Harmonics 295 III.88 Symplectic Manifolds 297 III.89 Tensor ...

Bochs - The cross platform IA-32 (x86) emulator

2011-03-14 上传
In addition to the default Bochs method using the CTRL key and the middle mouse button there are now the choices: - CTRL+F10 (like DOSBox) - CTRL+ALT (like QEMU) - F12 (replaces win32 'legacyF12'...

Graph Theory 3E Adrian Bondy, U.S.R Murty

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While the book could ably serve as a reference for many of the most important topics in graph theory, it fulfills the promise of being an effective textbook. The plentiful exercises in each ...

【R语言深度剖析】：解锁R语言函数与对象的神秘力量

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本章节旨在为读者提供R语言函数的基础知识，以及如何在不同的数据科学领域中有效地使用它们。 ## 1.1 R语言函数概述 R语言作为一种高级编程语言，其函数是一种封装了一系列语句的代码块，可以执行特定的任务。函数...

R语言cluster.stats快速入门：10个实用技巧助你轻松入门

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`cluster.stats`是R语言中一个用于聚类分析的函数，它在数据挖掘、模式识别、图像分析等领域扮演着重要角色。通过`cluster.stats`，数据分析人员能够对聚类结果进行深入的统计分析，验证聚类效果的稳定性和
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