Implementing PRNG Using Lehmer Random Number Generator in MATLAB

这是product quantization算法中基于ADC距离计算在Windows下的MATLAB实现源码。

多维随机变量及其分布可以通过mvnpdf和mvncdf函数计算二维正态分布随机变量在指定位置处的概率和累积分布函数值。下图分别为二维正态分布随机变量的概率密度图和累积分布图。

本项目建立PCA模型，使得PCA算子可以在任意时刻应用。实现基于MATLAB的PCA算法。

您可以使用MATLAB的randperm函数生成随机排列。不幸的是，randperm函数效率很低，因为它是通过对随机数列表进行排序来实现的。这需要时间O(n * log(n))。使用Knuth shuffle算法，这个操作只需要时间O(n)。这个包实现了Knuth shuffle。用法：只需使用randpermquick(n)而不是randperm(n)。请注意，在使用randpermquick之前，必须先编译文件randpermquick_helper.c。

此代码可以直接实现ARMA建模和预测。请注意，MATLAB自身说明文档无法实现预测功能。

欧拉公式求圆周率的Matlab代码 在Matlab中，可以使用欧拉公式计算圆周率，通过迭代逼近得到精确的结果。这种方法可以帮助研究人员和开发人员了解并计算常数π的值。以下是一个简单的Matlab代码示例： % Matlab代码示例 pi_approx = 0; for k = 0:n pi_approx = pi_approx + ((-1)^k)/(2*k + 1); end pi_approx = pi_approx * 4; PFLD面部地标检测器的非正式实现 PFLD是一种实用的面部地标检测器，适用于实时面部地标检测和头部姿势估计。使用Pytorch的非官方实现可以简化其安装过程，同时适应不同版本。下面是安装步骤： $ pip3 install -r requirements.txt # 替换成您PyTorch的版本 安装OpenCV和DNN（可选） OpenCV的DNN模块和Haar级联适用于面部检测，如果仅需要使用Haar级联则可以跳过以下部分。 sudo apt update && sudo apt install -y cmake g++ wget unzip wget -O opencv.zip https://github.com/opencv/archive/master.zip wget -O opencv_contrib.zip https://github.com/opencv_contrib/archive/master.zip unzip opencv.zip 这样就可以顺利安装用于PFLD的OpenCV模块，并开始进行实时地标检测。

Bridging an existing gap between mathematics and programming, Elementary Number Theory with Programming provides a unique introduction to elementary number theory with fundamental coverage of computer programming. Written by highly-qualified experts in the fields of computer science and mathematics, the book features accessible coverage for readers with various levels of experience and explores number theory in the context of programming without relying on advanced prerequisite knowledge and concepts in either area. Elementary Number Theory with Programming features comprehensive coverage of the methodology and applications of the most well-known theorems, problems, and concepts in number theory. Using standard mathematical applications within the programming field, the book presents modular arithmetic and prime decomposition, which are the basis of the public-private key system of cryptography.

In this guide, we explore how to implement GAN (Generative Adversarial Networks) using MatConvNet. MatConvNet is a MATLAB toolbox that simplifies deep learning network construction, enabling users to build GAN models with ease. This tutorial covers the setup, installation, and steps needed to create a basic GAN model within MatConvNet. Step-by-step instructions are provided to ensure clarity and smooth progress through the model-building process. GAN training involves two primary components: the generator and the discriminator, which are explained in detail along with examples.

快速生成字典文本格式，包含生日、数字、大小写英文的组合，以及各行字典的长度等。