glm r tutorial

GLM and MVP matrix operations shorthandWork 15 hours continuously, tired, sleep. include "GLM/GLM.HPP" include "GLM/GTC/MATRIX_TRANSFORM.HPP" If not specifically stated, the following example assumes that the matrix/vector is four-dimensionalGLM::MAT4 Mat;GLM::VEC4 VEC;For VEC, the fourth digit is 1 for the coordinates

R language GLM function learning:"Please specify the source when reproduced": http://www.cnblogs.com/runner-ljt/LjtAs a beginner, the level is limited, welcome to communicate correct.The GLM function describes:GLM ( formula , family= family.generator , Data,control = List (...)) Family: Each response distribution (exponential distribution family) allows various correlation functions to correlate the mean wi

^{2}) $So there are:$\eta = u$$T (y) = y $$a (\eta) = \frac{u^{2}}{2} = \frac{\eta^{2}}{2}$$b (Y) = (\frac{1}{\sqrt{2\pi}}) exp (-\frac{1}{2}y^{2}) $structure GLM1. $y \mid x;\theta \sim exponentialfamily (\eta) $2. Given x, our goal is to predict T (y), in most cases T (y) =y, so we can choose to predict output h (x), $h (x) =e\left [y \mid x \right]$3. The natural parameter $\eta$ and input x are linearly correlated, $\eta = \theta^{t}x$Ordinary least squaresOrdinary least squares is a special

has been heard of logistic regression logistic regression, such as Dr. Wu in the "beauty of mathematics" mentioned that Google is the use of logistic regression to predict the click-through of search ads. Because I have been interested in personalized advertising, so crazy Google over the logical return of data, but not a Web page data can be very good to tell the logic of the return is what. Fortunately, in the third section of CS229 introduced the logistic regression, the fourth lesson introdu

There are three parts in lecture4: Newton's method Newton Method Exceponential family exponential distribution family Generalized Linear models generalized linear model (GLMS) The Newton method, as mentioned in the previous article, is another optimization algorithm parallel to the gradient descent algorithm. Then, the video proves that the bernuoli distribution and Gaussian distribution belong to the special cases in the exponential distribution family. In fact, they are conve

As a fan of machine learning, he has recently been studying with Andrew Ng's machines learning. In the first part of the handout, Ng first explains what is called supervised learning, secondly, the linear model solved by least squares, the logistics regression of the response function by using the SIGMOD function, and then, using these two models, a widely used exponential distribution family is introduced. On the basis of the exponential distribution family, the model hypothesis is created, and

R Language Generalized linear Model GLM () functionGLM (formula, family=family.generator, Data,control = List (...))Formula data relationships, such as y~x1+x2+x3Family: Each response distribution (exponential distribution family) allows various correlation functions to correlate the mean with the linear predictor.Common family: Binomal (link= ' logit ')--the response variable is subject to two distributions, and the connection function is logit, i.e

GLM Generalized linear model George Box said: "All models is wrong, some is useful" 1. Starting with the Linear Model As a foundation of GLM, this section review the classic Linear Regression, and expounds some basic terms.The basic formula for our linear regression is essentially to look at x x and then predict Y y with a simple linear function h (x) H (x):Y=h (x) =wtx y=h (x) =w^tx 1.1 dependent variable

GLM: Linear regression GLM is the generalized linear model, the generalized linear models.Some software kits for Bayesian statistics jags, BUGS, Stan and PYMC, use these toolkits to have a good understanding of the models that will resume. the traditional form of linear regression In general, the frequency school expresses linear regression as:Y=xβ+ϵy = X\beta + \epsilonWhere y y is the output we expect to

is the exponential distribution family (the likelihood function part of the linear regression in the reference note); Our goal is to predict the expectations of T (y), by the above calculation we know T (y) =y , and the expectation of Y is also the parameter μ of the normal distribution; we know Μ=η by the above calculation, and Η=θt x. Therefore, linear regression is a special case of generalized linear regression, and its model is: Classical linear regression: The predictive value y is cont

We begin to contact linear equations from junior middle school, and linearity is the simplest relationship between variables, so I intend to start with the linear model to introduce the basic algorithm of machine learning. Generalized linear model (General Linear MODEL,GLM) is a generalization form of linear regression model, which can be deduced from linear regression, logistic regression, Softmax regression and so on. Most of the books that I have s

void sortByVector(TriMesh *mesh, glm::vec3 x) { std::sort(mesh->f.begin(), mesh->f.end(), [=](glm::ivec3 e1, glm::ivec3 e2){ return glm::dot(mesh->v[e1[0]] - mesh->v[e2[0]], x) > 0; });}In fact, the surface element that participates in the slicing algorithm is a sub-sequence of the sorted surface element sequ

The previous section said that matrices can be combined, and that multiplication is done in reverse order of application. Before we initialized Translationmatrix and Rotationmatrix, the first parameter was an initial matrix glm::matrix4 () used, and we could actually refine the code slightly to make the initialization process leaner.Compare the code before and after optimization:Before:1Glm::mat4 ProjectionMatrix =

depth buffer skyboxshader.use (); Glm::mat4 projection = GLM::p erspective ( Camera.mouse_zoom, (glfloat) (window_width)/window_height, 0.1f , 100.0f ); //projection matrix glm::mat4 view = GLM::MAT4 (GLM::MAT3 (Camera.getviewmatrix ())); //view transformation matrix Remo

(Gl_uniform_buffer, 2 * sizeof (GLM::MAT4), NULL, Gl_static_draw); Glbindbuffer (gl_uniform_buffer, 0); Glbindbufferrange (gl_uniform_buffer, 0, ubomatrices, 0, 2 * sizeof (GLM::MAT4)); We first allocate enough memory for the buffer, which is equal to twice times the GLM::MAT4. The size of the matrix type of GLM c

If we need to draw two (or more) of the same cube (or objects), just position, scale, rotation is not the same, then we can not need to the object's vertex information, color information, and so on to the video card, but sent once, drawn multiple times, just before each drawing to apply a different transformation matrix. This method is called OpenGL instancing. It is much more efficient than sending the data back to the video card every time.Look at the specific code:1 voidMyglwindow::p aintgl (

triangle mesh and the plane plane can be multiple closed parts (hand and torso, two feet), half of the structure can and can only get to the closed part of the given initial half, unable to obtain the number of closed parts of the intersection between the specified plane and the triangular mesh (the closed number of the next name). The algorithm framework is as follows: 1. 对共享顶点结构的三角网格进行排序2. 通过二分查找确定需要与平面求交的有序面元的最小子序列3. 遍历子序列，生成平面与三角形面元的边的交的集合,集合元素为{A,B,k},其中A,B为边的顶点索引，k为相交系数。4. 通过选择排序对3.生成的集合完

: ObjObject(); ObjObject(const char * path); virtual ~ObjObject(); int getVerticesCount(); vector Objobject. cpp #include "objobject.h"ObjObject::ObjObject(){ //ctor}ObjObject::~ObjObject(){ //dtor}ObjObject::ObjObject(const char * path){ Util u; u.loadOBJ(path, this->vertices, this->uvs, this->normals);}int ObjObject::getVerticesCount(){ return this->vertices.size();} The next step is to implement the steps mentioned above step by step in rende

need a camera to observe it. Assuming that the camera is initialized at the origin and moved through a translation matrix, glm::mat4 ViewMatrix = glm::translate(Tx, Ty ,Tz); This matrix is the view matrix, which corresponds to the conversion matrix of the world's moving far points to the camera. The process can be described. Here is a magic lookat function in GLM